THE PENNSYLVANIA STATE UNIVERSITY

SCHREYER HONORS COLLEGE

DEPARTMENT OF BIOCHEMISTRY AND MOLECULAR BIOLOGY

A SYSTEMATIC METHOD FOR ANALYZING STIMULUS-DEPENDENT ACTIVATION OF THE p53 TRANSCRIPTION NETWORK

SARAH L. MOORE SPRING 2013

A thesis submitted in partial fulfillment of the requirements for a baccalaureate degree in Biochemistry and Molecular Biology with honors in Biochemistry and Molecular Biology

Reviewed and approved* by the following:

Dr. Yanming Wang Associate Professor of Biochemistry and Molecular Biology Thesis Supervisor

Dr. Ming Tien Professor of Biochemistry and Molecular Biology Honors Advisor

Dr. Scott Selleck Professor and Head, Department of Biochemistry and Molecular Biology

* Signatures are on file in the Schreyer Honors College. i

ABSTRACT

The p53 protein responds to cellular stress, like DNA damage and nutrient depravation, by activating cell-cycle arrest, initiating apoptosis, or triggering autophagy (i.e., self eating). p53 also regulates a range of physiological functions, such as immune and inflammatory responses, metabolism, and cell motility. These diverse roles create the need for developing systematic methods to analyze which p53 pathways will be triggered or inhibited under certain conditions.

To determine the expression patterns of p53 modifiers and target genes in response to various stresses, an extensive literature review was conducted to compile a quantitative reverse transcription polymerase chain reaction (qRT-PCR) primer library consisting of 350 genes involved in apoptosis, immune and inflammatory responses, metabolism, cell cycle control, autophagy, motility, DNA repair, and differentiation as part of the p53 network. Using this library, qRT-PCR was performed in cells with inducible p53 over-expression, DNA-damage, cancer drug treatment, serum starvation, and serum stimulation. Heat-map and statistical analyses of these data, organized by cellular pathways or chromosome locations, have yielded insight into the complex response of the p53 network. Ultimately, the expression patterns of these p53-related genes will provide knowledge of cellular decision making mechanisms and allow researchers to evaluate the effect of particular drugs on the p53 pathways. A compilation of this data and analysis can be found at: https://protected.personal.psu.edu/s/l/slm5430/zoombablue/Home.html

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TABLE OF CONTENTS

List of Figures………………………………………………………………………………… iii List of Tables…………………………………………………………………………………. iv Acknowledgements…………………………………………………………………………… v

Chapter 1: The p53 Network .………………………………………………………………… 1 1.1 - NFκB Immune and Inflammatory Responses ……………………………...... 1 1.2- Apoptosis ………………………………………………………………..………….. 3 1.3- Cell Structure and Motility ....…………………… ………………………...... 4 1.4- Autophagy ……………………………………………...... …………….. 4 1.5- Cell Growth and Proliferation ………………………………………….…………… 5 1.6- Metabolism and Oxidative Stress ………………………………………...………… 6 1.7- DNA Repair ………………………………………………………………………… 7 1.8- Differentiation ……………………………………………………………………… 8 1.9- Upstream Regulation …………………………………………...... 9

Chapter 2: Materials and Methods …………………………………………………………… 10 2.1- Gene Selection ……………………………………………………………………… 10 2.2- Primer Design ………………………………………………………………………. 10 2.3- Gene Mapping ………………………………………………………………………. 11 2.4- Cell Culturing and Drug Treatment ………………………………………………… 12 2.5- RNA Collection and Reverse Transcription ……………………………………….. 12 2.6- q-RT PCR ...………………………………………………………………………… 13

Chapter 3: Results ……………………………………………………………………………. 14 3.1- The Gene Set ………………………………………………………………………… 14 3.2- Direct p53 Over-expression …………………………………………………………. 14 3.3- Doxorubicin treatment ………………………………………………………………. 16 3.4- Serum Starvation ……………………………………………………………………. 18 3.5- Serum Stimulation ……………………………………………………………………20 3.6- Testing the cancer drug 6e ………………………………………………………….. 21

Chapter 4: Discussion ………………………………………………………………………… 23

References……………………………………………………………………………………. 49 iii

List of Figures

Figure 1- The p53 protein responds to a battery of cellular pathways in response to various upstream signals…………………………………………………………………… 24 Figure 2- qRT-PCR data from six treatment conditions reveals context-dependent impact on each of the p53 pathways……………………………………………………………….. 25 Figure 3- p53 over-expression results in pathway trends of activation or inhibition……… 26 Figure 4- Doxorubicin treatment affects the p53 network differently than direct p53 over- expression………………………………………………………………………………….. 27 Figure 5- Numerous NFκB functions are repressed by p53 over-expression but not Doxorubicin treatment……………………………………………………………………... 28 Figure 6: Serum Starvation affects cell metabolism……………………………………….. 29 Figure 7: Serum stimulation rescues starved cells…………………………………………. 30 Figure 8: Analysis of 6e drug treatment allows prediction of the drug mechanism of action……………………………………………………………………………………….. 31

iv

List of Tables

Table 1- The p53 gene library responds uniquely to different stimuli…………………….. 32 Table 2- qRT-PCR Primers………………………………………………………………… 41

v

Acknowledgements

I would like to thank Dr. Wang for all of the support, guidance, and great ideas he has provided in this endeavor. I would also like to thank the graduate students in the lab, Jing Hu,

Shu Wang, Amy Chen, and Jinquan Sun for their support and assistance in learning lab protocols. This project was funded by a 2012 Undergraduate Summer Discovery Grant, the

Eberly College of Science, and the Penn State Biochemistry Department Whitfield Award.

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Chapter 1

The p53 Network

The p53 protein, commonly referred to as “The Guardian of the Genome,” is a stress- activated tumor suppressor widely known to be one of the most commonly mutated genes in human cancer. As such, extensive research has been conducted to determine the role of the protein, which has revealed downstream targets implicated in numerous cell pathways, including the immune and inflammatory response, apoptosis, cell motility, autophagy, cell cycle arrest, metabolism and oxidative stress, DNA repair, and differentiation (Figure 1). Furthermore, numerous mechanisms that regulate p53 function have been identified, including ubiquitination by the MDM2 protein [1], phosphorylation by ATR [2], and methylation by the lysine methyltransferase Set9 [3]. Acetylation of the p53 protein by KAT5 has been shown to play a role in regulating whether p53 activation induces apoptosis or cell cycle arrest [4]. However, further understanding of the role of p53 in cell-fate decision making mechanisms is needed to develop therapeutic targets for cancer and disease treatment. Information regarding how specific p53 pathways, as well as individual genes within the pathways, are affected by particular cellular stresses will be useful for gaining further insight into p53 function, aid in drug testing and design, and may ultimately provide a starting point for determining genes involved in cross-talk between the pathways.

1.1: NFκB Immune and Inflammatory Response

NFκB is a transcription factor constitutively present in the cytoplasm in an inactive form

[5]. While there are seven NFκB-family proteins, the heterodimer comprised of p50 (encoded by

NFκB1) and RELA is the most prevalent [6]. NFκB activation, triggered as part of an immune 2 response to viral particles and pathogen associated molecular patterns (PAMPs) such as lipopolysaccharide, results in migration of the transcription factor into the nucleus where it stimulates production of cytokines and chemokines central to inflammation [5]. This in turn results in propagation of the immune response.

Beyond its role in the immune response, NFκB has been shown to regulate apoptosis, cell proliferation, and differentiation, all of which are also implicated in p53 function [6].

Correspondingly, the NFκB and p53 DNA binding sites are extremely similar, indicating that the two transcription factors may compete [7]. NFκB has also been shown to induce p53 expression.

Furthermore, increased p53 expression in a Tet-On Saos2 p53-inducible cell line results in NFκB activation, and this activation is necessary for p53-induced apoptosis [8]. However, NFκB has been implicated in both anti-apoptotic and pro-apoptotic pathways. While promoting p53- induced apoptosis, for example, NFκB inhibits TNFα-mediated cell death, despite the fact that

TNFα is one of the primary activators of NFκB.

In addition to a clear connection between p53 and NFκB itself, numerous genes within the NFκB immune and inflammatory pathway have also been shown to affect or be affected by p53. For example, the cytokine CCL2 has been shown to be activated by ultraviolet-radiation

(UV)-induced p53 [9]. However, it is not clear if this response is consistent when p53 is activated by other stresses.

TRAIL (TNFα-related apoptosis-inducing ligand) is a p53 target gene which contributes to apoptosis [10]. It is also, as its name suggests, capable of inducing apoptosis via TNFα independent of p53. Furthermore, TRAIL activation of NFκB has been identified as a mechanism for cell protection [11], possibly reflecting NFκB-mediated inhibition of TNFα- induced apoptosis. Because it is both a pro-apoptotic molecule and NFκB activator in certain 3 contexts, with conflicting results, further analysis of this gene in the context of various stresses is needed to gain an understanding of its role in these pathways.

1.2: Apoptosis

The role of the p53 protein in inducing apoptosis, or programmed cell death, has long been known as one of the primary mechanisms for tumor suppression. As such, many cancer cells survive, proliferate, and resist chemotherapy due to mutations which inactivate p53 function, preventing the tumor cells from undergoing apoptosis [12]. An intensive area of research therefore involves activation of apoptosis in cancer cells containing mutant p53.

However, while p53-independent apoptotic pathways exist, such as TNF-α induced apoptosis, manipulation of these pathways will likely also affect other pathways in the p53 network, as is discussed with TNF-α and NFκB. This hypothesis is further supported by the discovery that several p53-dependent mediators of apoptosis, such as PIDD, are highly similar to the TNFα-receptor and Fas (TNFRSF6) [12]. Moreover, HDAC inhibitor sodium butyrate, which stimulates apoptosis synergistically in combination with TNFα independently of p53, relies on p21, a key cell growth inhibitor induced by p53 [13]. Additionally, while TNFα and

Fas are involved in p53-independent apoptosis, under certain conditions they are also implicated in p53-mediated apoptosis [14, 15]. Therefore, while some p53-independent pathways exist, they likely have extreme overlap with p53-induced apoptosis as well as other p53 functions, such as growth-arrest. When developing a cancer treatment, it is therefore important to first understand the cross-talk that is going on between pathways in the cell and the natural cell-fate decision making mechanisms that are in place.

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1.3 Cell Structure and Motility

The role of p53 in cell structure and motility is less defined. Never-the-less, it has been shown that p53 can induce production of extracellular matrix and cytoskeleton components, as well as inhibit the process of angiogenesis, or blood vessel formation [12]. Furthermore, CD82, a gene involved in metastasis inhibition, is activated by p53, but repressed by a complex comprising NFκB and the cAMP-dependent transcription factor ATF3 [16]. As metastasis, or the spread of cancer cells from a primary tumor to other parts of the body, is a big part of what makes cancer so lethal, understanding this cross-talk is important. To complicate matters, ATF3 has also been shown to both stabilize p53 and inhibit apoptosis by down-regulating Doxorubicin- induced p53 [17, 18]. This once again emphasizes a complex interplay within the p53 network, which will likely respond differently to different stimuli.

1.4 Autophagy

Autophagy, or “self-eating,” is the process whereby organelles are degraded in the lysosome [19]. The pathway serves to recycle old or malfunctioning organelles, as well as respond to stress, such as starvation or pathogen invasion. Its role as a central component of the stress response inevitably links it with p53 function and numerous pathways in the p53 network.

As an example, DRAM is an autophagy inducing protein which is activated by p53.

Additionally, DRAM is necessary for p53-mediated apoptosis, highlighting the role of both pathways in cell death and tumor suppression [20]. However, it has also been shown that while nuclear p53 induces expression of autophagy mediators, such as DRAM, cytoplasmic p53 functions in conjunction with apoptosis to repress autophagy [21]. Therefore, certain stresses, such as DNA damage, which result in translocation of p53 to the nucleus, lead to p53-induced 5 autophagy, as well as cell cycle arrest. The response to reactive oxygen species is mediated by nuclear p53 and NFκB, and also results in autophagy induction [19]. However, apoptotic proteases, such as Caspase 3, inhibit autophagy factors [22]. In turn, autophagy inhibits apoptosis by eliminating damaged mitochondria.

It is clear that autophagy is one of the key players concerning cross-talk within the p53 network. Autophagy factors have been shown to regulate inflammatory responses, and particularly to inhibit pro-inflammatory cytokines, which play a role in apoptosis as well as inflammation [19]. Additionally, autophagy is blocked by the cell cycle control pathway during mitosis, and many promoters of cell growth and proliferation also inhibit autophagy [23, 24].

Metabolism is also a clear player in autophagy, as autophagy induction is frequently caused by nutrient depravation.

Autophagy has become a prime target for cancer drug treatment due to its conflicting roles as both a tumor suppressor and mediator of cell-survival. However, a better understanding of these roles and the effect of autophagy induction or inhibition on the other p53-network pathways is essential for optimizing this treatment approach.

1.5 Cell Growth and Proliferation

As with apoptosis, the role of p53 in regulating cell growth and proliferation has long been known, particularly through the activation of the protein p21 (CKDN1B), an inhibitor of cyclin-dependent kinases. In response to stress, p53 halts cell cycle progression to give the cell time to recover, such as by repairing DNA damage. p53 mutation in tumor cells therefore not only results in cells resistant to apoptosis, but allows the cells to grow and proliferate out of control. 6

As mentioned previously, p21 has been implicated in TNFα-induced apoptosis independent of p53. It is also involved in regulation of the protein PCNA, which has roles both in DNA replication leading to cell growth and nucleotide excision DNA repair [25].

Interestingly, it appears that while p21 activation results in inhibition of cell proliferation by preventing PCNA-mediated DNA replication, it does not affect PCNA-dependent nucleotide excision repair. This indicates that p21 regulates specific functions of PCNA to allow DNA- repair to occur but prevent replication of damaged DNA. Furthermore, p53 itself has been shown to activate the PCNA promoter in certain contexts, but is associated with PCNA down- regulation in other reports [26, 27]. This balance emphasizes the role of p53-induced p21 in halting cell-cycle progression to allow for DNA repair.

While p53 function is generally associated with negative regulation of cell growth, it has been shown to directly activate several growth factors, most notably TGFα. It is hypothesized that this activation reflects a proliferative function for p53 that works to replace cells eliminated by p53-dependent apoptosis [28]. A better understanding of this response in different stress contexts is important when considering drugs that may affect it.

1.6 Metabolism

The role of p53 as a tumor suppressor is also clearly seen through its function in metabolism. While cancer cells tend to use excess amounts of glucose through the glycolysis pathway, instead of the more efficient oxidative phosphorylation, p53 works to down-regulate glycolysis and upregulate oxidative phosphorylation [29]. For example, p53 induces expression of the protein TIGAR, which not only represses glycolysis, but also decreases the level of damaging reactive oxygen species present. Interestingly, TIGAR is also a negative regulator of 7 autophagy [19]. As with the other pathways however, a balancing act, likely dependent on stimuli and cross-talk, is present, as p53 has also been shown to promote glycolysis in certain contexts. Furthermore, while activation of TIGAR has an antioxidant effect, p53 can also have pro-oxidant functions in certain conditions. [30].

The role of p53 in glycolysis is further complicated by the tendency for NFκB to increase glycolysis [29]. Part of p53’s mechanism for glycolysis inhibition may be through inhibition of this NFκB function, which is contrary to its role as an activator of NFκB discussed above.

p53 is also involved in fatty acid metabolism, and can both regulate and be regulated by

AMPK. Furthermore, when p53 is activated by AMPK due to insufficient nutrients, it in turn regulates AMPK and TSC1/2 to negatively regulate AKT and MTOR, which results in cell growth inhibition as well as activation of autophagy. p53 clearly has a central role in regulation of various metabolic pathways, both directly and through other parts of its network, such as

NFκB, cell growth, and autophagy. However these diverse, sometimes conflicting, roles for p53 in metabolism need to be reconciled.

1.7 DNA Repair

The role of p53 in DNA repair has already been briefly discussed with respect to PCNA

(see cell growth and proliferation above). One of p53’s main functions as “The Guardian of the

Genome” is maintaining genetic stability by ensuring both that the cell does not continue replicating DNA damage and by repairing the damage. However, while it has been shown that lack of p53 in a cell hinders the cell’s ability to respond to ultraviolet-radiation induced DNA damage via the nucleotide excision repair pathway [31], the mechanism of p53’s involvement in this process, as well as p53’s involvement in other DNA repair pathways, is less clear. 8

Supporting its role in DNA repair however, p53 is involved in activation of the nucleotide excision repair gene DDB2 [32], mismatch repair gene MSH2 [33], and the deoxyribonucleotide triphosphate provider RRM2B [34]. DDB2 has also been implicated in apoptosis [32] while p53-induced activation of MSH2 has been shown to depend on interaction with the protein c-Jun, which negatively regulates p53 in order to promote cell cycle progression

[35]. It is clear that the DNA-repair component of the p53 network follows the trend of cross- talk within the network. Further analysis of this pathway in response to stimuli other than UV- damage is necessary to further understand the role of p53 in DNA repair, as well as identify potential roles for DNA repair genes throughout the p53 network.

1.8 Differentiation

p53 has been shown to play a role in embryonic stem cell differentiation [36] and hematopoietic and muscle cell differentiation [37], among others. Furthermore, p53’s function in regulating cell growth and proliferation has been linked to the ability of the protein to control stem cell renewal [38]. As cancer stem cells pose one of the greatest challenges to treatment of the disease, exploring the role of p53 in differentiation is extremely important. Because p53 is so often mutated in cancers, it is possible that this inactivation not only allows uncontrolled cell growth in general, but also promotes stem cell renewal and inhibits proper differentiation.

Support for the role of p53 in differentiation comes from the discovery that repression of the p53-p21 pathway contributes to induction of pluripotency, making the process of generating induced-pluripotent stem (iPS) cells more efficient in p53-null cells [39]. It is already clear that p53 must be transiently repressed to allow for induction of pluripotency but subsequent maintenance of genomic integrity. Further understanding of the specific role of p53 in 9 differentiation is necessary, however, to ensure that repression of this pathway, even transiently, does not significantly impact other components of the p53 network, which would be a bar for therapeutic use of these iPS cells.

As with DNA repair, the mechanism behind p53’s involvement in differentiation is not well defined. Additionally, the majority of genes in the p53 network involved in the differentiation pathway are also players in another major pathway within the network, such as cell growth and proliferation, making determination of possible mechanisms all the more challenging.

1.9 Upstream Regulators

When analyzing any network, it is important to consider signals coming into the network in addition to downstream results. While the components of the p53 network that act upstream of p53 are not altogether part of a single cohesive pathway, analyzing them individually will be necessary in determining the differential responses of the network to various stimuli. In short, differences in the way p53 is activated are sure to have a role in determining its subsequent function.

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Chapter 2

Materials and Methods

2.1 Gene Selection

An extensive list of genes was compiled from the 2006 Wei et al. Cell paper titled “A

Global Map of p53 Transcription-Factor Binding Sites in the Human Genome.” Genes from the

SABiosciences Apoptosis, Autophagy, NFkB, Cancer, and p53 qRT-PCR arrays were added to the list, as well as genes from the p53 pathway on the Panther database (www.pantherdb.org). A literature review of these genes was conducted to determine which ones were known to be directly up or down-stream of p53. This literature review yielded roughly 300 genes from the approximately 1000 originally on the list. An additional 50 genes were added over the course of the experiment as necessary based on additional literature reviews and collaboration with the

Pugh Lab at The Pennsylvania State University.

2.2 qRT-PCR Primer Design

The reference sequence numbers for the genes were first found in the Gene database of the National Center for Biotechnology Information (NCBI). Primers were then designed using the idtdna.com qRT-PCR SciTool by entering the reference sequence numbers, selecting “design assays”, and then choosing “exons”. Genes with reference sequence numbers that were not recognized by the qRT-PCR SciTool were found in the NCBI Nucleotide database; the mRNA sequences were copied from this site to the SciTool application. Whenever possible, a primer set was chosen based on the SciTool recommendation as well as the requirement that an exon junction was spanned. Either the Set 1 or Set 2 primers were chosen in all cases (See Table S2 for primer sequences). The forward and reverse sequences were then copied into a DNA oligo 11 order form, which can be found at idtdna.com in the Order section. The primers designed were

25nmole DNA oligos and were ordered with standard desalting.

2.3 Gene mapping

The NCBI Gene database was used to determine the human chromosomal location of each of the genes of interest. Images of each human chromosome with location labels were found on the U.S. National Library of Medicine’s Genetics Home Reference webpage: http://ghr.nlm.nih.gov/chromosomes. These images were arrayed linearly head-to-tail in

Microsoft Publisher starting with the long arm of chromosome one at the far right of the page. A text-box was created for each gene and placed at the appropriate location on the opposite side of the chromosome from the location labels. Square colored boxes were placed above each textbox to designate the pathways the gene is involved in. These pathway labels are based on a compilation of information from the NCBI Gene database and genecards.org. To maintain upright orientation of all the text in a circular array, all of the text boxes and chromosome location labels were flipped from chromosomes eight to one. In the case of the location labels this required copying the image, cropping each label out of the image, rotating it, and placing it on top of the original label. All images, textboxes, and colored squares were grouped and saved as an image by right clicking on the grouped figure. This image was then opened in Adobe

Photoshop. To convert the linear coordinates to polar coordinates the image was resized such that the height of the original image was multiplied by pi while the width remained the same.

The canvas was resized to form a square with the width of the image as the length of each side.

Finally, the distort-Polar Coordinates function was used to convert the linear array into a circular array and the result was saved as a .png file. 12

2.4 Cell Culturing and Drug Treatment

Saos2 Tet-on p53 cells were cultured in DMEM medium supplemented with 10% Fetal-

Bovine Serum (FBS) and 1% Penicillin-Streptomycin (P/S) in a 37 ᴼC incubator with 5% CO2.

When cells reached greater than 90% confluency, one 10 cm plate was treated with 1.5 μg/mL

Doxycyclin for 24 hours while a second plate was used as a control.

Low passage U2OS cells were cultured under the conditions stated above. One confluent

10 cm plate was treated with .6 μM Doxorubicin for 6hours and a second plate was used as a control. Additional plates of U2OS cells were treated for 8 hours with 30μM 6e or 6m. Last, two plates of U2OS cells were deprived of serum for 16 hours. One of these plates was then stimulated with serum for 8 hours. Three separate control samples were used for Doxorubicin,

6e/6m, and Starvation/Stimulation.

2.5 RNA collection and Reverse Transcription

Following drug treatment, the medium was removed and the cells were washed with 5 ml

PBS and trypsinized with 3 ml of trypsin until the cells were visibly detached. 6 mL DMEM complete medium was added to inactivate the trypsin and the cells were pelleted by centrifuging for 5 minutes at 1000 rpm. The supernatant was removed and the cells were washed with 5 ml

PBS and re-pelleted. The PBS was removed. RNA was extracted using the RNeasy Mini Kit

(Qiagen) per manufacturer’s instructions.

To synthesize cDNA 1 μg RNA was combined with 4 μl qScript cDNA SuperMix

(Quanta Biosciences) and enough RNase free double distilled water to bring the total volume to

20 μL. The mixture was reverse transcribed into cDNA per manufacturer’s instructions, diluted

50 fold, and kept at -80 ᴼC. 13

2.6 Quantitative Real Time PCR

Quantitative PCR was performed using SYBR Green SuperMix (Quanta Biosciences) in the StepOnePlus Real-Time PCR System (Applied Biosystems). The primers used in quantitative PCR were designed as detailed above.

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Chapter 3

Results

A comprehensive literature review was conducted to identify gene targets in the p53 network. Accordingly, a p53 quantitative real-time PCR primer library was established and quantitative real-time PCR was conducted on cells stimulated with various stressful treatments

(see methods). The data collected (Table 1) validate the usefulness of this method for analyzing the p53-network response to unique stresses, and provides insight into context dependent p53 responses at both pathway and gene levels.

3.1- The Gene Set

In total, 350 genes were included in the primer library (Table 2). Each gene was considered part of each of the pathways it is known to be involved in. For example, TNFA is included in both the immune/inflammatory pathway and the apoptosis pathway. The complete list of pathways each gene is part of is included in Table 2. As shown in Figure 2, out of the 350 total genes, 61 are in the NFκB immune/inflammatory pathway, 110 are involved in apoptosis,

32 in cell structure and motility, 75 in autophagy, 107 in cell growth and proliferation, 25 in metabolism, 20 in DNA repair, 28 in differentiation, and 84 are upstream regulators. The pathways each individual gene is included in are denoted in Table 1 and can be found on the project website.

3.2- Direct p53 Over-expression

p53 was directly induced in a p53 TetOn Saos2 cell line to determine the effect of direct over-expression on the established gene library. Of all genes tested, forty seven percent were changed greater than 1.5 fold by p53 over-expression (Figure 2), reinforcing the value of this 15 library for analysis of p53-dependent mechanisms. Furthermore, each pathway tested was impacted, with change in 43% of upstream genes representing the least significant affect. The change in expression of many upstream genes due to p53 over-expression reflects the many feedback loops by which p53 is regulated, however it should be noted that for this condition, p53 was directly expressed. Therefore, upstream modifications of the p53 protein, which are integral in the various p53 responses, were not necessarily present. This lack of upstream modifications of p53 must be taken into account when analyzing the data, as p53 may only activate or repress specific genes when certain upstream events occur. None-the-less, several general trends due to p53 over-expression are evident.

First, p53 over-expression activates apoptosis (Figure 3a). Among the 110 genes classified within the apoptotic pathway, 36% were increased while 21% were repressed after p53 overexpression. 68% of the activated genes are pro-apoptotic, such as Caspases 1, 6, and 10 and apoptosome formation protein APAF1. Furthermore, 26% of the genes repressed are anti- apoptotic, including NAIP, PLK1, and PRKCE. Of the repressed genes that are generally considered pro-apoptotic, a majority are involved in another pathway, such as the immune/inflammatory response (eg. TRADD and TNFSF10) or autophagy (eg. DAPK1,

MAPK8, and SIRT1). Repression of these pro-apoptotic genes therefore likely reflects repression of non-apoptotic functions while allowing activation of alternative apoptotic pathways.

The second general trend is the repression of the autophagy pathway (Figure 2). Among the 75 genes classified as autophagy pathway genes, 12% were increased while 32% were repressed (Figure 2). All of the repressed genes are considered activators of autophagy, including ATG14, ATG5, and ATG4C (Figure 3b). Furthermore, while only a few genes are 16 activated, many of these have been shown to either induce both autophagy and apoptosis (eg.

FAS and DRAM1) or mediate the transition between autophagy and apoptosis (eg. ERN1) [40].

It seems, therefore, that in the absence of upstream signals, p53 represses autophagy, indicating that specific signals are required for p53-mediated autophagy induction.

In agreement with the known role of p53 in cell cycle control, cell cycle inhibition is the third trend (Figure 3c). Among the 107 genes classified as cell cycle pathway genes, 26% were increased while 23% were repressed (Figure 2). Approximately 50% of the genes activated in the cell growth and proliferation pathway promote cell cycle arrest and/or inhibit cell proliferation. Specifically, genes such as cyclin B1 and B2, which are required for cell cycle progression, are repressed. MYC and PIK3CA, two major factors that accelerate cell growth and proliferation, are also repressed by p53 overexpression. On the other hand, two growth factors,

TGFA and GDNF, are activated, reflecting the potential proliferative role for p53 (see further discussion above in Section 1.5).

Overall, the large effects of p53 over-expression in inducing changes in all of the p53- related pathways validate the usefulness of this gene library for analyzing the response of the p53 network to various stimuli. The library’s value is further supported by its ability to demonstrate the known pro-apoptotic and cell-cycle inhibitory functions of the protein. The results which demonstrate that p53 overexpression represses autophagy genes at the mRNA level represent a novel finding, suggesting that p53 has a systematic role in autophagy repression.

3.3- Doxorubicin treatment

U2OS Osteosarcoma cells were treated with 0.6µM Doxorubicin for 6 hours to induce

DNA damage. Doxorubicin (Dox) is a common cancer drug known to cause DNA damage. As 17 expected, this treatment has the most significant impact on genes in the DNA-repair pathway

(Figure 2), affecting 50% of them.

Specifically, mismatch repair genes MSH6, MSH2, and MLH1 are all inhibited, as is

DNA double-strand break repair gene WRN (Figure 4a). However, nucleotide excision repair gene DDB2 is activated, as well as DNA damage recognition gene XPC, and nucleotide supplier

RRM2B.

It is thought that Doxorubicin functions by covalently binding to the DNA [41]. It makes sense that this kind of mutation is targeted by nucleotide excision repair machinery such as

DDB2, as this pathway is responsible for the removal of bulky adducts on bases, such as what would result from Doxorubicin binding. XPC damage recognition is known to be directly associated with initiation of nucleotide excision repair. Furthermore, removed nucleotides must be replaced, explaining the need for suppliers such as RRM2B. It therefore appears that the following doxorubicin treatment the nucleotide excision repair pathway is activated in an attempt to repair the damage, while transcription of DNA repair genes involved in other pathways is repressed.

In addition to the affect on the DNA repair pathway, doxorubicin treatment has an effect similar to p53 over-expression on the apoptotic and cell growth pathways (Figures 4b and 4c).

However, these effects are much more modest. While pro-apoptotic genes are activated, only

21% of genes in the apoptotic pathway are activated by doxorubicin treatment, compared with

57% for p53 over-expression (Figure 2). Cell cycle regulators, most notably p21, are activated to a similar extent in both treatments. However, while cyclins such as CCNB1 and CCNB2 are repressed, only 12% of genes in the cell growth and proliferation pathway are repressed by doxorubicin compared to 23% for p53 over-expression. It appears that while the pro-apoptotic 18 and cell cycle inhibitory functions of p53 are activated, only select genes are targeted, as opposed to the many affected by direct p53 over-expression.

The effect of doxorubicin on the immune and inflammatory pathway is also less significant. p53 over-expression activates some NFκB functions, such as the production of pro- apoptotic inflammatory cytokines by CASP1 and up-regulation of the pro-apoptotic, TNF- receptor CD27, but strongly inhibits other functions of NFκB, particularly via inhibition of the

NFκB activator IKBKB (Figure 5a). Doxorubicin, on the other hand has minimal inhibitory effect (Figure 5b). This is made clear by the fact that 23% of genes in the NFκB pathway were inhibited by p53 over-expression, while only 3% were inhibited by doxorubicin treatment. A similar percentage of genes were activated in both treatments (Figure 2). CCL2, a cytokine shown to protect against apoptosis [42], is strongly repressed by p53 over-expression and just as strongly activated by doxorubicin treatment.

The more moderate overall effects of doxorubicin treatment likely reflect the action of p53 regulators. While direct p53 over-expression drastically affects gene expression, p53 induced by stress to the cell is controlled to allow the cell the chance to recover and return to homeostasis. Importantly, the success of this gene library in emphasizing the DNA repair pathway in response to a DNA damaging drug demonstrates the usefulness of this method for predicting the mechanism of action of novel drugs.

3.4- Serum Starvation

U2OS Osteosarcoma cells were starved of fetal bovine serum (FBS) for 16 hours to monitor the response of nutrient deprived cells. As expected, the impact is greatest for the metabolic pathway, where 52% of the genes are affected (Figure 2). There is minimal overlap 19 between the p53 over-expression and starvation conditions, with only 16% of total genes and

23% of metabolic genes similarly affected by both treatments.

Despite minimal overlap, p53 is clearly involved in the metabolic stress response. First,

SESN1 and its target TSC1 are activated by starvation (Figure 6a), both of which are p53 target genes. Furthermore, p53 activation of TSC1 in response to metabolic stress is known to result in repression of RHEB [29], which is reflected in the data as well (Figure 6a). RHEB repression results in decreased levels of MTOR, which is also evident in this experiment (Figure 6a).

Interestingly, TP53I3 is a p53-inducible gene, repressed by serum starvation (Figure 6a), which is responsible for carbohydrate metabolism and implicated in apoptosis [43]. In low nutrient conditions, it therefore makes sense that this gene would be repressed to prevent apoptosis with no effect on its role in metabolism because insufficient carbohydrates are present.

This regulation highlights the importance of stimuli in p53 function — while p53 over- expression strongly induces TP53I3, the p53 response to metabolic stress has the reverse affect.

In addition to influencing the metabolic pathway, serum starvation strongly induces numerous autophagy genes, including DRAM1 and DRAM2, LC3B, GABARAPL1 and

GABARAPL2, ULK1 and ULK2, and ATF4 (Figure 6b). This response perfectly aligns with the role for autophagy in allowing the cell to acquire energy from metabolism of its own organelles in conditions of extreme nutrient depravation.

Also as expected, starvation results in cell cycle inhibition. Cell cycle regulators including CDKN1B, LZTS1, and DDIT4 (which is also implicated in autophagy) are activated while several cyclin genes, including CCNB1, CCNE2, and CCNH are inhibited (Figure 6c). As with doxorubicin treatment however, the observed cell cycle inhibition is much more moderate than that seen with direct p53 over-expression. 20

The success of this gene library in reflecting the known effects of starvation on metabolism, autophagy, and the cell cycle serves to further validate the usefulness of this method for analyzing the response of the p53 network to various stimuli.

3.5- Serum Stimulation

U2OS Osteosarcoma cells were starved of FBS for 16 hours and then stimulated by re- introduction of FBS to the growth media for 8 hours. Like starvation, stimulation had a significant effect on the metabolic pathway (Figure 2). Overall, however, only 32% of genes affected by starvation are similarly changed by stimulation. To look specifically at the effect of serum stimulation following a period of starvation, heat map analysis was conducted for stimulation compared to an untreated control as well as stimulation compared to starvation as a control. The data reveals several general trends.

First, while both starvation and stimulation inhibit cell cycle progression, stimulation significantly decreases this inhibition, reflecting the cells return to growth phase in the presence of adequate nutrients. For example, while several cyclins remain repressed in the stimulation condition, growth factors such as TGFA, ABL1, and MYC, are strongly activated (Figure 7a).

Additionally, p27 (CDKN1B), a gene widely known to be directly involved in p53-dependent cell-cycle arrest, is activated by starvation and subsequently repressed by stimulation (Figures 6c and 7a).

The effect on the autophagy pathway follows a similar trend. While starvation induces autophagy genes, stimulation returns some of them to normal levels and actually inhibits others, such as ULK1, RB1CC1 and ATG3 (Figure 7b). This prevents the cells from dying via excessive autophagy when nutrients become available. 21

Genes involved metabolically in oxidation and regulation of reactive oxygen species, such as CYP3A7 and SESN3 are strongly activated by starvation and subsequently repressed by stimulation (Figures 6a and 7c). Proteins involved in lipid metabolism, such as STARD4, follow the same trend, being activated by starvation and repressed by stimulation. Factors involved in sugar metabolism however, such as HK2, are repressed by serum starvation and then strongly activated by serum stimulation. This pattern again reflects the inadequate supply of carbohydrates under starvation conditions, and additionally demonstrates the utilization of lipids for energy in such situations. Furthermore, these trends validate the usefulness of this gene library for analyzing the response of the p53 network to various stimuli.

3.6- Testing the cancer drug 6e

U2OS Osteosarcoma cells were treated with a final concentration of 30µM Nano-6e (6e), a novel cancer inhibitor, for 8 hours. 6e had the strongest effect on the cell structure and motility pathway, with 44% of genes being changed and a vast majority repressed. Of note for this sample, actin was significantly repressed by 6e treatment, so GAPDH was used as a control for both 6e and 6m. Additionally, the actin binding proteins ENC1, CALD1, and LASP1 are all repressed (Figure 8a), corresponding with the down-regulation of actin itself.

While there is a significant trend toward repression of structural genes and degradation of the extracellular matrix, analysis at the specific gene level reveals conflicting messages within the cell structure and motility pathway. 6e treatment appears to inhibit angiogenesis via inhibition of THBS1 and FLT1 but the anti-angiogenesis genes PARP9 and BAI1 are repressed

(Figure 8a). Furthermore, metastasis is inhibited via repression of MMP9 and MTA2 but 22 enhanced by MMP3 activation. Cell migration in general is inhibited via RB1CC1 and

PDGFRB.

Like the starvation treatment, 6e activates many of the major players in the autophagy pathway (Figure 8b). It is known that the process of autophagy involves the formation of novel membrane structures, called autophagosomes, through cytoskeleton rearrangement [44]. It is therefore no surprise that changes in cell structure and motility genes would be accompanied by an activation of autophagy. The discovery of this interplay points to the general mechanism of

6e action and provides a starting point for more in-depth exploration. 23

Chapter 4

Discussion

Use of a literature-based p53 qRT-PCR primer library and validation of the library through p53 over-expression, Doxorubicin-induced DNA damage and starvation-induced metabolic stress allowed the prediction of the mechanism of action of a novel cancer compound,

6e, which can serve as a starting point for further research. However, the data collected contains further valuable information. Additional analysis of the data from this experiment will yield further insight into specific mechanisms of each treatment and potentially allow determination of pathway interactions and cell-fate decision making mechanisms.

To this end, an interactive website has been developed

(http://www.personal.psu.edu/slm5430/zoombablue/Home.html). The website contains the entirety of the data collected from this experiment. Additionally, each of the genes included in the library is listed with a link to the genecard description of that gene’s function. An interactive chromosome array of the p53 network allows viewers to look at the genes in the library with respect to chromosome location as well as the pathways the genes are involved in.

The information can be accessed online and analyzed to reveal additional trends in the data and further study the response of individual genes to the various stimuli. Such analyses will yield insight into potential cell fate decision making mechanisms. This method can also be utilized to design qRT-PCR experiments and analyze microarrays to predict drug mechanisms or determine which pathways are being affected in particular disease models. Ultimately, researchers will then be able to design drugs targeting the pathway of concern and test those drugs to verify that they are indeed affecting the pathway of interest without adversely affecting other components of the p53 network. 24

Figure 1: The p53 protein responds to a battery of cellular pathways in response to various upstream signals.

The diverse pathways in the p53 network are affected by stress-activated p53. In this experiment DNA damage, serum starvation, serum stimulation, and drug treatments were used to influence and monitor p53 function. 25

Figure 2: qRT-PCR data from six treatment conditions reveals context-dependent impact on each of the p53 pathways.

Each pathway is color-coded as previously (see right), with the total number of genes in the pathway indicated. The heatmap shows the percent of genes in each pathway that were activated or repressed greater than 1.5 fold, as well as the total number of genes significantly changed by the treatment. Pathways experiencing the most change appear dark red, while pathways with the least change are blue. 26

Figure 3: p53 over-expression results in pathway trends of activation or inhibition.

Heatmaps show log2 values for only those genes which are activated or repressed greater than 1.5 fold. a. The apoptosis pathway is activated by activation of pro-apoptotic genes and repression of anti-apoptotic genes. b. Autophagy is generally repressed. c. The cell cycle is inhibited. Genes discussed in the text are highlighted in green.

27

Figure 4: Doxorubicin treatment affects the p53 network differently than direct p53 over- expression.

Heatmaps show log2 values for only those genes which are activated or repressed greater than 1.5 fold. a. The DNA repair pathway is most significantly affected. b. Apoptosis is moderately activated. c. The cell cycle is inhibited.

28

Figure 5: Numerous NFκB functions are repressed by p53 over-expression but not Doxorubicin treatment.

Heatmaps show log2 values for only those genes which are activated or repressed greater than 1.5 fold. a. While p53 over-expression activates some components of the NFκB pathway, a significant portion is repressed. b. Doxorubicin treatment does not result in a similar repression. Genes discussed in the text are highlighted in green.

29

Figure 6: Serum Starvation affects cell metabolism.

Heatmaps show log2 values for only those genes which are activated or repressed greater than 1.5 fold. a. Genes involved in metabolism or oxidative stress are significantly affected by serum starvation. b. The autophagy pathway is activated. c.

The cell cycle is inhibited. Genes discussed in the text are highlighted in green.

30

Figure 7: Serum stimulation rescues starved cells.

Heatmaps show log2 values for only those genes which are activated or repressed greater than

1.5 fold. Data shown here is compared to an untreated control. The fold change compared to the starvation treatment is available on-line. a. The cell cycle is less strongly inhibited and several growth factors are activated. b. Autophagy is inactivated. c.

The effect on the metabolic pathway is reversed.

Genes discussed in the text are highlighted in green.

31

Figure 8: Analysis of 6e drug treatment allows prediction of the drug mechanism of action.

Heatmaps show log2 values for only those genes which are activated or repressed greater than 1.5 fold. a. The cell structure and motility pathway is most significantly affected by 6e treatment. b. 6e treatment also results in activation of the autophagy pathway.

32

Table 1: The p53 gene library responds uniquely to different stimuli. gene 1 2 3 4 5 6 ABL1 -0.20 0.07 0.52 1.46 -1.03 -0.28 X X X AGT -0.92 X X AIFM2 1.95 0.12 -0.39 -0.32 0.02 -0.67 X AJAP1 -1.29 X AK1 X X AK123648 -0.24 -0.10 0.28 0.15 -0.09 -0.09 AK124961 0.06 1.34 0.34 1.77 AKT1 0.36 0.07 0.03 0.31 -0.66 -0.50 X AKT2 -0.44 0.08 -0.13 -0.19 -0.78 -0.65 X ALOX5 0.37 1.37 0.83 -0.57 -0.85 X X AMPK 0.38 0.70 -0.22 1.09 0.34 -0.22 X X ANKRD11 0.08 -0.40 -0.34 0.63 0.58 0.16 X ANO9 X APAF1 2.41 0.38 0.00 -0.51 -0.22 -0.26 X ATF3 -0.10 3.87 0.00 0.11 1.57 0.13 X ATF4 -0.08 -0.13 0.74 0.18 0.93 0.10 X X ATF6 -0.15 -0.02 -0.06 0.14 0.91 -0.44 X X ATG10 -0.47 0.26 -0.12 -0.21 -0.44 -0.50 X ATG101 0.53 -0.25 -0.18 0.13 0.48 -0.31 X ATG12 -0.54 0.15 0.01 -0.35 X ATG13 -0.94 0.26 0.38 0.12 0.80 -0.26 X ATG14 -1.53 0.30 0.31 0.13 0.42 0.17 X ATG16A -0.40 1.16 -0.34 -0.06 -0.01 0.05 X ATG16B -1.15 1.58 -0.02 0.38 -0.02 -0.07 X ATG2A -0.76 0.12 0.51 -0.23 0.69 0.51 X ATG2B -0.67 0.26 0.50 -0.37 0.65 -0.10 X ATG3 -0.05 0.40 0.22 -0.82 0.43 -0.15 X ATG4A -0.33 1.17 0.17 0.63 0.99 0.09 X ATG4B -0.26 -0.24 0.48 0.36 -0.34 1.03 X ATG4C -0.80 -0.12 0.45 -0.63 -0.46 0.87 X ATG4D 1.04 0.86 0.48 -0.34 -0.23 -0.06 X ATG5 -0.84 -0.01 -0.39 -0.63 -0.11 -0.28 X ATG7 -0.05 0.03 -0.14 -0.21 -0.24 -0.25 X X ATG9A -0.35 0.21 0.53 0.07 -0.18 1.04 X ATG9B X ATM -0.28 0.15 0.27 -0.28 -0.09 -0.28 X X X ATR -0.47 0.14 -0.23 0.19 -0.13 0.32 X X AURKA -0.36 -1.13 -0.05 -0.28 -0.84 -0.57 X X AURKB -0.63 -0.51 0.04 -0.45 -0.19 -0.25 X X 33

BACH1 0.01 0.59 0.55 0.93 -0.09 -0.54 X X BAD 0.15 -0.02 0.19 0.28 -0.05 0.15 X X BAI1 5.38 0.77 -0.01 0.60 -1.25 0.02 X BAK1 1.62 0.65 -0.74 0.29 -0.15 -0.54 X X BANF2 BAX 0.81 1.00 0.33 0.03 -0.65 -0.80 X BC031884 -0.43 1.02 0.56 0.81 -1.03 -0.06 BC034630 -0.43 0.80 -0.30 -0.26 -0.82 -0.21 BC037321 -1.02 2.50 -0.62 0.76 BC039546 -0.28 1.03 -0.27 1.30 -1.74 -0.49 BC039673 -1.29 -0.10 0.86 0.41 0.44 1.02 BC043370 -1.36 0.58 -0.73 0.63 BC045738 -0.03 1.35 0.01 -0.93 BC108660 -0.66 0.00 0.35 -0.23 -0.65 0.15 BCL2 -0.25 -1.05 0.95 0.24 0.36 0.41 X X BCLAF1 -0.39 -0.09 -0.11 -0.64 -0.21 -0.07 X BCLXL -0.04 0.15 0.31 0.00 -0.86 -0.42 BECN1 -0.68 0.51 0.55 0.41 -0.05 -0.10 X BID 0.32 0.13 0.36 -0.04 -0.71 -0.49 X X BIK 2.46 0.71 -0.24 0.75 -0.64 -0.10 X BIRC2 -0.45 0.42 -0.05 0.59 0.49 -0.36 X X X BIRC6 -0.62 -0.24 0.09 0.06 0.62 -0.22 X BNIP3 0.76 -0.08 -1.15 -0.65 -0.22 -0.15 X X BNIP3L -0.91 0.17 0.71 -0.40 0.51 -0.52 X BRCA1 -0.21 -0.52 -0.64 -1.16 0.04 -0.28 X X X BRCA2 -0.22 -0.03 -0.18 -0.21 -0.37 -0.42 X BTG2 4.44 2.43 -0.26 -2.07 -0.77 -0.86 X BTG3 -0.13 0.37 -0.04 0.71 -0.40 -0.63 X BTG4 0.17 -1.36 1.10 -2.58 -1.24 X C12ORF5 1.60 2.09 0.94 -0.07 -0.55 -0.47 X C18ORF56 -0.42 0.61 1.71 0.30 -0.77 -1.23 C21ORF91 -0.06 0.73 0.29 0.58 0.28 -0.42 CALD1 0.03 0.08 -0.13 0.16 -0.97 -0.64 X CASP1 5.22 1.01 0.44 0.38 X X CASP10 0.71 X CASP2 -0.80 -0.51 -0.25 -0.37 -0.39 -0.49 X CASP3 0.02 0.41 0.45 -0.06 -0.09 -0.28 X X CASP4 -0.22 0.00 0.65 0.05 1.26 0.06 X CASP6 0.95 0.52 0.31 -0.73 -0.82 -0.40 X CASP9 -1.46 -0.01 0.59 -0.31 0.62 -0.13 X CCDC90A -0.30 0.15 -0.65 -1.20 0.24 0.27 34

CCL18 -0.23 -0.04 3.59 0.41 -0.74 -1.15 X CCL2 -2.35 2.70 4.21 2.79 -3.80 -1.94 X CCNB1 -1.00 -0.80 -0.76 -0.96 -0.23 -0.42 X X CCNB2 -1.08 -0.91 0.52 -1.01 -0.24 -0.75 X CCND2 0.28 -0.04 -0.11 -0.31 0.22 -0.31 X X CCNE2 -0.68 -0.41 -1.38 -1.02 -1.34 -0.50 X CCNG1 X CCNH -0.57 0.01 -1.24 -0.74 0.61 1.42 X CD27 4.70 X X CD40 0.66 0.10 0.67 1.35 -0.56 -0.37 X X X CD82 4.28 1.31 0.08 1.99 -0.47 -0.56 X CDC25A 0.15 -0.29 -1.14 0.24 -0.98 -0.06 X CDK1 -0.20 -0.36 -0.55 -0.03 -0.54 -0.38 X X X CDK2 0.11 -0.14 -0.11 0.02 0.65 -0.42 X X CDK4 -0.31 0.23 -0.55 -0.90 0.08 -0.27 X X CDK5 -0.92 -0.39 -0.42 -0.72 -0.60 -0.53 X X CDK8 1.09 -0.46 -0.40 -0.27 -0.30 -0.81 X X CDK9 -0.58 -0.11 0.61 0.32 -0.47 -0.07 X X CDKN1A 4.40 2.96 0.39 0.37 -0.08 -0.38 X X X CDKN1B -1.01 -0.60 1.14 -1.31 0.20 -0.20 X X CDKN2A -1.30 X X CEBPB 1.13 0.57 0.00 0.29 3.50 0.15 X X CFLAR 0.83 0.13 1.05 0.64 -0.44 -0.53 X CHEK1 -0.41 -0.45 -0.10 -0.09 -0.13 -0.26 X X X CHEK2 0.01 -0.85 0.23 0.24 -0.29 -0.42 X X CHL1 -2.96 X CIGALT1 -0.26 0.39 0.11 0.05 0.72 -0.24 COL4A1 9.31 0.52 -1.16 1.08 -1.14 -0.81 X COP1 0.29 -0.22 -0.12 0.17 0.02 -0.20 X X COPS3 -0.36 -0.16 -0.40 -0.46 -0.18 -0.52 X CREBBP -0.82 -0.22 -0.05 0.02 -0.27 0.13 X X X CTSL1 -0.10 0.56 0.99 0.70 -0.07 -0.04 X X CXCR4 X X CYFIP2 3.76 0.90 1.24 0.19 -0.36 0.27 X CYP3A7 9.24 6.60 X DAPK1 -1.15 0.74 X X X DDB2 1.82 1.45 -0.08 -1.21 -1.02 -0.49 X DDIT3 -0.22 0.11 1.67 0.06 4.10 1.44 X X X DDIT4 -0.29 1.04 0.78 -0.92 4.10 1.74 X DEK 2.43 0.78 -0.69 -1.26 0.77 1.47 X X DRAM1 1.82 1.36 0.95 0.64 -0.15 -0.49 X 35

DRAM2 -0.91 -0.08 2.56 -0.37 -0.03 -0.29 X DYRK2 -1.60 -0.41 -0.48 -0.01 -0.26 -0.69 X X E2F1 -0.83 -0.11 -0.09 -0.53 -1.10 -0.60 EBNA1BP2 0.28 0.26 -0.91 -0.72 -0.24 -0.44 X EGFR 1.04 -0.54 1.12 0.76 0.71 -0.22 X EGR1 0.84 -0.07 -1.93 -4.84 1.07 2.36 X X X X X EHF -0.84 1.05 4.26 -0.14 X X EI24 1.20 1.00 -1.66 -1.14 -0.34 -0.46 X X EIF2AK2 0.18 -0.18 0.17 -0.50 0.02 -0.21 X X EIF2AK4 0.97 -0.20 -0.55 -0.41 -0.50 0.02 X X EIF4G1 -0.40 0.00 -0.08 0.75 -0.56 -0.34 X ENC1 -1.36 0.45 2.60 1.91 -1.58 0.18 X X ERBB4 -0.68 -0.05 0.68 0.80 -0.53 -0.66 X X X ERN1 1.89 0.09 0.01 1.09 0.74 0.02 X ESR1 X FADD -0.38 -0.37 -0.34 -0.09 -0.23 -0.38 X X X FAM86A -0.27 0.88 0.82 2.10 -0.23 0.58 FAS 2.64 2.72 0.41 0.44 -0.30 -0.27 X X FGF1 2.26 -0.75 -0.17 0.91 -1.32 0.45 X FGF2 0.12 0.44 -0.98 -0.29 0.06 -0.52 X X FLJ44511 -0.49 0.93 -0.36 -1.73 -1.36 -0.93 FLT1 -1.48 -0.11 X FOS -2.99 0.65 2.47 1.21 0.32 0.26 X X X FOXO3 -1.25 0.01 0.16 0.70 0.70 0.07 X X X X GABARAPL1 -1.21 1.28 1.00 0.25 1.53 -0.26 X GABARAPL2 -0.43 0.34 0.75 -0.30 -0.08 -0.28 X GADD45A 0.53 1.13 -0.31 0.35 0.47 -0.20 X X GDNF 2.14 X GML -0.94 0.77 -0.63 -1.64 -0.95 -0.15 X X GSN -0.35 -0.17 1.88 2.39 -0.55 -0.29 X GYG2P1 -0.52 0.15 0.36 0.44 0.65 0.16 HDAC1 -0.34 -0.10 -0.41 -0.20 -0.37 0.01 X X X X HDAC2 -0.74 0.16 -0.93 -1.07 -0.46 -0.18 X X HDAC3 -0.71 -0.09 -0.23 -0.55 0.72 0.56 X X HDAC4 0.10 -1.42 1.40 0.42 0.14 -0.32 X X HDAC9 0.13 -0.40 0.70 0.55 2.79 -0.16 X X HDGF2 -0.80 -0.67 0.39 -0.40 0.96 0.80 X HIF1A -0.82 0.03 -0.37 0.08 -0.31 -0.61 X HK2 0.18 0.01 -1.03 1.80 -0.19 0.39 X HMGB1 -0.24 0.39 0.02 -0.45 -0.78 -0.74 X HMOX1 1.64 -0.50 2.53 0.49 2.07 -0.26 X 36

HTT 0.06 -0.30 -0.03 -0.57 -0.60 -0.35 X ICAM1 -0.66 X IFNAR1 -0.40 -0.14 0.14 0.64 -0.13 -0.36 X X IFNG X X IGF1R 0.28 -0.41 0.06 0.89 -1.18 -0.73 X X IKBKB -0.74 -0.05 0.58 0.59 0.15 -0.24 X X IL-1 X X X X IL10 X IL6 0.98 0.63 -1.11 1.88 0.33 -1.20 X IL8 3.56 3.81 -0.13 4.72 2.00 -1.87 X IRF2BP2 -0.31 0.47 -0.01 -0.03 0.67 -0.50 X IRF5 1.52 -6.64 X IRF9 -1.04 -0.10 0.65 -0.11 -0.21 -0.53 X X JARID2 -1.07 -0.13 0.50 0.21 -0.36 -0.79 X X X JUN -1.40 0.00 -0.23 0.67 -0.36 0.39 X KAT5 -0.76 -0.36 0.16 -0.36 -0.28 -0.69 X X X KITLG 1.69 2.40 -0.62 0.07 -0.89 -1.29 X X LASP1 -0.36 0.17 -0.25 -0.57 -0.74 -0.78 X LATS2 0.19 -0.09 -0.03 0.32 -0.58 -0.65 X X LC3B -0.15 0.23 1.31 0.44 1.47 -0.15 X LGALS7 0.84 X X LITAF 0.26 0.35 0.44 0.45 -0.19 -0.57 X LOC201477 -0.63 0.20 1.27 0.93 -1.02 -0.82 LOC221710 -0.73 0.38 -0.83 -0.28 -0.22 -0.03 LOC374491 -0.22 0.00 0.41 -0.40 -0.36 -0.08 LOC389906 0.74 0.40 0.01 0.83 0.58 -0.25 LRRC30 -0.28 1.06 0.71 -0.03 -1.12 -1.04 LSD1 -0.61 -0.01 -0.20 -0.41 -1.63 -0.86 X LTA 0.01 2.29 -0.61 -1.28 -0.32 0.78 X X LZTS1 -1.06 1.70 -0.75 -1.37 -1.95 -0.21 X X MAD1L1 0.78 -0.77 0.28 -0.77 -0.62 -0.52 X MAP1LC3A -1.00 X MAP1LC3C X MAPK14 0.57 -0.30 -0.43 -0.76 -0.23 -0.12 X X X X MAPK8 -0.72 -0.64 -0.03 0.65 0.16 -0.25 X X X X X X MAPK9 0.59 -0.23 0.09 0.13 -0.31 -0.24 X X X X X X MCL1 0.78 -0.36 0.39 1.38 -0.62 -0.29 X MDM2 3.38 1.58 -0.35 -0.15 -0.26 -0.16 X X X MDM4 -0.47 -1.06 0.12 0.23 0.08 0.04 X X X MED1 -0.41 0.23 0.05 -0.02 -0.20 -0.29 X MLH1 -0.06 -0.76 0.54 -0.68 0.00 0.23 X X 37

MMP13 -1.26 0.25 0.87 2.27 0.03 -0.81 X MMP3 0.70 -0.01 -3.09 -0.27 2.52 0.32 X MMP9 3.08 -0.65 -0.80 3.88 -0.80 -0.15 X MSH2 -0.09 -1.01 0.03 -0.57 0.80 -0.21 X X MSH6 -0.86 -1.11 -0.26 -0.24 -0.78 -0.22 X X MTA2 0.01 0.09 -0.16 0.01 -0.71 -0.55 X X MTOR 0.12 -0.79 -0.32 0.37 -0.67 0.59 X X X MYC -0.93 -0.69 -0.52 1.74 0.24 -0.27 X X X NAIP -1.63 -0.59 0.63 -0.06 0.56 1.46 X NBEAP1 0.39 2.19 0.74 -0.09 -0.19 -0.31 NCK2 -0.48 -0.13 0.30 0.92 0.14 -0.51 X NEO1 -1.85 0.26 1.13 0.23 -0.47 -0.65 X NF1 -0.46 -0.08 -0.02 -0.26 -0.67 -0.30 X NFKB 0.70 -0.09 -0.82 -0.19 -0.82 -0.84 X X X X X NIR 0.23 0.20 -1.02 -0.01 -0.60 -0.35 X X NOTCH1 2.79 1.27 0.28 -0.17 -0.89 -0.19 X X NOXA 1.01 0.63 1.02 0.84 0.16 -0.03 X OKL38 X X OR52E8 -0.56 0.30 0.22 -0.67 -0.10 0.06 X OR52K1 -0.26 0.75 -0.51 X OR52K2 -1.45 0.79 0.15 -0.72 X OSBP -0.62 0.04 0.38 0.14 0.62 -0.44 X PAD4 3.98 1.23 1.30 0.61 -0.70 -0.67 X X PAK1IPI -0.13 -0.19 -1.76 -0.51 -0.21 -0.47 X PARK7 -0.59 0.16 -1.13 -1.15 -0.02 -0.17 X X PARP1 -0.38 0.01 -0.65 -0.44 -0.37 -0.46 X X X PARP9 -1.99 -0.15 0.48 -1.22 -0.72 -0.46 X PCAF 0.64 0.39 1.39 -0.76 -0.21 -0.41 X X X PCNA -0.90 1.59 0.71 -1.08 0.74 0.09 X PDGFRA -2.94 -0.76 1.43 -2.24 -1.01 -0.90 X PDGFRB 1.57 -0.88 0.64 0.53 -1.32 -0.60 X PDRG1 -0.11 -0.45 0.02 0.64 -0.42 -0.41 PERK -0.40 0.05 0.32 -0.16 1.57 0.21 X X PERP 3.33 0.09 0.02 -0.64 -0.48 -0.22 X X PHLDA3 1.92 1.07 -0.53 -0.26 -0.20 -0.29 X PIDD 1.15 0.47 0.44 0.99 -0.79 -0.38 X PIK3C3 -0.26 -0.01 -0.93 -1.57 0.22 -0.40 X X X PIK3CA -0.67 0.29 0.87 0.33 0.25 0.01 X PLK1 -0.87 -2.47 -0.18 -0.15 -0.36 -0.12 X X X PLK2 1.16 1.28 -1.14 -0.71 -1.85 -0.51 X X PLK3 1.39 1.91 0.29 0.97 -0.17 -0.26 X X X 38

PLK4 -0.49 -0.41 -0.04 -0.24 -0.81 -0.69 X X PML 0.62 0.55 0.06 0.33 -1.20 -1.26 X X X PMS2 0.11 -0.64 0.16 0.31 -0.45 -0.52 X PPARGC1B -0.85 -0.69 0.07 1.21 X X X PPM1D 0.06 1.47 0.06 -0.75 -0.39 -0.55 X X PPP1R13B 0.29 0.06 -0.05 0.36 -0.25 -0.45 X PRDM1 -0.69 0.59 1.19 0.30 -1.69 -0.08 X PRKCD 2.98 0.39 0.10 0.54 -0.75 -0.51 X X X X PRKCE -0.85 -0.65 -0.14 -0.30 1.35 0.06 X PRNP 0.27 0.02 0.66 0.89 0.77 -0.27 X PRODH 0.21 -0.28 0.28 0.55 -0.49 -0.68 X X PSRC1 -1.10 -2.06 -0.12 -0.68 -0.50 -0.46 X PTEN -0.39 0.11 0.54 0.09 -0.02 -0.70 X X PTGES 7.54 X X PTTG1 -0.05 0.09 0.71 1.30 0.69 -0.62 X X X PUMA 1.59 2.68 -0.42 -0.02 -0.01 -0.08 X PXDN 4.85 0.62 0.40 1.24 -0.11 0.09 X X PYCARD -0.58 0.31 0.22 -0.47 -1.19 -0.23 X RB1 0.60 -0.15 0.15 -0.47 -0.38 -0.35 X X RB1CC1 -0.80 0.57 0.36 -1.48 0.80 -0.23 X X X X RECK -0.95 -0.18 0.20 -0.28 -0.49 0.11 X X RELA -0.92 -0.74 -0.37 -0.22 -0.96 -1.01 X RHEB -0.33 0.04 -0.79 -0.79 0.38 -0.18 X X RHOA -0.45 -0.11 0.20 -0.56 -0.19 0.09 X X RHOB X RIPK1 -0.77 0.06 0.25 0.06 0.18 0.10 X X RNF144B 2.52 0.89 -0.74 -0.98 0.62 1.69 X RPRM 0.06 0.07 X RPS27L 0.73 0.73 1.59 1.22 -0.53 -0.52 X X RRAD 0.44 1.23 0.79 -0.92 -0.66 X RRM2B 2.57 0.46 -0.45 X RUBICON -2.44 X S100A2 0.68 -0.50 -0.13 1.16 -0.83 -0.40 X S100A6 -0.04 0.01 1.02 1.60 -0.56 -0.51 X SERPINB5 4.45 -0.14 -0.16 -0.04 -0.37 -0.17 X SESN1 1.88 2.02 -0.28 -0.41 -0.25 0.22 X X SESN2 2.14 1.78 -0.38 -0.38 2.49 0.74 X SESN3 -1.04 -0.09 3.78 -0.61 0.42 -1.00 X SGK223 0.12 0.85 1.02 0.20 -0.69 -0.16 SHISA5 -0.02 -0.24 -0.08 0.12 -0.38 -0.41 X SHISA9 4.60 0.72 1.74 0.83 0.60 0.25 X X 39

SIAH1 0.20 0.17 0.36 -0.26 -0.09 -0.21 X SIN3A -1.06 -0.11 0.29 -0.26 -0.03 -0.41 X X SIN3B -0.67 -0.10 -0.04 -0.16 -0.67 -0.36 X X SIRPG -0.19 X SIRT1 -0.82 0.43 -0.43 -0.54 0.30 0.01 X X X X X X SIVA -0.57 -0.25 -0.12 -0.79 -0.33 -0.42 X SNCA -0.49 0.16 0.81 -0.26 0.11 -0.66 X X SP1 -0.37 -0.43 0.07 -0.43 0.02 -0.42 X X X X X X SPAG9 -0.71 -0.18 0.10 -0.13 0.45 -0.43 X SQSTM1 0.24 0.22 1.71 0.98 2.30 0.15 X X X X STARD4 -1.04 0.53 0.62 -1.09 0.89 -0.07 X STAT1 -0.22 0.30 0.20 -0.12 0.05 -0.40 X X STK11 0.51 0.00 0.25 0.00 -0.25 -0.44 X X X TADA3 -0.89 -0.03 0.15 0.56 -0.13 0.34 X X TCF7L2 -1.08 -0.55 -0.53 -0.37 -0.85 -0.24 X X X X TERT 0.02 0.30 -0.71 -1.08 X TGFA 4.42 2.50 0.53 3.13 -1.02 -0.92 X X TGFB1 -0.25 -0.01 -0.05 0.13 -0.31 -0.28 X X X X THBS1 -0.21 0.17 -0.80 2.21 -4.10 -1.14 X TLE1 -1.45 0.27 0.22 0.87 0.32 0.22 X X TLR1 -0.56 -0.79 0.92 -0.21 0.76 1.65 X TLR2 1.12 X TLR3 -0.29 0.92 0.90 -0.84 -1.06 0.18 X TLR4 -0.06 0.12 1.71 1.76 -0.23 -0.17 X TLR6 -0.59 -0.42 0.32 -0.45 0.04 0.18 X TLR7 X TLR8 0.04 X TLR9 0.48 0.39 0.21 0.76 0.96 -0.21 X TNFA 0.84 X X X TNFAIP3 0.22 2.86 0.20 2.04 -0.39 -0.72 X TNFAIP8 -0.58 1.02 -0.38 -0.42 -1.11 -0.59 X TNFRSF10B 1.37 2.34 0.29 0.56 0.95 -0.01 X X X TNFSF10 -1.72 X X X TOPORS X TP53 11.81 0.87 -0.24 -0.37 -1.16 -1.43 X X X X X X X X X TP53AIP1 2.20 X TP53BP2 -0.69 0.34 -0.39 -0.59 0.02 -0.44 X TP53I13 1.18 -0.23 0.58 0.40 -0.72 -1.36 TP53I3 3.90 1.19 -0.96 -1.33 -0.73 0.29 X X TP53INP1 2.12 1.41 1.87 -0.92 1.39 0.22 X TP53INP2 40

TP53RK -0.16 0.09 0.42 0.17 -0.68 -0.88 X X TP53TG1 0.51 -0.16 0.75 0.42 -0.06 -0.75 X TPO 4.75 X TRADD -1.15 0.11 0.29 0.57 0.24 -0.68 X X TRAF3 -0.11 0.03 -0.60 1.12 -0.63 -0.72 X X TRIM13 -1.56 -0.01 0.54 0.04 -0.30 -0.24 X X X X TRIM22 2.91 1.52 1.14 -0.02 -0.60 -0.34 X X TRIM24 -0.98 0.16 -0.05 -0.54 0.13 -0.64 X X X TRIM29 -0.93 0.35 1.39 0.92 0.57 -0.23 X TRPM1 -2.03 X TSC1 -0.64 0.16 0.83 -0.23 -0.10 0.12 X TXN 0.18 0.66 -0.60 0.07 -0.04 0.25 X ULK1 0.04 0.57 0.82 -0.76 0.63 -0.30 X X ULK2 -0.87 -0.10 0.67 0.29 0.75 -0.50 X X UVRAG -0.79 -0.14 0.21 -0.55 1.08 0.45 X VMP1 -0.04 -0.20 0.05 0.17 0.22 -0.19 X X WIG1 0.79 1.54 0.25 -0.02 -0.81 -0.36 X WIPI1 -0.77 -0.24 2.12 1.39 2.54 0.40 X WRN -1.13 -0.72 -0.73 -1.71 0.18 0.19 X WT1 -0.15 0.31 -0.58 0.81 -0.87 -0.91 X X XAF1 -0.09 -0.54 0.61 1.23 -0.88 -0.79 X XPC -0.03 0.67 0.36 0.21 -0.09 -0.06 X ZNF251 -0.84 -0.13 0.63 0.45 -0.47 0.03 X

Genes in the p53 library were categorized based on pathway (NFκB – red, Apoptosis – orange, Structure and Motility – yellow, Autophagy – green, Cell Growth and Proliferation – blue, Metabolism – purple, DNA repair – pink, Differentiation – grey, and Upstream Regulators – Black), with an X marking the pathways in which each gene is involved. The expression of each gene was measured following stimulation with: 1. Direct p53-overexpression via a Tet-On p53 Saos2 cell line 2. Doxorubicin treatment of U2OS cells 3. Serum Starvation of U2OS cells 4. Serum Stimulation of U2OS cells 5. 6e treatment of U2OS cells and 6. 6m treatment of U2OS cells. Treatments are detailed in the methods. Heat map shows log2 values for extent of upregulation (red) or downregulation (blue) as determined by q-RT PCR. For a detailed chromosome map of the p53 network, including gene locations and their pathways, visit: http://www.personal.psu.edu/slm5430/zoombablue/Home.html and click on the circular chromosome array icon.

41

Table 2: qRT-PCR Primers

Gene Forward Primer Reverse Primer ABL1 ATGTTGGAGATCTGCCTGAAG AGGCTCAAAGTCAGATGCTAC AGT CTGATCCAGCCTCACTATGC AGGTCATAAGATCCTTGCAGC AIFM2 ACCGGCATCAAGATCAACAG TCGGCACAGTCACCAATG AJAP1 TGCAGTGTTCTCACGAGTG ACGAAGGCCACAGGAATAAG AK123648 GATAGCTTCACAGATCCACCG CCAGACACAGTAACCGCAG AK124961 GTTTTCACCTGCTCCATTTCC GCTGAAGTAAGAGGATCACTGG AK1B CCAACCGTAAAGTAGATGTTTCAG GCACATTGAGTACCTTTTCCAC AKT1 TCTATGGCGCTGAGATTGTG TCTTAATGTGCCCGTCCTTG AKT2 CGGTTTTATGGTGCAGAGATTG AGTCAGTGATCTTGATGTGGC AL832227 TTACACTTTCCTTATCCCAGCC AAGCACACCTCACATCCAG ALOX5 AAGTACATCACGCTGAAGACG CGGTGTTGCTTGAGAATGTG AMPK CCCAATCAGGTCATGCTGAA TTGCGCTGAGCACCATCA ANKRD11 AGCAGAGCAACAGGAAAGG AAGGTGCGAAGGATGGTG

ANO9 (PIG5) TCAAGATGGTCTGGTTGCAG GGGATGAACTCAGATGTGAAGG APAF1 GGCTGTGGGAAGTCTGTATTAG CAACCGTGTGCAAAGATTCTG ATF3 TCCATCACAAAAGCCGAGGTA TCTCGTCGCCTCTTTTTCCTT ATF4 CAGTCCCTCCAACAACAGCAA CCATTTTCTCCAACATCCAATCT ATF6 CCTGTCCTACAAAGTACCATGAG CCTTTAATCTCGCCTCTAACCC ATG10 TCAAAGGACTGTTCTGATGGC CATCCAAGGGTAGCTCGAAAG ATG101 CTTCTGAGGAACTGGATCGTG ATGCACTCGTCTGAGAATGG ATG12 CGTCTTCCGCTGCAGTTTC CCCACAGCCTTTAGCAAAATG ATG13 TTTGCCCTCAAGACTGTCC TGCTTCATGTGTAACCTCTGG ATG14 GGAAGTAAAGACGGGTGTGAG GTGTCTCCGTTGTGATCGTC ATG16A CCGAATCTGGACTGTGGATG GCGTAGATCCCAGAGTTTGAG ATG16B GTCTGGACACAAGGATAAGGTG ACAGTAGGAAAGGACATTGATGG ATG2A TGGAGCTGATGGTGAAGTTG AGTTCCTGAAGTTGCTGGAG ATG2B CCCCTTACACTTTCCCATTCC TGTGAGAAGGCGAACTGTG ATG3 GGCGGTGAAGATGCTATTTTG GTGCTCAACTGTTAAAGGCTG ATG4A GCCCTTATCTGTAGACACTTGG GCGTTGGTATTCTTTGGGTTG ATG4B CTTCAATGATTGGTGCCAGC CCAGTCGCTCTACATCAGAAG ATG4C CTCTTGGCTCAAGGACTCATAC TCCCCTGAAAGTGATGCTTC ATG4D GACAAGTTCAAGGCCAAGTTC AAACGCTGTATGTCACCCTC ATG5 AGCAACTCTGGATGGGATTG AGGTCTTTCAGTCGTTGTCTG ATG7 TTTTGCTATCCTGCCCTCTG GCTGTGACTCCTTCTGTTTGAC ATG9A CCTTGGCACCATATTGAAAACC AGGAAGGTAGTGAAGGCAAC ATG9B CACCTGCCTTCTCGATCAC GCCTTCTCACATCTGTCCAG ATM ATTCCGACTTTGTTCCCTCTG CATCTTGGTCCCCATTCTAGC ATR CCTTGAACATGAAAGCCTTGG CCTGAGTGATAACAGTAGACAGC AURKA TGTACCTCATCCTGTCTCCAG AGTCTTCCAAAGCCCACTG AURKB ACCCTTTGAGAGTGCATCAC CCTGAGCAGTTTGGAGATGAG CCNB1 GGCTTTCTCTGATGTAATTCTTGC GTATTTTGGTCTGACTGCTTGC BACH1 CACCGAAGGAGACAGTGAATC TGTTCTGGAGTAAGCTTGTGC BAD ACGTAACATCTTGTCCTCACAG GTCTTCCTGCTCACTCGG BAI1 TGTGATCTGGAAGGAGACCC CAATGGAAACACAGCGGATG BAK1 GGTTCTGGGTGTGGTTCTG AGGGAACAGAGAAGGCAAAG 42

BANF2 CTTCCTCTCCGAACCCATTG CGGCTTCATTCTTGTGCATC BAX TGGAGCTGCAGAGGATGATTG GCTGCCACTCGGAAAAAGAC BC034630 TTTGTGAGACCGTGATATTCCC TTCCTAAATGTGTCCTGCTATTAAAAG BC037321 TCAGTCATCCCAAGCCAAAAG AAAGCCCTTTCTCCCTTCTG BC039546 TCCTGAACTACAATGGCAACG TGGCAAGAGTGGATTAGCAG BC039673 CCTGCTCAACTCCTTCTGTG GTGCCTTGTGAAACCTTAGTG BC043370 CTGTGGGTTCTGTATAATGGTAGG AATGTGTTTCTGCTCCCCTC BC045738 CTGGAGTACGTGGAATACAGTG GCTACCTGGGAGAATCACTTG BC108660 ACCTATAATCCCAGCCCTTTG TTTGTAGAGATGTTGCCCAGG BCL2 GTGGATGACTGAGTACCTGAAC GCCAGGAGAAATCAAACAGAGG BCLAF1 AGAATACTCAGGCTTTGCAGG GCTCTTCCTCTGCCTCTAATTC BCLXL GACATCCCAGCTCCACATC GTTCCCATAGAGTTCCACAAAAG BECN1 AAGAGGTTGAGAAAGGCGAG TGGGTTTTGATGGAATAGGAGC BID ATTAACCAGAACCTACGCACC TGACCACATCGAGCTTTAGC BIK TCTTTGGAATGCATGGAGGG GTAGATGAAAGCCAGACCCAG BIRC2 TTGAGGTGTTGGGAATCTGG GGCCTTTCATTCGTATCAAGAAC BIRC6 TGGACTGGCTCTTATTGCTG AACTCCCCTGACTGTTCAATG BNIP3 GTTCCAGCCTCGGTTTCTATT AGCCCTGTTGGTATCTTGTG BNIP3L CAACTGCGAGGAAAATGAGC GGATGAGGATGGTACGTGTTC BRCA1 GCCTTCTAACAGCTACCCTTC CTTCTGGATTCTGGCTTATAGGG BRCA2 TTCATGGAGCAGAACTGGTG AGGAAAAGGTCTAGGGTCAGG BTG2 GCAGAGGCTTAAGGTCTTCAG CTTGTGGTTGATGCGAATGC BTG3 AGTGAAACCCAGTTCGGTG GAGGATAGTGATTCTGATGGCC BTG4 ACCTCGTGTCATTCCTAAAGTC AGCCATGGTAAGTGTTTCCC C12orf5 GGAAGAGTGCCCTGTGTTTAC AGTTGCTTGGAGATCCTTGG C18orf56 CGTCACCTCTCAGGCTGTAG AATTATCTCTTCATCTGGGACCTG C21orf91 ATAGTCACAACCAGGCACAG CAGTTGCTCTACCTCACCAAG CALD1 TGTGGGAGAAAGGGAATGTG AAGGTTTGGGAGCAGGTG CASP1 GGATATGGAAACAAAAGTCGGC CATTGTCATGCCTGTGATGTC CASP10 ACAACCACAGCTTTACCTCC ACCATCTCCATTTCCACTTTCG CASP2 TTCTATGTGACCAGACTGCAC AGTTTCCCATCCACACCATAG CASP3 ACTGGACTGTGGCATTGAG GAGCCATCCTTTGAATTTCGC CASP4 CCATAGAACGACTGTCCATGAC GCTGTACTAATGAAGGTGCTCC CASP6 CATGACAGAAACAGATGCCTTC GTGTTAAGTGCCAAAAGAACCTC CASP9 CCAACCCTAGAAAACCTTACCC TCTGCATTTCCCCTCAAACTC CCDC90A GACAGGAAGATCGAAACTGAGG ACAGGCGATAAAATCCCAGAG CCL18 CACTCTGACCACTTCTCTGC AGGAGGTATAGACGAGGCAG CCL2 CAGAAGTGGGTTCAGGATTCC ATTCTTGGGTTGTGGAGTGAG CCNB2 CAAAAGCCGTCAAAGACCTTG GGTTTCATGAGATAGACCAGAGG CCND2 CCTCCAAACTCAAAGAGACCAG TTCCACTTCAACTTCCCCAG CCNE2 CTGCCTTGTGCCATTTTACC GTCTTCAGCTTCACTGGACTAG CCNG1 TCTTGCCTACGAGTCCCC GAGAGTCAGTTGTTGTCAGTACC CCNH AGGAGAAGGCACTTGAACAG CAATATGGGATAGCGGGTCTT CD27 (TNFRSF7) GCTCCGATTTTATTCGCATCC TGTAACGACAAGGCTCTGC CD40 (TNFSF5) AAGCTGTGAGACCAAAGACC ATAAAGACCAGCACCAAGAGG CD82 AGAAAGCAGAACCCGCAG TGGTGACTTTGATACAGGCTG CDC25A TGTTGAAGAGACCAGAACGATC GGGAAGATGCCAGGGATAAAG CDK1 ACAAAGGAACAATTAAACTGGCTG CTGGAGTTGAGTAACGAGCTG 43

CDK2 TTTTGGAGTCCCTGTTCGTAC CGAGTCACCATCTCAGCAAAG CDK4 TTCCCATCAGCACAGTTCG TCTACATGCTCAAACACCAGG CDK5 GGCCAGAGTCTTAAAACCGAG TTTCAGAGCCACGATCTCATG CDK8 AAGAGGAAAGATGGGAAGGATG GAAGAGAAATGACGTTTGGATGC CDK9 GAAGGTGCTGATGGAAAACG GGAAGCTTTGGTTCGACAAATC CDKN1A (p21) TGTCACTGTCTTGTACCCTTG GGCGTTTGGAGTGGTAGAA CDKN1B (p27) TCTGAGGACACGCATTTGG TGTTCTGTTGGCTCTTTTGTTT CDKN2A GATGTCGCACGGTACCTG TCTCTGGTTCTTTCAATCGGG CEBPB CCGCCTGCCTTTAAATCCAT GTACGCAGCAGCCAAGCA CFLAR TTTGCCTCAGAGCATACCTG GGAAGTGAAGGTGTCTCGAAG CHEK1 TTGTGGAAGACTGGGACTTG ATTTTCTGGACAGTCTACGGC CHEK2 GCGCCTGAAGTTCTTGTTTC GTCCTATGCTCAGAGAAAGGTG CHL1 CTGGGAATCGCTATGTCAGAAG AATGTGTAAAGGTGGGAGGC CIGALT1 CTTCAATGCAGATTCTAGCCAAC GTAGCTTTGACGTGTTTGGC COL4A1 TGTGGATCGGCTACTCTTTTG TAGTAATTGCAGGTCCCACG COP1 GTGGCTTATACTCTCCTGTCAG CCAAGGCTGTTTCTTTGTCTG COPS3 CTTATCCCATCTGGACACTGTG ATGAAGAGCTGAACCTGTGAG CREBBP CAACCCCAAAAGAGCCAAAC GGTTCCCACTGTTTAAAAGGC CTSL1 CAATCAGGAATACAGGGAAGGG CTGGGCTTACGGTTTTGAAAG CXCR4 AGCAGGTAGCAAAGTGACG CCTCGGTGTAGTTATCTGAAGTG CYFIP2 CGTATCACCCTGCATGTCTTC TGTCTCGTTGTGGTTCTTGG CYP3A7 GTCCCTATCATTGCCCAGTATG AGTCGATGCTCACTCCAAATG DAPK1 TTTCCTGAAGTCCCTTGTCC TGTCATATCCAAACTCGCCTC DDB2 CCAAGAAACGCCCAGAAAC GGCAGTCTGAGTCACATCTTC DDIT3 CGCCTGACCAGGGAAGTAGA TCATGCTTGGTGCAGATTCAC DDIT4 CACTCTGAGTTCATCAGCAAACG ACGAGAAGCGGTCCCAAAG DEK CATTATGACAAAGGGCCATGC CCAGGAGTAACAAGGGTGAAG DRAM1 TTTGAAATTCTGCCACCTTGTTT ATGGGCAATTAGCAGCAAGAG DRAM2 GTTCAGACCATCCTTTCCTACC GTTCAGACCATCCTTTCCTACC DYRK2 TGCATTTTCCTCTCCAGCG ACTGTTGAACCTGGATCTGTC E2F1 TCTCCGAGGACACTGACAG ATCACCATAACCATCTGCTCTG EBNA1BP2 TCAGAACAAGGACCAGAAAGC ATAATCAGTGGGTCGCTTCG EGFR AAGCCATATGACGGAATCCC GGAACTTTGGGCGACTATCTG EGR1 TGTCACCAACTCCTTCAGC TCCTGTCCTTTAAGTCTCTTGTG EHF AGTCACCTTCCTGTTGCAG CTGGGTTCTTGTCTGGGTTC EI24 ATGGAGATGTTTGGTCGTGG CCTCCCTGATACCTCAAATGC EIF2AK2 CGATACATGAGCCCAGAACAG AGAATTAGCCCCAAAGCGTAG EIF2AK4 AAAGTGGATCTCTTCAGCCTG ATGCTCTCCATCGTCAAAGTC EIF4G1 AGTTCCCATCAGCAAAGGTAG GTTAGAATCGATGGAGCCAGG ENC1 (PIG10) TCGCTAGAACTCAGTTGTGC AGATGTTAATGGAGCCGCTG ERBB4 TGAAATTGGACACAGCCCTC TCTGGAATTGTGCTAGTTGGG ERN1 GCGAACAGAATACACCATCAC ACCAGCCCATCACCATTG ESR1 CGACTATATGTGTCCAGCCAC CCTCTTCGGTCTTTTCGTATCC FADD CCTGGTACAAGAGGTTCAGC CTGTGTAGATGCCTGTGGTC FAM86A GCCCTCTACCTTGCAGAATG AGTCGCTGAAGATGTATGCC FAS (TNFRSF6) AAGCTCTTTCACTTCGGAGG GGGCATTAACACTTTTGGACG FGF1 CACAGTGGATGGGACAAGG TGTGAGCCGTATAAAAGCCC FGF2 ACCCTCACATCAAGCTACAAC AAAAGAAACACTCATCCGTAACAC 44

FLJ44511 TGTCGTGGCAGAAAGGAAG GGCTGATCTTGAACTCCTGG FLT1 TGCCACCTCCATGTTTGATG CCCCGACTCCTTACTTTTACTG FOS AATCCGAAGGGAAAGGAATAAGA TCCGCTTGGAGTGTATCAGTCA FOXO3 CAGATCTACGAGTGGATGGTG TCTTGCCAGTTCCCTCATTC GABARAPL1 TCAACAACACCATCCCTCC ACCACTCATTTCCCATAGACAC GABARAPL2 CGAGCGAAATATCCCGACAG TGATCCACATGAACTGAGCC GADD45A GGGAAAGTCGCTACATGGATC GTGTAGGGAGTAACTGCTTGAG GDNF GGAGTTAATGTCCAACCTAGGG CAGACAGCCACGACATCC GML CTGTTGCTGAGTTTTGCCTC AGCAGTGGATAAGGGTCTCT GSN GTCCTACCTTTCCAGCCATATC TGTTGGAACCTTCGATTCTCC GYG2P1 GATGACATCTACTGCCAGGG TGGTATCACTGCACTCAAGC HDAC1 GAGATGACCAAGTACCACAGC TGACAGAACTCAAACAGGCC HDAC2 TGACAAACCAGAACACTCCAG CTTCTCCATCTTCATCTCCACTG HDAC3 GGACTTCTACCAACCCACG CAGCACGAGTAGAGGGATATTG HDAC4 ACAAGGAGAAGGGCAAAGAG GCGTTTTCCCGTACCAGTAG HDAC9 CCAAACAATGGGCCAACTG TAGAATGCGTTGCTGTGAAACC HDGF2 AAGCTGCACAGTGAGATCAAGTTT GGGCATTCAGGCACCTCTT HIF1A AACATAAAGTCTGCAACATGGAAG TTTGATGGGTGAGGAATGGG HK2 GGGACAATGGATGCCTAGATG GTTACGGACAATCTCACCCAG HMGB1 GATATGGCAAAAGCGGACAAG GGCGATACTCAGAGCAGAAG HMOX1 CCTCACTGGCAGGAAATCATC CCTCGTGGAGACGCTTTACATA HTT TGGATCTTCAGAACAGCACG TCGACTAAAGCAGGATTTCAGG ICAM1 CAATGTGCTATTCAAACTGCCC CAGCGTAGGGTAAGGTTCTTG IFNAR1 TTGACTCATTTACACCATTTCGC CATCCAAAGCCCACATAACAC IFNG GCATCGTTTTGGGTTCTCTTG AGTTCCATTATCCGCTACATCTG IGF1R AGTTATCTCCGGTCTCTGAGG TCTGTGGACGAACTTATTGGC IKBKB CAAAACCAGCATCCAGATTGAC ACCAGGAGTTTCACTTCGTTC IL1 ATGCACCTGTACGATCACTG ACAAAGGACATGGAGAACACC IL10 CGCATGTGAACTCCCTGG TAGATGCCTTTCTCTTGGAGC IL6 CCACTCACCTCTTCAGAACG CATCTTTGGAAGGTTCAGGTTG IL8 ATACTCCAAACCTTTCCACCC TCTGCACCCAGTTTTCCTTG IRF2BP2 AGGTTGTTGGGTTTCGAGG CTTCCTTTTCCTTGCTGTTCTTG IRF5 AGGGCTTCAATGGGTCAAC GTGTATTTCCCTGTCTCCTTGG IRF9 CTTGGTCAGGTACTTTCAGGG AGCAAGTATCGGGCAAAGG JARID2 TCCACAAGTGCATCTATAAGGG TCCTTCTCTTCCACTAGCCTC JUN AGCCCAAACTAACCTCACG TGCTCTGTTTCAGGATCTTGG KITLG CCAGAACAGCTAAACGGAGTC GACGAGAGGATTAAATAGGAGCAG LASP1 AACGCACACTACCCCAAG CCACTACGCTGAAACCTTTG LATS2 AACTCACAGATTTCGGCCTC ACACCGACAGTTAGACACATC LC3B CCATGCCGTCGGAGAAGA CTGCTCTCGAATAAGTCGGACAT LGALS7 GCCCAGTACCACCACTTC CTAACGCTTTATTTGCCAGCC LITAF TGATGGGAAGGGCATGAATC ACACATTTGGATAGGGCGG BC031884 TGACAGAGGGACACATTGAAG TCTGAACTCTGGGCTCTTAGG LOC201477 TCCTGAACTACAATGGCAACG TGGCAAGAGTGGATTAGCAG LOC221710 TGACTCTGCTTGTGTTCGTG GCTCATGATCTCCCTCTTGG LOC374491 GAAGGAGCCAACTGAGACAC AGTATCTCCACGGCAAATTCTG LOC389906 CCACACTGAGCTGAATATCGG TGTCAGGTGAATCGATGTGG LRRC30 CAACATCCACTCCTTCCCG GATCCTCACCAGTCTACACAG 45

LSD1 GCCAAAGCAGAGAAGGAAAAG TTGAGAAGTCATCCGGTCATG LTA TCTTCTTTGGAGCCTTCGC AGACTTGAGCTGTTGGAATGG LZTS1 CTCCCATCACCCAGATTACAC CTCACTGCACCCTTCTCG MAD1L1 GACTGGATATTTCTACCTCGGC CTTGTGACTCAGCTCCATCTG MAP1LC3A CTACGAGCAGGAGAAAGACG TCAGGAACCAGGAGCTCTC MAP1LC3C GACCATGACCCAGTTCCTC GCCATCCTCATCCTTGTAGTC MAPK14 TCAGTCCTTTGAAAGCAGGG ACAGTGAAGTGGGATCAACAG MAPK8 GACGCCTTATGTAGTGACTCG CTGGAAAGAGGATTTTGTGGC MAPK9 GGTATGGGCTACAAAGAGAACG TGGTCAGTGCCTTGGAATATC MCL1 AAGGACAAAACGGGACTGG ATATGCCAAACCAGCTCCTAC MDM2 TGGTTGGATCAGGATTCAGT TTCCAGTTTGGCTTTCTCAG MDM4 ACTTGAGAAGCAACTATACACCTAG TTCCAACACCTAACTGCTCTG MED1 (PPARGBP) CCTGACCCCATACCTTTGAATC GGGTGCTGAAAGGTGATTTTG MLH1 GGCACAGCATCAAACCAAG CAAGCATGGCAAGGTCAAAG MMP13 AAGGAGCATGGCGACTTCT TGGCCCAGGAGGAAAAGC MMP3 TGGCATTCAGTCCCTCTATGG AGGACAAAGCAGGATCACAGTT MMP9 ACCTCGAACTTTGACAGCGAC GAGGAATGATCTAAGCCCAGC MSH2 AAAGGGAGAGCAGATGAATAGTG TGATTACCGCAGACAGTGATG MSH6 GAGGCTTGAATTGGCAGTTTG CTAGATCCTTGTGTCTTAGGCTG MTA2 CAGTGTGACCCTCTTGAATGAG AACCAACTCTAATCTCGCCC MTOR GATGGCACTCAGCGAATGC ATGCGCACATCTCCATTGAC MYC TTCGGGTAGTGGAAAACCAG AGTAGAAATACGGCTGCACC NAIP (BIRC1) CCCATTAGACGATCACACCAG GCTTTCACTTGTGGTTTCCAG NCK2 GCCCCACAGATAAGCTACAC GACTCGCTGTCCCTAATGAG NEO1 TCCGACTACAAACCTCCAATG ACGGGTACAAAAGACAGCG NF1 GTTTCACTTCTAGCTGGTCTCC CGCACTTTCATCTTCAACTTCAC NFKB1 TGGGCTACAAGGAGAATGTTG ATGAACTCTGCGGATGGTG NIR ATTCTGTTCCTGTGACCGTC ACGCCTGTACCACTTCATG NOTCH1 TGCCTGGACAAGATCAATGAG CAGGTGTAAGTGTTGGGTCC NOXA GAGCTGGAAGTCGAGTGTG CTCTTTTGAAGGAGTCCCCTC OKL38 AAGGAAAAAAGCGGCCGCAGCTCC GGAATTCTTAGGGTGGCTTCCTGGTC OR52E8 GTACACCATGATCCTCACCAG ATGATACGATGCCCACAGAAG OR52K1 CTGTACCCTTCTCTTCATTATCCG CCTGATCCCTGAACCAGAATATG OR52K2 TGAACCCATGTACCTCTTTCTG GAAGTTTATCTCCCGATCCCTG OSBP TGTGGGTTCTGGTAAAGATCAG TGGCTCCACTGATATTGCTG PAD4 GATGAAATGGAGATCGGCTACAT TCAGGCCTCTGTTCCTTGGA PAK1IP1 ATGTTATTGTTTCAGCATCGAGTG TGTCTGCCACTTTGTCTAGC PARK7 GCTGTGAAGGAGATACTGAAGG TGTCTTTAGCAAGAGGGTGTG PARP1 AGAGAAAAGGCGATGAGGTG TTAGCTCGTCCTTGATGTTCC PARP9 TTTCAGCAAGTCCCATACCAG GATTTTCTTGGCCTTCTCTGC PCAF GAAGAGAACAGAAGCTCCAGG GCAATTGGTAAAGACTCGCTG PCNA CTAGCCTGACAAATGCTTGC AGGAAAGTCTAGCTGGTTTCG PDGFRA TTCCTCTGCCTGACATTGAC GTCTTCAATGGTCTCGTCCTC PDGFRB CCACACTCCTTGCCCTTTAAG CTCACAGACTCAATCACCTTCC PDRG1 TTATCAAGATGCCTCACCCTG CCCTTCAGCTCCGGTTTG PERK GAACCAGACGATGAGACAGAG GGATGACACCAAGGAACCG PERP TTGTCTTCCTGAGAGTGATTGG CCGTAGGCCCAGTTATAGATG PHLDA3 TCTTTCCTTCATGCTACCCAC CTCGTCCATTCCTTCAGCTC 46

PIDD TTCCAGAAATGCCCAGACTG GATAGCGGATGGTGATGGG PIG6 (PRODH) CAGAGCACAAGGAGATGGAG ATTGGCGTAGAAGTAGGTGC PIK3C3 GGGATTAGTGCTGAGGTCATG AGTCTATGTGGAAGAGTTTGCC PIK3CA GAGTACCTTGTTCCAATCCCAG TTCCTCTTTAGCACCCTTTCG PLK1 ACAGTTTCGAGGTGGATGTG GGTTGATGTGCTTGGGAATAC PLK2 AGAACCCTTGGAACACAGAAG GGCCTCCCTAGTAACATTGTATAC PLK3 AGTGTGGAAGAGGTAGAGGT TGAGAATCAGCTTGGTGTGG PLK4 AGAAGCATTACATCTCCGTTGG ACTCCTTTACAAGCTCCACAC PML GGACCCTATTGACGTTGACC TTGATGGAGAAGGCGTACAC PMS2 GCCTCATTCCTTTTGTTCAGC AACTCCTTCCAACTCCATGC PPARGC1B GAGACGCTCTATCATGCACC TTGATGTTGGGTAGGATGTGG PPM1D ATAAGCCAGAACTTCCCAAGG TGGTCAATAACTGTGCTCCTTC

PPP1R13B (ASPP1) AACGTCCCATACCCTTTGATC TTCGTTGCTCTTGGGTCTG PRDM1 TGTCAGAACGGGATGAACATCT AGGGATGGGCTTAATGGTGTA PRKCD GCTTCAAGGTTCACAACTACATG ACCTTCTCCCGGCATTTATG PRKCE CCTACCTTCTGCGATCACTG TACTTTGGCGATTCCTCTGG PRNP TGTATCACCCAGTACGAGAGG AGATGGTGAAAACAGGAAGACC PSRC1 TGTTGCGTGGAAGATAAGGC CCCAAAGTCCAAGGTCTCATC PTEN AAGGGACGAACTGGTGTAATG GCCTCTGACTGGGAATAGTTAC PTGES (PIG12) CCAGTATTGCAGGAGCGAC AGGCGACAAAAGGGTTAGG PTTG1 AAAGCTCTGTTCCTGCCTC CGTCAAGGATCATGAGAGGC PUMA CACGFCTTTGGAAAAAGGAA GTTTCTTTTTCAGTTTCTCATTGTTAC PXDN (PRG2) ATTCGTTCCAGCATCTCCC ACAGGATTTCACAGTCGCAG PYCARD AGCTCTTCAGTTTCACACCAG GAGTGTTGCTGGGAAGGAG RB1 GAACATCGAATCATGGAATCCC AGGAAGATTAAGAGGACAAGCAG RB1CC1 AGCCTCGAAAGTTTGACTGTG TGCTGGTGTAGAGGTTCAAAG RECK CCCGTCGATCACTATCCAAAAG GAGACCTGTACCTGGGAAATG RELA AATGGCTCGTCTGTAGTGC TGCTCAATGATCTCCACATAGG RHEB AAGACCTGCATATGGAAAGGG CTGCCTCCAAAATTATCCTTCG RHOA AGCTGGGCAGGAAGATTATG CGTTGGGACAGAAATGCTTG RHOB AGAACGGCTGCATCAACTG CTTGTGGGACACGGGTC RIPK1 CATGGAAAAGGCGTGATACAC ACTTCCCTCAGCTCATTGTG RNF144B CATTATGACAAAGGGCCATGC CCAGGAGTAACAAGGGTGAAG RPRM CTAGTCTGCGAGTGAGCG GTCCGTCTGGTTGCCTAG RPS27L AGAAACGCCTAGTACAAAGTCC CAACCTACACAAAGAACCACTG RRAD ACACCTATGATCGCTCCATTG CTTGTCCGTCACTGAGTACAC RRM2B GGTGCTGTCGTAGTTGGAG CTTTCGTTGGTGTCTGAAGATG rubicon TAAAAGTACAACCCGACTCCG CCTACATGGCTTTCAGATCCC S100A2 GCCCACATATAAATCCTCACCC GGAGTACTTGTGGAAGGTAGTG S100A6 CCATCTTCCACAAGTACTCCG CGGTCCAAGTCTTCCATCAG SERPINB5 AGCATCTCAGCATGTTCATCC TGGAGAGTTTGACCTTGGC SESN1 CGGAATTGGTACATGCAGTAGTTT GGCCACCATCACAATGAATTT SESN2 AATACCATCGCCATGCACAGT ATGCCAAAGACGCAGTGGAT SESN3 TGAAGACTTTGCCAGACGAG AGAACCCATGATTTTCCCAGG SGK233 GGACAGAAGTGGGATTGAGAC CTGAAGGATGGGCTGAGG SHISA5 TGACCATCTTTGTGCTGTCTG TGGAGGCTGAGGATAAGGG SHISA9 GACCAACCTGATCGTCTACATC TGTGCCTCCATAAACAGCA SIAH1 CATGAAGGAATTGCAACAGCC GCCAGAAAATGTTTGATTGCC 47

SIN3A ATCATGGAGGCACTGTCAAG CAGATTGAGCAGTTTGGGTTTC SIN3B TCCCCGTTGTCCTGAAAAG TGGTGTCGTTCTGCTTGAAG SIRPG CCTTTCCTGCTTCTGACTCTAC GGCTGTCTTTCCAACTGTGA SIRT1 CCCTCAAAGTAAGACCAGTAGC CACAGTCTCCAAGAAGCTCTAC SIVA CTGCCAGAGTCCCCAAAG TGAGGAACAGGCAATGGAC SNCA CTGGAAGATATGCCTGTGGATC AGCACTTGTACAGGATGGAAC SP1 GTGAAGCAGAGGCGAAGCAT CAGCACCTGGGACTCAAACTG SPAG9 TGGAGATGGATTGCTTACACC GTAGCTTTAGATCCGCCTTGG SQSTM1 AATCAGCTTCTGGTCCATCG TTCTTTTCCCTCCGTGCTC STARD4 CACTCTCATCCAGTACCATAGC GGTCATCTATAACACCTTGGGC STAT1 TGAACTTACCCAGAATGCCC CAGACTCTCCGCAACTATAGTG STK11 AGAAGCGTTTCCCAGTGTG GGTCGGAGATTTTGAGGGTG TADA3 CCAGCCATTAAACAGTCCCAC GGGCAGTCTTTCAACTCACT TCF7L2 AGAGGAAAAGGGACAAGCAG GAAGTGAAAGGCAAGGATTTAGG TERT GCACGGCTTTTGTTCAGATG CGGTTGAAGGTGAGACTGG TGFA ACACTCAGTTCTGCTTCCATG GCACCAACGTACCCAGAATG TGFB1 GCCTTTCCTGCTTCTCATGG TCCTTGCGGAAGTCAATGTAC THBS1 CTCCCCTATGCTATCACAACG AGGAACTGTGGCATTGGAG KAT5 CATCGTGGGCTACTTCTCC CCTGTTTTCCCTTCCACTTTG TLE1 AGAGGCACAGATAAACGCAG GGTCCTCATTAGACACATCCAC TLR1 CAAATGGAACAGACAAGCAGG GCCTGGTACCCCTATTAGTG TLR2 AGACCTATAGTGACTCCCAGG ACCCACACCATCCACAAAG TLR3 TCAACTTTCTGATAAAACCTTTGCC AGATGACAAGCCATTATGAGACA TLR4 TGCGTGAGACCAGAAAGC TTAAAGCTCAGGTCCAGGTTC TLR6 TGGACTCATATCAAGATGCTCTG GTCGGAGAACTGGATTCTGG TLR7 GAAAGTTGATGCTATTGGGCC GAATTTGTCTCTTCAGTGTCCAC TLR8 CTGCATAGAGGGTACCATTCTG CGCATAACTCACAGGAACCAG TLR9 CTATAACCGGAACTTCTGCCAG CTGCTCTGTGTCAGGTGTG TNFa AGGTCTACTTTGGGATCATTGC GAAGAGGTTGAGGGTGTCTG TNFAIP3 GATAGAAATCCCCGTCCAAGG CTGCCATTTCTTGTACTCATGC TNFAIP8 AGTACATGTGAGCGGTAATCG GATCTTCTTTTGTGCCTGAACG TNFRSF10B ACCACGACCAGAAACACAG CATTCGATGTCACTCCAGGG TNFSF10 CAGCTCACATAACTGGGACC CCATTCCTCAAGTGCAAGTTG

TOPORS (p53BP3) CCGGGACTTACCACTGAATAAA ACTTCTCCGCCTACCCTC TP53 CCTCCCGCCATAAAAAACTC CCTCCCCACAACAAAACA TP53AIP1 CTCACTCCGAAAGCCTCTG CTTGGCTTCTCCTCATTTGTTG TP53BP2 GCCGATGAAAATACAGACACTG TTTGAGGTACAGAAGCAGAGC TP53I13 CCCCAACTGCTTCTCCTG CTCGTGTGTAGGTCACTCTTG TP53I3 CAGATGCCTCAAGTCCCAAAA TTAATGCAGAGACAAGGCCAGTAT TP53INP1 TCGCCAAAATCTTACCAGGG GCACAAACCAAGAGAAACCAAC TP53INP2 CCGCACAACTACCTCAACG GGCAGCGATTTTATTGGACTC TP53RK TTAGATGAAGTGCGCCTGAG TCTTAACACCTTTACTTAGATCTCATCT

TP53TG1 AATCTTATTGTCCACCCCGTG GTTACTCAGACCTGCCAGC TPO AGTCTCGTGTCTCTAGCGTC TGTTGGCTCAGGAAGTTTGG TRADD TTTGAGTTGCATCCTAGCCC GCACTTCAGATTTCGCAGC TRAF3 AGTTAATGCTGGGACATCTGC TTTACACGCCTTCTCCACG TRIM13 (RFP2) GCCCTGGGAAATAGTAACCC AGAAGTTGTGGGAGCAAGG TRIM22 TGGGTTTGTGAACTGTCTCAG ATGTCATCTTCCAGCTTCTCAG 48

TRIM24 ACCAGAACATACCACGACAAG CTGGAAGGAGTAGAGGATGTG TRIM29 GCTACCTTTGCATGTTCCAG CCACTTCTCAGCTTCATCCTC TRPM1 CGGGTTTGTGCTATAGGAATTG GATGAAGTGGGTGTGGGAG TSC1 GCAAAAGGAAACACAGAGGAAG AGACTTGCTGGGTAAAGGC TXN GTATTCCAACGTGATATTCCTTGAAG GCTTTTCCTTATTGGCTCCAG ULK1 TGGGCAAGTTCGAGTTCTC CTTGTTAATGCACTTGACGGC ULK2 CGGATGACTTTGTTTTGGTGC TGTTTCGCTGTGAGGTGAC UVRAG CATCTGTGTCTTGTTTCGTGG TTCATTTTGGTTTCGGGCATG VMP1 ACCAGAGACGTGTAGCAATG AAGGTAATGAGCGGCTGTC WIG1 CAAGCAACATAAGAGCAAGGTG GCAGGGCAAGTTGACAAAAG WIPI1 GTGTTCTCTGTCCCTGATGG AGCTTGAAGATGTGTACCGTC WRN GATGTTATCCGAAACCCTCCC AAGGTTGAAGTCCGCTGTC WT1 CCAAATGACATCCCAGCTTG TGTATTCTGTATTGGGCTCCG XAF1 GAGAGCAGAACATGGAAGGAG GAGGGTGAAGTTGGCAGAG XPC GTCTCTACAGCCAATTCCTCTG CCTTTGCTGGTCTTTGGTTTG ZNF251 GTGATGCTGGAGAACTATGGG TTTCTGGCAGCTTTTCAAGATATC

49

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ACADEMIC VITA

Sarah Moore 5399 Guitner Road, Chambersburg Pa 17202 [email protected] Education:

The Pennsylvania State University, Schreyer Honors College (Fall 2010–Spring 2013) B.S. Biochemistry and Molecular Biology Schreyer Honors Thesis: A Systematic Method for Analyzing the p53-dependent Transcription Network

Pertinent Graduate Level Coursework Grade Earned

BMMB598K: Advanced Gene Expression A BMMB541: Molecular Biology of Animal Development Enrolled

Research Experience:

Penn State Center for Eukaryotic Gene Regulation (Fall 2010–Present) Undergraduate Research Assistant for Dr. Yanming Wang

 Working with peptidylarginine deaminase four (PAD4) knock-out mice to culture mouse embryonic fibroblasts (MEFs) and embryonic stem cells in preparation for studying the role of PAD4 in early development.  Testing novel synthetic cancer inhibitors in various cancer cell lines to determine toxicity and mechanisms of action, with an emphasis on autophagy inhibition and nanoparticle formation.  Performed a literature review on an extensive list of p53-related genes and designed and ordered quantitative real-time polymerase chain reaction (qRT-PCR) primers. Designing a website to visualize qRT-PCR data collected for these genes to provide a user-friendly resource for understanding the effect of different stresses upon the pathways in the p53 network (apoptosis, autophagy, cell cycle inhibition, DNA repair, immune/inflammatory response, metabolism, angiogenisis, differentiation) and determining the cross-talk between these pathways. This project will culminate in an honors thesis, manuscript, and public website.

Laboratory Skills:  Culturing cancer and stem cell lines  Handling, crossing, genotyping, and dissecting mice  Quantitative Real-Time Polymerase Chain Reaction  Western Blot  MTT-Cell Viability Assay  RNA, Protein, and DNA isolation  Immunostaining  Microsoft Office  Adobe Dreamweaver  HTML

Publications:

Moore, S. L., and Wang, Y. (2013). A Systematic Method for Analyzing Stimulus-Dependent Activation of the p53 Transcription Network. Manuscript submitted for publication.

Chen, X. A., Wang, Y., Wenjing, W., Wang, X., Ma, H., Pei, L., Moore, S., Lei, L., Wu, J., Xu, W., Zhao, S., Wang, Y., Zhu, H., Li, L., Gu, Y., Zhao, M., Wang, Y., and Peng, S. (2013) Nanoparticles formed by self-assembly small molecules as anticancer nanomedicine. Manuscript submitted for publication.

Presentations:

Penn State Cross-Campus Undergraduate Research Symposium and Poster Session A Systematic Method for Analyzing Stimulus-Dependent Activation of the p53 Transcription Network

Penn State BMMB Department Graduate Recruitment Fair A Systematic Method for Analyzing Stimulus-Dependent Activation of the p53 Transcription Network

Honors and Awards: Total Merit-Based Scholarship Dollars Granted: $45,466

Miller Joseph Scholarship in Science (2012) Mathues Scholarship in Science (2012) Herko Family Scholarship in Biochemistry and Molecular Biology (2011) Schreyer Honors College Ambassador Travel Grant (2011) Academic Excellence Scholarship (2010–Present) Lenfest Foundation Scholarship (2010–Present) Pre-eminence in Honors Education Scholarship (2010–2012) National AP Scholar; AP Scholar with Distinction (2010) Comcast Leaders and Achievers Scholarship (2010) Dean’s List Valedictorian of Chambersburg Area Senior High School Class of 2010

Grants Awarded: Total Research Funding Received: $8,820

2012 Undergraduate Summer Discovery Grant, The Pennsylvania State University 2012 Whitfield Summer Undergraduate Research Fellowship 2011 Schreyer Honors College Summer Research Grant

Service to the Scientific Community:  Creation of a public, on-line database for the p53 transcription network  Editing lab manuscripts and grant applications  Lab meeting presentations  Assisting with graduate students’ research projects

Collaboration:  Working with a bioinformatics and genomics graduate student in the Pugh Lab at Penn State who conducted a chromatin immunoprecipitation (CHIP) assay to analyze p53 binding sites throughout the genome and the effect of ultraviolet radiation on p53 binding.

Conferences Attended:  Summer Symposium in Molecular Biology: Chromatin and Epigenetic Regulation of Transcription The Pennsylvania State University, 2011

Teaching Experience:

Teaching Assistant for BIOL 322- Genetics (Spring 2013)  Held office hours once a week to answer students’ questions.

Coldbrook Elementary School, Chambersburg, PA (Spring 2009) 3rd grade Teaching Aid for Ms. Charlene Vanscyoc  Led reading group discussions, helped with science demonstrations, tutored students in math, and assisted with Study Island, a practice tool for the Pennsylvania System of School Assessment. Created a Social Studies website to provide the class with an on-line research tool for a project.