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DNA methylation inhibitors and epigenetic regulation of microRNA expression

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DNA methylation inhibitors and epigenetic regulation of microRNA expression

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DNA METHYLATION INHIBITORS AND EPIGENETIC REGULATION OF
MICRORNA EXPRESSION

by

Jody Chouying Chuang

A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BIOCHEMISTRY AND MOLECULAR BIOLOGY)

August 2008

Copyright 2008 Jody Chouying Chuang
ii
ACKNOWLEDGEMENTS

I would like to thank my mentor Dr. Peter A. Jones for his unfailing guidance and
support that made this work possible, and whom I will continue to look up to in the future.

I would also like to thank my committee members Drs. Ite Laird, Peter W. Laird,
Michael R. Stallcup, and Shao-Yao Ying for their invaluable advice and inputs for my
projects.

Furthermore, I would like to thank all the laboratory members for their help and
suggestions. In particular, I would like to thank Drs. Gerda Egger, Shinwu Jeong, Tony
Li, Gangning Liang, Tina Miranda, Yoshimasa Saito, and Christine Yoo for their
contintuous help and priceless advice. I also thank Dr. Terry Kelly and Jeffrey Friedman
for their critical reading of this thesis work, and Dr. Yvonne Tsai for overseeing all the
laboratory functions.

I would also like to thank the people in our laboratory group meetings for their
helpful suggestions, especially Drs. Gerry Coetzee, Judd Rice, and Allen Yang. I also
thank the NIH student training grant for supporting my research.

Lastly, I would like to thank my family for their continuous love and support.
iii
TABLE OF CONTENTS

Acknowledgements ii
List of Tables iv
List of Figures v
Abstract viii
Chapter 1: Introduction—Epigenetics, Epigenetic Therapy, and MicroRNAs 1
Chapter 2: Comparisons of DNA Methylation Inhibitors 20
Chapter 3: Examination of DNA Methylation Inhibitors in vivo 55
Chapter 4: Epigenetic Regulation of MicroRNA Expression 84
Chapter 5: Biological Functions of Epigenetically Regulated MicroRNAs 131
Chapter 6: Conclusion—Epigenetic Therapy, MicroRNAs, and Their 155
Clinical Potential

Bibliography 162

Appendix A: Complete MicroRNA Microarray Comparing Wildtype HCT116 178
Vs. Double Knock-Out Cells
Appendix B: Complete MicroRNA Microarray Comparing Untreated HCT116 196
Cells vs. Cells Treated with 5-Aza-2’-Deoxycytidine and
4-Phenylbutyric Acid
Appendix C: MicroRNA miR-10a, 10b, and 196a 214
Including complete MicroRNA Microarray Comparing Wildtype
HCT116 Cells vs. Double Knock-out (V) Cells
iv
LIST OF TABLES

Table 1.1: Tumor-Suppressor miRNAs and Oncogenic miRNAs 11

Table 3.1: Weight and tumor size changes in mice treated with PBS, 65
zebularine, and EPD-zebularine

Table 3.2: Weight and tumor size changes in mice treated with PBS, 77
5-Aza-CdR, and S110

Table 4.1: miRNA microarray—HCT116 WT vs. DKO 94

Table 4.2: miRNA microarray—HCT116 mock treated vs. HCT116 treated 97
with 5-Aza-CdR+PBA

Table 4.3: Composite table comparing the two microarray results 102

Table A.1: Differentially expressed transcripts—WT vs. DKO 181

Table B.1: Differentially expressed transcripts—mock vs. 5-Aza-CdR+PBA 198

Table C.1: miRNA microarray—HCT116 WT vs. DKO(V) 221

v
LIST OF FIGURES

Figure 1.1: Mechanism of DNA methylation 2

Figure 1.2: Distribution and changes in CpG sites in the genome 3

Figure 1.3: Metabolism of 5-azacytidine (5-Aza-CR) and 7
5-aza-2’-deoxycytidine (5-Aza-CdR)

Figure 1.4: Biogenesis of miRNAs 10

Figure 1.5: The expression of miRNAs can be controlled by epigenetic 16
mechanisms

Figure 2.1: Chemical structures of the DNA methylation inhibitors 22

Figure 2.2: Comparison of the methylation inhibition potencies of the 32
various agents by Ms-SNuPE

Figure 2.3: Comparison of the methylation inhibition potencies of the 33
various agents by Pyrosequencing

Figure 2.4: Effects of the various agents on the reactivation of p16 35

Figure 2.5: Effects of the various agents on the reactivation of MAGE-A1, 37
MAGE-B2, RAR β, and p16

Figure 2.6: Metabolism of zebularine 39

Figure 2.7: Thymidylate Synthase converts dUMP to dTMP and is 40
inhibited by dZMP

Figure 2.8: Effects of the various ODN compounds on CFPAC-1 cells 42

Figure 2.9: Effects of the varying concentrations of thymidine combined 44
with ODN compounds in CFPAC-1 cells

Figure 2.10: Re-Expression of p16 by TpZ in CFPAC-1, PL45, and 45
HepG2 cells

Figure 2.11: Effects of ODN compounds on HepG2 cells 47

Figure 2.12: Effects of dZTP transfection using Lipofectamine 49
vi
Figure 3.1: Structure of EPD-Zebularine 63

Figure 3.2: Relative Tumor Volume in EJ6 xenograft tumors treated with 66
zebularine and EPD-zebularine

Figure 3.3: Real Time RT-PCR of p16 expression in EJ6xenograft tumors 68
treated with zebularine and EPD-zebularine

Figure 3.4: Structure of S110 69

Figure 3.5: Re-expression of p16 by 5-Aza-CdR and S110 in EJ6 xenograft 71
tumors treated with 5-Aza-CdR and S110

Figure 3.6: DNA methylation level of p16 promoter in EJ6 xenograft tumors 73
treated with 5-Aza-CdR and S110 by Ms-SNuPE analysis

Figure 3.7: Relative tumor volume of each EJ6 xenograft tumor treated 76
with 5-Aza-CdR and S110

Figure 3.8: Percent weight change in mice after treatment with PBS, 78
5-Aza-CdR, and S110

Figure 4.1: miRNA microarrays comparison 107

Figure 4.2: Quantitative stem-loop mature miRNA RT-PCR results in 109
HCT116 cells

Figure 4.3: Transcription start sites of miR-205 and miR-377 as determined 111
by 5’ RLM-RACE

Figure 4.4: Northern Blot analysis of miR-205 and miR-377 115

Figure 4.5: Genomic bisulfite sequencing results of miR-377 region 119

Figure 4.6: ChIP analysis of miR-377 region 121

Figure 4.7: MSPA analysis of the miR-377 region 124

Figure 5.1: Northern Blot analysis of miR-377 Expression in human 139
cancer-free colon tissues

Figure 5.2: Northern Blot analysis of miR-377 expression in human colon 140
cancer tissues and matched adjacent normal colon tissues

Figure 5.3: Inhibition of HCT116 cell growth by miRNA expression vectors 142
vii
Figure 5.4: Quantitative real time RT-PCR analysis of miRNA expression 143
in human colon cancers vs. matched adjacent normal tissues

Figure 5.5: Northern analysis of transfection experiment with miR-377 147

Figure 5.6: Western Blot analysis of potential miR-377 targets 148

Figure 5.7: RT-PCR results of potential miR-377 targets 151

Figure C.1: The HOX gene clusters and included miRNAs 223

Figure C.2: Northern Blot analysis of miR-10a and 10b expression 226

Figure C.3: Expression of HOXB4, HOXB9, and HOXD4 in WT and 228
DKO(V) cells

Figure C.4: Transcription start sites of miR-10a and 10b 230

Figure C.5: Ms-SNuPE analysis of miR-10a and 10b 233

Figure C.6: Expression of miR-10a and 10b is only up-regulated in the 236
DKO(V) cells

viii
ABSTRACT

Epigenetics is defined as heritable changes in gene expression without a change in
the DNA sequence itself. DNA cytosine methylation and histone modifications are two
important mechanisms in the area of epigenetics that have profound roles in gene
regulation, development, and carcinogenesis. Methylation of CpG islands in promoter
regions is often associated with gene silencing, and aberrant DNA methylation occurs in
most cancers, leading to the silencing of some tumor suppressor genes. Many drugs have
effects in reversing abnormal epigenetic changes, and they can mainly be divided into
two classes—DNA methylation inhibitors and HDAC inhibitors. I first studied a few
nucleoside analog and non-nucleoside analog DNA methylation inhibitors in vitro. The
nucleoside analog DNA methylation inhibitors studied did not show general applicability,
and the results concerning the non-nucleoside agents tested did not support the idea that
they were likely to be effective as epigenetic therapies. I then expanded the study of
novel DNA methylation inhibitors to in vivo studies and found that the agent S110 (AzpG)
was effective in vivo.

My project received new directions after the discovery by Saito et al. that showed
epigenetic drugs could change the expression of microRNAs. miRNAs are ~22
nucleotides-long RNA molecules encoded in the genome that can have a profound effect
in controlling gene expression. miRNA mis-expression has been linked to cancer, and
these molecules can act as either oncogenes or tumor-suppressor genes. I examined the
potential epigenetic regulation of miRNA expression by both genetic and
ix
pharmacological approaches, and I narrowed down to focus on one specific miRNA,
miR-377, that could be epigenetically regulated. I then showed that miR-377 was
normally expressed in human colon tissues, and that its expression was down-regulated in
five out of the eight cases of colon cancers compared to their matching, adjacent normal
colon tissues. Furthermore, miR-377 was able to reduce cancer cell growth in vitro.
Taken together, the results suggested that the expression of microRNAs could be under
epigenetic regulation, and that miR-377 could be a tumor-suppressor gene.

1
CHAPTER ONE
EPIGENETICS, EPIGENETIC THERAPY AND MICRORNAS

Epigenetic mechanisms can regulate gene expression
Epigenetics is defined as heritable changes in gene expression without a change in
the DNA sequence itself (Egger, Liang et al. 2004). DNA cytosine methylation and
histone modifications are two important mechanisms in the area of epigenetics that have
profound roles in gene regulation, development, and carcinogenesis (Jones and Laird
1999; Jones and Baylin 2002; Baylin and Ohm 2006; Klose and Bird 2006).

DNA methylation is a process employed by mammalian cells in maintaining a
normal expression pattern; it is involved in the regulation of imprinted gene expression
and X-chromosome inactivation, among others (Csankovszki, Nagy et al. 2001; Jones
and Takai 2001; Kaneda, Okano et al. 2004). DNA methylation occurs almost
exclusively on a cytosine in a CpG dinucleotide, and is achieved by the addition of a
methyl group to the 5 position of a cytosine ring mediated by DNA methyltransferases
(DNMTs) (Figure 1.1). The majority of the genome is rather CpG-poor due to the
mutagenicity of a methylated cytosine, which can undergo spontaneous deamination to
become a thymine (Gardiner-Garden and Frommer 1987; Egger, Liang et al. 2004)
(Figure 1.2). The remaining CpG sites are asymmetrically distributed into CpG poor
regions and dense regions called “CpG islands” (Takai and Jones 2002; Takai and Jones
2003), which are often located in the promoter regions of approximately half of all
protein-coding genes. CpG islands normally remain unmethylated, while the sporadic
2
Figure 1.1: Mechanism of DNA methylation

DNMT DNMT DNMT

DNA methylation involves the addition of a methyl group onto the 5 position of a
cytosine residue, mediated by the enzymes DNA methyl transferases (DNMTs). DNA
methylation happens almost exclusively on cytosines in front of a guanine in a CpG
dinucleotide.

3
Figure 1.2: Distribution and changes in CpG sites in the genome

CpG island
Unmethylated CpG
Methylated CpG
gene
Normal
gene
X
Cancer
gene
Aging
CpG island
Unmethylated CpG
Methylated CpG
gene
Normal
gene
X
Cancer
gene
Aging

CpG sites in the genome are unevenly distributed. CpG islands can be found in the
promoter regions of roughly half of the genes. CpG islands normally remain
unmethylated, while the sporadic CpG sites located in the rest of the genome often are
methylated. With aging, there is a gradual reversal of this phenomenon. During
carcinogenesis, this change is much more dramatic, leading to a global hypomethylation
and hypermethylation of CpG islands. The results are chromosomal instability and
silencing of some important tumor-suppressor genes.

4
CpG sites in the rest of the genome are normally methylated. There is a gradual reversal
of this pattern during aging that leads to de novo methylation in the CpG islands and a
global loss of methylation, but this change is particular pronounced during carcinogenesis
(Bird 2002; Egger, Liang et al. 2004) (Figure 1.2). Methylation of CpG islands in
promoter regions is often associated with gene silencing, and aberrant DNA methylation
occurs in most cancers, leading to the silencing of some tumor suppressor genes (Bird
2002; Jones and Baylin 2002) (Figure 1.2). There are three major enzymes involved in
establishing and maintaining DNA methylation patterns: DNMTs 3A and 3B are de novo
methyltransferases, and DNMT1 is the maintenance DNMT that ensures that methylation
patterns are copied faithfully throughout each cell division (Klose and Bird 2006). They
cooperate with each other to establish and maintain the cellular DNA methylation
patterns (Egger, Liang et al. 2004).

Histone modifications, especially the post-translational modifications of amino-
terminal tail domains, are also important epigenetic mechanisms in regulating gene
expression (Egger, Liang et al. 2004). Certain histone modifications, such as histone
acetylation, are associated with active gene transcription, while others, such as the
methylation of histone H3 lysine 9 (H3K9), are indicators of condensed and inactive
chromatin (Heard, Rougeulle et al. 2001; Peters, Mermoud et al. 2002; Egger, Liang et al.
2004).

The relationships between DNA methylation and histone modifications have
recently become clearer, although much is still to be learned. It is now believed the two
5
mechanisms cooperate in controlling gene expression. For example, methylation of
histone H3K9 can be triggered by DNA methylation (Johnson, Cao et al. 2002; Soppe,
Jasencakova et al. 2002; Tariq, Saze et al. 2003). Furthermore, DNA methyltransferases
have also been shown to interact with histone deacetylaces (HDACs), histone
methyltransferases, and methyl-cytosine-binding proteins in a complex network (Nan, Ng
et al. 1998; Fuks, Burgers et al. 2000; Fuks, Hurd et al. 2003).

In addition to the increasing understanding of the fundamental roles of epigenetics
in normal gene regulation, the association between abnormal epigenetic changes such as
DNA hypermethylation and human diseases, including cancer, has become increasingly
clear (Egger, Liang et al. 2004). In contrast to genetic changes that cause cancer,
epigenetic modifications of gene expression are more general and usually involve many
more genes (Baylin, Esteller et al. 2001). There are a growing number of examples in the
literature of specific epigenetic changes that can lead to pathological consequences. For
example, mutations in the methyl CpG-binding protein 2 (MeCP2) gene, which encodes a
protein that binds to methylated DNA sequences, can lead to Rett Syndrome (Amir, Van
den Veyver et al. 1999). Mutations of the DNA methyltransferase gene DNMT3B can
lead to Immunodeficiency/Centromeric instability/Facial anomalies (ICF) syndrome (Xu,
Bestor et al. 1999). While genetic mutations are often difficult to correct clinically,
several drugs exist today which can reverse abnormal epigenetic changes, and some have
shown great promise in clinical studies (Yoo and Jones 2006).

6
Epigenetic Therapy Has a Promising Future as an Anticancer Treatment
Many drugs have effects in reversing abnormal epigenetic changes, and they can
mainly be divided into two classes—DNA methylation inhibitors and HDAC inhibitors
(Egger, Liang et al. 2004; Yoo and Jones 2006). Histone deacetylase (HDAC) inhibitors
target HDAC and therefore increase the level of histone acetylation, which induces a
more open chromatin structure. Many HDAC inhibitors have been shown to have anti-
tumor activities; however, the exact mechanisms by which these drugs work are still
largely unknown. (Egger, Liang et al. 2004; Yoo and Jones 2006)
On the other hand, many DNA methylation inhibitors have been well studied for
their mechanisms of actions and clinical potentials. DNA methylation inhibitors can be
further dividied into two groups—nucleoside analogs and non-nucleoside analogs (Yoo
and Jones 2006). The mechanisms of action for nucleoside analog DNA methylation
inhibitors have been well studied. They are converted into nucleotides and incorporated
into DNA, and there they can trap DNMTs by forming covalent complexes (Santi,
Garrett et al. 1983; Santi, Norment et al. 1984; Zhou, Cheng et al. 2002). 5-Azacytidine
(5-Aza-CR) and 5-Aza-2’-deoxycytidine (5-Aza-CdR) are two well-known DNA
methylation inhibitors, and they have been approved by the Food and Drug
Administration for the treatment of myelodysplastic syndrome (Kaminskas, Farrell et al.
2005; Ruter, Wijermans et al. 2006; Yoo and Jones 2006). The metabolism of these
drugs is shown in Figure 1.3. Zebularine, another nucleoside analog DNA methylation
inhibitor, is a promising new addition due to its high stability and ability to incorporate
into both RNA and DNA (Cheng, Matsen et al. 2003; Cheng, Yoo et al. 2004). Unlike
traditional chemotherapy agents, DNA methylation inhibitors do not induce immediate
7
Figure 1.3: Metabolism of 5-azacytidine (5-Aza-CR) and 5-aza-2’-deoxycytidine (5-
Aza-CdR).

DNA
5-Aza-CdR
dCyd Kinase
Ribonucleotide
Reductase
RNA
5-Aza-CR
Urd/Cyd Kinase
P 5-Aza-CR-
P P 5-Aza-CR-
P P P 5-Aza-CR-
P 5-Aza-CdR-
P P 5-Aza-CdR-
P P P 5-Aza-CdR-
DNA
5-Aza-CdR
dCyd Kinase
Ribonucleotide
Reductase
RNA
5-Aza-CR
Urd/Cyd Kinase
P 5-Aza-CR- P 5-Aza-CR-
P P 5-Aza-CR- P P 5-Aza-CR- P 5-Aza-CR-
P P P 5-Aza-CR- P P P P P 5-Aza-CR- P 5-Aza-CR-
P 5-Aza-CdR- P 5-Aza-CdR-
P P 5-Aza-CdR- P P 5-Aza-CdR- P 5-Aza-CdR-
P P P 5-Aza-CdR- P P P P P 5-Aza-CdR- P 5-Aza-CdR-

Figure courtesy of Christine Yoo

5-Aza-CR is a ribonucleotide analog, and it is phosphorylated by uridine/cytidine kinase
and other kinases into mono-, di-, and tri-phosphates and can get incorporated RNA.
Ribonucleotide reductase can convert 5-Aza-CR diphosphate into its deoxy-diphosphate
form, which can be subsequently phosphorylated and incorporated into DNA. 5-Aza-
CdR, on the other hand, only incorporates into DNA after phosphorylation by
deoxycitidine kinase and other kinases.

8
cell death at their optimal dosage, although cytotoxicity can occur at high concentrations.
Cells need to be proliferating for effective incorporation of these drugs, and the drugs act
to reactivate methylation-silenced tumor-suppressor genes that in turn make the cells
more responsive to apoptotic or cell-cycle regulating signals (Yoo and Jones 2006).
Given their promising potential in clinical applications, much effort has been invested to
develop more stable forms of these known DNA methylation inhibitors that can be
effectively delivered to cancer cells.

In addition to the nucleoside DNA methylation inhibitors, several non-nucleoside
analog DNA methylation inhibitors have also been reported (Yoo and Jones 2006). The
mechanisms of action for these drugs are mostly unclear, and I have shown that potential
of some of these drugs in inhibiting DNA methylation is questionable (Chapter 2)
(Chuang, Yoo et al. 2005).

MicroRNAs (miRNAs) are endogenous small RNA molecules that can regulate gene
expression
miRNAs are ~22 nucleotides-long RNA molecules encoded in the genome that
can have a profound effect in controlling gene expression. They are transcribed by RNA
polymerase II (Pol II) into primary miRNAs (Cai, Hagedorn et al. 2004; Lee, Kim et al.
2004), and are then processed in the nucleus by the RNase III Drosha and DGCR8
(microprocessor complex) into the precursor miRNAs. Precursor miRNAs are structured
as imperfect stem-loops that are exported into the cytoplasm by Exportin-5 where they
are further processed by another RNase III Dicer into the final functional mature
9
miRNAs (Figure 1.4) (Lee, Jeon et al. 2002; Meltzer 2005; Esquela-Kerscher and Slack
2006; Kim and Nam 2006).

miRNAs can regulate their target mRNAs using two mechanisms. When binding
to its target mRNA with complete complementarity, the miRNA can lead to the
degradation of the mRNA. miRNAs can also bind to their targets with incomplete
complementarity, often in the 3’ UTR regions, and this leads to the translational
suppression of their target genes by a mechanism that has yet to be completely elucidated
(Figure 1.4) (Meltzer 2005; Esquela-Kerscher and Slack 2006; Kim and Nam 2006).
Each miRNA is predicted to have many targets, and each mRNA may be regulated by
more than one miRNA (Lewis, Shih et al. 2003; Lim, Lau et al. 2005; Rajewsky 2006).
Currently, there are more than 460 human miRNAs known .

miRNA mis-expression has been linked to cancer, and these molecules can act as
either oncogenes or tumor-suppressor genes (Table 1.1). For example, miR-15a and
miR-16-1 can target the anti-apoptotic BCL2, and are often down-regulated in chronic
lymphocytic leukemia (Calin, Dumitru et al. 2002; Cimmino, Calin et al. 2005). miR-21
is found to be anti-apoptotic, and it is up-regulated in glioblastomas and breast cancers
(Chan, Krichevsky et al. 2005; Ciafre, Galardi et al. 2005; Iorio, Ferracin et al. 2005; Si,
Zhu et al. 2006) (Table 1.1). Lu et al. (Lu, Getz et al. 2005) showed that human cancers
can be classified using their miRNA expression profiles.

10
Figure 1.4: Biogenesis of miRNAs

miRNAs are endogenously encoded in the genome. They are generally transcribed by
RNA polymerase II (PolII) into the primary miRNAs (pri-miRNAs), which therefore
have 5’ caps and poly-A tails. The pri-miRNAs are processed by RNase III Drosha with
its partner DGCR8 (DiGeorge syndrome chromosomal region 8; also known as Pasha in
invertebrates) into the precursor miRNAs (pre-miRNAs), which are then exported by the
nuclear export factor Exportin 5 and its cofactor RAN-GTP into the cytoplasm. It is in
the cytoplasm that the pre-miRNAs are further processed by another RNase III Dicer into
the mature miRNAs. The mature miRNA is incorporated into the RISC complex
negatively regulates its target mRNA by one of two ways. When it binds its target with
complete complementarity, it leads to the target mRNA cleavage. On the other hand, it
can also bind to its target with incomplete complementarity, and leads to translational
repression of the latter by a yet poorly understood mechanism.

11
Table 1.1: Tumor-Suppressor miRNAs and Oncogenic miRNAs
Tumor-Suppressor miRNAs:
miRNA Regulation Role in Cancer References
miR-127 DNA
methylation &
histone
modifications
Decreased expression in
bladder and prostate
cancers. Targets proto-
oncogene BCL6
(Saito, Liang et al. 2006)
miR-15a,
miR-16-1
Often downregulated in B-
cell chronic lymphocytic
leukemias. Targets BCL2
(Calin, Dumitru et al.
2002; Cimmino, Calin et
al. 2005)
let-7 Targets RAS. Reduced
expression in human lung
cancer associates with
poor prognosis.
(Calin, Sevignani et al.
2004; Takamizawa,
Konishi et al. 2004;
Johnson, Grosshans et
al. 2005; Yanaihara,
Caplen et al. 2006)
miR-145 Reduced expression in
colon and breast cancers.
(Michael, SM et al.
2003; Iorio, Ferracin et
al. 2005)

12
Table 1.1, Continued
Oncogenic miRNAs:
miRNA Regulation Role in Cancer References
miR-17-92
cluster
c-Myc induces
the expression
Overexpressed in B-cell
lymphomas and can
accelerate tumor
progression in a mouse B-
cell lymphoma model.
Plays a role in tumor
angiogenesis.
Overexpressed in lung
cancers.
(Hayashita, Osada et al.
2005; He, Thomson et
al. 2005; O'Donnell,
Wentzel et al. 2005;
Dews, Homayouni et al.
2006)
miR-372,
miR-373
Overexpressed in
testicular germ cell
tumors. permit
proliferation and
tumorigenesis of primary
human cells that have both
oncogenic RAS and active
wild-type p53.
(Voorhoeve, le Sage et
al. 2006)
miR-21 Expressed in
glioblastomas and breast
cancers. An anti-apoptotic
factor
(Chan, Krichevsky et al.
2005; Iorio, Ferracin et
al. 2005; Si, Zhu et al.
2006)
miR-155 Overexpressed in breast,
colon, and lung cancers, as
well as in B-cell
lymphomas.
Overexpression correlates
with poor survival in lung
cancers and poor
prognosis in DLCBL.
High expression in
children with Burkitt’s
lymphoma
(Metzler, Wilda et al.
2004; Eis, Tam et al.
2005; Iorio, Ferracin et
al. 2005; Kluiver,
Poppema et al. 2005;
Tam and Dahlberg
2006; Volinia, Calin et
al. 2006; Yanaihara,
Caplen et al. 2006)
miR-146 NF- κB Overexpressed in breast,
pancreas, and prostate
cancers.
(Taganov, Boldin et al.
2006; Volinia, Calin et
al. 2006)

13
miRNA control of epigenetic mechanisms
The processing pathways of siRNAs and miRNAs share many of the enzymes
involved in the RNA interference (RNAi) pathway (He and Hannon 2004). Recent
evidence also suggests that they affect histone modifications. Small interfering RNAs
(siRNAs), often considered to be closely related to miRNAs, have been shown to be
involved in both DNA methylation and histone modifications. Because siRNAs and
miRNAs are closely related, miRNAs could also play important roles in controlling DNA
methylation and histone modifications. Maison et al. (Maison, Bailly et al. 2002) showed
that RNase treatment can abolish the localization of methylated H3K9 and HP1 to
pericentromeric chromatin, suggesting that certain RNA molecules are essential for the
epigenetic establishment of chromatin structure. Fukagawa et al. (Fukagawa, Nogami et
al. 2004) showed that Dicer-related RNAi machinery is necessary for the formation of
heterochromatin structure. In addition, miRNAs may regulate chromatin structure by
regulating key histone modifiers. miR-140, which is cartilage-specific, can target histone
deacetylase 4 in mice (Tuddenham, Wheeler et al. 2006). Costa et al. (Costa, Speed et al.
2006) suggested that miRNAs may be involved in meiotic silencing of unsynapsed
chromatin in mice.

miRNAs can be involved in establishing DNA methylation. miR-165 and miR-
166 are required for the methylation at the PHABULOSA (PHB) gene in Arabidopsis.
They interact with the newly processesd PHB mRNA to change the chromatin of the
template PHB gene (Bao, Lye et al. 2004). This presents an exciting new mechanism by
which miRNAs can control gene expression in addition to the RNAi pathway. A recent
14
study by Fabbri et al. (Fabbri, Garzon et al. 2007) showed that miRNA-29 family can
target DNMT 3A and 3B, and can revert aberrant methylation in lung cancer. This
exciting finding demonstrates the ability of miRNAs to regulate epigenetic mechanisms.
Taken together, miRNAs can be considered important players in the epigenetic control of
gene expression.

Epigenetic control of miRNA expression
Since their initial discovery, miRNAs had been assumed to be transcribed by
RNA polymerase III (Pol III) due to their small sizes (Lee, Feinbaum et al. 1993), yet the
biogenesis of miRNAs has been elucidated in recent years. Lee et al. (Lee, Jeon et al.
2002) showed that miRNAs are transcribed from long primary transcripts in 2002, and
two years later miRNAs were proven to be generally transcribed by Pol II (Cai, Hagedorn
et al. 2004; Lee, Kim et al. 2004). We are now only beginning to understand how
miRNA expression is regulated. Because miRNAs are generally transcribed by Pol II,
they can be spatially and temporally regulated (Kim and Nam 2006). In addition to
negatively regulating their target mRNAs, miRNAs themselves can be regulated by other
factors. Evidence suggests that, similar to protein-coding genes, miRNAs can be
regulated by transcription factors. c-Myc has been shown to activate the transcription of
the miR-17-92 cluster, which has a role in tumor neovascularization (O'Donnell, Wentzel
et al. 2005; Dews, Homayouni et al. 2006). NF-kB can induce the expression of miR-
146a, which can then down-regulate IRAK1 and TRAF6 and thus acts as a component in
a negative feedback loop that controls TLR signaling. Fazi et al. (Fazi, Rosa et al. 2005)
showed that the transcription factors NFI-A and C/EBPalpha compete for the binding to
15
the miR-223 promoter, leading to low and upregulated expression of miR-223,
respectively. In addition, miR-223 participates in its own feedback loop and favors the
C/EBPalpha binding by repressing the NFI-A translation (Fazi, Rosa et al. 2005).
Exciting new studies also show that p53 can regulate the miR-34 family and this adds
important understanding of the p53 network (Bommer, Gerin et al. 2007; Chang, Wentzel
et al. 2007; He, He et al. 2007; Welch, Chen et al. 2007). Despite mounting evidence for
the importance of miRNAs, the regulation of their expression is still poorly understood.

An exciting new discovery by Saito et al. (Saito, Liang et al. 2006) showed that
epigenetic mechanisms, such as DNA methylation and histone modifications, can affect
the expressions of miRNAs. In particular, miR-127 was found to be remarkably up-
regulated in cancer cell lines after the treatment with 5-Aza-CdR and 4-phenylbutyric
acid (PBA), a histone deacetylase inhibitor (Egger, Liang et al. 2004). Together, 5-Aza-
CdR and PBA lead to reduced DNA methylation levels and more open chromatin
structures, and therefore induce the re-expressions of genes that had been silenced
epigenetically (Figure 1.5) (Egger, Liang et al. 2004; Saito, Liang et al. 2006). The
finding that miR-127, among many other miRNAs, can be expressed after the treatment
with 5-Aza-CdR and PBA, suggests that epigenetic mechanisms can control the
expression of miRNAs.

Following the study by Saito et al., Lujambio et al. (Lujambio, Ropero et al. 2007)
and Han et al. (Han, Witmer et al. 2007) established that DNA methylation can control
the expression of miRNAs. A few additional examples exist of the regulation of
16
Figure 1.5: The expression of miRNAs can be controlled by epigenetic mechanisms

Epigenetic mechanisms such as DNA methylation and histone modifications can
contribute to the transcriptional control of miRNA expression. In the case of miR-127,
methylation of the CpG sites and deacetylation of the histones around its promoter region
contribute to its silencing in tumor cell lines. Treatment with 5’-Aza-CdR and PBA leads
to reduced DNA methylation and increased histone acetylation, allowing the miRNA to
be expressed. The gray circle depicts a nucleosome with histone tails. Open circles on
the DNA strand represent unmethylated CpG sites, and the filled circles methylated CpG
sites. Octagons on the histone tails represent acetyl groups.

17
miRNAs expression by DNA methylation. Fazi et al. (Fazi, Racanicchi et al. 2007)
showed that treatment with 5-Aza-CR can up-regulate the expression of miR-223, which
targets NFI-A (Fazi, Rosa et al. 2005). Lu et al. (Lu, Katsaros et al. 2007) showed that
let-7a-3 is hypermethylated in epithelial ovarian cancer, and this may affect insulin-like
growth factor-II and patient survival. Lehmann et al. (Lehmann, Hasemeier et al. 2008)
observed aberrant hypermethylation for miR-9-1, 124a3, 148, 152, and 663 in human
breast cancer specimens, and that treatment with 5-Aza-CdR can reactivate miR-9-1. In
addition, Scott et al. (Scott, Mattie et al. 2006) also showed that the treatment of breast
cancer cell line SKBr3 with HDAC inhibitor LAQ824 lead to a rapid change in the
miRNA expression profile, suggesting that histone modifications can also regulate the
expression of miRNAs.

Many miRNAs are located in the introns of protein-coding genes (Kim and Nam
2006). It is believed that such miRNAs are co-expressed with their host genes (Ying and
Lin 2005). However, it is possible that these miRNAs can have their own promoters.
The finding that CpG islands within introns can act as promoters suggests that perhaps
intronic miRNAs that have CpG islands upstream within the same intron could be
transcribed from their own promoters that are regulated by DNA methylation (Wutz,
Smrzka et al. 1997; Lyle, Watanabe et al. 2000). Additional studies should be conducted
to further illuminate the epigenetic control of miRNA expression.

18
Clinical significance and future directions
Epigenetics and miRNAs are two important areas of study that warrant significant
growth in their fields in the future, and the relationship between epigenetics and miRNA
is just beginning to be understood. Some miRNAs have been found to play important
roles in carcinogenesis. These miRNAs can serve as therapeutic targets in future cancer
therapies. For example, knockdown of the oncogenic miRNA miR-21 can trigger
apoptosis in cultured glioblastoma cells (Chan, Krichevsky et al. 2005). Other examples
of oncogenic miRNAs exist. miR-372 and 373 are up-regulated in testicular germ cell
tumors (Voorhoeve, le Sage et al. 2006). miR-155 is over-expressed in B-cell
lymphomas and breast cancers (Eis, Tam et al. 2005; Iorio, Ferracin et al. 2005; Tam and
Dahlberg 2006). These, and other oncogenic miRNAs, can all serve as potential targets
in cancer therapy; knocking down of these miRNAs may stunt the cancer growth. On the
other hand, restoring tumor-suppressor miRNAs can also be a powerful approach in
treating cancer. The finding that epigenetic drugs 5-Aza-CdR and PBA are able to lead
to the up-regulation of miR-127, which can down-regulate BCL6, is especially exciting
(Saito, Liang et al. 2006). It demonstrates that epigenetic drugs may exert their anti-
tumor effects on two fronts: they not only turn on tumor-suppressor genes that were
aberrantly silenced epigenetically, but they also turn on tumor-suppressor miRNAs that
down-regulate target oncogenic mRNAs. Knowing that these drugs can affect the
expression of miRNAs helps us further understand the mechanisms of action of these
agents. More studies are needed in these areas to further illuminate the therapeutic
potential of epigenetic modifiers and miRNAs.

19
Thesis Outline
To investigate the areas of novel epigenetic therapies and epigenetics and
miRNAs, I first studied a few nucleoside analog and non-nucleoside analog DNA
methylation inhibitors in vitro (Chapter 2). I then expanded the study of novel DNA
methylation inhibitors to in vivo studies (Chapter 3). Next, I examined the potential
epigenetic regulation of miRNA expression by both genetic and pharmacological
approaches, and I narrowed down to focus on one specific miRNA, miR-377, that could
be epigenetically regulated (Chapter 4). The studies on the biological functions of miR-
377 were detailed in Chapter 5.
20
CHAPTER TWO
COMPARISON OF DNA METHYLATION INHIBITORS

INTRODUCTION

The relationship between epigenetic alterations such as DNA methylation and
human carcinogenesis has become increasingly evident (Baylin, Esteller et al. 2001;
Esteller and Herman 2002; Jones and Baylin 2002). DNA cytosine methylation is
employed in normal cells as a mechanism to silence gene expression, and plays a role in
genomic imprinting and X chromosome inactivation (Csankovszki, Nagy et al. 2001;
Jones and Takai 2001; Kaneda, Okano et al. 2004). During cancer development, cells
can undergo abnormal hypermethylation of CpG islands at the promoters of tumor-
suppressor genes that leads to the silencing of these genes (Baylin, Esteller et al. 2001;
Esteller and Herman 2002; Jones and Baylin 2002; Issa 2004). Reactivation of tumor-
suppressor genes by demethylating agents has thus become a potential and promising
area of cancer therapy (Pinto and Zagonel 1993; Lubbert 2000; Aparicio, Eads et al. 2003;
Egger, Liang et al. 2004). There is a growing list of DNA methylation inhibitors in
addition to 5-azacytidine and 5-aza-2’-deoxycytidine (5-Aza-CdR) (Jones and Taylor
1980), the first demethylating agents with well characterized mechanisms of action. The
list includes, but is not limited to, 5-fluoro-deoxycitidine, zebularine , antisense
oligodeoxynucleotides, mitoxantrone, psammaplin A, procaine, N-acetylprocainamide,
procainamide, hydralazine, and EGCG (Cornacchia, Golbus et al. 1988; Richardson,
Cornacchia et al. 1988; Scheinbart, Johnson et al. 1991; Lin, Asgari et al. 2001; Zhou,
21
Cheng et al. 2002; Cheng, Matsen et al. 2003; Deng, Lu et al. 2003; Fang, Wang et al.
2003; Parker, Cutts et al. 2003; Pina, Gautschi et al. 2003; Segura-Pacheco, Trejo-
Becerril et al. 2003; Villar-Garea, Fraga et al. 2003; Cheng, Yoo et al. 2004; Egger,
Liang et al. 2004; Gorbunova, Seluanov et al. 2004; Saikawa, Kubota et al. 2004).

Hydralazine and procainamide were first reported to have DNA methylation
inhibition properties in 1988 (Cornacchia, Golbus et al. 1988). Hydralazine is a
vasodilator and is used clinically as an antihypertensive drug. It has been found to
decrease the mRNA expression of DNA methyltransferases (DNMT) 1 and 3A and the
DNMT catalytic activities, and induce autoimmunity (Deng, Lu et al. 2003).
Procainamide is used clinically as an antiarrhythmic, and previous studies have shown
that it inhibits DNMT activity, thus leading to DNA hypomethylation (Scheinbart,
Johnson et al. 1991; Lin, Asgari et al. 2001). Recently, EGCG, the major polyphenol in
green tea that has been reported to have chemopreventive activity (Moyers and Kumar
2004; Park and Surh 2004), has been reported to directly inhibit the DNMT enzymes and
reactivate methylation-silenced genes such as RAR β and p16 (Fang, Wang et al. 2003).

Despite the identifications of an increasing number of DNA methylation
inhibitors, there has not been a systemic study comparing the DNA demethylating effects
and potencies of these agents. In this study, I compare several potential non-nucleoside
DNA methylation inhibitors—EGCG, hydralazine, and procainamide—to the nucleoside
analog methylation inhibitor 5-Aza-CdR (Figure 2.1). I found that 5-Aza-CdR is far
22
Figure 2.1: Chemical structures of the DNA methylation inhibitors

A. 5-Aza-2’-Deoxycitidine

B. Zebularine

C. EGCG

D. Hydralazine

E. Procainamide

F. dZTP

23
Figure 2.1, Continued

G. TpZ

H. TTZ

I. TTTZ

J. TTTTZ

K. TZZ

more effective both in its DNA methylation inhibition activity and in its ability to
reactivate methylation-silenced genes in cancer cells.

In addition to examining the potential non-nucleoside DNA methylation inhibitors
mentioned above, I also studied several pro-drugs of zebularine. Zebularine, though less
potent than 5-Aza-CdR in its DNA methylation inhibition activity, is a promising
candidate for clinical use for its superior stability, as it is not susceptible to deamination
(Cheng, Yoo et al. 2004; Yoo and Jones 2006). Yoo et al. (Yoo, Jeong et al. 2007) have
shown that the delivery of 5-Aza-CdR in an oligonucleotide format can yield successful
DNA methylation inhibition, and the method also makes the compound more stable by
preventing degradation by deamination. Knowing that drugs in oligonucleotide forms
can be successfully delivered for treatment, I therefore examined several
oligodeoxynucleotides (ODNs) consisting of zebularine and thymidine, and found that
the actions of these compounds were cell-line specific and thus limited. Lastly, I
examined the possibility of treating cancer cells with deoxy-zebularine-triphosphate
(dZTP), and found it to be largely ineffective.

25
MATERIALS AND METHODS

Cell Lines
T24 (urinary bladder transitional cell carcinoma) cells, PC3 (prostate
adenocarcinoma) cells, HT29 (colorectal adenocarcinoma), CFPAC-1 (pancreatic ductal
adenocarcinomas), PL45 (pancreatic adenocarcinomas), Capan-1 (pancreatic
adenocarcinomas), and HepG2 (hepatocellular carcinoma) cells were obtained from
American Type Culture Collection (Manassas, VA). T24 and HT29 cells were cultured
in McCoy’s 5A medium supplemented with 10% FBS. PC3 cells were cultured in RPMI
medium plus 10% FBS. CFPAC-1 and Capan-1 cells were cultured in IMDM medium
supplemented with 10% and 20% FBS, respectively. PL45 and HepG2 cells were
cultured in DMEM medium plus 10% FBS. All cells were grown in a humidified 37 °C
incubator containing 5% CO
2
.

Cell Treatments
For the non-nucleoside DNA methylation inhibitors study, cells were seeded at
2X10
5
cells per 100mm dish 24 hrs prior to treatments. Cells were treated with 1 μM 5-
Aza-CdR (Sigma-Aldrich Chemical Company, St. Louis, MO), 20 and 30 μM of EGCG,
10 and 20 μM of hydralazine (Sigma-Aldrich Chemical Company), and 100 and 200 μM
of procainamide (Sigma-Aldrich Chemical Company). EGCG sample was a generous
gift from Dr. Chung S. Yang (from Unilever Bestfoods) (Fang, Wang et al. 2003), and a
separate sample was obtained from Sigma-Aldrich Chemical Company. 5-Aza-CdR was
made up in PBS and was removed after 24 hrs, while the other treatments were

26
continuous. EGCG was prepared in DMSO and replaced every two days. Hydralazine
and procainamide were made up in PBS fresh daily and replaced daily with new medium.
All treatment regimens have been shown to be effective in inhibiting DNA methylation in
previous studies (Deng, Lu et al. 2003; Fang, Wang et al. 2003; Segura-Pacheco, Trejo-
Becerril et al. 2003). Cells were collected after 6 days of treatment. Genomic DNA and
total RNA were extracted for subsequent methylation and expression studies using
standard methods.

For the oligodeoxynucleotides (ODN) compounds, cells were seeded at 150,000
cells per 60mm dish 24 hrs prior to treatment. Cells were treated with 1, 3, and 10 μM of
TpZ, TTZ, TTTZ, TTTTZ, and TZZ with and without 10 μM of thymidine. The ODNs
were provided by Dr. Victor Marquez at the NCI, and they were dissolved in DMSO.
Zebularine and thymidine were made up in PBS and were each administered at 100 μM
unless otherwise noted. The treatments were continuous except for the 5-Aza-CdR
control, which was removed after 24 hrs. Cells were collected after eight days of
treatment. Total RNA was extracted for subsequent expression studies using standard
methods.

For the dZTP study, cells were seeded at 200,000 cells per 60mm dish 48 hrs
prior to transfection treatment. I aimed to have 50% confluency of cells for transfection.
The dZTP compound was dissolved in PBS, and transfected into T24 cells using
Lipofectamine (Invitrogen, Carlsbad, CA) at final concentrations of 5, 10, and 50 μM.
Briefly, 2 μL of 100mM dZTP was mixed with 8 μL of Opti-MEM media, while

27
separately 200 μL of Lipofectamine was mixed with 190 μL of Opti-MEM. After 5min of
incubation, the two tubes were mixed together and incubated for another 20 min. After
incubation, the lipoplexes were then added to the cells in varying amounts to achieve the
final concentrations of 5, 10, and 50 μM of dZTP. The transfection reagents were
removed after 18 hrs. Cells were collected after 6 days of treatment. Total RNA was
extracted for subsequent expression studies using standard methods.

Quantitative DNA Methylation Analysis by Methylation-Specific Single Nucleotide
Extension (Ms-SNuPE)
Genomic DNA was extracted from cells with the Qiagen DNeasy Tissue Kit
(Valencia, CA). Two μg of each DNA sample was converted with sodium bisulfite as
previously described (Frommer, McDonald et al. 1992), and each region of interest was
amplified by PCR. The PCR conditions for MAGE-A1 were as follows: 94 ° for 4 min,
followed by 40 cycles of denaturation at 94 °C for 1 min, annealing at 53 °C for 1 min, and
extension at 72 °C for 1 min, and a final extension at 72 °C for 1 min. The PCR
conditions for LINE elements were as follows: 95 °C for 3 min, followed by 35 cycles
denaturation at 95 °C for 1 min, annealing at 51 °C, and extension at 72 °C for 1 min, and a
final extension at 72 °C for 10 min. The PCR conditions for p16 were as follows: 95 °C
for 3 min, followed by 40 cycles of denaturation at 95 °C for 1 min, annealing at 62 °C for
1 min, and extension at 72 °C for 1 min, and a final extension at 72 °C for 10min. The
bisulfite specific-PCR primer sequences are as follows: MAGE-A1 sense, 5’- GTT TAT
TTT TAT TTT TAT TTA GGT AGG ATT-3’, MAGE-A1 antisense, 5’- TTA CCT CCT

28
CAC AAA ACC TAA A-3’; LINE sense, 5’- TTT TTT GAG TTA GGT GTG GG-3’,
LINE antisense, 5’- CAT CTC ACT AAA AAA TAC CAA ACA A-3’; p16 sense, 5’-
GTA GGT GGG GAG GAG TTT AGT T-3’, p16 antisense, 5’- TCT AAT AAC CAA
CCA ACC CCT CCT-3’. The Ms-SNuPE conditions for MAGE-A1 and p16 were as
follows: 95 °C for 2 min, 50 °C for 2 min, and 72 °C for 1 min. The Ms-SNuPE
conditions for LINE elements were as follows: 95 °C for 1 min, 50 °C for 1 min, and
72°C for 1 min. The MAGE-A1 SNuPE primers are as follows: 5’-TTT TAT TTT TAT
TTA GGT AGG ATT-3’, 5’-TGG GGT AGA GAG AAG-3’, and 5’-AGG TTT TTA
TTT TGA GGG A-3’. The LINE SNuPE primers are as follows: 5’-GGG TGG GAG
TGA TT-3’, 5’-GAA AGG GAA TTT TTT GAT TTT TTG-3’, and 5’-TTT TTT AGG
TGA GGT AAT GTT T-3’. The p16 SNuPE primers are as follows: 5’-TTT TAG GGG
TGT TAT ATT-3’, 5’-TTT TTT TGT TTG GAA AGA TAT-3’, and 5’-TTT GAG GGA
TAG GGT-3’.
The PCR amplicons were extracted with the Qiagen Gel Extraction Kit, and Ms-SNuPE
analysis was performed to examine the methylation level changes as previously described
(Gonzalgo and Jones 2002).

Pyrosequencing
Bisulfite-converted DNA was used for pyrosequencing analysis as previously
described (Yang, Estecio et al. 2004). Pyrosequencing was performed for LINE elements,
Alu elements, and MAGE-A1 gene. The primers used are listed as follows: LINE
elements sense, 5’-TTT TTT GAG TTA GGT GTG GG-3’; LINE elements antisense, 5’-
biotin-TCT CAC TAA AAA ATA CCA AAC AA-3’; LINE elements sequencing, 5’-

29
GGG TGG GAG TGA T-3’; Alu elements sense, 5’-biotin-TTT TTA TTA AAA ATA
TAA AAA TT-3’; Alu elements antisense, 5’-CCC AAA CTA AAA TAC AAT AA-3’;
Alu elements sequencing, 5’-AAT AAC TAA AAT TAC AAA C-3’; MAGE-A1 sense,
5’-biotin-TAT TGT GGG GTA GAG AGA AG-3’; MAGE-A1 antisense, 5’-AAA TCC
TCA ATC CTC CCT CAA-3’; MAGE-A1 sequencing, 5’-AAC CTA AAT CAA ATT
CCT T-3’.

Reverse-Transcription PCR and Quantitative Real-Time Reverse-Transcription
PCR
Total RNA was extracted from cells with the Qiagen RNeasy Kit (Valencia, CA)
or the Invitrogen Trizol reagent (Carlsbad, CA). Reverse transcription (RT) was
performed with M-MLV reverse transcriptase and random hexamers from Promega
(Madison, WI). Reverse transcription PCR (RT-PCR) was performed for the p16 gene as
previously described (Cheng, Yoo et al. 2004) with the following primers: p16 sense, 5’-
AGC CTT CGG CTG ACT GGC TGG-3’; p16 antisense, 5’-CTG CCC ATC ATC ATG
ACC TGG A-3’. PCR conditions for p16 gene were as follows: 94 °C for 3 min,
followed by 35 cycles of denaturation at 94 °C for 1 min, annealing at 57 °C for 30 s, and
extension at 72 °C for 40 s, and a final extension at 72 °C for 5 min. I also performed
quantitative real-time RT-PCR analysis as previously described (Heid, Stevens et al.
1996) with DNA Engine Opticon System (MJ Research, Hercules, CA). The primers
used are listed below: MAGE-A1 sense, 5’-GAA CCT GAC CCA GGC TCT GTG-3’;
MAGE-A1 antisense, 5’-CCA CAG GCA GAT CTT CTC CTT G-3’; MAGE-A1
fluorogenic probe, 5’-CAA GGT TTT CAG GGG ACA GGC CAA C-3’; MAGE-B2

30
sense, 5’-CGG CAG TCA AGC CAT CAT G-3’; MAGE-B2 antisense, 5’-TTG CGG
CGT TTC TCA CG-3’; MAGE-B2 fluorogenic probe, 5’-TCG TGG TCA GAA GAG
TAA GCT CCG TGC-3’; RAR β sense, 5’-CCC TTC ACT CTG CCA GCT G-3’; RAR β
antisense, 5’-GCC CAG GTC CAG TCG GA-3’; RAR β fluorogenic probe, 5’-AAA TAC
ACC ACG AAT TCC AGT GCT GAC CA-3’; p16 sense, 5’-AGC CTT CGG CTG ACT
GGC TGG-3’; p16 antisense, 5’-CTG CCC ATC ATC ATG ACC TGG A-3’; p16
fluorogenic probe, 5’-TGG ATC GGC CTC CGA CCG TAA CT-3’. The real-time RT-
PCR conditions for all 4 genes were as follows: 95 °C for 9min, followed by 45 cycles of
denaturation at 95 °C for 15 s and annealing at 60 °C for 1min.

31
RESULTS

5-Aza-CdR is considerably more effective in DNA methylation inhibition than non-
nucleoside agents
The quantitative Ms-SNuPE and pyrosequencing methods were used to compare
the methylation status of several loci in the genome before and after treatment with
potential inhibitors. Ms-SNuPE analysis was performed to examine the methylation
levels of the p16 promoter, MAGE-A1, and LINE repetitive elements (Fig.2.2). EGCG
from Sigma appeared to be more toxic than EGCG from Unilever Bestfoods; the highest
doses of EGCG with surviving cells tested are shown in Figure 2.2 and 2.3: for T24 cells,
EGCG from Unilever Bestfoods (UB) was used at 20 μM. For HT29 cells, 30 μM of
EGCG from UB and 30 μM of EGCG from Sigma were used. For PC3 cells, 30 μM of
EGCG from UB and 20 μM of EGCG from Sigma were used.

The data also shows that the three cell lines have different sensitivities to the
agents tested. Only 5-Aza-CdR treatment was able to consistently reduce methylation
levels in T24, HT29, and PC3 cells. Of the non-nucleoside agents tested, 200 μM of
procainamide reduced the methylation level of LINE repetitive elements in T24 cells by
roughly 6%. No other non-nucleoside agents tested showed any measurable
demethylating activity. Minor reductions in LINE repetitive element methylation levels
(5-10%) were observed in HT29 cells treated with EGCG, hydralazine, and procainamide
treatments. Pyrosequencing analysis was performed for MAGE-A1, Alu, and LINE
repetitive elements (Figure 2.3) to further analyze the methylation level changes after

Figure 2.2: Comparison of the methylation inhibition potencies of the various agents by
Ms-SNuPE

A
B
C
0%
10%
20%
30%
40%
50%
60%
70%
T24 HT29 PC3
Unt
Aza
H-20
P-200
E(UB)-20 or 30
E(S)-20 or 30
% Methylation
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
T24 HT29 PC3
Unt
Aza
H-20
P-200
E(UB)-20 or 30
E(S)-20 or 30
% Methylation
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
T24 HT29 PC3
Unt
Aza
H-20
P-200
E(UB)-20 or 30
E(S)-20 or 30
% Methylation
5-Aza-CdR
5-Aza-CdR
5-Aza-CdR
A
B
C
0%
10%
20%
30%
40%
50%
60%
70%
T24 HT29 PC3
Unt
Aza
H-20
P-200
E(UB)-20 or 30
E(S)-20 or 30
% Methylation
0%
10%
20%
30%
40%
50%
60%
70%
T24 HT29 PC3
Unt
Aza
H-20
P-200
E(UB)-20 or 30
E(S)-20 or 30
% Methylation
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
T24 HT29 PC3
Unt
Aza
H-20
P-200
E(UB)-20 or 30
E(S)-20 or 30
% Methylation
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
T24 HT29 PC3
Unt
Aza
H-20
P-200
E(UB)-20 or 30
E(S)-20 or 30
% Methylation
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
T24 HT29 PC3
Unt
Aza
H-20
P-200
E(UB)-20 or 30
E(S)-20 or 30
% Methylation
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
T24 HT29 PC3
Unt
Aza
H-20
P-200
E(UB)-20 or 30
E(S)-20 or 30
% Methylation
5-Aza-CdR
5-Aza-CdR
5-Aza-CdR

Ms-SNuPE results of the methylation levels of (A) p16 promoter, (B) MAGE-A1, and (C)
LINE elements after treatments with hydralazine, procainamide, and EGCG in T24,
HT29, and PC3 cells. Cells were treated with the various agents for 6 days. The results
of two independent experiments are shown in the graph as the percentage methylation ±
standard deviation. The percentage methylation is calculated as the average C/(C+T)
signal ratio of three separate CpG sites for each region examined. Unt: untreated; 5-Aza-
CdR: 1 μM 5-Aza-CdR; E-20/30(S): 20/30 μM of EGCG from Sigma; E-20/30(UB):
20/30 μM of EGCG from Unilever Bestfoods; H-20: 20 μM of hydralazine; P-200:
200μM of procainamide.

33
Figure 2.3: Comparison of the methylation inhibition potencies of the various agents by
Pyrosequencing

A
B
C
0%
5%
10%
15%
20%
25%
30%
35%
T24 HT29 PC3
% M e th ylatio n
Unt
Aza
H-20
P-200
E(UB)-20 or 30
E(S)-20 or 30
0%
20%
40%
60%
80%
100%
T24 HT29 PC3
% M eth ylatio n
Unt
Aza
H-20
P-200
E(UB)-20 or 30
E(S)-20 or 30
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
T24 HT29 PC3
% M e t hy l a t i on
Unt
Aza
H-20
P-200
E(UB)-20 or 30
E(S)-20 or 30
% Methylation % Methylation % Methylation
5-Aza-CdR
5-Aza-CdR
5-Aza-CdR
A
B
C
0%
5%
10%
15%
20%
25%
30%
35%
T24 HT29 PC3
% M e th ylatio n
Unt
Aza
H-20
P-200
E(UB)-20 or 30
E(S)-20 or 30
0%
20%
40%
60%
80%
100%
T24 HT29 PC3
% M eth ylatio n
Unt
Aza
H-20
P-200
E(UB)-20 or 30
E(S)-20 or 30
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
T24 HT29 PC3
% M e t hy l a t i on
Unt
Aza
H-20
P-200
E(UB)-20 or 30
E(S)-20 or 30
% Methylation % Methylation % Methylation
5-Aza-CdR
5-Aza-CdR
5-Aza-CdR

Pyrosequencing results of the methylation levels of (A) Alu repetitive elements, (B)
MAGE-A1, and (C) LINE repetitive elements after treatments with hydralazine,
procainamide, and EGCG in T24, HT29, and PC3 cells. The percentage methylation is
calculated as the C/(C+T) ratio of the first CpG sites. Results are shown as the average
percentage methylation of two independent experiments ± standard deviation. Unt:
untreated; 5-Aza-CdR: 1 μM 5-Aza-CdR; E-20/30(S): 20/30 μM of EGCG from Sigma;
E-20/30(UB): 20/30 μM of EGCG from Unilever Bestfoods; H-20: 20 μM of hydralazine;
P-200: 200 μM of procainamide.

34
treatment and to confirm my Ms-SNuPE data. Pyrosequencing results also showed that
only 5-Aza-CdR was able to reduce methylation levels after treatment. Treatments with
10μM hydralazine, 100µM procainamide, and EGCG from both Sigma and Unilever
Bestfoods (Lubbert) (20µM) were also performed and did not show any notable decrease
in methylation by Ms-SNuPE and pyrosequencing analyses (data not shown).

5-Aza-CdR is considerably more effective in reactivating silenced genes in cancer
cells than non-nucleoside agents
To examine the ability of 5-Aza-CdR and the non-nucleoside agents to reactivate
gene expression, I performed RT-PCR for the p16 gene. Figure 2.4A shows a
representative result of three independent RT-PCR experiments using p16 as the indicator
gene. Only 5-Aza-CdR was able to turn on the expression of the p16 gene.

Given such dissimilarities between my results and previously published data, I
compared the experimental details to examine for possible sources of discrepancies. I
found that the RT-PCR primers for p16 expression used by Fang et al. were not specific
for p16; they bound to both transcripts of the CDKN2A gene—p14 and p16 (Figure
2.4B). The erroneous primer designs by Fang et al. (Fang, Wang et al. 2003) led to
misinterpretation of their RT-PCR data.

Real-time RT-PCR was also performed to check for the expression of MAGE-A1,
MAGE-B2, RAR β, and p16 after treatments with 5-Aza-CdR, hydralazine, procainamide,
and EGCG (Figure 2.5). The RAR β gene was resistant to any of the agents tested. The

35
Figure 2.4: Effects of the various agents on the reactivation of p16

2.4.A
Unt
DMSO
5-Aza-CdR
E-20(UB)
H-10
H-20
P-100
P-200
Unt
DMSO
5-Aza-CdR
E-20(S)
E-20(UB)
E-30(UB)
H-10
H-20
P-100
P-200
Unt
DMSO
5-Aza-CdR
E-20(S)
E-20(UB)
E-30(UB)
H-10
H-20
P-100
P-200
p16
GAPDH
p16
GAPDH
p16
GAPDH
T24
HT29
PC3
Unt
DMSO
5-Aza-CdR
E-20(UB)
H-10
H-20
P-100
P-200
Unt
DMSO
5-Aza-CdR
E-20(S)
E-20(UB)
E-30(UB)
H-10
H-20
P-100
P-200
Unt
DMSO
5-Aza-CdR
E-20(S)
E-20(UB)
E-30(UB)
H-10
H-20
P-100
P-200
p16
GAPDH
p16
GAPDH
p16
GAPDH
T24
HT29
PC3

RT-PCR with p16 gene in T24, HT29, and PC3 cells. The three cell lines were treated
with the various agents for 6 days. Reactions were run on ethidium bromide stained
agarose gels. GAPDH was used as a loading control. Unt: untreated; DMSO: DMSO
control, same amount added as the EGCG 30 μM sample; 5-Aza-CdR: 1 μM 5-Aza-CdR;
E-20/30(S): 20/30 μM of EGCG from Sigma; E-20/30(UB): 20/30 μM of EGCG from
Unilever Bestfoods; H-10/20: 10/20 μM of hydralazine; P-100/200: 100/200 μM of
procainamide.

36
Figure 2.4, Continued

2.4.B

1 β 1 α
p14
ARF
p16
INK4a
CDKN2A
Our primers
Primers by Fang et al.
1 β 1 α
p14
ARF
p16
INK4a
CDKN2A
Our primers
Primers by Fang et al.

Schematic representation of CDKN2A gene and the two alternative transcripts—p16 and
p14, with the locations of the two primer sets labeled above. The primers we used were
p16-specific, while the primers by Fang et al. detected both p16 and p14 expression.
Figure adopted from Inoue et al. (Gene Therapy (2004) 11, 1195–1204)

37
Figure 2.5: Effects of the various agents on the reactivation of MAGE-A1, MAGE-B2,
RAR β, and p16

A
B
C
0
5
10
30
40
0
5
10
24
44
U A E20 (S) E 30(U B ) H20 P2 00
0
5
30
80
MAG E -A1
MAG E -B 2
RA Rb
p1 6
Expression (Gene/GAPDH) Expression (Gene/GAPDH) Expression (Gene/GAPDH)
Unt 5-Aza-CdR E-20(UB) H-20 P-200
Unt 5-Aza-CdR E-30(S) E-30(UB) H-20 P-200
Unt 5-Aza-CdR E-20(S) E-30(UB) H-20 P-200
A
B
C
0
5
10
30
40
0
5
10
24
44
U A E20 (S) E 30(U B ) H20 P2 00
0
5
30
80
MAG E -A1
MAG E -B 2
RA Rb
p1 6
Expression (Gene/GAPDH) Expression (Gene/GAPDH) Expression (Gene/GAPDH)
Unt 5-Aza-CdR E-20(UB) H-20 P-200
Unt 5-Aza-CdR E-30(S) E-30(UB) H-20 P-200
Unt 5-Aza-CdR E-20(S) E-30(UB) H-20 P-200

Real time RT-PCR with MAGE-A1, MAGE-B2, RAR β, and p16 in (A)T24, (B)HT29, and
(C)PC3 cells. The three cell lines were treated with the various agents for 6 days.
Quantitative real time RT-PCR analysis of the various genes was done with
normalization against the GAPDH reference gene. Representative results of three
independent experiments are shown. Unt: untreated; DMSO: DMSO control, same
amount added as the EGCG 30 μM sample; 5-Aza-CdR: 1 μM 5-Aza-CdR; E-20/30(S):
20/30 μM of EGCG from Sigma; E-20/30(UB): 20/30 μM of EGCG from Unilever
Bestfoods; H-10/20: 10/20 μM of hydralazine; P-100/200: 100/200 μM of procainamide.

38
remaining three genes examined—MAGE-A1, MAGE-B2, and p16—were all reactivated
by 5-Aza-CdR but not by any of the non-nucleoside methylation inhibition agents.

ODN compounds are effective in reactivating p16, although effects are greatly
increased by the addition of thymidine
Zebularine has been shown to be an effective and stable DNA methylation
inhibitor. Its stability makes it available for oral administration and thus an attractive
alternative to 5-Aza-CdR (Yoo and Jones 2006). However, because the majority of the
zebularine administered gets incorporated into RNA and only a small fraction in DNA,
efforts have been made to bypass this shortcoming (Ben-Kasus, Ben-Zvi et al. 2005).
While deoxyzebularineularine is not phosphorylated by deoxycytidine kinase inside cells,
deoxyzebularineularine monophosphate (dZMP) can be phosphorylated into
deoxyzebularineularine di- (dZDP) and triphosphate (dZTP) and thus incorporated into
DNA (Figure 2.6) (Yoo and Jones 2006). There have been efforts in developing
alternative forms of zebularine to increase its incorporation into DNA, and therefore its
potency as a DNA methylation inhibitor. Yoo et al. (manuscript completed) have shown
that phosphoramidite pronucleotide pro-drugs that release zebularine as dZMP inside
cells to be effective with the addition of thymidine. (data not yet published) The
thymidine is believed to overcome the inhibition of thymidylate synthetase (TS) by
dZMP (Figure 2.7) (Votruba 1973); however, the need for thymidine addition makes this
approach impractical for clinical use.

39
Figure 2.6: Metabolism of zebularine

DNA
2’-Deoxyzebularine
dCyd Kinase
Ribonucleotide
Reductase
RNA
Zebularine
Urd/Cyd Kinase
P Zebularine-
P P
Zebularine-
P P P Zebularine-
P 2’-Deoxyzebularine-
P P
2’-Deoxyzebularine-
P P P 2’-Deoxyzebularine-
DNA
2’-Deoxyzebularine
dCyd Kinase
Ribonucleotide
Reductase
RNA
Zebularine
Urd/Cyd Kinase
P Zebularine- P Zebularine-
P P
Zebularine-
P P P P
Zebularine-
P P P Zebularine- P P P P P P Zebularine-
P 2’-Deoxyzebularine- P 2’-Deoxyzebularine-
P P
2’-Deoxyzebularine-
P P P P
2’-Deoxyzebularine-
P P P 2’-Deoxyzebularine- P P P P P P 2’-Deoxyzebularine-

Figure courtesy of Christine Yoo

Zebularine is a ribonucleotide analog, and it is phosphorylated by uridine/cytidine kinase
and other kinases into mono-, di-, and tri-phosphates and can get incorporated RNA.
Ribonucleotide reductase can convert zebularine diphosphate into its deoxy-diphosphate
form, which can be subsequently phosphorylated and incorporated into DNA. However,
2’-deoxyzebularine is not a good substrate for deoxycytidine kinase and therefore is
ineffective for inhibiting DNA methylation.

40
Figure 2.7: Thymidylate Synthase converts dUMP to dTMP and is inhibited by dZMP

7,8-dihydrofolate
N
5
,N
10
-methylene-
tetrahydrofolate
dTMP dUMP
N
CH
C
O
HN
CH C
O
OH
-
O-P-O-CH
2
O
-
O
O
CH
3
N
C
C
O
HN
CH C
O
OH
-
O-P-O-CH
2
O
-
O
Thymidylate
Synthetase
5-FdUMP or dZMP
7,8-dihydrofolate
N
5
,N
10
-methylene-
tetrahydrofolate
dTMP dUMP
N
CH
C
O
HN
CH C
O
N
CH
C
O
HN
CH C
O
N
CH
C
O
C
O
HN
CH C
O
OH
-
O-P-O-CH
2
O
-
O
OH OH
-
O-P-O-CH
2
O
-
O
-
O-P-O-CH
2
O
-
O
-
O O
O
CH
3
N
C
C
O
C
O
HN
CH C
O
OH
-
O-P-O-CH
2
O
-
O
OH
-
O-P-O-CH
2
O
-
O
OH OH
-
O-P-O-CH
2
O
-
O
-
O-P-O-CH
2
O
-
O
-
O O
Thymidylate
Synthetase
5-FdUMP or dZMP

Figure courtesy of Christine Yoo

41
Following the study by Yoo et al. (Yoo, Jeong et al. 2007) in which the authors
show that delivery of oligonucleotide molecules containing 5-Aza-CdR can be effective,
we designed drugs that contain both thymidine and zebularine in an ODN prodrug.
Phosphodiesterases cleave ODNs into individual nucleotides, leaving each phosphate
group on the nucleotide on its 3’ end. The ODNs would therefore deliver zebularine in
the form of dZMP for effective DNA incorporation, and also thymidine (if only one
thymidine in front of zebularine) and thymidine and thymidine monophosphate (if more
than one thymidine in front of zebularine) for overcoming the inhibition of TS.

Dinucleotides containing thymidine followed by zebularine (TpZ) are processed
in the cells by phosphodiesterases into thymidine and dZMP, and have been shown by
Christine Yoo to be effective at high concentrations in inducing p16 expression without
the addition of thymidine. However, its ability is greatly boosted by the addition of
thymidine (unpublished data). I therefore tested TpZ, two thymidines followed by
zebularine (TTZ), tri-thymidine followed by zebularine (TTTZ), and quadruple
thymidines followed by zebularine (TTTTZ) to determine if the extra thymidine
nucleotides released by longer ODN molecules could eliminate the need for thymidine
co-treatment. In addition, I also tested ODNs containing thymidine followed by two
zebularines (TZZ). The results in CFPAC-1 cells (Figure 2.8) show that although the
effects of each compound were still increased with the addition of thymidine, TTTZ and
TTTTZ at 10 μM were effective in re-expressing p16 without the addition of thymidine.
In fact, at 10 μM, TTTZ and TTTTZ were more effective than zebularine at 100 μM. I
then tested if lower concentrations of thymidine would lead to the similar levels of

42
Figure 2.8: Effects of the various ODN compounds on CFPAC-1 cells

p1 6 E x p r essio n in C F P A C
0. 00E + 00
5. 00E - 03
1. 00E - 02
1. 50E - 02
2. 00E - 02
2. 50E - 02
3. 00E - 02
3. 50E - 02
4. 00E - 02
4. 50E - 02
unt
DMSO
Thy
Zeb
TpZ 1
TpZ 3
TpZ 10
TpZ1+Thy
TpZ3+Thy
TpZ10+Thy
TTZ 1
TTZ 3
TTZ 10
TTZ1+Thy
TTZ3+Thy
TTZ10+Thy
TTTZ 1
TTTZ 3
TTTZ 10
TTTZ1+Thy
TTTZ3+Thy
TTTZ10+Thy
TTTTZ 1
TTTTZ 3
TTTTZ 10
TTTTZ1+Thy
TTTTZ3+Thy
TTTTZ10+Thy
TZZ 1
TZZ 3
TZZ 10
TZZ1+Thy
TZZ3+Thy
TZZ10+Thy
t r eat m en t
p16/GAPDH
p16 Expression in CFPAC-1
Treatment
p1 6 E x p r essio n in C F P A C
0. 00E + 00
5. 00E - 03
1. 00E - 02
1. 50E - 02
2. 00E - 02
2. 50E - 02
3. 00E - 02
3. 50E - 02
4. 00E - 02
4. 50E - 02
unt
DMSO
Thy
Zeb
TpZ 1
TpZ 3
TpZ 10
TpZ1+Thy
TpZ3+Thy
TpZ10+Thy
TTZ 1
TTZ 3
TTZ 10
TTZ1+Thy
TTZ3+Thy
TTZ10+Thy
TTTZ 1
TTTZ 3
TTTZ 10
TTTZ1+Thy
TTTZ3+Thy
TTTZ10+Thy
TTTTZ 1
TTTTZ 3
TTTTZ 10
TTTTZ1+Thy
TTTTZ3+Thy
TTTTZ10+Thy
TZZ 1
TZZ 3
TZZ 10
TZZ1+Thy
TZZ3+Thy
TZZ10+Thy
t r eat m en t
p16/GAPDH
p16 Expression in CFPAC-1
p1 6 E x p r essio n in C F P A C
0. 00E + 00
5. 00E - 03
1. 00E - 02
1. 50E - 02
2. 00E - 02
2. 50E - 02
3. 00E - 02
3. 50E - 02
4. 00E - 02
4. 50E - 02
unt
DMSO
Thy
Zeb
TpZ 1
TpZ 3
TpZ 10
TpZ1+Thy
TpZ3+Thy
TpZ10+Thy
TTZ 1
TTZ 3
TTZ 10
TTZ1+Thy
TTZ3+Thy
TTZ10+Thy
TTTZ 1
TTTZ 3
TTTZ 10
TTTZ1+Thy
TTTZ3+Thy
TTTZ10+Thy
TTTTZ 1
TTTTZ 3
TTTTZ 10
TTTTZ1+Thy
TTTTZ3+Thy
TTTTZ10+Thy
TZZ 1
TZZ 3
TZZ 10
TZZ1+Thy
TZZ3+Thy
TZZ10+Thy
t r eat m en t
p16/GAPDH
p16 Expression in CFPAC-1
Treatment

Re-expression of p16 by the various ODN prodrugs in CFPAC-1 cells. Cells were
treated with the various drugs continuously for eight days. Quantitative real time RT-
PCR analysis of the p16 gene was done with normalization against the GAPDH reference
gene. Representative results of two independent experiments are shown. Unt: untreated;
DMSO: mock treatment with DMSO; Thy: thymidine at 100 μM; Zeb: Zebularine at
100μM; TpZ/TTZ/TTTZ/TTTTZ/TZZ 1/3/10: TpZ/TTZ/TTTZ/TTTTZ/TZZ at 1/3/10
μM.

43
increase in activity for the ODN compounds (Figure 2.9), but my results show that only
thymidine at 100 μM can lead to an appreciable increase in p16 expression when
combined with the ODNs.

I expanded the study to include more cell lines—PL45, Capan, and HepG2. PL45
and Capan cells, like CFPAC-1 cells, are pancreatic adenocarcinoma cell lines and
therefore may behave similarly in their responses to the ODNs because they likely share
similar physiological and pathological molecular profiles (Figure 2.10). I found that
Capan cells were completely resistant to any of the drugs used, including my zebularine
and 5-Aza-CdR controls. PL45 cells showed re-expression of p16 by zebularine and 5-
Aza-CdR, but not by TpZ, even with the addition of thymidine. HepG2 responded
minimally to TpZ at 10 μM + thymidine, and therefore I examined the response of this
cell line to different ODNs (Figure 2.11). However, the results demonstrated that the
ODN compounds were largely ineffective in HepG2 cells. Taken together, the ODN
compounds have been shown to be effective only in CFPAC-1 cells, and thus have
limited clinical applicability.

Delivery of dZTP using Lipofectamine transfection is ineffective in inducing p16 re-
expression
In another attempt to design a form of zebularine for effective, direct
incorporation into DNA, I tested the possibility of delivering dZTP molecules into cells
directly using the Lipofectamine transfection reagent. dZTP can be incorporated into
DNA directly; however, it cannot enter the cells directly without means of transportation

44
Figure 2.9: Effects of the varying concentrations of thymidine combined with ODN
compounds in CFPAC-1 cells

p16 Expression in CFPAC cells
0.00E+00
4.00E-03
8.00E-03
1.20E-02
1.60E-02
2.00E-02
unt
DMSO
Thy1
Thy10
Thy100
Zeb10+Thy1
Zeb10+Thy10
Zeb10+Thy100
Zeb100
Aza
TTTZ
TTTZ+Thy1
TTTZ+Thy10
TTTZ+Thy100
TTTTZ
TTTTZ+Thy1
TTTTZ+Thy10
TTTTZ+Thy100
Treatment
p16/GAPDH
5-Aza-CdR
p16 Expression in CFPAC-1
p16 Expression in CFPAC cells
0.00E+00
4.00E-03
8.00E-03
1.20E-02
1.60E-02
2.00E-02
unt
DMSO
Thy1
Thy10
Thy100
Zeb10+Thy1
Zeb10+Thy10
Zeb10+Thy100
Zeb100
Aza
TTTZ
TTTZ+Thy1
TTTZ+Thy10
TTTZ+Thy100
TTTTZ
TTTTZ+Thy1
TTTTZ+Thy10
TTTTZ+Thy100
Treatment
p16/GAPDH
5-Aza-CdR
p16 Expression in CFPAC cells
0.00E+00
4.00E-03
8.00E-03
1.20E-02
1.60E-02
2.00E-02
unt
DMSO
Thy1
Thy10
Thy100
Zeb10+Thy1
Zeb10+Thy10
Zeb10+Thy100
Zeb100
Aza
TTTZ
TTTZ+Thy1
TTTZ+Thy10
TTTZ+Thy100
TTTTZ
TTTTZ+Thy1
TTTTZ+Thy10
TTTTZ+Thy100
Treatment
p16/GAPDH
5-Aza-CdR
p16 Expression in CFPAC-1

Re-expression of p16 by TTTZ and TTTTZ with varying concentrations of thymidine in
CFPAC-1 cells. Cells were treated with the various drugs for eight days; 5-Aza-CdR was
removed after 24 hrs, while the rest of the treatments were continuous. Quantitative real
time RT-PCR analysis of the p16 gene was done with normalization against the GAPDH
reference gene. Results are shown as the average of two independent PCR reactions ±
standard deviation. Unt: untreated; DMSO: mock treatment with DMSO; Thy 1/10/100:
thymidine at 1/10/100 μM; Zeb 10/100: Zebularine at 10/100 μM; 5-Aza-CdR: 5-Aza-
CdR at 1 μM; TTTZ/TTTTZ: TTTZ/TTTTZ at 10 μM.

45
Figure 2.10: Re-Expression of p16 by TpZ in CFPAC-1, PL45, and HepG2 cells

2.10.A
p16 Expression in CFPAC cells
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
unt
DMSO
Thy
Zeb
Aza
TpZ1
TpZ3
TpZ10
TpZ1+thy
TpZ3+thy
TpZ10+thy
treatment
p16/GAPDH
p16 Expression in CFPAC-1
5-Aza-CdR
p16 Expression in CFPAC cells
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
unt
DMSO
Thy
Zeb
Aza
TpZ1
TpZ3
TpZ10
TpZ1+thy
TpZ3+thy
TpZ10+thy
treatment
p16/GAPDH
p16 Expression in CFPAC-1
5-Aza-CdR

2.10.B
p16 Expression in PL45 cells
0.00E+00
1.00E-03
2.00E-03
3.00E-03
4.00E-03
unt
DMSO
Thy
Zeb
Aza
TpZ1
TpZ3
TpZ10
TpZ1+thy
TpZ3+thy
TpZ10+thy
treatment
p16/GAPDH
Unt
DMSO
Thy
Zeb
5-Aza-CdR
TpZ1
TpZ3
TpZ10
TpZ1+thy
TpZ3+thy
TpZ10+thy
p16 Expression in PL45
p16 Expression in PL45 cells
0.00E+00
1.00E-03
2.00E-03
3.00E-03
4.00E-03
unt
DMSO
Thy
Zeb
Aza
TpZ1
TpZ3
TpZ10
TpZ1+thy
TpZ3+thy
TpZ10+thy
treatment
p16/GAPDH
Unt
DMSO
Thy
Zeb
5-Aza-CdR
TpZ1
TpZ3
TpZ10
TpZ1+thy
TpZ3+thy
TpZ10+thy
p16 Expression in PL45

Figure 2.10, Continued

2.10.C
p16 Expression in HepG2 cells
0.00E+00
1.00E-03
2.00E-03
3.00E-03
4.00E-03
5.00E-03
6.00E-03
unt
DMSO
Thy
Zeb
Aza
TpZ1
TpZ3
TpZ10
TpZ1+Thy
TpZ3+Thy
TpZ10+Thy
treatm ent
p16/GAPDH
Unt
DMSO
Thy
Zeb
5-Aza-CdR
TpZ1
TpZ3
TpZ10
TpZ1+thy
TpZ3+thy
TpZ10+thy
p16 Expression in HepG2
p16 Expression in HepG2 cells
0.00E+00
1.00E-03
2.00E-03
3.00E-03
4.00E-03
5.00E-03
6.00E-03
unt
DMSO
Thy
Zeb
Aza
TpZ1
TpZ3
TpZ10
TpZ1+Thy
TpZ3+Thy
TpZ10+Thy
treatm ent
p16/GAPDH
Unt
DMSO
Thy
Zeb
5-Aza-CdR
TpZ1
TpZ3
TpZ10
TpZ1+thy
TpZ3+thy
TpZ10+thy
p16 Expression in HepG2

Re-expression of p16 in A) CFPAC-1, B) PL45, and C) HepG2 cells. Cells were treated
for eight days; 5-Aza-CdR was removed after 24 hrs, while the rest of the treatments
were continuous. Quantitative real time RT-PCR analysis of the p16 gene was done with
normalization against the GAPDH reference gene. Results are shown as the average of
two independent PCR reactions ± standard deviation. Unt: untreated; DMSO: mock
treatment with DMSO; Thy: thymidine at 100 μM; Zeb: Zebularine at 100 μM; 5-Aza-
CdR: 5-Aza-CdR at 1 μM; TpZ 1/3/10: TpZ at 1/3/10 μM.

47
Figure 2.11: Effects of ODN compounds on HepG2 cells

p16 Expression in HepG2 Cells
0.00E+00
5.00E-04
1.00E-03
1.50E-03
2.00E-03
2.50E-03
3.00E-03
3.50E-03
4.00E-03
unt
DMSO
Thy
Zeb
dAza
TpZ1
TpZ3
TpZ10
TpZ1+Thy
TpZ3+Thy
TpZ10+Thy
TTZ1
TTZ3
TTZ10
TTZ1+Thy
TTZ3+Thy
TTZ10+Thy
TTTZ1
TTTZ3
TTTZ10
TTTZ1+Thy
TTTZ3+Thy
TTTZ10+Thy
treatment
p16/GAPDH
p16 Expression in HepG2 Cells
5-Aza-CdR
p16 Expression in HepG2 Cells
0.00E+00
5.00E-04
1.00E-03
1.50E-03
2.00E-03
2.50E-03
3.00E-03
3.50E-03
4.00E-03
unt
DMSO
Thy
Zeb
dAza
TpZ1
TpZ3
TpZ10
TpZ1+Thy
TpZ3+Thy
TpZ10+Thy
TTZ1
TTZ3
TTZ10
TTZ1+Thy
TTZ3+Thy
TTZ10+Thy
TTTZ1
TTTZ3
TTTZ10
TTTZ1+Thy
TTTZ3+Thy
TTTZ10+Thy
treatment
p16/GAPDH
p16 Expression in HepG2 Cells
5-Aza-CdR

Re-Expression of p16 in HepG2 cells. Cells were treated for eight days; 5-Aza-CdR was
removed after 24 hrs, while the rest of the treatments were continuous. Quantitative real
time RT-PCR analysis of the p16 gene was done with normalization against the GAPDH
reference gene. Unt: untreated; DMSO: mock treatment with DMSO; Thy: thymidine
at 100 μM; Zeb: Zebularine at 100 μM; 5-Aza-CdR: 5-Aza-CdR at 1 μM;
TpZ/TTZ/TTTZ 1/3/10: TpZ/TTZ/TTTZ at 1/3/10 μM.

48
because of its high polarity. I therefore used Lipofectamine to deliver dZTP compounds
into T24 cells at 5, 10, and 50 μM final concentrations. Because the ratio of
Lipofectamine and dZTP had to be kept consistent to balance the charges of both
molecules, 50 μM of dZTP was the highest concentration I could achieve using this
method. I observed that the cells became much bigger and more angular after the
treatment with dZTP + Lipofectamine at 50 μM, and this morphological change was not
observed in lower doses or any of the other control treatments. Cells treated with
Lipofectamine only at 50 μM and zebularine transfected with Lipofectamine at 50 μM,
which served as my controls for the dZTP experiment, all died. This was most likely due
to the extra charges of Lipofectamine that was not balanced. None of the dZTP
concentrations tested was effective in re-expressing p16 in T24 cells (Figure 2.12). I
conclude that this method of delivery is ineffective in directing dZTP for DNA
incorporation.

49
Figure 2.12: Effects of dZTP transfection using Lipofectamine

Expression of p16 in T24 cells
0.00E+00
1.00E-03
2.00E-03
3.00E-03
4.00E-03
unt
lipo5
lipo10
Zeb100
Zeb5
Zeb10
Zeb50
Zeb5+lipo
Zeb10+lipo
dZTP5
dZTP10
dZTP50
dZTP5+lipo
dZTP10+lipo
dZTP50+lipo
Treatment
p16/GAPDH
p16 Expression in T24 Cells
Expression of p16 in T24 cells
0.00E+00
1.00E-03
2.00E-03
3.00E-03
4.00E-03
unt
lipo5
lipo10
Zeb100
Zeb5
Zeb10
Zeb50
Zeb5+lipo
Zeb10+lipo
dZTP5
dZTP10
dZTP50
dZTP5+lipo
dZTP10+lipo
dZTP50+lipo
Treatment
p16/GAPDH
p16 Expression in T24 Cells

Reactivation of p16 in T24 cells. Cells were transfected for 18 hrs and collected on day 6
for analysis. Quantitative real time RT-PCR analysis of the p16 gene was done with
normalization against the GAPDH reference gene. Unt: untreated; lipo 5/10:
Lipofectamine only at 5/10 μM; Zeb 5/10/50: Zebularine at 5/10/50 μM; Zeb 5/10 + lipo:
Zebularine transfected using Lipofectamine for the final concentration of 5/10 μM; dZTP
5/10/50: dZTP at 5/10/50 μM; dZTP 5/10/50 + lipo: dZTP transfected using
Lipofectamine for the final concentration of 5/10/50 μM. The increase in p16 expression
with dZTP 10 μM was not significant.

50
Discussion
Previous studies have shown that EGCG, hydralazine, and procainamide are able
to reduce DNA methylation and reactivate gene expression in cancer cells (Cornacchia,
Golbus et al. 1988; Scheinbart, Johnson et al. 1991; Lin, Asgari et al. 2001; Deng, Lu et
al. 2003; Fang, Wang et al. 2003; Segura-Pacheco, Trejo-Becerril et al. 2003; Gorbunova,
Seluanov et al. 2004). I examined a total of six different genes/repetitive elements in
three separate cell lines for their DNA methylation levels using quantitative Ms-SNuPE
and Pyrosequencing, and their mRNA expression levels by real time RT-PCR and RT-
PCR. Both Ms-SNuPE and pyrosequencing results show that only the nucleoside analog
5-Aza-CdR can significantly reduce methylation in all three cell lines. All three non-
nucleoside agents have much weaker, if any, demethylating activities, with procainamide
being the only agent able to reduce DNA methylation of LINE elements in T24 cells.
The slight differences between the Ms-SNuPE and the Pyrosequencing results for the
LINE elements may be attributed by the fact that the Ms-SNuPE method examined three
separate CpG sites, whereas only one CpG site was assayed in the Pyrosequencing
method. The expression studies with RT-PCR also show that only 5-Aza-CdR was able
to appreciably reactivate MAGE-A1, MAGE-B2, and p16 genes as shown previously in
other studies (Weber, Salgaller et al. 1994; Lurquin, De Smet et al. 1997; Gonzalgo,
Hayashida et al. 1998; De Smet, Lurquin et al. 1999). The RAR β gene may require the
simultaneous administration of 5-Aza-CdR along with a histone deacetylase inhibitor
such as Trichostatin A for its reactivation in these cell lines (Bovenzi and Momparler
2001).

51
At present, I cannot explain the discrepancy between my data and earlier studies.
There are many potential reasons for this, however, these other agents seem unlikely to
be robust and reliable inhibitors of DNA methylation. The discrepancies could arise from
one or more of the following possibilities: the actions of the non-nucleoside agents could
be gene-specific or cell-line-specific, the treatment methods might have been ineffective
to show efficacy, or the methods of analysis were different from previous studies.

I do not believe that the discrepancies were solely due to the set of genes in my
study because I examined some of the genes that have been shown to be responsive to
these agents in other studies, such as p16 and RAR β (Fang, Wang et al. 2003; Segura-
Pacheco, Trejo-Becerril et al. 2003). In addition, I examined global methylation level
changes with LINE and Alu repetitive elements and did not observe methylation
inhibition by the non-nucleoside agents.

Testing with different cell lines could be another source of discrepancy
(Cornacchia, Golbus et al. 1988; Scheinbart, Johnson et al. 1991; Lin, Asgari et al. 2001;
Deng, Lu et al. 2003). I examined the effect of DNA methylation inhibitors on T24,
HT29, and PC3 cells. From my results, it is apparent that different cell lines have
different sensitivities to these agents. Other studies with Jurkat (Deng, Lu et al. 2003)
and LnCAP (Lin, Asgari et al. 2001) cell lines have shown apparent methylation
inhibition activities of hydralazine and procainamide, respectively. Perhaps studies with
different cell lines and/or a higher dose regimen will show the demethylating effect of
these agents. However, I followed the treatment methods that were reported to be

52
effective in previous studies for the non-nucleoside agents, and therefore I do not believe
this to be the cause of the discrepancy (Deng, Lu et al. 2003; Fang, Wang et al. 2003;
Segura-Pacheco, Trejo-Becerril et al. 2003). Nevertheless, longer treatments with these
agents might be able to induce an appreciable decrease in DNA methylation (Lin, Asgari
et al. 2001).

Finally, discrepancies could arise from different methods of study. I used Ms-
SNuPE and Pyrosequencing analyses, two quantitative and reliable methods, to measure
methylation levels. It is possible that the differences between the methods I employed
and other methods such as methylation-specific PCR could lead to different results. One
should also pay attention to experimental details, as I have discovered that the wrong
primer designs by Fang et al. (Fang, Wang et al. 2003) could lead to erroneous results.

Green tea, which contains EGCG, is often consumed chronically. Additionally,
hydralazine and procainamide are both used for long-term management of hypertension
and cardiac arrhythmia, respectively. The possible weaker demethylating effects of these
agents should not be ignored. While they are considerably weaker in their DNA
methylation inhibition activity compared to 5-Aza-CdR, it is feasible that long term usage
of these agents might have small effects. However, one should consider the potential and
feasibility of these non-nucleoside agents in chemotherapy regimens. Plasma levels of
procainamide above 10 μg/ml (~36.8 μM) are associated with toxicity in a patient such as
ventricular tachycardia or fibrillation (2004). The concentrations I tested in cell culture
were much higher than the toxic plasma level. Taken together, my results do not support

53
the idea that the three non-nucleoside agents tested are likely to be effective as epigenetic
therapies with clinical or preventative actions.

On the other hand, my attempts to create a more effective form of zebularine have
been largely unsuccessful. The ODN compounds, thought successful in CFPAC-1 cells,
are not effective in all the other cell lines I have examined. Their clinical applicability is
therefore likely to be limited. The ODN compounds are believed to be processed in the
cells by phosphodiesterases (PDEs), and therefore it is likely that their effectiveness
depends on the cellular PDE activities. Hence, these compounds may still be employed
for treatment if a particular tumor type presents with high PDE activity and abnormal
hypermethylation.

Lastly, my attempt to deliver dZTP molecules into cells by Lipofectamine failed
to induce p16 gene expression, despite a dramatic change in cellular appearances. The
fact that dZTP + Lipofectamine at the highest concentration tested was able to induce cell
morphology changes led us to believe that the dZTPs were most likely successfully
delivered into the cells. The exact reason why these molecules did not lead to successful
p16 re-expression needs further analysis. For example, it would be of interest to
investigate if similar morphological changes would be induced by Lipofectamine
transfection with dCTPs.

Additionally, because of the high incorporation of zebularine into RNA, many
cellular changes observed from the treatment with zebularine could be attributed to their

54
potential effects on the cellular RNA molecules (Ben-Kasus, Ben-Zvi et al. 2005). For
example, they have the potential of affecting mRNA stability or miRNA processing.
Either one of these actions would likely have a pronounced effect on the cells.
Zebularine pro-drugs that lead to direct incorporation into DNA instead of both DNA and
RNA, when compared to zebularine, will likely induce changes in addition to a more
pronounced decrease in DNA methylation by eliminating the changes zebularine induces
at the RNA level. Thus, when comparing zebularine with its pro-drugs, one needs to
consider the biological functions of the drugs in addition to their effects on DNA
methylation.

Epigenetic drugs are an important new area of potential cancer therapy, and initial
studies have shown very promising results (Yoo and Jones 2006). They can be combined
with traditional chemotherapy to increase the effectiveness in reducing carcinogenetic
developments. Given their promising potential, future efforts should be continued to
improve our existing library of available epigenetic drugs.

55
CHAPTER THREE
EXAMINATION OF DNA METHYLATION INHIBITORS IN VIVO

INTRODUCTION

Epigenetic therapy has become an exciting new addition to the currently available
selections for chemotherapy. Unlike traditional chemotherapy that targets rapidly
dividing cells for immediate cell cycle arrest and apoptosis, epigenetic drugs aim to
induce cellular differentiation and thus make the cancer cells more susceptible to their
internal apoptotic signals (Egger, Liang et al. 2004; Yoo and Jones 2006). Epigenetic
drugs can be roughly divided into two groups—DNA methylation inhibitors and histone
deacetylase (HDAC) inhibitors. Many epigenetic drugs exist today, and most of them are
undergoing intensive studies for their potential clinical benefits. DNA methylation
inhibitors 5-azacytidine (5-Aza-CR) and 5-aza-2’-deoxycytidine (5-Aza-CdR) have now
been Food and Drug Administration (FDA) approved for the treatment of
myelodysplastic syndrome (Yoo and Jones 2006).

Given the promising results from the first epigenetic drugs that have undergone
clinical trials, much effort has been invested in improving the current list of available
epigenetic drugs. In addition to studying potential DNA methylation inhibitors in vitro, I
also examined two DNA methylation inhibitors in vivo by employing a mouse xenograft
model. Because animal responses do not always agree with results from cell culture, and
because animal studies are believed to mirror the potential clinical patient response much

56
closer than cell culture studies, it is important to test the effectiveness and safety of the
drugs in animal models.

Since Laird et al. (Laird, Jackson-Grusby et al. 1995) showed that 5-Aza-CdR
was effective in reducing intestinal adenomas in Apc
Min/+
mice, there has been many
animal studies that examine the actions of epigenetic drugs. Zebularine, another
promising DNA methylation inhibitor, has been shown to be effective in reducing tumor
growth in vivo (Cheng, Matsen et al. 2003; Herranz, Martin-Caballero et al. 2006).
Karam et al. (Karam, Fan et al. 2007) reported that HDAC inhibitor FK228 can inhibit
transitional cell carcinoma xenograft growth with minimal undesirable side effects.
Moreover, 5-Aza-CdR and zebularine have been shown to decrease vessel formation, a
necessary step for tumor formation, in mouse tumor models (Hellebrekers, Jair et al.
2006). Many more studies also examine the combinatorial effects of different epigenetic
drugs in mouse tumor models (Morita, Iida et al. 2006; Cantor, Iliopoulos et al. 2007;
Venturelli, Armeanu et al. 2007).

I examined two DNA methylation inhibitors in EJ6 human bladder carcinoma cell
xenograft mouse models. Both 5-Aza-CdR and zebularine have been shown to be
effective DNA methylation inhibitors in vivo, and much effort has been invested to search
for prodrugs that can have higher potency and stability and less toxicity (Yoo and Jones
2006). I first examined the effect of a zebularine prodrug, EPD-zebularine ([1-( β-D-
Ribofuranosyl)-2(1H)pyrimidinone] 5’-phosphoric acid, [2-(decyloxy)-3-
(dodecylthio)propyl]ester, sodium salt), and found that it was ineffective in re-expressing

57
p16 in the EJ6 xenograft tumors. I then examined the dinucleotide S110, which consists
of 5-Aza-CdR followed by a deoxyguanosine. S110 has been shown to be effective in
vitro in inducing p16 expression and is more stable than 5-Aza-CdR due to decreased
deamination by cytidine deaminase (Yoo, Jeong et al. 2007). My study showed that S110
was also effective in vivo in inducing p16 and MAGE-A1 expression and reducing DNA
methylation at the p16 promoter region. In addition, I found that the S110 dosage that
yielded similar levels of p16 and MAGE-A1 re-expression compared to 5-Aza-CdR was
much less toxic to the mice. S110 therefore serves as a promising new agent that acts
similarly to 5-Aza-CdR and has better stability and less toxicity.

58
MATERIALS AND METHODS

In vivo experiments
Experiments were done similarly to previously described (Cheng, Matsen et al.
2003). EJ6 cells (5 x 10
5
/injection) suspended in PBS were inoculated subcutaneously
into the right and left back (along the midaxillary lines) of 4- to 6-week-old female
BALB/c athymic nude-Foxn1nu mice (Harlan, San Diego, CA). Mice were randomly
divided into 3-4 groups. After 2–3 weeks and after macroscopic tumors (50–200 mm
3
)
had formed, treatments were initiated. Tumors were measured with calipers, and tumor
volumes (TVs) were calculated with the following formula: TV = LD
2
/2 (where L is the
longest diameter and D is the shortest diameter). The

fold differences in tumor growth
among the various mice groups

were calculated using relative TVs (RTVs), which are
calculated

as follows: RTV = TV
n
/TV
0
, where TV
n
is the tumor volume in

mm
3
at a given
day n and TV
0
is the tumor volume in mm
3
at day

0 (initial treatment). Mice were
weighed at the beginning and end of treatment to determine toxicity. The percent weight
change for each mouse was calculated with the following formula: [(W
6
-W
0
)/W
0
] X
100% (where W
n
is the mouse weight on day
n
.

For the EPD-zebularine study, zebularine was used as the positive control and
0.45% PBS was used as the negative control. PBS, zebularine (dose of 500 mg/kg in
PBS), and EPD-zebularine (dose of 100mg/kg in PBS) were administered daily by
intraperitoneal (IP) injection over a period of 18 days.

59
For the S110 study, 5-Aza-CdR was used as the positive control and 0.45% PBS
was used as the negative control. PBS, 5-Aza-CdR (dose of 5 mg/kg in PBS), and S110
(dose of 5, 10, and 20mg/kg in PBS) were administered daily by IP injection over a
period of 6 days.

All mice were killed 24 hours after the last treatment. At this time, tumors were
removed and each tumor was divided into two separate portions. One portion was
immediately homogenized in TRIzol reagent (Invitrogen, Carlsbad, CA) for RNA
extraction, and the other portion was immediately frozen in liquid nitrogen for DNA
extraction later. Genomic DNA and RNA would be used for analysis of the methylation
status of p16 promoter by Ms-SNuPE and of gene expression by real time RT–PCR,
respectively. All experimental protocols were approved by the Institutional Animal Care
and Use Committee, in compliance with the Guide for the Care and Use of Laboratory
Animals, University of Southern California.

Nucleic acid extraction
RNA was extracted by first homogenizing tumor samples in TRIzol reagent
(Invitrogen, Carlsbad, CA), and then following the manufacturer’s instructions.

Genomic DNA was extracted by dissolving each tumor sample in 500 μl of lysis
buffer (100mM Tris HCl pH8.5, 5mM EDTA pH8.0, 0.2% SDS, 400mM NaCl,
100μg/ml proteinase K, and 10ug/ml RNase) overnight at 55°C, and then followed by
phenol-chloroform extraction (Laird, Zijderveld et al. 1991; Wu, Chen et al. 1995).

60
Quantitative Real-Time Reverse-Transcription PCR
Total RNA was extracted from cells with the Invitrogen Trizol reagent (Carlsbad,
CA). Reverse transcription (RT) was performed with M-MLV reverse transcriptase and
random hexamers from Promega (Madison, WI). I performed quantitative real-time RT-
PCR analysis as previously described (Heid, Stevens et al. 1996) with DNA Engine
Opticon System (MJ Research, Hercules, CA). The primers used are listed below:
MAGE-A1 sense, 5’-GAA CCT GAC CCA GGC TCT GTG-3’; MAGE-A1 antisense, 5’-
CCA CAG GCA GAT CTT CTC CTT G-3’; MAGE-A1 fluorogenic probe, 5’-CAA GGT
TTT CAG GGG ACA GGC CAA C-3’; p16 sense, 5’-AGC CTT CGG CTG ACT GGC
TGG-3’; p16 antisense, 5’-CTG CCC ATC ATC ATG ACC TGG A-3’; p16 fluorogenic
probe, 5’-TGG ATC GGC CTC CGA CCG TAA CT-3’. The real-time RT-PCR
conditions for both genes were as follows: 95 °C for 9min, followed by 45 cycles of
denaturation at 95 °C for 15 s and annealing at 60 °C for 1min.

Quantitative DNA Methylation Analysis by Methylation-Specific Single Nucleotide
Extension (Ms-SNuPE)
Two μg of each genomic DNA sample was converted with sodium bisulfite as
previously described (Frommer, McDonald et al. 1992), and each region of interest was
amplified by PCR. The PCR conditions for p16 were as follows: 95 °C for 3 min,
followed by 40 cycles of denaturation at 95 °C for 1 min, annealing at 62 °C for 1 min, and
extension at 72 °C for 1 min, and a final extension at 72 °C for 10min. The bisulfite
specific-PCR primer sequences are as follows: p16 sense, 5’- GTA GGT GGG GAG
GAG TTT AGT T-3’, p16 antisense, 5’- TCT AAT AAC CAA CCA ACC CCT CCT-3’.

61
The Ms-SNuPE conditions for p16 were as follows: 95 °C for 2 min, 50 °C for 2 min, and
72°C for 1 min. The p16 SNuPE primers are as follows: 5’-TTT TAG GGG TGT TAT
ATT-3’, 5’-TTT TTT TGT TTG GAA AGA TAT-3’, and 5’-TTT GAG GGA TAG
GGT-3’.

The PCR amplicons were extracted with the Qiagen Gel Extraction Kit, and Ms-
SNuPE analysis was performed to examine the methylation level changes as previously
described (Gonzalgo and Jones 2002).

62
RESULTS

Effects of EPD-zebularine on human bladder carcinoma cells in Vivo
Zebularine, a DNA methylation inhibitor, has been shown to be effective both in
vitro and in vivo in reducing DNA methylation and reactivating methylation-silenced
genes (Cheng, Matsen et al. 2003; Cheng, Yoo et al. 2004). In addition to its ability to
reduce tumor growth in vivo, Yoo et al. (manuscript accepted) also showed that long-
term administration of zebularine could serve as an effective chemo-preventive agent
(manuscript accepted). Because of its high stability, zebularine can be administered
orally and is thus an attractive alternative to 5-Aza-CdR, although the potency of
zebularine as a DNA methylation inhibitor is still lower than that of 5-Aza-CdR (Cheng,
Matsen et al. 2003). Given the promising studies with zebularine, many a search for a
more effective form of zebularine has been under investigation. Here I tested a
zebularine pro-drug EPD-zebularine (Figure 3.1), which consisted of zebularine-
monophosphate (ZMP) and long hydrocarbon chains, to determine if it was effective in
vivo in a mouse xenograft model.

ZMP alone is too polar for effective delivery, thus adding the long hydrocarbon
chains would add a lipophilic protecting group to deliver the compounds to the cells.
Previous work in the lab found that EPD-zebularine was ineffective at reactivating p16 in
vitro (Christine Yoo, data unpublished). I hypothesized that the protecting group was not
sufficiently cleaved off in vitro to yield ZMP for effective DNA methylation inhibition.

63
Figure 3.1: Structure of EPD-Zebularine

64
I reasoned that this would not be the case in vivo, and thus the protecting group would be
cleaved off to yield ZMP to effectively inhibit DNA methylation.

I used EJ6 bladder cells, which are a tumorigenic derivative of T24 cells that also
have a methylated p16 gene promoter (Cheng, Matsen et al. 2003). EJ6 cells were
inoculated subcutaneously into the right and left backs of 4- to 6-week-old female
BALB/c athymic nude-Foxn1nu mice. When macroscopic tumors were evident
(approximately 2–3 weeks later, roughly 50-200 mm
3
), the mice were treated with PBS
(negative control), zebularine at 500 mg/kg of mouse weight (positive control), or EPD-
zebularine administered by daily intraperitoneal (IP) injections. I first tested EPD-
zebularine at 500mg/kg; however, the dose was too high for the mice, and the mice died
within two days. I then reduced the EPD-zebularine concentration to 100mg/kg.

A total of three mice were in each group; mice 3-1 to 3-3 received PBS treatment,
mice 4-1 to 4-3 received zebularine, and mice 5-1 to 5-3 received EPD-zebularine.
Zebularine treatment was toxic for the mice, and induced weight loss in two of the three
mice (10-15% loss of body weight), and death for the third mouse in the group before the
completion of the experiment. EPD-zebularine induced 9-14% of mouse weight loss and
was less toxic compared to zebularine (Table 3.1). However, contrary to previously
published data (Cheng, Matsen et al. 2003), neither zebularine nor EPD-zebularine
reduced the tumor sizes by the end of the treatment compared to the original sizes on day
0. Zebularine slowed the rate of tumor growth compared to PBS on average, but EPD-
zebularine was ineffective in retarding tumor growth (Figure 3.2).

65
Table 3.1: Weight and tumor size changes in mice treated with PBS, zebularine, and
EPD-zebularine

Mouse Treatment Wt on
Day 0
(g)
Wt on
Day
18 (g)
% Wt
change
(%)
Tumor
size on
Day 0
(mm
3
)
Tumor
size on
Day 18
(mm
3
)
RTV
3-1 PBS 24.1 25.4 5.4 L: 105.88
R: 117.00
L: 210.94
R: 220.5
L: 1.99
R: 1.88
3-2 PBS 23.8 24.4 2.5 L: 108.00
R: 65.81
L: 606.38
R: 496.38
L: 5.61
R: 7.54
3-3 PBS 24.5 23.2 -5.3 L: 550.00
R: 162.00
L: 1267.5
R: 578.81
L: 2.30
R: 3.57
4-1 Zebularine 25.3 21.4 -15.4 L: 144.00
R: 158.44
L: 445.5
R: 343.19
L: 3.09
R: 2.17
4-2 Zebularine 23.0 Died L: 135.00
R: 135.00
Died
4-3 Zebularine 23.3 20.9 -10.3 L: 40.00
R: 60.75
L: 81.25
R: 105.88
L: 2.03
R: 1.74
5-1 EPD-
zebularine
26.0 22.4 -13.8 L: 87.50
R: 98.31
L: 600
R: 158.44
L: 6.86
R: 1.61
5-2 EPD-
zebularine
23.3 21.1 -9.4 L: 75.00
R: 106.25
L: 336
R: 445.5
L: 4.48
R: 4.19
5-3 EPD-
zebularine
25.3 21.7 -14.2 L: 144.00
R: 232.75
L: 496.38
R: 976.56
L: 3.45
R: 4.20

Mice were weighed at the beginning and the end of the treatment. Tumors were
measured with calipers, and tumor volumes (TVs) were calculated with the following
formula: TV = LD
2
/2 (where L is the longest diameter and D is the shortest diameter).
Percent weight change (% wt change) is calculated as follows: % wt change = [(W
18
-
W
0
)/W
0
] X 100%, where W
18
is the weight on day 18 and W
0
is the weight on day 0
(initial treatment). The

fold differences in tumor growth among the various mice groups

were calculated using relative TVs (RTVs), which are calculated

as follows: RTV =
TV
18
/TV
0
, where TV
18
is the tumor volume in

mm
3
at day 18 and TV
0
is the tumor
volume in mm
3
at day

0 (initial treatment). Wt: weight

66
Figure 3.2: Relative tumor volume in EJ6 xenograft tumors treated with zebuarline and
EPD-zebularine

Relative Tumor Volume (Day 18/Day 0) in EJ6
Xenograft Tumors
0
1
2
3
4
5
6
7
8
3-1L
3-1R
3-2L
3-2R
3-3L
3-3R
4-1L
4-1R
4-3L
4-3R
5-1L
5-1R
5-2L
5-2R
5-3L
5-3R
Tumor
Relative Tumor Volume
(Day18/Day0)

PBS zebularine EPD-zebularine PBS zebularine EPD-zebularine

Relative tumor volume of each EJ6 xenograft tumor. Tumors were measured with
calipers, and tumor volumes (TVs) were calculated with the following formula: TV =
LD
2
/2 (where L is the longest diameter and D is the shortest diameter). The

fold
differences in tumor growth among the various mice groups

were calculated using RTVs,
which are calculated

as follows: RTV = TV
18
/TV
0
, where TV
6
is the tumor volume in

mm
3
at day 18 and TV
0
is the tumor volume in mm
3
at day

0 (initial treatment).

Mice were treated by IP injections of PBS, zebularine, or EPD-zebularine daily for 18
days. Mice 3-1 ~ 3-3 received PBS treatment, 4-1 ~ 4-3 received zebularine at 500mg/kg,
and 5-1 ~ 5-3 received EPD-zebularine at 100mg/kg. L: left tumor; R: right tumor.

67
Zebularine treatment was able to reactivate p16 in two out of the four tumors, but
EPD-zebularine was ineffective (Figure 3.3). Taken together, EPD-zebularine was
ineffective in vivo to either reduce tumor growth or reactivate p16 in my study, and
therefore is unlikely to be a promising zebularine pro-drug. The compound might not
have been effectively transported to the tumor cells in vivo, or it might not have been
effectively processed within the cells. Given the weight loss I observed in the mice, I
concluded that EPD-zebularine had a systemic effect on the mice, although to a lesser
extent than zebularine at the concentrations I tested.

S110 is an effective DNA methylation inhibitor in Vivo
In addition to EPD-zebularine, I studied S110 in vivo using the same xenograft
system. S110 is a dinucleotide consisting of 5-Aza-CdR followed by a deoxyguanosine
(Figure 3.4). It has been previously shown by Yoo et al. (Yoo, Jeong et al. 2007) to be
effective in vitro as a DNA methylation inhibitor and is comparable to 5-Aza-CdR in its
action to decrease DNA methylation, reactivate p16 expression, deplete DNA
methyltransferases, and reduce cancer cell growth. Moreover, it has the advantage of
being less prone to deamination by cytidine deaminase, making it a promising alternative
to 5-Aza-CdR.

Three mice each were injected IP with PBS or 5-Aza-CdR (5mg/kg of mouse
weight), and four mice each were injected with S110 at 10 or 20mg/kg of mouse weight.

68
Figure 3.3: Real Time RT-PCR of p16 expression in EJ6 xenograft tumors treated with
zebularine and EPD-zebularine

PBS zebularine EPD-zebularine PBS zebularine EPD-zebularine

Re-expression of p16 by zebularine in EJ6 xenograft tumors. Mice were treated by IP
injections of PBS, zebularine, or EPD-zebularine daily for 18 days. Xenograft tumors
were harvested on day 18, and total RNA was extracted for RT-PCR analysis.
Quantitative real time RT-PCR analysis for the p16 gene was done with normalization
against the GAPDH reference gene. Representative results of two independent RT-PCR
reactions are shown above.

Mice 3-1 ~ 3-3 received PBS treatment, 4-1 ~ 4-3 received zebularine at 500mg/kg, and
5-1 ~ 5-3 received EPD-zebularine at 100mg/kg. L: left tumor; R: right tumor.

p16 Expression in EJ6 tumors in nude mice
0.00E+00
5.00E-06
1.00E-05
1.50E-05
2.00E-05
2.50E-05
3.00E-05
3.50E-05
4.00E-05
4.50E-05
F 3-1
L
F 3-1
R
F 3-2
L
F 3-2
R
F 3-3
L
F 3-3
R
F 4-1
L
F 4-1
R
F 4-3
L
F 4-3
R
F 5-1
L
F 5-1
R
F 5-2
L
F 5-2
R
F 5-3
L
F 5-3
R
Tumor
p16/GAPDH

69
Figure 3.4: Structure of S110

Yoo, Cancer Research 67, 6400-6408, July 1, 2007

70
Real time RT-PCR analysis of the tumor tissues revealed that S110 was effective
in vivo at reactivating the expression of p16 (Figure 3.5.A) and MAGE-A1 (Figure 3.5.B).
In addition, S110 at 10mg/kg, which was at the same molar concentration as 5-Aza-CdR
at 5mg/kg, induced similar levels of p16 and MAGE-A1 expression as 5-Aza-CdR at
5mg/kg. I also noticed that unlike in vitro studies, there was greater variation within
treatment groups. This could be attributed to the different levels of response among
different mice, or even between the two different tumors on the same mouse. For
example, mouse 12-1 apparently did not respond to S110 at 20mg/kg as much as its peers
in the same group. The two tumors in each mouse could have different levels of response
as well, as evidenced in the case of 12-3 left and right tumors. Overall, S110 was
effective in vivo at reactivating silenced genes, and its activity was comparable to 5-Aza-
CdR at the same molar concentrations.

In addition, S110 was effective in reducing the level of DNA methylation in vivo
at the p16 promoter region (Figure 3.6.A). While the PBS group showed relatively
consistent levels of high methylation, variations among different tumors were much more
obvious in the 5-Aza-CdR and the two S110 groups. On average, the DNA methylation
level was 95.08% in the PBS group, 80.81% in the 5-Aza-CdR group, 83.28% in the
10mg/kg S110 group, and 80.97% in the 20mg/kg S110 group (Figure 3.6.B). Though
the changes in magnitude were small, they were statistically significant as determined by
the Student t-test (Figure 3.6.B) when compared to the PBS group. I noticed, however,
that the levels of decreases in methylation were very similar between the two S110
groups, even though at 20mg/kg, S110 induced much higher expression of p16 compared

71
Figure 3.5: Re-expression of p16 by 5-Aza-CdR and S110 in EJ6 xenograft tumors
treated with 5-Aza-CdR and S110

3.5.A

PBS 5-Aza-CdR 5mg/kg S110 10mg/kg S110 20mg/kg PBS 5-Aza-CdR 5mg/kg S110 10mg/kg S110 20mg/kg

Mice were treated by IP injections of PBS, 5-Aza-CdR at 5mg/kg, or S110 at 10 or
20mg/kg daily for 6 days. Xenograft tumors were harvested on day 6, and total RNA was
extracted for RT-PCR analysis. Quantitative real time RT-PCR analysis for the p16 gene
was done with normalization against the GAPDH reference gene. Representative results
of two independent RT-PCR reactions are shown above.

Mice 9-1 ~ 9-3 received PBS treatment, 10-1 ~ 10-3 received 5-Aza-CdR at 5mg/kg, 11-
1 ~ 11-4 received S110 at 10mg/kg, and 12-1 ~ 12-4 received S110 at 20mg/kg. L: left
tumor; R: right tumor.

p16 Expression in EJC tumors in nude mice
0
0.0002
0.0004
0.0006
0.0008
0.001
0.0012
9-1L
9-1R
9-2L
9-2R
9-3L
9-3R
10-1L
10-1R
10-2L
10-2R
10-3L
10-3R
11-1L
11-1R
11-2L
11-2R
11-3L
11-3R
11-4L
11-4R
12-1L
12-1R
12-2L
12-2R
12-3L
12-3R
12-4L
12-4R
tumor
p16/GAPDH

72
Figure 3.5, Continued

3.5.B

PBS 5-Aza-CdR 5mg/kg S110 10mg/kg S110 20mg/kg PBS 5-Aza-CdR 5mg/kg S110 10mg/kg S110 20mg/kg

Re-expression of MAGE-A1 by 5-Aza-CdR and S110 in EJ6 xenograft tumors. Mice
were treated by IP injections of PBS, 5-Aza-CdR at 5mg/kg, or S110 at 10 or 20mg/kg
daily for 6 days. Xenograft tumors were harvested on day 6, and total RNA was
extracted for RT-PCR analysis. Quantitative real time RT-PCR analysis for the MAGE-
A1 gene was done with normalization against the GAPDH reference gene.
Representative results of two independent RT-PCR reactions are shown above.

Mice 9-1 ~ 9-3 received PBS treatment, 10-1 ~ 10-3 received 5-Aza-CdR at 5mg/kg, 11-
1 ~ 11-4 received S110 at 10mg/kg, and 12-1 ~ 12-4 received S110 at 20mg/kg. L: left
tumor; R: right tumor.
MAGE-A1 Expression in EJ6 Tumors in Nude Mice
0
0.00005
0.0001
0.00015
0.0002
0.00025
0.0003
0.00035
0.0004
0.00045
9-1L
9-1R
9-2L
9-2R
9-3L
9-3R
10-1L
10-1R
10-2L
10-2R
10-3L
10-3R
11-1L
11-1R
11-2L
11-2R
11-3L
11-3R
11-4L
11-4R
12-1L
12-1R
12-2L
12-2R
12-3L
12-3R
12-4L
12-4R
Tumor
MAGE-A1/GAPDH

73
Figure 3.6: DNA methylation level of p16 promoter in EJ6 xenograft tumors treated
with 5-Aza-CdR and S110 by Ms-SNuPE analysis

3.6.A

PBS 5-Aza-CdR 5mg/kg S110 10mg/kg S110 20mg/kg PBS 5-Aza-CdR 5mg/kg S110 10mg/kg S110 20mg/kg

Mice were treated by IP injections of PBS, 5-Aza-CdR at 5mg/kg, or S110 at 10 or
20mg/kg daily for 6 days. Xenograft tumors were harvested on day 6, and genomic DNA
was extracted for Ms-SNuPE analysis.

Mice 9-1 ~ 9-3 received PBS treatment, 10-1 ~ 10-3 received 5-Aza-CdR at 5mg/kg, 11-
1 ~ 11-4 received S110 at 10mg/kg, and 12-1 ~ 12-4 received S110 at 20mg/kg. L: left
tumor; R: right tumor.

p16 prom oter DNA m ethylation level in m ouse EJ6 xenografts
50%
60%
70%
80%
90%
100%
9-1 L
9-1R
9-2L
9-2R
9-3L
9-3R
10-1R
10-2L
10-2R
10-3L
10-3R
11-1L
11-1R
11-2L
11-3L
11-3R
11-4L
11-4R
12-1L
12-2L
12-2R
12-3R
12-4L
12-4R
xenograft tumor
% methylation

74
Figure 3.6, Continued

3.6.B

DNA methylation level of p16 promoter in EJ6 xenograft tumors by SNuPE analysis.
The graph shows the average percent methylation level in each group, and the numbers of
tumors in each group are labeled above. The changes in percent methylation level in 5-
Aza-CdR- and S110-treated groups are found to be statistically significant by Student t-
test.

Mice were treated by IP injections of PBS, 5-Aza-CdR at 5mg/kg, or S110 at 10 or
20mg/kg daily for 6 days. Mice 9-1 ~ 9-3 received PBS treatment, 10-1 ~ 10-3 received
5-Aza-CdR at 5mg/kg, 11-1 ~ 11-4 received S110 at 10mg/kg, and 12-1 ~ 12-4 received
S110 at 20mg/kg. L: left tumor; R: right tumor.

p16 promoter DNA methylation level average for various
treatments of EJ6 xenografts by IP injections
70%
75%
80%
85%
90%
95%
100%
PBS 5-Aza-CdR
5mg/kg
S110 10mg/kg S110 20mg/kg
treatment
% methylation
p<0.0001
p<0.0001
p<0.0001
N=6 N=6 N=7 N=5

75
to S110 at 10mg/kg (Figure 3.5.A). Because Ms-SNuPE only examined the methylation
changes at a few CpG sites, a different method such as genomic bisulfite sequencing
might reveal more dramatic methylation changes than was determined by Ms-SNuPE.

S110 was effective at slowing tumor growth and caused less toxicity compared to 5-
Aza-CdR
In addition to examining the ability of S110 as a DNA methylation inhibitor in
vivo, I also studied its potential action on inhibiting tumor growth. I found that neither 5-
Aza-CdR nor S110 reduced the tumor sizes by the end of the treatment compared to the
original sizes on day 0, but they all retarded the growth of tumors on average compared
to the PBS treatment (Figure 3.7, Table 3.2). The large increase in RTV in 12-3R was
mostly due to the small volume of the tumor on day 0 (Table 3.2), and thus the fold
increase in its size appeared particular large.

Lastly, I estimated the toxicities from the treatments by measuring the weight of
the mice at the beginning and the end of the treatment (Figure 3.8, Table 3.2). Mice
treated with PBS did not experience much change in weight. While 5-Aza-CdR and
S110 at 20mg/kg induced dramatic weight loss in mice, S110 at 10mg/kg induced much
less weight change, and the mice in that group were relatively healthy both by appearance
and by their level of activity. On the contrary, the mice treated with 5-Aza-CdR and
S110 at 20mg/kg appeared sick and were not active. These data suggested that S110
could be a promising alternative to 5-Aza-CdR because, at the same molar concentration,

76
Figure 3.7: Relative tumor volume of each EJ6 xenograft tumor treated with 5-Aza-CdR
and S110

Relative Tumor Volume (Day6/Day0)
of EJ6 Xenografts
0.0
1.0
2.0
3.0
4.0
5.0
9-1L
9-1R
9-2L
9-2R
9-3L
9-3R
10-1L
10-1R
10-2L
10-2R
10-3L
10-3R
11-1L
11-1R
11-2L
11-2R
11-3L
11-3R
11-4L
11-4R
12-1L
12-1R
12-2L
12-2R
12-3L
12-3R
12-4L
12-4R
Tumor
Relative tumor volume RTV
Relative Tumor Volume (Day6/Day0)
of EJ6 Xenografts
0.0
1.0
2.0
3.0
4.0
5.0
9-1L
9-1R
9-2L
9-2R
9-3L
9-3R
10-1L
10-1R
10-2L
10-2R
10-3L
10-3R
11-1L
11-1R
11-2L
11-2R
11-3L
11-3R
11-4L
11-4R
12-1L
12-1R
12-2L
12-2R
12-3L
12-3R
12-4L
12-4R
Tumor
Relative tumor volume RTV

PBS 5-Aza-CdR 5mg/kg S110 10mg/kg S110 20mg/kg PBS 5-Aza-CdR 5mg/kg S110 10mg/kg S110 20mg/kg

Tumors were measured with calipers, and tumor volumes (TVs) were calculated with the
following formula: TV = LD
2
/2 (where L is the longest diameter and D is the shortest
diameter). The

fold differences in tumor growth among the various mice groups

were
calculated using RTVs, which are calculated

as follows: RTV = TV
6
/TV
0
, where TV
6
is
the tumor volume in

mm
3
at day 6 and TV
0
is the tumor volume in mm
3
at day

0 (initial
treatment).

Mice were treated by IP injections of PBS, 5-Aza-CdR at 5mg/kg, or S110 at 10 or
20mg/kg daily for 6 days. Mice 9-1 ~ 9-3 received PBS treatment, 10-1 ~ 10-3 received
5-Aza-CdR at 5mg/kg, 11-1 ~ 11-4 received S110 at 10mg/kg, and 12-1 ~ 12-4 received
S110 at 20mg/kg. L: left tumor; R: right tumor.

77
Table 3.2: Weight and tumor size changes in mice treated with PBS, 5-Aza-CdR, and
S110

Mouse Treatment Wt on
Day 0
(g)
Wt on
Day 6
(g)
% wt
change
(%)
Tumor
size on
Day 0
(mm
3
)
Tumor size
on Day 6
(mm
3
)
RTV
9-1 PBS 22.3 21.5 -3.6 L: 68.75
R: 22.5
L: 137.31
R: 60.75
L: 2.00
R: 2.70
9-2 PBS 20.8 21.4 2.9 L: 20.25
R: 12.5
L: 48
R: 33.69
L: 2.37
R: 2.70
9-3 PBS 22.0 22.6 2.7 L: 18
R: 27.56
L: 55.69
R: 62.5
L: 3.09
R: 2.27
10-1 5-Aza-
CdR
25.0 19.2 -23.2 L: 8
R: 60
L: 3.38
R: 81
L: 0.42
R: 1.35
10-2 5-Aza-
CdR
22.4 19.3 -13.8 L: 40
R: 39.81
L: 36
R: 70.88
L: 0.90
R: 1.78
10-3 5-Aza-
CdR
20.8 16.5 -20.7 L: 36
R: 40
L: 32
R: 27.56
L: 0.89
R: 0.69
11-1 S110
10mg/kg
22.8 21.7 -4.8 L: 48
R: 31.5
L: 87.5
R: 42.88
L: 1.82
R: 1.36
11-2 S110
10mg/kg
26.8 22.1 -17.5 L: 87.5
R: 9.38
L: 126
R: 18
L: 1.44
R: 1.92
11-3 S110
10mg/kg
24.5 21.3 -13.1 L: 27
R: 40
L: 36.75
R: 117
L: 1.36
R: 2.93
11-4 S110
10mg/kg
24.5 21.5 -12.2 L: 48
R: 44
L: 87.5
R: 65.81
L: 1.82
R: 1.50
12-1 S110
20mg/kg
22.4 17.0 -24.1 L: 22.5
R: 6
L: 30.63
R: 6
L: 1.36
R: 1.00
12-2 S110
20mg/kg
19.8 16.1 -18.7 L: 70.88
R: 36.75
L: 75.94
R: 56
L: 1.07
R: 1.52
12-3 S110
20mg/kg
25.1 19.2 -23.5 L: 65.81
R: 6
L: 87.5
R: 27.56
L: 1.33
R: 4.59
12-4 S110
20mg/kg
23.2 19.9 -14.2 L: 56
R: 62.5
L: 60
R: 75
L: 1.07
R: 1.20

Mice were weighed at the beginning and the end of the treatment. Tumors were
measured with calipers, and tumor volumes (TVs) were calculated with the following
formula: TV = LD
2
/2 (where L is the longest diameter and D is the shortest diameter).
The % wt changes are calculated as follows: % wt change = [(W
6
-W
0
)/W
0
] X 100%,
where W
6
is the weight on day 6 and W
0
is the weight on day 0 (initial treatment). The

fold differences in tumor growth among the various mice groups

were calculated using
RTVs, which are calculated

as follows: RTV = TV
6
/TV
0
, where TV
6
is the tumor volume
in

mm
3
at day 6 and TV
0
is the tumor volume in mm
3
at day

0 (initial treatment). Wt:
weight

78
Figure 3.8: Percent weight change in mice after treatment with PBS, 5-Aza-CdR, and
S110

Percent Weight Change (Day 6 compared to Day 0) in
Treated Mice
-30%
-25%
-20%
-15%
-10%
-5%
0%
5%
9-1
9-2
9-3
10-1
10-2
10-3
11-1
11-2
11-3
11-4
12-1
12-2
12-3
12-4
Mouse
% W t Change
avg=0.67% avg=20.13% avg=11.92% avg=19.24%

PBS 5-Aza-CdR 5mg/kg S110 10mg/kg S110 20mg/kg PBS 5-Aza-CdR 5mg/kg S110 10mg/kg S110 20mg/kg

Mice were weighed at the beginning and the end of treatment to determine toxicity. The
percent weight change for each mouse was calculated with the following formula: [(W
6
-
W
0
)/W
0
] X 100% (where W
n
is the mouse weight on day n. The average percent weight
change for each group is labeled above.

Mice were treated by IP injections of PBS, 5-Aza-CdR at 5mg/kg, or S110 at 10 or
20mg/kg daily for 6 days. Mice 9-1 ~ 9-3 received PBS treatment, 10-1 ~ 10-3 received
5-Aza-CdR at 5mg/kg, 11-1 ~ 11-4 received S110 at 10mg/kg, and 12-1 ~ 12-4 received
S110 at 20mg/kg. L: left tumor; R: right tumor.

79
the former caused less toxicity in mice while remaining comparable in terms of
reactivating genes and reducing DNA methylation. Additional studies would be needed
to prove if the toxicity differences I observed are of clinical significance.

80
DISCUSSION

In vivo studies of drug action can provide a wealth of information that is not
obtainable from in vitro studies alone, and in vivo results are believed to mirror the
potential clinical response more closely. Therefore, it is essential to test a drug in an
animal system if it has shown promising clinical potential, and mice are often used as the
first animals for in vivo studies given their ease for handling. I used the mouse xenograft
system to study two compounds—EPD-zebularine and S110.

My study with EPD-zebularine showed that it was not effective in vivo at
reactivating methylation-silenced gene p16, and it did not slow down tumor growth. The
exact reason that EPD-zebularine failed to act as an effective DNA methylation inhibitor
in vivo was unknown. Compounds that are delivered by IP injections are absorbed by the
lymphatic system to enter the circulation before entering different tissues (Zee-Cheng and
Cheng 1989). It was not known if the EPD-zebularine molecules were effectively
delivered to the tumor tissues. Furthermore, I did not have evidence if EPD-zebularine
could be cleaved successfully in vivo to yield ZMP. Lastly, the concentrations I tested
might not have been the right concentrations to be effective. I found that EPD-zebularine
at 500mg/kg to be too toxic, and at 100mg/kg to be ineffective. It is possible that at a
concentration in between the two I tested, EPD-zebularine would prove to be effective in
vivo.

81
On the other hand, my results with S110 showed that it was an effective DNA
methylation inhibitor in vivo. It was effective in reducing DNA methylation as
determined by Ms-SNuPE, and was effective in reactivating p16 and MAGE-A1
expression. My data showed that p16 and MAGE-A1 were up-regulated to different
extents by the treatments. S110 at 20mg/kg induced p16 expression much more strongly
than S110 at 10mg/kg, but the difference was much less pronounced in MAGE-A1
expression. It has been shown that genes that are abnormally silenced by methylation,
such as p16, respond more readily to DNA methylation inhibitors than genes are that
normally silenced by methylation, such as MAGE-A1 (Yang, Doshi et al. 2006). Thus,
my results are consistent with previously published data. Moreover, the tumors which
expressed MAGE-A1 also expressed p16, suggesting that there was consistency between
the two experiments.

I noticed that while S110 was effective in most tumors, there were individual
variations in response to S110. For example, mouse 12-1 did not exhibit much response
to S110 in terms of reactivation of methylation-silenced genes, and yet it suffered
dramatic weight loss. It would be of interest to determine which factors contribute to the
differences in response. Having this knowledge would also aid us in the future to
determine which patients would respond well to DNA methylation inhibitors clinically.

The results from Ms-SNuPE indicated that S110 was effective at reducing DNA
methylation in vivo; however, the decrease in DNA methylation at the p16 promoter did
not correlate completely with p16 expression. S110 at 20mg/kg did not induce much

82
further decrease in DNA methylation level compared to S110 at 10mg/kg, and yet the
former induced p16 expression to a much higher level compared to the latter. This could
be due to the fact that Ms-SNuPE only measured the methylation level at a few CpG sites,
and thus might have masked a much greater difference in methylation levels.

In addition, I found that S110 was also effective at retarding tumor growth
compared to the PBS control, and this showed that S110 could be a potential
chemotherapy agent. I measured the weight of the mice to estimate the toxicities of the
treatments, and I found that S110 at 10mg/kg caused less weight loss compared to 5-Aza-
CdR at 5mg/kg, while the two were comparable in terms of reducing DNA methylation
and reactivating methylation-silenced genes. Toxicities and side effects from
chemotherapy are generally hard to tolerate for patients and they can compromise patient
compliance. It is therefore very encouraging that at the same efficacy level, S110 caused
less toxicity compared to 5-Aza-CdR.

The above difference I observed also suggested that 5-Aza-CdR and S110 could
have secondary effects. Although both drugs have been shown to be effective DNA
methylation inhibitors, they could have actions in addition to inhibiting DNA
methyltransferases. Given that at the same level of efficacy, 5-Aza-CdR caused more
toxicity in mice than S110, it is reasonable to hypothesize that 5-Aza-CdR can cause
changes in addition to affecting DNA methylation. Further studies should be conducted
to investigate the actions of these drugs by examining global gene expression changes by
microarray analysis. Such studies would illuminate on the global actions of these drugs

83
and allow us to compare the actions of the different DNA methylation inhibitors side by
side.

Because the drugs were delivered by IP injections, they entered the general
circulation before reaching the tumor tissues. I therefore expected that they could also
affect other mouse tissues and organs in addition to the xenograft tumors. It would be of
great interest to study the effects of the drugs on other mouse tissues to examine if the
drugs have any adverse effects, and also to see if different tissues have preferential
responses to the drugs.

Given the promising results from my S110 data, further studies should be
conducted to investigate the chemotherapeutic potential and toxicity of S110. A wider
range of concentrations of S110 should be tested to find the optimal dose, and more mice
should be included in the experiment to verify if S110 is indeed less toxic than 5-Aza-
CdR. In addition, 5-Aza-CdR at 5mg/kg proved to be rather toxic for the mice; thus it
might be more instrumental to test a lower concentration of 5-Aza-CdR as my positive
control, as the goal of the treatment is to reduce methylation in tumors while causing
minimal toxicity in the animals. Lastly, in addition to comparing S110 to 5-Aza-CdR at
equal molar concentrations, it would be informative to also compare the two drugs at
equal toxicity levels because most clinical studies compare the efficacies of different
drugs at equal toxicity levels. Having more knowledge of S110 would help us develop
more effective DNA methylation inhibitors for chemotherapy in the future.

84
CHAPTER FOUR
EPIGENETIC CONTROL OF MICRORNA EXPRESSION

INTRODUCTION

After investigating the different DNA methylation inhibitors, I broadened my
project to examine additional aspects of the epigenetic control of genes and the potential
of using epigenetic drugs to affect gene expression. Epigenetic control of protein-coding
genes, especially tumor-suppressor genes, has been well studied; however, much less is
known about epigenetic control of other transcripts (Jones and Baylin 2002). Saito et al.
(Saito, Liang et al. 2006) found that treatment of human cancer cells with a combination
of the DNA methylation inhibitor 5-Aza-2’-deoxycytidine (5-Aza-CdR) and histone
deacetylace (HDAC) inhibitor 4-phenylbutyric acid (PBA) can induce the expression of
microRNAs (miRNAs), demonstrating epigenetic regulation of miRNA expression.

miRNAs are small, non-coding RNA molecules roughly 18-24 nucleotides long
that can regulate target translation. miRNAs are encoded in the genome and are
transcribed mainly by RNA polymerase II (pol II) into primary miRNAs. Primary
miRNAs are processed in the nucleus by the RNase III, Drosha, with its partner DGCR8,
into precursor miRNAs, which are then exported into the cytoplasm. Precursor miRNAs,
which are roughly 60-70 nucleotides long, are then processed into the functional mature
miRNAs by the cytoplasmic RNase III Dicer and incorporated into the RNA-induced
silencing (RISC) complex (Cai, Hagedorn et al. 2004; He and Hannon 2004). Each

85
miRNA is predicted to have hundreds of targets due to its flexibility in binding its target
mRNA, and most protein-coding genes are predicted to be controlled by miRNAs (John,
Enright et al. 2004; Kim and Nam 2006).

The importance of miRNAs has only recently been elucidated, and the regulation
of their expression is still poorly understood. Several studies exist that shed light on the
control of miRNA expression. For example, c-Myc has been shown to activate the miR-
17 cluster (O'Donnell, Wentzel et al. 2005), and NF- κB can regulate the expression of
miR-146 (Taganov, Boldin et al. 2006). Recent publications have found that p53 can
control the expression of the miR-34 family, thereby linking miRNAs to cancer
development (He, He et al. 2007; Raver-Shapira, Marciano et al. 2007; Welch, Chen et al.
2007). While several studies have examined the regulation of individual miRNAs, a
comprehensive, systemic study of the regulation of miRNA expression has yet to be
conducted. Here I hypothesize that epigenetic mechanisms, specifically DNA
methylation and histone modifications, can control the expression of miRNAs.

After our initial study linking epigenetics and miRNAs, there have been a few
recent studies examining the relationship between the two. Lujambio et al. (Lujambio,
Ropero et al. 2007) and Han et al. (Han, Witmer et al. 2007) report that DNA methylation
can control the expression of miRNAs. miR-9-1 is silenced in human breast cancers by
aberrant hypermethylation and that plays a role in carcinogenesis (Lehmann, Hasemeier
et al. 2008). Scott et al. (Scott, Mattie et al. 2006) report that histone de-acetylation can
alter the expression of miRNAs. Moreover, the relationship between epigenetics and

86
miRNAs has been found to be reciprocal by the discovery of Fabbri et al. (Fabbri,
Garzon et al. 2007) that the miR-29 family can target DNA methyltransferases (DNMTs)
3a and 3b. These recent findings have begun to shed light on our understanding of how
epigenetics and miRNAs regulate each other.

To elucidate the extent to which miRNA expression is regulated by epigenetic
mechanisms, I examined global miRNA expression using miRNA microarrays in human
colon carcinoma cells HCT116 with varying epigenetic states. I compared wild-type
(WT) HCT116 cells vs. HCT116 cells that have reduced DNMT 1 levels and no
DNMT3b. The later will be referred to as “double knock-out” (DKO) cells, even though
the two enzymes are not both completely knocked out (Rhee, Bachman et al. 2002; Egger,
Jeong et al. 2006). In addition, I compared untreated HCT116 cells vs. cells treated with
5-Aza-CdR and PBA. From the microarray results, I discovered that many miRNAs are
potentially epigenetically regulated. I selected miR-377 for further study and found that
it is regulated by both DNA methylation and histone acetylation.

87
MATERIALS AND METHODS

Cell line and drug treatment
HCT116 colon carcinoma cells were obtained from the American Type Culture
Collection (AATC, Rockville, MD) and cultured in McCoy’s 5A medium supplemented
with 10% heat-inactivated fetal bovine serum. DKO cells (clone 1) were a generous gift
from Dr. Baylin (Rhee, Bachman et al. 2002) and cultured with the same media for
HCT116. Cells were cultured in a humidified incubator at 37 °C in 5% CO
2
. 5-Aza-CdR
and PBS were obtained from Sigma-Aldrich (St. Louis, MO). 5-Aza-CdR was dissolved
in PBS and stored in -80 °C, and PBA was dissolved in 100% ethanol before the
experiment and stored in -20 °C. Cells were seeded at the density of 1X10
6
cells/25mm
dish 24 hours prior to treatment. I used the dosage of 0.1 μM 5-Aza-CdR for 24 hours
and 1mM PBA continuously, and the cells were collected on day 6 of the treatment.

MicroRNA microarray
miRNA microarray analyses were conducted by LC sciences
(www.lcsciences.com; Houston, TX). Briefly, poly-A tails were added to the RNA
sequences at the 3 ′ ends using a poly(A) polymerase, and nucleotide tags were then
ligated to the poly-A tails. For each dual-sample experiment, two sets of RNA sequences
were added with tags of two different sequences. The tagged RNA sequences were then
hybridized to the miRNA microarray chip containing 328 human miRNA probes. For the
complete probe sequences please see Appendix A. The labeling reaction was carried out
during the second hybridization reaction using tag-specific dendrimer Cy3 and Cy5 dyes.

88
For the first array, RNA from WT and DKO cells were labeled with Cy3 and Cy5,
respectively. For the second array, RNA from untreated cells and cells treated with 5-
Aza-CdR and/or PBA were labeled with Cy3 and Cy5, respectively. The human miRNA
chip includes seven redundancies for each miRNA. The data were corrected by
subtracting the background and normalizing to the statistical median of all detectable
transcripts. Background was calculated from the median of 5% to 25% of the lowest-
intensity cells. The data normalization balances the intensities of Cy3- and Cy5-labeled
transcripts so that differential expression ratios can be correctly calculated (Saito, Liang
et al. 2006).

RNA isolation
Total RNA was extracted with Invitrogen TRIzol reagent (Carlsbad, CA)
according to the manufacturer’s instructions.

Quantitative mature miRNA stem-loop RT-PCR
Reagents were purchased from Applied Biosystems (Foster City, CA). Reverse
transcription (RT) and RT-PCR were performed according to manufacturer’s instructions.
The primers used for stem-loop RT-PCR for U6 are U6 forward, 5’-CTC GCT TCG
GCA GCA CA-3’; U6 reverse, 5’-AAC GCT TCA CGA ATT TGC GT-3’.

5’ RNA Ligase mediated rapid amplification of cDNA ends (5’ RLM-RACE)
Total RNA was extracted as described above, and the 5’ ends of primary miRNAs
were determined by using the RLM-RACE Kit (Ambion) according to the manufacturer's

89
instructions. PCR products were cloned into a TOPO TA cloning vector (Invitrogen,
Carlsbad, CA) and sequenced. The sequences used for the 5’ RLM-RACE PCR are miR-
146a inner, 5’-GTA AAT CCA ATG CAG CTG CTT CC-3’; miR-146a outer, 5’-GAG
ATA TCC CAG CTG AAG AAC TG-3’; miR-184 inner, 5’-CAG GAC AAG ATG TAA
ATG GAC GTT TG-3’; miR-184 outer, 5’-GAG GCT GTG AGT GTC AAT CAC C-3’;
miR-205 inner, 5’-CAA GAG AAG CAC ATG GAT TGT CTG AG-3’; miR-205 outer,
5’-CTC CAC TGA AAT CTG GTT GGG TAT G-3’; miR-214 inner, 5’-GCA GAC
ACA TGA CAA CTC TGT CC-3’; miR-214 outer, 5’-CTT TCA ATG GCT GGT TGT
CAT TCA GG-3’; miR-342 inner, 5’-CTT GAG CCC AGT TTC ACA GGT TG-3’;
miR-342 outer, 5’-GGT GGT GAT AAG TAG GCC AAG G-3’; miR-377 inner, 5’-CAT
AAA TAA AGC GAA TTC ACC AAG GGC-3’; miR-377 outer, 5’-CAT CCC AAG
CAG GAT TTG ATA CTC-3’; miR-485 inner, 5’-CCA CAC ATG AAC ACT GGT
GAG AAA TC-3’; miR-485 outer, 5’-CTC CGA GGC AGA ATT TGA CAC TAA
AAG-3’; miR-489 inner, 5’-CTA AAG GTT CAA GTA AAT GGC GTC ACA C-3’;
miR-489 outer, 5’-CTT AAT TGT TGT CCC ATG TAG CAG TTT AGC-3’; miR-510
inner, 5’-CAC ACA TAC ACA GCA TTC CTG AAA ATG G-3’; miR-510 outer, 5’-
GTC ATG TGT TAC TCC ACT CTT AGA GG-3’.

Northern blot analysis
Total RNA (20 μg) was loaded onto a 15% polyacrylamide denaturing gel and
transferred to a nylon membrane. The StarFire radiolabeled probes (Integrated DNA
Technologies, Coralville, IA) were prepared by incorporation of [ α-
32
P] dATP 6000
Ci/mmol following the manufacturer's recommendation. Prehybridization and

90
hybridization were carried out using ExpressHyb Hybridization Solution (Clontech,
Mountain View, CA). Hybridization was carried out at 42 °C overnight, and then the
membrane was washed with 1X SSC+0.05% SDS until the background is clear as
detected by a Geiger counter. U6 was used as a loading control. The Northern probe
sequences are listed as follows: miR-205, 5’-CAG ACT CCG GTG GAA TGA AGG A-
3’; miR-377, 5’-ACA AAA GTT GCC TTT GTG TGA T-3’; U6, 5’-GCA GGG GCC
ATG CTA ATC TTC TCT GTA TCG-3’.

Genomic bisulfite sequencing
Genomic DNA was extracted using the Qiagen DNeasy kit (Qiagen, Valencia,
CA) according to the manufacturer’s instructions. 2ug of the genomic DNA was
converted with sodium bisulfite as previously described (Frommer, McDonald et al.
1992). After amplification of the bisulfite-converted DNA with specific primers for miR-
377, DNA methylation levels were analyzed by bisulfite genomic sequencing as
previously described (Daskalakis, Nguyen et al. 2002). The primers used for PCR
amplification of the miR-377 genomic region 1 are: Forward, 5’-GGA GTA TAG TTA
TTT GAG TTT GTG TTG TTT-3’; Reverse, 5’-AAC AAC CTC TAC TCA AAA ATC
AAA CAT CAA-3’. The primers for region 2 are: Forward, 5’-TTG ATG TTT GAT
TTT TGA GTA GAG GTT GTT-3’; Reverse, 5’-TCA CAA ACC CTA CCA AAT ATA
AAC TCT TTA-3’.

91
Chromatin immunoprecipitation (ChIP)
The ChIP assay was performed as previously described (Nguyen, Weisenberger et
al. 2002). Ten μl of anti-trimethylated histone H3-K4 (Upstate Biochemistry, Lake
Placid, NY) and 10 μl of anti-acetylated histone H3 antibodies (Upstate) were used.
Quantitative analysis was performed by real-time PCR with CYBR green using the DNA
Engine Opticon System (MJ Research, Waltham, MA). The fraction of
immunoprecipitated DNA was calculated as follows: (immunoprecipitated DNA with
each antibody − nonspecific antibody control [NAC])/(input DNA − NAC). The primer
sequences used for ChIP PCR are listed below: Region 1 Forward, 5’-CTA CCT TAT
GTT GCC AGC TGT GTG CCC-3’; Reverse, 5’-GTT TCC ACT CCG CTG TTA ACC
CTG C-3’. Region 2 Forward, 5’-CTT CAG GTC GGG TCC TCC CTC AAA C-3’;
Reverse, 5’-CTG GAA GTC TCT GAA GCA GGA TTT CAC AC-3’. Region 3
Forward, 5’-CCA TGT TCT GAC CAC TGC CTG GG-3’; Reverse, 5’-GCT GCA GGC
TCG CTG GCT TGC TG-3’. Region 4 Forward, 5’-GAG TAT CAA ATC CTG CTT
GGG ATG GCT TC-3’; Reverse, 5’-CCA CGT CTA CCC CGT CCA TTC TTG-3’.

M.SssI Treatment
Nuclei preparation and M.SssI reactions were performed as described previously
(Fatemi, Pao et al. 2005; Lin, Jeong et al. 2007). Freshly extracted nuclei were treated
with M.SssI for 15 min at 37°C. M.SssI, methylates all accessible CpG sites in purified
DNA except those protected by nucleosomes or bound by tight-binding transcription
factors. Reactions were stopped by the addition of an equal volume of stop solution (20
nM Tris-HCl [pH 7.9], 600 mM NaCl, 1% SDS, 10 mM EDTA, 400 μg/ml proteinase K),

92
and then incubated at 55°C overnight. DNA was purified by phenol/chloroform
extraction and ethanol precipitation. The methylation patterns were then analyzed by
genomic bisulfite sequencing.

93
RESULTS

MicroRNA microarrays reveal that many miRNAs are potentially epigenetically
regulated
To examine the effects of the changes in cellular DNA methylation level on
miRNA expression by genetic modifications, I first compared WT with DKO HCT116
cells that have a partial DNMT1 knock-down and a complete DNMT 3b knock-out (Rhee,
Bachman et al. 2002; Egger, Jeong et al. 2006). While the DKO cells did not have a
complete loss of both DNMT1 and 3b, they had a dramatic decrease of methylation levels
and thus were still suitable for my study (Rhee, Bachman et al. 2002).

I found 100 of the 328 miRNAs on the microarray were differentially expressed
in the DKO cells compared to the WT, 41 of which were up-regulated and 59 of which
were down-regulated (Table 4.1). The fact that roughly 30% of the miRNAs examined
showed differential expression suggested that DNA methylation could play a role in
regulating miRNA expression. I decided to focus on the miRNAs whose expression was
the most highly up-regulated in the DKO cells because they had a higher probability of
being directly affected by DNA methylation. For the complete microarray data,
including probe sequences, please see Appendix A.

To examine the role of histone modifications as well as DNA methylation, I
performed a second miRNA microarray experiment comparing mock treated HCT116
cells with cells that were treated with 5-Aza-CdR and PBA (Table 4.2). The combination

94
Table 4.1: miRNA microarray—HCT116 WT vs. DKO
Probe ID WT DKO
log2
(DKO/WT)
Absolute Ratio
(DKO/WT)
hsa-miR-146a 7.932,111.93 7.99 266.25
hsa-miR-377 13.47 641.29 5.57 47.61
hsa-miR-184 15.80 615.84 5.40 38.98
hsa-miR-489 10.07 366.32 5.19 36.39
hsa-miR-205 13.15 449.88 5.10 34.20
hsa-miR-510 10.24 347.63 5.09 33.95
hsa-miR-342 239.10 5,372.51 4.41 22.47
hsa-miR-486 33.67 593.17 4.13 17.62
hsa-miR-214 29.05 448.48 3.95 15.44
hsa-miR-127 17.33 265.35 3.94 15.31
hsa-miR-485-3p 19.10 268.48 3.70 14.06
hsa-miR-210 295.24 3,201.66 3.57 10.84
hsa-miR-125a 945.14 8,431.39 3.26 8.92
hsa-miR-182 1,138.07 9,896.13 3.09 8.70
hsa-miR-34c 18.54 160.79 3.12 8.67
hsa-miR-320 8,891.02 75,956.26 3.09 8.54
hsa-miR-193b 340.62 2,825.99 3.01 8.30
hsa-miR-200b 609.60 4,407.46 2.94 7.23
hsa-miR-197 269.88 1,744.68 2.75 6.46
hsa-miR-99b 2,687.39 15,912.14 2.56 5.92
hsa-miR-198 138.79 821.22 2.56 5.92
hsa-miR-409-3p 57.62 332.83 2.53 5.78
hsa-miR-188 241.33 1,375.36 2.51 5.70
hsa-miR-500 103.55 552.30 2.43 5.33
hsa-miR-155 179.47 884.21 2.30 4.93
hsa-miR-154* 335.37 1,617.25 2.27 4.82
hsa-miR-362 154.25 741.00 2.35 4.80
hsa-miR-331 170.89 812.71 2.35 4.76
hsa-miR-200c 14,925.06 53,342.65 1.84 3.57
hsa-miR-361 1,083.44 3,756.66 1.74 3.47
hsa-miR-191 6,978.48 23,472.16 1.75 3.36
hsa-let-7e 838.482,610.53 1.39 3.11
hsa-miR-191* 229.36 653.99 1.51 2.85
hsa-miR-324-5p 1,230.09 3,018.09 1.32 2.45
hsa-miR-145 1,650.30 3,904.71 1.14 2.37
hsa-miR-30d 958.89 2,130.91 1.15 2.22
hsa-miR-151 892.07 1,950.20 1.13 2.19
hsa-miR-30c 916.30 1,886.63 1.04 2.06

95
Table 4.1, Continued
hsa-miR-185 1,218.13 2,413.85 0.99 1.98
hsa-miR-26a 4,048.19 7,172.13 0.85 1.77
hsa-miR-222 8,375.18 12,475.38 0.54 1.49
hsa-miR-16 11,521.97 8,769.27 -0.44 0.76
hsa-miR-107 4,737.83 3,314.26 -0.52 0.70
hsa-let-7a 4,756.373,278.65 -0.66 0.69
hsa-miR-23a 9,574.94 6,531.64 -0.56 0.68
hsa-miR-93 5,611.37 3,377.15 -0.77 0.60
hsa-miR-125b 2,405.28 1,306.04 -0.88 0.54
hsa-miR-130a 975.69 498.68 -0.97 0.51
hsa-miR-23b 6,505.49 3,252.66 -1.03 0.50
hsa-miR-30b 1,497.23 741.38 -1.01 0.50
hsa-miR-221 6,095.63 2,861.46 -1.18 0.47
hsa-miR-21 27,621.22 11,958.93 -1.17 0.43
hsa-miR-25 7,678.83 3,280.99 -1.14 0.43
hsa-miR-128b 486.82 202.93 -1.30 0.42
hsa-miR-128a 504.02 199.47 -1.35 0.40
hsa-miR-100 2,896.84 1,123.81 -1.36 0.39
hsa-miR-30a-5p 501.76 187.02 -1.40 0.37
hsa-miR-26b 1,117.51 413.73 -1.43 0.37
hsa-miR-195 324.85 109.25 -1.48 0.34
hsa-miR-22 479.78 138.13 -1.71 0.29
hsa-miR-17-5p 11,057.93 3,149.10 -1.76 0.28
hsa-miR-106a 8,318.95 2,311.07 -1.88 0.28
hsa-miR-7 4,148.96 1,117.05 -1.93 0.27
hsa-let-7c 1,176.89 297.65 -2.01 0.25
hsa-let-7f 4,421.451,113.41 -1.97 0.25
hsa-miR-148a 348.74 73.83 -2.23 0.21
hsa-miR-200a 193.06 35.17 -2.54 0.18
hsa-miR-106b 5,075.90 897.10 -2.54 0.18
hsa-miR-29a 9,053.32 1,572.80 -2.59 0.17
hsa-miR-422a 200.57 32.60 -2.41 0.16
hsa-miR-27b 1,317.77 207.35 -2.67 0.16
hsa-miR-181a 507.25 76.82 -2.76 0.15
hsa-miR-18a 1,438.04 213.26 -2.72 0.15
hsa-miR-20a 10,418.97 1,281.45 -2.91 0.12
hsa-miR-34a 525.65 62.36 -3.18 0.12
hsa-miR-27a 4,230.85 456.35 -3.21 0.11
hsa-let-7d 2,726.99 285.86 -3.20 0.10
hsa-miR-31 7,942.72 798.89 -3.38 0.10
hsa-miR-505 296.99 29.85 -3.41 0.10

96
Table 4.1, Continued
hsa-miR-542-5p 210.62 20.32 -3.07 0.10
hsa-miR-99a 129.33 11.85 -3.26 0.09
hsa-miR-9* 187.79 16.07 -3.57 0.09
hsa-miR-9 207.42 14.79 -3.88 0.07
hsa-miR-29b 1,863.02 127.09 -3.99 0.07
hsa-miR-126 714.61 44.99 -3.69 0.06
hsa-miR-18b 787.57 43.41 -4.13 0.06
hsa-miR-186 1,265.55 68.47 -4.43 0.05
hsa-miR-424 122.50 6.49 -3.86 0.05
hsa-let-7i 3,604.61 186.24 -4.22 0.05
hsa-let-7g 3,538.76 169.95 -4.38 0.05
hsa-miR-503 139.20 6.61 -4.33 0.05
hsa-miR-20b 3,694.84 161.65 -4.54 0.04
hsa-miR-19b 2,877.55 122.78 -4.49 0.04
hsa-miR-301 1,168.71 49.19 -4.48 0.04
hsa-miR-148b 879.42 36.79 -4.50 0.04
hsa-miR-30e-5p 334.11 12.31 -4.48 0.04
hsa-miR-98 501.85 14.91 -4.99 0.03
hsa-miR-141 3,729.02 90.32 -5.36 0.02
hsa-miR-101 921.48 14.20 -5.55 0.02
hsa-miR-19a 1,695.34 11.49 -7.28 0.01

One miRNA microarray was performed, and a total of 328 human miRNAs were
analyzed on the array (LC Sciences; http://www.lcsciences.com/). WT and DKO cells
were labeled with Cy3 and Cy5, respectively. The table lists all differentially expressed
transcripts with p-value < 0.01. These values represent the mean signal intensity of seven
redundancies on the chip for each miRNA, and the log and absolute ratios of the mean
signal intensity in DKO cells over WT cells for each miRNA. * indicates sequenes
derived from precursor miRNAs that are outside of the mature miRNA sequences.

97
Table 4.2: miRNA microarray—HCT116 mock treated vs. HCT116 treated with 5-Aza-
CdR+PBA
Probe ID
HCT116
mock
HCT116
A0.1P1
log2 (A0.1P1/
mock)
Absolute Ratio
(A0.1P1/mock)
hsa-miR-493-5p 30.16 1,209.73 5.34 40.11
hsa-miR-127 26.921,032.84 5.30 38.37
hsa-miR-376a 117.77 3,407.71 4.80 28.94
hsa-miR-517a 20.55 484.50 4.64 23.58
hsa-miR-517b 18.52 379.73 4.47 20.51
hsa-miR-376b 49.47 929.67 4.22 18.79
hsa-miR-368 110.30 2,038.37 4.14 18.48
hsa-miR-495 102.18 1,475.46 3.88 14.44
hsa-miR-154* 52.87 741.29 3.77 14.02
hsa-miR-373 61.59 848.78 3.85 13.78
hsa-miR-379 36.90 506.56 3.69 13.73
hsa-miR-329 55.01 720.27 3.58 13.09
hsa-miR-487b 76.25 987.93 3.58 12.96
hsa-miR-382 63.51 747.98 3.40 11.78
hsa-miR-515-5p 18.15 213.69 3.64 11.77
hsa-miR-369-3p 24.17 281.33 3.76 11.64
hsa-miR-323 34.35 328.28 3.29 9.56
hsa-miR-519c 18.82 145.61 3.15 7.74
hsa-miR-487a 50.63 372.07 2.78 7.35
hsa-miR-381 30.80 200.84 2.99 6.52
hsa-miR-493-3p 28.97 181.14 2.63 6.25
hsa-miR-485-3p 102.75 617.28 2.59 6.01
hsa-miR-409-3p 183.49 1,059.06 2.56 5.77
hsa-miR-139 102.56 550.07 2.41 5.36
hsa-miR-410 49.95 239.41 2.49 4.79
hsa-miR-372 52.43 246.92 2.32 4.71
hsa-miR-494 286.96 1,171.72 2.16 4.08
hsa-miR-371 41.24 155.26 1.98 3.76
hsa-miR-512-3p 47.64 171.06 1.96 3.59
hsa-miR-155 246.09 874.71 1.83 3.55
hsa-miR-218 386.90 1,348.54 1.82 3.49
hsa-miR-431 107.18 362.63 1.90 3.38
hsa-miR-146b 310.88 1,017.43 1.66 3.27
hsa-miR-199a* 172.98 565.92 1.58 3.27
hsa-miR-194 773.70 1,948.41 1.28 2.52
hsa-miR-21 33,786.82 83,751.08 1.34 2.48

98
Table 4.2, Continued
hsa-miR-182 6,506.31 15,987.98 1.33 2.46
hsa-miR-375 608.96 1,433.50 1.30 2.35
hsa-miR-424 7,491.50 16,035.75 1.14 2.14
hsa-miR-23b 20,228.28 37,895.75 0.95 1.87
hsa-miR-23a 21,469.47 39,910.94 0.93 1.86
hsa-miR-152 235.88 434.37 0.90 1.84
hsa-miR-9 6,192.9211,299.08 0.83 1.82
hsa-miR-126* 571.81 988.52 0.82 1.73
hsa-miR-183 5,177.00 8,855.66 0.77 1.71
hsa-miR-203 3,117.13 5,262.08 0.79 1.69
hsa-miR-27b 11,369.30 18,902.08 0.66 1.66
hsa-miR-148a 4,201.97 6,854.37 0.72 1.63
hsa-miR-26a 13,080.26 20,593.48 0.59 1.57
hsa-miR-27a 15,319.38 24,003.12 0.59 1.57
hsa-miR-96 3,004.74 4,514.44 0.56 1.50
hsa-miR-450 1,108.89 1,664.64 0.48 1.50
hsa-miR-132 2,079.83 3,055.48 0.55 1.47
hsa-miR-196a 1,024.01 1,474.31 0.51 1.44
hsa-miR-7 23,310.7133,171.34 0.49 1.42
hsa-miR-28 7,807.9910,978.85 0.39 1.41
hsa-miR-24 18,817.23 26,189.61 0.43 1.39
hsa-miR-29a 21,197.86 27,762.28 0.41 1.31
hsa-miR-9* 3,672.36 4,697.35 0.41 1.28
hsa-miR-30b 12,059.20 15,310.56 0.36 1.27
hsa-miR-29c 2,215.77 2,804.25 0.35 1.27
hsa-miR-26b 10,136.58 12,452.42 0.28 1.23
hsa-miR-126 5,883.47 7,201.40 0.28 1.22
hsa-miR-30c 10,449.14 12,579.96 0.29 1.20
hsa-miR-374 6,539.16 7,859.57 0.27 1.20
hsa-miR-191 18,889.07 22,635.41 0.28 1.20
hsa-miR-342 6,077.16 7,083.55 0.23 1.17
hsa-miR-30d 7,504.80 8,703.49 0.18 1.16
hsa-miR-16 23,733.24 27,310.34 0.23 1.15
hsa-miR-30a-5p 8,094.11 9,284.14 0.18 1.15
hsa-miR-195 14,010.55 15,905.21 0.19 1.14
hsa-miR-200b 16,672.25 15,124.68 -0.14 0.91
hsa-miR-200c 22,330.04 20,256.86 -0.15 0.91
hsa-miR-25 26,494.45 23,937.77 -0.14 0.90
hsa-miR-20a 28,131.03 24,377.91 -0.24 0.87
hsa-miR-20b 23,809.87 20,605.15 -0.28 0.87
hsa-miR-222 21,817.72 18,657.09 -0.19 0.86

99
Table 4.2, Continued
hsa-miR-186 9,786.41 8,316.76 -0.25 0.85
hsa-miR-106a 24,977.90 20,978.36 -0.29 0.84
hsa-miR-320 18,084.58 14,987.80 -0.27 0.83
hsa-miR-29b 12,757.47 10,541.09 -0.26 0.83
hsa-miR-103 17,204.66 14,171.18 -0.29 0.82
hsa-miR-93 15,826.68 12,858.75 -0.32 0.81
hsa-let-7f 24,554.2419,862.63 -0.29 0.81
hsa-let-7g 21,759.8317,478.22 -0.31 0.80
hsa-miR-107 16,729.95 13,422.99 -0.33 0.80
hsa-miR-22 5,797.09 4,648.79 -0.32 0.80
hsa-miR-92 22,537.04 17,977.18 -0.31 0.80
hsa-let-7a 25,180.2820,083.08 -0.30 0.80
hsa-miR-106b 15,425.82 12,261.76 -0.35 0.79
hsa-miR-99b 7,618.97 5,983.99 -0.37 0.79
hsa-let-7e 14,973.3211,708.84 -0.27 0.78
hsa-miR-148b 7,575.71 5,918.21 -0.36 0.78
hsa-miR-324-3p 2,866.33 2,165.36 -0.38 0.76
hsa-miR-422a 2,920.47 2,155.98 -0.38 0.74
hsa-let-7c 16,062.6111,766.24 -0.45 0.73
hsa-miR-125a 8,811.15 6,422.80 -0.46 0.73
hsa-miR-505 2,665.60 1,875.97 -0.57 0.70
hsa-miR-98 11,458.08 8,019.45 -0.56 0.70
hsa-miR-125b 16,789.60 11,733.49 -0.51 0.70
hsa-let-7d 19,483.2013,102.31 -0.54 0.67
hsa-miR-181a 2,536.32 1,701.93 -0.52 0.67
hsa-miR-31 20,500.04 13,697.60 -0.56 0.67
hsa-let-7i 17,447.6211,609.11 -0.58 0.67
hsa-miR-331 2,544.63 1,644.38 -0.69 0.65
hsa-miR-100 13,358.48 8,592.73 -0.62 0.64
hsa-miR-99a 7,669.36 4,925.69 -0.61 0.64
hsa-miR-15a 1,635.19 1,041.25 -0.69 0.64
hsa-miR-130a 7,011.21 4,454.30 -0.61 0.64
hsa-let-7b 10,659.61 6,703.90 -0.64 0.63
hsa-miR-30e-5p 4,626.77 2,839.34 -0.70 0.61
hsa-miR-149 2,287.14 1,384.25 -0.74 0.61
hsa-miR-34a 2,504.50 1,458.69 -0.68 0.58
hsa-miR-210 678.82 385.31 -0.81 0.57
hsa-miR-324-5p 3,840.14 2,166.27 -0.78 0.56
hsa-miR-130b 8,233.01 4,623.00 -0.80 0.56
hsa-miR-145 9,768.33 5,307.65 -0.88 0.54
hsa-miR-18b 6,783.80 3,588.53 -0.95 0.53

100
Table 4.2, Continued
hsa-miR-18a 8,479.61 4,478.42 -0.92 0.53
hsa-miR-498 563.21 290.90 -0.98 0.52
hsa-miR-197 4,358.14 2,201.23 -0.95 0.51
hsa-miR-330 718.38 350.35 -1.03 0.49
hsa-miR-492 1,057.83 513.53 -1.03 0.49
hsa-miR-345 1,651.53 801.17 -1.07 0.49
hsa-miR-301 7,291.18 3,510.55 -0.98 0.48
hsa-miR-484 1,654.76 787.71 -1.03 0.48
hsa-miR-193b 2,482.18 1,181.37 -1.09 0.48
hsa-miR-198 1,816.11 849.92 -1.09 0.47
hsa-miR-140 627.14 288.22 -1.16 0.46
hsa-miR-200a 2,164.09 978.11 -1.18 0.45
hsa-miR-191* 604.01 263.37 -1.22 0.44
hsa-miR-101 5,803.35 2,520.21 -1.21 0.43
hsa-miR-181b 616.86 257.65 -1.35 0.42
hsa-miR-497 551.95 223.90 -1.43 0.41
hsa-miR-200a* 543.74 217.11 -1.35 0.40
hsa-miR-196b 1,485.16 589.32 -1.36 0.40
hsa-miR-19b 15,321.70 5,766.64 -1.50 0.38
hsa-miR-141 10,826.15 3,966.30 -1.43 0.37
hsa-miR-328 277.08 100.17 -1.51 0.36
hsa-miR-339 351.15 122.65 -1.33 0.35
hsa-miR-346 347.37 118.58 -1.46 0.34
hsa-miR-423 2,764.56 930.82 -1.51 0.34
hsa-miR-18a* 777.12 238.50 -1.89 0.31
hsa-miR-19a 12,328.85 3,436.73 -1.95 0.28
hsa-miR-188 651.71 181.08 -1.82 0.28
hsa-miR-17-3p 320.24 85.86 -1.94 0.27
hsa-miR-542-5p 412.14 47.31 -3.16 0.11

One miRNA microarray was performed, and a total of 328 human miRNAs were
analyzed on the array (LC Sciences; http://www.lcsciences.com/). Mock-treated cells
and cells treated with 5-Aza-CdR (0.1 μM) and PBA (1mM) for 6 days were labeled with
Cy3 and Cy5, respectively. The table lists all differentially expressed transcripts with p-
value < 0.01. These values represent the mean signal intensity of seven redundancies on
the chip for each miRNA, and the log and absolute ratios of the mean signal intensity in
DKO cells over WT cells for each miRNA. * indicates sequenes derived from precursor
miRNAs that are outside of the mature miRNA sequences.
Mock: mock treatment with EtOH; A0.1P1: 5-Aza-CdR at 0.1 μM and PBA at 1mM

101
treatment would be expected to result in decreased DNA methylation and increased
histone acetylation (Egger, Liang et al. 2004; Saito, Liang et al. 2006). A total of 147 out
of the 328 miRNAs on the array showed differential expression; 71 miRNAs showed up-
regulation and 76 showed down-regulation. Again, the fact that more than one third of
the miRNAs examined showed differential expression in the treated cells compared to
untreated cells suggests epigenetic mechanisms control the expression of many miRNAs.
I focused on the most highly up-regulated miRNAs for the same reason as outlined above,
namely that their expression had the highest probability of being directly affected by
epigenetics. The complete microarray data, including probe sequences, are shown in
Appendix B. By combining the two array results—genetic and pharmacological
manipulation of the cellular epigenetic states—I obtained a more complete understanding
of the roles epigenetic mechanisms play in controlling miRNA expression.

Table 4.3 is a composite of the two miRNA microarrays, and Figure 4.1 shows
that there was little overlap between the data obtained from the two arrays. Although I
had expected to identify the same set of miRNAs when DNA methylation levels were
manipulated pharmacologically or genetically, my result and a previous study
demonstrated that this was not the case. Gius et al. (Gius, Cui et al. 2004) reported that
HCT116 DKO cells and 5-Aza-CdR treated cells showed different gene expression
profiles. The varied results could potentially be explained by the different physiological
states of cells. DKO cells had been selected to grow stably in the absence of normal
DNMT levels, whereas the pharmacologically treated cells were undergoing acute
changes caused by 5-Aza-CdR and PBA that induced significant changes in the

102
Table 4.3: Composite table comparing the two microarray results

Probe ID Double Treatment DKO
hsa-let-7a 0.80 0.69
hsa-let-7b 0.63
hsa-let-7c 0.73 0.25
hsa-let-7d 0.67 0.10
hsa-let-7e 0.78 3.11
hsa-let-7f 0.81 0.25
hsa-let-7g 0.80 0.05
hsa-let-7i 0.67 0.05
hsa-miR-100 0.64 0.39
hsa-miR-101 0.43 0.02
hsa-miR-103 0.82
hsa-miR-106a 0.84 0.28
hsa-miR-106b 0.79 0.18
hsa-miR-107 0.80 0.70
hsa-miR-125a 0.73 8.92
hsa-miR-125b 0.70 0.54
hsa-miR-126 1.22 0.06
hsa-miR-126* 1.73
hsa-miR-127 38.37 15.31
hsa-miR-128a 0.40
hsa-miR-128b 0.42
hsa-miR-130a 0.64 0.51
hsa-miR-130b 0.56
hsa-miR-132 1.47
hsa-miR-139 5.36
hsa-miR-140 0.46
hsa-miR-141 0.37 0.02
hsa-miR-145 0.54 2.37
hsa-miR-146a 266.25
hsa-miR-146b 3.27
hsa-miR-148a 1.63 0.21
hsa-miR-148b 0.78 0.04
hsa-miR-149 0.61
hsa-miR-151 2.19
hsa-miR-152 1.84
hsa-miR-154* 14.02 4.82
hsa-miR-155 3.55 4.93

103
Table 4.3, Continued
hsa-miR-15a 0.64
hsa-miR-16 1.15 0.76
hsa-miR-17-5p 0.28
hsa-miR-17-3p 0.27
hsa-miR-181a 0.67 0.15
hsa-miR-181b 0.42
hsa-miR-182 2.46 8.70
hsa-miR-183 1.71
hsa-miR-184 38.98
hsa-miR-185 1.98
hsa-miR-186 0.85 0.05
hsa-miR-188 0.28 5.70
hsa-miR-18a 0.53 0.15
hsa-miR-18a* 0.31
hsa-miR-18b 0.53 0.06
hsa-miR-191 1.20 3.36
hsa-miR-191* 0.44 2.85
hsa-miR-193b 0.48 8.30
hsa-miR-194 2.52
hsa-miR-195 1.14 0.34
hsa-miR-196a 1.44
hsa-miR-196b 0.40
hsa-miR-197 0.51 6.46
hsa-miR-198 0.47 5.92
hsa-miR-199a* 3.27
hsa-miR-19a 0.28 0.01
hsa-miR-19b 0.38 0.04
hsa-miR-200a 0.45 0.18
hsa-miR-200a* 0.40
hsa-miR-200b 0.91 7.23
hsa-miR-200c 0.91 3.57
hsa-miR-203 1.69
hsa-miR-205 34.20
hsa-miR-20a 0.87 0.12
hsa-miR-20b 0.87 0.04
hsa-miR-21 2.48 0.43
hsa-miR-210 0.57 10.84
hsa-miR-214 15.44
hsa-miR-218 3.49

104
Table 4.3, Continued
hsa-miR-22 0.80 0.29
hsa-miR-221 0.47
hsa-miR-222 0.86 1.49
hsa-miR-23a 1.86 0.68
hsa-miR-23b 1.87 0.50
hsa-miR-24 1.39
hsa-miR-25 0.90 0.43
hsa-miR-26a 1.57 1.77
hsa-miR-26b 1.23 0.37
hsa-miR-27a 1.57 0.11
hsa-miR-27b 1.66 0.16
hsa-miR-28 1.41
hsa-miR-29a 1.31 0.17
hsa-miR-29b 0.83 0.07
hsa-miR-29c 1.27
hsa-miR-301 0.48 0.04
hsa-miR-30a-5p 1.15 0.37
hsa-miR-30b 1.27 0.50
hsa-miR-30c 1.20 2.06
hsa-miR-30d 1.16 2.22
hsa-miR-30e-5p 0.61 0.04
hsa-miR-31 0.67 0.10
hsa-miR-320 0.83 8.54
hsa-miR-323 9.56
hsa-miR-324-3p 0.76
hsa-miR-324-5p 0.56 2.45
hsa-miR-328 0.36
hsa-miR-329 13.09
hsa-miR-330 0.49
hsa-miR-331 0.65 4.76
hsa-miR-339 0.35
hsa-miR-342 1.17 22.47
hsa-miR-345 0.49
hsa-miR-346 0.34
hsa-miR-34a 0.58 0.12
hsa-miR-34c 8.67
hsa-miR-361 3.47
hsa-miR-362 4.80
hsa-miR-368 18.48

105
Table 4.3, Continued
hsa-miR-369-3p 11.64
hsa-miR-371 3.76
hsa-miR-372 4.71
hsa-miR-373 13.78
hsa-miR-374 1.20
hsa-miR-375 2.35
hsa-miR-376a 28.94
hsa-miR-376b 18.79
hsa-miR-377 47.61
hsa-miR-379 13.73
hsa-miR-381 6.52
hsa-miR-382 11.78
hsa-miR-409-3p 5.77 5.78
hsa-miR-410 4.79
hsa-miR-422a 0.74 0.16
hsa-miR-423 0.34
hsa-miR-424 2.14 0.05
hsa-miR-431 3.38
hsa-miR-450 1.50
hsa-miR-484 0.48
hsa-miR-485-3p 6.01 14.06
hsa-miR-486 17.62
hsa-miR-487a 7.35
hsa-miR-487b 12.96
hsa-miR-489 36.39
hsa-miR-492 0.49
hsa-miR-493-3p 6.25
hsa-miR-493-5p 40.11
hsa-miR-494 4.08
hsa-miR-495 14.44
hsa-miR-497 0.41
hsa-miR-498 0.52
hsa-miR-500 5.33
hsa-miR-503 0.05
hsa-miR-505 0.70 0.10
hsa-miR-510 33.95
hsa-miR-512-3p 3.59
hsa-miR-515-5p 11.77
hsa-miR-517a 23.58

106
Table 4.3, Continued
hsa-miR-517b 20.51
hsa-miR-519c 7.74
hsa-miR-542-5p 0.11 0.10
hsa-miR-7 1.42 0.27
hsa-miR-9 1.82 0.07
hsa-miR-9* 1.28 0.09
hsa-miR-92 0.80
hsa-miR-93 0.81 0.60
hsa-miR-96 1.50
hsa-miR-98 0.70 0.03
hsa-miR-99a 0.64 0.09
hsa-miR-99b 0.79 5.92

Table lists the absolute ratio of signal intensity of each miRNA in 5-Aza-CdR+PBA
treated cells vs. mock-treated cells, and in DKO cells compared to WT cells. Blank space
indicates that the miRNA was not found to be significantly differentially expressed in
that array.

107
Figure 4.1: miRNA microarrays comparison

A. Total number of miRNAs

B. Up-regulated miRNAs C. Down-regulated miRNAs

Diagrams showing the overlap between the DKO array and the 5-Aza-
CdR(0.1 μM)+PBA(1mM) treatment array (A0.1P1). A) The total numbers of
differentially expressed miRNAs (differentially expressed transcripts with p-value < 0.01
among the seven redundancies on each chip) are shown in the circles. We performed one
miRNA microarray comparing WT vs. DKO HCT116 cells, labeld Cy3 and Cy5,
respectively. (DKO array) We also performed one miRNA microarray comparing mock-
treated HCT116 cells vs. cells treated with 5-Aza-CdR (0.1 μM) and PBA (1mM) for 6
days, labeled with Cy3 and Cy5, respectively. (LC Sciences;
http://www.lcsciences.com/).

A total of 100 miRNAs in the DKO array and a total of 147 miRNAs in the AP array are
found to be differentially expressed. 48 miRNAs are found to be concordantly
differentially expressed in both arrays (up- or down-regulated in both arrays). B) The
total numbers of up-regulated miRNAs are shown in the circles. A total of 41 miRNAs
in the DKO array and 71 in the AP array are up-regulated, and 11 are up-regulated in
both. C) The total numbers of down-regulated miRNAs are shown in the circles. 59
miRNAs from the DKO array and 76 miRNAs from the AP array are found to be down-
regulated, and 37 miRNAs are down-regulated in both.
52 48 99
DKO A0.1P1
30 11 60
DKO A0.1P1
22 37 39
DKO A0.1P1

108
epigenetic states of these cells. Moreover, the treatment included PBA, which altered the
histone acetylation level, and therefore added one more variable to the experiment in
addition to the decrease in DNA methylation. Therefore, even though both approaches—
genetic and pharmacological manipulations—led to decreased DNA methylation in the
cellular epigenetic states, there were many significant differences that could potentially
explain the lack of overlap between the two microarrays.

I confirmed the microarray results using quantitative mature miRNA stem-loop
RT-PCR for two miRNAs from each array (Figure 4.2). miR-205 and 489 were found to
be up-regulated in the DKO array, whereas miR-368 and 376a were found to be up-
regulated in the treatment array. These four miRNAs were chosen because they were
some of the most highly up-regulated miRNAs on the arrays, and because of the
availability of the commercial stem-loop RT-PCR reagents. miR-377 was not included in
this study because no commercial RT-PCR reagents were available for it then. In all four
cases, the RT-PCR results confirmed the microarray results, suggesting that the array
results were representative. In addition, miR-489, 368, and 376a show up-regulation in
both the DKO and the treated cells, even though they were each only detected by one
array. This suggests that there might be more overlap between the two arrays than is
suggested by the array results alone. I therefore believed the array analysis yielded high
specificity, although the sensitivity was lower than optimal. Therefore, it is likely that
the miRNAs found from the array analyses were truly differentially regulated, and I
recognized that there could be more miRNAs affected that were not found to be
statistically significant from my analyses.

109
Figure 4.2: Quantitative stem-loop mature miRNA RT-PCR results in HCT116 cells

Expression of miRNAs in HCT116 cells. Quantitative real time RT-PCR analysis of
each miRNA was done with normalization to the U6 RNA. Representative results of two
independent RT-PCR reactions are shown. MiR-205 and 489 were found to be up-
regulated in the DKO array, and miR-368 and 376a were found to be up-regulated in the
treatment array. All four miRNAs examined here confirm the miRNA microarray results.
In addition, miR-489, 368, and 376a show up-regulation by both DKO and treatment,
suggesting that there is more overlap than suggested by the array results alone.

WT, wildtype; mock, mock treatment with EtOH only; A0.1P1, treatment with 5-Aza-
CdR 0.1 μM and PBA 1mM for 6 days; P3, treatment with PBA 3mM for 6 days; DKO,
double knock-out (DNMT1 hypomorph and DNMT3b knock-out); 1KO, DNMT1
hypomorph; 3bKO, DNMT3b knock-out. Treatment with 5-Aza-CdR was removed after
24 hrs, while treatment with PBA was continuous.

miR-205 Expression in HCT116 cells
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
WT mock A0.1P1 P3 DKO 1 KO 3bKO
miR-368 Expression in HCT116 cells
0
0.00005
0.0001
0.00015
0.0002
0.00025
0.0003
WT mock A0.1P1 P3 DKO 1 KO 3bKO
miR-376a Expression in HCT116 Cells
0
0.0005
0.001
0.0015
0.002
0.0025
0.003
0.0035
0.004
0.0045
0.005
WT mock A0.1P1 P3 DKO 1 KO 3bKO
miR-489 Expression in HCT116 Cells
0
0.0002
0.0004
0.0006
0.0008
0.001
0.0012
WT mock A0.1P1 P3 DKO 1 KO 3bKO

110
Determination of miRNA transcription start sites narrows down to candidate
miRNAs miR-205 and miR-377
Epigenetic mechanisms generally control gene expression at the proximal
promoter region, thus to properly study the epigenetic control of a gene, I needed to know
the transcription start site (TSS) of that particular gene (Baylin, Esteller et al. 2001; Jones
and Takai 2001; Jones and Baylin 2002). To determine the TSSs of the most highly
regulated miRNAs (miR-146a, 377, 184, 489, 205, 510, 342, 486, and 214), I performed
5’ RLM-RACE. Of the nine miRNAs I examined, I was able to determine the TSSs for
miR-377 and 205 (Figure 4.3.C). Because 5’ RLM-RACE is best for detecting TSSs
within 500bp of the designed primer site, it is possible that the miRNAs whose TSSs I
was unable to determine reside in a long primary transcript, or that they belong to a
cluster of miRNAs with a common start site farther upstream. Additionally, a minority of
miRNAs is transcribed by pol III (Borchert, Lanier et al. 2006), and 5’ RLM-RACE
would not be suitable for determining the TSSs of those miRNAs. Lastly, primary
transcripts are often very quickly processed into precursor miRNAs (Lee, Kim et al.
2004), and thus making it even more technically challenging for determining the TSSs of
the primary transcripts.

The TSS of miR-205 in DKO cells (Figure 4.3A) was found to be 80bp upstream
from the precursor miRNA sequence, and it resides within an initiator element
(YYANWYY, where Y=C/T, N=A/G/C/T, W=A/T) (Subramaniam 1998). The TSS of
miR-377 in DKO cells is 118 bp upstream from the start of the precursor sequence
(Figure 4.3B). There is a small CpG island of roughly 300bp, as determined by the CpG

111
Figure 4.3: Transcription start sites of miR-205 and miR-377 as determined by 5’ RLM-
RACE

4.3.A. miR-205

gtctgtctgtgcagcaggtgcaaggacgtgttgaactagctctctgcagcctccttggaggatgtgatcctatgggaggggtagg
agtattcaggtccttgacatctcccaaatgtgtgAttccgggatgccaaaggcctttggccaggtaatgcagtgtctacaggctga
ggttgacatgcatccccaccctctgagaaaaagatcctcagacaatccatgtgcttctcttgTCCTTCATTCCAC
CGGAGTCTGtctcatacccaaccagatttcagtggagtgaagttcaggaggcatggagctgacaaccatgaggc
ctcggcagccaccgccaccaccgccgccgccaccaccgtagcagcagcagcagcagcagcagcagcagcagcagcagca

Map and genomic sequence showing the precursor and mature miR-205 and the
transcription start site (TSS) as determined by 5’ RLM-RACE in DKO cells. Precursor
miR-205 is depicted as the red bar in the map, and the wider region represents the mature
miR-205. The tick marks on the lower half represent individual CpG sites. The TSS is
shown as the green arrow. In the genomic sequence the precursor miR-205 is underlined,
and the mature miR-205 is in capital letters. The TSS is in a green capital letter. Dotted
underline shows the transcription initiator element the TSS is located in.

>>>
100bp

112
Figure 4.3, Continued

4.3.B. miR-377

atgtgtgtgtgcatagcctcgcccccactgcctgctccagggctacagaagctggtccatgaccaaccatgttctgaccactgcct
ggggggcttctcggAggtagagagagagagagagcaagaggggagAgagcgagccagcaagccagcgagcctgcagc
tggagtcagcagggaggtcccgcaggcaatgccgcctttggtgAagaggcatctcggtgtgttcttgcccgtgctgatgtttga
cccttgagcagaggttgcccttggtgaattcgctttatttatgttgaATCACACAAAGGCAACTTTTGT
ttgagtatcaaatcctgcttgggatggcttccgggacccagtggcaagctcaggggcatctacacccctcccgtgagcaagaat

Map and genomic sequence showing the precursor and mature miR-377 and the
transcription start sites (TSSs) as determined by 5’ RLM-RACE in DKO (green),
DKO+PBA1mM (purple), and LD419 (blue) cells. Precursor miR-377 is depicted as the
red bar in the map, and the wider region represents the mature miR-377. The tick marks
on the lower half represent individual CpG sites. The small CpG island is labeled on the
map by the green bar. The TSSs are shown as the colored arrows. In the genomic
sequence the precursor miR-377 is underlined, and the mature miR-377 is in capital
letters. The TSSs are in corresponding colored capital letters. Note the rich GAGA
repeats in the genomic region around the TSSs.

>>>
100bp
DKO DKO+P1 LD419
CpG island

113
Figure 4.3, Continued

4.3.C: 5’ RLM-RACE products
miR-205 in DKO
miR-377 in DKO
miR-377 in LD419
miR-377 in DKO+P1
miRNA
-TAP
miR-205 in DKO
miR-377 in DKO
miR-377 in LD419
miR-377 in DKO+P1
miRNA
-TAP

PCR products of 5’ RLM-RACE for miR-205 and miR-377 in each cell line are shown
above. The PCR products were cloned into TA TOPO vectors and sequenced. P1: PBA
1mM; -TAP: minus Tobacco Acid Pyrophosphatase control (negative control).

114
Island Searcher (Takai and Jones 2003), downstream but close to the mature miRNA
sequence. Many GAGA repeats are present around the TSS region, and they may serve
as binding sites for the GAGA transcription factor (Adkins, Hagerman et al. 2006). 5’
RLM-RACE was also performed for miR-377 using DKO cells treated with PBA 1mM
and LD419 cells, both of which expressed miR-377 even more strongly than DKO cells
(Figure 4.4B). The TSSs from these later two cell populations are located very close to
that found in the DKO cells. The results demonstrated that there are multiple TSSs for
miR-377; the fact that they are located close to each other suggested that they could be
under similar transcription regulation mechanisms.

Expression studies showed that miR-377 could be epigenetically regulated
To further analyze the expression pattern of my candidate miRNAs miR-205 and
377, I performed Northern blot analysis, which is considered the gold standard in
examining miRNA expression. I was easily able to detect the mature miRNAs but not
the precursor or the primary miRNA transcripts, suggesting that these miRNAs are very
efficiently processed within the cells.

The Northern blot of miR-205 (Figure 4.4A) showed that it was expressed in the
DKO cells and undetected in the WT, and this was consistent with the microarray and
quantitative stem-loop RT-PCR results. I was unable to detect increased miR-205
expression in WT cells treated with 5-Aza-CdR, PBA, or 5-Aza-CdR+PBA, suggesting
that its expression was not truly regulated by epigenetic mechanisms. miR-205

115
Figure 4.4: Northern Blot analysis of miR-205 and miR-377

4.4.A. miR-205

Expression of miR-205 in HCT116 and LD419 cells by Northern Blot analysis. U6 is
used as the loading control. Cells were treated with 5-Aza-CdR and/or PBS for 6 days,
and total RNA was harvested and run on a denaturing polyacrylamide gel. The treatment
with 5-Aza-CdR was removed after 24 hrs, while the PBA treatment was continuous.
MiR-205 is expressed at equal levels in DKO, DKO+A0.1, and DKO+A0.3 cells.

Mock, mock treatment with EtOH; A0.1/0.3/0.5, 5-Aza-CdR at 0.1/0.3/0.5 μM; P2/3,
PBA at 2/3mM; A0.1P1, 5-Aza-CdR 0.1 μM and PBA 1mM; 1KO, DNMT1 hypomorph;
3bKO, DNMT 3b knock-out.

miR-205
U6
HCT116 WT
HCT116 mock
HCT116 A0.1
HCT116 A0.3
HCT116 A0.5
HCT116 P2
HCT116 P3
HCT116 A0.1P1
HCT116 1KO
HCT116 3bKO
HCT116 DKO
HCT116 DKO A0.1
HCT116 DKO A0.3
LD419

116
Figure 4.4, Continued

4.4.B. miR-377

Expression of miR-377 in HCT116 and LD419 cells by Northern Blot analysis. U6 is
used as the loading control. Cells were treated with 5-Aza-CdR and/or PBS for 6 days,
and total RNA was harvested and run on a denaturing polyacrylamide gel. The treatment
with 5-Aza-CdR was removed after 24 hrs, while the PBA treatment was continuous.

Mock, mock treatment with EtOH; A0.3, Aza at 0.3 μM; P2/3, PBA at 2/3mM; A0.1P1,
Aza 0.1 μM and PBA 1mM; 1KO, DNMT1 hypomorph; 3bKO, DNMT 3b knock-out.

miR-377
U6
HCT116 WT
HCT116 mock
HCT116 A0.3
HCT116 P2
HCT116 P3
HCT116 A0.1P1
HCT116 1KO
HCT116 3bKO
HCT116 DKO
HCT116 DKO mock
HCT116 DKO A0.3
HCT116 DKO P1
HCT116 DKO A0.1P1
LD419

117
expression may be specific to DKO cells. Since I was unable to detect an increase of
miR-205 in response to epigenetic drugs, I did not analyze it further.

The Northern blot analysis of miR-377 (Figure 4.4B) showed that the miRNA
was highly expressed in the DKO cells relative to the WT, thus confirming the
microarray result. In addition, I observed that miR-377 was up-regulated in WT cells
treated with either 5-Aza-CdR or 5-Aza-CdR+PBA, suggesting that miR-377 was
regulated by DNA methylation. Moreover, treatment of DKO cells with PBA (1mM)
dramatically increased the expression of miR-377, suggesting that histone acetylation
could also regulate the expression of this miRNA. Treatment of DKO cells with 5-Aza-
CdR only minimally increased the expression of miR-377, probably because the DNA
methylation level in DKO cells was already dramatically reduced before treatment. Since
the expression of miR-377 could be manipulated by genetic and pharmacological
regulators of epigenetic states, I focused on it for further analysis.

Genomic bisulfite sequencing suggested that DNA methylation could control miR-
377 expression
In order for DNA methylation to regulate the expressions of miR-377, the
methylation level should correlate with the expression level. That is, when miR-377 is
expressed, the region surrounding the TSSs should be low in methylation; when it is not
expressed, this region should be highly methylated.

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Using genomic bisulfite sequencing, I found the region around the miR-377 TSSs
was heavily methylated in the non-expressing WT HCT116 cells, and was largely devoid
of methylation in the expressing DKO cells except in some small patches. In addition,
treatment of the WT cells with either 5-Aza-CdR or 5-Aza-CdR+PBA, both of which
could up-regulate miR-377 expression, led to ~50% decrease in the DNA methylation
levels (Figure 4.5). These results suggested that DNA methylation might regulate miR-
377 expression. However, when I examined the methylation level in DKO cells treated
with PBA, which showed a dramatic increase in miR-377 expression compared to the
untreated DKO cells, I did not see a further reduction in DNA methylation. This
suggested that DNA methylation was not the only mechanism contributing to the control
of miR-377 expression. I therefore concluded that while miR-377 expression could be
regulated by DNA methylation, it was most likely controlled by additional mechanisms
such as histone acetylation.

Histone acetylation controls the expression of miR-377
To investigate the role of histone modifications in regulating miR-377 expression
by ChIP analysis, I examined the levels of total histone H3, acetylated H3, and tri-
methylated H3K4, which is usually associated with active transcription start sites
(Ruthenburg, Allis et al. 2007). I compared the above three markers in WT, DKO, and
DKO cells treated with PBA; the three different cell populations expressed miR-377 at
increasing levels, respectively (Figure 4.6).

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Figure 4.5: Genomic bisulfite sequencing results of miR-377 region

HCT
116
WT
DKO
miR-377
100bp
DKO + P1
HCT116 + A0.3
HCT116 + A0.1P1

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Figure 4.5, Continued

Genomic bisulfite sequencing results around the miR-377 gene. The top is a map
showing the miR-377 gene (red bar), the transcription start sites (green arrows), and the
CpG sites (tick marks in lower half). Each row represents a single cloned allele. A
filled-in circle represents a methylated CpG site, and an open circle represents an un-
methylated CpG site. The results show the region to be highly methylated in HCT116
WT cells, and the methylation is decreased by roughly 50% by the treatment of 5-Aza-
CdR or 5-Aza-CdR+PBA. In DKO cells, this region is virtually devoid of methylation
except in small discrete patches, where there is still methylation. The addition of PBA to
DKO cells does not induce further de-methylation.

Cells were treated with 5-Aza-CdR and/or PBA for 6 days; the treatment with 5-Aza-
CdR was removed after 24 hrs, while the PBA treatment was continuous. Genomic DNA
was harvested on day 6 for genomic bisulfite sequencing.
A0.3: 5-Aza-CdR 0.3 μM; A0.1P1: 5-Aza-CdR 0.1 μM and PBA 1mM; P1: PBA 1mM

121
Figure 4.6: ChIP analysis of miR-377 region

Alterations in histone modifications around the promoter region of the miR-377 gene in
WT and DKO HCT116 cells and in DKO cells treated with PBA. The levels of
acetylated histone H3 and trimethylated histone H3-K4 in four regions around the miR-
377 gene determined by ChIP assay. Regions 2, 3, and 4 are shown on the map above,
and region 1 is approximately 900bp upstream from the precursor start site (outside of the
map). IP/Input = (immunoprecipitated DNA with each antibody − nonspecific antibody
control [NAC])/(input DNA − NAC). Values represent mean + standard deviation of two
experiments from two independent ChIP samples.

The total H3 level is unchanged among WT, DKO, and DKO+PBA1mM cells. The level
of acetylated H3 increased minimally in the DKO cells compared to the WT, and it
increased dramatically in DKO cells treated with PBA 1mM. The level of tri-methylated
H3K4 is roughly the same in WT and DKO cells, and it increased in region 1 and 2 in the
DKO cells treated with PBA 1mM.

H3, histone 3; AcH3, acetylated H3; TriMe H3K4, tri-methylated H3 lysine 4; unt,
untreated (WT); P1, continuous treatment with PBA 1mM for 6 days.
>>>
100
2 3 4
HCT116 unt H3
0
5
10
15
20
25
30
35
40
45
1234
Region
H3/input
DKO unt H3
0
5
10
15
20
25
30
35
40
45
1234
Region
H3/input
DKO P1 H3
0
5
10
15
20
25
30
35
40
45
1234
Region
H3/input
HCT116 unt AcH3
0
0.5
1
1.5
2
12 34
Region
AcH3/input
DKO unt AcH3
0
0.5
1
1.5
2
12 34
Region
AcH3/input
DKO P1 AcH3
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
12 34
Region
AcH3/input
HCT116 unt TriMe H3K4
0
0.5
1
1.5
2
2.5
3
12 34
Region
TriMe H3K4/input
DKO unt TriMe H3K4
0
0.5
1
1.5
2
2.5
3
12 34
Region
TriMe H3K4/input
DKO P1 TriMe H3K4
0
0.5
1
1.5
2
2.5
3
12 34
Region
TriMe H3K4/input

122
The levels of total H3 did not vary much among the three cell populations I
examined. The levels of acetylated H3 were minimally increased in the DKO cells
compared to the WT; however, they were increased in the DKO cells treated with PBA in
all four regions examined. The levels of tri-methylated H3K4 were the same in WT and
DKO cells, and were increased in region 1 and 2 in the DKO cells treated with PBA.
Taken together, my results suggested that histone acetylation, in addition to DNA
methylation, also controlled the expression of miR-377. The decrease in DNA
methylation level most likely accounted for the increase in miR-377 expression in DKO
cells compared to the WT, and the further increase in the expression in DKO cells treated
with PBA was likely due to the increase in histone acetylation levels.

Nucleosome positioning is unlikely to control the expression of miR-377
In addition to DNA methylation and histone modifications, nucleosome
positioning has recently been discovered to be an important epigenetic mechanism for
controlling gene expression (Fatemi, Pao et al. 2005; Gal-Yam, Jeong et al. 2006; Lin,
Jeong et al. 2007). I hypothesized that the dramatic increase in miR-377 expression in
DKO cells treated with PBA, compared to untreated DKO cells, could be partially due to
changes in nucleosomal positioning. To investigate if this was the case, I analyzed the
region around miR-377 using the methylase-based single-promoter analysis (MSPA)
assay (Fatemi, Pao et al. 2005). This technique enables the visualization of protection
due to transcription factors or nucleosomes on individual promoter molecules, and thus
can reveal if changes in nucleosomal occupancy regulate the expression of miR-377.

123
My results (Figure 4.7) show that the region around miR-377 was highly
protected. This protection could be from nucleosomes or from tight-binding transcription
factors. In addition, the pattern of protection was virtually the same between untreated
DKO cells and DKO cells treated with PBA. While I could not be certain about the cause
of the protection, I could conclude that nucleosomal positioning was unlikely to
contribute to the control of miR-377 expression because no changes in nucleosome
positioning were observed with treatment of PBA when I saw dramatic changes in miR-
377 expression.

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Figure 4.7: MSPA analysis of the miR-377 region

Accessibility of native chromatin to M.SssI at the miR-377 promoter region in DKO cells
untreated and treated with PBA. Nuclei were extracted from DKO cells untreated and
treated with PBA and were then treated with M.SssI for 15 min, followed by bisulfite
genomic sequencing. The top shows a map of miR-377. The precursor miR-377 is
shown in the red bar, and the mature miR-377 is represented by the wider red bar region.
The transcription start sites are represented by the bent arrows, and the lower half tick
marks depict individual CpG sites. An open circle denotes an un-methylated CpG site,
and a filled-in circle denotes a methylated CpG site.

The top panel shows the DKO cells untreated with M.SssI, the middle shows DKO
treated with M.SssI, and the bottom shows DKO cells + PBA1mM treated with M.SssI.
The results show that the region is highly protected, either by nucleosomes or tight-
binding transcription factors. In addition, there is no change between untreated DKO
cells and DKO cells treated with PBA. This shows that the dramatic increase in miR-377
expression by the PBA treatment is not contributed by changes in nucleosomal
positioning.

P1: continuous treatment with PBA 1mM for 6 days.
DKO + SssI
miR-377
100bp
DKO+P1 + SssI
DKO

125
DISCUSSION

Our emerging understanding and appreciation of the importance of miRNAs in
normal and pathological processes have prompted studies into the function and regulation
of these endogenous RNA molecules (Esquela-Kerscher and Slack 2006). miRNAs have
been shown to play essential roles in a plethora of cellular functions. Much effort has
been made to elucidate the functions of miRNAs; however, the regulation of miRNAs—
controls at the transcription and the processing levels—is not as well understood and still
requires much further study. Abnormalities in miRNA expression have been shown to be
involved in pathological processes. For example, miR-15 and 16, which target BCL2, are
found to be frequently down-regulated in B cell chronic lymphocytic leukemias
(Cimmino, Calin et al. 2005). Because miRNAs play considerable roles in cellular
functions, it is important to understand how their expression is regulated.

Our understanding of miRNA expression comes from a handful of examples.
Only recently was it demonstrated that most miRNAs are processed from RNA pol II
transcripts (Lee, Kim et al. 2004). Other studies show that miRNAs, similar to protein-
coding genes, can be regulated by transcription factors such as c-Myc and NF- κB
(O'Donnell, Wentzel et al. 2005; Taganov, Boldin et al. 2006). From these studies, and
our previous study that shows epigenetic drugs can up-regulate the expression of
miRNAs (Saito, Liang et al. 2006), I came to hypothesize that epigenetic mechanisms can
regulate miRNA expression

126
To address the question if epigenetic mechanisms—DNA methylation, histone
modification, and nucleosomal positioning—can regulate the expression of miRNAs, I
examined global changes in miRNA expression. Using HCT116 cells, I compared the
miRNA expression when the epigenetic status is manipulated genetically—in the case of
the DKO cells—and pharmacologically—by the treatment with 5-Aza-CdR and PBA.
By combining the two different approaches, I obtained a more complete picture of
epigenetic regulation of miRNA expression. My array results suggest that epigenetic
mechanisms have the potential to control the expression of many miRNAs. Roughly one
third of all miRNAs examined on either array was significantly differentially expressed
(differentially expressed transcripts with p-value < 0.01 among the seven redundancies
for each miRNA), suggesting that DNA methylation and histone acetylation—the two
primary epigenetic mechanisms examined in my arrays—control the expression of many
miRNAs.

There is little overlap between the miRNAs I indentified and those from similar
studies by Lujambio et al (Lujambio, Ropero et al. 2007) and Han et al. (Han, Witmer et
al. 2007), who also included an array comparing WT and DKO HCT116 cells. The
differences are not easy to explain, but may be due to the use of different microarray
platforms that did not include the exact same set of miRNAs. Secondly, a total of eight
clones of DKO cells were generated Rhee et al. (Rhee, Bachman et al. 2002) and I do not
know which clones were used by the other groups. While it is likely they all contain
genes and miRNAs that are re-expressed by the decrease in DNA methylation, they also
contain miRNAs that are specific to each clone and that may not be controlled by

127
epigenetics. When making comparisons between different studies involving DKO cells,
one has to carefully note which clone of DKO cells was used.

While it is tempting to conclude that epigenetics directly regulates the expression
of miRNAs, one must be cautious when doing epigenetic analysis. First, genetic or
pharmacological manipulations of cellular epigenetic mechanisms can have indirect
effects. One needs to differentiate between a miRNA whose expression is directly
controlled by epigenetic mechanisms such as DNA methylation, versus a miRNA whose
expression is governed by a transcription factor that is epigenetically regulated. Secondly,
the majority of miRNAs reside within introns of protein-coding genes, and these
miRNAs are believed to be co-regulated with their host genes (Ying and Lin 2005).
When studying the regulation of expression of these miRNAs, one needs to study the
regulation of their host genes as well. While some intronic miRNAs can also have their
own promoters, often the control of their expression resides in the promoter regions of
their host genes (Ying and Lin 2005). Analyzing the epigenetic control of these intronic
miRNAs without analyzing the epigenetic control of their host genes would lead to
incomplete or erroneous interpretations. Lastly, epigenetic mechanisms largely control
the expression of genes at the proximal promoter sites (Baylin, Esteller et al. 2001; Jones
and Takai 2001; Bird 2002). It is therefore of extreme importance to identify the correct
start site for any miRNA being studied, and the TSSs of miRNAs have yet to be
experimentally determined. Studies that analyze epigenetic changes at regions that are
not confirmed to be real promoter regions can lead to unreliable conclusions.

128
I focused on the miRNAs that showed the highest up-regulation from the
microarrays because they had the highest probability of being epigenetically regulated.
Using 5’ RLM-RACE, I was able to determine the transcription start sites of miR-205
and miR-377. I eliminated miR-205 from further study because its expression appeared
to be DKO-specific and not truly regulated by epigenetics. This is important for our
analysis because both genetic knock-out of DNMTs and pharmacological treatments with
epigenetic drugs can induce secondary changes that are not affected by epigenetic
mechanisms directly.

I demonstrated that miR-377 expression can be regulated by DNA methylation
and histone acetylation by genomic bisulfite sequencing and ChIP analysis, respectively.
In addition, my results from the MSPA assay suggested that the changes in nucleosome
occupancy do not control the expression of miR-377. However, it is likely that there are
genetic mechanisms contributing to the control of miR-377 expression in addition to
DNA methylation and histone acetylation. One intriguing feature of the proximal
promoter region of miR-377 is the abundance of GAGA repeats. These repeats could
serve as optimal docking sites for the transcription factor GAGA binding factor (GAF)
(Adkins, Hagerman et al. 2006). However, due to the lack of a commercially available
antibody to the human GAF protein, I have not been able to investigate whether the
binding of GAF to this region controls the miR-377 expression.

To fully understand the impact that epigenetic controls have over miR-377
expression, I am now in the process of determining the biological functions of miR-377

129
(Chapter 5). From my study, I observed that epigenetic changes can affect the expression
of many miRNAs. Individual miRNAs can have multiple targets, thus the combined
effect of the expression changes of so many miRNAs is likely to be significant. Past
studies examining epigenetic drugs such as DNA methylation inhibitors and histone
deacetylase inhibitors have focused on their direct effects on tumor-suppressor genes and
have largely neglected their effects on miRNAs. From my study and others, I can
hypothesize that many of the effects of 5-Aza-CdR and PBA could be mediated through
their effects on miRNA expression. For example, 5-Aza-CdR + PBA can up-regulate the
expression of miR-127, which can then down-regulate BCL6 (Saito, Liang et al. 2006).
In addition, DNA methylation inhibitors 5-azacytidine and zebularine are incorporated
into both DNA and RNA (Yoo and Jones 2006), and it is conceivable that their
incorporation into RNA may affect miRNA processing or stability. Thus, it is possible
that the actions of these drugs are two-fold—inhibition of DNA methylation and
alteration of miRNA expression. By discovering how miRNA expression is affected by
epigenetic therapy, we also broaden our understanding of the mechanism of action of
epigenetic drugs such as 5-Aza-CdR and PBA.

In conclusion, I have demonstrated from my miRNA microarray results that many
miRNAs may be epigenetically regulated. Thus, I believe that epigenetics controls the
expression of miRNAs just as it controls protein-coding genes. I then further studied the
epigenetic control of one miRNA, miR-377, and showed that it is indeed regulated by
DNA methylation and histone acetylation. This study adds valuable insight into the
transcriptional control of miRNAs. Furthermore, it demonstrates that epigenetic

130
treatment can affect the expression of many miRNAs. This understanding is also
valuable in helping us understand the pharmacological actions of these drugs, and one
needs to consider the affected miRNAs and their downstream effects on their targets
when using these drugs for future studies.

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CHAPTER FIVE
BIOLOGICAL FUNCTIONS OF EPIGENETICALLY
REGULATED MICRORNAS

INTRODUCTION
MicroRNAs (miRNAs) play essential roles in many normal and pathological
cellular processes mainly by acting as negative controls of cellular post transcriptional
activities. They comprise an additional layer of control for gene expression, and the
functions of individual miRNAs are beginning to be understood (He and Hannon 2004).
miRNAs have been shown to be involved in normal development, apoptosis, cellular
stress response, proliferation, and carcinogenesis (He and Hannon 2004). Given their
crucial roles in cellular physiology, many miRNAs are under active investigation for their
potential as pharmacological targets. This is especially true for miRNAs that are
involved in carcinogenesis. A miRNA can be a tumor suppressor gene if it down-
regulates oncogene targets; on the other hand, a miRNA can be an oncogene if it down-
regulates tumor-suppressor targets (Esquela-Kerscher and Slack 2006). Mis-expression
of miRNAs has been associated with cancer development, and previously published data
suggests that miRNA expression profiles can be used to classify human cancers (Lu, Getz
et al. 2005).

miRNAs can down-regulate their target mRNAs by two different mechanisms.
When binding to their targets with complete complementarity, they can target the

132
mRNAs to degradation by cleavage. Much more often, however, miRNAs bind to their
targets with partial complementarity and lead to translational inhibition by a poorly
understood mechanism (Kim and Nam 2006). The nucleotides 2-7 from the 5’ end of
each miRNA are believed to be important in determining the specificity of each miRNA
and are referred to as the “seed sequence,” although 100% complementarity within the
seed sequence is not required for inhibition (Kim and Nam 2006). Due to the flexibility
in its target binding, each miRNA is predicted to have hundreds of targets. Additionally,
each mRNA can be controlled by more than one miRNA, so that the combined effect of
several miRNAs often creates a significant change in cellular function (Lewis, Shih et al.
2003; He and Hannon 2004; Kiriakidou, Nelson et al. 2004; Krek, Grun et al. 2005;
Rajewsky 2006). To fully understand how miRNAs control our cellular functions is
therefore a difficult task. However, we can begin to address the question by discovering
one target at a time.

We and others have shown that epigenetics can control the expression of miRNAs,
much like the way they control the expression of protein-coding genes (Saito, Liang et al.
2006; Han, Witmer et al. 2007; Lujambio, Ropero et al. 2007). In Chapter 4 I focused on
the epigenetic regulation of one miRNA, miR-377, and showed that its expression can be
regulated by DNA methylation and histone acetylation. In this chapter I aimed to further
understand the biological functions of miR-377. By examining its expression in human
colon cancer tissues, I found that miR-377 was down-regulated in five out of the eight
cases of human colon cancers compared to their adjacent normal tissues. Moreover, cell
proliferation and colony formation assays showed that miR-377, along with miR-154,

133
376a, and 489, could suppress the growth of HCT116 cells, suggesting that miR-377
could be a potential tumor-suppressor gene. Subsequent attempts to identify a target for
miR-377 have not been successful, and I aim to continue the study by performing
microarray experiments to identify a target, or to examine if miR-377 is indeed a tumor
suppressor gene by in vivo studies.

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MATERIALS AND METHODS

RNA and DNA extraction from primary tissues
Matched sets of primary colon tumors and adjacent normal tissues (~10cm away
from tumor) from the same patients, and cancer-free colon tissues were obtained through
the USC/Norris Tissue Procurement Core Resource after informed consent and
Institutional Review Board (IRB) approval (IRB #886005 and #926041) at the
USC/Norris Comprehensive Cancer Center. Fresh frozen blocks of tumor and
corresponding adjacent normal tissue identified by a pathologist were used for nucleic
acid extraction. RNA was extracted using the ToTALLY RNA Isolation Kit (Ambion,
Austin, TX) (Saito, Liang et al. 2006).

Cell proliferation and colony formation assays
miRNA expression vectors were constructed by inserting each precursor miRNA
flanked by ~200bp of genomic sequences into pcDNA3.1 vectors. The expression
vectors were transfected into HCT116 cells using Lipofectamine 2000 (Invitrogen,
Carlsbad, CA) following the manufacturer’s instructions. Forty-eight hrs after
transfection, cells were seeded at 900,000/100mm dish for cell proliferation assays, and
20,000/well in a 6-well dish for colony formation assays. Both were subjected to G418
selection at 500µM/ml. For the cell proliferation assay, media containing G418 was
replaced every 2 to 3 days, and the total number of cells was counted for each sample
after 10 to 14 days. For the colony formation assay, colonies were allowed to form

135
undisturbed for 10 to 14 days, fixed with methanol, and stained with 10% Giemsa.
Triplicate dishes were used.

Cell line and drug treatment
HCT116 (colon carcinoma cells) and HeLa (cervical carcinoma cells) were
obtained from the American Type Culture Collection (ATCC, Rockville, MD). HCT116
cells were cultured in McCoy’s 5A medium supplemented with 10% heat-inactivated
fetal bovine serum (FBS). HeLa cells were cultured in MEM medium supplemented with
10% FBS. Cells were cultured in a humidified incubator at 37 °C in 5% CO
2
.

RNA isolation from cell lines
Total RNA was extracted with Invitrogen TRIzol reagent (Carlsbad, CA)
according to the manufacturer’s instructions.

Northern Blot analysis
Total RNA (15 μg) was loaded onto a 15% polyacrylamide denaturing gel and
transferred to a nylon membrane. The StarFire radiolabeled probes (Integrated DNA
Technologies, Coralville, IA) were prepared by incorporation of [ α-
32
P] dATP 6000
Ci/mmol following the manufacturer's recommendation. Prehybridization and
hybridization were carried out using ExpressHyb Hybridization Solution (Clontech,
Mountain View, CA). Hybridization was carried out at 42 °C overnight, and then the
membrane was washed with 1X SSC+0.05% SDS until the background is clear as
detected by a Geiger counter. U6 was used as a loading control. The Northern probe

136
sequences are listed as follows: miR-377, 5’-ACA AAA GTT GCC TTT GTG TGA T-
3’; U6, 5’-GCA GGG GCC ATG CTA ATC TTC TCT GTA TCG-3’.

Transfection with miR-377 precursor molecules for target identification
miR-377 precursor molecules and negative control 1 precursor molecules were
purchased from Ambion (Austin, TX). The precursors were transfected into HCT116 and
HeLa cells at final concentrations of 100 nM each using Lipofectamine 2000 (Invitrogen)
according to the manufacturer's instruction. Three days after transfection, cells were
collected and total cellular protein and total RNA were extracted.

Total protein extraction and Western Blot analysis
Total cellular proteins were extracted by freezing and thawing twice. Briefly,
cells were trypsinized, pelleted, and resuspended in protein extraction buffer (20mM
Tris-HCl, pH8.0; 100mM NaCl; 1mM EDTA; 0.5% NP-40; and fresh protease inhibitor).
The samples were then immediately frozen in liquid N
2
, thawed at 37 °C for 5min, re-
frozen in liquid N
2
, and thawed on ice for 15min. Following centrifugation at 10,000 rpm
at 4 °C for 10min, the supernatants, which contained the protein extracts, were collected.
The protein extracts were then separated by SDS/polyacrylamide gel electrophoresis and
transferred onto a nitrocellulose membrane. Membranes were hybridized with antibodies
against SOD1 (Abcam), CTCF (Upstate), and β-actin (Sigma).

137
RT-PCR of potential miR-377 targets
RT-PCR was performed to examine the expression changes of B4GALT6, CLCN3,
EIF4E, and PTPRC. The RT-PCR conditions are: denature at 95 °C for 3 min, followed
by 25-34 cycles of 95°C for 1min, 60 °C for 1min, and 72 °C for 1min, and then final
extension at 72 °C for 8min. The primer sequences are as follows: B4GALT6 Forward,
5’-GCT CAA CGG TAC AGA TTA TCC CGA AG-3’; Reverse, 5’-CGG TCA TTT
TCA GGT AGA TGA TCC ACA TC-3’. CLCN3 Forward, 5’-CAC CGA TTT TGC
TAT GTC TCT GAG CTG-3’; Reverse, 5’-GTT GTA CCA CAA CGC ACT AAG GCA
AAT G-3’. EIF4E Forward, 5’-GAC ATA TCC GTC ACG TGG CCA GAA G-3’;
Reverse, 5’-CTG GAT ATG GTT GTA CAG AGC CCA AAA GTC-3’. PTPRC
Forward, 5’-CAA CAA TAG CTA CTA CTC CAT CTA AGC C-3’; Reverse, 5’-CAC
ACT TAT ACT CAT GTT CGG GTT CAA G-3’.

138
RESULTS

miR-377 is expressed in normal human colon tissues
To understand the biological functions of miR-377, it would be helpful to
understand its expression in normal tissues first. miR-377 is a relatively new discovery
for the human miRNA family, and thus it was not included in many earlier studies that
examined miRNA expression in human tissues . I first examined its expression in colon
tissues since I discovered the epigenetic regulation of miR-377 in a human colon
carcinoma cell line HCT116. A total of eight cancer-free human colon tissue samples
representative of normal colon tissues were used for the analysis. In all eight cases
examined by Northern Blot, miR-377 was strongly expressed. The expression level was
even stronger than that in DKO cells (Figure 5.1). This result indicated that miR-377 was
normally expressed in human colons.

miR-377 expression changes in human colon carcinoma
Knowing that miR-377 was expressed in normal human colon tissues, I next
investigated potential changes in expression during carcinogenesis by examining its
expression in eight matched cases of human colon cancers and their adjacent normal
colon tissues by Northern Blot (Figure 5.2). miR-377 was observed to be down-regulated
in five of the eight cases examined—2044, 2596, 2046, 2099, and 2277 (Figure 5.2).
This dramatic reduction in cancer tissues compared to the matched normal tissues
suggested that miR-377 could potentially play a role as a tumor-suppressor gene.

139
Figure 5.1: Northern Blot analysis of miR-377 Expression in human cancer-free colon
tissues

miR-377 is expressed in normal colon tissues. The expression of miR-377 in HCT116
WT and DKO cells are included for comparison. U6 was used as a loading control
(lower panel).

miR-377
U6
3049 N
2930 N
3046 N
3186 N
2945 N
2947 N
3105 N
3280 N
HCT116 WT
DKO

140
Figure 5.2: Northern Blot analysis of miR-377 expression in human colon cancer tissues
and matched adjacent normal colon tissues.

The expression of miR-377 is decreased in five out of eight cases of colon cancers
compared to their adjacent normal colon tissues. U6 was used as a loading control (lower
panel). Each matched set is indicated by the same number.

N: adjacent normal tissue; T: tumor

U6
2044 N
T
2337 N
T
2276 N
T
2596 N
T
2046 N
T
2457 N
T
2099 N
T
2277 N
T
miR-377

141
miR-377 is able to reduce human cancer cell growth in vitro
Given the promising results from the Northern Blot analyses, the tumor-
suppressor potential of miR-377 was further investigated. Cell proliferation (Figure 5.3A)
and colony formation assays (Figure 5.3B) were performed using HCT116 cells
transfected with the pcDNA3.1 expression vectors I constructed to investigate whether
over-expression of miR-377 reduced cancer cell growth. In addition to miR-377, miR-
154, 376a, and 489 were included in the assays. miR-154 was found to be up-regulated
in both the WT vs. DKO array and the untreated vs. 5-Aza-CdR+PBA-treated array
(Table 4.3), and miR-376a and 489 were shown be to up-regulated by both by
quantitative RT-PCR methods (Figure 4.2). Therefore, these three miRNAs could
possibly be epigenetically regulated. In addition, two separate and randomly chosen
clones of the expression vectors were each included for miR-154 (clone #3, 34) and 377
(clone #3, 6), and the two clones for each miRNA showed identical sequences of the
insert. Results from the cell proliferation and colony formation assays were well
correlated. All four miRNAs reduced cancer cell growth, although to varying extents.
miR-376a, in particular, reduced cancer cell growth dramatically. The two separate
clones for miR-154 (#3, 34) and miR-377 (#3, 6) led to different levels of growth
reduction, and the reason for that was unclear; nevertheless, both clones of each miRNA
led to reduction in tumor growth. Overall, my results indicated that miR-377, along with
miR-154, 376a, and 489, could have tumor-suppressor activities.

The expression pattern for other potential epigenetically regulated miRNAs, miR-
368 (Figure 5.4.A), 376a (5.4.B), and 489 (5.4.C), was examined by quantitative stem-

142
Figure 5.3: Inhibition of HCT116 cell growth by miRNA expression vectors

5.3.A: Cell proliferation assay

5.3.B: Colony formation assay

miR-154, 376a, 377, and 489 were able to suppress HCT116 cell growth. HCT116 cells
were transfected with various expression vectors using Lipofectamine, and cells were
seeded for cell proliferation and colony formation assays 48 hrs after transfection. Total
cell number (5.6.A) and total colony number (5.6.B) were counted after 10-14 days. Data
from triplicate dishes, and error bars represent 1 standard deviation of the mean.
Columns, mean; bars, standard deviation.
HCT116 Colony Formation Assay with miRNA Expression Vectors
0
20
40
60
80
100
120
140
E.V. p16 miR-154
#3
miR-154
#34
miR-
376a
miR-377
#3
miR-377
#6
miR-489
Expression Vector
colony number
HCT116 Cell Proliferation Assay with miRNA Expression Vectors
0.E+00
1.E+06
2.E+06
3.E+06
4.E+06
5.E+06
6.E+06
7.E+06
E.V. p16 miR-154
#3
miR-154
#34
miR-
376a
miR-377
#3
miR-377
#6
miR-489
Expression Vector
Cell number

143
Figure 5.4: Quantitative real time RT-PCR analysis of miRNA expression in human
colon cancers vs. matched adjacent normal tissues
5.4.A
miR-368 Expression in Colon Cancers vs.
Adjacent Normal Tissues
0
0.0002
0.0004
0.0006
0.0008
0.001
0.0012
2044N
2044T
2337N
2337T
2276N
2276T
2196N
2196T
2596N
2596T
2046N
2046T
2457N
2457T
2099N
2099T
Colon Samples
miR-368/U6 miR-368/U6
miR-368 Expression in Colon Cancers vs.
Adjacent Normal Tissues
0
0.0002
0.0004
0.0006
0.0008
0.001
0.0012
2044N
2044T
2337N
2337T
2276N
2276T
2196N
2196T
2596N
2596T
2046N
2046T
2457N
2457T
2099N
2099T
Colon Samples
miR-368/U6 miR-368/U6

5.4.B
miR-376a Expression in Colon Cancers vs.
Adjacent Normal Tissues
0
0.0005
0.001
0.0015
0.002
0.0025
0.003
0.0035
2044N
2044T
2337N
2337T
2276N
2276T
2196N
2196T
2596N
2596T
2046N
2046T
2457N
2457T
2099N
2099T
Colon Samples
miR-376a/U6 miR-376a/U6
miR-376a Expression in Colon Cancers vs.
Adjacent Normal Tissues
0
0.0005
0.001
0.0015
0.002
0.0025
0.003
0.0035
2044N
2044T
2337N
2337T
2276N
2276T
2196N
2196T
2596N
2596T
2046N
2046T
2457N
2457T
2099N
2099T
Colon Samples
miR-376a/U6 miR-376a/U6

5.4.C
miR-489 Expression in Colon Cancers vs.
Adjacent Normal Tissues
0
0.001
0.002
0.003
0.004
0.005
0.006
0.007
0.008
2044N
2044T
2337N
2337T
2276N
2276T
2196N
2196T
2596N
2596T
2046N
2046T
2457N
2457T
2099N
2099T
Colon Samples
miR-489/U6 miR-489/U6
miR-489 Expression in Colon Cancers vs.
Adjacent Normal Tissues
0
0.001
0.002
0.003
0.004
0.005
0.006
0.007
0.008
2044N
2044T
2337N
2337T
2276N
2276T
2196N
2196T
2596N
2596T
2046N
2046T
2457N
2457T
2099N
2099T
Colon Samples
miR-489/U6 miR-489/U6

144
Figure 5.4, Continued

Expression of miR-368 (5.7.A), 376a (5.7.B), and 489 (5.7.C) does not show consistent
changes in tumor development. A total of eight cases of matched colon tumors and their
adjacent normal tissues were examined. Quantitative RT-PCR analysis was done with
reference to U6 RNA.

N: adjacent normal colon tissues; T: colon tumors

145
loop RT-PCR. Although miR-368 was not included in the colony formation or cell
proliferation assays, its expression was found to be up-regulated in both 5-Aza-
CdR+PBA-treated HCT116 cells and in DKO cells compared to the WT (Figure 4.2) and
was therefore potentially epigenetically regulated. miR-154 and 377 were not included in
this analysis due to the lack of commercially available reagents for the stem-loop RT-
PCR analysis at the time. The result (Figure 5.4) showed that none of the three miRNA
examined demonstrated consistent down-regulation in the tumors compared to the
adjacent normal tissues. The down-regulation of miR-377 in the colon cancer tissues was
therefore unique to miR-377, and was unlikely the result of a general reduction in
miRNA expression or processing. Taken together, miR-377 proved to be of great interest
to further study its tumor-suppressor potential due to its expression being down-regulated
in several human colon cancer cases, as well as its ability to reduce tumor cell growth in
vitro.

Search for miR-377 targets using prediction algorithms
Given the tumor-suppressor potential of miR-377, I aimed to further dissect its
biological functions by identifying its target(s). Because miRNAs exert their actions
through controlling their target mRNAs, the way to understand the functions of a miRNA
is generally to identify its targets. However, because miRNAs can bind to their targets
with incomplete complementarity, it is predicted that one miRNA can have hundreds of
targets, and it is also technically difficult to identify the targets. Many computer
prediction algorithms exist to help identify potential targets, and the following three
commonly-used algorithms were employed—miRanda (John, Enright et al. 2004),

146
TargetScan (Lewis, Shih et al. 2003; Lewis, Burge et al. 2005; Grimson, Farh et al. 2007),
and PicTar (Krek, Grun et al. 2005)—to predict the targets of miR-377.

Each computer algorithm gave hundreds to thousands of predicted targets for
miR-377, and there was not a large degree of overlap in the predicted targets from the
three algorithms. I narrowed down the candidate targets by focusing on ones that are
predicted by all three computer algorithms because these candidates might have had a
higher probability of being true targets. I then chose to study superoxide dismutase one
(SOD1), a well studied protein involved in aging (Oh, Shin et al. 2008), and CCCTC-
binding factor (CTCF), a well known zinc finger transcription factor (Dunn and Davie
2003).

The miR-377 precursor molecules were transfected into HCT116 and HeLa cells,
neither of which expressed miR-377 endogenously. These cells were also transfected
with a scrambled, negative control precursor molecule. The cells were harvested after
three days of transfection, and protein and RNA were extracted from the cells. Northern
Blot analysis on the transfected cells showed that the precursor molecules were
successfully transfected into both cells and that the precursor molecules were effectively
processed in the cells into mature miR-377 (Figure 5.5).

After verifying that miR-377 was successfully transfected and processed into
mature miRNA, the protein expression levels of the predicted targets SOD1 (Figure 5.6.A)
and CTCF (Figure 5.6.B) were analyzed using Western Blots. The results showed that

147
Figure 5.5: Northern analysis of transfection experiment with miR-377

mock
neg cntrl
miR-377
mock
neg cntrl
miR-377
mock
neg cntrl
miR-377
mock
neg cntrl
miR-377
HCT116 HeLa
Precursor
Mature
48hr 72hr 48hr 72hr
mock
neg cntrl
miR-377
mock
neg cntrl
miR-377
mock
neg cntrl
miR-377
mock
neg cntrl
miR-377
HCT116 HeLa
Precursor
Mature
48hr 72hr 48hr 72hr

miR-377 precursor molecules were successfully transfected and processed inside cells.
HCT116 and HeLa cells were transfected with Lipofectamine only (mock), a negative
control precursor molecule (neg cntrl), or the miR-377 precursor molecule (miR-377)
using Lipofectamine. Cells were collected at 48 and 72 hrs after transfection, and total
RNA and cellular proteins were extracted. The Northern Blot result confirmed the
successful transfection, processing, and expression of miR-377 in the cells. Both
precursor and mature molecules were visible on the Northern Blot.

148
Figure 5.6: Western Blot analysis of potential miR-377 targets

5.6.A: Western Blot of SOD1

mock
neg cntrl
miR-377
mock
neg cntrl
miR-377
DKO
DKO + PBA 1mM
mock
neg cntrl
miR-377
mock
neg cntrl
miR-377
HCT116 HeLa
SOD1
β-actin
48hr 72hr 48hr 72hr
mock
neg cntrl
miR-377
mock
neg cntrl
miR-377
DKO
DKO + PBA 1mM
mock
neg cntrl
miR-377
mock
neg cntrl
miR-377
HCT116 HeLa
SOD1
β-actin
48hr 72hr 48hr 72hr

5.6.B: Western Blot of CTCF

CTCF
β-actin
CTCF
β-actin

SOD1 and CTCF are not targets of miR-377. Western blots of total cellular protein
extracts isolated from HCT116 and HeLa cells transfected with miR-377 precursor
molecules or negative control precursor miRNAs (neg cntrl) sequentially probed with
antibodies against SOD1 (5.2.A) and CTCF (5.2.B) (top panel) and β-actin as a loading
control (bottom panel).

mock: Lipofectamine only; neg cntrl: negative control precursor molecule; miR-377:
miR-377 precursor.

149
neither protein was down-regulated in the miR-377 transfected cells compared to the
Lipofectamine only control or the negative precursor control, indicating that SOD1 and
CTCF were not true targets of miR-377. A reduction in both SOD1 and CTCF in DKO
cells treated with 1mM PBA compared to untreated DKO cells was observed. As noted
previously in Chapter 4, DKO cells treated with 1mM PBA expressed miR-377 much
more highly than untreated DKO cells. However, because PBA treatment could change
the expression of many genes in addition to inducing miR-377, I could not conclude that
the increase in miR-377 was causing the reduction in SOD1 and CTCF. Taken together,
the results suggested that SOD1 and CTCF were not true targets of miR-377.

Verification of proposed miR-377 targets
Palmieri et al. (Palmieri, Pezzetti et al. 2007) proposed that miR-377 can target
B4GALT6, CLCN3, EIF4E, and PTPRC from their miRNA and mRNA microarrays, and
thus I decided to investigate if these were true targets of miR-377. The expression level
changes of these four candidate mRNA targets were measured by RT-PCR on samples
collected from the transfection experiment. Although miRNAs target most of their
targets at the translation level, they can affect the mRNA level of their targets as well by
affecting the mRNA stability (Wu and Belasco 2008). As a result, changes in the target
mRNA levels can be seen even though the inhibition of expression is exerted at the
translation step.

150
RT-PCR showed no expression of PTPRC in HCT116 or HeLa cells. B4GALT6
and CLCN3 were expressed robustly, and EIF4E was expressed weakly in these cells.
RT-PCR reaction was performed at varying PCR cycle numbers to make sure I compared
the expression at a linear range. Representative results of several different experiments
are shown in Figure 5.7. No change in expression level in any of the genes studied was
observed with the miR-377 transfection. I could not confirm the previous published
results, and as a result, I concluded that none of these proposed targets were regulated by
miR-377 at the mRNA level.

151
Figure 5.7: RT-PCR results of potential miR-377 targets

GAPDH
EIF4E
CLCN3
B4GALT6
mock
neg cntrl
miR-377
mock
neg cntrl
miR-377
mock
neg cntrl
miR-377
mock
neg cntrl
miR-377
HCT116 HeLa
48hr 72hr 48hr 72hr
GAPDH
EIF4E
CLCN3
B4GALT6
mock
neg cntrl
miR-377
mock
neg cntrl
miR-377
mock
neg cntrl
miR-377
mock
neg cntrl
miR-377
HCT116 HeLa
48hr 72hr 48hr 72hr

B4GALT6, CLCN3, and EIF4E are not targets of miR-377. HCT116 and HeLa cells were
transfected with Lipofectamine only, a negative control precursor molecule, or the miR-
377 precursor molecule using Lipofectamine. Cells were collected at 48 and 72 hrs after
transfection, and total RNA was extracted. RT-PCR was performed with different cycle
numbers to ensure the comparison of expression level was at a linear range.
Representative results of several different experiments are shown above.

mock, Lipofectamine only; neg cntrl, negative control precursor molecule; miR-377,
miR-377 precursor molecule

152
DISCUSSION
Our understanding of the importance of miRNAs has expanded exponentially in
recent years due to increasing evidence for their involvement in fundamental cellular
functions. Our previous studies have focused on miR-377, which can be regulated by
DNA methylation and histone acetylation, and here I aimed to further understand this
miRNA by studying its biological functions.

miR-377 was found to be highly expressed in normal human colon tissues, and
was dramatically down-regulated in five out of the eight human colon cancers compared
to their matched adjacent normal tissues. These results suggest that miR-377 could be a
tumor-suppressor gene, and the loss of its expression could augment cellular
transformation or tumor growth. However, one cannot be certain whether the decrease of
miR-377 expression in cancers was causal or consequential of tumor carcinogenesis. In
addition, it would be helpful to examine miR-377 expression in more cases of colon
cancers to obtain statistically significant correlation of its expression and carcinogenesis.
It would also be of interest to examine its expression in other cancers, such as bladder
and prostate cancers, and see if down-regulation in the tumor can also be observed.

I also examined the expression of miR-368, 376a, and 489 in matched sets of
colon cancers and their adjacent normal tissues, and found that none of the three showed
the same pattern of reduction as miR-377, nor did they demonstrate consistent down-
regulation in tumors compared to the adjacent normal tissues. miR-377 was therefore
unique in my search for epigenetically regulated miRNAs that also demonstrated tumor-

153
suppressor potential. I could also conclude that the reduction of the expression was
specific to miR-377, and not due to a general decrease in most miRNA levels.
Furthermore, miR-377 is capable of reducing tumor growth in vitro as demonstrated by
the colony formation and cell proliferation assays.

To further understand the biological functions of miR-377, I attempted to identify
its targets through computer prediction algorithms and literature search. The two targets I
chose from the prediction algorithms—SOD1 and CTCF—turned out not to be true
targets of miR-377. In addition, I showed that the potential targets suggested by Palmieri
et al. (Palmieri, Pezzetti et al. 2007)—B4GALT6, EIF4E, and CLCN3—were not
regulated by miR-377 at the mRNA level. These results demonstrated that the prediction
algorithms are still limited in their abilities to predict true targets, and microarray
experiment results require additional verification because many differentially expressed
mRNAs are not true targets.

One issue to consider is the transfection efficiency of Lipofectamine in my
experiments. Although Northern Blot analysis showed that the miR-377 precursor
molecules were effectively taken up by the cells and processed, it did not indicate that the
precursor molecules were transfected into most of the cells. It is possible that only a
minority of the cell population was successfully transfected, and the expression of the
precursor and mature miR-377 I observed was from that minority group. If the
transfection efficiency were low, then the effect on the target proteins would likewise be

154
unremarkable. Using a higher transfection efficiency system, such as lentiviral
expression vectors, could result in a stronger reduction in the target protein levels.

In addition, one should also consider the stability and half-life of the potential
target proteins. If a protein is stable or rapidly replenished in the cells, it would be less
likely to show changes in its expression level by Western Blot analysis. Although I
attempted to circumvent this problem by collecting the transfected cells at two different
time points—48 and 72 hrs after transfection—I could not be absolutely certain that the
time points were optimal for observing changes in the levels of the target protein.

Knowing the functions of miRNAs can help in the understanding of their
biological roles, and how epigenetically regulated miRNAs can be relevant to patients
treated with epigenetic therapy. Given the results I obtained, further analyses should be
done to investigate the tumor-suppressor potential of miR-377. A better understanding of
the functions of miR-377 may prove to have beneficial clinical implications.

155
CHAPTER SIX
CONCLUSION: EPIGENETIC THERAPY, MicroRNAs,
AND THEIR CLINICAL POTENTIAL

Studies of DNA Methylation Inhibitors
The importance of epigenetic regulation of gene expression has become
increasingly apparent, and the field of epigenetic therapies has experienced revolutionary
development (Yoo and Jones 2006). Epigenetic drugs, such as DNA methylation
inhibitors and histone deacetylase (HDAC) inhibitors, are under active preclinical and
clinical investigation, and some have shown promising results (Yoo and Jones 2006). In
particular, DNA methylation inhibitors 5-azacytidine (5-Aza-CR) and 5-Aza-2’-
deoxycytidine (5-Aza-CdR) have been FDA-approved for the treatment of
myelodysplastic syndrome (Yoo and Jones 2006). Given the promising results, I wanted
to examine more potential DNA methylation inhibitors for their clinical potential.

I first examined three potential non-nucleoside DNA methylation inhibitors from
published data—EGCG (Fang, Wang et al. 2003), Hydralazine (Cornacchia, Golbus et al.
1988), and procainamide (Scheinbart, Johnson et al. 1991; Lin, Asgari et al. 2001;
Segura-Pacheco, Trejo-Becerril et al. 2003) (Chapter 2). All three agents had been
previously reported to have DNA methylation inhibition activities, although their
mechanisms of action were unclear. Using 5-Aza-CdR as the positive control, I
demonstrated that none of the three agents tested was able to reduce DNA methylation as
determined by Ms-SNuPE and pyrosequencing. In addition, none of the three agents was

156
able to induce expression of p16, MAGE-A1, or MAGE-B2 as determined by traditional
and real-time RT-PCR. My results do not support the idea that these non-nucleoside
agents are likely to be effective as epigenetic therapies (Chuang, Yoo et al. 2005).

In addition to studying the non-nucleoside DNA methylation inhibitors, I also
investigated several prodrugs of zebularine, a stable cytidine-analog DNA methylation
inhibitor that has been shown to be effective both in vitro and in vivo (Cheng, Matsen et
al. 2003; Cheng, Yoo et al. 2004). I first examined oligo-deoxynucleotides (ODNs)
consisting of one or more thymidines followed by one zebularine molecule, and found
that TTTZ and TTTTZ were able to induce p16 expression in CFPAC-1 cells without the
need for additional thymidine, a necessary addition for TpZ and TTZ compounds to
overcome the inhibition of thymidylate synthetase by deoxy-zebularine monophosphate
(Votruba 1973). However, the efficacy of the ODNs was limited to only CFPAC-1 cells;
the reason was unclear and would be of interest for further investigation. Lastly, I
explored the possibility of delivering deoxy-zebularine tri-phosphate using Lipofectamine
to T24 cells, but this method failed to induce p16 expression despite inducing obvious
morphological changes in the cells.

Moreover, I also examined two DNA methylation inhibitors in vivo using mouse
xenograft systems (Chapter 3). Study with EPD-zebularine, which conjugated zebularine
to a lipophilic protecting group that could theoretically be cleaved off in vivo to yield
zebularine for effective delivery, showed that it was ineffective in vivo as a DNA
methylation inhibitor. On the other hand, my study with the compound S110 (Yoo,

157
Jeong et al. 2007)—dinucleotide consisting of a 5-Aza-CdR followed by a
deoxyguanosine—showed promising results. At the same molar concentration, S110 was
comparable to 5-Aza-CdR in terms of reducing DNA methylation levels and inducing
p16 expression in vivo. Furthermore, S110 was less toxic than 5-Aza-CdR as determined
by the weight loss in the mice. These preliminary results show great promise and warrant
further investigation into the clinical potential of S110.

Epigenetic Regulation of MicroRNAs (miRNAs)

My studies with DNA methylation inhibitors received new directions by the
discovery by Saito et al. (Saito, Liang et al. 2006) showing that treatment with 5-Aza-
CdR and the HDAC inhibitor 4-phenylbutyric acid (PBA) can induce the expression of
several miRNAs in cancer cell lines. I wanted to further investigate how epigenetic
therapies could affect miRNA expression, and to expand our study to examine the
epigenetic regulation of miRNA expression (Chapter 4).

Using miRNA microarrays, I found that the expression of many miRNAs could
be under epigenetic regulation. I focused on one miRNA in particular, miR-377, and
showed that its expression could be epigenetically regulated by DNA methylation and
histone acetylation.

158
The biological functions of miR-377 was further investigated in Chapter 5. miR-
377 was highly expressed in normal human colon tissues; more interestingly, its
expression was down-regulated several cases of human colon cancers, suggesting that
miR-377 had tumor-suppressor potential. Cell proliferation and colony formation assays
showed that over-expression of miR-377 was able to reduce the tumor cell growth in
vitro. Further attempts to identify a target for miR-377, however, have not been
successful yet. I aim to perform a microarray comparing wildtype HCT116 cells, which
do not express miR-377, vs. HCT116 cells transfected with miR-377 precursors, and to
look for mRNAs that are down-regulated in the transfected cells as these could be targets
of miR-377. Although miRNAs down-regulate their targets mainly by translational
inhibition rather than mRNA cleavage, it is often observed that the mRNA levels could
be affected as well. This could be due to the fact that the mRNA molecules are degraded
faster in the cells when they are not protected by the translational machinery (He and
Hannon 2004; Wu and Belasco 2008). As a result, one can often identify miRNA targets
by performing a microarray experiment and to identify genes whose expression levels are
decreased when the miRNA of interest is over-expressed. Moreover, I can also
investigate if miR-377 is a tumor-suppressor gene by using mouse models for in vivo
studies. Knowing the biological functions of miR-377 would not only expand our
knowledge of the physiological roles of miRNAs, but would also help us further
understand the pharmacological actions of epigenetic therapies.

159
Implications for Clinical Significance in the Future

The field of medicine has seen revolutionary advancement in the past few decades.
In addition to the establishment of “evidence-based medicine” as the standard basis for
treatment selection (Beltran 2008), emphasis has been placed on “personalized therapy.”
As our understanding of the molecular mechanisms behind different pathologies
improves, physicians no longer use the “one-size-for-all” approach for the treatments.
This is especially true in the field of cancer research and cancer therapy development, as
physicians and scientists continue to characterize the underlying molecular mechanisms
of different forms and grades of cancers that drive the phenotypic abnormalities.
Equipped with this information, physicians would be better able to predict which
treatment options would be most effective against a particular type of cancer. Moreover,
knowing the molecular mechanisms underlying each cancer type can also help scientists
design targeted therapy that is effective and less toxic. A good example is the
development of Gleevec for the treatment of most cases of chronic myeloid leukemia
(CML), a disease that used to be largely fatal but can now be considered “curable” by the
treatment (Nadal and Olavarria 2004).

The advance in the field of epigenetics has enriched our understanding of cancer
biology and has increased the currently available treatment options for different cancers.
We now know that abnormalities in epigenetic mechanisms, such as abnormal
hypermethylation in the promoter regions of tumor suppressor genes, can also be
involved in the process of tumorigenesis (Jones and Baylin 2002). Epigenetic therapy,

160
therefore, can complement traditional chemotherapy for the treatment of cancer (Yoo and
Jones 2006). Together, chemotherapy and epigenetic therapy can lead to more effective
treatment and less toxicity for the patients.

Moreover, we have witnessed different epigenetic signatures of different cancer
types, and it would be beneficial to categorize each cancer according to their epigenetic
alterations in addition to genetic changes. With the development of microarray-based
DNA methylation profiling (Schumacher, Kapranov et al. 2006), it is possible to obtain
the DNA methylation profile of each cancer to determine if it is a good candidate for
DNA methylation inhibitor treatment. It is conceivable that such technologies will also
be available for analyzing histone acetylation, and therefore be helpful for deciding if a
HDAC inhibitor should be used. The decision about what treatment should be employed
would no longer be a shot in the dark if we know the underlying molecular pathology and
are able to predict the responses to each drug.

Furthermore, knowing that epigenetic therapies can regulate the expression of
miRNAs adds to our understanding of the pharmacological mechanisms of these drugs.
miRNAs play fundamental roles in normal cellular physiology, and their mis-expression
can have serious effects on tumor development (Table 1.1). In addition to using miRNAs
for cancer therapy, miRNAs can also be drug targets. Saito et al. (Saito, Liang et al. 2006)
have shown that the treatment of 5-Aza-CdR and PBA can lead to the up-regulation of
miR-127, which can down-regulate BCL6. Our results also show that the expression of
many miRNAs can be affected by epigenetic treatment. Thus, epigenetic therapies can

161
not only up-regulate tumor-suppressor genes directly, but they can also up-regulate
miRNAs that can then down-regulate target oncoproteins. Because miRNA expression
profiles have been shown to be effective in classifying human cancers (Lu, Getz et al.
2005), it is conceivable that in the future they can also be used to monitor patient
response to epigenetic therapies.

Given the above, we are optimistic that in the near future physicians and scientists
will be able to combine the genetic, epigenetic, and miRNA profiles of each cancer, and
use these information complementarily to obtain a truly “personalized” cancer profile and
to make the best informed decision for the most effective, personalized therapy. The
hope for more complete cures for cancers will depend on further studies of the
fundamental molecular biology of cancer cell physiology and drug mechanisms.

162
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APPENDIX A

miRNA Microarray Service

Data Summary S60210

Date: 3/24/2006

Prepared for
USC Norris Cancer Center, Jody Chuang

Prepared by
LC Sciences, LLC

I. Data List
Chip 1
Data Files
Folder Name Chip1_ H8.0_060263
Data File
H8.0_060263-0322-w52c20-425-
650_Data.xls
Layout File MiHuman_8.0_060307.xls
Original Image File
H8.0_060263-0322-w52c20-425-
650.tif
Processed Cy3 File
H8.0_060263-0322-w52c20-
425cy3.tif
Processed Cy5 File
H8.0_060263-0322-w52c20-
650cy5.tif

Assay Information
Date of Assay 3/22/2006
Chip ID H8.0_060263
Sample source USC Norris Cancer Center

Sample A
Sample ID HCT116 WT
Sample Receiving Date 3/9/2005
Labeling Dye Cy3

Sample B
Sample ID HCT116 DKO2
Sample Receiving Date 3/9/2006
Labeling Dye Cy5

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II. Chip Content
The content of each chip is listed in a Layout File of a corresponding folder. Multiple
redundant regions are included. Each region further comprises a miRNA probe
region, which detects miRNA transcripts listed in Sanger miRBase Release 8.0
(http://www.sanger.ac.uk/Software/Rfam/mirna/).
Multiple control probes are included in each chip. The control probes are used for
quality controls of chip production, sample labeling and assay conditions. Among the
control probes, PUC2PM-20B and PUC2MM-20B are the perfect match and single-
based match detection probes, respectively, of a 20-mer RNA positive control
sequence that is spiked into the RNA samples before labeling. One may assess assay
stringency from the intensity ratio of PUC2PM-20B and PUC2MM-20B, which is
normally larger than 30.
When the option for custom probes is selected, custom probes are also included.

III. Summary of Results
Following are representative regions of chips images. From Cy3 and Cy5 images one
may directly read miRNA profiles and from Ratio images one may get a quick sense
of differential expressions between the corresponding samples. The images are
displayed in pseudo colors so as to expand visual dynamic range. In the Cy3 and Cy5
intensity images, as signal intensity increases from 1 to 65,535 the corresponding
color changes from blue to green, to yellow, and to red. In the Cy3/Cy5 ratio image,
when Cy3 level is higher than Cy5 level the color is green; when Cy3 level is equal to
Cy5 level the color is yellow; and when Cy5 level is higher than Cy3 level the color
is red.
In this section, a list of differentially expressed transcripts is also provided following
the chip images. From the list, one can have a quick overview of the difference
between the two samples on the chip.

Chip 1: H8.0_060263
LC Control
cy3 cy5
Ratio
miRNA

HCT116 WT HCT116 DKO2 Cy3/Cy5

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Call list (differentially expressed transcripts with p-value < 0.01)
Sample A: HCT116 WT – Cy3
Sample B: HCT116 DKO2 – Cy5
Table A.1: Differentially expressed transcripts—WT vs. DKO
No. Probe_ID
Sample A
Signal
Sample B
Signal
log2
(Sample B /
Sample A)
1 hsa-miR-146a 7.93 2,111.93 7.99
2 hsa-miR-19a 1,695.34 11.49 -7.28
3 hsa-miR-377 13.47 641.29 5.57
4 hsa-miR-101 921.48 14.20 -5.55
5 hsa-miR-184 15.80 615.84 5.40
6 hsa-miR-141 3,729.02 90.32 -5.36
7 hsa-miR-489 10.07 366.32 5.19
8 hsa-miR-205 13.15 449.88 5.10
9 hsa-miR-510 10.24 347.63 5.09
10 hsa-miR-98 501.85 14.91 -4.99
11 hsa-miR-20b 3,694.84 161.65 -4.54
12 hsa-miR-148b 879.42 36.79 -4.50
13 hsa-miR-19b 2,877.55 122.78 -4.49
14 hsa-miR-30e-5p 334.11 12.31 -4.48
15 hsa-miR-301 1,168.71 49.19 -4.48
16 hsa-miR-186 1,265.55 68.47 -4.43
17 hsa-miR-342 239.10 5,372.51 4.41
18 hsa-let-7g 3,538.76 169.95 -4.38
19 hsa-miR-503 139.20 6.61 -4.33
20 hsa-let-7i 3,604.61 186.24 -4.22
21 hsa-miR-18b 787.57 43.41 -4.13
22 hsa-miR-486 33.67 593.17 4.13
23 hsa-miR-29b 1,863.02 127.09 -3.99
24 hsa-miR-214 29.05 448.48 3.95
25 hsa-miR-127 17.33 265.35 3.94
26 hsa-miR-9 207.42 14.79 -3.88
27 hsa-miR-424 122.50 6.49 -3.86
28 hsa-miR-485-3p 19.10 268.48 3.70
29 hsa-miR-126 714.61 44.99 -3.69
30 hsa-miR-210 295.24 3,201.66 3.57
31 hsa-miR-9* 187.79 16.07 -3.57

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Table A.1, Continued
32 hsa-miR-505 296.99 29.85 -3.41
33 hsa-miR-31 7,942.72 798.89 -3.38
34 hsa-miR-125a 945.14 8,431.39 3.26
35 hsa-miR-99a 129.33 11.85 -3.26
36 hsa-miR-27a 4,230.85 456.35 -3.21
37 hsa-let-7d 2,726.99 285.86 -3.20
38 hsa-miR-34a 525.65 62.36 -3.18
39 hsa-miR-34c 18.54 160.79 3.12
40 hsa-miR-320 8,891.02 75,956.26 3.09
41 hsa-miR-182 1,138.07 9,896.13 3.09
42 hsa-miR-542-5p 210.62 20.32 -3.07
43 hsa-miR-193b 340.62 2,825.99 3.01
44 hsa-miR-200b 609.60 4,407.46 2.94
45 hsa-miR-20a 10,418.97 1,281.45 -2.91
46 hsa-miR-181a 507.25 76.82 -2.76
47 hsa-miR-197 269.88 1,744.68 2.75
48 hsa-miR-18a 1,438.04 213.26 -2.72
49 hsa-miR-27b 1,317.77 207.35 -2.67
50 hsa-miR-29a 9,053.32 1,572.80 -2.59
51 hsa-miR-198 138.79 821.22 2.56
52 hsa-miR-99b 2,687.39 15,912.14 2.56
53 hsa-miR-200a 193.06 35.17 -2.54
54 hsa-miR-106b 5,075.90 897.10 -2.54
55 hsa-miR-409-3p 57.62 332.83 2.53
56 hsa-miR-188 241.33 1,375.36 2.51
57 hsa-miR-500 103.55 552.30 2.43
58 hsa-miR-422a 200.57 32.60 -2.41
59 hsa-miR-362 154.25 741.00 2.35
60 hsa-miR-331 170.89 812.71 2.35
61 hsa-miR-155 179.47 884.21 2.30
62 hsa-miR-154* 335.37 1,617.25 2.27
63 hsa-miR-148a 348.74 73.83 -2.23
64 hsa-let-7c 1,176.89 297.65 -2.01
65 hsa-let-7f 4,421.45 1,113.41 -1.97
66 hsa-miR-7 4,148.96 1,117.05 -1.93
67 hsa-miR-106a 8,318.95 2,311.07 -1.88
68 hsa-miR-200c 14,925.06 53,342.65 1.84
69 hsa-miR-17-5p 11,057.93 3,149.10 -1.76
70 hsa-miR-191 6,978.48 23,472.16 1.75
71 hsa-miR-361 1,083.44 3,756.66 1.74

183
Table A.1, Continued
72 hsa-miR-22 479.78 138.13 -1.71
73 hsa-miR-191* 229.36 653.99 1.51
74 hsa-miR-195 324.85 109.25 -1.48
75 hsa-miR-26b 1,117.51 413.73 -1.43
76 hsa-miR-30a-5p 501.76 187.02 -1.40
77 hsa-let-7e 838.48 2,610.53 1.39
78 hsa-miR-100 2,896.84 1,123.81 -1.36
79 hsa-miR-128a 504.02 199.47 -1.35
80 hsa-miR-324-5p 1,230.09 3,018.09 1.32
81 hsa-miR-128b 486.82 202.93 -1.30
82 hsa-miR-221 6,095.63 2,861.46 -1.18
83 hsa-miR-21 27,621.22 11,958.93 -1.17
84 hsa-miR-30d 958.89 2,130.91 1.15
85 hsa-miR-145 1,650.30 3,904.71 1.14
86 hsa-miR-25 7,678.83 3,280.99 -1.14
87 hsa-miR-151 892.07 1,950.20 1.13
88 hsa-miR-30c 916.30 1,886.63 1.04
89 hsa-miR-23b 6,505.49 3,252.66 -1.03
90 hsa-miR-30b 1,497.23 741.38 -1.01
91 hsa-miR-185 1,218.13 2,413.85 0.99
92 hsa-miR-130a 975.69 498.68 -0.97
93 hsa-miR-125b 2,405.28 1,306.04 -0.88
94 hsa-miR-26a 4,048.19 7,172.13 0.85
95 hsa-miR-93 5,611.37 3,377.15 -0.77
96 hsa-let-7a 4,756.37 3,278.65 -0.66
97 hsa-miR-23a 9,574.94 6,531.64 -0.56
98 hsa-miR-222 8,375.18 12,475.38 0.54
99 hsa-miR-107 4,737.83 3,314.26 -0.52
100 hsa-miR-16 11,521.97 8,769.27 -0.44

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Following is the probe layout of the above mouse miRNA array images.
hsa-let-7a hsa-miR-137 hsa-miR-186 hsa-miR-21 hsa-miR-302c hsa-miR-363* hsa-miR-432* hsa-miR-507 hsa-miR-520d*
PUC2PM hsa-let-7b hsa-miR-138 hsa-miR-187 hsa-miR-210 hsa-miR-302c* hsa-miR-365 hsa-miR-433 hsa-miR-508 hsa-miR-520e
PUC2MM hsa-let-7c hsa-miR-139 hsa-miR-188 hsa-miR-211 hsa-miR-302d hsa-miR-367 hsa-miR-448 hsa-miR-509 hsa-miR-520f
BKG0 hsa-let-7d hsa-miR-140 hsa-miR-189 hsa-miR-212 hsa-miR-30a-3p hsa-miR-368 hsa-miR-449 hsa-miR-510 hsa-miR-520g
PUC2PM-20B hsa-let-7e hsa-miR-141 hsa-miR-18a hsa-miR-213 hsa-miR-30a-5p hsa-miR-369-3p hsa-miR-450 hsa-miR-511 hsa-miR-520h
PUC2MM-20B hsa-let-7f hsa-miR-142-3p hsa-miR-18a* hsa-miR-214 hsa-miR-30b hsa-miR-369-5p hsa-miR-451 hsa-miR-512-3p hsa-miR-521
hsa-let-7g hsa-miR-142-5p hsa-miR-18b hsa-miR-215 hsa-miR-30c hsa-miR-370 hsa-miR-452 hsa-miR-512-5p hsa-miR-522
a1-PUC2PM-20B hsa-let-7i hsa-miR-143 hsa-miR-190 hsa-miR-216 hsa-miR-30d hsa-miR-371 hsa-miR-452* hsa-miR-513 hsa-miR-523
a1-PUC2MM-20B hsa-miR-1 hsa-miR-144 hsa-miR-191 hsa-miR-217 hsa-miR-30e-3p hsa-miR-372 hsa-miR-453 hsa-miR-514 hsa-miR-524
a2-PUC2PM-20B hsa-miR-100 hsa-miR-145 hsa-miR-191* hsa-miR-218 hsa-miR-30e-5p hsa-miR-373 hsa-miR-455 hsa-miR-515-3p hsa-miR-524*
a2-PUC2MM-20B hsa-miR-101 hsa-miR-146a hsa-miR-192 hsa-miR-219 hsa-miR-31 hsa-miR-373* hsa-miR-483 hsa-miR-515-5p hsa-miR-525
a3-PUC2PM-20B hsa-miR-103 hsa-miR-146b hsa-miR-193a hsa-miR-22 hsa-miR-32 hsa-miR-374 hsa-miR-484 hsa-miR-516-3p hsa-miR-525*
a3-PUC2MM-20B hsa-miR-105 hsa-miR-147 hsa-miR-193b hsa-miR-220 hsa-miR-320 hsa-miR-375 hsa-miR-485-3p hsa-miR-516-5p hsa-miR-526a
hsa-miR-106a hsa-miR-148a hsa-miR-194 hsa-miR-221 hsa-miR-323 hsa-miR-376a hsa-miR-485-5p hsa-miR-517* hsa-miR-526b
5S-rRNA-1 hsa-miR-106b hsa-miR-148b hsa-miR-195 hsa-miR-222 hsa-miR-324-3p hsa-miR-376a* hsa-miR-486 hsa-miR-517a hsa-miR-526b*
5S-rRNA-2 hsa-miR-107 hsa-miR-149 hsa-miR-196a hsa-miR-223 hsa-miR-324-5p hsa-miR-376b hsa-miR-487a hsa-miR-517b hsa-miR-526c
5S-rRNA-3 hsa-miR-10a hsa-miR-150 hsa-miR-196b hsa-miR-224 hsa-miR-325 hsa-miR-377 hsa-miR-487b hsa-miR-517c hsa-miR-527
5S-rRNA-4 hsa-miR-10b hsa-miR-151 hsa-miR-197 hsa-miR-23a hsa-miR-326 hsa-miR-378 hsa-miR-488 hsa-miR-518a hsa-miR-539
5S-rRNA-5 hsa-miR-122a hsa-miR-152 hsa-miR-198 hsa-miR-23b hsa-miR-328 hsa-miR-379 hsa-miR-489 hsa-miR-518a-2* hsa-miR-542-3p
5S-rRNA-6 hsa-miR-124a hsa-miR-153 hsa-miR-199a hsa-miR-24 hsa-miR-329 hsa-miR-380-3p hsa-miR-490 hsa-miR-518b hsa-miR-542-5p
hsa-miR-125a hsa-miR-154 hsa-miR-199a* hsa-miR-25 hsa-miR-33 hsa-miR-380-5p hsa-miR-491 hsa-miR-518c hsa-miR-544
a1-PUC2PM-20B hsa-miR-125b hsa-miR-154* hsa-miR-199b hsa-miR-26a hsa-miR-330 hsa-miR-381 hsa-miR-492 hsa-miR-518c* hsa-miR-545
a1-PUC2MM-20B hsa-miR-126 hsa-miR-155 hsa-miR-19a hsa-miR-26b hsa-miR-331 hsa-miR-382 hsa-miR-493-3p hsa-miR-518d hsa-miR-7
a2-PUC2PM-20B hsa-miR-126* hsa-miR-15a hsa-miR-19b hsa-miR-27a hsa-miR-335 hsa-miR-383 hsa-miR-493-5p hsa-miR-518e hsa-miR-9
a2-PUC2MM-20B hsa-miR-127 hsa-miR-15b hsa-miR-200a hsa-miR-27b hsa-miR-337 hsa-miR-384 hsa-miR-494 hsa-miR-518f hsa-miR-9*
a3-PUC2PM-20B hsa-miR-128a hsa-miR-16 hsa-miR-200a* hsa-miR-28 hsa-miR-338 hsa-miR-409-3p hsa-miR-495 hsa-miR-518f* hsa-miR-92
a3-PUC2MM-20B hsa-miR-128b hsa-miR-17-3p hsa-miR-200b hsa-miR-296 hsa-miR-339 hsa-miR-409-5p hsa-miR-496 hsa-miR-519a hsa-miR-93
PUC2PM-20B hsa-miR-129 hsa-miR-17-5p hsa-miR-200c hsa-miR-299-3p hsa-miR-340 hsa-miR-410 hsa-miR-497 hsa-miR-519b hsa-miR-95
PUC2MM-20B hsa-miR-130a hsa-miR-181a hsa-miR-202 hsa-miR-299-5p hsa-miR-342 hsa-miR-412 hsa-miR-498 hsa-miR-519c hsa-miR-96
BKG0 hsa-miR-130b hsa-miR-181b hsa-miR-202* hsa-miR-29a hsa-miR-345 hsa-miR-422a hsa-miR-499 hsa-miR-519d hsa-miR-98
PUC2PM hsa-miR-132 hsa-miR-181c hsa-miR-203 hsa-miR-29b hsa-miR-346 hsa-miR-422b hsa-miR-500 hsa-miR-519e hsa-miR-99a
PUC2MM hsa-miR-133a hsa-miR-181d hsa-miR-204 hsa-miR-29c hsa-miR-34a hsa-miR-423 hsa-miR-501 hsa-miR-519e* hsa-miR-99b
hsa-miR-133b hsa-miR-182 hsa-miR-205 hsa-miR-301 hsa-miR-34b hsa-miR-424 hsa-miR-502 hsa-miR-520a
a1-PUC2PM-20B hsa-miR-134 hsa-miR-182* hsa-miR-206 hsa-miR-302a hsa-miR-34c hsa-miR-425 hsa-miR-503 hsa-miR-520a*
a1-PUC2MM-20B hsa-miR-135a hsa-miR-183 hsa-miR-208 hsa-miR-302a* hsa-miR-361 hsa-miR-429 hsa-miR-504 hsa-miR-520b
a2-PUC2PM-20B hsa-miR-135b hsa-miR-184 hsa-miR-20a hsa-miR-302b hsa-miR-362 hsa-miR-431 hsa-miR-505 hsa-miR-520c
a2-PUC2MM-20B hsa-miR-136 hsa-miR-185 hsa-miR-20b hsa-miR-302b* hsa-miR-363 hsa-miR-432 hsa-miR-506 hsa-miR-520d
a3-PUC2PM-20B
a3-PUC2MM-20B
a-PUC2PM
a-PUC2MM2d

IV. Data Analysis
We provide the result of a data analysis in data files (Chip#_Data.xls). There are
seven worksheets in each file as described in the following.
• Worksheet “File Info” – provides information on data files, samples, and data
analysis parameters.
• Worksheet “Simple Differential” – lists all differentially expressed transcripts
with p-value < 0.01. Mature miRNAs are sorted separately according to
differential ratios. The ratio values are presented in log
2
scale for quick and easy
assessing differential direction as well as magnitude. A positive log
2
value
indicates an upper regulation and a negative log
2
value indicates a down
regulation. One can easily convert a log
2
value into a arithmetic ratio on a
calculator by typing in 2^(value). Detailed data processing statistics are listed in
Worksheet “Differential Data”.
• Worksheet “Simple Detectable” – lists average signal values all transcripts on the
array. The signal values are derived by background subtraction and
normalization. Blank spaces represent signal values below detection level. A
transcript to be listed as detectable must meets at least two conditions: signal
intensity higher than 3×(background standard deviation) and spot CV < 0.5. CV
is calculated by (standard deviation)/(signal intensity). When repeating probes
are present on an array, a transcript is listed as detectable only if the signals from

185
at least 50% of the repeating probes are above detection level. Detailed data
processing statistics are listed in Worksheet “Detectable Transcripts”.
• Worksheet “Raw Data” – lists raw data extracted from image files with
corresponding probe, sequence, and location information.
• Worksheet “Processed Data” – lists processed data, including background-
subtracted and normalized signals, p-values, statistically significant log ratios of
Cy3 and Cy5 labeled transcripts, and a scatter plot of the processed data.
• Worksheet “Differential Data” – lists all differentially expressed transcripts with
p-value < 0.01 along with data processing statistics. Signals are listed in median
signal values of repeating probes of p-value < 0.01 that are listed in the upper
portion of the table on Worksheet “Processed Data”. Median values are used to
minimize the effect of occasional “non-uniform spots” that may have signal
values deviate from average signal values but have p-values < 0.01.
• Worksheet “Detectable Transcripts” – lists all the transcripts with signals above
detection levels along with data processing statistics. Signal intensities are listed
in average values of repeating spots. During data process, “bad spots” that have
signal values deviated more than 50% of average values of repeating spots and/or
spot CV larger than 0.5 are discarded.
In data file package a probe layout file containing a complete list of probe positions
and target sequences is included in the file directory of each chip.
V. Suggestions for Data Analysis
In case you want to perform your own data analysis we have the following
suggestions.
1. Background should be calculated from the median of 5% to 25% of low intensity
cells. BKG0 and blank cells should be excluded for the background calculation.
2. All “Production_Use_Probes” (including BKG0, PUC2 …,) and blank cells
which are listed in a supplied layout file, should be excluded during data
normalization.

186

MiHuman_8.0_060307 - Based on Sanger miRBase Release 8.0
Probe Information
Group Probe Name Target Sequence (5' to 3')
Human miRNA Probes v8.0
hsa-let-7a UGAGGUAGUAGGUUGUAUAGUU
hsa-let-7b UGAGGUAGUAGGUUGUGUGGUU
hsa-let-7c UGAGGUAGUAGGUUGUAUGGUU
hsa-let-7d AGAGGUAGUAGGUUGCAUAGU
hsa-let-7e UGAGGUAGGAGGUUGUAUAGU
hsa-let-7f UGAGGUAGUAGAUUGUAUAGUU
hsa-let-7g UGAGGUAGUAGUUUGUACAGU
hsa-let-7i UGAGGUAGUAGUUUGUGCUGU
hsa-miR-1 UGGAAUGUAAAGAAGUAUGUA
hsa-miR-100 AACCCGUAGAUCCGAACUUGUG
hsa-miR-101 UACAGUACUGUGAUAACUGAAG
hsa-miR-103 AGCAGCAUUGUACAGGGCUAUGA
hsa-miR-105 UCAAAUGCUCAGACUCCUGU
hsa-miR-106a AAAAGUGCUUACAGUGCAGGUAGC
hsa-miR-106b UAAAGUGCUGACAGUGCAGAU
hsa-miR-107 AGCAGCAUUGUACAGGGCUAUCA
hsa-miR-10a UACCCUGUAGAUCCGAAUUUGUG
hsa-miR-10b UACCCUGUAGAACCGAAUUUGU
hsa-miR-122a UGGAGUGUGACAAUGGUGUUUGU
hsa-miR-124a UUAAGGCACGCGGUGAAUGCCA
hsa-miR-125a UCCCUGAGACCCUUUAACCUGUG
hsa-miR-125b UCCCUGAGACCCUAACUUGUGA
hsa-miR-126 UCGUACCGUGAGUAAUAAUGC
hsa-miR-126* CAUUAUUACUUUUGGUACGCG
hsa-miR-127 UCGGAUCCGUCUGAGCUUGGCU
hsa-miR-128a UCACAGUGAACCGGUCUCUUUU
hsa-miR-128b UCACAGUGAACCGGUCUCUUUC
hsa-miR-129 CUUUUUGCGGUCUGGGCUUGC
hsa-miR-130a CAGUGCAAUGUUAAAAGGGCAU
hsa-miR-130b CAGUGCAAUGAUGAAAGGGCAU
hsa-miR-132 UAACAGUCUACAGCCAUGGUCG
hsa-miR-133a UUGGUCCCCUUCAACCAGCUGU
hsa-miR-133b UUGGUCCCCUUCAACCAGCUA
hsa-miR-134 UGUGACUGGUUGACCAGAGGG
hsa-miR-135a UAUGGCUUUUUAUUCCUAUGUGA

187
hsa-miR-135b UAUGGCUUUUCAUUCCUAUGUG
hsa-miR-136 ACUCCAUUUGUUUUGAUGAUGGA
hsa-miR-137 UAUUGCUUAAGAAUACGCGUAG
hsa-miR-138 AGCUGGUGUUGUGAAUC
hsa-miR-139 UCUACAGUGCACGUGUCU
hsa-miR-140 AGUGGUUUUACCCUAUGGUAG
hsa-miR-141 UAACACUGUCUGGUAAAGAUGG
hsa-miR-142-3p UGUAGUGUUUCCUACUUUAUGGA
hsa-miR-142-5p CAUAAAGUAGAAAGCACUAC
hsa-miR-143 UGAGAUGAAGCACUGUAGCUCA
hsa-miR-144 UACAGUAUAGAUGAUGUACUAG
hsa-miR-145 GUCCAGUUUUCCCAGGAAUCCCUU
hsa-miR-146a UGAGAACUGAAUUCCAUGGGUU
hsa-miR-146b UGAGAACUGAAUUCCAUAGGCU
hsa-miR-147 GUGUGUGGAAAUGCUUCUGC
hsa-miR-148a UCAGUGCACUACAGAACUUUGU
hsa-miR-148b UCAGUGCAUCACAGAACUUUGU
hsa-miR-149 UCUGGCUCCGUGUCUUCACUCC
hsa-miR-150 UCUCCCAACCCUUGUACCAGUG
hsa-miR-151 ACUAGACUGAAGCUCCUUGAGG
hsa-miR-152 UCAGUGCAUGACAGAACUUGGG
hsa-miR-153 UUGCAUAGUCACAAAAGUGA
hsa-miR-154 UAGGUUAUCCGUGUUGCCUUCG
hsa-miR-154* AAUCAUACACGGUUGACCUAUU
hsa-miR-155 UUAAUGCUAAUCGUGAUAGGGG
hsa-miR-15a UAGCAGCACAUAAUGGUUUGUG
hsa-miR-15b UAGCAGCACAUCAUGGUUUACA
hsa-miR-16 UAGCAGCACGUAAAUAUUGGCG
hsa-miR-17-3p ACUGCAGUGAAGGCACUUGU
hsa-miR-17-5p CAAAGUGCUUACAGUGCAGGUAGU
hsa-miR-181a AACAUUCAACGCUGUCGGUGAGU
hsa-miR-181b AACAUUCAUUGCUGUCGGUGGG
hsa-miR-181c AACAUUCAACCUGUCGGUGAGU
hsa-miR-181d AACAUUCAUUGUUGUCGGUGGGUU
hsa-miR-182 UUUGGCAAUGGUAGAACUCACA
hsa-miR-182* UGGUUCUAGACUUGCCAACUA
hsa-miR-183 UAUGGCACUGGUAGAAUUCACUG
hsa-miR-184 UGGACGGAGAACUGAUAAGGGU
hsa-miR-185 UGGAGAGAAAGGCAGUUC
hsa-miR-186 CAAAGAAUUCUCCUUUUGGGCUU

188
hsa-miR-187 UCGUGUCUUGUGUUGCAGCCG
hsa-miR-188 CAUCCCUUGCAUGGUGGAGGGU
hsa-miR-189 GUGCCUACUGAGCUGAUAUCAGU
hsa-miR-18a UAAGGUGCAUCUAGUGCAGAUA
hsa-miR-18a* ACUGCCCUAAGUGCUCCUUCU
hsa-miR-18b UAAGGUGCAUCUAGUGCAGUUA
hsa-miR-190 UGAUAUGUUUGAUAUAUUAGGU
hsa-miR-191 CAACGGAAUCCCAAAAGCAGCU
hsa-miR-191* GCUGCGCUUGGAUUUCGUCCCC
hsa-miR-192 CUGACCUAUGAAUUGACAGCC
hsa-miR-193a AACUGGCCUACAAAGUCCCAG
hsa-miR-193b AACUGGCCCUCAAAGUCCCGCUUU
hsa-miR-194 UGUAACAGCAACUCCAUGUGGA
hsa-miR-195 UAGCAGCACAGAAAUAUUGGC
hsa-miR-196a UAGGUAGUUUCAUGUUGUUGG
hsa-miR-196b UAGGUAGUUUCCUGUUGUUGG
hsa-miR-197 UUCACCACCUUCUCCACCCAGC
hsa-miR-198 GGUCCAGAGGGGAGAUAGG
hsa-miR-199a CCCAGUGUUCAGACUACCUGUUC
hsa-miR-199a* UACAGUAGUCUGCACAUUGGUU
hsa-miR-199b CCCAGUGUUUAGACUAUCUGUUC
hsa-miR-19a UGUGCAAAUCUAUGCAAAACUGA
hsa-miR-19b UGUGCAAAUCCAUGCAAAACUGA
hsa-miR-200a UAACACUGUCUGGUAACGAUGU
hsa-miR-200a* CAUCUUACCGGACAGUGCUGGA
hsa-miR-200b UAAUACUGCCUGGUAAUGAUGAC
hsa-miR-200c UAAUACUGCCGGGUAAUGAUGG
hsa-miR-202 AGAGGUAUAGGGCAUGGGAAAA
hsa-miR-202* UUUCCUAUGCAUAUACUUCUUU
hsa-miR-203 GUGAAAUGUUUAGGACCACUAG
hsa-miR-204 UUCCCUUUGUCAUCCUAUGCCU
hsa-miR-205 UCCUUCAUUCCACCGGAGUCUG
hsa-miR-206 UGGAAUGUAAGGAAGUGUGUGG
hsa-miR-208 AUAAGACGAGCAAAAAGCUUGU
hsa-miR-20a UAAAGUGCUUAUAGUGCAGGUAG
hsa-miR-20b CAAAGUGCUCAUAGUGCAGGUAG
hsa-miR-21 UAGCUUAUCAGACUGAUGUUGA
hsa-miR-210 CUGUGCGUGUGACAGCGGCUGA
hsa-miR-211 UUCCCUUUGUCAUCCUUCGCCU
hsa-miR-212 UAACAGUCUCCAGUCACGGCC

189
hsa-miR-213 ACCAUCGACCGUUGAUUGUACC
hsa-miR-214 ACAGCAGGCACAGACAGGCAG
hsa-miR-215 AUGACCUAUGAAUUGACAGAC
hsa-miR-216 UAAUCUCAGCUGGCAACUGUG
hsa-miR-217 UACUGCAUCAGGAACUGAUUGGAU
hsa-miR-218 UUGUGCUUGAUCUAACCAUGU
hsa-miR-219 UGAUUGUCCAAACGCAAUUCU
hsa-miR-22 AAGCUGCCAGUUGAAGAACUGU
hsa-miR-220 CCACACCGUAUCUGACACUUU
hsa-miR-221 AGCUACAUUGUCUGCUGGGUUUC
hsa-miR-222 AGCUACAUCUGGCUACUGGGUCUC
hsa-miR-223 UGUCAGUUUGUCAAAUACCCC
hsa-miR-224 CAAGUCACUAGUGGUUCCGUUUA
hsa-miR-23a AUCACAUUGCCAGGGAUUUCC
hsa-miR-23b AUCACAUUGCCAGGGAUUACC
hsa-miR-24 UGGCUCAGUUCAGCAGGAACAG
hsa-miR-25 CAUUGCACUUGUCUCGGUCUGA
hsa-miR-26a UUCAAGUAAUCCAGGAUAGGC
hsa-miR-26b UUCAAGUAAUUCAGGAUAGGUU
hsa-miR-27a UUCACAGUGGCUAAGUUCCGC
hsa-miR-27b UUCACAGUGGCUAAGUUCUGC
hsa-miR-28 AAGGAGCUCACAGUCUAUUGAG
hsa-miR-296 AGGGCCCCCCCUCAAUCCUGU
hsa-miR-299-3p UAUGUGGGAUGGUAAACCGCUU
hsa-miR-299-5p UGGUUUACCGUCCCACAUACAU
hsa-miR-29a UAGCACCAUCUGAAAUCGGUU
hsa-miR-29b UAGCACCAUUUGAAAUCAGUGUU
hsa-miR-29c UAGCACCAUUUGAAAUCGGU
hsa-miR-301 CAGUGCAAUAGUAUUGUCAAAGC
hsa-miR-302a UAAGUGCUUCCAUGUUUUGGUGA
hsa-miR-302a* UAAACGUGGAUGUACUUGCUUU
hsa-miR-302b UAAGUGCUUCCAUGUUUUAGUAG
hsa-miR-302b* ACUUUAACAUGGAAGUGCUUUCU
hsa-miR-302c UAAGUGCUUCCAUGUUUCAGUGG
hsa-miR-302c* UUUAACAUGGGGGUACCUGCUG
hsa-miR-302d UAAGUGCUUCCAUGUUUGAGUGU
hsa-miR-30a-3p CUUUCAGUCGGAUGUUUGCAGC
hsa-miR-30a-5p UGUAAACAUCCUCGACUGGAAG
hsa-miR-30b UGUAAACAUCCUACACUCAGCU
hsa-miR-30c UGUAAACAUCCUACACUCUCAGC

190
hsa-miR-30d UGUAAACAUCCCCGACUGGAAG
hsa-miR-30e-3p CUUUCAGUCGGAUGUUUACAGC
hsa-miR-30e-5p UGUAAACAUCCUUGACUGGA
hsa-miR-31 GGCAAGAUGCUGGCAUAGCUG
hsa-miR-32 UAUUGCACAUUACUAAGUUGC
hsa-miR-320 AAAAGCUGGGUUGAGAGGGCGAA
hsa-miR-323 GCACAUUACACGGUCGACCUCU
hsa-miR-324-3p CCACUGCCCCAGGUGCUGCUGG
hsa-miR-324-5p CGCAUCCCCUAGGGCAUUGGUGU
hsa-miR-325 CCUAGUAGGUGUCCAGUAAGUGU
hsa-miR-326 CCUCUGGGCCCUUCCUCCAG
hsa-miR-328 CUGGCCCUCUCUGCCCUUCCGU
hsa-miR-329 AACACACCUGGUUAACCUCUUU
hsa-miR-33 GUGCAUUGUAGUUGCAUUG
hsa-miR-330 GCAAAGCACACGGCCUGCAGAGA
hsa-miR-331 GCCCCUGGGCCUAUCCUAGAA
hsa-miR-335 UCAAGAGCAAUAACGAAAAAUGU
hsa-miR-337 UCCAGCUCCUAUAUGAUGCCUUU
hsa-miR-338 UCCAGCAUCAGUGAUUUUGUUGA
hsa-miR-339 UCCCUGUCCUCCAGGAGCUCA
hsa-miR-340 UCCGUCUCAGUUACUUUAUAGCC
hsa-miR-342 UCUCACACAGAAAUCGCACCCGUC
hsa-miR-345 UGCUGACUCCUAGUCCAGGGC
hsa-miR-346 UGUCUGCCCGCAUGCCUGCCUCU
hsa-miR-34a UGGCAGUGUCUUAGCUGGUUGUU
hsa-miR-34b UAGGCAGUGUCAUUAGCUGAUUG
hsa-miR-34c AGGCAGUGUAGUUAGCUGAUUGC
hsa-miR-361 UUAUCAGAAUCUCCAGGGGUAC
hsa-miR-362 AAUCCUUGGAACCUAGGUGUGAGU
hsa-miR-363 AAUUGCACGGUAUCCAUCUGUA
hsa-miR-363* CGGGUGGAUCACGAUGCAAUUU
hsa-miR-365 UAAUGCCCCUAAAAAUCCUUAU
hsa-miR-367 AAUUGCACUUUAGCAAUGGUGA
hsa-miR-368 ACAUAGAGGAAAUUCCACGUUU
hsa-miR-369-3p AAUAAUACAUGGUUGAUCUUU
hsa-miR-369-5p AGAUCGACCGUGUUAUAUUCGC
hsa-miR-370 GCCUGCUGGGGUGGAACCUGG
hsa-miR-371 GUGCCGCCAUCUUUUGAGUGU
hsa-miR-372 AAAGUGCUGCGACAUUUGAGCGU
hsa-miR-373 GAAGUGCUUCGAUUUUGGGGUGU

191
hsa-miR-373* ACUCAAAAUGGGGGCGCUUUCC
hsa-miR-374 UUAUAAUACAACCUGAUAAGUG
hsa-miR-375 UUUGUUCGUUCGGCUCGCGUGA
hsa-miR-376a AUCAUAGAGGAAAAUCCACGU
hsa-miR-376a* GGUAGAUUCUCCUUCUAUGAG
hsa-miR-376b AUCAUAGAGGAAAAUCCAUGUU
hsa-miR-377 AUCACACAAAGGCAACUUUUGU
hsa-miR-378 CUCCUGACUCCAGGUCCUGUGU
hsa-miR-379 UGGUAGACUAUGGAACGUA
hsa-miR-380-3p UAUGUAAUAUGGUCCACAUCUU
hsa-miR-380-5p UGGUUGACCAUAGAACAUGCGC
hsa-miR-381 UAUACAAGGGCAAGCUCUCUGU
hsa-miR-382 GAAGUUGUUCGUGGUGGAUUCG
hsa-miR-383 AGAUCAGAAGGUGAUUGUGGCU
hsa-miR-384 AUUCCUAGAAAUUGUUCAUA
hsa-miR-409-3p CGAAUGUUGCUCGGUGAACCCCU
hsa-miR-409-5p AGGUUACCCGAGCAACUUUGCA
hsa-miR-410 AAUAUAACACAGAUGGCCUGU
hsa-miR-412 ACUUCACCUGGUCCACUAGCCGU
hsa-miR-422a CUGGACUUAGGGUCAGAAGGCC
hsa-miR-422b CUGGACUUGGAGUCAGAAGGCC
hsa-miR-423 AGCUCGGUCUGAGGCCCCUCAG
hsa-miR-424 CAGCAGCAAUUCAUGUUUUGAA
hsa-miR-425 AUCGGGAAUGUCGUGUCCGCC
hsa-miR-429 UAAUACUGUCUGGUAAAACCGU
hsa-miR-431 UGUCUUGCAGGCCGUCAUGCA
hsa-miR-432 UCUUGGAGUAGGUCAUUGGGUGG
hsa-miR-432* CUGGAUGGCUCCUCCAUGUCU
hsa-miR-433 AUCAUGAUGGGCUCCUCGGUGU
hsa-miR-448 UUGCAUAUGUAGGAUGUCCCAU
hsa-miR-449 UGGCAGUGUAUUGUUAGCUGGU
hsa-miR-450 UUUUUGCGAUGUGUUCCUAAUA
hsa-miR-451 AAACCGUUACCAUUACUGAGUUU
hsa-miR-452 UGUUUGCAGAGGAAACUGAGAC
hsa-miR-452* UCAGUCUCAUCUGCAAAGAAG
hsa-miR-453 GAGGUUGUCCGUGGUGAGUUCG
hsa-miR-455 UAUGUGCCUUUGGACUACAUCG
hsa-miR-483 UCACUCCUCUCCUCCCGUCUUCU
hsa-miR-484 UCAGGCUCAGUCCCCUCCCGAU
hsa-miR-485-3p GUCAUACACGGCUCUCCUCUCU

192
hsa-miR-485-5p AGAGGCUGGCCGUGAUGAAUUC
hsa-miR-486 UCCUGUACUGAGCUGCCCCGAG
hsa-miR-487a AAUCAUACAGGGACAUCCAGUU
hsa-miR-487b AAUCGUACAGGGUCAUCCACUU
hsa-miR-488 CCCAGAUAAUGGCACUCUCAA
hsa-miR-489 AGUGACAUCACAUAUACGGCAGC
hsa-miR-490 CAACCUGGAGGACUCCAUGCUG
hsa-miR-491 AGUGGGGAACCCUUCCAUGAGGA
hsa-miR-492 AGGACCUGCGGGACAAGAUUCUU
hsa-miR-493-3p UGAAGGUCUACUGUGUGCCAG
hsa-miR-493-5p UUGUACAUGGUAGGCUUUCAUU
hsa-miR-494 UGAAACAUACACGGGAAACCUCUU
hsa-miR-495 AAACAAACAUGGUGCACUUCUUU
hsa-miR-496 AUUACAUGGCCAAUCUC
hsa-miR-497 CAGCAGCACACUGUGGUUUGU
hsa-miR-498 UUUCAAGCCAGGGGGCGUUUUUC
hsa-miR-499 UUAAGACUUGCAGUGAUGUUUAA
hsa-miR-500 AUGCACCUGGGCAAGGAUUCUG
hsa-miR-501 AAUCCUUUGUCCCUGGGUGAGA
hsa-miR-502 AUCCUUGCUAUCUGGGUGCUA
hsa-miR-503 UAGCAGCGGGAACAGUUCUGCAG
hsa-miR-504 AGACCCUGGUCUGCACUCUAU
hsa-miR-505 GUCAACACUUGCUGGUUUCCUC
hsa-miR-506 UAAGGCACCCUUCUGAGUAGA
hsa-miR-507 UUUUGCACCUUUUGGAGUGAA
hsa-miR-508 UGAUUGUAGCCUUUUGGAGUAGA
hsa-miR-509 UGAUUGGUACGUCUGUGGGUAGA
hsa-miR-510 UACUCAGGAGAGUGGCAAUCACA
hsa-miR-511 GUGUCUUUUGCUCUGCAGUCA
hsa-miR-512-3p AAGUGCUGUCAUAGCUGAGGUC
hsa-miR-512-5p CACUCAGCCUUGAGGGCACUUUC
hsa-miR-513 UUCACAGGGAGGUGUCAUUUAU
hsa-miR-514 AUUGACACUUCUGUGAGUAG
hsa-miR-515-3p GAGUGCCUUCUUUUGGAGCGU
hsa-miR-515-5p UUCUCCAAAAGAAAGCACUUUCUG
hsa-miR-516-3p UGCUUCCUUUCAGAGGGU
hsa-miR-516-5p CAUCUGGAGGUAAGAAGCACUUU
hsa-miR-517* CCUCUAGAUGGAAGCACUGUCU
hsa-miR-517a AUCGUGCAUCCCUUUAGAGUGUU
hsa-miR-517b UCGUGCAUCCCUUUAGAGUGUU

193
hsa-miR-517c AUCGUGCAUCCUUUUAGAGUGU
hsa-miR-518a AAAGCGCUUCCCUUUGCUGGA
hsa-miR-518a-2* UCUGCAAAGGGAAGCCCUUU
hsa-miR-518b CAAAGCGCUCCCCUUUAGAGGU
hsa-miR-518c CAAAGCGCUUCUCUUUAGAGUG
hsa-miR-518c* UCUCUGGAGGGAAGCACUUUCUG
hsa-miR-518d CAAAGCGCUUCCCUUUGGAGC
hsa-miR-518e AAAGCGCUUCCCUUCAGAGUGU
hsa-miR-518f AAAGCGCUUCUCUUUAGAGGA
hsa-miR-518f* CUCUAGAGGGAAGCACUUUCUCU
hsa-miR-519a AAAGUGCAUCCUUUUAGAGUGUUAC
hsa-miR-519b AAAGUGCAUCCUUUUAGAGGUUU
hsa-miR-519c AAAGUGCAUCUUUUUAGAGGAU
hsa-miR-519d CAAAGUGCCUCCCUUUAGAGUGU
hsa-miR-519e AAAGUGCCUCCUUUUAGAGUGU
hsa-miR-519e* UUCUCCAAAAGGGAGCACUUUC
hsa-miR-520a AAAGUGCUUCCCUUUGGACUGU
hsa-miR-520a* CUCCAGAGGGAAGUACUUUCU
hsa-miR-520b AAAGUGCUUCCUUUUAGAGGG
hsa-miR-520c AAAGUGCUUCCUUUUAGAGGGUU
hsa-miR-520d AAAGUGCUUCUCUUUGGUGGGUU
hsa-miR-520d* UCUACAAAGGGAAGCCCUUUCUG
hsa-miR-520e AAAGUGCUUCCUUUUUGAGGG
hsa-miR-520f AAGUGCUUCCUUUUAGAGGGUU
hsa-miR-520g ACAAAGUGCUUCCCUUUAGAGUGU
hsa-miR-520h ACAAAGUGCUUCCCUUUAGAGU
hsa-miR-521 AACGCACUUCCCUUUAGAGUGU
hsa-miR-522 AAAAUGGUUCCCUUUAGAGUGUU
hsa-miR-523 AACGCGCUUCCCUAUAGAGGG
hsa-miR-524 GAAGGCGCUUCCCUUUGGAGU
hsa-miR-524* CUACAAAGGGAAGCACUUUCUC
hsa-miR-525 CUCCAGAGGGAUGCACUUUCU
hsa-miR-525* GAAGGCGCUUCCCUUUAGAGC
hsa-miR-526a CUCUAGAGGGAAGCACUUUCU
hsa-miR-526b CUCUUGAGGGAAGCACUUUCUGUU
hsa-miR-526b* AAAGUGCUUCCUUUUAGAGGC
hsa-miR-526c CUCUAGAGGGAAGCGCUUUCUGUU
hsa-miR-527 CUGCAAAGGGAAGCCCUUUCU
hsa-miR-539 GGAGAAAUUAUCCUUGGUGUGU
hsa-miR-542-3p UGUGACAGAUUGAUAACUGAAA

194
hsa-miR-542-5p UCGGGGAUCAUCAUGUCACGAG
hsa-miR-544 AUUCUGCAUUUUUAGCAAGU
hsa-miR-545 AUCAGCAAACAUUUAUUGUGUG
hsa-miR-7 UGGAAGACUAGUGAUUUUGUUG
hsa-miR-9 UCUUUGGUUAUCUAGCUGUAUGA
hsa-miR-9* UAAAGCUAGAUAACCGAAAGU
hsa-miR-92 UAUUGCACUUGUCCCGGCCUG
hsa-miR-93 AAAGUGCUGUUCGUGCAGGUAG
hsa-miR-95 UUCAACGGGUAUUUAUUGAGCA
hsa-miR-96 UUUGGCACUAGCACAUUUUUGC
hsa-miR-98 UGAGGUAGUAAGUUGUAUUGUU
hsa-miR-99a AACCCGUAGAUCCGAUCUUGUG
hsa-miR-99b CACCCGUAGAACCGACCUUGCG

Controls
cont01A LC Internal Use
cont01B LC Internal Use
cont01C LC Internal Use
cont02A LC Internal Use
cont02B LC Internal Use
cont02C LC Internal Use
cont03A LC Internal Use
cont03B LC Internal Use
cont03C LC Internal Use
cont04A LC Internal Use
cont04B LC Internal Use
cont04C LC Internal Use
cont05A LC Internal Use
cont05B LC Internal Use
cont05C LC Internal Use
cont06A LC Internal Use
cont06B LC Internal Use
cont06C LC Internal Use
cont07A LC Internal Use
cont07B LC Internal Use
cont07C LC Internal Use
cont08A LC Internal Use
cont08B LC Internal Use
cont08C LC Internal Use
cont09A LC Internal Use

195
cont09B LC Internal Use
cont09C LC Internal Use
cont10A LC Internal Use
cont10B LC Internal Use
cont10C LC Internal Use
cont11A LC Internal Use
cont11B LC Internal Use
cont11C LC Internal Use

Process_use_probe
BKG0 LC Internal Use
PUC2PM LC Internal Use
PUC2MM LC Internal Use
a-PUC2PM LC Internal Use
a-PUC2MM LC Internal Use
a-PUC2MM2d LC Internal Use
PUC2PM-20B LC Internal Use
PUC2MM-20B LC Internal Use
a1-PUC2PM-20B LC Internal Use
a1-PUC2MM-20B LC Internal Use
a2-PUC2PM-20B LC Internal Use
a2-PUC2MM-20B LC Internal Use
a3-PUC2PM-20B LC Internal Use
a3-PUC2MM-20B LC Internal Use
5S-rRNA-1 LC Internal Use
5S-rRNA-2 LC Internal Use
5S-rRNA-3 LC Internal Use
5S-rRNA-4 LC Internal Use
5S-rRNA-5 LC Internal Use
5S-rRNA-6 LC Internal Use

196
APPENDIX B

miRNA Microarray Service

Data Summary S60277

Date: 5/31/2006

Prepared for
University of Southern California, Jody Chuang

Prepared by
LC Sciences, LLC

VI. Data List
Chip 1
Data Files
Folder Name Chip01_H8.0_060584
Data File
01_H8.0_060584-0530-w52c20-450-
455_Data.xls
Layout File MiHuman_8.0_060307.xls
Original Image File
01_H8.0_060584-0530-w52c20-450-
455.tif
Processed Cy3 File
01_H8.0_060584-0530-w52c20-
450cy3.tif
Processed Cy5 File
01_H8.0_060584-0530-w52c20-
455cy5.tif

Assay Information
Date of Assay 5/30/2006
Chip ID 01_H8.0_060584
Sample source University of Southern California

Sample A
Sample ID HCT116 mock (yellow tube)
Sample Receiving Date 5/4/2006
Labeling Dye Cy3

Sample B
Sample ID HCT116 A0.1P1 (green tube)
Sample Receiving Date 5/4/2006
Labeling Dye Cy5

VII. Chip Content
The content of each chip is listed in a Layout File of a corresponding folder. Multiple
redundant regions are included. Each region further comprises a miRNA probe
region, which for a standard array detects miRNA transcripts listed in Sanger
miRBase Release 8.1 (http://www.sanger.ac.uk/Software/Rfam/mirna/). When the
option for custom probes is selected, custom probes are also included.
Multiple control probes are included in each chip. The control probes are used for
quality controls of chip production, sample labeling and assay conditions. Among the
control probes, PUC2PM-20B and PUC2MM-20B are the perfect match and single-
based match detection probes, respectively, of a 20-mer RNA positive control
sequence that is spiked into the RNA samples before labeling. One may assess assay
stringency from the intensity ratio of PUC2PM-20B and PUC2MM-20B, which is
normally larger than 30.

198
VIII. Summary of Results
Following are representative regions of chips images. From Cy3 and Cy5 images one
may directly read miRNA profiles and from Ratio images one may get a quick sense
of differential expressions between the corresponding samples. The images are
displayed in pseudo colors so as to expand visual dynamic range. In the Cy3 and Cy5
intensity images, as signal intensity increases from 1 to 65,535 the corresponding
color changes from blue to green, to yellow, and to red. In the Cy3/Cy5 ratio image,
when Cy3 level is higher than Cy5 level the color is green; when Cy3 level is equal to
Cy5 level the color is yellow; and when Cy5 level is higher than Cy3 level the color
is red.
In this section, a list of differentially expressed transcripts is also provided following
the chip images. From the list, one can have a quick overview of the difference
between the two samples on the chip.

Chip: 01_H8.0_060584
Sample A: HCT116 mock (yellow tube) – Cy3
Sample B: HCT116 A0.1P1 (green tube) – Cy5
LC Control
cy3 cy5
Ratio
miRNA

Sample A Sample B Cy3/Cy5

Table B.1: Differentially expressed transcripts—mock vs. 5-Aza-CdR+PBA
No. Probe_ID
Sample A
Signal
Sample B
Signal
log2
(Sample B /
Sample A)
1 hsa-miR-493-5p 30.16 1,209.73 5.34
2 hsa-miR-127 26.92 1,032.84 5.30
3 hsa-miR-376a 117.77 3,407.71 4.80
4 hsa-miR-517a 20.55 484.50 4.64
5 hsa-miR-517b 18.52 379.73 4.47
6 hsa-miR-376b 49.47 929.67 4.22
7 hsa-miR-368 110.30 2,038.37 4.14
8 hsa-miR-495 102.18 1,475.46 3.88

199
Table B.1, Continued
9 hsa-miR-373 61.59 848.78 3.85
10 hsa-miR-154* 52.87 741.29 3.77
11 hsa-miR-369-3p 24.17 281.33 3.76
12 hsa-miR-379 36.90 506.56 3.69
13 hsa-miR-515-5p 18.15 213.69 3.64
14 hsa-miR-329 55.01 720.27 3.58
15 hsa-miR-487b 76.25 987.93 3.58
16 hsa-miR-382 63.51 747.98 3.40
17 hsa-miR-323 34.35 328.28 3.29
18 hsa-miR-542-5p 412.14 47.31 -3.16
19 hsa-miR-519c 18.82 145.61 3.15
20 hsa-miR-381 30.80 200.84 2.99
21 hsa-miR-487a 50.63 372.07 2.78
22 hsa-miR-493-3p 28.97 181.14 2.63
23 hsa-miR-485-3p 102.75 617.28 2.59
24 hsa-miR-409-3p 183.49 1,059.06 2.56
25 hsa-miR-410 49.95 239.41 2.49
26 hsa-miR-139 102.56 550.07 2.41
27 hsa-miR-372 52.43 246.92 2.32
28 hsa-miR-494 286.96 1,171.72 2.16
29 hsa-miR-371 41.24 155.26 1.98
30 hsa-miR-512-3p 47.64 171.06 1.96
31 hsa-miR-19a 12,328.85 3,436.73 -1.95
32 hsa-miR-17-3p 320.24 85.86 -1.94
33 hsa-miR-431 107.18 362.63 1.90
34 hsa-miR-18a* 777.12 238.50 -1.89
35 hsa-miR-155 246.09 874.71 1.83
36 hsa-miR-218 386.90 1,348.54 1.82
37 hsa-miR-188 651.71 181.08 -1.82
38 hsa-miR-146b 310.88 1,017.43 1.66
39 hsa-miR-199a* 172.98 565.92 1.58
40 hsa-miR-328 277.08 100.17 -1.51
41 hsa-miR-423 2,764.56 930.82 -1.51
42 hsa-miR-19b 15,321.70 5,766.64 -1.50
43 hsa-miR-346 347.37 118.58 -1.46
44 hsa-miR-141 10,826.15 3,966.30 -1.43
45 hsa-miR-497 551.95 223.90 -1.43
46 hsa-miR-196b 1,485.16 589.32 -1.36
47 hsa-miR-200a* 543.74 217.11 -1.35
48 hsa-miR-181b 616.86 257.65 -1.35

200
Table B.1, Continued
49 hsa-miR-21 33,786.82 83,751.08 1.34
50 hsa-miR-339 351.15 122.65 -1.33
51 hsa-miR-182 6,506.31 15,987.98 1.33
52 hsa-miR-375 608.96 1,433.50 1.30
53 hsa-miR-194 773.70 1,948.41 1.28
54 hsa-miR-191* 604.01 263.37 -1.22
55 hsa-miR-101 5,803.35 2,520.21 -1.21
56 hsa-miR-200a 2,164.09 978.11 -1.18
57 hsa-miR-140 627.14 288.22 -1.16
58 hsa-miR-424 7,491.50 16,035.75 1.14
59 hsa-miR-198 1,816.11 849.92 -1.09
60 hsa-miR-193b 2,482.18 1,181.37 -1.09
61 hsa-miR-345 1,651.53 801.17 -1.07
62 hsa-miR-484 1,654.76 787.71 -1.03
63 hsa-miR-492 1,057.83 513.53 -1.03
64 hsa-miR-330 718.38 350.35 -1.03
65 hsa-miR-498 563.21 290.90 -0.98
66 hsa-miR-301 7,291.18 3,510.55 -0.98
67 hsa-miR-197 4,358.14 2,201.23 -0.95
68 hsa-miR-23b 20,228.28 37,895.75 0.95
69 hsa-miR-18b 6,783.80 3,588.53 -0.95
70 hsa-miR-23a 21,469.47 39,910.94 0.93
71 hsa-miR-18a 8,479.61 4,478.42 -0.92
72 hsa-miR-152 235.88 434.37 0.90
73 hsa-miR-145 9,768.33 5,307.65 -0.88
74 hsa-miR-9 6,192.92 11,299.08 0.83
75 hsa-miR-126* 571.81 988.52 0.82
76 hsa-miR-210 678.82 385.31 -0.81
77 hsa-miR-130b 8,233.01 4,623.00 -0.80
78 hsa-miR-203 3,117.13 5,262.08 0.79
79 hsa-miR-324-5p 3,840.14 2,166.27 -0.78
80 hsa-miR-183 5,177.00 8,855.66 0.77
81 hsa-miR-149 2,287.14 1,384.25 -0.74
82 hsa-miR-148a 4,201.97 6,854.37 0.72
83 hsa-miR-30e-5p 4,626.77 2,839.34 -0.70
84 hsa-miR-331 2,544.63 1,644.38 -0.69
85 hsa-miR-15a 1,635.19 1,041.25 -0.69
86 hsa-miR-34a 2,504.50 1,458.69 -0.68
87 hsa-miR-27b 11,369.30 18,902.08 0.66
88 hsa-let-7b 10,659.61 6,703.90 -0.64

201
Table B.1, Continued
89 hsa-miR-100 13,358.48 8,592.73 -0.62
90 hsa-miR-130a 7,011.21 4,454.30 -0.61
91 hsa-miR-99a 7,669.36 4,925.69 -0.61
92 hsa-miR-26a 13,080.26 20,593.48 0.59
93 hsa-miR-27a 15,319.38 24,003.12 0.59
94 hsa-let-7i 17,447.62 11,609.11 -0.58
95 hsa-miR-505 2,665.60 1,875.97 -0.57
96 hsa-miR-96 3,004.74 4,514.44 0.56
97 hsa-miR-31 20,500.04 13,697.60 -0.56
98 hsa-miR-98 11,458.08 8,019.45 -0.56
99 hsa-miR-132 2,079.83 3,055.48 0.55
100 hsa-let-7d 19,483.20 13,102.31 -0.54
101 hsa-miR-181a 2,536.32 1,701.93 -0.52
102 hsa-miR-125b 16,789.60 11,733.49 -0.51
103 hsa-miR-196a 1,024.01 1,474.31 0.51
104 hsa-miR-7 23,310.71 33,171.34 0.49
105 hsa-miR-450 1,108.89 1,664.64 0.48
106 hsa-miR-125a 8,811.15 6,422.80 -0.46
107 hsa-let-7c 16,062.61 11,766.24 -0.45
108 hsa-miR-24 18,817.23 26,189.61 0.43
109 hsa-miR-29a 21,197.86 27,762.28 0.41
110 hsa-miR-9* 3,672.36 4,697.35 0.41
111 hsa-miR-28 7,807.99 10,978.85 0.39
112 hsa-miR-324-3p 2,866.33 2,165.36 -0.38
113 hsa-miR-422a 2,920.47 2,155.98 -0.38
114 hsa-miR-99b 7,618.97 5,983.99 -0.37
115 hsa-miR-30b 12,059.20 15,310.56 0.36
116 hsa-miR-148b 7,575.71 5,918.21 -0.36
117 hsa-miR-29c 2,215.77 2,804.25 0.35
118 hsa-miR-106b 15,425.82 12,261.76 -0.35
119 hsa-miR-107 16,729.95 13,422.99 -0.33
120 hsa-miR-22 5,797.09 4,648.79 -0.32
121 hsa-miR-93 15,826.68 12,858.75 -0.32
122 hsa-miR-92 22,537.04 17,977.18 -0.31
123 hsa-let-7g 21,759.83 17,478.22 -0.31
124 hsa-let-7a 25,180.28 20,083.08 -0.30
125 hsa-let-7f 24,554.24 19,862.63 -0.29
126 hsa-miR-103 17,204.66 14,171.18 -0.29
127 hsa-miR-106a 24,977.90 20,978.36 -0.29
128 hsa-miR-30c 10,449.14 12,579.96 0.29

202
Table B.1, Continued
129 hsa-miR-20b 23,809.87 20,605.15 -0.28
130 hsa-miR-191 18,889.07 22,635.41 0.28
131 hsa-miR-26b 10,136.58 12,452.42 0.28
132 hsa-miR-126 5,883.47 7,201.40 0.28
133 hsa-miR-374 6,539.16 7,859.57 0.27
134 hsa-miR-320 18,084.58 14,987.80 -0.27
135 hsa-let-7e 14,973.32 11,708.84 -0.27
136 hsa-miR-29b 12,757.47 10,541.09 -0.26
137 hsa-miR-186 9,786.41 8,316.76 -0.25
138 hsa-miR-20a 28,131.03 24,377.91 -0.24
139 hsa-miR-342
6,077.16 7,083.55 0.23
140 hsa-miR-16 23,733.24 27,310.34 0.23
141 hsa-miR-195 14,010.55 15,905.21 0.19
142 hsa-miR-222 21,817.72 18,657.09 -0.19
143 hsa-miR-30a-5p 8,094.11 9,284.14 0.18
144 hsa-miR-30d 7,504.80 8,703.49 0.18
145 hsa-miR-200c 22,330.04 20,256.86 -0.15
146 hsa-miR-200b 16,672.25 15,124.68 -0.14
147 hsa-miR-25 26,494.45 23,937.77 -0.14

Following is the probe layout of the above miRNA array images.
hsa-let-7a hsa-miR-137 hsa-miR-186 hsa-miR-21 hsa-miR-302c hsa-miR-363* hsa-miR-432* hsa-miR-507 hsa-miR-520d*
PUC2PM hsa-let-7b hsa-miR-138 hsa-miR-187 hsa-miR-210 hsa-miR-302c* hsa-miR-365 hsa-miR-433 hsa-miR-508 hsa-miR-520e
PUC2MM hsa-let-7c hsa-miR-139 hsa-miR-188 hsa-miR-211 hsa-miR-302d hsa-miR-367 hsa-miR-448 hsa-miR-509 hsa-miR-520f
BKG0 hsa-let-7d hsa-miR-140 hsa-miR-189 hsa-miR-212 hsa-miR-30a-3p hsa-miR-368 hsa-miR-449 hsa-miR-510 hsa-miR-520g
PUC2PM-20B hsa-let-7e hsa-miR-141 hsa-miR-18a hsa-miR-213 hsa-miR-30a-5p hsa-miR-369-3p hsa-miR-450 hsa-miR-511 hsa-miR-520h
PUC2MM-20B hsa-let-7f hsa-miR-142-3p hsa-miR-18a* hsa-miR-214 hsa-miR-30b hsa-miR-369-5p hsa-miR-451 hsa-miR-512-3p hsa-miR-521
hsa-let-7g hsa-miR-142-5p hsa-miR-18b hsa-miR-215 hsa-miR-30c hsa-miR-370 hsa-miR-452 hsa-miR-512-5p hsa-miR-522
a1-PUC2PM-20B hsa-let-7i hsa-miR-143 hsa-miR-190 hsa-miR-216 hsa-miR-30d hsa-miR-371 hsa-miR-452* hsa-miR-513 hsa-miR-523
a1-PUC2MM-20B hsa-miR-1 hsa-miR-144 hsa-miR-191 hsa-miR-217 hsa-miR-30e-3p hsa-miR-372 hsa-miR-453 hsa-miR-514 hsa-miR-524
a2-PUC2PM-20B hsa-miR-100 hsa-miR-145 hsa-miR-191* hsa-miR-218 hsa-miR-30e-5p hsa-miR-373 hsa-miR-455 hsa-miR-515-3p hsa-miR-524*
a2-PUC2MM-20B hsa-miR-101 hsa-miR-146a hsa-miR-192 hsa-miR-219 hsa-miR-31 hsa-miR-373* hsa-miR-483 hsa-miR-515-5p hsa-miR-525
a3-PUC2PM-20B hsa-miR-103 hsa-miR-146b hsa-miR-193a hsa-miR-22 hsa-miR-32 hsa-miR-374 hsa-miR-484 hsa-miR-516-3p hsa-miR-525*
a3-PUC2MM-20B hsa-miR-105 hsa-miR-147 hsa-miR-193b hsa-miR-220 hsa-miR-320 hsa-miR-375 hsa-miR-485-3p hsa-miR-516-5p hsa-miR-526a
hsa-miR-106a hsa-miR-148a hsa-miR-194 hsa-miR-221 hsa-miR-323 hsa-miR-376a hsa-miR-485-5p hsa-miR-517* hsa-miR-526b
5S-rRNA-1 hsa-miR-106b hsa-miR-148b hsa-miR-195 hsa-miR-222 hsa-miR-324-3p hsa-miR-376a* hsa-miR-486 hsa-miR-517a hsa-miR-526b*
5S-rRNA-2 hsa-miR-107 hsa-miR-149 hsa-miR-196a hsa-miR-223 hsa-miR-324-5p hsa-miR-376b hsa-miR-487a hsa-miR-517b hsa-miR-526c
5S-rRNA-3 hsa-miR-10a hsa-miR-150 hsa-miR-196b hsa-miR-224 hsa-miR-325 hsa-miR-377 hsa-miR-487b hsa-miR-517c hsa-miR-527
5S-rRNA-4 hsa-miR-10b hsa-miR-151 hsa-miR-197 hsa-miR-23a hsa-miR-326 hsa-miR-378 hsa-miR-488 hsa-miR-518a hsa-miR-539
5S-rRNA-5 hsa-miR-122a hsa-miR-152 hsa-miR-198 hsa-miR-23b hsa-miR-328 hsa-miR-379 hsa-miR-489 hsa-miR-518a-2* hsa-miR-542-3p
5S-rRNA-6 hsa-miR-124a hsa-miR-153 hsa-miR-199a hsa-miR-24 hsa-miR-329 hsa-miR-380-3p hsa-miR-490 hsa-miR-518b hsa-miR-542-5p
hsa-miR-125a hsa-miR-154 hsa-miR-199a* hsa-miR-25 hsa-miR-33 hsa-miR-380-5p hsa-miR-491 hsa-miR-518c hsa-miR-544
a1-PUC2PM-20B hsa-miR-125b hsa-miR-154* hsa-miR-199b hsa-miR-26a hsa-miR-330 hsa-miR-381 hsa-miR-492 hsa-miR-518c* hsa-miR-545
a1-PUC2MM-20B hsa-miR-126 hsa-miR-155 hsa-miR-19a hsa-miR-26b hsa-miR-331 hsa-miR-382 hsa-miR-493-3p hsa-miR-518d hsa-miR-7
a2-PUC2PM-20B hsa-miR-126* hsa-miR-15a hsa-miR-19b hsa-miR-27a hsa-miR-335 hsa-miR-383 hsa-miR-493-5p hsa-miR-518e hsa-miR-9
a2-PUC2MM-20B hsa-miR-127 hsa-miR-15b hsa-miR-200a hsa-miR-27b hsa-miR-337 hsa-miR-384 hsa-miR-494 hsa-miR-518f hsa-miR-9*
a3-PUC2PM-20B hsa-miR-128a hsa-miR-16 hsa-miR-200a* hsa-miR-28 hsa-miR-338 hsa-miR-409-3p hsa-miR-495 hsa-miR-518f* hsa-miR-92
a3-PUC2MM-20B hsa-miR-128b hsa-miR-17-3p hsa-miR-200b hsa-miR-296 hsa-miR-339 hsa-miR-409-5p hsa-miR-496 hsa-miR-519a hsa-miR-93
PUC2PM-20B hsa-miR-129 hsa-miR-17-5p hsa-miR-200c hsa-miR-299-3p hsa-miR-340 hsa-miR-410 hsa-miR-497 hsa-miR-519b hsa-miR-95
PUC2MM-20B hsa-miR-130a hsa-miR-181a hsa-miR-202 hsa-miR-299-5p hsa-miR-342 hsa-miR-412 hsa-miR-498 hsa-miR-519c hsa-miR-96
BKG0 hsa-miR-130b hsa-miR-181b hsa-miR-202* hsa-miR-29a hsa-miR-345 hsa-miR-422a hsa-miR-499 hsa-miR-519d hsa-miR-98
PUC2PM hsa-miR-132 hsa-miR-181c hsa-miR-203 hsa-miR-29b hsa-miR-346 hsa-miR-422b hsa-miR-500 hsa-miR-519e hsa-miR-99a
PUC2MM hsa-miR-133a hsa-miR-181d hsa-miR-204 hsa-miR-29c hsa-miR-34a hsa-miR-423 hsa-miR-501 hsa-miR-519e* hsa-miR-99b
hsa-miR-133b hsa-miR-182 hsa-miR-205 hsa-miR-301 hsa-miR-34b hsa-miR-424 hsa-miR-502 hsa-miR-520a
a1-PUC2PM-20B hsa-miR-134 hsa-miR-182* hsa-miR-206 hsa-miR-302a hsa-miR-34c hsa-miR-425 hsa-miR-503 hsa-miR-520a*
a1-PUC2MM-20B hsa-miR-135a hsa-miR-183 hsa-miR-208 hsa-miR-302a* hsa-miR-361 hsa-miR-429 hsa-miR-504 hsa-miR-520b
a2-PUC2PM-20B hsa-miR-135b hsa-miR-184 hsa-miR-20a hsa-miR-302b hsa-miR-362 hsa-miR-431 hsa-miR-505 hsa-miR-520c
a2-PUC2MM-20B hsa-miR-136 hsa-miR-185 hsa-miR-20b hsa-miR-302b* hsa-miR-363 hsa-miR-432 hsa-miR-506 hsa-miR-520d
a3-PUC2PM-20B
a3-PUC2MM-20B
a-PUC2PM
a-PUC2MM2d

203

IX. Data Analysis
We provide the result of a data analysis in data files (Chip#_Data.xls). There are
seven worksheets in each file as described in the following.
• Worksheet “File Info” – provides information on data files, samples, and data
analysis parameters.
• Worksheet “Simple Differential” – lists all differentially expressed transcripts
with p-value < 0.01. Mature miRNAs are sorted separately according to
differential ratios. The ratio values are presented in log
2
scale for quick and easy
assessing differential direction as well as magnitude. A positive log
2
value
indicates an upper regulation and a negative log
2
value indicates a down
regulation. One can easily convert a log
2
value into a arithmetic ratio on a
calculator by typing in 2^(value). Detailed data processing statistics are listed in
Worksheet “Differential Data”.
• Worksheet “Simple Detectable” – lists average signal values all transcripts on the
array. The signal values are derived by background subtraction and
normalization. Blank spaces represent signal values below detection level. A
transcript to be listed as detectable must meets at least two conditions: signal
intensity higher than 3×(background standard deviation) and spot CV < 0.5. CV
is calculated by (standard deviation)/(signal intensity). When repeating probes
are present on an array, a transcript is listed as detectable only if the signals from
at least 50% of the repeating probes are above detection level. Detailed data
processing statistics are listed in Worksheet “Detectable Transcripts”.
• Worksheet “Raw Data” – lists raw data extracted from image files with
corresponding probe, sequence, and location information.
• Worksheet “Processed Data” – lists processed data, including background-
subtracted and normalized signals, p-values, statistically significant log ratios of
Cy3 and Cy5 labeled transcripts, and a scatter plot of the processed data.
• Worksheet “Differential Data” – lists all differentially expressed transcripts with
p-value < 0.01 along with data processing statistics. Signals are listed in median
signal values of repeating probes of p-value < 0.01 that are listed in the upper
portion of the table on Worksheet “Processed Data”. Median values are used to
minimize the effect of occasional “non-uniform spots” that may have signal
values deviate from average signal values but have p-values < 0.01.
• Worksheet “Detectable Transcripts” – lists all the transcripts with signals above
detection levels along with data processing statistics. Signal intensities are listed
in average values of repeating spots. During data process, “bad spots” that have
signal values deviated more than 50% of average values of repeating spots and/or
spot CV larger than 0.5 are discarded.
In data file package a probe layout file containing a complete list of probe positions
and target sequences is included in the file directory of each chip.

204
X. Suggestions for Data Analysis
In case you want to perform your own data analysis we have the following
suggestions.
3. Background should be calculated from the median of 5% to 25% of low intensity
cells. BKG0 and blank cells should be excluded for the background calculation.
4. All “Production_Use_Probes” (including BKG0, PUC2 …,) and blank cells
which are listed in a supplied layout file, should be excluded during data
normalization.
5. The systematic dye bias has been found on the following probes. Therefore, it is
advised to exclude the data of these probes from further consideration.
Probes hsa-miR-377
MiHuman_8.0_060307 - Based on Sanger miRBase Release 8.0
Probe Information
Group Probe Name Target Sequence (5' to 3')
Human miRNA Probes v8.0
hsa-let-7a UGAGGUAGUAGGUUGUAUAGUU
hsa-let-7b UGAGGUAGUAGGUUGUGUGGUU
hsa-let-7c UGAGGUAGUAGGUUGUAUGGUU
hsa-let-7d AGAGGUAGUAGGUUGCAUAGU
hsa-let-7e UGAGGUAGGAGGUUGUAUAGU
hsa-let-7f UGAGGUAGUAGAUUGUAUAGUU
hsa-let-7g UGAGGUAGUAGUUUGUACAGU
hsa-let-7i UGAGGUAGUAGUUUGUGCUGU
hsa-miR-1 UGGAAUGUAAAGAAGUAUGUA
hsa-miR-100 AACCCGUAGAUCCGAACUUGUG
hsa-miR-101 UACAGUACUGUGAUAACUGAAG
hsa-miR-103 AGCAGCAUUGUACAGGGCUAUGA
hsa-miR-105 UCAAAUGCUCAGACUCCUGU
hsa-miR-106a AAAAGUGCUUACAGUGCAGGUAGC
hsa-miR-106b UAAAGUGCUGACAGUGCAGAU
hsa-miR-107 AGCAGCAUUGUACAGGGCUAUCA
hsa-miR-10a UACCCUGUAGAUCCGAAUUUGUG
hsa-miR-10b UACCCUGUAGAACCGAAUUUGU
hsa-miR-122a UGGAGUGUGACAAUGGUGUUUGU
hsa-miR-124a UUAAGGCACGCGGUGAAUGCCA
hsa-miR-125a UCCCUGAGACCCUUUAACCUGUG
hsa-miR-125b UCCCUGAGACCCUAACUUGUGA
hsa-miR-126 UCGUACCGUGAGUAAUAAUGC
hsa-miR-126* CAUUAUUACUUUUGGUACGCG
hsa-miR-127 UCGGAUCCGUCUGAGCUUGGCU

205
hsa-miR-128a UCACAGUGAACCGGUCUCUUUU
hsa-miR-128b UCACAGUGAACCGGUCUCUUUC
hsa-miR-129 CUUUUUGCGGUCUGGGCUUGC
hsa-miR-130a CAGUGCAAUGUUAAAAGGGCAU
hsa-miR-130b CAGUGCAAUGAUGAAAGGGCAU
hsa-miR-132 UAACAGUCUACAGCCAUGGUCG
hsa-miR-133a UUGGUCCCCUUCAACCAGCUGU
hsa-miR-133b UUGGUCCCCUUCAACCAGCUA
hsa-miR-134 UGUGACUGGUUGACCAGAGGG
hsa-miR-135a UAUGGCUUUUUAUUCCUAUGUGA
hsa-miR-135b UAUGGCUUUUCAUUCCUAUGUG
hsa-miR-136 ACUCCAUUUGUUUUGAUGAUGGA
hsa-miR-137 UAUUGCUUAAGAAUACGCGUAG
hsa-miR-138 AGCUGGUGUUGUGAAUC
hsa-miR-139 UCUACAGUGCACGUGUCU
hsa-miR-140 AGUGGUUUUACCCUAUGGUAG
hsa-miR-141 UAACACUGUCUGGUAAAGAUGG
hsa-miR-142-3p UGUAGUGUUUCCUACUUUAUGGA
hsa-miR-142-5p CAUAAAGUAGAAAGCACUAC
hsa-miR-143 UGAGAUGAAGCACUGUAGCUCA
hsa-miR-144 UACAGUAUAGAUGAUGUACUAG
hsa-miR-145 GUCCAGUUUUCCCAGGAAUCCCUU
hsa-miR-146a UGAGAACUGAAUUCCAUGGGUU
hsa-miR-146b UGAGAACUGAAUUCCAUAGGCU
hsa-miR-147 GUGUGUGGAAAUGCUUCUGC
hsa-miR-148a UCAGUGCACUACAGAACUUUGU
hsa-miR-148b UCAGUGCAUCACAGAACUUUGU
hsa-miR-149 UCUGGCUCCGUGUCUUCACUCC
hsa-miR-150 UCUCCCAACCCUUGUACCAGUG
hsa-miR-151 ACUAGACUGAAGCUCCUUGAGG
hsa-miR-152 UCAGUGCAUGACAGAACUUGGG
hsa-miR-153 UUGCAUAGUCACAAAAGUGA
hsa-miR-154 UAGGUUAUCCGUGUUGCCUUCG
hsa-miR-154* AAUCAUACACGGUUGACCUAUU
hsa-miR-155 UUAAUGCUAAUCGUGAUAGGGG
hsa-miR-15a UAGCAGCACAUAAUGGUUUGUG
hsa-miR-15b UAGCAGCACAUCAUGGUUUACA
hsa-miR-16 UAGCAGCACGUAAAUAUUGGCG
hsa-miR-17-3p ACUGCAGUGAAGGCACUUGU
hsa-miR-17-5p CAAAGUGCUUACAGUGCAGGUAGU

206
hsa-miR-181a AACAUUCAACGCUGUCGGUGAGU
hsa-miR-181b AACAUUCAUUGCUGUCGGUGGG
hsa-miR-181c AACAUUCAACCUGUCGGUGAGU
hsa-miR-181d AACAUUCAUUGUUGUCGGUGGGUU
hsa-miR-182 UUUGGCAAUGGUAGAACUCACA
hsa-miR-182* UGGUUCUAGACUUGCCAACUA
hsa-miR-183 UAUGGCACUGGUAGAAUUCACUG
hsa-miR-184 UGGACGGAGAACUGAUAAGGGU
hsa-miR-185 UGGAGAGAAAGGCAGUUC
hsa-miR-186 CAAAGAAUUCUCCUUUUGGGCUU
hsa-miR-187 UCGUGUCUUGUGUUGCAGCCG
hsa-miR-188 CAUCCCUUGCAUGGUGGAGGGU
hsa-miR-189 GUGCCUACUGAGCUGAUAUCAGU
hsa-miR-18a UAAGGUGCAUCUAGUGCAGAUA
hsa-miR-18a* ACUGCCCUAAGUGCUCCUUCU
hsa-miR-18b UAAGGUGCAUCUAGUGCAGUUA
hsa-miR-190 UGAUAUGUUUGAUAUAUUAGGU
hsa-miR-191 CAACGGAAUCCCAAAAGCAGCU
hsa-miR-191* GCUGCGCUUGGAUUUCGUCCCC
hsa-miR-192 CUGACCUAUGAAUUGACAGCC
hsa-miR-193a AACUGGCCUACAAAGUCCCAG
hsa-miR-193b AACUGGCCCUCAAAGUCCCGCUUU
hsa-miR-194 UGUAACAGCAACUCCAUGUGGA
hsa-miR-195 UAGCAGCACAGAAAUAUUGGC
hsa-miR-196a UAGGUAGUUUCAUGUUGUUGG
hsa-miR-196b UAGGUAGUUUCCUGUUGUUGG
hsa-miR-197 UUCACCACCUUCUCCACCCAGC
hsa-miR-198 GGUCCAGAGGGGAGAUAGG
hsa-miR-199a CCCAGUGUUCAGACUACCUGUUC
hsa-miR-199a* UACAGUAGUCUGCACAUUGGUU
hsa-miR-199b CCCAGUGUUUAGACUAUCUGUUC
hsa-miR-19a UGUGCAAAUCUAUGCAAAACUGA
hsa-miR-19b UGUGCAAAUCCAUGCAAAACUGA
hsa-miR-200a UAACACUGUCUGGUAACGAUGU
hsa-miR-200a* CAUCUUACCGGACAGUGCUGGA
hsa-miR-200b UAAUACUGCCUGGUAAUGAUGAC
hsa-miR-200c UAAUACUGCCGGGUAAUGAUGG
hsa-miR-202 AGAGGUAUAGGGCAUGGGAAAA
hsa-miR-202* UUUCCUAUGCAUAUACUUCUUU
hsa-miR-203 GUGAAAUGUUUAGGACCACUAG

207
hsa-miR-204 UUCCCUUUGUCAUCCUAUGCCU
hsa-miR-205 UCCUUCAUUCCACCGGAGUCUG
hsa-miR-206 UGGAAUGUAAGGAAGUGUGUGG
hsa-miR-208 AUAAGACGAGCAAAAAGCUUGU
hsa-miR-20a UAAAGUGCUUAUAGUGCAGGUAG
hsa-miR-20b CAAAGUGCUCAUAGUGCAGGUAG
hsa-miR-21 UAGCUUAUCAGACUGAUGUUGA
hsa-miR-210 CUGUGCGUGUGACAGCGGCUGA
hsa-miR-211 UUCCCUUUGUCAUCCUUCGCCU
hsa-miR-212 UAACAGUCUCCAGUCACGGCC
hsa-miR-213 ACCAUCGACCGUUGAUUGUACC
hsa-miR-214 ACAGCAGGCACAGACAGGCAG
hsa-miR-215 AUGACCUAUGAAUUGACAGAC
hsa-miR-216 UAAUCUCAGCUGGCAACUGUG
hsa-miR-217 UACUGCAUCAGGAACUGAUUGGAU
hsa-miR-218 UUGUGCUUGAUCUAACCAUGU
hsa-miR-219 UGAUUGUCCAAACGCAAUUCU
hsa-miR-22 AAGCUGCCAGUUGAAGAACUGU
hsa-miR-220 CCACACCGUAUCUGACACUUU
hsa-miR-221 AGCUACAUUGUCUGCUGGGUUUC
hsa-miR-222 AGCUACAUCUGGCUACUGGGUCUC
hsa-miR-223 UGUCAGUUUGUCAAAUACCCC
hsa-miR-224 CAAGUCACUAGUGGUUCCGUUUA
hsa-miR-23a AUCACAUUGCCAGGGAUUUCC
hsa-miR-23b AUCACAUUGCCAGGGAUUACC
hsa-miR-24 UGGCUCAGUUCAGCAGGAACAG
hsa-miR-25 CAUUGCACUUGUCUCGGUCUGA
hsa-miR-26a UUCAAGUAAUCCAGGAUAGGC
hsa-miR-26b UUCAAGUAAUUCAGGAUAGGUU
hsa-miR-27a UUCACAGUGGCUAAGUUCCGC
hsa-miR-27b UUCACAGUGGCUAAGUUCUGC
hsa-miR-28 AAGGAGCUCACAGUCUAUUGAG
hsa-miR-296 AGGGCCCCCCCUCAAUCCUGU
hsa-miR-299-3p UAUGUGGGAUGGUAAACCGCUU
hsa-miR-299-5p UGGUUUACCGUCCCACAUACAU
hsa-miR-29a UAGCACCAUCUGAAAUCGGUU
hsa-miR-29b UAGCACCAUUUGAAAUCAGUGUU
hsa-miR-29c UAGCACCAUUUGAAAUCGGU
hsa-miR-301 CAGUGCAAUAGUAUUGUCAAAGC
hsa-miR-302a UAAGUGCUUCCAUGUUUUGGUGA

208
hsa-miR-302a* UAAACGUGGAUGUACUUGCUUU
hsa-miR-302b UAAGUGCUUCCAUGUUUUAGUAG
hsa-miR-302b* ACUUUAACAUGGAAGUGCUUUCU
hsa-miR-302c UAAGUGCUUCCAUGUUUCAGUGG
hsa-miR-302c* UUUAACAUGGGGGUACCUGCUG
hsa-miR-302d UAAGUGCUUCCAUGUUUGAGUGU
hsa-miR-30a-3p CUUUCAGUCGGAUGUUUGCAGC
hsa-miR-30a-5p UGUAAACAUCCUCGACUGGAAG
hsa-miR-30b UGUAAACAUCCUACACUCAGCU
hsa-miR-30c UGUAAACAUCCUACACUCUCAGC
hsa-miR-30d UGUAAACAUCCCCGACUGGAAG
hsa-miR-30e-3p CUUUCAGUCGGAUGUUUACAGC
hsa-miR-30e-5p UGUAAACAUCCUUGACUGGA
hsa-miR-31 GGCAAGAUGCUGGCAUAGCUG
hsa-miR-32 UAUUGCACAUUACUAAGUUGC
hsa-miR-320 AAAAGCUGGGUUGAGAGGGCGAA
hsa-miR-323 GCACAUUACACGGUCGACCUCU
hsa-miR-324-3p CCACUGCCCCAGGUGCUGCUGG
hsa-miR-324-5p CGCAUCCCCUAGGGCAUUGGUGU
hsa-miR-325 CCUAGUAGGUGUCCAGUAAGUGU
hsa-miR-326 CCUCUGGGCCCUUCCUCCAG
hsa-miR-328 CUGGCCCUCUCUGCCCUUCCGU
hsa-miR-329 AACACACCUGGUUAACCUCUUU
hsa-miR-33 GUGCAUUGUAGUUGCAUUG
hsa-miR-330 GCAAAGCACACGGCCUGCAGAGA
hsa-miR-331 GCCCCUGGGCCUAUCCUAGAA
hsa-miR-335 UCAAGAGCAAUAACGAAAAAUGU
hsa-miR-337 UCCAGCUCCUAUAUGAUGCCUUU
hsa-miR-338 UCCAGCAUCAGUGAUUUUGUUGA
hsa-miR-339 UCCCUGUCCUCCAGGAGCUCA
hsa-miR-340 UCCGUCUCAGUUACUUUAUAGCC
hsa-miR-342 UCUCACACAGAAAUCGCACCCGUC
hsa-miR-345 UGCUGACUCCUAGUCCAGGGC
hsa-miR-346 UGUCUGCCCGCAUGCCUGCCUCU
hsa-miR-34a UGGCAGUGUCUUAGCUGGUUGUU
hsa-miR-34b UAGGCAGUGUCAUUAGCUGAUUG
hsa-miR-34c AGGCAGUGUAGUUAGCUGAUUGC
hsa-miR-361 UUAUCAGAAUCUCCAGGGGUAC
hsa-miR-362 AAUCCUUGGAACCUAGGUGUGAGU
hsa-miR-363 AAUUGCACGGUAUCCAUCUGUA

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hsa-miR-363* CGGGUGGAUCACGAUGCAAUUU
hsa-miR-365 UAAUGCCCCUAAAAAUCCUUAU
hsa-miR-367 AAUUGCACUUUAGCAAUGGUGA
hsa-miR-368 ACAUAGAGGAAAUUCCACGUUU
hsa-miR-369-3p AAUAAUACAUGGUUGAUCUUU
hsa-miR-369-5p AGAUCGACCGUGUUAUAUUCGC
hsa-miR-370 GCCUGCUGGGGUGGAACCUGG
hsa-miR-371 GUGCCGCCAUCUUUUGAGUGU
hsa-miR-372 AAAGUGCUGCGACAUUUGAGCGU
hsa-miR-373 GAAGUGCUUCGAUUUUGGGGUGU
hsa-miR-373* ACUCAAAAUGGGGGCGCUUUCC
hsa-miR-374 UUAUAAUACAACCUGAUAAGUG
hsa-miR-375 UUUGUUCGUUCGGCUCGCGUGA
hsa-miR-376a AUCAUAGAGGAAAAUCCACGU
hsa-miR-376a* GGUAGAUUCUCCUUCUAUGAG
hsa-miR-376b AUCAUAGAGGAAAAUCCAUGUU
hsa-miR-377 AUCACACAAAGGCAACUUUUGU
hsa-miR-378 CUCCUGACUCCAGGUCCUGUGU
hsa-miR-379 UGGUAGACUAUGGAACGUA
hsa-miR-380-3p UAUGUAAUAUGGUCCACAUCUU
hsa-miR-380-5p UGGUUGACCAUAGAACAUGCGC
hsa-miR-381 UAUACAAGGGCAAGCUCUCUGU
hsa-miR-382 GAAGUUGUUCGUGGUGGAUUCG
hsa-miR-383 AGAUCAGAAGGUGAUUGUGGCU
hsa-miR-384 AUUCCUAGAAAUUGUUCAUA
hsa-miR-409-3p CGAAUGUUGCUCGGUGAACCCCU
hsa-miR-409-5p AGGUUACCCGAGCAACUUUGCA
hsa-miR-410 AAUAUAACACAGAUGGCCUGU
hsa-miR-412 ACUUCACCUGGUCCACUAGCCGU
hsa-miR-422a CUGGACUUAGGGUCAGAAGGCC
hsa-miR-422b CUGGACUUGGAGUCAGAAGGCC
hsa-miR-423 AGCUCGGUCUGAGGCCCCUCAG
hsa-miR-424 CAGCAGCAAUUCAUGUUUUGAA
hsa-miR-425 AUCGGGAAUGUCGUGUCCGCC
hsa-miR-429 UAAUACUGUCUGGUAAAACCGU
hsa-miR-431 UGUCUUGCAGGCCGUCAUGCA
hsa-miR-432 UCUUGGAGUAGGUCAUUGGGUGG
hsa-miR-432* CUGGAUGGCUCCUCCAUGUCU
hsa-miR-433 AUCAUGAUGGGCUCCUCGGUGU
hsa-miR-448 UUGCAUAUGUAGGAUGUCCCAU

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hsa-miR-449 UGGCAGUGUAUUGUUAGCUGGU
hsa-miR-450 UUUUUGCGAUGUGUUCCUAAUA
hsa-miR-451 AAACCGUUACCAUUACUGAGUUU
hsa-miR-452 UGUUUGCAGAGGAAACUGAGAC
hsa-miR-452* UCAGUCUCAUCUGCAAAGAAG
hsa-miR-453 GAGGUUGUCCGUGGUGAGUUCG
hsa-miR-455 UAUGUGCCUUUGGACUACAUCG
hsa-miR-483 UCACUCCUCUCCUCCCGUCUUCU
hsa-miR-484 UCAGGCUCAGUCCCCUCCCGAU
hsa-miR-485-3p GUCAUACACGGCUCUCCUCUCU
hsa-miR-485-5p AGAGGCUGGCCGUGAUGAAUUC
hsa-miR-486 UCCUGUACUGAGCUGCCCCGAG
hsa-miR-487a AAUCAUACAGGGACAUCCAGUU
hsa-miR-487b AAUCGUACAGGGUCAUCCACUU
hsa-miR-488 CCCAGAUAAUGGCACUCUCAA
hsa-miR-489 AGUGACAUCACAUAUACGGCAGC
hsa-miR-490 CAACCUGGAGGACUCCAUGCUG
hsa-miR-491 AGUGGGGAACCCUUCCAUGAGGA
hsa-miR-492 AGGACCUGCGGGACAAGAUUCUU
hsa-miR-493-3p UGAAGGUCUACUGUGUGCCAG
hsa-miR-493-5p UUGUACAUGGUAGGCUUUCAUU
hsa-miR-494 UGAAACAUACACGGGAAACCUCUU
hsa-miR-495 AAACAAACAUGGUGCACUUCUUU
hsa-miR-496 AUUACAUGGCCAAUCUC
hsa-miR-497 CAGCAGCACACUGUGGUUUGU
hsa-miR-498 UUUCAAGCCAGGGGGCGUUUUUC
hsa-miR-499 UUAAGACUUGCAGUGAUGUUUAA
hsa-miR-500 AUGCACCUGGGCAAGGAUUCUG
hsa-miR-501 AAUCCUUUGUCCCUGGGUGAGA
hsa-miR-502 AUCCUUGCUAUCUGGGUGCUA
hsa-miR-503 UAGCAGCGGGAACAGUUCUGCAG
hsa-miR-504 AGACCCUGGUCUGCACUCUAU
hsa-miR-505 GUCAACACUUGCUGGUUUCCUC
hsa-miR-506 UAAGGCACCCUUCUGAGUAGA
hsa-miR-507 UUUUGCACCUUUUGGAGUGAA
hsa-miR-508 UGAUUGUAGCCUUUUGGAGUAGA
hsa-miR-509 UGAUUGGUACGUCUGUGGGUAGA
hsa-miR-510 UACUCAGGAGAGUGGCAAUCACA
hsa-miR-511 GUGUCUUUUGCUCUGCAGUCA
hsa-miR-512-3p AAGUGCUGUCAUAGCUGAGGUC

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hsa-miR-512-5p CACUCAGCCUUGAGGGCACUUUC
hsa-miR-513 UUCACAGGGAGGUGUCAUUUAU
hsa-miR-514 AUUGACACUUCUGUGAGUAG
hsa-miR-515-3p GAGUGCCUUCUUUUGGAGCGU
hsa-miR-515-5p UUCUCCAAAAGAAAGCACUUUCUG
hsa-miR-516-3p UGCUUCCUUUCAGAGGGU
hsa-miR-516-5p CAUCUGGAGGUAAGAAGCACUUU
hsa-miR-517* CCUCUAGAUGGAAGCACUGUCU
hsa-miR-517a AUCGUGCAUCCCUUUAGAGUGUU
hsa-miR-517b UCGUGCAUCCCUUUAGAGUGUU
hsa-miR-517c AUCGUGCAUCCUUUUAGAGUGU
hsa-miR-518a AAAGCGCUUCCCUUUGCUGGA
hsa-miR-518a-2* UCUGCAAAGGGAAGCCCUUU
hsa-miR-518b CAAAGCGCUCCCCUUUAGAGGU
hsa-miR-518c CAAAGCGCUUCUCUUUAGAGUG
hsa-miR-518c* UCUCUGGAGGGAAGCACUUUCUG
hsa-miR-518d CAAAGCGCUUCCCUUUGGAGC
hsa-miR-518e AAAGCGCUUCCCUUCAGAGUGU
hsa-miR-518f AAAGCGCUUCUCUUUAGAGGA
hsa-miR-518f* CUCUAGAGGGAAGCACUUUCUCU
hsa-miR-519a AAAGUGCAUCCUUUUAGAGUGUUAC
hsa-miR-519b AAAGUGCAUCCUUUUAGAGGUUU
hsa-miR-519c AAAGUGCAUCUUUUUAGAGGAU
hsa-miR-519d CAAAGUGCCUCCCUUUAGAGUGU
hsa-miR-519e AAAGUGCCUCCUUUUAGAGUGU
hsa-miR-519e* UUCUCCAAAAGGGAGCACUUUC
hsa-miR-520a AAAGUGCUUCCCUUUGGACUGU
hsa-miR-520a* CUCCAGAGGGAAGUACUUUCU
hsa-miR-520b AAAGUGCUUCCUUUUAGAGGG
hsa-miR-520c AAAGUGCUUCCUUUUAGAGGGUU
hsa-miR-520d AAAGUGCUUCUCUUUGGUGGGUU
hsa-miR-520d* UCUACAAAGGGAAGCCCUUUCUG
hsa-miR-520e AAAGUGCUUCCUUUUUGAGGG
hsa-miR-520f AAGUGCUUCCUUUUAGAGGGUU
hsa-miR-520g ACAAAGUGCUUCCCUUUAGAGUGU
hsa-miR-520h ACAAAGUGCUUCCCUUUAGAGU
hsa-miR-521 AACGCACUUCCCUUUAGAGUGU
hsa-miR-522 AAAAUGGUUCCCUUUAGAGUGUU
hsa-miR-523 AACGCGCUUCCCUAUAGAGGG
hsa-miR-524 GAAGGCGCUUCCCUUUGGAGU

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hsa-miR-524* CUACAAAGGGAAGCACUUUCUC
hsa-miR-525 CUCCAGAGGGAUGCACUUUCU
hsa-miR-525* GAAGGCGCUUCCCUUUAGAGC
hsa-miR-526a CUCUAGAGGGAAGCACUUUCU
hsa-miR-526b CUCUUGAGGGAAGCACUUUCUGUU
hsa-miR-526b* AAAGUGCUUCCUUUUAGAGGC
hsa-miR-526c CUCUAGAGGGAAGCGCUUUCUGUU
hsa-miR-527 CUGCAAAGGGAAGCCCUUUCU
hsa-miR-539 GGAGAAAUUAUCCUUGGUGUGU
hsa-miR-542-3p UGUGACAGAUUGAUAACUGAAA
hsa-miR-542-5p UCGGGGAUCAUCAUGUCACGAG
hsa-miR-544 AUUCUGCAUUUUUAGCAAGU
hsa-miR-545 AUCAGCAAACAUUUAUUGUGUG
hsa-miR-7 UGGAAGACUAGUGAUUUUGUUG
hsa-miR-9 UCUUUGGUUAUCUAGCUGUAUGA
hsa-miR-9* UAAAGCUAGAUAACCGAAAGU
hsa-miR-92 UAUUGCACUUGUCCCGGCCUG
hsa-miR-93 AAAGUGCUGUUCGUGCAGGUAG
hsa-miR-95 UUCAACGGGUAUUUAUUGAGCA
hsa-miR-96 UUUGGCACUAGCACAUUUUUGC
hsa-miR-98 UGAGGUAGUAAGUUGUAUUGUU
hsa-miR-99a AACCCGUAGAUCCGAUCUUGUG
hsa-miR-99b CACCCGUAGAACCGACCUUGCG

Controls
cont01A LC Internal Use
cont01B LC Internal Use
cont01C LC Internal Use
cont02A LC Internal Use
cont02B LC Internal Use
cont02C LC Internal Use
cont03A LC Internal Use
cont03B LC Internal Use
cont03C LC Internal Use
cont04A LC Internal Use
cont04B LC Internal Use
cont04C LC Internal Use
cont05A LC Internal Use
cont05B LC Internal Use
cont05C LC Internal Use

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cont06A LC Internal Use
cont06B LC Internal Use
cont06C LC Internal Use
cont07A LC Internal Use
cont07B LC Internal Use
cont07C LC Internal Use
cont08A LC Internal Use
cont08B LC Internal Use
cont08C LC Internal Use
cont09A LC Internal Use
cont09B LC Internal Use
cont09C LC Internal Use
cont10A LC Internal Use
cont10B LC Internal Use
cont10C LC Internal Use
cont11A LC Internal Use
cont11B LC Internal Use
cont11C LC Internal Use

Process_use_probe
BKG0 LC Internal Use
PUC2PM LC Internal Use
PUC2MM LC Internal Use
a-PUC2PM LC Internal Use
a-PUC2MM LC Internal Use
a-PUC2MM2d LC Internal Use
PUC2PM-20B LC Internal Use
PUC2MM-20B LC Internal Use
a1-PUC2PM-20B LC Internal Use
a1-PUC2MM-20B LC Internal Use
a2-PUC2PM-20B LC Internal Use
a2-PUC2MM-20B LC Internal Use
a3-PUC2PM-20B LC Internal Use
a3-PUC2MM-20B LC Internal Use
5S-rRNA-1 LC Internal Use
5S-rRNA-2 LC Internal Use
5S-rRNA-3 LC Internal Use
5S-rRNA-4 LC Internal Use
5S-rRNA-5 LC Internal Use
5S-rRNA-6 LC Internal Use

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APPENDIX C
MICRORNA MIR-10A, 10B, AND 196A

BACKGROUND
A total of eight clones of double knock-out (DKO) HCT116 cells were generated
by Rhee et al.(Rhee, Bachman et al. 2002), and Egger et al. (Egger, Jeong et al. 2006)
have subsequently shown that the DKO cells have reduced levels of DNA
methyltransferase (DNMT) 1 and a complete DNMT 3b knock-out, and therefore are not
truly DKO cells. Nevertheless, most of the clones have dramatic decreases in their DNA
methylation levels and are therefore still suitable for studies that compare them to
wildtype (WT) HCT116 cells to examine various changes induced by the decrease in
DNA methylation. Of the eight DKO clones, clone 8 had the highest DNA methylation
level; it showed 49% methylation at the p16
INK4a
promoter when determined by
quantitative methylation-specific polymerase chain reaction (MSP), while clone 1-7 had
methylation levels around 1% (Rhee, Bachman et al. 2002). We initially received clone 8
DKO cells, which will be denoted as “DKO(V)” cells from here on, from Dr. Vogelstein
at Johns Hopkins University. We received clone 1 DKO cells later from Dr. Baylin at
Johns Hopkins University; the clone 1 DKO cells will simply be denoted as “DKO” cells,
and these were the cells employed for the studies in Chapter 4 and 5. A microRNA
(miRNA) microarray was performed by LC Sciences (http://www.lcsciences.com/)
comparing WT vs. DKO(V) HCT116 cells before we received the clone 1 DKO cells.
We focused on miR-10a and 10b from the WT vs. DKO(V) microarray for further study;
however, their expression turned out to be not directly controlled by DNA methylation.

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MATERIALS AND METHODS:
Cell line and drug treatment
HCT116 colon carcinoma cells and T24 bladder cancer cells were obtained from
the American Type Culture Collection (AATC, Rockville, MD) and cultured in McCoy’s
5A medium supplemented with 10% heat-inactivated fetal bovine serum. DKO(V)
(clone 8) and DKO (clone 1) cells were a generous gift from Dr. Bert Vogelstein and Dr.
Stephen Baylin, respectively, and cultured with the same media for HCT116. Cells were
cultured in a humidified incubator at 37 °C in 5% CO
2
. 5-Aza-2’-deoxycytidine (5-Aza-
CdR) and 4 phenylbutyric acid (PBA) were obtained from Sigma-Aldrich (St. Louis,
MO). 5-Aza-CdR was dissolved in PBS and stored in -80 °C, and PBA was dissolved in
100% ethanol freshly before the experiment and stored in -20 °C. Cells were seeded at
the density of 1X10
6
cells/25mm dish 24 hours prior to treatment. Treatment with 5-Aza-
CdR was removed after 24 hrs, while the PBA treatment was continuous and replaced
every 2 to 3 days. The cells were treated for 6 days unless otherwise unless otherwise
indicated, and total RNA and genomic DNA were extracted at the end of the treatment.

miRNA microarray
miRNA microarray analyses were conducted by LC sciences
(www.lcsciences.com; Houston, TX). Briefly, poly-A tails were added to the RNA
sequences at the 3 ′ ends using a poly(A) polymerase, and nucleotide tags were then
ligated to the poly-A tails. For each dual-sample experiment, two sets of RNA sequences
were added with tags of two different sequences. The tagged RNA sequences were then
hybridized to the miRNA microarray chip containing 328 human miRNA probes. For the

217
complete probe sequences please see the end of appendix C. The labeling reaction was
carried out during the second hybridization reaction using tag-specific dendrimer Cy3
and Cy5 dyes. RNA from WT and DKO(V) cells were labeled with Cy3 and Cy5,
respectively. The human miRNA chip includes seven redundancies for each miRNA.
The data were corrected by subtracting the background and normalizing to the statistical
median of all detectable transcripts. Background was calculated from the median of 5%
to 25% of the lowest-intensity cells. The data normalization balances the intensities of
Cy3- and Cy5-labeled transcripts so that differential expression ratios can be correctly
calculated.

Nucleic acid isolation
Total RNA was extracted with Invitrogen TRIzol reagent (Carlsbad, CA)
according to the manufacturer’s instructions.

5’ RNA Ligase mediated rapid amplification of cDNA ends (5’ RLM-RACE)
Total RNA was extracted as described above, and the 5’ ends of primary miRNAs
were determined by using the RLM-RACE Kit (Ambion) according to the manufacturer's
instructions. PCR products were cloned into a TOPO TA cloning vector (Invitrogen,
Carlsbad, CA) and sequenced. The primers used for 5’ RLM RACE are listed as follows:
miR-10a inner, 5’-CCA GTT CTC CTA CTT TCC GC-3’; miR-10a outer, 5’-CCC TAG
ATA CGA ATT TGT GAC CAC-3’; miR-10b inner, 5’-GAG TGA GGT ACC TAG
GTC G-3’; miR-10b outer, 5’-CTT TGG TCT CTG GCT ATT CCG-3’; miR-196a-1
inner, 5’-CTA GGT CAA GAG CTC ACT GG-3’; miR-196a-1 outer, 5’-GCA GTA

218
ACT GCC GTG AAT CG-3’; miR-196a-2 inner, 5’-GCT TTG CAT CTA TCT GGA
GGA G-3’; miR-196a-2 outer, 5’-CCG ACT GAT GTA ACT CAG GC-3’.

Northern blot analysis
Total RNA (20 μg) was loaded onto a 15% polyacrylamide denaturing gel and
transferred to a nylon membrane. The StarFire radiolabeled probes (Integrated DNA
Technologies, Coralville, IA) were prepared by incorporation of [ α-
32
P] dATP 6000
Ci/mmol following the manufacturer's recommendation. Prehybridization and
hybridization were carried out using ExpressHyb Hybridization Solution (Clontech,
Mountain View, CA). Hybridization was carried out at 42 °C overnight, and then the
membrane was washed with 1X SSC+0.05% SDS until the background is clear as
detected by a Geiger counter. U6 was used as a loading control. The Northern probe
sequences are listed as follows: miR-10a, 5’-CAC AAA TTC GGA TCT ACA GGG
TA-3’; miR-10b, 5’-ACA AAT TCG GTT CTA CAG GGT A-3’; U6, 5’-GCA GGG
GCC ATG CTA ATC TTC TCT GTA TCG-3’.

Reverse-Transcription PCR and Quantitative Real-Time Reverse-Transcription
PCR
Total RNA was extracted from cells with the Invitrogen Trizol reagent (Carlsbad,
CA). Reverse transcription (RT) was performed with M-MLV reverse transcriptase and
random hexamers from Promega (Madison, WI). Reverse transcription PCR (RT-PCR)
was performed for the HOXB4,HOX-B9, and HOXD4 genes with the following primers:
HOXB4 forward, 5’-GCA AAG AGC CCG TCG TCT ACC C-3’; HOXB4 reverse, 5’-

219
CGG ATC TTG GTG TTG GGC AAC TTG TG-3’; HOXB9 forward, 5’-GCT GTC
TAA TCA AAG ACC CGG CTA CG-3’; HOXB9 reverse, 5’-GAT GGG GAA GAG
CTA GGG AGG AC-3’; HOXD4 forward, 5’-CCC CTG GAT GAA GAA GGT GCA
CG-3’; HOXD4 reverse, 5’-CAT CGG CTG TAA ATG CTG GCT GGG-3’. PCR
conditions for all three genes were as follows: 94 °C for 3 min, followed by 32-35 cycles
of denaturation at 94 °C for 1 min, annealing at 64 °C for 30 s, and extension at 72 °C for
40 s, and a final extension at 72 °C for 5 min. We also performed quantitative real-time
RT-PCR analysis using the same primers and SYBR green for signal detection as
previously described (Heid, Stevens et al. 1996) with DNA Engine Opticon System (MJ
Research, Hercules, CA). The real-time RT-PCR conditions for all 3 genes were as
follows: 95 °C for 10min, followed by 45 cycles of denaturation at 95°C for 15 s and
annealing at 60 °C for 1min.

Quantitative DNA Methylation Analysis by Methylation-Specific Single Nucleotide
Extension (Ms-SNuPE)
Genomic DNA was extracted from cells with the Qiagen DNeasy Tissue Kit
(Valencia, CA). Two μg of each DNA sample was converted with sodium bisulfite as
previously described (Frommer, McDonald et al. 1992), and each region of interest was
amplified by PCR. The PCR conditions for both miR-10a and 10b were as follows: 95 °
for 3 min, followed by 45 cycles of denaturation at 95 °C for 1 min, annealing at 56 °C for
1 min, and extension at 72 °C for 1 min, and a final extension at 72 °C for 10 min. The
bisulfite specific-PCR primers are listed as follows: miR-10a forward, 5’-ATA AAT

220
TAA AGG TTT GGA GGG GTA-3’; miR-10a reverse, 5’-CCT CTT CTA TTA AAA
TTT CAA ATA ACC AA-3’; miR-10b forward, 5’-GGG GGA GGG TGG TTA ATA
AAG T-3’; miR-10b reverse, 5’-CAA CCT CTA AAT CCC ACC CAA AAT AAA A-3’.
The PCR amplicons were extracted with the Qiagen Gel Extraction Kit, and Ms-SNuPE
analysis was performed to examine the methylation level changes as previously described
(Gonzalgo and Jones 2002).

The Ms-SNuPE conditions for miR-10a set 1 (Ms-SNuPE primers 1 and 2) were
as follows: 95 °C for 2 min, 51.5 °C for 1 min, and 72 °C for 1 min. The Ms-SNuPE
conditions for miR-10a set 2 (Ms-SNuPE primer 3) were as follows: 95 °C for 2 min,
46.8°C for 1 min, and 72 °C for 1 min. The Ms-SNuPE conditions for miR-10b set 1
(Ms-SNuPE primers 1 and 5) were as follows: 95 °C for 2 min, 60.8 °C for 1 min, and
72°C for 1 min. The Ms-SNuPE conditions for miR-10b set 2 (Ms-SNuPE primers 2-4)
were as follows: 95 °C for 2 min, 49.5 °C for 1 min, and 72 °C for 1 min. The MS-SNuPE
primer sequences are listed as follows: miR-10a #1, 5’-AGG TTT GGA GGG GTA-3’;
miR-10a #2, 5’-TAT TTA GAA AGG TTT GAA TTT GAT TT-3’; miR-10a #3, 5’-TTA
ATG TTT ATA TTT TAT AAT TTA TG-3’; miR-10b #1, 5’-GGG GGA GGG TGG
TTA ATA AAG T-3’; miR-10b #2, 5’-TTT TTA AGA AGA ATA TTT TGG TTG TT-
3’; miR-10b #3, 5’-AGG GAA TTT ATT GTT TTA AAT TGT-3’; miR-10b #4, 5’-ATT
TGA ATT GTT TTA GAA AGT G-3’; miR-10b #5, 5’-TGG TTT AGA GGA AGA
GAT TGG GGT-3’.

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Results:
Three highly up-regulated miRNAs from the WT vs. DKO(V) array reside in the
HOX clusters.
Table C.1 lists the differentially expressed miRNAs from the miRNA array
comparing WT and DKO(V) HCT116 cells. For the complete miRNA data, including
probe sequences, please refer to the end of the appendix.

From the microarray results, we observed that four miRNAs were up-regulated
above 5-fold in the DKO(V) cells compared to the WT—miR-10a, 10b, 9, and 196a. It
was intriguing that of the four most highly up-regulated miRNAs, three of them—miR-
10a, 10b, and 196a—all laid within the HOX gene clusters. There are two other known
miRNAs in the HOX clusters—miR-196b and miR-615—the latter was not included in
the array. There are two primary transcripts that can yield the mature miR-196a—miR-
196a-1 and miR-196a-2—and they both reside in the HOX clusters. The two primary
transcripts, although located on different chromosomes and contain different primary
transcript sequences, give rise to the same mature sequence of miR-196a. A schematic
diagram showing the locations of the miRNAs in the HOX clusters is depicted in Figure
C.1.

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Table C.1: miRNA microarray—HCT116 WT vs. DKO(V)

Probe ID WT DKO(V)
log2(DKO(V)/
WT)
Absolute Ratio
(DKO(V)/WT)
hsa-miR-10a 300.60 10,687.92 5.17 35.56
hsa-miR-10b 104.07 1,196.03 3.52 11.49
hsa-miR-9 94.07 908.81 3.27 9.66
hsa-miR-196a 31.07 225.96 2.97 7.27
hsa-miR-517a 64.65 258.72 1.93 4.00
hsa-miR-155 208.21 689.45 1.86 3.31
S-hsa-mir-361 91.32 300.25 1.81 3.29
hsa-miR-125b 4,963.81 12,809.07 1.41 2.58
hsa-miR-9* 193.86 465.65 1.28 2.40
hsa-miR-130a 253.16 575.97 1.19 2.28
hsa-let-7e 9,012.65 16,596.80 0.90 1.84
hsa-let-7i 2,372.61 4,344.36 0.85 1.83
hsa-miR-27b 618.50 1,057.98 0.64 1.71
hsa-miR-221 2,628.26 4,407.81 0.76 1.68
hsa-miR-182 2,570.63 3,961.37 0.61 1.54
hsa-let-7c 14,962.4323,047.88 0.67 1.54
hsa-let-7b 9,667.11 14,701.50 0.72 1.52
hsa-miR-222 6,432.15 9,659.70 0.56 1.50
hsa-miR-7 7,187.33 10,325.02 0.44 1.44
hsa-miR-183 1,772.55 2,437.57 0.44 1.38
hsa-miR-30c 1,211.03 1,657.62 0.38 1.37
hsa-miR-26a 3,136.99 4,231.37 0.29 1.35
hsa-miR-30b 1,197.76 1,540.41 0.42 1.29
hsa-let-7f 20,706.7125,912.56 0.31 1.25
hsa-let-7a 26,058.9731,055.56 0.29 1.19
hsa-miR-125a 3,219.42 3,809.08 0.27 1.18
hsa-let-7d 17,650.5020,345.76 0.22 1.15
hsa-miR-17-5p 6,129.56 4,546.96 -0.42 0.74
hsa-miR-107 3,112.87 2,176.90 -0.53 0.70
hsa-miR-103 3,745.14 2,505.27 -0.58 0.67
hsa-miR-320 3,207.61 2,011.90 -0.67 0.63
hsa-miR-31 4,201.28 1,945.65 -1.11 0.46
hsa-miR-200c 15,061.606,067.13 -1.28 0.40
hsa-miR-498 1,670.74 532.73 -1.65 0.32
S-hsa-let-7d 755.22 233.72 -1.74 0.31
S-hsa-mir-186 2,285.26 511.17 -2.14 0.22
hsa-miR-409-3p 1,322.48 178.74 -2.90 0.14

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Table C.1, Continued

miRNA microarray comparing WT and DKO(V) HCT116 cells.
One miRNA microarray was performed, and a total of 328 human miRNAs were
analyzed on the array (LC Sciences; http://www.lcsciences.com/). WT and DKO(V)
cells were labeled with Cy3 and Cy5, respectively. The table lists all differentially
expressed transcripts with p-value < 0.01. These values represent the mean signal
intensity of seven redundancies on the chip for each miRNA, and the log and absolute
ratios of the mean signal intensity in DKO(V) cells over WT cells for each miRNA are
listed.

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Figure C.1: The HOX gene clusters and included miRNAs

Direction of Transcription of Human HOX genes
5’ 3’
miR-196b
miR-196a-1
miR-10a
miR-196a-2
miR-10b
miR-615
Direction of Transcription of Human HOX genes
5’ 3’
miR-196b
miR-196a-1
miR-10a
miR-196a-2
miR-10b
miR-615

A total of four HOX clusters are present in the human genome. There are a total of six
known miRNAs within the clusters, and their locations are marked by the bent arrows.
The arrows in green show the miRNAs that were found to be differentially expressed in
the WT vs. DKO(V) HCT116 microarray, and the purple arrows show the other two
miRNAs in the clusters.

Figure adopted from Strachan & Read, Human Molecular Genetics 2E, Wiley-Liss, 1999

225
HOX genes encode a large family of closely related transcription factors with
similar DNA binding preferences, and they are present in all bilateral animals. They
control morphologies on the main body axis of nearly all metazoans and therefore are of
fundamental importance in development (Lemons and McGinnis 2006). The relationship
between the miRNAs residing in the HOX clusters and the HOX genes has been studied
by several groups. miR-196 has been found to act upstream of Hoxb8 and has a role in
hind limb development (Hornstein, Mansfield et al. 2005); in addition, Yekta et al.
(Yekta, Shih et al. 2004) showed that miR-196 could cleave HOXB8 mRNA and
proposed that it could also regulate HOXC8, HOXD8, and HOXA7. miR-10 has been
shown to repress HoxB1a and HoxB3a in zebrafish (Woltering and Durston 2008), and
miR-10a can directly target HOXA1 (Garzon, Pichiorri et al. 2006). These reports
suggest that the functions of these miRNAs are intimately involved with the HOX gene
regulation, and it is very likely the HOX genes and their intergenic miRNAs co-evolved
to create an intrinsic regulatory network for patterning in development.

226
miRNA-10a and 10b are up-regulated in DKO(V) cells and may have their own
promoters
We confirmed the microarray results by performing Northern Blot analyses to
examine the expression of miR-10a and 10b; the results are shown in Figure C.2. The
Northern Blot results showed that miR-10a and 10b were indeed highly expressed in the
DKO(V) cells and not in the WT. Interestingly, the expression of miR-10b decreased as
the passage number of DKO(V) increased. The expression of both miR-10a and 10b was
high in T24 cells, and it went down for both after treatment with 5-Aza-CdR and PBA.
The reason for the decrease in their expression after treatment was unclear. Because
treatment with epigenetic drugs generally leads to up-regulation of genes that had been
abnormally epigenetically silenced (Yoo and Jones 2006), this decrease in expression is
unlikely a direct effect. It is conceivable that the treatment with 5-Aza-CdR and PBA led
to the up-regulation of a transcription inhibitor that inhibited the expression of miR-10a
and 10b. The expression of miR-10a and 10b also decreased after treatment with α-
amanitin, suggesting that these miRNAs, like the majority of human miRNAs, were RNA
polymerase II (pol II) transcripts (Lee, Kim et al. 2004).

227
Figure C.2: Northern Blot analysis of miR-10a and 10b expression
T24 untreated
T24 + AP3
T24 + α-amanitin 50
T24 + α-amanitin 25
HCT116 WT
HCT116 DKO(V) P8
HCT116 DKO(V) P30
miR-10a
miR-10b
T24 untreated
T24 + AP3
T24 + α-amanitin 50
T24 + α-amanitin 25
HCT116 WT
HCT116 DKO(V) P8
HCT116 DKO(V) P30
miR-10a
miR-10b

Expression changes of miR-10a and 10b in T24 and HCT116 cells. T24 cells were
treated with 5-Aza-CdR at 3 μM and PBA at 3mM for 6 days, and total RNA was
extracted on day 6 and probed with miR-10a and 10b for Northern Blot analysis. The
treatment with 5-Aza-CdR was removed after 24 hrs, while the PBA treatment was
continuous. Methylene-blue staining revealed even loading for the samples (data not
shown).
AP3: 5-Aza-CdR 3 μM and PBA 3mM; α-amanitin 25/50: α-amanitin at 25/50 μM;
P8/P30: passage 8/30.

228
Because miR-10a and 10b lie within the HOX clusters, it is possible that their
expression is co-regulated with their upstream HOX genes. We examined the expression
of HOXB4, HOXB9, and HOXD4, which are positioned immediately up-stream of miR-
10a, 196a-1, and 10b, respectively. Both traditional RT-PCR and quantitative real time
RT-PCR results indicated that, unlike the expression of the three miRNAs, all three HOX
genes examined were strongly expressed in both the WT and the DKO(V) cells (Figure
C.3). The expression of each of the three HOX genes did not vary much between the WT
and the DKO(V) cells. The difference in the expression patterns between the miRNAs
and their upstream HOX genes suggested that the miRNAs could have their own
promoters.

At the same time, Mansfield et al. (Mansfield, Harfe et al. 2004) showed that
miR-10a and miR-196a were expressed in patterns that were markedly reminiscent of
those of Hox genes in mouse embryogenesis, and Debernardi et al. (Debernardi,
Skoulakis et al. 2007) showed that the expression of miR-10a, miR-10b and miR-196a-1
exhibited a distinctive correlation with the expression of the HOX genes in 30 primary
adult acute myeloid leukaemia (AML) with a normal karyotype. Our results and these
data suggest that while the HOX intergenic miRNAs and the HOX genes can exhibit
similar expression patterns in development, they have separate promoters and the
correlation may not be causal. It is more likely that the miRNAs and the HOX genes
share the same regulatory elements across paralog clusters (Debernardi, Skoulakis et al.
2007)

229
Figure C.3: Expression of HOXB4, HOXB9, and HOXD4 in WT and DKO(V) cells

C.3.1
WT
-RT
DKO(V)
-RT
HOXB4
HOXB9
HOXD4
GAPDH
WT
-RT
DKO(V)
-RT
HOXB4
HOXB9
HOXD4
GAPDH

Expression of HOXB4, HOXB9, and HOXD4 is similar between WT and DKO(V) cells.
RT-PCR reactions were performed at 32-35 cycles to ensure that the comparisons of
expression levels were done at a linear range, and the PCR products were analyzed on 2%
agarose gels. Representative results are shown above.

C.3.2

Expression of HOXB4, HOXB9, and HOXD4 is similar between WT and DKO(V) cells.
Quantitative real time RT-PCR analysis of the various genes was done with
normalization against the GAPDH reference gene. Representative results of two
independent RT-PCR reactions are shown above.

Expression of HOX Genes in WT vs DKO(V)
0
0.005
0.01
0.015
0.02
0.025
0.03
WT DKO (V)
Cells
Expression
HOXB4
HOXB9
HOXD4

230
Transcription start sites of miR-10a and 10b were determined by 5’ RLM-RACE
After confirming that the expression of miR-10a and 10b was indeed up-regulated
in the DKO(V) cells compared to the WT, we then performed 5’ RLM-RACE to
determine the transcription start sites (TSSs) of the miRNAs. Knowing the TSSs of the
miRNAs would help us understand which region to focus on for studying the potential
epigenetic regulation of the miRNAs, as epigenetic mechanisms usually control the
expression of a gene at its proximal promoter region (Bird 2002; Jones and Baylin 2002;
Yoo and Jones 2006). We found three TSSs for miR-10a, all close to each other, and one
TSS for miR-10b. (Figure C.4) The three TSSs for miR-10a were 180, 256, and 375 bp
upstream from the beginning of its precursor transcript, and the second TSS laid within a
transcription initiator element YYANWYY (where Y= C or T, N= A, C, G, or T, and W=
A or T). The TSS for miR-10b was 87 bp upstream from the beginning of its precursor
transcript. It would be of interest to also determine the TSSs of these miRNAs in T24
cells and to see if the miRNAs could have different TSSs in different cell lines. A small
CpG island, as determined by the CpG island searcher (Takai and Jones 2003), was
present around the TSS regions for both miR-10a and 10b (Figure C.4.B and C.4.C).

231
Figure C.4: Transcription start sites of miR-10a and 10b

C.4.A: 5’ RLM-RACE products
miR-10a
miR-10b
miRNA
-TAP
-TAP
miRNA
miR-10a
miR-10b
miRNA
-TAP
-TAP
miRNA

PCR products of 5’ RLM-RACE for miR-10a and miR-10b, using DKO(V) as the source
of mRNA, are shown above. The PCR products were cloned into TA TOPO vectors and
sequenced. A total of three TSSs were found for miR-10a, and one TSS was found for
miR-10b. -TAP: minus Tobacco Acid Pyrophosphatase control (negative control).

232
Figure C.4, Continued
C.4.B: miR-10a
>>>>>>
miR-10a
0 100
>>>>>>
miR-10a
>>>>>>
miR-10a
0 100

TCCTCCTCCC TTCGCTGAGA GGCGCTTTGG AAAATATCTA GATATTCGTT
TGATCACAAA CTAAAGGCTT GGAGGGGCAC GGAGGAGCCG ATTGGGGTTT
TGTTTTTTCT AAAAAAAAAA AAAAAAAAAA AGTCCTGGGG GGAGGGGAGG
GGCGCGAAGG CACCCAGAAA GGCCTGAATC TGACTTCGTG GCAGCCTCAG
AATGACCCTT CCTCCTTTTG TGCTTAGCTA ATGTTTACAT CTCATAATTC
ATGCGCCACC GAGAGTTGCG CGGCGGCGGC GGAGGCAAGG TTCTCGTCCC
TTTGCGAACT GGCTACTTGA AATTCTAATA GAAGAGGAGA ATTGAAAACC
TTGTAATCCC AAGAACAGAC TCGCACTGCC TTTTTCTGTT CCCAGAGCTC
AAAACTAGAA CAAAACGAAA TAAAACCAAA GCACTCAAAC CACACCCCAA
ACGAAGAAGG CGCGGAAAGT AGGAGAACTG GAAAATTTCTGGGCCAAGAA
GATCTGTCTG TCTTCTGTAT ATACCCTGTA GATCCGAATT TGTGTAAGGA
ATTTTGTGGT CACAAATTCG TATCTAGGGG AATATGTAGT TGACATAAAC
ACTCCGCTCT TATTTTTCCA GAAGAAAAAA ATATATATAT ATGTATATGT
AGTATTTTTC TGAATGAGGA CAGTCTGGTG ACTGGCCACA CGAAGACTCC

Map and genomic sequence showing the precursor and mature miR-10a. The TSSs, as
determined by 5’ RLM-RACE in DKO(V), are shown by the red bent arrows and
indicated in the sequence by blue, double-underlined, letters. Precursor miR-10a is
depicted as the red bar in the map, and the wider region represents the mature miR-10a.
The tick marks on the lower half represent individual CpG sites. The small CpG island is
labeled on the map by the green bar. In the genomic sequence the precursor miR-10a is
underlined, and the mature miR-10a is in red letters. The second TSS lies in a
transcription initiator element YYANWYY (where Y= C or T, N= A, C, G, or T, and W=
A or T)

233
Figure C.4, Continued

C.4.C: miR-10b
miR-10b
0 100
>>>>>>
miR-10b
0 100
>>>>>>

CCTTGGGATG GATTATTTTT CTTTCTTTCT TTCTTTTTTT CTTTCTTAAG
AAGAATATTC TGGTTGTTCG CCTGCTTGGT AACCCTGACC CTGGCAGAAG
AATGAGGGAA CTCATTGCTT CAAATTGTCG CCAAGCCCAT TAGGCTACCT
GAACTGTCTC AGAAAGTGCG GGTGGCTGCG TCGAACGGTG GTGGCTCAGA
GGAAGAGATT GGGGCCGGCA GCGACCTAGG TACCTCACTC TGGGTGGGAC
CCAGAGGTTG TAACGTTGTC TATATATACC CTGTAGAACC GAATTTGTGT
GGTATCCGTA TAGTCACAGA TTCGATTCTA GGGGAATATA TGGTCGATGC
AAAAACTTCA CGTTTCTTCG GAATAGCCAG AGACCAAAGT GCGACATGGA
GACTAGAAGC AGCCGGCGCT GGTCAGCCGC CTCGTTCTGT TTTATTACCT
TGGACTCCAG GAGGATCAGC TGCGCCTGGT GACATAGAGC AGCTTTTCCT

Map and genomic sequence showing the precursor and mature miR-10b. The TSS, as
determined by 5’ RLM-RACE in DKO(V), is shown by the red bent arrow and indicated
in the sequence by the blue, double-underlined, letter. Precursor miR-10b is depicted as
the red bar in the map, and the wider region represents the mature miR-10b. The tick
marks on the lower half represent individual CpG sites. The small CpG island is labeled
on the map by the green bar. In the genomic sequence the precursor miR-10b is
underlined, and the mature miR-10b is in red letters.

234
DNA methylation levels around miR10a and 10b TSS regions decrease in DKO,
DKO(V), and 5-Aza-CdR treated WT cells
After determining the TSSs of miR-10a and 10b, we then performed Ms-SNuPE
analysis to examine the DNA methylation changes around the TSS regions of these two
miRNAs in HCT116 cells (Figure C.5). The Ms-SNuPE sites analyzed are shown in
Figure C.5.A. We found that the level of DNA methylation was decreased in both
DKO(V) and DKO cells compared to the WT; in addition, treatment with 5-Aza-CdR at 1
and 3 μM also led to a dramatic decrease in DNA methylation levels in the WT cells.

Figure C.5: Ms-SNuPE analysis of miR-10a and 10b
C.5.A: Map of miR-10a and 10b and the CpG sites examined by Ms-SNuPE
>>>>
CpG island
0 100
0 100
>>>>
CpG Island
miR-10a
miR-10b
>>>>
CpG island
0 100
0 100
>>>>
CpG Island
>>>>
CpG island
0 100
0 100
>>>>
CpG Island
miR-10a
miR-10b

The TSSs, as determined by 5’ RLM-RACE in DKO(V), are shown by the red bent
arrows. The tick marks on the lower half represent individual CpG sites. The small CpG
islands are labeled on the map by the green bar. Blue straight arrows indicate the CpG
sites examined by Ms-SNuPE.

235
Figure C.5, Continued
C.5.B
Methylation Changes of miR-10a in HCT116 cells
0%
20%
40%
60%
80%
100%
WT DKO DKO(B) mock A1 P1 A3
Methylation Changes of miR-10b in HCT116 cells
0%
20%
40%
60%
80%
100%
WT DKO DKO(B) mock A1 P1 A3
DNA Methylation Changes of miR-10a in HCT116 Cells
DNA Methylation Changes of miR-10b in HCT116 Cells
WT DKO(V) DKO mock A1 P1 A3
WT DKO(V) DKO mock A1 P1 A3
Methylation Changes of miR-10a in HCT116 cells
0%
20%
40%
60%
80%
100%
WT DKO DKO(B) mock A1 P1 A3
Methylation Changes of miR-10b in HCT116 cells
0%
20%
40%
60%
80%
100%
WT DKO DKO(B) mock A1 P1 A3
DNA Methylation Changes of miR-10a in HCT116 Cells
DNA Methylation Changes of miR-10b in HCT116 Cells
WT DKO(V) DKO mock A1 P1 A3
WT DKO(V) DKO mock A1 P1 A3

Ms-SNuPE results of the methylation levels of miR-10a and miR-10b. WT HCT116
cells were treated with the various agents for 6 days. The treatment with 5-Aza-CdR was
removed after 24 hrs, while the PBA treatment was continuous. The results are shown as
the percentage methylation ± standard deviation. The percentage methylation is
calculated as the average C/(C+T) signal ratio of the separate CpG sites for each region
examined.
WT: wildtype; DKO(V): clone 8 DKO; DKO: clone 1 DKO; mock: mock treatment
with 100% EtOH; A1/3: 5-Aza-CdR 1/3 μM; P1: PBA 1mM

236
Expression of miR-10a and 10b is not regulated by DNA methylation
While the decrease in DNA methylation in DKO(V) cells compared to WT
correlated well with the expression changes for miR-10a and 10b, further analysis of the
expression in DKO cells and in 5-Aza-CdR treated WT cells by Northern Blot (Figure
C.6) suggested that miR-10a and 10b were not truly regulated by DNA methylation.
Neither miRNA was strongly expressed in the DKO cells, even though DKO cells had
lower DNA methylation levels than DKO(V) cells. Moreover, the expression of miR-10a
and 10b could not be up-regulated in the WT cells by 5-Aza-CdR treatment, despite the
fact that the treatment led to a decrease in DNA methylation levels similar to that in the
DKO(V) cells. We therefore concluded that the two miRNAs were not controlled by
DNA methylation, and the up-regulation of their expression in the DKO(V) cells was
most likely a DKO(V) cell-specific feature. It is likely that the miRNAs had been
selected to be up-regulated in the DKO(V) cells in order to cope with the decrease in
DNA methylation and the subsequent changes. We did not perform any more studies for
the miRNAs from the DKO(V) array.

237
Figure C.6: Expression of miR-10a and 10b is only up-regulated in the DKO(V) cells
WT D3
DKO(V)
DKO
DKO
A1 D3
A3 D3
WT D6
A1 D6
WT
DKO(V)
DKO
DKO
A1
A3
Mock
P1
P3
miR-10a
U6
miR-10b
WT D3
DKO(V)
DKO
DKO
A1 D3
A3 D3
WT D6
A1 D6
WT
DKO(V)
DKO
DKO
A1
A3
Mock
P1
P3
miR-10a
U6
miR-10b

Cells were treated with 5-Aza-CdR or PBA at varying concentrations for six days unless
otherwise indicated, and total RNA was extracted at the end of the treatment. The
treatment with 5-Aza-CdR was removed after 24 hrs, while the PBA treatment was
continuous. Total RNA was run on denaturing polyacrylamide gels and then transferred
onto nitrocellulose membranes. U6 was used as the loading control for the miR-10a
Northern blot. Methylene blue staining revealed even loading for the miR-10b Northern
blot (data not shown).

WT: wildtype; DKO(V): clone 8 DKO(V) cells; DKO: clone 1 DKO cells; A1/3: 5-
Aza-CdR at 1/3 μM; mock: mock treatment with 100% EtOH; P1/3: PBA at 1/3mM;
D3/6: treatment for 3/6 days.

238
CONCLUSION
A total of eight clones of DKO cells were generated by Rhee et al.(Rhee,
Bachman et al. 2002), and the different clones have differences that set them apart from
each other in addition to the decrease in DNA methylation levels. While many of the
genes and miRNAs that are up-regulated in the different DKO clones could be controlled
by DNA methylation, some of them may not be controlled directly by DNA methylation,
and their expression could be a clone-specific feature. Here we show that miR-10a and
10b are up-regulated in the DKO(V) cells, and yet their expression is not controlled by
DNA methylation. Although this study was inconclusive, it provides valuable insight
when examining other published literature data. For example, Han et al. (Han, Witmer et
al. 2007) reported that DNA methylation regulates miRNA expression, and they showed
that miR-10a was one of the differentially expressed miRNAs from their miRNA
microarray study. Although the authors did not specify which clone of DKO cells was
used in their study, it is very likely they used the DKO(V) cells instead of the other
clones, and we have clearly shown that miR-10a is not regulated by DNA methylation.
Therefore, one needs to be critical when analyzing studies that employ the HCT116 DKO
cells, as each clone is different and many of the changes in gene expression may not be
directly contributed by the decrease in DNA methylation in these cells.

239

miRNA Microarray Service

Data Summary S50024

Date: 7/28/2005

Prepared for
USC, Norris Cancer Center

Prepared by
LC Sciences, LLC

XI. Data List
Chip 1
Data Files
Folder Name H5_050258_Chip1
Data File
H5_050258-0725-w55c20-475-
495f135-let7_Data.xls
Layout File Layout_Human_V5C_050714.xls
Original Image File
H5_050258-0725-w55c20-475-
495f135-let7.tif
Processed Cy3 File
H5_050258-0725-w55c20-
475Cy3.tif
Processed Cy5 File
H5_050258-0725-w55c20-
495Cy5.tif

Assay Information
Date of Assay 7/25/2005
Chip ID H5_050258
Sample source USC, Norris Cancer Center

Channel I
Sample ID HCT116 wildtype
Sample Receiving Date 7/20/2005
Dye type Cy3
Sample Type Total RNA

Channel II
Sample ID HCT116 DKO
Sample Receiving Date 7/20/2005
Dye type Cy5
Sample Type Total RNA

241

XII. Chip Content
The content of each chip is listed in a Layout File of a corresponding folder. For
miRNA Human chip, seven redundant regions are included. Each region further
comprises a miRNA probe region, which detects miRNA transcripts listed in Sanger
miRBase Release 7.0 (http://microrna.sanger.ac.uk/sequences/), and a miRNA* probe
region (labeled as S_hsa_###), which is designed to detect miRNA*. The miRNA*
sequences are extracted from the stem-loop sequences (opposite of mature sequences)
and are not experimentally verified. These probes may be used to detect the presence
of pre-miRNAs or to facilitate any other discovery uses.
Multiple control probes are included in each chip. The control probes are used for
quality controls of chip production, sample labeling and assay conditions. Among the
control probes, PUC2PM-20B and PUC2MM-20B are the perfect match and single-
based match detection probes, respectively, of a 20-mer RNA positive control
sequence that is spiked into the RNA samples before labeling. One may assess assay
stringency from the intensity ratio of PUC2PM-20B and PUC2MM-20B, which is
normally larger than 20.
When the option for custom probes is selected, custom probes are also included.

242

XIII. Summary of Results
Following are representative regions of chips images. From Cy3 and Cy5 images one
may directly read miRNA profiles and from Ratio images one may get a quick sense
of differential expressions between the corresponding samples.

Chip 1: H5_050258
Cy3 Cy5 Ratio
miRNA* miRNA Control
LC Internal Use

Sample: HCT116 wildtype – Cy3 Sample: HCT116 DKO – Cy5 Cy3/Cy5

243
Following is the probe layout of the above images.
S-hsa-mir-1-2 S-hsa-mir-103-2 S-hsa-mir-194-1 S-hsa-mir-377 hsa-miR-1 hsa-miR-133a hsa-miR-221 hsa-miR-453
PUC2PM S-hsa-mir-1-1 S-hsa-mir-103-1 S-hsa-mir-194-2 S-hsa-mir-379 hsa-let-7a hsa-miR-133b hsa-miR-222 hsa-miR-485-5p
PUC2MM S-hsa-let-7a-1 S-hsa-mir-105-1 S-hsa-mir-195 S-hsa-mir-381 hsa-let-7b hsa-miR-134 hsa-miR-223 hsa-miR-485-3p
BKG0 S-hsa-let-7a-2 S-hsa-mir-105-2 S-hsa-mir-196a-1 S-hsa-mir-382 hsa-let-7c hsa-miR-135a hsa-miR-224 hsa-miR-488
PUC2PM-20B S-hsa-let-7a-3 S-hsa-mir-106a S-hsa-mir-196a-2 S-hsa-mir-383 hsa-let-7d hsa-miR-135b hsa-miR-296 hsa-miR-489
PUC2MM-20B S-hsa-let-7b S-hsa-mir-106b S-hsa-mir-196b S-hsa-mir-384 hsa-let-7e hsa-miR-136 hsa-miR-299-5p hsa-miR-490
S-hsa-let-7c S-hsa-mir-107 S-hsa-mir-197 S-hsa-mir-422a hsa-let-7f hsa-miR-137 hsa-miR-299-3p hsa-miR-491
a1-PUC2PM-20B S-hsa-let-7d S-hsa-mir-122a S-hsa-mir-198 S-hsa-mir-423 hsa-miR-7 hsa-miR-138 hsa-miR-301 hsa-miR-492
a1-PUC2MM-20B S-hsa-let-7e S-hsa-mir-124a-1 S-hsa-mir-199b S-hsa-mir-424 hsa-let-7g hsa-miR-139 hsa-miR-302a* hsa-miR-493
a2-PUC2PM-20B S-hsa-let-7f-1 S-hsa-mir-124a-2 S-hsa-mir-200b S-hsa-mir-425 hsa-let-7i hsa-miR-140 hsa-miR-302a hsa-miR-494
a2-PUC2MM-20B S-hsa-let-7f-2 S-hsa-mir-124a-3 S-hsa-mir-200c S-hsa-mir-429 hsa-miR-9 hsa-miR-141 hsa-miR-302b* hsa-miR-495
a3-PUC2PM-20B S-hsa-mir-7-1 S-hsa-mir-125b-1 S-hsa-mir-203 S-hsa-mir-448 hsa-miR-9* hsa-miR-142-5p hsa-miR-302b hsa-miR-496
a3-PUC2MM-20B S-hsa-mir-7-2 S-hsa-mir-125a S-hsa-mir-204 S-hsa-mir-449 hsa-miR-10a hsa-miR-142-3p hsa-miR-302c* hsa-miR-497
S-hsa-mir-7-3 S-hsa-mir-125b-2 S-hsa-mir-205 S-hsa-miR-410 hsa-miR-10b hsa-miR-143 hsa-miR-302c hsa-miR-498
S-hsa-let-7g S-hsa-mir-127 S-hsa-mir-206 S-hsa-miR-412 hsa-miR-15a hsa-miR-144 hsa-miR-302d hsa-miR-499
S-hsa-let-7i S-hsa-mir-128a S-hsa-mir-208 S-hsa-miR-431 hsa-miR-15b hsa-miR-145 hsa-miR-320 hsa-miR-500
S-hsa-mir-10a S-hsa-mir-128b S-hsa-mir-210 S-hsa-miR-433 hsa-miR-16 hsa-miR-146a hsa-miR-323 hsa-miR-501
S-hsa-mir-10b S-hsa-mir-129-1 S-hsa-mir-211 S-hsa-mir-450-1 hsa-miR-17-5p hsa-miR-146b hsa-miR-324-5p hsa-miR-502
S-hsa-mir-15a S-hsa-mir-129-2 S-hsa-mir-212 S-hsa-mir-450-2 hsa-miR-17-3p hsa-miR-147 hsa-miR-324-3p hsa-miR-503
S-hsa-mir-15b S-hsa-mir-130a S-hsa-mir-214 S-hsa-mir-451 hsa-miR-18a hsa-miR-148a hsa-miR-325 hsa-miR-504
S-hsa-mir-16-1 S-hsa-mir-130b S-hsa-mir-215 S-hsa-mir-453 hsa-miR-18b hsa-miR-148b hsa-miR-326 hsa-miR-505
a1-PUC2PM-20B S-hsa-mir-16-2 S-hsa-mir-132 S-hsa-mir-216 S-hsa-mir-488 hsa-miR-19a hsa-miR-149 hsa-miR-328 hsa-miR-506
a1-PUC2MM-20B S-hsa-mir-18a S-hsa-mir-133a-1 S-hsa-mir-217 S-hsa-mir-489 hsa-miR-19b hsa-miR-150 hsa-miR-329 hsa-miR-507
a2-PUC2PM-20B S-hsa-mir-18b S-hsa-mir-133a-2 S-hsa-mir-218-1 S-hsa-mir-490 hsa-miR-20a hsa-miR-151 hsa-miR-330 hsa-miR-508
a2-PUC2MM-20B S-hsa-mir-19a S-hsa-mir-133b S-hsa-mir-218-2 S-hsa-mir-491 hsa-miR-20b hsa-miR-152 hsa-miR-331 hsa-miR-509
a3-PUC2PM-20B S-hsa-mir-19b-1 S-hsa-mir-134 S-hsa-mir-219-1 S-hsa-mir-492 hsa-miR-21 hsa-miR-153 hsa-miR-335 hsa-miR-510
a3-PUC2MM-20B S-hsa-mir-19b-2 S-hsa-mir-135a-1 S-hsa-mir-219-2 S-hsa-mir-493 hsa-miR-22 hsa-miR-154 hsa-miR-337 hsa-miR-511
PUC2PM-20B S-hsa-mir-20a S-hsa-mir-135a-2 S-hsa-mir-220 S-hsa-mir-494 hsa-miR-23a hsa-miR-154* hsa-miR-338 hsa-miR-512-5p
PUC2MM-20B S-hsa-mir-20b S-hsa-mir-135b S-hsa-mir-221 S-hsa-mir-495 hsa-miR-23b hsa-miR-155 hsa-miR-339 hsa-miR-512-3p
BKG0 S-hsa-mir-21 S-hsa-mir-136 S-hsa-mir-222 S-hsa-mir-496 hsa-miR-24 hsa-miR-181a hsa-miR-340 hsa-miR-513
PUC2PM S-hsa-mir-22 S-hsa-mir-137 S-hsa-mir-223 S-hsa-mir-497 hsa-miR-25 hsa-miR-181b hsa-miR-342 hsa-miR-514
PUC2MM S-hsa-mir-23a S-hsa-mir-138-2 S-hsa-mir-224 S-hsa-mir-498 hsa-miR-26a hsa-miR-181c hsa-miR-345 hsa-miR-515-5p
S-hsa-mir-23b S-hsa-mir-138-1 S-hsa-mir-296 S-hsa-mir-499 hsa-miR-26b hsa-miR-181d hsa-miR-346 hsa-miR-515-3p
a1-PUC2PM-20B S-hsa-mir-24-2 S-hsa-mir-139 S-hsa-mir-301 S-hsa-mir-500 hsa-miR-27a hsa-miR-182 hsa-miR-361 hsa-miR-516-5p
a1-PUC2MM-20B S-hsa-mir-25 S-hsa-mir-140 S-hsa-mir-302d S-hsa-mir-501 hsa-miR-27b hsa-miR-182* hsa-miR-362 hsa-miR-516-3p
a2-PUC2PM-20B S-hsa-mir-26a-1 S-hsa-mir-141 S-hsa-mir-320 S-hsa-mir-502 hsa-miR-28 hsa-miR-183 hsa-miR-363 hsa-miR-517*
a2-PUC2MM-20B S-hsa-mir-26b S-hsa-mir-143 S-hsa-mir-323 S-hsa-mir-503 hsa-miR-29a hsa-miR-184 hsa-miR-365 hsa-miR-517a
a3-PUC2PM-20B S-hsa-mir-26a-2 S-hsa-mir-144 S-hsa-mir-325 S-hsa-mir-504 hsa-miR-29b hsa-miR-185 hsa-miR-367 hsa-miR-517b
a3-PUC2MM-20B S-hsa-mir-27a S-hsa-mir-145 S-hsa-mir-326 S-hsa-mir-505 hsa-miR-29c hsa-miR-186 hsa-miR-368 hsa-miR-517c
S-hsa-mir-27b S-hsa-mir-146a S-hsa-mir-328 S-hsa-mir-506 hsa-miR-30a-5p hsa-miR-187 hsa-miR-369-5p hsa-miR-518f*
S-hsa-mir-28 S-hsa-mir-146b S-hsa-mir-329-1 S-hsa-mir-507 hsa-miR-30a-3p hsa-miR-188 hsa-miR-369-3p hsa-miR-518f
5S-rRNA-a1 S-hsa-mir-29a S-hsa-mir-147 S-hsa-mir-329-2 S-hsa-mir-508 hsa-miR-30c hsa-miR-189 hsa-miR-370 hsa-miR-518b
5S-rRNA-a2 S-hsa-mir-29b-1 S-hsa-mir-148a S-hsa-mir-330 S-hsa-mir-509 hsa-miR-30d hsa-miR-190 hsa-miR-371 hsa-miR-518c*
5S-rRNA-a3 S-hsa-mir-29b-2 S-hsa-mir-148b S-hsa-mir-331 S-hsa-mir-510 hsa-miR-30b hsa-miR-191 hsa-miR-372 hsa-miR-518c
5S-rRNA-a4 S-hsa-mir-29c S-hsa-mir-149 S-hsa-mir-335 S-hsa-mir-511-1 hsa-miR-30e-5p hsa-miR-191* hsa-miR-373* hsa-miR-518e
5S-rRNA-a5 S-hsa-mir-30c-2 S-hsa-mir-150 S-hsa-mir-337 S-hsa-mir-511-2 hsa-miR-30e-3p hsa-miR-192 hsa-miR-373 hsa-miR-518a
5S-rRNA-a6 S-hsa-mir-30d S-hsa-mir-151 S-hsa-mir-338 S-hsa-mir-513-1 hsa-miR-31 hsa-miR-193a hsa-miR-374 hsa-miR-518d
5S-rRNA-a7 S-hsa-mir-30b S-hsa-mir-152 S-hsa-mir-339 S-hsa-mir-513-2 hsa-miR-32 hsa-miR-193b hsa-miR-375 hsa-miR-518a-2*
5S-rRNA-a8 S-hsa-mir-30c-1 S-hsa-mir-153-1 S-hsa-mir-340 S-hsa-mir-514-1 hsa-miR-33 hsa-miR-194 hsa-miR-376a hsa-miR-519e*
5S-rRNA-a9 S-hsa-mir-31 S-hsa-mir-153-2 S-hsa-mir-342 S-hsa-mir-514-2 hsa-miR-34a hsa-miR-195 hsa-miR-376b hsa-miR-519e
5S-rRNA-a10 S-hsa-mir-32 S-hsa-mir-155 S-hsa-mir-345 S-hsa-mir-514-3 hsa-miR-34b hsa-miR-196a hsa-miR-377 hsa-miR-519c
S-hsa-mir-33 S-hsa-mir-181a S-hsa-mir-346 S-hsa-mir-516-1 hsa-miR-34c hsa-miR-196b hsa-miR-378 hsa-miR-519b
DM5880-a1 S-hsa-mir-34a S-hsa-mir-181b-1 S-hsa-mir-361 S-hsa-mir-516-2 hsa-miR-92 hsa-miR-197 hsa-miR-379 hsa-miR-519d
DM5881-a1 S-hsa-mir-34b S-hsa-mir-181c S-hsa-mir-362 S-hsa-mir-518b hsa-miR-93 hsa-miR-198 hsa-miR-380-5p hsa-miR-519a
DM5880-a2 S-hsa-mir-34c S-hsa-mir-181b-2 S-hsa-mir-363 S-hsa-mir-519a-2 hsa-miR-95 hsa-miR-199a hsa-miR-380-3p hsa-miR-520e
DM5881-a2 S-hsa-mir-92-1 S-hsa-mir-181d S-hsa-mir-365-1 S-hsa-mir-519d hsa-miR-96 hsa-miR-199a* hsa-miR-381 hsa-miR-520f
DM5880-a3 S-hsa-mir-92-2 S-hsa-mir-183 S-hsa-mir-365-2 S-hsa-mir-520b hsa-miR-98 hsa-miR-199b hsa-miR-382 hsa-miR-520a*
DM5881-a3 S-hsa-mir-93 S-hsa-mir-184 S-hsa-mir-367 S-hsa-mir-520e hsa-miR-99a hsa-miR-200b hsa-miR-383 hsa-miR-520a
S-hsa-mir-95 S-hsa-mir-185 S-hsa-mir-368 S-hsa-mir-520f hsa-miR-99b hsa-miR-200c hsa-miR-384 hsa-miR-520b
S-hsa-mir-96 S-hsa-mir-186 S-hsa-mir-370 S-hsa-mir-520g hsa-miR-100 hsa-miR-200a* hsa-miR-409-5p hsa-miR-520c
S-hsa-mir-98 S-hsa-mir-187 S-hsa-mir-371 S-hsa-mir-520h hsa-miR-101 hsa-miR-200a hsa-miR-409-3p hsa-miR-520d*
S-hsa-mir-99a S-hsa-mir-188 S-hsa-mir-372 S-hsa-mir-521-1 hsa-miR-103 hsa-miR-202* hsa-miR-410 hsa-miR-520d
S-hsa-mir-99b S-hsa-mir-190 S-hsa-mir-374 S-hsa-mir-521-2 hsa-miR-105 hsa-miR-202 hsa-miR-412 hsa-miR-520g
S-hsa-mir-100 S-hsa-mir-192 S-hsa-mir-375 S-hsa-mir-526a-1 hsa-miR-106a hsa-miR-203 hsa-miR-422b hsa-miR-520h
S-hsa-mir-101-1 S-hsa-mir-193a S-hsa-mir-376a S-hsa-mir-526a-2 hsa-miR-106b hsa-miR-204 hsa-miR-422a hsa-miR-521
S-hsa-mir-101-2 S-hsa-mir-193b S-hsa-mir-376b S-hsa-mir-527 hsa-miR-107 hsa-miR-205 hsa-miR-423 hsa-miR-522
hsa-miR-122a hsa-miR-206 hsa-miR-424 hsa-miR-523
5S-rRNA-1 DG9990-3 DP5880-3 DT9990-3 hsa-miR-124a hsa-miR-208 hsa-miR-425 hsa-miR-524*
5S-rRNA-2 DG9992-3 DP9990-3 DT9991-3 hsa-miR-125b hsa-miR-210 hsa-miR-429 hsa-miR-524
5S-rRNA-3 DH5880-3 DQ5880-3 DT9992-3 hsa-miR-125a hsa-miR-211 hsa-miR-431 hsa-miR-525
5S-rRNA-4 DI5880-3 DQ9990-3 DV5880-3 hsa-miR-126* hsa-miR-212 hsa-miR-432 hsa-miR-525*
a1-PUC2PM-20B 5S-rRNA-5 DK5880-3 DQ9991-3 DV9990-3 hsa-miR-126 hsa-miR-213 hsa-miR-432* hsa-miR-526c
a1-PUC2MM-20B 5S-rRNA-6 DK9990-3 DQ9992-3 DV9993-3 hsa-miR-127 hsa-miR-214 hsa-miR-433 hsa-miR-526b
a2-PUC2PM-20B DA5880-3 DK9991-3 DR5880-3 DV9994-3 hsa-miR-128a hsa-miR-215 hsa-miR-448 hsa-miR-526b*
a2-PUC2MM-20B DA5881-3 DL5880-3 DR5881-3 DV9995-3 hsa-miR-128b hsa-miR-216 hsa-miR-449 hsa-miR-526a
a3-PUC2PM-20B DC5880-3 DL5881-3 DR9990-3 DW5880-3 hsa-miR-129 hsa-miR-217 hsa-miR-450 hsa-miR-527
a3-PUC2MM-20B DD5880-3 DL9990-3 DS5880-3 DX5880-3 hsa-miR-130a hsa-miR-218 hsa-miR-451
PUC2PM-20B DE5880-3 DM5880-3 DS5881-3 DX9990-3 hsa-miR-130b hsa-miR-219 hsa-miR-452
PUC2MM-20B DE5881-3 DM5881-3 DS9990-3 DX9991-3 hsa-miR-132 hsa-miR-220 hsa-miR-452*
BKG0 DE9990-3 DN5880-3 DS9993-3 DY5880-3
PUC2PM DF5880-3 DN9990-3 DS9994-3 DY9990-3
PUC2MM DG5880-3 DN9991-3 DT5880-3 DY9991-3

XIV. Courtesy Data Analysis
In the data files, we provide the result of a courtesy data analysis. From the file, you
can quickly find a list of up and down regulated transcripts in the worksheet
“Summary Data” and “Simple List”. All the transcripts listed in the tables have
statistically significant difference between two samples labeled in Cy3 and Cy5.
Calls are made on the spots with p-values less than 0.01. Global normalization is
used in the data process, unless a different normalization method is requested.
When color reversal assays are ordered, data analysis is performed to correlate ratios
of called transcripts of the pairing chips.

244

XV. Suggestions for Data Analysis
In case you want to perform your own data analysis we have the following
suggestions.
6. Background should be calculated from the median of 5% to 25% of low intensity
cells. BKG0 and blank cells should be excluded for the background calculation.
7. All “Production_Use_Probes” (including BKG0, PUC2 …,) and blank cells
which are listed in a supplied layout file, should be excluded during data
normalization.

245

Human_V5C_050714 - Based on Sanger miRBase Release 7.0

Probe Information
Probe Name Correspondin
g miRNA
Target Sequence (5'
to 3')
Note
Production Use Probes
BKG0 LC Internal Use Quality Control
PUC2PM LC Internal Use Quality Control Perfect
Match
PUC2MM LC Internal Use Quality Control Mismatch
PUC2PM-20B LC Internal Use Positive Control Perfect
Match (spiking RNA)
PUC2MM-20B LC Internal Use Positive Control Mismatch
a1-PUC2PM-20B LC Internal Use Positive Control Perfect
Match (spiking RNA)
a1-PUC2MM-20B LC Internal Use Positive Control Mismatch
a2-PUC2PM-20B LC Internal Use Positive Control Perfect
Match (spiking RNA)
a2-PUC2MM-20B LC Internal Use Positive Control Mismatch
a3-PUC2PM-20B LC Internal Use Positive Control Perfect
Match (spiking RNA)
a3-PUC2MM-20B LC Internal Use Positive Control Mismatch
miRNA Probe
hsa-miR-1 hsa-miR-1 UGGAAUGUAAAGAA
GUAUGUA
Detection probe for miRNA
sequence
hsa-let-7a hsa-let-7a UGAGGUAGUAGGUU
GUAUAGUU
Detection probe for miRNA
sequence
hsa-let-7b hsa-let-7b UGAGGUAGUAGGUU
GUGUGGUU
Detection probe for miRNA
sequence
hsa-let-7c hsa-let-7c UGAGGUAGUAGGUU
GUAUGGUU
Detection probe for miRNA
sequence
hsa-let-7d hsa-let-7d AGAGGUAGUAGGUU
GCAUAGU
Detection probe for miRNA
sequence
hsa-let-7e hsa-let-7e UGAGGUAGGAGGUU
GUAUAGU
Detection probe for miRNA
sequence
hsa-let-7f hsa-let-7f UGAGGUAGUAGAUU
GUAUAGUU
Detection probe for miRNA
sequence
hsa-miR-7 hsa-miR-7 UGGAAGACUAGUGA
UUUUGUUG
Detection probe for miRNA
sequence
hsa-let-7g hsa-let-7g UGAGGUAGUAGUUU
GUACAGU
Detection probe for miRNA
sequence
hsa-let-7i hsa-let-7i UGAGGUAGUAGUUU
GUGCUGU
Detection probe for miRNA
sequence
hsa-miR-9 hsa-miR-9 UCUUUGGUUAUCUAG
CUGUAUGA
Detection probe for miRNA
sequence
hsa-miR-9* hsa-miR-9* UAAAGCUAGAUAACC
GAAAGU
Detection probe for miRNA
sequence

246
hsa-miR-10a hsa-miR-10a UACCCUGUAGAUCCG
AAUUUGUG
Detection probe for miRNA
sequence
hsa-miR-10b hsa-miR-10b UACCCUGUAGAACCG
AAUUUGU
Detection probe for miRNA
sequence
hsa-miR-15a hsa-miR-15a UAGCAGCACAUAAUG
GUUUGUG
Detection probe for miRNA
sequence
hsa-miR-15b hsa-miR-15b UAGCAGCACAUCAUG
GUUUACA
Detection probe for miRNA
sequence
hsa-miR-16 hsa-miR-16 UAGCAGCACGUAAAU
AUUGGCG
Detection probe for miRNA
sequence
hsa-miR-17-5p hsa-miR-17-5p CAAAGUGCUUACAGU
GCAGGUAGU
Detection probe for miRNA
sequence
hsa-miR-17-3p hsa-miR-17-3p ACUGCAGUGAAGGCA
CUUGU
Detection probe for miRNA
sequence
hsa-miR-18a hsa-miR-18a UAAGGUGCAUCUAGU
GCAGAUA
Detection probe for miRNA
sequence
hsa-miR-18b hsa-miR-18b UAAGGUGCAUCUAGU
GCAGUUA
Detection probe for miRNA
sequence
hsa-miR-19a hsa-miR-19a UGUGCAAAUCUAUGC
AAAACUGA
Detection probe for miRNA
sequence
hsa-miR-19b hsa-miR-19b UGUGCAAAUCCAUGC
AAAACUGA
Detection probe for miRNA
sequence
hsa-miR-20a hsa-miR-20a UAAAGUGCUUAUAG
UGCAGGUAG
Detection probe for miRNA
sequence
hsa-miR-20b hsa-miR-20b CAAAGUGCUCAUAGU
GCAGGUAG
Detection probe for miRNA
sequence
hsa-miR-21 hsa-miR-21 UAGCUUAUCAGACUG
AUGUUGA
Detection probe for miRNA
sequence
hsa-miR-22 hsa-miR-22 AAGCUGCCAGUUGAA
GAACUGU
Detection probe for miRNA
sequence
hsa-miR-23a hsa-miR-23a AUCACAUUGCCAGGG
AUUUCC
Detection probe for miRNA
sequence
hsa-miR-23b hsa-miR-23b AUCACAUUGCCAGGG
AUUACC
Detection probe for miRNA
sequence
hsa-miR-24 hsa-miR-24 UGGCUCAGUUCAGCA
GGAACAG
Detection probe for miRNA
sequence
hsa-miR-25 hsa-miR-25 CAUUGCACUUGUCUC
GGUC
Detection probe for miRNA
sequence
hsa-miR-26a hsa-miR-26a UUCAAGUAAUCCAGG
AUAGGC
Detection probe for miRNA
sequence
hsa-miR-26b hsa-miR-26b UUCAAGUAAUUCAGG
AUAGGUU
Detection probe for miRNA
sequence
hsa-miR-27a hsa-miR-27a UUCACAGUGGCUAAG
UUCCGC
Detection probe for miRNA
sequence
hsa-miR-27b hsa-miR-27b UUCACAGUGGCUAAG
UUCUGC
Detection probe for miRNA
sequence
hsa-miR-28 hsa-miR-28 AAGGAGCUCACAGUC
UAUUGAG
Detection probe for miRNA
sequence
hsa-miR-29a hsa-miR-29a UAGCACCAUCUGAAA
UCGGUU
Detection probe for miRNA
sequence
hsa-miR-29b hsa-miR-29b UAGCACCAUUUGAAA
UCAGUGUU
Detection probe for miRNA
sequence
hsa-miR-29c hsa-miR-29c UAGCACCAUUUGAAA Detection probe for miRNA

247
UCGGU sequence
hsa-miR-30a-5p hsa-miR-30a-5p UGUAAACAUCCUCGA
CUGGAAG
Detection probe for miRNA
sequence
hsa-miR-30a-3p hsa-miR-30a-3p CUUUCAGUCGGAUGU
UUGCAGC
Detection probe for miRNA
sequence
hsa-miR-30c hsa-miR-30c UGUAAACAUCCUACA
CUCUCAGC
Detection probe for miRNA
sequence
hsa-miR-30d hsa-miR-30d UGUAAACAUCCCCGA
CUGGAAG
Detection probe for miRNA
sequence
hsa-miR-30b hsa-miR-30b UGUAAACAUCCUACA
CUCAGCU
Detection probe for miRNA
sequence
hsa-miR-30e-5p hsa-miR-30e-5p UGUAAACAUCCUUGA
CUGGA
Detection probe for miRNA
sequence
hsa-miR-30e-3p hsa-miR-30e-3p CUUUCAGUCGGAUGU
UUACAGC
Detection probe for miRNA
sequence
hsa-miR-31 hsa-miR-31 GGCAAGAUGCUGGCA
UAGCUG
Detection probe for miRNA
sequence
hsa-miR-32 hsa-miR-32 UAUUGCACAUUACUA
AGUUGC
Detection probe for miRNA
sequence
hsa-miR-33 hsa-miR-33 GUGCAUUGUAGUUGC
AUUG
Detection probe for miRNA
sequence
hsa-miR-34a hsa-miR-34a UGGCAGUGUCUUAGC
UGGUUGUU
Detection probe for miRNA
sequence
hsa-miR-34b hsa-miR-34b UAGGCAGUGUCAUUA
GCUGAUUG
Detection probe for miRNA
sequence
hsa-miR-34c hsa-miR-34c AGGCAGUGUAGUUA
GCUGAUUGC
Detection probe for miRNA
sequence
hsa-miR-92 hsa-miR-92 UAUUGCACUUGUCCC
GGCCUG
Detection probe for miRNA
sequence
hsa-miR-93 hsa-miR-93 AAAGUGCUGUUCGUG
CAGGUAG
Detection probe for miRNA
sequence
hsa-miR-95 hsa-miR-95 UUCAACGGGUAUUUA
UUGAGCA
Detection probe for miRNA
sequence
hsa-miR-96 hsa-miR-96 UUUGGCACUAGCACA
UUUUUGC
Detection probe for miRNA
sequence
hsa-miR-98 hsa-miR-98 UGAGGUAGUAAGUU
GUAUUGUU
Detection probe for miRNA
sequence
hsa-miR-99a hsa-miR-99a AACCCGUAGAUCCGA
UCUUGUG
Detection probe for miRNA
sequence
hsa-miR-99b hsa-miR-99b CACCCGUAGAACCGA
CCUUGCG
Detection probe for miRNA
sequence
hsa-miR-100 hsa-miR-100 AACCCGUAGAUCCGA
ACUUGUG
Detection probe for miRNA
sequence
hsa-miR-101 hsa-miR-101 UACAGUACUGUGAUA
ACUGAAG
Detection probe for miRNA
sequence
hsa-miR-103 hsa-miR-103 AGCAGCAUUGUACAG
GGCUAUGA
Detection probe for miRNA
sequence
hsa-miR-105 hsa-miR-105 UCAAAUGCUCAGACU
CCUGU
Detection probe for miRNA
sequence
hsa-miR-106a hsa-miR-106a AAAAGUGCUUACAGU
GCAGGUAGC
Detection probe for miRNA
sequence
hsa-miR-106b hsa-miR-106b UAAAGUGCUGACAGU
GCAGAU
Detection probe for miRNA
sequence

248
hsa-miR-107 hsa-miR-107 AGCAGCAUUGUACAG
GGCUAUCA
Detection probe for miRNA
sequence
hsa-miR-122a hsa-miR-122a UGGAGUGUGACAAU
GGUGUUUGU
Detection probe for miRNA
sequence
hsa-miR-124a hsa-miR-124a UUAAGGCACGCGGUG
AAUGCCA
Detection probe for miRNA
sequence
hsa-miR-125b hsa-miR-125b UCCCUGAGACCCUAA
CUUGUGA
Detection probe for miRNA
sequence
hsa-miR-125a hsa-miR-125a UCCCUGAGACCCUUU
AACCUGUG
Detection probe for miRNA
sequence
hsa-miR-126* hsa-miR-126* CAUUAUUACUUUUGG
UACGCG
Detection probe for miRNA
sequence
hsa-miR-126 hsa-miR-126 UCGUACCGUGAGUAA
UAAUGC
Detection probe for miRNA
sequence
hsa-miR-127 hsa-miR-127 UCGGAUCCGUCUGAG
CUUGGCU
Detection probe for miRNA
sequence
hsa-miR-128a hsa-miR-128a UCACAGUGAACCGGU
CUCUUUU
Detection probe for miRNA
sequence
hsa-miR-128b hsa-miR-128b UCACAGUGAACCGGU
CUCUUUC
Detection probe for miRNA
sequence
hsa-miR-129 hsa-miR-129 CUUUUUGCGGUCUGG
GCUUGC
Detection probe for miRNA
sequence
hsa-miR-130a hsa-miR-130a CAGUGCAAUGUUAAA
AGGGCAU
Detection probe for miRNA
sequence
hsa-miR-130b hsa-miR-130b CAGUGCAAUGAUGAA
AGGGCAU
Detection probe for miRNA
sequence
hsa-miR-132 hsa-miR-132 UAACAGUCUACAGCC
AUGGUCG
Detection probe for miRNA
sequence
hsa-miR-133a hsa-miR-133a UUGGUCCCCUUCAAC
CAGCUGU
Detection probe for miRNA
sequence
hsa-miR-133b hsa-miR-133b UUGGUCCCCUUCAAC
CAGCUA
Detection probe for miRNA
sequence
hsa-miR-134 hsa-miR-134 UGUGACUGGUUGACC
AGAGGG
Detection probe for miRNA
sequence
hsa-miR-135a hsa-miR-135a UAUGGCUUUUUAUUC
CUAUGUGA
Detection probe for miRNA
sequence
hsa-miR-135b hsa-miR-135b UAUGGCUUUUCAUUC
CUAUGUG
Detection probe for miRNA
sequence
hsa-miR-136 hsa-miR-136 ACUCCAUUUGUUUUG
AUGAUGGA
Detection probe for miRNA
sequence
hsa-miR-137 hsa-miR-137 UAUUGCUUAAGAAU
ACGCGUAG
Detection probe for miRNA
sequence
hsa-miR-138 hsa-miR-138 AGCUGGUGUUGUGA
AUC
Detection probe for miRNA
sequence
hsa-miR-139 hsa-miR-139 UCUACAGUGCACGUG
UCU
Detection probe for miRNA
sequence
hsa-miR-140 hsa-miR-140 AGUGGUUUUACCCUA
UGGUAG
Detection probe for miRNA
sequence
hsa-miR-141 hsa-miR-141 UAACACUGUCUGGUA
AAGAUGG
Detection probe for miRNA
sequence
hsa-miR-142-5p hsa-miR-142-5p CAUAAAGUAGAAAGC
ACUAC
Detection probe for miRNA
sequence
hsa-miR-142-3p hsa-miR-142-3p UGUAGUGUUUCCUAC Detection probe for miRNA

249
UUUAUGGA sequence
hsa-miR-143 hsa-miR-143 UGAGAUGAAGCACUG
UAGCUCA
Detection probe for miRNA
sequence
hsa-miR-144 hsa-miR-144 UACAGUAUAGAUGA
UGUACUAG
Detection probe for miRNA
sequence
hsa-miR-145 hsa-miR-145 GUCCAGUUUUCCCAG
GAAUCCCUU
Detection probe for miRNA
sequence
hsa-miR-146a hsa-miR-146a UGAGAACUGAAUUCC
AUGGGUU
Detection probe for miRNA
sequence
hsa-miR-146b hsa-miR-146b UGAGAACUGAAUUCC
AUAGGCU
Detection probe for miRNA
sequence
hsa-miR-147 hsa-miR-147 GUGUGUGGAAAUGC
UUCUGC
Detection probe for miRNA
sequence
hsa-miR-148a hsa-miR-148a UCAGUGCACUACAGA
ACUUUGU
Detection probe for miRNA
sequence
hsa-miR-148b hsa-miR-148b UCAGUGCAUCACAGA
ACUUUGU
Detection probe for miRNA
sequence
hsa-miR-149 hsa-miR-149 UCUGGCUCCGUGUCU
UCACUCC
Detection probe for miRNA
sequence
hsa-miR-150 hsa-miR-150 UCUCCCAACCCUUGU
ACCAGUG
Detection probe for miRNA
sequence
hsa-miR-151 hsa-miR-151 ACUAGACUGAAGCUC
CUUGAGG
Detection probe for miRNA
sequence
hsa-miR-152 hsa-miR-152 UCAGUGCAUGACAGA
ACUUGGG
Detection probe for miRNA
sequence
hsa-miR-153 hsa-miR-153 UUGCAUAGUCACAAA
AGUGA
Detection probe for miRNA
sequence
hsa-miR-154 hsa-miR-154 UAGGUUAUCCGUGUU
GCCUUCG
Detection probe for miRNA
sequence
hsa-miR-154* hsa-miR-154* AAUCAUACACGGUUG
ACCUAUU
Detection probe for miRNA
sequence
hsa-miR-155 hsa-miR-155 UUAAUGCUAAUCGUG
AUAGGGG
Detection probe for miRNA
sequence
hsa-miR-181a hsa-miR-181a AACAUUCAACGCUGU
CGGUGAGU
Detection probe for miRNA
sequence
hsa-miR-181b hsa-miR-181b AACAUUCAUUGCUGU
CGGUGGG
Detection probe for miRNA
sequence
hsa-miR-181c hsa-miR-181c AACAUUCAACCUGUC
GGUGAGU
Detection probe for miRNA
sequence
hsa-miR-181d hsa-miR-181d AACAUUCAUUGUUGU
CGGUGGGUU
Detection probe for miRNA
sequence
hsa-miR-182 hsa-miR-182 UUUGGCAAUGGUAG
AACUCACA
Detection probe for miRNA
sequence
hsa-miR-182* hsa-miR-182* UGGUUCUAGACUUGC
CAACUA
Detection probe for miRNA
sequence
hsa-miR-183 hsa-miR-183 UAUGGCACUGGUAGA
AUUCACUG
Detection probe for miRNA
sequence
hsa-miR-184 hsa-miR-184 UGGACGGAGAACUGA
UAAGGGU
Detection probe for miRNA
sequence
hsa-miR-185 hsa-miR-185 UGGAGAGAAAGGCA
GUUC
Detection probe for miRNA
sequence
hsa-miR-186 hsa-miR-186 CAAAGAAUUCUCCUU
UUGGGCUU
Detection probe for miRNA
sequence

250
hsa-miR-187 hsa-miR-187 UCGUGUCUUGUGUUG
CAGCCG
Detection probe for miRNA
sequence
hsa-miR-188 hsa-miR-188 CAUCCCUUGCAUGGU
GGAGGGU
Detection probe for miRNA
sequence
hsa-miR-189 hsa-miR-189 GUGCCUACUGAGCUG
AUAUCAGU
Detection probe for miRNA
sequence
hsa-miR-190 hsa-miR-190 UGAUAUGUUUGAUA
UAUUAGGU
Detection probe for miRNA
sequence
hsa-miR-191 hsa-miR-191 CAACGGAAUCCCAAA
AGCAGCU
Detection probe for miRNA
sequence
hsa-miR-191* hsa-miR-191* GCUGCGCUUGGAUUU
CGUCCCC
Detection probe for miRNA
sequence
hsa-miR-192 hsa-miR-192 CUGACCUAUGAAUUG
ACAGCC
Detection probe for miRNA
sequence
hsa-miR-193a hsa-miR-193a AACUGGCCUACAAAG
UCCCAG
Detection probe for miRNA
sequence
hsa-miR-193b hsa-miR-193b AACUGGCCCUCAAAG
UCCCGCUUU
Detection probe for miRNA
sequence
hsa-miR-194 hsa-miR-194 UGUAACAGCAACUCC
AUGUGGA
Detection probe for miRNA
sequence
hsa-miR-195 hsa-miR-195 UAGCAGCACAGAAAU
AUUGGC
Detection probe for miRNA
sequence
hsa-miR-196a hsa-miR-196a UAGGUAGUUUCAUG
UUGUUGG
Detection probe for miRNA
sequence
hsa-miR-196b hsa-miR-196b UAGGUAGUUUCCUGU
UGUUGG
Detection probe for miRNA
sequence
hsa-miR-197 hsa-miR-197 UUCACCACCUUCUCC
ACCCAGC
Detection probe for miRNA
sequence
hsa-miR-198 hsa-miR-198 GGUCCAGAGGGGAGA
UAGG
Detection probe for miRNA
sequence
hsa-miR-199a hsa-miR-199a CCCAGUGUUCAGACU
ACCUGUUC
Detection probe for miRNA
sequence
hsa-miR-199a* hsa-miR-199a* UACAGUAGUCUGCAC
AUUGGUU
Detection probe for miRNA
sequence
hsa-miR-199b hsa-miR-199b CCCAGUGUUUAGACU
AUCUGUUC
Detection probe for miRNA
sequence
hsa-miR-200b hsa-miR-200b UAAUACUGCCUGGUA
AUGAUGAC
Detection probe for miRNA
sequence
hsa-miR-200c hsa-miR-200c UAAUACUGCCGGGUA
AUGAUGG
Detection probe for miRNA
sequence
hsa-miR-200a* hsa-miR-200a* CAUCUUACCGGACAG
UGCUGGA
Detection probe for miRNA
sequence
hsa-miR-200a hsa-miR-200a UAACACUGUCUGGUA
ACGAUGU
Detection probe for miRNA
sequence
hsa-miR-202* hsa-miR-202* UUUCCUAUGCAUAUA
CUUCUUU
Detection probe for miRNA
sequence
hsa-miR-202 hsa-miR-202 AGAGGUAUAGGGCA
UGGGAAAA
Detection probe for miRNA
sequence
hsa-miR-203 hsa-miR-203 GUGAAAUGUUUAGG
ACCACUAG
Detection probe for miRNA
sequence
hsa-miR-204 hsa-miR-204 UUCCCUUUGUCAUCC
UAUGCCU
Detection probe for miRNA
sequence
hsa-miR-205 hsa-miR-205 UCCUUCAUUCCACCG Detection probe for miRNA

251
GAGUCUG sequence
hsa-miR-206 hsa-miR-206 UGGAAUGUAAGGAA
GUGUGUGG
Detection probe for miRNA
sequence
hsa-miR-208 hsa-miR-208 AUAAGACGAGCAAAA
AGCUUGU
Detection probe for miRNA
sequence
hsa-miR-210 hsa-miR-210 CUGUGCGUGUGACAG
CGGCUGA
Detection probe for miRNA
sequence
hsa-miR-211 hsa-miR-211 UUCCCUUUGUCAUCC
UUCGCCU
Detection probe for miRNA
sequence
hsa-miR-212 hsa-miR-212 UAACAGUCUCCAGUC
ACGGCC
Detection probe for miRNA
sequence
hsa-miR-213 hsa-miR-213 ACCAUCGACCGUUGA
UUGUACC
Detection probe for miRNA
sequence
hsa-miR-214 hsa-miR-214 ACAGCAGGCACAGAC
AGGCAG
Detection probe for miRNA
sequence
hsa-miR-215 hsa-miR-215 AUGACCUAUGAAUUG
ACAGAC
Detection probe for miRNA
sequence
hsa-miR-216 hsa-miR-216 UAAUCUCAGCUGGCA
ACUGUG
Detection probe for miRNA
sequence
hsa-miR-217 hsa-miR-217 UACUGCAUCAGGAAC
UGAUUGGAU
Detection probe for miRNA
sequence
hsa-miR-218 hsa-miR-218 UUGUGCUUGAUCUAA
CCAUGU
Detection probe for miRNA
sequence
hsa-miR-219 hsa-miR-219 UGAUUGUCCAAACGC
AAUUCU
Detection probe for miRNA
sequence
hsa-miR-220 hsa-miR-220 CCACACCGUAUCUGA
CACUUU
Detection probe for miRNA
sequence
hsa-miR-221 hsa-miR-221 AGCUACAUUGUCUGC
UGGGUUUC
Detection probe for miRNA
sequence
hsa-miR-222 hsa-miR-222 AGCUACAUCUGGCUA
CUGGGUCUC
Detection probe for miRNA
sequence
hsa-miR-223 hsa-miR-223 UGUCAGUUUGUCAAA
UACCCC
Detection probe for miRNA
sequence
hsa-miR-224 hsa-miR-224 CAAGUCACUAGUGGU
UCCGUUUA
Detection probe for miRNA
sequence
hsa-miR-296 hsa-miR-296 AGGGCCCCCCCUCAA
UCCUGU
Detection probe for miRNA
sequence
hsa-miR-299-5p hsa-miR-299-5p UGGUUUACCGUCCCA
CAUACAU
Detection probe for miRNA
sequence
hsa-miR-299-3p hsa-miR-299-3p UAUGUGGGAUGGUA
AACCGCUU
Detection probe for miRNA
sequence
hsa-miR-301 hsa-miR-301 CAGUGCAAUAGUAUU
GUCAAAGC
Detection probe for miRNA
sequence
hsa-miR-302a* hsa-miR-302a* UAAACGUGGAUGUAC
UUGCUUU
Detection probe for miRNA
sequence
hsa-miR-302a hsa-miR-302a UAAGUGCUUCCAUGU
UUUGGUGA
Detection probe for miRNA
sequence
hsa-miR-302b* hsa-miR-302b* ACUUUAACAUGGAAG
UGCUUUCU
Detection probe for miRNA
sequence
hsa-miR-302b hsa-miR-302b UAAGUGCUUCCAUGU
UUUAGUAG
Detection probe for miRNA
sequence
hsa-miR-302c* hsa-miR-302c* UUUAACAUGGGGGU
ACCUGCUG
Detection probe for miRNA
sequence

252
hsa-miR-302c hsa-miR-302c UAAGUGCUUCCAUGU
UUCAGUGG
Detection probe for miRNA
sequence
hsa-miR-302d hsa-miR-302d UAAGUGCUUCCAUGU
UUGAGUGU
Detection probe for miRNA
sequence
hsa-miR-320 hsa-miR-320 AAAAGCUGGGUUGA
GAGGGCGAA
Detection probe for miRNA
sequence
hsa-miR-323 hsa-miR-323 GCACAUUACACGGUC
GACCUCU
Detection probe for miRNA
sequence
hsa-miR-324-5p hsa-miR-324-5p CGCAUCCCCUAGGGC
AUUGGUGU
Detection probe for miRNA
sequence
hsa-miR-324-3p hsa-miR-324-3p CCACUGCCCCAGGUG
CUGCUGG
Detection probe for miRNA
sequence
hsa-miR-325 hsa-miR-325 CCUAGUAGGUGUCCA
GUAAGUGU
Detection probe for miRNA
sequence
hsa-miR-326 hsa-miR-326 CCUCUGGGCCCUUCC
UCCAG
Detection probe for miRNA
sequence
hsa-miR-328 hsa-miR-328 CUGGCCCUCUCUGCC
CUUCCGU
Detection probe for miRNA
sequence
hsa-miR-329 hsa-miR-329 AACACACCUGGUUAA
CCUCUUU
Detection probe for miRNA
sequence
hsa-miR-330 hsa-miR-330 GCAAAGCACACGGCC
UGCAGAGA
Detection probe for miRNA
sequence
hsa-miR-331 hsa-miR-331 GCCCCUGGGCCUAUC
CUAGAA
Detection probe for miRNA
sequence
hsa-miR-335 hsa-miR-335 UCAAGAGCAAUAACG
AAAAAUGU
Detection probe for miRNA
sequence
hsa-miR-337 hsa-miR-337 UCCAGCUCCUAUAUG
AUGCCUUU
Detection probe for miRNA
sequence
hsa-miR-338 hsa-miR-338 UCCAGCAUCAGUGAU
UUUGUUGA
Detection probe for miRNA
sequence
hsa-miR-339 hsa-miR-339 UCCCUGUCCUCCAGG
AGCUCA
Detection probe for miRNA
sequence
hsa-miR-340 hsa-miR-340 UCCGUCUCAGUUACU
UUAUAGCC
Detection probe for miRNA
sequence
hsa-miR-342 hsa-miR-342 UCUCACACAGAAAUC
GCACCCGUC
Detection probe for miRNA
sequence
hsa-miR-345 hsa-miR-345 UGCUGACUCCUAGUC
CAGGGC
Detection probe for miRNA
sequence
hsa-miR-346 hsa-miR-346 UGUCUGCCCGCAUGC
CUGCCUCU
Detection probe for miRNA
sequence
hsa-miR-361 hsa-miR-361 UUAUCAGAAUCUCCA
GGGGUAC
Detection probe for miRNA
sequence
hsa-miR-362 hsa-miR-362 AAUCCUUGGAACCUA
GGUGUGAG
Detection probe for miRNA
sequence
hsa-miR-363 hsa-miR-363 AUUGCACGGUAUCCA
UCUGUAA
Detection probe for miRNA
sequence
hsa-miR-365 hsa-miR-365 UAAUGCCCCUAAAAA
UCCUUAU
Detection probe for miRNA
sequence
hsa-miR-367 hsa-miR-367 AAUUGCACUUUAGCA
AUGGUGA
Detection probe for miRNA
sequence
hsa-miR-368 hsa-miR-368 ACAUAGAGGAAAUUC
CACGUUU
Detection probe for miRNA
sequence
hsa-miR-369-5p hsa-miR-369-5p AGAUCGACCGUGUUA Detection probe for miRNA

253
UAUUCGC sequence
hsa-miR-369-3p hsa-miR-369-3p AAUAAUACAUGGUU
GAUCUUU
Detection probe for miRNA
sequence
hsa-miR-370 hsa-miR-370 GCCUGCUGGGGUGGA
ACCUGG
Detection probe for miRNA
sequence
hsa-miR-371 hsa-miR-371 GUGCCGCCAUCUUUU
GAGUGU
Detection probe for miRNA
sequence
hsa-miR-372 hsa-miR-372 AAAGUGCUGCGACAU
UUGAGCGU
Detection probe for miRNA
sequence
hsa-miR-373* hsa-miR-373* ACUCAAAAUGGGGGC
GCUUUCC
Detection probe for miRNA
sequence
hsa-miR-373 hsa-miR-373 GAAGUGCUUCGAUUU
UGGGGUGU
Detection probe for miRNA
sequence
hsa-miR-374 hsa-miR-374 UUAUAAUACAACCUG
AUAAGUG
Detection probe for miRNA
sequence
hsa-miR-375 hsa-miR-375 UUUGUUCGUUCGGCU
CGCGUGA
Detection probe for miRNA
sequence
hsa-miR-376a hsa-miR-376a AUCAUAGAGGAAAA
UCCACGU
Detection probe for miRNA
sequence
hsa-miR-376b hsa-miR-376b AUCAUAGAGGAAAA
UCCAUGUU
Detection probe for miRNA
sequence
hsa-miR-377 hsa-miR-377 AUCACACAAAGGCAA
CUUUUGU
Detection probe for miRNA
sequence
hsa-miR-378 hsa-miR-378 CUCCUGACUCCAGGU
CCUGUGU
Detection probe for miRNA
sequence
hsa-miR-379 hsa-miR-379 UGGUAGACUAUGGA
ACGUA
Detection probe for miRNA
sequence
hsa-miR-380-5p hsa-miR-380-5p UGGUUGACCAUAGAA
CAUGCGC
Detection probe for miRNA
sequence
hsa-miR-380-3p hsa-miR-380-3p UAUGUAAUAUGGUCC
ACAUCUU
Detection probe for miRNA
sequence
hsa-miR-381 hsa-miR-381 UAUACAAGGGCAAGC
UCUCUGU
Detection probe for miRNA
sequence
hsa-miR-382 hsa-miR-382 GAAGUUGUUCGUGG
UGGAUUCG
Detection probe for miRNA
sequence
hsa-miR-383 hsa-miR-383 AGAUCAGAAGGUGA
UUGUGGCU
Detection probe for miRNA
sequence
hsa-miR-384 hsa-miR-384 AUUCCUAGAAAUUGU
UCAUA
Detection probe for miRNA
sequence
hsa-miR-409-5p hsa-miR-409-5p AGGUUACCCGAGCAA
CUUUGCA
Detection probe for miRNA
sequence
hsa-miR-409-3p hsa-miR-409-3p CGAAUGUUGCUCGGU
GAACCCCU
Detection probe for miRNA
sequence
hsa-miR-410 hsa-miR-410 AAUAUAACACAGAUG
GCCUGUU
Detection probe for miRNA
sequence
hsa-miR-412 hsa-miR-412 ACUUCACCUGGUCCA
CUAGCCGU
Detection probe for miRNA
sequence
hsa-miR-422b hsa-miR-422b CUGGACUUGGAGUCA
GAAGGCC
Detection probe for miRNA
sequence
hsa-miR-422a hsa-miR-422a CUGGACUUAGGGUCA
GAAGGCC
Detection probe for miRNA
sequence
hsa-miR-423 hsa-miR-423 UCGGUCUGAGGCCCC
UCAG
Detection probe for miRNA
sequence

254
hsa-miR-424 hsa-miR-424 CAGCAGCAAUUCAUG
UUUUGAA
Detection probe for miRNA
sequence
hsa-miR-425 hsa-miR-425 AUCGGGAAUGUCGUG
UCCGCC
Detection probe for miRNA
sequence
hsa-miR-429 hsa-miR-429 UAAUACUGUCUGGUA
AAACCGU
Detection probe for miRNA
sequence
hsa-miR-431 hsa-miR-431 UGUCUUGCAGGCCGU
CAUGCA
Detection probe for miRNA
sequence
hsa-miR-432 hsa-miR-432 UCUUGGAGUAGGUCA
UUGGGUGG
Detection probe for miRNA
sequence
hsa-miR-432* hsa-miR-432* CUGGAUGGCUCCUCC
AUGUCU
Detection probe for miRNA
sequence
hsa-miR-433 hsa-miR-433 AUCAUGAUGGGCUCC
UCGGUGU
Detection probe for miRNA
sequence
hsa-miR-448 hsa-miR-448 UUGCAUAUGUAGGA
UGUCCCAU
Detection probe for miRNA
sequence
hsa-miR-449 hsa-miR-449 UGGCAGUGUAUUGU
UAGCUGGU
Detection probe for miRNA
sequence
hsa-miR-450 hsa-miR-450 UUUUUGCGAUGUGU
UCCUAAUA
Detection probe for miRNA
sequence
hsa-miR-451 hsa-miR-451 AAACCGUUACCAUUA
CUGAGUUU
Detection probe for miRNA
sequence
hsa-miR-452 hsa-miR-452 UGUUUGCAGAGGAA
ACUGAGAC
Detection probe for miRNA
sequence
hsa-miR-452* hsa-miR-452* UCAGUCUCAUCUGCA
AAGAAG
Detection probe for miRNA
sequence
hsa-miR-453 hsa-miR-453 GAGGUUGUCCGUGGU
GAGUUCG
Detection probe for miRNA
sequence
hsa-miR-485-5p hsa-miR-485-5p AGAGGCUGGCCGUGA
UGAAUUC
Detection probe for miRNA
sequence
hsa-miR-485-3p hsa-miR-485-3p GUCAUACACGGCUCU
CCUCU
Detection probe for miRNA
sequence
hsa-miR-488 hsa-miR-488 CCCAGAUAAUGGCAC
UCUCAA
Detection probe for miRNA
sequence
hsa-miR-489 hsa-miR-489 AGUGACAUCACAUAU
ACGGCAGC
Detection probe for miRNA
sequence
hsa-miR-490 hsa-miR-490 CAACCUGGAGGACUC
CAUGCUG
Detection probe for miRNA
sequence
hsa-miR-491 hsa-miR-491 AGUGGGGAACCCUUC
CAUGAGGA
Detection probe for miRNA
sequence
hsa-miR-492 hsa-miR-492 AGGACCUGCGGGACA
AGAUUCUU
Detection probe for miRNA
sequence
hsa-miR-493 hsa-miR-493 UUGUACAUGGUAGGC
UUUCAUU
Detection probe for miRNA
sequence
hsa-miR-494 hsa-miR-494 UGAAACAUACACGGG
AAACCUCUU
Detection probe for miRNA
sequence
hsa-miR-495 hsa-miR-495 AAACAAACAUGGUGC
ACUUCUUU
Detection probe for miRNA
sequence
hsa-miR-496 hsa-miR-496 AUUACAUGGCCAAUC
UC
Detection probe for miRNA
sequence
hsa-miR-497 hsa-miR-497 CAGCAGCACACUGUG
GUUUGU
Detection probe for miRNA
sequence
hsa-miR-498 hsa-miR-498 UUUCAAGCCAGGGGG Detection probe for miRNA

255
CGUUUUUC sequence
hsa-miR-499 hsa-miR-499 UUAAGACUUGCAGUG
AUGUUUAA
Detection probe for miRNA
sequence
hsa-miR-500 hsa-miR-500 AUGCACCUGGGCAAG
GAUUCUG
Detection probe for miRNA
sequence
hsa-miR-501 hsa-miR-501 AAUCCUUUGUCCCUG
GGUGAGA
Detection probe for miRNA
sequence
hsa-miR-502 hsa-miR-502 AUCCUUGCUAUCUGG
GUGCUA
Detection probe for miRNA
sequence
hsa-miR-503 hsa-miR-503 UAGCAGCGGGAACAG
UUCUGCAG
Detection probe for miRNA
sequence
hsa-miR-504 hsa-miR-504 AGACCCUGGUCUGCA
CUCUAU
Detection probe for miRNA
sequence
hsa-miR-505 hsa-miR-505 GUCAACACUUGCUGG
UUUCCUC
Detection probe for miRNA
sequence
hsa-miR-506 hsa-miR-506 UAAGGCACCCUUCUG
AGUAGA
Detection probe for miRNA
sequence
hsa-miR-507 hsa-miR-507 UUUUGCACCUUUUGG
AGUGAA
Detection probe for miRNA
sequence
hsa-miR-508 hsa-miR-508 UGAUUGUAGCCUUUU
GGAGUAGA
Detection probe for miRNA
sequence
hsa-miR-509 hsa-miR-509 UGAUUGGUACGUCUG
UGGGUAGA
Detection probe for miRNA
sequence
hsa-miR-510 hsa-miR-510 UACUCAGGAGAGUGG
CAAUCACA
Detection probe for miRNA
sequence
hsa-miR-511 hsa-miR-511 GUGUCUUUUGCUCUG
CAGUCA
Detection probe for miRNA
sequence
hsa-miR-512-5p hsa-miR-512-5p CACUCAGCCUUGAGG
GCACUUUC
Detection probe for miRNA
sequence
hsa-miR-512-3p hsa-miR-512-3p AAGUGCUGUCAUAGC
UGAGGUC
Detection probe for miRNA
sequence
hsa-miR-513 hsa-miR-513 UUCACAGGGAGGUGU
CAUUUAU
Detection probe for miRNA
sequence
hsa-miR-514 hsa-miR-514 AUUGACACUUCUGUG
AGUAG
Detection probe for miRNA
sequence
hsa-miR-515-5p hsa-miR-515-5p UUCUCCAAAAGAAAG
CACUUUCUG
Detection probe for miRNA
sequence
hsa-miR-515-3p hsa-miR-515-3p GAGUGCCUUCUUUUG
GAGCGU
Detection probe for miRNA
sequence
hsa-miR-516-5p hsa-miR-516-5p AUCUGGAGGUAAGA
AGCACUUU
Detection probe for miRNA
sequence
hsa-miR-516-3p hsa-miR-516-3p UGCUUCCUUUCAGAG
GGU
Detection probe for miRNA
sequence
hsa-miR-517* hsa-miR-517* CCUCUAGAUGGAAGC
ACUGUCU
Detection probe for miRNA
sequence
hsa-miR-517a hsa-miR-517a AUCGUGCAUCCCUUU
AGAGUGUU
Detection probe for miRNA
sequence
hsa-miR-517b hsa-miR-517b UCGUGCAUCCCUUUA
GAGUGUU
Detection probe for miRNA
sequence
hsa-miR-517c hsa-miR-517c AUCGUGCAUCCUUUU
AGAGUGU
Detection probe for miRNA
sequence
hsa-miR-518f* hsa-miR-518f* CUCUAGAGGGAAGCA
CUUUCUCU
Detection probe for miRNA
sequence

256
hsa-miR-518f hsa-miR-518f AAAGCGCUUCUCUUU
AGAGGA
Detection probe for miRNA
sequence
hsa-miR-518b hsa-miR-518b CAAAGCGCUCCCCUU
UAGAGGU
Detection probe for miRNA
sequence
hsa-miR-518c* hsa-miR-518c* UCUCUGGAGGGAAGC
ACUUUCUG
Detection probe for miRNA
sequence
hsa-miR-518c hsa-miR-518c CAAAGCGCUUCUCUU
UAGAGUG
Detection probe for miRNA
sequence
hsa-miR-518e hsa-miR-518e AAAGCGCUUCCCUUC
AGAGUGU
Detection probe for miRNA
sequence
hsa-miR-518a hsa-miR-518a AAAGCGCUUCCCUUU
GCUGGA
Detection probe for miRNA
sequence
hsa-miR-518d hsa-miR-518d CAAAGCGCUUCCCUU
UGGAGC
Detection probe for miRNA
sequence
hsa-miR-518a-
2*
hsa-miR-518a-2* UCUGCAAAGGGAAGC
CCUUU
Detection probe for miRNA
sequence
hsa-miR-519e* hsa-miR-519e* UUCUCCAAAAGGGAG
CACUUUC
Detection probe for miRNA
sequence
hsa-miR-519e hsa-miR-519e AAAGUGCCUCCUUUU
AGAGUGU
Detection probe for miRNA
sequence
hsa-miR-519c hsa-miR-519c AAAGUGCAUCUUUUU
AGAGGAU
Detection probe for miRNA
sequence
hsa-miR-519b hsa-miR-519b AAAGUGCAUCCUUUU
AGAGGUUU
Detection probe for miRNA
sequence
hsa-miR-519d hsa-miR-519d CAAAGUGCCUCCCUU
UAGAGUGU
Detection probe for miRNA
sequence
hsa-miR-519a hsa-miR-519a AAAGUGCAUCCUUUU
AGAGUGUUAC
Detection probe for miRNA
sequence
hsa-miR-520e hsa-miR-520e AAAGUGCUUCCUUUU
UGAGGG
Detection probe for miRNA
sequence
hsa-miR-520f hsa-miR-520f AAGUGCUUCCUUUUA
GAGGGUU
Detection probe for miRNA
sequence
hsa-miR-520a* hsa-miR-520a* CUCCAGAGGGAAGUA
CUUUCU
Detection probe for miRNA
sequence
hsa-miR-520a hsa-miR-520a AAAGUGCUUCCCUUU
GGACUGU
Detection probe for miRNA
sequence
hsa-miR-520b hsa-miR-520b AAAGUGCUUCCUUUU
AGAGGG
Detection probe for miRNA
sequence
hsa-miR-520c hsa-miR-520c AAAGUGCUUCCUUUU
AGAGGGUU
Detection probe for miRNA
sequence
hsa-miR-520d* hsa-miR-520d* UCUACAAAGGGAAGC
CCUUUCUG
Detection probe for miRNA
sequence
hsa-miR-520d hsa-miR-520d AAAGUGCUUCUCUUU
GGUGGGUU
Detection probe for miRNA
sequence
hsa-miR-520g hsa-miR-520g ACAAAGUGCUUCCCU
UUAGAGUGU
Detection probe for miRNA
sequence
hsa-miR-520h hsa-miR-520h ACAAAGUGCUUCCCU
UUAGAGU
Detection probe for miRNA
sequence
hsa-miR-521 hsa-miR-521 AACGCACUUCCCUUU
AGAGUGU
Detection probe for miRNA
sequence
hsa-miR-522 hsa-miR-522 AAAAUGGUUCCCUUU
AGAGUGUU
Detection probe for miRNA
sequence
hsa-miR-523 hsa-miR-523 AACGCGCUUCCCUAU Detection probe for miRNA

257
AGAGGG sequence
hsa-miR-524* hsa-miR-524* CUACAAAGGGAAGCA
CUUUCUC
Detection probe for miRNA
sequence
hsa-miR-524 hsa-miR-524 GAAGGCGCUUCCCUU
UGGAGU
Detection probe for miRNA
sequence
hsa-miR-525 hsa-miR-525 CUCCAGAGGGAUGCA
CUUUCU
Detection probe for miRNA
sequence
hsa-miR-525* hsa-miR-525* GAAGGCGCUUCCCUU
UAGAGC
Detection probe for miRNA
sequence
hsa-miR-526c hsa-miR-526c CUCUAGAGGGAAGCG
CUUUCUGUU
Detection probe for miRNA
sequence
hsa-miR-526b hsa-miR-526b CUCUUGAGGGAAGCA
CUUUCUGUU
Detection probe for miRNA
sequence
hsa-miR-526b* hsa-miR-526b* AAAGUGCUUCCUUUU
AGAGGC
Detection probe for miRNA
sequence
hsa-miR-526a hsa-miR-526a CUCUAGAGGGAAGCA
CUUUCU
Detection probe for miRNA
sequence
hsa-miR-527 hsa-miR-527 CUGCAAAGGGAAGCC
CUUUCU
Detection probe for miRNA
sequence
Star Sequence Probes
S-hsa-mir-1-2 hsa-mir-1-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-1-1 hsa-mir-1-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-let-7a-1 hsa-let-7a-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-let-7a-2 hsa-let-7a-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-let-7a-3 hsa-let-7a-3 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-let-7b hsa-let-7b Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-let-7c hsa-let-7c Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-let-7d hsa-let-7d Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-let-7e hsa-let-7e Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-let-7f-1 hsa-let-7f-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-let-7f-2 hsa-let-7f-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-7-1 hsa-mir-7-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-7-2 hsa-mir-7-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-7-3 hsa-mir-7-3 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-let-7g hsa-let-7g Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-let-7i hsa-let-7i Selected from stem loop
sequence
Detection probe for miRNA*
sequence

258
S-hsa-mir-10a hsa-mir-10a Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-10b hsa-mir-10b Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-15a hsa-mir-15a Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-15b hsa-mir-15b Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-16-1 hsa-mir-16-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-16-2 hsa-mir-16-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-18a hsa-mir-18a Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-18b hsa-mir-18b Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-19a hsa-mir-19a Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-19b-1 hsa-mir-19b-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-19b-2 hsa-mir-19b-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-20a hsa-mir-20a Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-20b hsa-mir-20b Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-21 hsa-mir-21 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-22 hsa-mir-22 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-23a hsa-mir-23a Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-23b hsa-mir-23b Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-24-2 hsa-mir-24-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-25 hsa-mir-25 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-26a-1 hsa-mir-26a-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-26b hsa-mir-26b Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-26a-2 hsa-mir-26a-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-27a hsa-mir-27a Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-27b hsa-mir-27b Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-28 hsa-mir-28 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-29a hsa-mir-29a Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-29b-1 hsa-mir-29b-1 Selected from stem loop Detection probe for miRNA*

259
sequence sequence
S-hsa-mir-29b-2 hsa-mir-29b-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-29c hsa-mir-29c Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-30c-2 hsa-mir-30c-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-30d hsa-mir-30d Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-30b hsa-mir-30b Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-30c-1 hsa-mir-30c-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-31 hsa-mir-31 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-32 hsa-mir-32 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-33 hsa-mir-33 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-34a hsa-mir-34a Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-34b hsa-mir-34b Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-34c hsa-mir-34c Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-92-1 hsa-mir-92-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-92-2 hsa-mir-92-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-93 hsa-mir-93 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-95 hsa-mir-95 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-96 hsa-mir-96 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-98 hsa-mir-98 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-99a hsa-mir-99a Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-99b hsa-mir-99b Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-100 hsa-mir-100 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-101-1 hsa-mir-101-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-101-2 hsa-mir-101-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-103-2 hsa-mir-103-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-103-1 hsa-mir-103-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-105-1 hsa-mir-105-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence

260
S-hsa-mir-105-2 hsa-mir-105-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-106a hsa-mir-106a Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-106b hsa-mir-106b Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-107 hsa-mir-107 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-122a hsa-mir-122a Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-124a-
1
hsa-mir-124a-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-124a-
2
hsa-mir-124a-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-124a-
3
hsa-mir-124a-3 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-125b-
1
hsa-mir-125b-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-125a hsa-mir-125a Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-125b-
2
hsa-mir-125b-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-127 hsa-mir-127 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-128a hsa-mir-128a Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-128b hsa-mir-128b Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-129-1 hsa-mir-129-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-129-2 hsa-mir-129-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-130a hsa-mir-130a Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-130b hsa-mir-130b Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-132 hsa-mir-132 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-133a-
1
hsa-mir-133a-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-133a-
2
hsa-mir-133a-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-133b hsa-mir-133b Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-134 hsa-mir-134 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-135a-
1
hsa-mir-135a-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-135a-
2
hsa-mir-135a-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-135b hsa-mir-135b Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-136 hsa-mir-136 Selected from stem loop Detection probe for miRNA*

261
sequence sequence
S-hsa-mir-137 hsa-mir-137 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-138-2 hsa-mir-138-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-138-1 hsa-mir-138-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-139 hsa-mir-139 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-140 hsa-mir-140 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-141 hsa-mir-141 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-143 hsa-mir-143 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-144 hsa-mir-144 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-145 hsa-mir-145 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-146a hsa-mir-146a Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-146b hsa-mir-146b Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-147 hsa-mir-147 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-148a hsa-mir-148a Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-148b hsa-mir-148b Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-149 hsa-mir-149 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-150 hsa-mir-150 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-151 hsa-mir-151 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-152 hsa-mir-152 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-153-1 hsa-mir-153-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-153-2 hsa-mir-153-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-155 hsa-mir-155 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-181a hsa-mir-181a Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-181b-
1
hsa-mir-181b-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-181c hsa-mir-181c Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-181b-
2
hsa-mir-181b-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-181d hsa-mir-181d Selected from stem loop
sequence
Detection probe for miRNA*
sequence

262
S-hsa-mir-183 hsa-mir-183 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-184 hsa-mir-184 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-185 hsa-mir-185 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-186 hsa-mir-186 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-187 hsa-mir-187 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-188 hsa-mir-188 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-190 hsa-mir-190 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-192 hsa-mir-192 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-193a hsa-mir-193a Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-193b hsa-mir-193b Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-194-1 hsa-mir-194-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-194-2 hsa-mir-194-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-195 hsa-mir-195 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-196a-
1
hsa-mir-196a-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-196a-
2
hsa-mir-196a-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-196b hsa-mir-196b Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-197 hsa-mir-197 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-198 hsa-mir-198 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-199b hsa-mir-199b Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-200b hsa-mir-200b Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-200c hsa-mir-200c Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-203 hsa-mir-203 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-204 hsa-mir-204 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-205 hsa-mir-205 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-206 hsa-mir-206 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-208 hsa-mir-208 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-210 hsa-mir-210 Selected from stem loop Detection probe for miRNA*

263
sequence sequence
S-hsa-mir-211 hsa-mir-211 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-212 hsa-mir-212 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-214 hsa-mir-214 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-215 hsa-mir-215 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-216 hsa-mir-216 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-217 hsa-mir-217 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-218-1 hsa-mir-218-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-218-2 hsa-mir-218-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-219-1 hsa-mir-219-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-219-2 hsa-mir-219-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-220 hsa-mir-220 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-221 hsa-mir-221 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-222 hsa-mir-222 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-223 hsa-mir-223 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-224 hsa-mir-224 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-296 hsa-mir-296 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-301 hsa-mir-301 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-302d hsa-mir-302d Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-320 hsa-mir-320 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-323 hsa-mir-323 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-325 hsa-mir-325 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-326 hsa-mir-326 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-328 hsa-mir-328 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-329-1 hsa-mir-329-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-329-2 hsa-mir-329-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-330 hsa-mir-330 Selected from stem loop
sequence
Detection probe for miRNA*
sequence

264
S-hsa-mir-331 hsa-mir-331 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-335 hsa-mir-335 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-337 hsa-mir-337 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-338 hsa-mir-338 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-339 hsa-mir-339 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-340 hsa-mir-340 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-342 hsa-mir-342 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-345 hsa-mir-345 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-346 hsa-mir-346 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-361 hsa-mir-361 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-362 hsa-mir-362 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-363 hsa-mir-363 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-365-1 hsa-mir-365-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-365-2 hsa-mir-365-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-367 hsa-mir-367 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-368 hsa-mir-368 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-370 hsa-mir-370 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-371 hsa-mir-371 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-372 hsa-mir-372 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-374 hsa-mir-374 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-375 hsa-mir-375 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-376a hsa-mir-376a Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-376b hsa-mir-376b Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-377 hsa-mir-377 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-379 hsa-mir-379 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-381 hsa-mir-381 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-382 hsa-mir-382 Selected from stem loop Detection probe for miRNA*

265
sequence sequence
S-hsa-mir-383 hsa-mir-383 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-384 hsa-mir-384 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-422a hsa-mir-422a Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-423 hsa-mir-423 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-424 hsa-mir-424 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-425 hsa-mir-425 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-429 hsa-mir-429 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-448 hsa-mir-448 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-449 hsa-mir-449 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-miR-410 hsa-miR-410 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-miR-412 hsa-miR-412 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-miR-431 hsa-miR-431 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-miR-433 hsa-miR-433 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-450-1 hsa-mir-450-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-450-2 hsa-mir-450-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-451 hsa-mir-451 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-453 hsa-mir-453 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-488 hsa-mir-488 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-489 hsa-mir-489 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-490 hsa-mir-490 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-491 hsa-mir-491 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-492 hsa-mir-492 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-493 hsa-mir-493 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-494 hsa-mir-494 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-495 hsa-mir-495 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-496 hsa-mir-496 Selected from stem loop
sequence
Detection probe for miRNA*
sequence

266
S-hsa-mir-497 hsa-mir-497 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-498 hsa-mir-498 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-499 hsa-mir-499 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-500 hsa-mir-500 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-501 hsa-mir-501 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-502 hsa-mir-502 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-503 hsa-mir-503 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-504 hsa-mir-504 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-505 hsa-mir-505 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-506 hsa-mir-506 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-507 hsa-mir-507 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-508 hsa-mir-508 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-509 hsa-mir-509 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-510 hsa-mir-510 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-511-1 hsa-mir-511-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-511-2 hsa-mir-511-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-513-1 hsa-mir-513-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-513-2 hsa-mir-513-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-514-1 hsa-mir-514-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-514-2 hsa-mir-514-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-514-3 hsa-mir-514-3 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-516-1 hsa-mir-516-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-516-2 hsa-mir-516-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-518b hsa-mir-518b Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-519a-
2
hsa-mir-519a-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-519d hsa-mir-519d Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-520b hsa-mir-520b Selected from stem loop Detection probe for miRNA*

267
sequence sequence
S-hsa-mir-520e hsa-mir-520e Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-520f hsa-mir-520f Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-520g hsa-mir-520g Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-520h hsa-mir-520h Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-521-1 hsa-mir-521-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-521-2 hsa-mir-521-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-526a-
1
hsa-mir-526a-1 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-526a-
2
hsa-mir-526a-2 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
S-hsa-mir-527 hsa-mir-527 Selected from stem loop
sequence
Detection probe for miRNA*
sequence
Internal Use Probes
5S-rRNA-1 LC Internal Use LC Internal Use
5S-rRNA-2 LC Internal Use LC Internal Use
5S-rRNA-3 LC Internal Use LC Internal Use
5S-rRNA-4 LC Internal Use LC Internal Use
5S-rRNA-5 LC Internal Use LC Internal Use
5S-rRNA-6 LC Internal Use LC Internal Use
5S-rRNA-a1 LC Internal Use LC Internal Use
5S-rRNA-a2 LC Internal Use LC Internal Use
5S-rRNA-a3 LC Internal Use LC Internal Use
5S-rRNA-a4 LC Internal Use LC Internal Use
5S-rRNA-a5 LC Internal Use LC Internal Use
5S-rRNA-a6 LC Internal Use LC Internal Use
5S-rRNA-a7 LC Internal Use LC Internal Use
5S-rRNA-a8 LC Internal Use LC Internal Use
5S-rRNA-a9 LC Internal Use LC Internal Use
5S-rRNA-a10 LC Internal Use LC Internal Use
DA5880-3 LC Internal Use LC Internal Use
DA5881-3 LC Internal Use LC Internal Use
DC5880-3 LC Internal Use LC Internal Use
DD5880-3 LC Internal Use LC Internal Use
DE5880-3 LC Internal Use LC Internal Use
DE5881-3 LC Internal Use LC Internal Use
DE9990-3 LC Internal Use LC Internal Use
DF5880-3 LC Internal Use LC Internal Use
DG5880-3 LC Internal Use LC Internal Use

268
DG9990-3 LC Internal Use LC Internal Use
DG9992-3 LC Internal Use LC Internal Use
DH5880-3 LC Internal Use LC Internal Use
DI5880-3 LC Internal Use LC Internal Use
DK5880-3 LC Internal Use LC Internal Use
DK9990-3 LC Internal Use LC Internal Use
DK9991-3 LC Internal Use LC Internal Use
DL5880-3 LC Internal Use LC Internal Use
DL5881-3 LC Internal Use LC Internal Use
DL9990-3 LC Internal Use LC Internal Use
DM5880-3 LC Internal Use LC Internal Use
DM5881-3 LC Internal Use LC Internal Use
DN5880-3 LC Internal Use LC Internal Use
DN9990-3 LC Internal Use LC Internal Use
DN9991-3 LC Internal Use LC Internal Use
DP5880-3 LC Internal Use LC Internal Use
DP9990-3 LC Internal Use LC Internal Use
DQ5880-3 LC Internal Use LC Internal Use
DQ9990-3 LC Internal Use LC Internal Use
DQ9991-3 LC Internal Use LC Internal Use
DQ9992-3 LC Internal Use LC Internal Use
DR5880-3 LC Internal Use LC Internal Use
DR5881-3 LC Internal Use LC Internal Use
DR9990-3 LC Internal Use LC Internal Use
DS5880-3 LC Internal Use LC Internal Use
DS5881-3 LC Internal Use LC Internal Use
DS9990-3 LC Internal Use LC Internal Use
DS9993-3 LC Internal Use LC Internal Use
DS9994-3 LC Internal Use LC Internal Use
DT5880-3 LC Internal Use LC Internal Use
DT9990-3 LC Internal Use LC Internal Use
DT9991-3 LC Internal Use LC Internal Use
DT9992-3 LC Internal Use LC Internal Use
DV5880-3 LC Internal Use LC Internal Use
DV9990-3 LC Internal Use LC Internal Use
DV9993-3 LC Internal Use LC Internal Use
DV9994-3 LC Internal Use LC Internal Use
DV9995-3 LC Internal Use LC Internal Use
DW5880-3 LC Internal Use LC Internal Use
DX5880-3 LC Internal Use LC Internal Use
DX9990-3 LC Internal Use LC Internal Use

269
DX9991-3 LC Internal Use LC Internal Use
DY5880-3 LC Internal Use LC Internal Use
DY9990-3 LC Internal Use LC Internal Use
DY9991-3 LC Internal Use LC Internal Use
DM5880-a1 LC Internal Use LC Internal Use
DM5881-a1 LC Internal Use LC Internal Use
DM5880-a2 LC Internal Use LC Internal Use
DM5881-a2 LC Internal Use LC Internal Use
DM5880-a3 LC Internal Use LC Internal Use
DM5881-a3 LC Internal Use LC Internal Use

Abstract (if available)

Abstract Epigenetics is defined as heritable changes in gene expression without a change in the DNA sequence itself. DNA cytosine methylation and histone modifications are two important mechanisms in the area of epigenetics that have profound roles in gene regulation, development, and carcinogenesis. Methylation of CpG islands in promoter regions is often associated with gene silencing, and aberrant DNA methylation occurs in most cancers, leading to the silencing of some tumor suppressor genes. Many drugs have effects in reversing abnormal epigenetic changes, and they can mainly be divided into two classes -- DNA methylation inhibitors and HDAC inhibitors. I first studied a few nucleoside analog and non-nucleoside analog DNA methylation inhibitors in vitro. The nucleoside analog DNA methylation inhibitors studied did not show general applicability, and the results concerning the non-nucleoside agents tested did not support the idea that they were likely to be effective as epigenetic therapies. I then expanded the study of novel DNA methylation inhibitors to in vivo studies and found that the agent S110 (AzpG) was effective in vivo.

Linked assets

University of Southern California Dissertations and Theses

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Asset Metadata

Creator Chuang, Jody Chouying (author)

Core Title DNA methylation inhibitors and epigenetic regulation of microRNA expression

School Keck School of Medicine

Degree Doctor of Philosophy

Degree Program Biochemistry and Molecular Biology

Degree Conferral Date 2008-08

Publication Date 07/29/2008

Defense Date 05/08/2008

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag DNA methylation,epigenetics,microRNA,OAI-PMH Harvest

Language English

Advisor Jones, Peter A. (committee chair), Laird, Peter W. (committee member), Stallcup, Michael R. (committee member), Ying, Shao-Yao (committee member)

Creator Email jchuang@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m1436

Unique identifier UC1358236

Identifier etd-Chuang-20080729 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-196604 (legacy record id),usctheses-m1436 (legacy record id)

Legacy Identifier etd-Chuang-20080729.pdf

Dmrecord 196604

Document Type Dissertation

Rights Chuang, Jody Chouying

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu