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Downregulation of METTL7A gene in liver-specific Pten-null mouse model

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Downregulation of METTL7A gene in liver-specific Pten-null mouse model

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Content Downregulation of METTL7A gene in liver-specific Pten-null mouse model
By
Yiren Zhou
A Thesis Presented to the
FACULTY OF THE USC ALFRED E. MANN SCHOOL OF PHARMACY AND
PHARMACEUTICAL SCIENCES
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(PHARMACEUTICAL SCIENCES)
August 2023
Copyright [2023] Yiren Zhou

ii
Dedication

To
my family, and friends
for their sincere help, care, and support
I am not grateful for the trouble that I met, I am grateful for myself

iii
Acknowledgments
I sincerely appreciate Dr. Bangyan Stiles, who served as my adviser when I was pursuing my
master’s degree at USC. You have always supported us in our efforts to further our research aims
and urged us to think critically like scientists. My education and future have both been advanced
by the enthusiasm and knowledge I have gained from you. I also want to express my gratitude to
Dr. Shih and Dr. Zaro from my committee for their assistance and wise counsel.

Mario Alba has been a huge help to me. When I initially joined the lab, he spent his time
teaching me mass spectrometry knowledge and providing me with creative inspiration. I can’t be
successful without his help. It’s my pleasure that I can have him as my mentor. I also want to
express my gratitude to Qi Tang for her patience in giving me lots of advice as I developed my
wet lab skills. Thanks to Lina He, Brittney Hua, Lelyzaveta Slarve for their extensive experience
that taught me. Also, thanks to Yunji Jia who become my friend so that we could explore the
food in LA.

Sincere gratitude is extended to every member of our lab, particularly Zixin Zong, Aditi Ashish
Datta, Yushan Wang, Jared A Khan, Pranav Pammidimukkala, and Diala Alhousari. They taught
me what teamwork is and made the lab environment full of joy. And also, thanks to the rest of
the lab members, they made me reflect on myself constantly and helped me become a better
person. I realized that being a good man is the most important thing in our life. Last but not least,
I would like to thank myself for surviving.

四

Table of Contents
Dedication ....................................................................................................................................... ii
Acknowledgments .......................................................................................................................... iii
List of Tables ................................................................................................................................. iv
List of Figures ................................................................................................................................. v
Abstract .......................................................................................................................................... vi
Chapter 1. Liver Cancer and PTEN Signaling Pathway ................................................................. 1
1.1 Liver cancer background ........................................................................................................... 1
1.2 Tumor Suppressor: Phosphatase and tensin homolog deleted on chromosome 10 (PTEN) ..... 2
1.2.1 Introduction of PTEN ............................................................................................................ 2
1.2.2 Regulation of PTEN ............................................................................................................... 3
1.2.3 PTEN’s role as a tumor suppressor ........................................................................................ 4
1.2.4 The lipid metabolism related PTEN/PI3K signaling ............................................................. 5
1.3 Hypothesis and Aims ................................................................................................................ 6
Chapter 2. Using Liquid Chromatography Mass Spectrometry-based Proteomics to Understand
Liver Disease Progression ............................................................................................................... 8
2.1 General Principles for Mass-Spectrometry ............................................................................... 8
2.2 Protocol selection ...................................................................................................................... 9
2.3 TMT Labeling ......................................................................................................................... 11
Chapter 3. Results ......................................................................................................................... 13
3.1 Sample Selection ..................................................................................................................... 13
3.2 Overall Data Analysis ............................................................................................................. 14
3.3 Identification of METTL7A as a consistently downregulated protein when PTEN is lost .... 20
3.4 Validation of METTL7A downregulation in liver tissues with PTEN loss ............................ 23
3.5 METTL7A is downregulated in liver cancer and some other cancers .................................... 24
3.6 Regulation of METTL7A by PTEN/PI3K/AKT signaling pathway ...................................... 27
Chapter 4. Discussion ................................................................................................................... 29
Chapter 5. Methods and Materials ............................................................................................... 32
5. 1 Animals .................................................................................................................................. 32
5.2 Mass spectrometry workflow .................................................................................................. 32
5.3 RNA isolation, reverse transcription, and real-time PCR ....................................................... 33

五
5.4 Western Blot ........................................................................................................................... 34
5.5 Statistics .................................................................................................................................. 35
References ..................................................................................................................................... 36

iv
List of Tables

Table 1 Pathway enrichment analysis of 3 months of upregulated proteins ................................ 15
Table 2 PEA of 3 months of downregulated proteins .................................................................. 15
Table 3 PEA of 6 months of upregulated proteins ....................................................................... 16
Table 4 PEA of 6 months of downregulated proteins .................................................................. 16
Table 5 PEA of 9 months of upregulated proteins ....................................................................... 17
Table 6 PEA of 9 months of downregulated proteins .................................................................. 17
Table 7 PEA of 15 months of upregulated proteins ..................................................................... 19
Table 8 PEA of 15 months of downregulated proteins ................................................................ 19
Table 9 Primers for Real-Time PCR ............................................................................................ 32
Table 10 Antibodies used in Western Blot .................................................................................. 33

v
List of Figures

Figure 1 Pten Protein Abundance VS Relative Protein Abundance (TMT Log2 ratio) .............. 3
Figure 2 Structure of PTEN ........................................................................................................ 3
Figure 3 Overall workflow ........................................................................................................... 9
Figure 4 Sample TMT labeling proteomic workflow .................................................................. 11
Figure 5 Liver cancer progression in Pten-null mice over time .................................................. 12
Figure 6 Volcano plot of 3,6,9,15 Months mice proteins ............................................................ 14
Figure 7 Venn diagram showing proteins that are significantly upregulated and
downregulated ............................................................................................................................. 20
Figure 8 Detailed description of the downregulated proteins in each month .............................. 21
Figure 9 qPCR analysis of METTL7A ........................................................................................ 23
Figure 10 Western blot analysis of Pten-null mice contrasted with WT mice in 3 months, 9
months, and 15 months ................................................................................................................ 23

Figure 11 METTL7A protein abundance in Liver Cancer, LUAD, Breast cancer, PDAC, and
Kidney Cancer ............................................................................................................................. 24

Figure 12 The transcript expression of METTL7A in LIHC, LUAD, BRCA, PAAD
, and KIRC ................................................................................................................................... 25
Figure 13 The overall survival plot of METTL7A in LIHC, LUAD, BRCA, PAAD
, and KIRC ................................................................................................................................... 26
Figure 14 Correction between PTEN and METTL7A ................................................................. 27
Figure 15 Analysis of METTL7A by Western blotting in Huh7 cells with LY294002 .............. 27

vi
Abstract

PTEN (phosphatase and tensin homolog deleted on chromosome 10) is a tumor suppressor which
was discovered by three laboratories in 1997. Since then, studies have established that PTEN plays
an important role in metabolic regulation in addition to cell growth and death. Although the many
downstream signals regulated by PTEN have been identified, particularly for its role as a tumor
suppressor, how PTEN may regulate metabolism is largely unknown. In this study, we employed
a Mass Spectrometry-based bottom-up proteomics technique to analyze differential protein
expressions in mouse livers lacking PTEN vs. those with intact PTEN. Here, we explored the
differential protein expressions in 3-, 9-, and 15 months-old mouse livers. Our data identified
methyltransferase-like 7A (METTL7A) as a protein of which the levels are significantly reduced
in all stages when PTEN is lost. METTL7A is the most significantly altered in 9 months and 15
months of the proteomics database. As an integral lipid droplet protein, METTL7A is found in the
cytoplasm and is involved in mRNA translation. We validated the MS results in liver tissues using
western blot analysis. However, the mRNA expression of Mettl7a did not change accordingly,
suggesting that the PTEN signal may regulate METTL7A protein expression post-transcriptionally.
In summary, we identified METTL7A as a potential downstream target for PTEN. As PTEN loss
induces liver steatosis, METTL7A, a lipid droplet protein may contribute to this phenotypical
change. Our future studies will explore the signaling pathways leading to the regulation of
METTL7A by PTEN and the role of METTL7A in PTEN-regulated lipid metabolism.

1
Chapter 1. Liver Cancer and PTEN Signaling Pathway

1.1 Liver cancer background
Liver cancer is a chronic illness and ranked as the sixth most prevalent cancer worldwide in 2020
[1]. In 2022, in the United States, approximately 41,260 cases of liver cancer were estimated with
30.7% for women and 69.3% for men [2]. In these cases, 30,520 deaths have been reported, of
which 33.1% are women and 66.9% are men [2]. Despite recent stabilization of mortality rates,
liver cancer still has one of the highest fatality rates of all malignancies [2]. American Indians and
Alaska Natives have the highest death rate among all the groups (18.1%), and are 7.2% for Whites,
10.9% for Blacks, 12.4% for Asians with Pacific Islanders, and 13.8% for Hispanics [2].

Primary liver cancer and secondary metastatic liver cancer are the two types of liver cancer.
Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma which originate from the
liver tissues, are two main types of primary liver cancer. Liver cancer occurs highly in patients
infected with hepatitis B (HBV) or hepatitis C (HCV) though the main risk factors might be
different in different population [3]. In China, the Republic of Korea, and sub-Saharan Africa
Excessive, chronic HBV infection is high in prevalence whereas in Japan, Italy, and Egypt
infection of HCV is higher [2]. In addition, excessive alcohol consumption, aflatoxin, or a calorie-
rich diet that leads to the accumulation of a high level of fat also increases the risk of developing
HCC [3].

In the late 1970s, throughout the world, the rate of death from liver cancer decreased [4]. These
declines are probably caused by the concurrent declines in aflatoxin exposure and population

2
seroprevalence of HCV and HBV. A wide distribution of vaccines against HBV also contributed
to the reduced rate of HBV infection and HCC incidence in the 1980s particularly in China [5].
Nevertheless, in recent years, the primary risk factors for liver cancer are diabetes and obesity [6].
The National Expanded Program of Immunization and the aflatoxin abatement program in China
stabilized the incidence rate of liver cancer in the birth cohort since 1978 [7] while people in
regions like Austria and Europe, of having diabetes and being overweight increased the incidence
rate of liver cancer even though they have lower rate before [8].

1.2 Tumor Suppressor: Phosphatase and tensin homolog deleted on chromosome 10 (PTEN)
1.2.1 Introduction of PTEN

Genetic alteration is considered a main contributing factor to tumor progression [9]. During late
tumor development, loss of heterozygosity (LOH) at chromosome 10q23 is one of the most
common events in various human malignancies that was founded by three different laboratories in
1997 [9-11]. Moreover, a number of studies found LOH at 10q23 in 60% of prostate tumors and
70% of advanced glial tumors which suggests that there is a tumor suppressor gene is encoded on
the 10q23 locus [12]. They named it as PTEN for phosphatase and tensin homologue deleted on
chromosome 10 [11] after a sequence analysis showed that it encoded a protein tyrosine
phosphatase similar to the chicken tensin [13].

Phosphatase and Tensin Homolog deleted on Chromosome 10 (PTEN) is a dual protein- and lipid-
phosphatase [14]. The role of PTEN as a tumor suppressor was identified by homozygous deletion
mapping of the human chromosome 10q23 in cancer [9]. In human malignancies, the PTEN gene

3
is one of the most frequently mutated tumor suppressors [10, 12, 15-17]. In liver cancer, the PTEN
protein level is downregulated in tumor tissue as compared to adjacent normal tissue (Figure 1).

1.2.2 Regulation of PTEN

The PTEN protein is 403 amino acids
long, and its amino-terminal portion has
sequence homology with the actin
filament capping protein TENSIN and
the putative tyrosine-protein phosphatase
AUXILIN [18] (Figure 2). PTEN has a
phosphatase domain with the CX5R hallmark motif and a C2 domain on membranes [14].
Additionally, PTEN dephosphorylates phospho-peptides and phospholipids in vitro. Hence, PTEN
is characterized as a dual phosphatase of lipids and proteins [14]. Ubiquitination controls the
Figure 2. Structure of PTEN.
Both membrane binding and catalytic activity
depend on the N-terminal phosphatase domain.
The C2 domain is a membrane-binding domain.
[19].
Figure 1. Reduced PTEN Protein
Abundance in Liver Tumors vs.
Surrounding Tissues.
Relative Protein Abundance (TMT
Log2 ratio) is compared in tumors
vs. nearby normal tissues using
publicly available datasets from
CPTAC proteogenomic databank at
cProSite
(https://cprosite.ccr.cancer.gov/).

4
movement of PTEN between the nucleus and cytosol [20]. The nuclear transportation is controlled
by ubiquitination site K13 and Monoubiquitination of lysine 289 (K289) of PTEN [14]. The
transcription of PTEN is regulated by active transcription factor 2 (ATF2), metabolic regulatory
gene peroxisome proliferation-activator receptor γ (PPARγ), the early growth response protein 1
(EGR-1), and tumor suppressor p53 [22]. Furthermore, the member of the Slug/Snail family
(SLUG), binds to the two E-boxes in PTEN’s promoter to inhibit its expression [23].

1.2.3 PTEN’s role as a tumor suppressor

By converting phosphatidylinositol 3,4,5-triphosphate (PIP3) into phosphatidylinositol 4,5-
bisphosphate (PIP2) in its role as a lipid phosphatase, PTEN reduces PI3K/AKT signaling
activity [24] and assumes many of its effects through its modulation of the PI3K signaling
pathway. The PI3K signaling pathways regulate cell survival, cell growth, and metabolism [25].
The activation of the PI3K signal which was stimulated by the growth factor will result in lipid
PIP3 accumulation [14]. This pro-growth effect of the PI3K signal is consistent with the fact that
the activated form of PI3K is an oncogene. PI3K mutations and amplifications can usually be
found in many human cancers [26] consequently with the observed loss of PTEN function during
the development of cancer.

Based on the mode of activation, distribution, and structure, the PI3K family can fall into three
groups in mammalian cells [27]. Class I PI3Ks are named class IA or IB based on the
corresponding adaptors [28]. class IA PI3Ks are activated by receptor tyrosine kinases, whereas
class IB PI3Ks are activated by G-protein-coupled receptors [24]. PI3Ks can produce PIP3,
which is a phospholipid second messenger [24]. Serine-threonine kinase (AKT) is then attracted

5
to the membrane and activated by phosphorylation due to PIP3's binding to AKT's pleckstrin
homology (PH) domain [29]. AKT prevents the GTPase-activating protein (GAP) to active
tuberous sclerosis conplex1 (TSC1) and TSC2 [29]. This will lead to the increasing
concentration of the serine/threonine protein kinase (mTOR) [30]. The term mTOR refers to
mTORC1 and mTORC2 which are two different complexes and each of them has a unique
function [31]. mLST8, Raptor, and proline-rich AKT substrate 40 (PRAS40) are what made up
mTORC1, which promotes anabolic cell growth and modifies cellular metabolism [32], while
SIN1, mLST8(GL), and Rictor (mAVO3) compose mTOR2, which functions as serine/threonine
protein kinases [33].

1.2.4 The lipid metabolism related PTEN/PI3K signaling

Other than cell growth and survival, PTEN also has inhibitory effects on insulin signaling. PTEN
overexpression in 3T3-L1 adipocytes inhibits AKT activity, which results in less glucose
transporter 4 (GLUT4) being transported to the cell membrane [34-35]. Further, the PTEN/PI3K
signaling pathway also plays an important role in lipid metabolism [14]. Sterol receptor element
binding protein (SREBP) takes part in fatty acid production and then further integrates these fatty
acids into cholesterol and triglycerides [14]. The downstream target of AKT, Forkhead
transcriptional factor (FoxO1), reduces the transcription of SREBP and consequently lipogenesis
[14, 36-37]. The PTEN deletion in the liver would also result in the up expression of SREBP and
fatty acid synthase (Fasn) [38-39]. In the SREBP pathway, the insulin-induced gene (Insig) and
SREBP cleavage-activating protein (SCAP) are two important players [14]. In reacting to sterol
demand, SCAP cleaves SREBP to produce the transcriptional factor's mature active form [14].
When Insig binds to SCAP, this process stops, and the SREBP processing also stops [14].

6
The reduction of PI3K/AKT activity reverses the inhibition of oxysterols to the Insig-1
expression and increases SREBP processing, suggesting that AKT involves in SREBP
processing in addition to its transcriptional regulation [40].

1.3 Hypothesis and Aims

To further dissect the PTEN regulated signals and its function in liver cancer initiation and
progression, we took a non-biased approach to explore the proteome of the liver tissues with or
without PTEN. Recently, proteomics based-mass spectrometry (MS) appears to become a popular
method for detecting and measuring an organism's proteome [41]. The "bottom-up" approach to
proteomics involves first breaking down complicated protein mixtures using tryptic enzymes, then
based on the physical or chemical properties of the resulting peptide products to separate them,
and finally using a MS to analyze [41]. Thus, the protein can be identified and quantified [41].

In this study, we applied the Liquid Chromatography MS-based proteomics to study the PTEN-
regulated protein changes during the progression of liver cancer. To do this, we used the liver-
specific Pten deletion mice models that our lab previously developed and characterized [37-38].
In the Pten liver-specific deletion mouse model, fatty acid synthesis is significantly elevated, alone
with fatty liver phenotype and hepatomegaly [38]. The livers in these mice displayed steatosis,
triglyceride accumulation, steatohepatitis, and hepatomegaly, a phenotype similar to nonalcoholic
steatohepatitis (NAFLD) in humans, a condition predisposes patients to liver cancer development.
Following chronic NAFLD, the Pten liver-specific deleted mice develop inflammation, fibrosis
and eventually HCC where steatosis is required for HCC development [37]. Similar phenotypes

7
were also reported elsewhere for the function of PTEN in the liver [24,42]. Consequently, a Pten
liver-specific deletion mice model serves as a valuable model for research HCC.

8
Chapter 2. Using Liquid Chromatography Mass Spectrometry-based Proteomics to Understand
Liver Disease Progression

2.1 General Principles for Mass-Spectrometry
Proteins are crucial for the study of systems biology since they are essential to structure, movement,
communication, and cell division [41]. Assessing messenger ribonucleic acid (mRNA) levels as
in DNA microarrays alone fails to show the levels of related proteins in a cell and their regulatory
activities given proteins are subject to thousands of post-translational modifications and regulated
by other biological stimulations independent of gene transcription [41]. Thus, studying the
proteomic changes is crucial to complement that of the genome/transcriptome in an effort to
understand the mechanisms for disease progression [41]. To achieve this goal, Mass spectrometry
(MS) has shown its power in the identification of proteins and the analysis of complicated protein
samples [43].

Mass spectrometry (MS) is developed to measure the mass of a given molecule. The mass can
indicate the protein’s structure, chemical linkages, and chemical changes [44]. In the late 1970s,
MS could only be used to evaluate components that were flammable and had low molecular
weights [45]. Electrospray ionization (ESI), which generates highly charged, stable analyte ions,
was developed by John Fenn and made proteomics possible [46]. According to their mass or mass-
to-charge ratio (m/z), the ions that the source molecule produces are separated for examination and
identified using charged particles [45]. Because the mass of a protein is usually large. After
dividing the charge by mass, the mass spectrometer's mass-to-charge ratios are often used for
protein identification instead of the absolute mass [45]. The parts of peptides are first detected for
protein identification. It is done by comparing the m/z ratio of the observed features to those of

9
data in an inventory that already discovered peptides [41]. With more precision than single mass
spectrometry, tandem mass spectrometry, or MS/MS, selects an ion (parent) that might correspond
to a peptide for further fragmentation in MS2 [47]. To further verify the identification of the
potential peptide, the resulting fragmentation spectra are then compared to fragmentation spectra
that have been recorded in a database [47].

2.2 Protocol selection
Despite the fact that several MS methods for exploring the proteome have been invented, the pros
and cons of each MS technique vary, affecting their specific applications. For example, laser
desorption ionization (LDI) technique air-dries a soluble analyte on a metal surface, followed by
ultraviolet laser irradiation [48]. However, this technique has low sensitivity [48]. However,
matrix-Assisted Laser Desorption Ionization (MALDI), solves this problem by decoupling the
energy required for analyte desorption and ionization [48], thus gives us high sensitivity. However,
obtaining highly significant search results is one of the most challenging aspects of using MALDI-
TOF for protein identification. This is because not all predicted tryptic peptides are present in the
experimental MS spectrum [49]. Another technique called Surface Enhanced Laser Desorption
Ionization (SELDI), is suitable for proteins of molecular weight less than 20 kDa and proteins with
femtomole sensitivity [49]. Both MALDI and SELDI can identify protein in a spatial manner [48].
Here, we aim to examine the protein change in a large spectrum, LC-MS is the best technique to
employ due to the high degree of specificity and the capacity to manage complex mixtures [50].
The study, therefore, uses a LC- MS, "bottom-up" approach to MS-based proteomics. The
experiment extract sample’s protein at first, subsequently fractionated to eliminate salts and non-
disease related proteins, digest it into peptides (Figure 3), and using MS at the end [41].

10

Figure 3. Overall workflow (A. Extracted the liver tissue from Pten-null mouse or wild-type
mouse by using lysis buffer, homogenized the sample solution, and used trypsin to digest the
protein into peptide. B. The sample solution first will be ionized into the parent ion (MS1) and
then further fragmented into the daughter ion (MS2). C. The results will be matched with the
database and analyzed by the software. The blue peaks and red peaks together represent the
peptide sequence inside the database. The blue peaks indicate the reality peptide sequences
matched with the database peptide sequences. The red peaks indicate the reality peptide
sequences that did not match with the database peptide sequences.)

11
2.3 TMT Labeling
To perform MS on liver tissues, previously frozen liver tissues were cut with a razor while kept
on dry ice. Proteins were extracted and homogenized with lysis buffer. C18 HPLC columns are
used to eliminate the salts inside the mixture based on their hydrophobic properties [51]. Tandem
mass tags (TMT) were then introduced to the samples (Figure 4). TMT is an artificial label that
enables sample multiplexing for the identification and measurement of biological macromolecules
like nucleic acids, peptides, and proteins [52]. TMT are groups of the same massed molecules that
produce reporter ions with different masses after fragmentation [52]. In order to isobaric label
multiple samples with one of the tags, then mix all samples and analyzed them in the mass
spectrometer [53]. Then based on the different reporter groups, each individual sample can be then
identified [53]. The MS captures the tagged peptides with various m/z ratios at each time point for
a period of time. When sufficient peptides have been collected, the reporter groups are removed,
and the peptides are counted. The reporter groups have various masses, but they are balanced by
the normalization group. In this way, the peptides with different tags are not processed differently
by the MS prior to quantification [54].

12

Figure 4. Sample TMT labeling proteomic workflow [53]

13
Chapter 3. Results

3.1 Sample Selection
Previously, our lab generated a mouse model by breeding albumin-Cre transgenic mice with
Pten
loxP/loxP
transgenic mice [38]. These mice develop a spectrum of liver diseases that replicate
the carcinogenesis and fibrogenesis of NAFLD disease and liver cancer illness in humans. Mice
begin to develop fatty liver disease between the ages of 1 and 3 months. Inflammation-related
events and oxidative stress cause fibrosis, hepatitis, and cirrhosis after 6 months. Liver cancer
begins at approximately 8-9 months of age, and after 12 months, nearly all the mice get cancer
(Figure 5). As a result, by collecting Pten-null mice liver tissues at ages 3 months, 6 months, 9
months, and 15 months and utilizing MS-based proteomics, we can generate a profile of protein
changes with the progression of liver disease.

Figure 5. Liver cancer progression in Pten-null mice over time (The Pten-null mice develops
early NAFLD at 3 months; The NAFLD is fully established at 6 months; The NASH begins at 9
months; At 15 months, the tumor is mature.) Figure created by Biorender

14

3.2 Overall Data Analysis

To understand the molecular alterations carried on by the liver's loss of PTEN, we performed a
bottom-up proteomics on liver tissues isolated from the 3, 6, 9, 15-months old Pten-null mice
and the age matched genotype controls. The proteomics identified 5106, 5168, 1694 and 1762
proteins that are differentially expressed between Pten deleted livers and controls in 3-, 6-, 9-
and 15-months old mice respectively. These proteins were plotted in Figure 6 based on their log
2 ratio and P values when comparing Pten-null mice to wild-type mice. To find proteins that are
expressed quite differently in liver that lack PTEN compared to controls, we identified the
proteins with a log 2 ratio larger than 1 or less than -1, and P values less than 0.05. These
proteins are marked with green box to indicate downregulated proteins and red box to indicate
upregulated proteins. This analysis identified 57, 28, 4, and 10 proteins that are significantly
upregulated due to PTEN loss in 3, 6, 9, 15 months livers; and 57, 18, 5, and 14 proteins that are
significantly downregulated due to PTEN loss in 3, 6, 9, 15 months livers, respectively.

15

Figure 6. Volcano plot of 3, 6, 9 and 15Months mice proteins. (Red box shows upregulated
proteins with p-value less than 0.05, log2 ratio larger than 1; Green box shows downregulated
proteins with p-value less than 0.05, log2 ratio less than -1)

We subjected these significantly changed proteins from the red and green shaded areas to
Pathway Enrichment Analysis (PEA). The PEA tool finds biological activities that are dominant
in a set of genes and arranges these functions based on their correlation [55]. Tables 1-8 show the
pathways that were ranked based on p-value from the smallest to the largest with a cut-off at
0.05. In the 3-month-old mice, these differentially expressed proteins are predominantly enriched
for metabolism. In addition, several other upregulated signals are also enriched including the
“Regulation of Actin Cytoskeleton pathway”, the “Novel Jun-Dmp1 Pathway” and the “MAPK
Cascade” that are related to stimulated cellular processes and might contribute to tumor
development. In addition to being indicative of lipid metabolic changes, the “Prostaglandin

16
Synthesis and Regulation” is also linked with inflammation, possibly contributing to the
inflammation phenotype observed later in the 6 month old mice. The down-regulated proteins are
enriched for the biosynthesis of cholesterol and estrogen metabolism pathways, both involved
with steroid metabolism. Consistent with this observation, xenobiotic metabolism such as
aflatoxin and glucuronidation signaling are among the enriched pathways. Eicosanoids
metabolism that relies on Cyp450 enzymes is also found enriched, indicating that the liver
tissues may be trying to compensate for the upregulation of prostaglandin signal induced
inflammation. While the mouse WP157 pathway shows downregulation of
Glycolysis/gluconeogenesis pathway, the protein molecules that contributed to this analysis are
Enolase 2 (ENO2) and Enolase 3 (ENO3). The downregulation of these two enzymes is
expected as the induction of insulin/PI3K signaling pathway due to loss of PTEN has been
shown to suppress gluconeogenesis [14, 56].

Table 1. Pathway enrichment analysis of 3 months of upregulated proteins

Table 2. PEA of 3 months of downregulated proteins

17

More severe steatosis is observed in the 6 months old PTEN deleted livers. Along with these
changes, more severe steatosis phenotypes are observed than the 3-month-old livers [38].
Previously, our lab showed that AKT2 mediates the lipogenesis signal regulated by PTEN [37].
Consistently, fatty acid biosynthesis is the top signaling pathway that the upregulated proteins
are enriched for in this age group. Upregulation of other lipid metabolic pathways is also
observed, including: “Dysregulated miRNA Targeting in Insulin/PI3K-AKT Signaling”,
“Nuclear receptors in lipid metabolism and toxicity”, “Cholesterol metabolism”, “Selenium
metabolism/Selenoproteins” and “Oxidative phosphorylation” pathways. Meanwhile, the
downregulated proteins from the 6-month-old livers are also largely involved in metabolism,
particularly amino acid metabolism and also energy production.

Table 3. PEA of 6 months of upregulated proteins

18

Table 4. PEA of 6 months of downregulated proteins

As the phenotype progresses to inflammation and malignancy in the 9 months old livers,
metabolic pathways such as “Glutathione metabolism”, “Prostaglandin Synthesis and
Regulation”, “Tryptophan metabolism” and “PPAR signaling” pathways continue to be the top
signals that are altered due to PTEN loss. While the downregulated proteins are also enriched for
lipid metabolism, these proteins are significantly enriched for inflammatory processes. In
particular, interleukin, chemokine, and cytokine signals that involve STAT1 pathway appears to
be dominant signals these proteins are enriched for. This finding is interesting as significant
inflammatory cell infiltration is observed at this stage. Downregulation of these signals may play
a role in triggering the infiltration of inflammatory cells or is an adaptive tissue response to
inflammation. Specific analysis of the molecules within these pathways may further illuminate
their functions.

Table 5. PEA of 9 months of upregulated proteins

19

Table 6. PEA of 9 months of downregulated proteins

In the tumor livers of the 15 months-old mice, cellular metabolism continues to be enriched in
the upregulated proteins due to PTEN loss, including “Glycolysis and Gluconeogenesis”, “PPAR
signaling”, and also “Amino Acid metabolism”. Signals involved in cytoskeleton remodeling
such as “Spinal Cord Injury” and “Focal Adhesion-PI3K-Akt-mTOR-Signaling” are also found
to be enriched. In addition, other cell signaling pathways are also enriched in this tumor stage
including “G Protein Signaling”, “Novel Jun-DMP1”, “MAPK Cascade” pathways. For the
downregulated protein, the top significant pathways overlapped with the 3 months of
downregulated proteins’ pathways, like the “Glycolysis and Gluconeogenesis”, “Eicosanoid
metabolism via Cytochrome P450 Mono-Oxygenases (CYP)” and “Nuclear receptors in lipid

20
metabolism and toxicity” pathways. The consistent changes of these metabolic signals suggest
that they may play a tumor-promoting role in directly inducing the progress of tumor
development through regulating tumor metabolism or establishing the tumor microenvironment.

Table 7. PEA of 15 months of upregulated proteins

Table 8. PEA of 15 months of downregulated proteins

3.3 Identification of METTL7A as a consistently downregulated protein when PTEN is lost

To address how PTEN may have induced the metabolic changes, we searched for proteins that
are consistently altered with PTEN loss regardless of phenotype and age. To do this, we
generated a Venn diagram with the differentially expressed proteins. For the significantly

21
upregulated proteins, the Venn diagram (Figure 7) identified no proteins that are consistently
changed due to PTEN loss. For the significantly downregulated protein, the Venn diagram
(Figure 7) identified one protein methyltransferase-like protein 7A (METTL7A) that is
consistently changed in all age group/disease stage except for 6 months of age. As shown in
Figure 8, MS detection of METTL7A showed a 3.76-fold lower in the PTEN deleted livers vs.
controls in the 3 months old mice. In 9 months and 15 months livers, METTL7A is the most
significantly downregulated protein with 4.59 and 6.36-fold lower levels in the PTEN deleted vs.
WT livers, respectively.

Figure 7. Venn diagram showing proteins that are significantly upregulated and downregulated
(The Venn diagram showing proteins that are significantly upregulated and downregulated of 3-,
6-, 9-, and 15-months group. The significantly upregulated protein is not overlap in all age
groups while there is one protein overlap in the 3-, 9-, and 15-months group with the exception
of 6 months group)

22

Figure 8. Detailed description of the downregulated proteins in each month (The METTL7A is a
significantly downregulated protein in 3-, 9-, and 15-months group while is not a significantly
downregulated protein in 6 months group. The table was cut off at the top 17 most significant
downregulated proteins to give the best picture quality.)

23
3.4 Validation of METTL7A downregulation in liver tissues with PTEN loss

To validate the proteomics data, we first determined METTL7A mRNA expression levels in the
livers of 3-, 9-, and 15-month-old wild-type and Pten-null mice using qPCR analysis. At 3 months
of age, METTL7A mRNA expression is 25 fold higher in wild-type livers than Pten-null mics,
consistent with our proteomics results (Figure 9). However, we did not observe similar
downregulation of METTL7A mRNA at 9 and 15 months of age (Figure 9). On the contrary, at 9
and 15 months of age, respectively, Pten-null mice showed a modest 1.4117- and 1.408-fold
increase in METTL7A mRNA expression in comparison to wild-type mice. However, significant
variation is observed for the RNA expression data. Further validation with increased samples size
is needed to confirm this data.

Then, a Western blot analysis was carried out to examine METTL7A's protein levels (Figure 10).
Similar to our proteomics data, our results showed consistently reduced METTL7A protein levels
in the PTEN deleted livers in all three age groups compared with wild-type livers. In the 3 months
old livers, METTL7A is essentially undetectable when PTEN is lost. The PTEN deleted groups
have significantly lower levels of METTL7A than the controls in the livers that are 9 months old.
In the 15 months old livers, this difference is less pronounced when compared with the other two
groups. The contribution of other cell types than hepatocytes may explain the less significant
effects observed in the 15 months old livers. The 15 months old Pten-mice are heavily burdened
with tumors [38]. The tumors developed in these mice are infiltrated by inflammatory cells and
fibroblasts that compose the tumor stroma. These infiltrating immune cells and stroma fibroblasts
carry intact PTEN as can be observed with the reappearance of PTEN bands in some samples of
the 15 months old livers (Fig 10). A similar contribution of immune cells may also contribute to

24
the observed METTL7A in Pten-deleted livers at 9 months of age. Future studies may need to
separate the normal tissue from tumor tissue to confirm that the downregulation of METTL7A
only occurs in cells lacking PTEN.

Figure 9. qPCR analysis of METTL7A (The METTL7A is downregulated in 3 Months group
while is upregulated in 9- and 15-months group)

Figure 10. Western blot analysis of Pten-null mice contrasted with WT mice in 3 months, 9
months, and 15 months (The METTL7A is downregulated after PTEN deleting in all age groups)

3.5 METTL7A is downregulated in liver cancer and some other cancers

To explore the relevancy of this discovery, we performed a protein expression analysis of datasets
available at the Cancer Proteogenomic Data Analysis Site. Consistent with our proteomic analysis,
METTL7A protein expression levels are lower in Liver cancer, Lung Adenocarcinoma (LUAD),

25
Breast cancer, Pancreatic Ductal Adenocarcinoma (PDAC), and Kidney Cancer when comparing
tumor tissue to adjacent normal tissue (Figure 11).

Figure 11. METTL7A protein abundance in Liver Cancer, LUAD, Breast cancer, PDAC, and
Kidney Cancer (Figure created via https://cprosite.ccr.cancer.gov/) (METTL7A protein
expression levels have declined in Liver cancer, Lung Adenocarcinoma, Breast cancer,
Pancreatic Ductal Adenocarcinoma, and Kidney Cancer comparing tumor tissue to adjacent
normal tissue)

Other than protein expression, we also explore the transcript expression of METTL7A. The
Figure 12 were generated at UALCAN website. As shown in Figure 12, the transcript expression
of METTL7A is also lower in primary tumor tissue as compared to normal tissue.

26

Figure 12. The transcript expression of METTL7A in LIHC, LUAD, BRCA, PAAD, and KIRC
(Breast invasive carcinoma: BRCA; Pancreatic adenocarcinoma: PAAD; Kidney renal clear cell
carcinoma: KIRC. The transcript expression of METTL7A is reduced in these five cancer types
when comparing normal tissue with primary tumor tissue.) [57]

In addition, we generated survival plots using the UALCAN website (Figure 13). Our analysis
shows that the higher the survival rate of patients, the higher the METTL7A expression with the
exception of Breast invasive carcinoma. It’s interesting to notice that METTL7A expression
more profoundly affected the survival of kidney cancer patients. Many studies suggest that the
over-accumulation of Hypoxia-inducible factor 1a (HIF-1a) is crucial for Renal Cell Carcinoma
(RCC) tumorigenesis and mTOR is required for HIF protein synthesis [58]. METTL7A may
participate within the PTEN/PI3K/AKT/mTOR pathway, which could inhibit mTOR, hence
leading to a higher survival rate of RCC patients with a p-value equal to 1.5e-11.

27

Figure 13. The overall survival plot of METTL7A in LIHC, LUAD, BRCA, PAAD, and KIRC
(The patients with higher METTL7A expression can live longer than the patients with lower
METTL7A expression apart from BRCA) [57]

3.6 Regulation of METTL7A by PTEN/PI3K/AKT signaling pathway

To understand more about how PTEN and METTL7A are related, the correlation plot was
generated by using the GEPIA (Gene Expression Profiling Interactive Analysis) website and
TCGA dataset. METTL7A and PTEN showed a moderate positive correlation with R= 0.091 in
TCGA LIHC tumor datasets but they have a negative relation with R= -0.27 in TCGA LIHC
normal datasets (Figure 14). The significance of this change from negative to positive correlation
in normal vs. tumor tissues is unclear. To fully understand the relation between the PI3K/AKT
signal and METTL7A, more research is needed.

28

Figure 14. Correction between PTEN and METTL7A (The overexpression of PTEN will lead to
the overexpression of METTL7A in LIHC tumor environment while the overexpression of
PTEN will lead to the down expression of METTL7A in LIHC normal environment.)

To further explore the relationship between PTEN/PI3K signal and METTL7A in the liver, we
treated Huh7 cells with LY294002 (PI3K inhibitor) to inhibit PI3K/AKT signaling and explore
whether METTL7A would be affected by such manipulation. After LY294002 treatment for 1, 2,
and 4 hours, the protein expression of METTL7A was examined using western blot. In Figure
15, we show that METTL7A’s protein expression was time-dependently increased with the
treatment of LY294002 to inhibit PI3K/AKT pathway.

29
Figure 15. Analysis of METTL7A by Western blotting in Huh7 cells with LY294002 (The
protein expression of METTL7A continued increasing as time progressed.)
Chapter 4. Discussion

Liver cancer is ranked as the sixth most prevalent cancer with high mortality rates in the world in
2020 [1]. Here, we used liquid chromatography-mass spectrometry-based proteomics to explore
the molecule pathway during liver disease development. This study aims to investigate the protein
expression change when the deletion of a tumor suppressor gene, which it is PTEN, is coocured,
and tumors develop with the progression of time. Based on the results of MS data, we discovered
METTL7A as a consistently and significantly downregulation protein in Pten-null livers compared
to the normal wild type across all phenotypes. In patients, METTL7A is downregulated in many
cancer types, including liver cancer. How METTL7A is associated with tumor development is still
unclear. This study suggests a possible relationship between METTL7A and PTEN and suggests
that it might be regulated by the PTEN/PI3K/AKT pathway.

Human METTL7A is an integral membrane protein that attaches to the endoplasmic reticulum
(ER) and produces lipid droplets [59]. Under metabolic stress, METTL7A promotes cell survival
rate [60], osteogenic differentiation, and odontogenetic differentiation [61]. Methotrexate
resistance has been shown to be regulated by METTL7A by decreasing the formation of reactive
oxygen species and stimulating pro-survival signaling pathways [62]. Elsewhere, Guo’s study
group reported that in lung cancer, the expression of METTL7A was downregulated, revealing
that METTL7A could be a possible target for lung adenocarcinoma therapy [63]. Using functional
enrichment analysis of four GEO datasets, Guo's team created a protein-protein interaction (PPI)
network and reported 11 hub genes that are highly correlated, and METTL7A was identified as

30
one of them [63]. METTL7A expression was also found to decrease in transformed thyroid cell
lines and primary thyroid cancers [64]. In HCC, the expression of METTL7A was investigated
using the NCBI Gene Expression Omnibus (GEO) database’s (GEO542361) microarray gene
expression [65] and the Cancer Genome Atlas (TCGA) project’s RNA-Seq datasets [66].
According to the secondary analysis of these datasets, when compared to adjacent NT tissues, the
METTL7A expression in HCC tumor tissues was lower, and the overall survival time for HCC
patients was predicted to be shorter.

The regulation and cellular functions of METTL7A are largely unknown. In a study, the protein
levels of METTL7A were evaluated between the HCC tumor tissue and the associated NT liver
tissue using 15 matched pairs of primary HCC tumor tissues and NT liver tissues [67]. The samples
were categorized into ADAR1/2 (high) and ADAR1/2 (low or normal) groups. [67]. In the
ADAR1/2 (high) group, low METTL7A levels were observed compared with the paired NT liver
tissues [67]. On the other hand, comparing tumor and non-tumor tissues, around three-quarters of
the ADAR1/2 (low or normal) group showed an increase in METTL7A expression or showed no
change in its expression [67]. Thus, they hypothesize that METTL7A downregulation in HCCs
may be mediated by ADARs [67]. Adenosine is deaminated to inosine (A-to-I editing) by a family
of enzymes known as Adenosine DeAminases Acting on dsRNA (ADAR) [68]. Target gene
expression is impacted by the removal of mRNAs that have undergone A-to-I editing at their
3′UTRs through RNA editing-dependent processes including as control of microRNA (miRNA)
targeting, nuclease-mediated degradation, and nuclear retention [69-70]. A codon change and
subsequent alterations to protein-coding sequences in coding regions could result through this A-

31
to-I RNA editing [71]. Thus, differential editing frequencies may influence human diseases like
cancer [65, 67, 72-73].
Three ADAR proteins have been identified in humans: ADAR1, ADAR2, and ADAR3, with
ADAR3 only being found in the central nervous system [74]. The three ADARs have a modular
structure with two to three dsRNA binding domains (dsRBDs) at the N-terminus and a catalytic
deaminase domain at the C-terminus [71, 75]. In HCC patients, ADAR1 is overexpressed whereas
ADAR2 is downregulated [67]. A recent study identified ADAR2 and ADAR1p110 are direct
substrates for AKT1, AKT2, and AKT3 in CCRF-CEM cells [76]. In this study, AKT inhibition
resulted in ADAR1p110 moving more quickly and losing phosphorylation at locations identified
by the anti-phospho-AKT substrate antibody [76]. In our study, we found that PTEN also regulates
METTL7A. Here, we hypothesize that the deletion of PTEN induces upregulation of AKT,
resulting in a rise in the expression of ADAR1 and hence a decline in the expression of METTL7A.
Excessive activation of the PI3K/AKT pathway in HCC, whether as a result of PTEN loss or
something else, promotes tumor cell survival, invasion, and metastasis while preventing apoptosis.
[77]. Thus, the observed downregulation of METTL7A due to PTEN loss regulated by AKT-
ADAR signal may have contributed to the HCC phenotype observed with PTEN loss.

In conclusion, we found that METTL7A is a prospective target of PTEN regulation, but we still
don't know how PTEN loss results in the dysregulation of METTL7A. Despite this potential
regulation of METTL7A by the PI3K/AKT/PTEN pathway, the function of METTL7A is not well
understood. Its potential role in lipid droplet formation as an ER membrane/lipid droplet protein
[59] may be important for the steatosis phenotype shown by liver PTEN loss and the initiation of

32
liver disease due to alterations in this pathway. The potential role of METTL7A in ADAR
regulated nucleotide editing may also contribute to the PTEN regulated liver cancer development.
Chapter 5. Methods and Materials

5. 1 Animals

Mice with liver-specific deletion were generated by breeding !"#$
!"#$/!"#$
mice with Alb-Cre
mice together [38]. The experiments were conducted on animals that were 3, 6, 9, and 15 months
old. Mice were fasted overnight. Liquid nitrogen was used to freeze the liver tissues.
The University of Southern California Institutional Animal Care and Use Committee's guidelines
were followed when conducting the experiments.

5.2 Mass spectrometry workflow

Liver tissue from 3 months, 6 months, 9 months, and 15 months of mouse were diluted in Halt™
Protease and Phosphatase Inhibitor Cocktail (Thermo Scientific, Waltham, MA) and lysis buffer
[50 mM triethylammonium bicarbonate (TEAB), 0.5% sodium deoxycholate, 12 mM sodium
lauroyl sarcosine]. Next digested with Sequencing Grade Modified Trypsin (Promega, Madison,
WI; 1 ug, 37°C, overnight) [78]. Then use C18 StageTips [79] to desalt the samples. Later, used
a TMT11plex Isobaric Label Reagent Set (Thermo Fisher Scientific) to label the samples. Used
C18 StageTips [79] to desalt the samples again. The eluants were injected onto HPLC column
using a Dionex UltiMate 3,000 RSLCnano System (Thermo Fisher Scientific) based on Cohn’s
protocol [78].

33
Using SEQUEST-HT, Proteome Discoverer (Version 2,4; Thermo Scientific) searched the raw
data against the Uniprot human-reviewed protein database, yielding measures of the found
peptides' relative abundance [78].

Using decoy database searching, high confidence tryptic peptides (FDR 1%) were produced.
To identify and provide relative quantification between the proteins in each sample, tryptic
peptides with amino acid sequences specific to individual proteins were employed [78].

5.3 RNA isolation, reverse transcription, and real-time PCR

According to the manufacturer's instructions, total RNA was extracted from mouse liver tissues
and hepatocyte cell lysates with TRIzol reagent (Invitrogen, Carlsbad, CA). The concentrations
of RNA were determined using a NanoDropTM 1000 Spectrophotometer (Thermo Fisher,
Waltham, MA). M-MLV reverse transcriptase system (Promega, Madison, WI) was used to carry
out reverse transcription. The StepOnePlusTM Real-Time PCR System (Thermo Fisher,
Waltham, MA) was used to perform the real-time Polymerase chain reaction. And SYBR Green
qPCR Master Mix (Thermo Fisher, Waltham, MA; Bioland, Paramount, CA) was used to
quantify. Delta Ct was used to calculate the relative gene expression, and GAPDH was employed
as an internal control. The table below includes a list of the primers that target different genes.
(Table 9)
Table 9. Primers for Real-Time PCR
Full name Sequences (5’- 3’) *
METTL7A Methyltransferase-like
protein 7A
F: CGTGATGTACAATGAGCAGATGG

R: TGGGGTTGGGGTCGATACA

34
GAPDH** Glyceraldehyde 3-phosphate
dehydrogenase
F: TGTGTCCGTCGTGGATCTGA
R: TTGCTGTTGAAGTCGCAGGAG

F: Forward primer; R: Reverse primer
* Specificities of all primers were checked by Primer-BLAST
** primers target total gene expression including all mRNA variants and isoforms

5.4 Western Blot

Mouse liver tissue lysates were generated using RIPA lysis buffer (25 mM Tris-HCl [pH 7.6],
150 mM NaCl, 1% NP-40, 1% sodium deoxycholate, 0.1% SDS), added with a combination of
protease inhibitors (MedChemExpress, Monmouth Junction, NJ). Protein concentration was
measured using the colorimetric DCTM (detergent compatible) protein assay (Bioland,
Paramount, CA), or the BCA Protein Assay (Thermo Fisher Scientific, Waltham, MA). Equal
amounts of protein from tissue lysates were added to a 5X protein loading dye solution before
being put through a 10% Tris-glycine SDS-PAGE. The proteins were then transferred from the
gel to a polyvinylidene fluoride (PVDF) membrane (Bio-Rad Laboratories, Hercules, CA, USA)
for immunoblotting. PVDF membranes were blocked using 0.1% Tween-20 and 5% nonfat milk
added in phosphate buffer saline (PBST). Antibodies for METTL7A (Proteintech, Rosemont, IL,
USA), PTEN (Cell Signaling Technology, Danvers, MA, USA), and GAPDH (Cell Signaling
Technology, Danvers, MA, USA) as a control gene were then used to incubate with the
membrane at 4 °C overnight. The membrane was then treated with HRP (horseradish
peroxidase)-linked secondary antibodies (GE Healthcare Chicago IL) following PBST washing.
Solutions with HRP substrate-enhanced chemiluminescence solutions (Thermo Fisher Waltham,
MA) were used to detect signals.
Table 10. Antibodies used in Western Blot

35
Antibodies Source Manufacturer Catalog numbers
Primary METTL7A Mouse Proteintech

67905-1-Ig
PTEN Rabbit Cell signaling
Technology
9559S
GAPDH Rabbit Cell signaling
Technology
5174S
Secondary Anti-mouse Sheep Ge Healthcare RPN4301
Anti-rabbit Donkey Ge Healthcare NA9340-1ML

5.5 Statistics

All data are statistically analyzed by using Excel (Microsoft). The qPCR data was analyzed using
student t test. The build-in equations inside the Proteome Discoverer (Version 2,4; Thermo
Scientific) were used to analyze the raw MS data. P-values less or equal to 0.05 is considered
statistically significant.

36
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Abstract (if available)

Abstract PTEN (phosphatase and tensin homolog deleted on chromosome 10) is a tumor suppressor which was discovered by three laboratories in 1997. Since then, studies have established that PTEN plays an important role in metabolic regulation in addition to cell growth and death. Although the many downstream signals regulated by PTEN have been identified, particularly for its role as a tumor suppressor, how PTEN may regulate metabolism is largely unknown. In this study, we employed a Mass Spectrometry-based bottom-up proteomics technique to analyze differential protein expressions in mouse livers lacking PTEN vs. those with intact PTEN. Here, we explored the differential protein expressions in 3-, 9-, and 15 months-old mouse livers. Our data identified methyltransferase-like 7A (METTL7A) as a protein of which the levels are significantly reduced in all stages when PTEN is lost. METTL7A is the most significantly altered in 9 months and 15 months of the proteomics database. As an integral lipid droplet protein, METTL7A is found in the cytoplasm and is involved in mRNA translation. We validated the MS results in liver tissues using western blot analysis. However, the mRNA expression of Mettl7a did not change accordingly, suggesting that the PTEN signal may regulate METTL7A protein expression post-transcriptionally. In summary, we identified METTL7A as a potential downstream target for PTEN. As PTEN loss induces liver steatosis, METTL7A, a lipid droplet protein may contribute to this phenotypical change. Our future studies will explore the signaling pathways leading to the regulation of METTL7A by PTEN and the role of METTL7A in PTEN-regulated lipid metabolism.

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Asset Metadata

Creator Zhou, Yiren (author)

Core Title Downregulation of METTL7A gene in liver-specific Pten-null mouse model

School School of Pharmacy

Degree Master of Science

Degree Program Pharmaceutical Sciences

Degree Conferral Date 2023-08

Publication Date 07/27/2023

Defense Date 07/26/2023

Publisher University of Southern California. Libraries (digital)

Tag downregulation,liver cancer,METTL7A,OAI-PMH Harvest,PTEN

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Stiles, Bangyan (committee chair), Shih, Jean (committee member), Zaro, Jennica (committee member)

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