University of Southern California Dissertations and Theses

LINC00261 induces a G2/M cell cycle arrest and activation of the DNA damage response in lung adenocarcinoma

(USC Thesis Other)

LINC00261 induces a G2/M cell cycle arrest and activation of the DNA damage response in lung adenocarcinoma

PDF

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content LINC00261 induces a G2/M cell cycle arrest and activation of
the DNA damage response
in Lung Adenocarcinoma

by
Vishaly Kumaran
A Thesis Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(Biochemistry and Molecular Medicine)

August 2017

Dedication

I would like to dedicate this thesis to my dearest parents Lakshmi Prabha and Kumaran
Kandasamy, and my sister Karishni.
Acknowledgements:

I would first like to thank the members of this committee for their support. A special
thank you to Dr. Crystal Marconett who has always been by my side through the two
years. Her passion and dedication have helped me bring this task to completion. I would
like to express my gratitude to Dr. Ite Laird-Offringa for being a mentor, a guide and an
inspiration to grow during my time here. I would also like to thank Dr. Fabbri for his
kindness and support.

I would like to thank the members of the Laird-Offringa lab being very helpful during the
past two years. I would like to specifically thank Evelyn Tran, Theresa Ryan Stueve,
Chenchen Yang, Prerna Sehgal, Chunli Yan, Daniel Mullen, Anusha Muralidhar, Laura
St. Pierre and Madhura Lotlikar. I would also like to thank Johnathan Castillo for his help
with the project.

Contents

CHAPTER 1 : Introduction ................................................................................................. 1
1.1 Lung Cancer ............................................................................................................. 1
1.2 Long noncoding RNA ................................................................................................ 5
1.3 LncRNA in Cancer..................................................................................................... 8
1.4 Preliminary Data ...................................................................................................... 10
CHAPTER 2: Characterizing the tumor- suppressive properties of LINC00261 .......... 12
2.1 Introduction ............................................................................................................. 12
2.2 Materials and Methods ............................................................................................ 13
2.3 Results .................................................................................................................... 16
2.4 Discussion ............................................................................................................... 21
CHAPTER 3: Investigating the role of LINC00261 in the DNA Damage checkpoint ........ 23
3.1 Introduction ............................................................................................................. 23
3.2 Materials and Methods ............................................................................................ 26
3.3 Results .................................................................................................................... 37
3.4 Discussion ............................................................................................................... 43
CHAPTER 4 : Summary ................................................................................................... 45
CHAPTER 5: Future directions ........................................................................................ 46
References ....................................................................................................................... 48
Supplemental Data ........................................................................................................ 51

Figure 1 ......................................................................................................................................... 2
Figure 2 ......................................................................................................................................... 3
Figure 3 ......................................................................................................................................... 4
Figure 4 ......................................................................................................................................... 7
Figure 5 ....................................................................................................................................... 10
Figure 6 ....................................................................................................................................... 17
Figure 7 ....................................................................................................................................... 19
Figure 8 ....................................................................................................................................... 20
Figure 9 ....................................................................................................................................... 25
Figure 10 ..................................................................................................................................... 27
Figure 11 ..................................................................................................................................... 39
Figure 12 ..................................................................................................................................... 40
Figure 13 ..................................................................................................................................... 41
Figure 14 ..................................................................................................................................... 42
Figure 15 ..................................................................................................................................... 43
Figure 16 ..................................................................................................................................... 44
Figure 17 ..................................................................................................................................... 47

Table 1 .......................................................................................................................................... 6
Table 2 .......................................................................................................................................... 9
Table 3 ........................................................................................................................................ 52
Table 4 ........................................................................................................................................ 53
Abstract

Lung cancer is the leading cause of cancer related death worldwide.(Fabricius and Lange,
2003) The lung cancer five-year survival rate (17.7 percent) is lower than many other
leading cancer subtypes such as colon (64.4 percent), breast (89.7 percent) and prostate
(98.9 percent). More than half of the people with lung cancer die within one year of being
diagnosed. Lung adenocarcinoma (LUAD) is the largest histological subtype of lung cancer.
Recent advances in sequencing techniques revealed that most transcription in our genome
(~80%) occurs on long non-coding RNAs (lncRNAs). Little is known about the altered
expression of lncRNAs in cancer. We profiled lncRNA expression in LUAD cell lines and
purified alveolar epithelial cells, which are the purported cells of origin for LUAD. We then
cross-referenced these with lncRNAs altered in primary human tumors, and eliminated all
lncRNAs without expression-based difference in survival. We identified LINC00261, a
lncRNA predicted to have a tumor suppression activity that lies adjacent to the transcription
factor FOXA2. LINC00261 blocked cellular proliferation by inducing a G2/M cell cycle arrest.
RNA-Seq analysis indicated that the G2/M arrest was mediated by DNA damage pathway
induction. Western blots confirmed that DNA damage checkpoint control proteins underwent
ATM-dependent phosphorylation-mediated activation when LINC00261 was present. DNA
damage repair and cell cycle checkpoints have been intrinsically linked to cancer and the
molecular mechanisms that connect these dysfunctional pathways to the onset of
carcinogenesis are not very well understood. This novel finding gives us a unique insight
into the mechanism of this lncRNA and has huge therapeutic implications for treatment of
LUAD.

1
CHAPTER 1 : Introduction

1.1 Lung Cancer
Lung cancer is the leading cause of cancer-related deaths globally. The overall 5-year
survival rate for a person affected by lung cancer is 18%, as shown in Figure 1A. The 5-
year survival rate for men is 15%; for women, it’s 21%. Approximately 6.4 percent of
men and women will be diagnosed with lung and bronchus cancer at some point during
their lifetime (DeSantis et al., 2014). In 2017, it is estimated that there will be 222,500
new cases of lung and bronchus cancer, in the US and an estimated 155,870 people
will die of this disease, and it has been predicted that in 2017 that 13.2% of the all new
cases of cancer in the US will be lung cancer. Lung cancer causes more deaths than
colon cancer, breast cancer and pancreatic cancer combined. It accounts for 27% of all
cancer deaths (Figure 1B).
Over the last 16 years, there has been a reduction in the overall number of cancer
related deaths.(DeSantis et al., 2014) The discovery of genetic mutations that drive lung
cancer is rapidly improving the outlook, even for some patients with late stage
metastatic disease.(Palliat, 2017) Although there has been a progress in reducing the
incidence, mortality rates and improving survival, cancer still accounts for the second
most highest deaths in 2015, after heart disease (CDC, 2015). Further progress can be
accelerated by understanding the underlying molecular basis of the disease.
2
N

Figure 1

Epidemiology of Lung Cancer: A. Gray figures represent the number of people who have died from lung
and bronchus cancer within 5 years after being diagnosed with it. Green figures represent those who have
survived for 5 years or more (18.1%) This figure is taken from US National Cancer Institute, Surveillance,
Epidemiology, and Endresults Program Cancer Statistics Review 1975–2011(Institute, 2016)B. Lung
cancer kills more people per year than the other 3 leading cancers combined (breast cancer, prostate
cancer, colorectal cancer) – it accounts for 27% of all cancer deaths. This figure is taken from United
States Cancer Statistics: 1999–2011 ( Incidence and Mortality Report, 2011)

The four most prominent histological types of lung cancer are adenocarcinoma, squamous
cell carcinoma, large cell carcinoma, and small cell carcinoma (Figure 2). The first three
classes are collectively named Non-Small Cell Lung Cancer (NSCLC) (Risteski et al.,
2013). NSCLC account for more than 85% of all lung cancer cases and of the three
subtypes, Lung Adenocarcinomas (LUAD) occur most often (50% of lung cancer cases).

LUAD is thought to be of alveolar origin (Rowbotham and Kim, 2014).
3

Figure 2

Lung Cancer Subtypes: The main types of Lung Cancer are adenocarcinoma, squamous cell carcinoma,
large cell carcinoma, and small cell carcinoma. This figure is taken from US National Cancer Institute,
Surveillance, Epidemiology, and End Results Program Cancer Statistics Review 1975–2011.

Different subtypes of cancer have distinct mutations. Molecular profiling performed by
the cancer genome atlas (TCGA) on a cohort of 230 LUAD patient tumors showed that
a group of 18 genes are statistically significantly mutated. (Rodger, 2014)These include
TP53, KRAS, KEAP, STK11, EGFR, NF1 and BRAF (Figure 3A). Identification of the
most frequent driver gene alteration, without the added burden of a passenger events is
essential for the discovery of novel targets for treatment of LUAD. Based on the study

performed at Massachusetts General, it can be said that approximately half of NSCLCs
have identifiable driver mutations (Figure 3C) (Kumarakulasinghe et al., 2015)

Figure 3

Mutations associated with LUAD A. A co-mutation plot from whole exome sequencing of 230 LUADs.
Data was combined with TCGA data and statistically analyzed. Significant genes are arranged in order of
decreasing prevalence – This figure was taken from A comprehensive molecular profiling of lung
adenocarcinoma by Rodgers et al., 2014. B.A study done at Massachusetts General looking at more
than 550 NSLCs. It can be seen less than 49% have no mutation – This figure is taken from Sequist lv et
al., 2011 ASCO meeting C. Therapy Targeting common mutations in NSCLC taken from Molecular
targeted therapy of advanced NSLC(Kumarakulasinghe et al., 2015).

Identifiable driver mutations play a vital role in therapy, as they can be targeted. As it can be
seen in Figure 3B, while some LUADs have identifiable protein markers, almost half of them
do not. In Figure 3C the current molecular targets that can be used for therapies are
enlisted. Though effective, they haven’t changed the survival statistics drastically. For

4
5
example Gefitinib, a EGFR targeted drug for lung cancer that is widely used has shown
to be have a delayed reoccurrence. (Healio, May 17, 2017)The need for a non-protein
biomarker is evident. A major class of RNA described as long non-coding RNA
(lncRNA) are found to play vital roles in disease conditions (Sun and Kraus, 2015).
These RNA could be vital for investigating new therapies and understanding the
disease state of lung adenocarcinoma.

1.2 Long noncoding RNA
LncRNAs have be identified as a novel class of functional RNA that are longer than 200
nucleotides in length (Devaux et al., 2015). There have been several attempts at
systematic classification of lncRNAs (Table 1), but in general lncRNAs are categorized
by their genomic localization as either antisense transcripts, long intergenic non-coding
RNAs, circular RNA, enhancer associated RNAs and promotor-associated RNAs
(Jeffares et al., 1998). However, the cellular function of any given lncRNA is not strictly
determined by its genomic position (Cheng et al., 2005). LncRNAs provide a new,
relatively unstudied class of transcripts to identify both functional drivers and cancer-
type-specific markers (Brunner et al., 2012). Various groups have developed catalogues
of human lncRNAs. GENCODE is the largest (Table 1). They performed computational
analysis, manual curation, and finally targeted experimental validation to classify more
than 18,000 lncRNAs (Derrien et al., 2012).
6

Table 1

Table 1: LncRNA Databases LncRNA are depicted and classified in different databases with varying
content. General content databases such as lncRNome and LNCipedia offer a good coverage and depth.
Databases like lncRNAdb, Noncode and lncRNome provide adequate links between lncRNAs and
relevant literature sources. For molecular functions of the LncRNA can be understood by using
ChlPBase, DIANA-LncBase and lncRnome can be used. (Fritah et al., 2014)

There are to date four primary methods via which lncRNAs are known to execute their
cellular functions: as signals, decoys, scaffolds, or guides (Fritah et al., 2014). The function
of a signal lncRNA is to transfer molecular information to regulate transcription and its
presence can serve as an indicator of transcriptional activity (Ravasi et al., 2006). Decoy
lncRNAs limit the availability of regulatory factors by presenting alternate binding sites(Li et
al., 2014). Scaffold lncRNAs play a structural role by providing platforms for assembly of
complex structures such as ribonucleoprotein (RNP) complexes (Sakthianandeswaren et
al., 2016; Wu et al., 2013). Guide lncRNAs interact with RNPs and direct them to specific
targets, thereby enabling proper localization of these RNPs. (Wu et al., 2013). For each of
these methods, lncRNAs can act in cis, that is, when they
7

regulate neighboring genes within the same chromosomal regions. Conversely,
lncRNAs can affect the expression of genes in trans, effecting expression of genes
located great distances from the genomic locus of the lncRNA, from several kb to
altogether different chromosomes (Figure 4) (He et al., 2016).

Figure 4

Gene Regulation by LncRNA A. LncRNA guide chromatin remodeling complexes to the correct chromosomal
locations. B. LncRNA can inhibit or facilitate the recruitment of RNA pol II, transcription factors to gene
promotors, thereby controlling transcription C. They can regulate alternative splicing of pre-mRNAs. D. LncRNA
can bind with mRNA and thereby affect the stability and translation of mRNA. E. LncRNAs compete with
miRNA binding and thereby prevent their functioning F. LncRNA can be processed into small, single or double-
stranded siRNAs that target other RNA, leading to degradation. G. LncRNAbind to multiple protein factors
thereby making them functionally cooperate. H. LncRNA are important in protein activity. I. LncRNA form
important subcellular structures.(Karlsson and Baccarelli, 2016)

From Figure 4 we can deduce that LncRNA have multiple functions which make them
essential to carry out important cellular processes. A dysfunctional change in the cell is
marked with a drastic effect on LncRNA expression. Subsequently, differential
expression of LncRNA affects the normal functioning of the cell. Long noncoding RNAs
8

are being increasingly recognized to contribute to diseases that disrupt normal cellular
processes, like cancer.

1.3 LncRNA in Cancer
Cancer occurs when the regulation of tissue homeostasis is disturbed and this results in
increased cell growth and proliferation (Prensner and Chinnaiyan, 2011). It is believed to be
a result of accumulated mutations which results in the deregulation of vital processes
controlling the growth and proliferation of cells (Owens and Naylor, 2013). Initial evidence
suggests that lncRNAs play essential roles in tumorigenesis (Hao et al., 2016). The recent
implementation of next-generation sequencing to many cancer transcriptomes has revealed
thousands of lncRNAs whose expression is aberrant in multiple types of cancers (Huarte
and Rinn, 2010). Abnormal lncRNA regulation and expression could directly influence
cancer development and progression – thereby revealing novel pathways that can be
targeted (Huarte, 2015).
Because lncRNAs are involved in various important physiological processes,
their dysfunction has severe consequences for cell homeostasis. Analysis of
the transcripts of noncoding genome revealed that lncRNAs are deregulated in
all stages of carcinogenesis. LncRNAs may regulate signaling pathways
involved in tumor initiation, progress and spreading. PINC and PGEM1 were
two of the first oncogenic lncRNAs
found overexpressed in breast and prostate cancers (Shore et al., 2012). Since
then many have been many lncRNAs discovered with oncogenic properties
(ANRIL,HOTAIR, MALAT1, HOTTIP, etc.) as well as those that exhibit tumor
suppressive properties, such as MEG3(Balik et al., 2013) (Table 2).
9

Table 2

GENE Locus
Nearby gene
Mechanism(s) of action Associated cancers
implicated in
cancer

ANRIL 9p21
CDKN2B Oncogene. Antagonizes the basal cell, breast, cervical,
(INK4-

CDKN2A and CDKN2B tumor esophageal, gallbladder,
ARF) tumor suppressors via recruitment of gastric,
suppressor PRC2 and PRC1 liver, melanoma, ovarian.
H19 11p15
IGF2 growth Oncogene. Targets multiple adrenal, bladder, cervical,
factor

tumor colorectal, gallbladder,

suppressive miRNAs; parent gastric, esophageal,

transcript of miRNAs involved in laryngeal, nasopharyngeal,

regulation of tight junction ovarian,
dynamics pancreatic, thyroid.
HOTAIR 12q13
HOXC Oncogene. Long-range bladder, colorectal, ER (+)
transcription epigenetic action breast, liver, nasopharyngeal,
factors

via recruitment of PRC2 and oral,

LSD1; serves as a miRNA ovarian, pancreatic, pituitary,

sponge to block small

cell lung.

miR-331-3p mediated

destruction of HER2
transcripts
HOTTIP 7p15
HOXA Oncogene. Regulates chromatin colorectal, pancreatic,
transcription structure osteosarcoma, tongue.
factors at the HOXA transcription factor

locus

MALAT1 11q13
NEAT1 Tumor Suppressor. Long-range glioma, multiple myeloma,
lncRNA

epigenetic pituitary, renal clear cell,

action, leading to suppression of tongue.

TGF; blocks oncogenic activity

of miR-21.

MEG3 14q32
DLK1 growth Tumor Suppressor. Long-range AML, cervical, colorectal,
factor

epigenetic gastric,
receptor action, leading to suppression of meningioma, ovarian,

TGFbeta; blocks oncogenic pancreatic,

activity of miR-21 pituitary, prostate, thyroid.
NEAT1 11q13
MALAT1 Oncogene. Promotes survival colorectal, esophageal,
lncRNA

when DNA gastric,

Damage present via glioma, leukemia, ovarian,

paraspeckle formation prostate.

Table 2. Emerging roles for long non-coding RNAs in cancer (Castillo et al, 2017)

10

1.4 Preliminary Data
Our lab has previously identified a lncRNA with potential tumor suppressor
activity, LINC00261. Expression of LINC00261 was downregulated in multiple
LUAD cell lines as compared to primary alveolar epithelium (Figure 6A). The lab
had previously investigated if LINC00261 was differentially expressed in other
epithelial cancers to determine if this was unique to LUAD. Using expression data
from TCGA, our lab determined that LINC00261 was down regulated in prostate,
liver, and lung squamous cancers (Figure 6C). In addition, LINC00261 is
significantly downregulated in expression as early as stage 1A in LUAD and
maintains a low expression until stage IV (Figure 6D). The expression of
LINC00261 was correlated with better survival outcomes (Figure 6E). This indicated
to us that LINC00261 is an ideal candidate for in vitro functional studies.

Figure 5
11

Analysis of differential lncRNA expression in lung adenocarcinoma reveals LINC00261 as
candidate tumor suppressor gene. A. Volcano plot of differential analysis of lncRNA expression. B.
Table of top deregulated lncRNAs in LUAD. C. Stratification of LINC00261 expression in multiple TCGA
(Rodgers Et Al.,2014) datasets available from lncRNAtor (Park et al., 2014) D. Stratification of LINC00261
expression in TCGA (2014) dataset by stage as computed by TANRIC (Li et al., 2015). ANOVA tested all
groups, difference is significant between normal and stage 1A, 1B, 2A, 2B, and 3. Stage 4 = ns E. Overall
Survival of patients stratified in 10 datasets by expression level of LINC00261 present in KMplot(p=2.3 e-10).
(Gyorffy et al., 2013)

According to the RNA-seq that was performed on multiple LUAD cell lines to identify
candidate cell lines to model LINC00261, H2228s do not express LINC00261 and A549s
have endogenous expression of LINC00261, which was verified by qRT-PCR. These cell
lines were selected as models for presence or absence of LINC00261 in subsequent
experiments.
12

CHAPTER 2: Characterizing the tumor- suppressive
properties of LINC00261

2.1 Introduction
A tumor suppressor gene is broadly defined as a gene whose product inhibits tumor
initiation and progression. In many cases these genes are lost or inactivated, thereby
removing the negative regulators of cell proliferation and contributing to the abnormal
proliferation of cells. Therefore, the search for new tumor suppressors and investigation
of their functions are of great importance to understanding the development of lung
adenocarcinoma. To date, most tumor suppressors idenfied are protein-coding genes.
However, recent have identified lncRNAs with tumor-suppressor properties (Benetatos
et al., 2011).
An example of lncRNA acting as a tumor suppressor is p53. Regulation of the tumor
suppressor p53 by lncRNAs has been a topic of intense interest. The lncRNA MEG3 is said
to directly bind p53, mediating activation of known downstream p53 targets (Zhou et al.,
2012). MEG3 is highly expressed in normal human tissue. however its expression is lost
in most human tumors like meningioma, colon cancer, nasopharyngeal carcinoma and
leukemia.(Benetatos et al., 2011) Another p53 interacting lncRNA, TUG1a 7.1kb, binds
p53 directly and is downregulated in NSCLC tissues (Wu et al., 2013). Yet another
lncRNA, MT1JP, acts by enhancing the translation of p53, thereby increasing the
production of p53 protein and mediating a series of downstream pathways, such as the
cell cycle, apoptosis and proliferation (Liu et al., 2016).
13

Our lab has identified LINC00261 as a candidate tumor suppressor gene involved in
LUAD and verified its expression levels in different LUAD cell lines. As with other
lncRNAs, the possible functional contributions of LINC00261 to carcinogenesis include
altering cellular proliferation, migration, adhesion, angiogenesis and apoptosis (Yu et
al., 2017). It was previously shown than LINC00261 is downregulated in gastric cancer
(Kang et al., 2014) but to date no molecular characterization of the underlying
mechanism LINC00261 exerts in the cell has been published. Identifying what cellular
processes LINC00261 is involved in will give us a better understanding of LINC00261’s
role in carcinogenesis.

2.2 Materials and Methods
2.2.1 Cell Culturing
A549 and H2228 LUAD cell lines were maintained in RPMI-1640 (Corning)
supplemented with 10% fetal bovine serum (FBS) and 1% penicillin streptomycin (PS).

2.2.2 RNA Isolation
Cells were washed once with cold PBS and harvested using a cell lifter (cs100, VWR).
Cells were then lysed with 500 μL of TRIzol (Ambion) and incubated at room
temperature for 5 minutes. 200 μL of chloroform was then added, the sample shaken
vigorously for 15 seconds, and incubated at room temperature for 2 minutes. Samples
were then centrifuged at 14,000 rpm for 15 minutes at 4°C. The upper aqueous phase
was collected and transferred to a new tube. 200 μL of isopropanol was then added,
mixed, and incubated at room temperature for overnight at -20°C. Samples were then
14

centrifuged at 14,0000 rpm for 10 minutes at 4°C. The supernatant was then removed,
leaving only the pellet, and left to evaporate the residual isopropanol for 5 minutes at
room temperature. 500 μL of 75% ethanol was then added to wash the pellet. This was
then centrifuged at 14,000 rpm for 5 minutes at 4°C. The supernatant was removed,
and the pellet was left to air dry for 10 minutes at room temperature. Crude RNA pellets
were then resuspended in 20 μL of ice-cold water and the concentration was
determined using a nanophotometer (IMPLEN).

2.2.3 Quantitative Real-Time PCR
PCR Complementary DNA (cDNA) was prepared using the iScript cDNA Synthesis Kit
(Bio-Rad). Reactions consisted of 1 μg RNA, 1x iScript reaction mix, 1 μL reverse
transcriptase, nuclease-free water to a total volume of 20 μL. This was then incubated
for 5 minutes at 25°C, 30 minutes at 42°C, and 5 minutes at 85°C using the MJ Mini
Thermocycler (Bio-Rad). Samples were then diluted 1:5 with water and stored at -20°C.

2.2.4 Generation of Stable Cell Lines
Stable cell lines for A549 and H2228 were generated using previously optimized
conditions in a 6-well format. Optimized antibiotic concentrations of G418 (13-394N,
Lonza) or puromycin ( CAS 58-58-2, Chemcruz). The optimized dose that took 5-7 days
for complete cell death to occur was considered optimal. 0.625μg/mL was used for
propagation of stably transfected A549 cells. For H2228, a G418 concentration of 166
μg/mL was used.
15

H2228 cells were transfected with either the pCMV LINC00261-plasmid or the control C-
terminal Myc-DDK tagged pCMV-6 entry mammalian vector. Optimized transfection
consisted of 800 ng of plasmid DNA, 2 μl of Fugene-HD and Opti-MEM (GIBCO) at a
ratio of 1:2.5 – total volume was 30 μl.
Selection media was refreshed every 48 hours. Using a P20 pipette colonies were
isolated. Isolated stable clones were transferred to a 24-well plate for monoclonal
expansion. After expansion, they were harvested and expression of LINC00261 verified
using qRT-PCR (Section 2.2.3). The list of primers used are in supplemental materials -
Table 3. Three monoclonal colonies were selected for further studies from the controls
and stables. It should be noted that the expression of LINC00261 in the stable cell lines
was like that of AT1 cells (as confirmed by the RNA-seq performed in section 3.2.1)
(Figure 8A). These cell lines were grown and frozen down using freezing media which
consisted of 10% DMSO (sigma Aldrich) and 90% Fetal Bovine Serum (FBS).

2.2.5 Migration Assay
The protocol for the in vitro migration assay was modified from that found in(Liang et al.,
2007). Approximately 400,000 cells per well were plated to a 6-well dish, and the cells
were allowed to grow to confluence. The plate was then vertically scratched using a P20
pipette tip. It was then visually inspected using an inverted phase contrast microscope.
After 24 hours, the cells were washed carefully with PBS and the media was replaced,
followed by visualization. The scratch assay was repeated 5 times using distinct stable
cell lines (N=3). The plates were then analyzed the T-Scratch software v1.0 (CSE Lab,
ETH) (Geback et al., 2009)
16

2.2.6 Proliferation Assay
10,000 cells per well of either the H2228 CMV-LINC00261 or H2228 CMV-NEO control
cells were plated on 4 wells of a 24-well dish. Control or ectopic expression cells were
then harvested from the wells by trypsinization at 24-hour intervals over a total of 4 days
and counted using a Bright-Line hemocytometer (Sigma-Aldrich), following the
manufacturer’s protocol.

2.2.7 Fluorescent Activated Cell Sorting (FACS)
A549 cells and H2228 cells, along with their controls, were plated in a 6-well format. Once
50% confluent, they were washed three times with PBS. Cells were trypsinized using 500
uL of Trypsin-EDTA(0.05%). The cells were then harvested and spun down at 2500 rpm for
5 minutes. 300 uL of Propidium Iodide (Invitrogen) was added to the pellet, which was then
resuspended 10-15 times and passed through Corning Filter Top Tubes (mesh size 40µm,
Corning Inc.352340) The resuspended cells were processed using (BDFACs-
ARIA, 535/617 excitation-emission) in the lab of Dr Omid Akbari. Cell cycle distribution
was calculated using FloJo v10.1.

2.3 Results

2.3.1 Generation of stable LINC00261 expression in the H2228 cell line
LINC00261 overexpression stable cell lines were generated in H2228 cells. The
expression plasmid with CMV promotor and LINC00261 gene was transfected into
H2228 cells. The colonies were tested for expression of LINC00261 using qRT-PCR.
17

2.3.2 Re- introduction of LINC00261 decreases cellular migration
A fundamental property of cancer cells is continuous and unregulated proliferation. I
generated H2228-CMV-LINC00261 cells which ectopically over expressed the
LINC00261 gene (Figure 9A) and the levels of the LINC00261 were comparable to that
of alveolar cells (Figure 9B).
Alterations in cell migration are a hallmark of cancer cell invasion and metastasis. Tumor
suppressor genes are known to prevent cancer cell migration. H2228 cells with and without
LINC00261 were assayed for their ability to migrate. Reintroduction of LINC00261 into
H2228 cells inhibited the ability of these cells to migrate in 2D culture conditions (Figure
8A). Quantification of the cells ability to migrate, indicated that LINC00261 significantly
impaired their ability to restore a monolayer within 24 hours (Figure 8B). This
experiment showed that reintroduction of LINC00261 profoundly decreased the migrative

capacity of the cells.

Figure 6
18

LINC00261 affects the migratory behavior of H2228 LUAD cells. A. Equal number of cells of H2228-
CMV-NEO and H2228 CMV-LINC00261 were placed in a 6-well plate and allowed to grow to confluency.
The plate was vertically scratched at approximately 500 µm cross-section and observed at 0 and 24 hrs.
The experiment was repeated with 3 biological replicates in triplicates. B. Quantification of H2228
cellular migration from (a). Distances were measured using T-SCRATCH software (CSE LAB,
ETH).(Geback et al., 2009)

2.3.3 Ectopic expression of LINC00261 increases cellular proliferation
The H2228-CMV-NEO and H2228-CMV-LINC00261 were assessed for their differences
in proliferative capacity. Using three stable cell lines and performing the experiment in
triplicate, the growth rate and doubling times between H2228 CMV-NEO and H2228
CMV-LINC00261 were found to statistically significantly distinct (Figure 9C). Proliferation
relies on the accelerating and braking mechanisms which act on the cell cycle. Tumor
suppressors systems act as transduction systems of negative signals which induce a
brake in the cell cycle. This experiment demonstrated that reintroduction of LINC00261
decreased the proliferation of cells. Fluorescent activated cell sorting was performed to
determine how the phases of the cell cycle were influenced by LINC00261 to affect
proliferation.

19

FIGURE 7

LINC00261 AFFECTS IN VITRO PROLIFERATION OF LUAD CANCER CELL LINES A. Quantitation of
ectopic LINC00261 reintroduction H2228-CMV-LINC00261 and H2228-CMV NEO control levels (n=3). B.
Proliferation of H2228-CMV-NEO vs. H2228-CMV-LINC00261. CMV-LINC00261 cells grew significantly
slower (n=3 p=0.016) The experiment was repeated with 3 biological replicates in triplicates. C. The
LINC00261 locus and RNASEQ of primary at cells, H2228 CMV-NEO and CMV-LINC00261 stable cell
lines. The blue bigwig tracks are of primary at cells from three human donors. The orange tracks
represent H2228-CMV-NEO and the purple tracks are H2228-CMV-LINC00261. All bigwig tracks are
binned into 30bp windows and scaled [0-100]. Reference genome is Hg19.

2.3.4 FACs analysis shows large number of cells in G2 phase
Cancer cells can bypass the cell cycle checkpoint machinery and grow indefinitely.
Tumor suppressor genes tend to sensitize the cancer cells to the cell cycle checkpoints
and help them to retain properties of normal cells. Thus, determining the cell cycle
phases of the cells on adding a tumor suppressor gene, would give us an insight into
the mechanistic approach of the tumor suppressor.
Propidium Iodide staining and subsequent FACS analysis H2228-CMV-NEOs versus
the H2228-CMV-LINC00261 stable cell lines showed a significant shift in the population
that resulted in an accumulation of cells in the G2/M phase of cell cycle (Figure 10A).
The number of cells in G2/M phase in the H2228-CMV-LINC00261 stables was
statistically significantly more than the control H2228-CMV-NEO stables (Figure 10B).
Figure 7
20

We repeated this analysis using A549 stable cell lines with shLINC00261 or control sh
plasmids (Figure 10C). In A549 cells we observed that loss of LINC00261 resulted in a
population shift of cells out of G2/M and a concomitant increase in cells with greater
than G2/M DNA content (Figure 10D).
Figure 8

FACS analysis of LINC00261 reintroduction or knock down on cell cycle distribution. A. Flow
cytometery profiling of H2228-CMV-NEO and H2228-CMV-LINC00261 using Propidium Iodide FlowJo
v10.1. N = 10,000 per sample. B. Quantification of Flow analysis was performed and it is seen that there
is large increase in the G2/M checkpoint. C Flow cytometry profiling of A549-shScrambled and A549-
shLINC00261 cells using Propidium Iodide. Population was analyzed using FlowJo v10.1. N = 10,000 per
sample. D. Quantification of Flow analysis was performed and it is seen that 12% of the cells accumulate
in the region greater than G2 (n = 3).

The G2/M cell cycle checkpoint is activated primarily by DNA damage that, when
presents detected by specific proteins, initiating a phosphorylation cascade to halt the
cell cycle and give repair enzymes time to fix the damage. Because I observed an
accumulation of cells in G2/M, we wanted to determine if LINC00261 was affecting the
function of the DNA damage response. This will be addressed in the next chapter.
21

2.4 Discussion

I utilized the cell line H2228, which lacks endogenous expression of LINC00261, to
generated three stable cell lines where LINC00261 was ectopically expressed
(alongside vector-matched controls). This provided a good model system for studying
the effect of LINC00261 on the proliferative capacity of LUAD cells. I demonstrated that
my stable H2228-CMV-LINC00261 cell lines had LINC00261 expression via qRT-PCR
(Figure 9A). It is important to note that while these cells do have a strong promotor, they
express an amount of LINC00261 that is comparable to the expression seen in alveolar
epithelial cells (Figure 9B). This would mean they are recapitulating what we would
expect endogenous expression levels to be like in primary alveolar epithelium.
Results from the proliferation and migration assays indicate that LINC00261 plays an
important role in the physiological processes of the cell (Figure 8). The statistically
significant decrease in proliferation upon reintroducing LINC00261 into the cells concurs
with our hypothesis that LINC00261 acts as a tumor suppressor (Figure 9). It also
implies that reintroduction of the LINC00261 could slow down the progression of
cancers in vitro, though in vivo implications in tumors must be investigated. Mouse
xenographts could help us with this investigation. These results necessitate further
mechanistic interrogation of the involvement of LINC00261
FACS analyses showed that LINC00261 reintroduction results in an accumulation of
cells in the G2/M phase of the cell cycle (Figure 10). The A549 cells with LINC00261
knockdown showed a shift in the cellular population that moved toward greater than G2
22

DNA content (Figure 10C). This suggested that the lack of LINC00261 allowed the cells
to bypass G2/M checkpoint. (Figure 10D), however further experimentation such as
isolation of the >G2 population and karyotyping is needed to confirm this hypothesis.

Most tumor suppressor and oncogenes are thought to act on the G1 cell cycle
checkpoint. This is where the cells make the fate decision to commit to replication and
division, or cease growing and senesce. The G2/M checkpoint is to determine if DNA
replication has occurred accurately. G2/M is also known as the DNA damage
checkpoint as it checks for incomplete replication. The DNA damage signaling
machinery is active at this phase of the cell cycle, with many components expression
tied to this phase, such as Cyclin B (cdc25). If there is damage, checkpoint regulators
such as ATM/ATR/CHEK1/CHEK2 etc. initiate a phosphorylation cascade. This will
further recruit repair enzymes and ultimately will pause the cell cycle to allow for DNA
repair. As LINC00261 halts the cell cycle at G2 checkpoint, our next step was to find out
if the mechanism of LINC00261 mediated G2/M cell cycle arrest involved activation of
the DNA damage response pathway. To assess whether the cells were undergoing a
G2/M cell cycle arrest we proceeded to the experiments outlined in chapter 3.
23

CHAPTER 3: Investigating the role of LINC00261 in the
DNA Damage checkpoint

3.1 Introduction

The cell cycle is a system present in the cells, that controls various physiological processes
like growth, proliferation and senescence (Figure 1A). The cell cycle checkpoints monitor
the changes that occur in the cell and determine whether the cell is competent to pass on its
genetic information. A change in homeostasis of the cell can be attributed to the change in
genetic composition of the cell or due to an external stimulus. Cancer development and
progression are closely linked to the cell cycle (Sotak et al., 2014). Cancer cells trick the
cells into entering the cell cycle continuously thereby leading to its proliferation and
progression. For example, in the studies conducted by Filipski et al., tumor growth was
aggressive when the cell cycle was disrupted. The cell cycle checkpoint proteins thereby
function as gate keepers and try to ensure that the cells do not suffer from mutations, DNA
damage or DNA defects. (Filipski et al., 2009)
Cells respond to DNA damage by activating a complex downstream signaling that decides
its fate. The cells can either decide to fix the DNA damage by recruiting proteins involved in
DNA repair or cause apoptosis by using caspase-mediated or caspase independent
pathways (Roos and Kaina, 2013). The decision of the cell fate resides in the proteins
responsible for the DNA damage pathway. These proteins are collectively called the DNA
Damage Response (DDR) proteins and are activated when a damage is detected.
24

DNA damage is predominantly determined in the G2/M phase by Mre11, Rad50 and
Nbs1 complex (MRN complex). Immediate detection of the DNA damage and active
recruitment of the DNA damage pathways is crucial for the maintenance of the integrity
of the genome. ATM is said to be the immediate downstream response element, it gets
auto phosphorylated and phosphorylates critical proteins like Chk1, Chk2 and H2AX.
Recent studies show that in addition to protein coding regions of the transcriptome, non-
protein coding regions tend to play a major role in DDR. For example, Gadd7 is involved
in regulating CDK6 expression. Gadd7 is known to cause cell cycle arrest at G1/S
transition. It binds to the CDK6 mRNA and the mRNA is then degraded. Gadd7 is
upregulated in DNA damage mediated by UV (Hollander et al., 1996).
Our previous results show us that the DNA damage pathway might be getting activated
on reintroduction of LINC00261. To investigate this further and to quantitate the
mechanism by LINC00261 activates the DNA damage pathway, we perform the
following experiments.
25

Figure 9

Cancer and the Cell Cycle A. Cell cycle checkpoints – The cell cycle is controlled at three
checkpoints, the integrity of the DNA is assessed at each checkpoint and it moves to the next phase.
G1 checkpoint is for checking if the cells have enough growth factors, nutrients etc to proceed. At
G2/M the cell is checked for DNA damage. At M it is checker for attachemnt of the kinectochore. This
figure is taken from – Oncogenes and their Role in Cancer (Wordpress, 2015) B. DNA Damage
Response DNA damage response recruits kinases like ataxia-telangiectasia mutated(ATM) and ataxia
telangiectasia and Rad3-related(ATR). They get auto phosphorylated and recruit other downstream
proteins like p53, BRCA1, CHK1 and CHK2. These are all kinases that phosphorylate downstream
affecters: This figure is taken from - DNA damage-induced cell death: from specific DNA lesions to the
DNA damage response and apoptosis(Roos and Kaina, 2013).
26

3.2 Materials and Methods

3.2.1 RNA-Sequencing analysis
RNA-seq was performed to check the differential expression of genes between H2228-
CMV-LINC00261 and H2228-CMV-NEO. Total RNA content was measured after using
a RiboZero purification (Illumina NextSeq500), and samples were sequenced at the
USC Epigenome Core. The raw FASTQ files were uploaded to the GALAXY web server
https://usegalaxy.org/ and processed as follows: FASTQ files were subject to quality
assessment using NGS:QC and manipulation – Compute Quality Statistics., then each
read had the first 12bp trimmed to remove sequence adapters. Then, each read was
filtered using a quality score of 30 for greater than 90% of the read. The resultant
FASTQ files were then run through the Tophat2.1.1 alignment program and aligned to
hg38 (Figure 12A). Once aligned, BAM files were downloaded and technical quality of
the samples assessed in R (version3.4.0) using DESeq2 (v1.16.1). DESeq provides
methods to test for differential expression by use of the negative binomial distribution by
using raw count tables. The values are pre-filtered and normalized. We visualize the
differentially expressed genes by using a volcano plot. The results are then expressed
in terms of an unsupervised clustered heatmap. Once differential expression was
calculated, pathway analysis in IPA was conducted using the Core Analysis tool, was
used to study functions of differential mRNAs. Similar analysis was carried out with
A549shLINC00261 and A549shScramble RNA-seq samples were sent to Novogene for
library preparation and RNA sequencing.
27

Figure 10

RNA-Seq Workflow: Raw reads were obtained From the USC Genomics Core as FASTQ files
and quality control was performed and filtered by quality (90% and >30). The transcriptome was
then assembled onto hg38 Genome, using TOPHAT. The aligned files were moved to R for
differential analysis using DESeq2.

3.2.2 CODE

source("http://bioconductor.org/biocLite.R")

biocLite()
library(GenomicFeatures)
28

library(GenomicRanges)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(GenomicAlignments)
library(DESeq2)
library("pheatmap")
library("RColorBrewer")
library("ggplot2")
library("AnnotationDbi")
library("org.Hs.eg.db")

##CREATE THE TRANSCRIPT DATABASE THAT WE WILL USE TO ANALYZE
DIFFERENTIAL EXPRESSION
hg38.tx<-TxDb.Hsapiens.UCSC.hg38.knownGene
hg38.tx

##TURN OFF ALL THE HALF-FORMED CHROMOSOMAL PIECES AND JUT LEAVE
BEHIND THE ACTUAL CHROMOSOMES
isActiveSeq(hg38.tx)
29

isActiveSeq(hg38.tx)[seqlevels(hg38.tx)]<-FALSE
isActiveSeq(hg38.tx)[paste("chr",c(1:22,"X","Y","M"),sep="")]<-TRUE
hg38.tx

setwd("C:/Users/Vishaly/Desktop/H2228_DESEQ2")
getwd()
list.files()

##READ IN THE BAM FILES

p <- ScanBamParam(what=c("rname", "strand", "pos", "qwidth","mapq"))

my.aln1<-readGAlignments("H2228CMVNEO1.bam")
my.aln2<-readGAlignments("H2228CMVNEO2.bam")
my.aln3<-readGAlignments("H2228CMVNEO3.bam")
my.aln4<-readGAlignments("H2228CMVLINC1.bam")

##BUILD TABLE OF COUNTS BY EXONS

exbygene<-exonsBy(hg38.tx,by="gene")
30

cnts_Neo1=summarizeOverlaps(exbygene,my.aln1)
length(assays(cnts_Scr1)$counts)
cnts_Neo2=summarizeOverlaps(exbygene,my.aln2)
length(assays(cnts_Scr2)$counts)
cnts_Neo3=summarizeOverlaps(exbygene,my.aln3)
length(assays(cnts_Scr3)$counts)
cnts_LINC00261_1=summarizeOverlaps(exbygene,my.aln4)
length(assays(cnts_LINC00261_1)$counts)
cnts_LINC00261_2=summarizeOverlaps(exbygene,my.aln5)
length(assays(cnts_LINC00261_2)$counts)
cnts_LINC00261_3=summarizeOverlaps(exbygene,my.aln6)
length(assays(cnts_LINC00261_2)$counts)

tableofcounts=cbind(assays(cnts_Scr1)$counts,
assays(cnts_Scr2)$counts,
31

assays(cnts_LINC00261_1)$counts,
assays(cnts_LINC00261_2)$counts)
write.csv(tableofcounts,file="H2228_tableofcounts.csv")
head(tableofcounts)

## CHECKING FOR DIFFERENTIAL EXPRESSION

library(DESeq2)

setwd("C:/Users/Vishaly/Desktop/RNAseqAnalysis/H2228_DESeq2_1")

##USING COUNT MATRIX AS INPUT

countData <- read.csv("H2228_NEO_LINCOO261_Stables_tableofcounts.csv.csv",
row.names=1)
colData <- read.csv("SampleAttribute.csv")
colData <- colData[,c("Name","Type")]

## CONSTRUCTING A DESeqDataSet

dds <- DESeqDataSetFromMatrix(countData = countData,
32

colData = colData,
design = ~ Type)
## PRE-FILTERING

dds <- dds[ rowSums(counts(dds)) > 1, ]

##Result Table
dds<-DESeq(dds)
res<-results(dds)
res

## SUMMARY

summary(res)

##PLOTMA - SHOWS THE LOG2 FOLD CHANGES ATTRIBUTABLE TO A GIVEN
VARIABLE OVER THE MEAN OF NORMALIZED COUNTS
plotMA(res,main="DESeq2", ylim=c(-2,2))
33

## ESTIMATE THE COUNT OF READS BETWEEN TREATED AND NOT TREATED.

plotCounts(dds, gene=which.min(res$padj), intgroup="Type")

## EXTRACTING TRANSFORMED VALUES

rld <- rlog(dds, blind=FALSE)

vsd <- varianceStabilizingTransformation(dds, blind=FALSE)
vsd.fast <- vst(dds, blind=FALSE)
head(assay(rld), 3)

##MAKING A VOLCANO PLOT

volcano<-read.csv("results1.csv", header=T, row.names=1)
colnames(volcano)
attach(volcano)
log10<- -log10(padj)
plot(log2FoldChange, log10, type="n", xlab="Log Fold Change in Gene Expression",
ylab="log2FoldChange", main="Volcano Plot for Differential Gene Expression")
points(log2FoldChange, log10, col="black", cex=0.5, pch=19)
points(log2FoldChange[(log2FoldChange>1&log10>1.3)],
log10[(log2FoldChange>1&log10>1.3)], col="red", pch=19, cex=0.5)
34

points(log2FoldChange[(log2FoldChange< -1&log10>1.3)], log10[(log2FoldChange< -
1&log10>1.3)], col="green3", pch=19, cex=0.5)
abline(h=1.3, lty=3, lwd=3, col="grey")
abline(v=1, lty=3, lwd=3, col="grey")
abline(v= -1, lty=3, lwd=3, col="grey")
##CLUSTERING AND VISUALIZATION - HEATMAP_NORMALIZED

library("pheatmap")

select <- order(rowMeans(counts(dds,normalized=TRUE)),
decreasing=TRUE)[1:200]
nt <- normTransform(dds) # defaults to log2(x+1)
log2.norm.counts <- assay(nt)[select,]
df <- as.data.frame(colData(dds)[,c("Name","Type")])
pheatmap(log2.norm.counts, cluster_rows=FALSE, show_rownames=FALSE,
cluster_cols=FALSE, annotation_col=df)

## GENE CLUSTERING AND MAKING DENDROGRAM of top 1000 VARIANT

GENES library("genefilter")

topVarGenes <- head(order(-rowVars(assay(rld))),1000)
mat <- assay(rld)[ topVarGenes, ]
35

mat <- mat - rowMeans(mat)

df <- as.data.frame(colData(rld)[,c("Name","Type")])

colfunc <- colorRampPalette(c("darkgreen", "black", "darkred"))
pheatmap(mat,col=colfunc(15),scale="row", trace="none")
display.brewer.all()

##PRINCIPAL COMPONENT ANALYSIS

plotPCA(rld, intgroup=c("Name", "Type"))

#ANNOTATION OF RESULTS TABLES CONVERT THE ROW NAMES TO ENTREZ IDS

res$symbol <- mapIds(org.Hs.eg.db,

keys=row.names(res),
column="SYMBOL",
keytype="ENTREZID",
multiVals="first")
36
resOrdered <- res[order(res$pvalue),]

head(resOrdered)##Export Results

resOrderedDF <- as.data.frame(resOrdered)[1:103 ]
write.csv(resOrderedDF, file = "results1.csv")

3.2.2 Western Blot

Protein lysates from both H2228-CMV-NEO and H2228-CMV-LINC00261 were obtained by
washing 70% confluent plates with PBS, then harvesting in PBS and spinning down the
cells at 2500prm for 5min at 4C. Once harvested, the supernatant was removed and. RIPA
buffer (radioimmunoprecipiate buffer with 1mM phenymethylsulfonyl fluoride) was used to
lyse the cells. Cell suspensions underwent shearing using a 21G needle (15x dounced) and
the cells pelleted at 10,000rpm for 10min at 4C to remove cellular debris. Supernatant
containing the protein fraction underwent Bradford quantification. All samples were
standardized and 30µg of lysate was added in each well. Samples were resuspended with
5x GLB (Sodium Dodecyl Sulphate, bromophenol blue, DDT and betamercaptoethanol) and
then run on 10% Tris-polyacrylamide gels for 3 hours at 15mAmp per gel. Gels were transferred
at 22volts overnight, onto a Immuno-Blot PVDF membrane. Antibodies for phosphorylated ATM,
ATR, BRCA1, CHK1, CHK2, and H2AX from Cell Signaling DNA damage repair kit (Cell
Signaling, CS#9947) were used at a dilution of 1:1000 and incubated overnight. HSP90 alpha
antibody (Genetex, GTX109753 )was used as loading control The membrane was washed 3
times for 15 minutes using TBST. The membrane was blocked using nonfat milk (5%).
Horseradish peroxidase(HRP)- conjugated goat anti-rabbit immunoglobulin was added at a
37
dilution of 1:10,000 for 1 hour at room temperature. The membrane was then washed 3 times
for 5 minutes and Super Signal West Femto Maximum Sensitivity Substrate (Fischer Scientific,
34095) was used to visualize the membranes. The blots were visualized in the Bio-rad Chemi
doc XRS chemiluminescence setting was used The inhibitor Ku- 55933 (Abcam, ab120637).
was used as a specific inhibitor of ATM kinase phosphorylation. The optimal concentration of
Ku-55933 was taken from the paper: Identification and Characterization of a Novel and Specific
Inhibitor of the Ataxia- Telangiectasia Mutated Kinase ATM(Hickson et al., 2004)

3.3 Results

RNA sequencing (RNA-Seq) uses next-generation sequencing to reveal the presence and
quantity of RNA in a biological sample at a given moment. Our previous data showed that
re-introduction of LINC00261 enabled the cells to halt at the G2/M DNA damage checkpoint.
To validate the results, we performed RNA- sequencing on the H2228- CMV-NEO and H2228-
CMV-LINC00261 stable cell lines we previously generated. RNA-seq analysis using the
DESeq2 package (Love et al., 2014) indicated that 103 genes were differentially expressed
upon ectopic LINC00261 reintroduction (Supplemental DATA Table 4).
Principal component analysis converts a set of observations into linearly uncorrelated
variables called principal components. This transformation is performed in such a way
that the first principal component has the largest possible variance and the next
component the second largest etc. It can be seen from the figure that the controls and
the stables have the largest variability (Figure 11A).
Volcano plot shows the significant fold changes on the x-axes and y-axes. The negative

log of the adjusted p-value is plotted against the log2 fold changes (to make the points
38
equidistant). The points on the top left indicate substantial negative fold changes, i.e.
these genes are significantly downregulated in the presence of the LINC00261. The top
103 differentially expressed genes are listed in Supplemental Data table 4. The green
points are the statistically downregulated genes and the red points are the statistically
up regulated genes (Figure 11B).

Figure 11

RNA-Seq Analysis of H2228 cells. A. PCA plot of 4 samples in 2D plane using DESEQ2. B. Volcano
plot of differential gene expression. The red dots represent the significantly upregulated genes in H2228-
CMV-LINC00261 and the green dots represent the significantly downregulated genes.
Clustering makes the analysis of distances in high dimensional data more effective and
can be visualized using a heatmap. Clustering helps to identify candidate subgroups in
complex data. Hierarchical clustering is used to display the distances between
subgroups. The closest distances are measured as 1 and so forth. Unsupervised
hierarchical clustering of the samples showed us that H2228-CMV-LINC00261 and
H2228-CMV-NEO were intrinsically different. The figure shows the top 1000 most
39

variant genes amongst the two groups (Figure 12).

Figure 12

Unsupervised hierarchical clustering of H2228-CMV-LINC00261 and H2228-CMV-NEO normalized
microarray data using pheatmap (v1.0.8) (Kolde, 2015, 2015-12-11). The top 1000 variant probes across
the dataset were included. The colors of the tiles on the heatmap represent the measured experiment
value of the particular gene. Red = High expression level, Yellow =intermediate levels and blue = low
expression levels.

Ingenuity® Pathway Analysis (IPA ®) by Qiagen is a robust analysis and search tool that
identifies the relationship between the differentially expressed genes (Figure13). IPA
pathways analysis revealed that significant pathways altered were Cyclins and Cell Cycle
Regulation and G2/M Checkpoint Regulation. Granzyme A signaling, involved in caspase
independent apoptosis seemed to be highly activated in the cell lines. Various genes that
involved in the cell cycle checkpoints and apoptosis seemed to be highlighted. Other cyclins
involved in G1/S checkpoint and mitotic spindle disruptions also have an increased
expression. This along with the observations seen while performing FACs
40
(Section 2.3.4) led us to believe that LINC00261 played a significant role in sensitizing
the cancer cells to DNA damage and activated the proteins at the G2 checkpoint.

Figure 13

IPA pathway enrichment of differentially expressed genes between H2228 CMV-NEO and CMV-
LINC00261. Pathways are corrected by Fischer’s exact Test p-value. Granzyme A signaling, a caspase-
independent apoptosis pathway is highly altered. It can be observed that various pathways associated
with the cell cycle are altered. The DNA damage pathway is altered that is the G2/M checkpoint.

To further investigate this intriguing result we performed Western Blots on the proteins
involved in DNA damage checkpoint using the DNA damage signaling kit, obtained from
cell signaling (Figure 14). The results revealed that several of the top G2/M checkpoint
proteins were phosphorylated on the introduction of LINC00261. The amount of ATM,
Chk2, BRACA1 proteins were increased in the cells when LINC00261 was reintroduced.
To check whether the lncRNA was acting upstream of ATM, we introduced an ATM
inhibitor, Ku-55933 (Stiff et al., 2006). Ku55933 blocks ATM phosphorylation and
prevents the downstream phosphorylation.(Hickson et al., 2004) This indicates that
41
LINC00261 activates the DDR protein phosphorylation cascade (Figure 14).

Figure 14

Western blot of lysates from H2228-CMV-LINC00261 and H2228-CMV-NEO controls. (Proteins in decreasing
order of molecular weight) DNA damage response proteins like ATM, CHK2, CHK1, H2AX and BRACA1 are
phosphorylated in the presence of LINC00261. Use of ATM inhibitor Ku-55933 shows us that action of
LINC00261 occurs upstream of ATM. There is no obvious change in the phosphorylation of ATR.

RNA-Seq of H2228 samples with and without LINC00261 revealed that some of the
genes involved in the G2/M checkpoint were differentially expressed. Reintroduction of
LINC00261 causes the cells to become sensitized to DNA damage. To check if the
converse was true we performed RNA-Seq on the A549 cells whose LINC00261 levels
had been knocked down using shRNA. These stable cell lines were generated by both
myself (2 A549shLINC00261 lines, and 1 control line) as well as previous student in the
42
lab (1 A549shLINC00261 line, 2 control lines). Upon visualizing the counts of the genes
in all the samples (Figure 15A) and the PCA plot (Figure 15B), we concluded that the
data was not of a high enough quality to continue with downstream analysis. This was
because LINC00261 levels were not >2fold knocked down in 2 of the 3
A549shLINC00261 lines. (Figure 16).

Figure 15

RNA-Seq Analysis of A549 cells. A Total read counts for the samples. B. PCA plot. The distribution
of the samples is inconsistent.
43

Figure 16

Unsupervised hierarchical clustering of A549shLINC00261 and A549shSCRAMBLED normalized data
using pheatmapv1.0.8 (Kolde, 2015) .The top 1000 variant genes across the dataset were included. The
clustering between the 6 samples is inconsistent. The colors of the tiles on the heatmap represent the
measured experiment value of the gene. Red = High expression level, Yellow =intermediate levels and
blue = low expression levels.

3.4 Discussion
The bioinformatic analysis of LINC00261 provided interesting results. On analyzing the
differential expression of genes between the H2228-CMV-NEOs and H2228-CMV-
LINC00261 lines, it can be seen that 103 genes are differentially expressed. On
performing IPA on these genes, the pathways that are altered are the ones involved in
DNA damage, apoptosis and cell cycle regulation. Granzyme A signaling is highly
44
activated in these cells. Granzyme A is involved in a caspase-independent programmed
cell death. Other pathways involving cell cycle regulation and DNA damage are also
altered in these two states. TOP2A and ATM important players involved in DNA
damage are significantly altered by the reintroduction of LINC00261.
Reintroduction of LINC00261 into H2228 cells which lacked endogenous expression
resulted in a prominent G2/M cell cycle arrest as is seen in the previous results. The
phosphorylation of ATM, one of the primary responders of the DNA damage is
quantitatively higher in H2228-CMV-LINC00261 cells as compared to the H2228-CMV-
NEOs. This substantiates the fact the LINC00261 functions to sensitize the cells to DNA
damage occurring during cancer proliferation. Other DNA Damage pathway response
proteins are seen to be phosphorylated. These include BRCA1, CHK1, CHK2 and
H2AX. The phosphorylation of these proteins indicates the downstream progression of
the signal and the determination of cell fate.
Ku-55933 inhibitor is used to block the phosphorylation of ATM. The phosphorylation
proteins downstream of ATM are also inhibited by this compound. This indicates that
the signaling cascade is affected upstream of ATM. We can derive from our
experiments that LINC00261 acts upstream of ATM.
RNA-Seq was performed in A549s was performed to check if knocking down
LINC00261 initiated altercation of any pathways. On obtaining the differentially
expressed genes and plotting them in a heatmap it could be seen that the data obtained
from A549 cells were not conclusive.
45
CHAPTER 4 : Summary

The findings presented suggest that LINC00261 plays a role in tumorigenesis and acts
as a tumor suppressor gene. The H2228 cell lines did not have endogenous expression
of LINC00261. This provided a good model system to study the proliferative, migration
and anti -tumorigenic property of LINC00261. From the studies, it could be concluded
that LINC00261 slows down the migration of cancers in vitro. The FACs analysis
introduced us to further evidence that LINC00261 acts as a tumor suppressor and
suggested that it occurs at the G2/M checkpoint.
RNA-Seq analysis indicated that G2/M cell arrest was induced by the activating the
DNA damage protein. Western blots confirmed that DNA damage checkpoint control
proteins underwent phosphorylation. It can be deduced that LINC00261 plays an
important role in sensitizing the cells to DNA damage. This could have tremendous
clinical impacts. Chemo therapeutic treatments are largely based on damaging DNA in
cancer cells. As LINC00261 appears to sensitize the cells with respect to DNA damage,
it could have huge therapeutic values. For example, etoposide is a common
chemotherapeutic agent used for NSCLC, along with radiation. Etoposide induces the
inhibition of topoisomerase2 (TOP2A) and thereby causes DNA breaks in the
cell(Hande, 1998). LINC00261 appears to also sensitize the cells to DNA damage and a
combination of the two might lead to an increase efficiency of the therapy.
46
CHAPTER 5: Future directions

We hypothesize that ATM might be binding to LINC00261 and performed analysis on
catRapid (Cirillo et al., 2016). The protein-RNA pair is predicted to interact with
propensity (Global Score) 1.00. CatRAPID predicts protein-RNA associations by
combining secondary structure, hydrogen bonding and van der Waals contributions.
ATM is a relatively large protein (350kDa). The software predicts that the LINC00261
binds to the protein at its active site. This could be the method by which LINC00261
instigates the DNA Damage Response (Figure 17).
Figure 17

ATM BINDING REGION A. RESULTS FROM CATRAPID THAT PREDICT THE LINC00261-ATM PROTEIN BINDING SITE. B. IT
CAN BE SEEN THAT LINC00261 BINDS AT THE ACTIVE SITE OF THE PROTEIN AND THIS EFFECT DOWNSTREAM
PHOSPHORYLATION (SHILOH AND ZIV, 2013).
47

Preliminary experiments implicate that loss of LINC00261 results in the stimulation of
DNA damage pathway signaling. It is unknown how LINC00261 affects downstream
signaling. Through these experiments we can hypothesize that LINC00261 can act at
the level of ATM and sensitize the cells to DNA damage. LncRNA are known to bind to
proteins and act as a guide. To check if this is the case we can use ATM antibodies to
perform an RNA Immuno-Precipitation (RIP). In the RIP protocol, RNA is crosslinked to
protein, and the complex is purified from the cellular lysate. The complex then
undergoes protein digestion, and RNA is converted into cDNA and quantitated using
qRT-PCR. Another approach to find out if LINC00261 is binding to ATM would be to
generate aptamers that bind to streptavidin. The RNA can then be pulled down using
biotin and the proteins that bind to it can be determined. Direct interaction of LINC00261
with mRNA of ATM could also be possible. Determination of the mechanism of action of
this LncRNA could give rise to insights in the mechanism of LUAD progression and
therapeutic implications.
48

References

1999–2011 Incidence and Mortality Report., United states Cancer Statistics.
2014, Comprehensive molecular profiling of lung adenocarcinoma: Nature, v. 511, p. 543-
50. Balik, V., J. Srovnal, I. Sulla, O. Kalita, T. Foltanova, M. Vaverka, L. Hrabalek, and M.
Hajduch, 2013, MEG3: a novel long noncoding potentially tumour-suppressing RNA in
meningiomas: J Neurooncol, v. 112, p. 1-8.
Benetatos, L., G. Vartholomatos, and E. Hatzimichael, 2011, MEG3 imprinted gene
contribution in tumorigenesis: Int J Cancer, v. 129, p. 773-9.
Brunner, A. L., A. H. Beck, B. Edris, R. T. Sweeney, S. X. Zhu, R. Li, K. Montgomery, S. Varma,
T. Gilks, X. Guo, J.
W.Foley, D. M. Witten, C. P. Giacomini, R. A. Flynn, J. R. Pollack, R. Tibshirani, H. Y. Chang,
M. van de Rijn, and R. B. West, 2012, Transcriptional profiling of long non-coding RNAs
and novel transcribed regions across a diverse panel of archived human cancers:
Genome Biol, v. 13, p. R75.
CDC, 2015, Health, United States, US department of Health and Human Services.
Cheng, J., P. Kapranov, J. Drenkow, S. Dike, S. Brubaker, S. Patel, J. Long, D. Stern, H. Tammana,
G. Helt,
X. Sementchenko, A. Piccolboni, S. Bekiranov, D. K. Bailey, M. Ganesh, S. Ghosh, I. Bell, D.
S. Gerhard, and T. R. Gingeras, 2005, Transcriptional maps of 10 human chromosomes
at 5- nucleotide resolution: Science, v. 308, p. 1149-54.
Cirillo, D., M. Blanco, A. Armaos, A. Buness, P. Avner, M. Guttman, A. Cerase, and G. G.
Tartaglia, 2016, Quantitative predictions of protein interactions with long
noncoding RNAs: Nat Methods, v. 14, p. 5-6.
Derrien, T., R. Johnson, G. Bussotti, A. Tanzer, S. Djebali, H. Tilgner, G. Guernec, D. Martin,
A. Merkel, D.
G. Knowles, J. Lagarde, L. Veeravalli, X. Ruan, Y. Ruan, T. Lassmann, P. Carninci, J. B. Brown,
L. Lipovich, J.
M. Gonzalez, M. Thomas, C. A. Davis, R. Shiekhattar, T. R. Gingeras, T. J. Hubbard, C.
Notredame, J. Harrow, and R. Guigo, 2012, The GENCODE v7 catalog of human long
noncoding RNAs: analysis of their gene structure, evolution, and expression: Genome Res, v.
22, p. 1775-
89. DeSantis, C. E., C. C. Lin, A. B. Mariotto, R. L. Siegel, K. D. Stein, J. L. Kramer, R. Alteri, A.
S. Robbins, and A.
Jemal, 2014, Cancer treatment and survivorship statistics, 2014: CA Cancer J Clin, v. 64,
p. 252-71.
Devaux, Y., J. Zangrando, B. Schroen, E. E. Creemers, T. Pedrazzini, C. P. Chang, G. W. Dorn, 2nd,
T. Thum, and S. Heymans, 2015, Long noncoding RNAs in cardiac development and
ageing: Nat Rev Cardiol, v. 12, p. 415-25.
Fabricius, P., and P. Lange, 2003, Diet and lung cancer: Monaldi Arch Chest Dis, v. 59, p. 207-11.
Filipski, E., P. Subramanian, J. Carriere, C. Guettier, H. Barbason, and F. Levi, 2009,
Circadian disruption accelerates liver carcinogenesis in mice: Mutat Res, v. 680, p.
95-105.
Fritah, S., S. P. Niclou, and F. Azuaje, 2014, Databases for lncRNAs: a comparative evaluation
of emerging tools: Rna, v. 20, p. 1655-65.
49

Geback, T., M. M. Schulz, P. Koumoutsakos, and M. Detmar, 2009, TScratch: a novel and simple
software tool for automated analysis of monolayer wound healing assays: Biotechniques, v.
46, p. 265-74.
Gyorffy, B., P. Surowiak, J. Budczies, and A. Lanczky, 2013, Online survival analysis software
to assess the prognostic value of biomarkers using transcriptomic data in non-small-
cell lung cancer: PLoS One, v. 8, p. e82241.
Hande, K. R., 1998, Etoposide: four decades of development of a topoisomerase II inhibitor:
Eur J Cancer, v. 34, p. 1514-21.
Hao, D., L. Wang, and L. J. Di, 2016, Distinct mutation accumulation rates among
tissues determine the variation in cancer risk: Sci Rep, v. 6, p. 19458.
He, C., H. Hu, K. D. Wilson, H. Wu, J. Feng, S. Xia, J. Churko, K. Qu, H. Y. Chang, and J. C. Wu,
2016, Systematic Characterization of Long Noncoding RNAs Reveals the Contrasting
Coordination of Cis-and Trans-Molecular Regulation in Human Fetal and Adult
Hearts: Circ Cardiovasc Genet, v. 9, p. 110-8.
Healio, May 17, 2017, Gefitinib Delayed Recurrence in Intermediate-Stage EGFR Positive
Lung Cancer Cancer Network.
Hickson, I., Y. Zhao, C. J. Richardson, S. J. Green, N. M. Martin, A. I. Orr, P. M. Reaper, S. P.
Jackson, N. J. Curtin, and G. C. Smith, 2004, Identification and characterization of a
novel and specific inhibitor of the ataxia-telangiectasia mutated kinase ATM: Cancer
Res, v. 64, p. 9152-9.
Hollander, M. C., I. Alamo, and A. J. Fornace, Jr., 1996, A novel DNA damage-inducible
transcript, gadd7, inhibits cell growth, but lacks a protein product: Nucleic Acids Res,
v. 24, p. 1589-93.
Huarte, M., 2015, The emerging role of lncRNAs in cancer: Nat Med, v. 21, p. 1253-61.
Huarte, M., and J. L. Rinn, 2010, Large non-coding RNAs: missing links in cancer?: Hum
Mol Genet, v. 19, p. R152-61.
Institute, N. C., 2016, Cancer Stat Facts - SEER.
Jeffares, D. C., A. M. Poole, and D. Penny, 1998, Relics from the RNA world: J Mol Evol, v. 46,
p. 18-36.
Johnathan Castillo, T. S., Crystal Marconett, 2017, Intersecting transcriptomic
profiling technologies and long: Oncotarget.
Karlsson, O., and A. A. Baccarelli, 2016, Environmental Health and Long Non-coding RNAs:
Curr Environ Health Rep, v. 3, p. 178-87.
Kolde, R., 2015, pheatmap: Pretty
Heatmaps. Kolde, R., 2015-12-11,
Pretty Heatmaps.
Kumarakulasinghe, N. B., N. van Zanwijk, and R. A. Soo, 2015, Molecular targeted therapy in
the treatment of advanced stage non-small cell lung cancer (NSCLC): Respirology, v. 20,
p.
370-8.
Li, X., Z. Wu, X. Fu, and W. Han, 2014, lncRNAs: insights into their function and mechanics
in underlying disorders: Mutat Res Rev Mutat Res, v. 762, p. 1-21.
Liang, C. C., A. Y. Park, and J. L. Guan, 2007, In vitro scratch assay: a convenient and
inexpensive method for analysis of cell migration in vitro: Nat Protoc, v. 2, p. 329-
33.
Liu, L., H. Yue, Q. Liu, J. Yuan, J. Li, G. Wei, X. Chen, Y. Lu, M. Guo, J. Luo, and R. Chen, 2016,
LncRNA MT1JP functions as a tumor suppressor by interacting with TIAR to
modulate the p53 pathway: Oncotarget, v. 7, p. 15787-800.
50

Love, M. I., W. Huber, and S. Anders, 2014, Moderated estimation of fold change and
dispersion for RNA-seq data with DESeq2: Genome Biol, v. 15, p. 550.
Owens, T. W., and M. J. Naylor, 2013, Breast cancer stem cells: Front Physiol, v. 4, p. 225.
Palliat, G. A. e. a., 2017, Preparation for the End of Life and Life Completion During Late-Stage
Lung Cancer:
An Exploratory Analysis.
Park, C., N. Yu, I. Choi, W. Kim, and S. Lee, 2014, lncRNAtor: a comprehensive resource for
functional investigation of long non-coding RNAs: Bioinformatics, v. 30, p. 2480-5.
Pathak, E., - 2016, Is Heart Disease or Cancer the Leading Cause of Death in United
Prensner, J. R., and A. M. Chinnaiyan, 2011, The emergence of lncRNAs in cancer biology:
Cancer Discov, v. 1, p. 391-407.
Ravasi, T., H. Suzuki, K. C. Pang, S. Katayama, M. Furuno, R. Okunishi, S. Fukuda, K. Ru, M. C.
Frith, M. M. Gongora, S. M. Grimmond, D. A. Hume, Y. Hayashizaki, and J. S. Mattick,
2006, Experimental validation of the regulated expression of large numbers of non-
coding RNAs from the mouse genome: Genome Res, v. 16, p. 11-9.
Risteski, M., S. Crvenkova, Z. Atanasov, and R. Isjanovska, 2013, Epidemiological analysis of
progression- free survival (PFS) and overall survival (OS) in non-small-cell lung cancer
patients in Republic of Macedonia: Pril (Makedon Akad Nauk Umet Odd Med Nauki), v.
34, p. 49-61.
Rodger, K., 2014, Comprehensive molecular profiling of lung adenocarcinoma, Nature.
Roos, W. P., and B. Kaina, 2013, DNA damage-induced cell death: from specific DNA lesions to
the DNA damage response and apoptosis: Cancer Lett, v. 332, p. 237-48.
Rowbotham, S. P., and C. F. Kim, 2014, Diverse cells at the origin of lung adenocarcinoma: Proc
Natl Acad Sci U S A, v. 111, p. 4745-6.
Sakthianandeswaren, A., S. Liu, and O. M. Sieber, 2016, Long noncoding RNA LINP1:
scaffolding non- homologous end joining: Cell Death Discov, v. 2, p. 16059.
Shiloh, Y., and Y. Ziv, 2013, The ATM protein kinase: regulating the cellular response to
genotoxic stress, and more: Nat Rev Mol Cell Biol, v. 14, p. 197-210.
Shore, A. N., E. B. Kabotyanski, K. Roarty, M. A. Smith, Y. Zhang, C. J. Creighton, M. E. Dinger,
and J. M. Rosen, 2012, Pregnancy-induced noncoding RNA (PINC) associates with
polycomb repressive complex 2 and regulates mammary epithelial differentiation: PLoS
Genet, v. 8, p. e1002840.
Sotak, M., A. Sumova, and J. Pacha, 2014, Cross-talk between the circadian clock and the cell
cycle in cancer: Ann Med, v. 46, p. 221-32.
Sun, M., and W. L. Kraus, 2015, From discovery to function: the expanding roles of long
noncoding RNAs in physiology and disease: Endocr Rev, v. 36, p. 25-64.
Wordpress, 2015, cell-cycle-checkpoints-and-effect-of-oncogenes-2.
Wu, P., X. Zuo, H. Deng, X. Liu, L. Liu, and A. Ji, 2013, Roles of long noncoding RNAs in brain
development,
functional diversification and neurodegenerative diseases: Brain Res Bull, v. 97, p. 69-80.
Zhou, Y., X. Zhang, and A. Klibanski, 2012, MEG3 noncoding RNA: a tumor suppressor: J Mol
Endocrinol, v. 48, p. R45-53.
51

Supplemental Data

Table 3

LIST OF PRIMERS

Primer Name Primer Sequence
LINC00261 exon junction qPCR forward GGATAAAGACCAGCTCAACCA
LINC00261 exon junction qPCR reverse CTCCAAGACAAAGAAGAGTAGG
GAPDH exon junction qPCR forward GGTGAAGGTCGGAGTCAACG
GAPDH exon junction qPCR reverse GTTGAGGTCAATGAAGGGGTC
52

Table 4

Differentially Expressed Genes
ENTEREZID baseMean log2FoldChange pvalue symbol

140828 20.45956 7.753488 8.50E-07 LINC00261
780853 107.8892 -1.9603 7.68E-08 SNORD3C
780851 116.5034 -1.85645 1.46E-07 SNORD3A
100008589 1453.909 -1.4121 3.02E-07 LOC100008589
116228 216.6147 1.703752 1.07E-06 COX20
472 57.70778 2.476951 1.11E-06 ATM
378938 694.2892 1.360814 3.07E-06 MALAT1
165215 146.7495 1.795484 6.68E-06 FAM171B
84852 305.0854 -1.20288 1.55E-05 ATP1A1-AS1
6080 41.87814 -2.29123 2.14E-05 SNORA73A
9088 154.8139 -1.36077 2.25E-05 PKMYT1
10216 112.8367 2.00559 3.18E-05 PRG4
5279 92.13299 1.835131 4.38E-05 PIGC
780852 82.89395 -1.60072 5.21E-05 SNORD3B-2
147968 268.5445 -1.16691 5.57E-05 CAPN12
23469 51.89743 2.291497 5.78E-05 PHF3
8349 125.9378 -1.37893 6.24E-05 HIST2H2BE
3008 163.0538 -1.28828 7.42E-05 HIST1H1E
148398 313.0698 -1.12554 8.91E-05 SAMD11
11251 109.7872 -1.39132 0.0001 PTGDR2
3009 309.3733 -1.10239 0.000104 HIST1H1B
3065 88.949 -1.4833 0.000105 HDAC1
64864 71.87091 1.859965 0.000116 RFX7
1063 40.27133 2.384869 0.000193 CENPF
134728 84.67589 1.800551 0.000193 IRAK1BP1
53

8357 37.31005 -2.01574 0.000198 HIST1H3H
9263 236.9036 1.322487 0.000199 STK17A
8607 187.5527 -1.15264 0.000211 RUVBL1
7175 31.0435 2.239097 0.000237 TPR
9648 103.2109 1.361004 0.000256 GCC2
5921 59.86892 2.213749 0.000264 RASA1
79856 941.9839 -0.92233 0.000297 SNX22
83463 542.6257 -0.90334 0.000332 MXD3
11168 108.9654 1.519561 0.000332 PSIP1
65217 29.16689 2.592341 0.000344 PCDH15
11162 1229.195 1.213832 0.000349 NUDT6
102465432 2414.889 -1.05179 0.000367 MIR6723
3799 25.52659 2.371261 0.000392 KIF5B
10417 168.1384 1.113304 0.000416 SPON2
7005 51.73301 -1.63843 0.000466 TEAD3
100505876 118.6949 1.341474 0.000476 CEBPZOS
9144 39.29248 -1.93178 0.000533 SYNGR2
440689 100.1022 -1.27679 0.000556 HIST2H2BF
4703 190.3879 1.327279 0.000571 NEB
6023 71.7062 -1.41353 0.000592 RMRP
124512 36.47405 -1.92343 0.000644 METTL23
29081 162.5591 1.343211 0.000692 METTL5
7913 77.78752 1.465129 0.000708 DEK
401024 60.15521 1.528308 0.000712 FSIP2
146956 273.3331 -0.95511 0.000723 EME1
646626 165.2538 1.158032 0.00078 LOC646626
54

7342 20.04026 2.593938 0.000841 UBP1
3006 203.3348 -1.024 0.000854 HIST1H1C
51529 281.571 -1.20737 0.000887 ANAPC11
339829 47.36469 1.837045 0.00091 CCDC39
2186 47.72051 1.675463 0.000944 BPTF
124989 55.62622 1.797497 0.001014 EFCAB13
10420 47.78194 -1.61494 0.001073 TESK2
64135 51.33914 1.651868 0.001088 IFIH1
125050 791.9607 -0.95984 0.001142 RN7SK
254552 40.58627 -1.73892 0.001147 NUDT8
729177 401.6858 1.147711 0.001287 NBAT1
199800 69.95802 -1.32424 0.001317 ADM5
1030 97.17262 1.329326 0.00141 CDKN2B
23098 151.7428 -1.03365 0.001481 SARM1
79912 275.659 1.066447 0.001568 PYROXD1
9603 64.64585 1.392613 0.001578 NFE2L3
100132618 101.5929 1.221674 0.001663 ZRANB2-AS1
9108 164.3289 1.188142 0.001783 MTMR7
100287042 129.1814 -1.04124 0.001886 LOC100287042
100628315 948.8542 2.263526 0.002023 DNM3OS
27429 59.63255 -1.34655 0.002059 HTRA2
245973 1260.487 -0.73738 0.002295 ATP6V1C2
276 5.196185 5.764463 0.002544 AMY1A
647979 20.04829 2.18941 0.002642 NORAD
9512 132.2797 1.138328 0.002678 PMPCB
55

729987 22.59748 2.265493 0.002849 LINC01776
55796 23.53254 2.223613 0.002859 MBNL3
5528 290.7371 -0.85065 0.002874 PPP2R5D
388697 208.9914 0.883346 0.002906 HRNR
8448 214.8786 -0.87925 0.002967 DOC2A
6571 122.4222 1.297869 0.002992 SLC18A2
102659353 440.9995 -0.78653 0.003095 THRIL
284801 1859.878 -0.92849 0.003242 MIR663AHG
5192 82.54744 -1.17033 0.003384 PEX10
85001 57.55931 -1.31723 0.003452 MGC16275
8564 94.91096 -1.10713 0.003454 KMO
163259 127.853 -0.98197 0.003495 DENND2C
93650 48.16245 -1.49443 0.003606 ACPT
7153 49.49123 1.423863 0.003679 TOP2A
55016 85.94052 1.384576 0.003729 1-Mar
85007 421.6387 -0.77529 0.003886 PHYKPL
84281 46.49651 1.497297 0.003978 C2orf88
8331 15.04141 -2.41913 0.004224 HIST1H2AJ
96764 4.373718 5.527481 0.00446 TGS1
101927813 76.07042 1.422093 0.00463 HMMR-AS1
79172 199.9423 -0.8634 0.004648 CENPO
64151 31.97047 2.049104 0.004689 NCAPG
552900 131.8869 -0.95884 0.004887 BOLA2
150759 11.62258 2.991813 0.004918 LINC00342
114793 83.1866 1.152757 0.00502 FMNL2
56

9415 95.69806 -1.07489 0.00513 FADS2
101929319 58.08415 1.238915 0.005426 LOC101929319

Abstract (if available)

Abstract Lung cancer is the leading cause of cancer related death worldwide.(Fabricius and Lange, 2003) The lung cancer five-year survival rate (17.7 percent) is lower than many other leading cancer subtypes such as colon (64.4 percent), breast (89.7 percent) and prostate (98.9 percent). More than half of the people with lung cancer die within one year of being diagnosed. Lung adenocarcinoma (LUAD) is the largest histological subtype of lung cancer. Recent advances in sequencing techniques revealed that most transcription in our genome (~80%) occurs on long non-coding RNAs (lncRNAs). Little is known about the altered expression of lncRNAs in cancer. We profiled lncRNA expression in LUAD cell lines and purified alveolar epithelial cells, which are the purported cells of origin for LUAD. We then cross-referenced these with lncRNAs altered in primary human tumors, and eliminated all lncRNAs without expression-based difference in survival. We identified LINC00261, a lncRNA predicted to have a tumor suppression activity that lies adjacent to the transcription factor FOXA2. LINC00261 blocked cellular proliferation by inducing a G2/M cell cycle arrest. RNA-Seq analysis indicated that the G2/M arrest was mediated by DNA damage pathway induction. Western blots confirmed that DNA damage checkpoint control proteins underwent ATM-dependent phosphorylation-mediated activation when LINC00261 was present. DNA damage repair and cell cycle checkpoints have been intrinsically linked to cancer and the molecular mechanisms that connect these dysfunctional pathways to the onset of carcinogenesis are not very well understood. This novel finding gives us a unique insight into the mechanism of this lncRNA and has huge therapeutic implications for treatment of LUAD.

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

Defining the functional region of LINC00261 in lung adenocarcinoma

PDF

Establishing the non-coding RNA LINC00261 as a tumor suppressor in lung adenocarcinoma

PDF

Generation of an epigenetic toggle switch to test LINC00261 function on lung adenocarcinoma cellular response to the chemotherapeutics oxaliplatin and carboplatin

PDF

Evaluating the impact of long non-coding RNAs on tumor mutational burden in cancer

PDF

Modeling lung adenocarcinoma progression in vitro using immortalized human alveolar epithelial cells

PDF

DNA methylation markers for blood-based detection of small cell lung cancer in mouse models

PDF

Development of immunotherapy for small cell lung cancer using iso-aspartylated antigen

PDF

Tight junction protein CLDN18.1 attenuates malignant properties and related signaling pathways of human lung adenocarcinoma in vivo and in vitro

Asset Metadata

Creator Kumaran, Vishaly (author)

Core Title LINC00261 induces a G2/M cell cycle arrest and activation of the DNA damage response in lung adenocarcinoma

School Keck School of Medicine

Degree Master of Science

Degree Program Biochemistry and Molecular Biology

Publication Date 07/20/2019

Defense Date 06/06/2017

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

Tag DNA damage repair,FACs,G2 cell cycle arrest,long non RNAs(lncRNAs),lung cancer,migration,OAI-PMH Harvest,proliferation

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Marconett, Crystal (committee chair), Fabbri, Muller (committee member), Laid, Ite (committee member)

Creator Email vishalykumaran@gmail.com,vkumaran@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c40-412531

Unique identifier UC11214597

Identifier etd-KumaranVis-5613.pdf (filename),usctheses-c40-412531 (legacy record id)

Legacy Identifier etd-KumaranVis-5613.pdf

Dmrecord 412531

Document Type Thesis

Format application/pdf (imt)

Rights Kumaran, Vishaly

Type texts

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

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA