Computational biology currently faces challenges in a big data world with thousands of data samples across multiple disease types including cancer. The challenging problem is how to extract biologically meaningful information from large-scale genomic data. Next-generation Sequencing (NGS) can now produce high quality data at DNA and RNA levels. However, in cells there exist a lot of non-specific (background) signals that affect the detection accuracy of true (foreground) signals. In this dissertation work, under Bayesian framework, we aim to develop and apply approaches to learn the distribution of genomic signals in each type of NGS data for reliable identification of specific foreground signals. We propose a novel Bayesian approach (ChIP-BIT) to reliably detect transcription factor (TF) binding sites (TFBSs) within promoter or enhancer regions by jointly analyzing the sample and input ChIP-seq data for one specific TF. Specifically, a Gaussian mixture model is used to capture both binding and background signals in the sample data; and background signals are modeled by a local Gaussian distribution that is accurately estimated from the input data. An Expectation-Maximization algorithm is used to learn the model parameters according to the distributions on binding signal intensity and binding locations. Extensive simulation studies and experimental validation both demonstrate that ChIP-BIT has a significantly improved performance on TFBS detection over conventional methods, particularly on weak binding signal detection. To infer cis-regulatory modules (CRMs) of multiple TFs, we propose to develop a Bayesian integration approach, namely BICORN, to integrate ChIP-seq and RNA-seq data of the same tissue. Each TFBS identified from ChIP-seq data can be either a functional binding event mediating target gene transcription or a non-functional binding. The functional bindings of a set of TFs usually work together as a CRM to regulate the transcription processes of a group of genes. We develop a Gibbs sampling approach to learn the distribution of CRMs (a joint distribution of multiple TFs) based on their functional bindings and target gene expression. The robustness of BICORN has been validated on simulated regulatory network and gene expression data with respect to different noise settings. BICORN is further applied to breast cancer MCF-7 ChIP-seq and RNA-seq data to identify CRMs functional in promoter or enhancer regions. In tumor cells, the normal regulatory mechanism may be interrupted by genome mutations, especially those somatic mutations that uniquely occur in tumor cells. Focused on a specific type of genome mutation, structural variation (SV), we develop a novel pattern-based probabilistic approach, namely PSSV, to identify somatic SVs from whole genome sequencing (WGS) data. PSSV features a mixture model with hidden states representing different mutation patterns; PSSV can thus differentiate heterozygous and homozygous SVs in each sample, enabling the identification of those somatic SVs with a heterozygous status in the normal sample and a homozygous status in the tumor sample. Simulation studies demonstrate that PSSV outperforms existing tools. PSSV has been successfully applied to breast cancer patient WGS data for identifying somatic SVs of key factors associated with breast cancer development. In this dissertation research, we demonstrate the advantage of the proposed distributional learning-based approaches over conventional methods for NGS data analysis. Distributional learning is a very powerful approach to gain biological insights from high quality NGS data. Successful applications of the proposed Bayesian methods to breast cancer NGS data shed light on underlying molecular mechanisms of breast cancer, enabling biologists or clinicians to identify major cancer drivers and develop new therapeutics for cancer treatment. / Ph. D.

Recent breakthroughs in high-throughput biotechnology have promoted the integration of multi-platform data to investigate signal transduction pathways within a cell. In order to model complicated dynamics and heterogeneity of biological pathways, sophisticated computational models are needed to address unique properties of both the biological hypothesis and the data. In this dissertation work, we have proposed and developed methods using Markov Chain Monte Carlo (MCMC) techniques to solve complex modeling problems in human cancer research by integrating multi-platform data. We focus on two research topics: 1) identification of transcriptional regulatory networks and 2) uncovering of aberrant intracellular signal transduction pathways. We propose a robust method, called GibbsOS, to identify condition specific gene regulatory patterns between transcription factors and their target genes. A Gibbs sampler is employed to sample target genes from the marginal function of outlier sum of regression t statistic. Numerical simulation has demonstrated significant performance improvement of GibbsOS over existing methods against noise and false positive connections in binding data. We have applied GibbsOS to breast cancer cell line datasets and identified condition specific regulatory rewiring in human breast cancer. We also propose a novel method, namely Gibbs sampler to Infer Signal Transduction (GIST), to detect aberrant pathways that are highly associated with biological phenotypes or clinical information. By converting predefined potential functions into a Gibbs distribution, GIST estimates edge directions by learning the distribution of linear signaling pathway structures. Through the sampling process, the algorithm is able to infer signal transduction directions which are jointly determined by both gene expression and network topology. We demonstrate the advantage of the proposed algorithms on simulation data with respect to different settings of noise level in gene expression and false-positive connections in protein-protein interaction (PPI) network. Another major contribution of the dissertation work is that we have improved traditional perspective towards understanding aberrant signal transductions by further investigating structural linkage of signaling pathways. We develop a method called Structural Organization to Uncover pathway Landscape (SOUL), which emphasizes on modularized pathways structures from reconstructed pathway landscape. GIST and SOUL provide a very unique angle to computationally model alternative pathways and pathway crosstalk. The proposed new methods can bring insight to drug discovery research by targeting nodal proteins that oversee multiple signaling pathways, rather than treating individual pathways separately. A complete pathway identification protocol, namely Infer Modularization of PAthway CrossTalk (IMPACT), is developed to bridge downstream regulatory networks with upstream signaling cascades. We have applied IMPACT to breast cancer treated patient datasets to investigate how estrogen receptor (ER) signaling pathways are related to drug resistance. The identified pathway proteins from patient datasets are well supported by breast cancer cell line models. We hypothesize from computational results that HSP90AA1 protein is an important nodal protein that oversees multiple signaling pathways to drive drug resistance. Cell viability analysis has supported our hypothesis by showing a significant decrease in viability of endocrine resistant cells compared with non-resistant cells when 17-AAG (a drug that inhibits HSP90AA1) is applied. We believe that this dissertation work not only offers novel computational tools towards understanding complicated biological problems, but more importantly, it provides a valuable paradigm where systems biology connects data with hypotheses using computational modeling. Initial success of using microarray datasets to study endocrine resistance in breast cancer has shed light on translating results from high throughput datasets to biological discoveries in complicated human disease studies. As the next generation biotechnology becomes more cost-effective, the power of the proposed methods to untangle complicated aberrant signaling rewiring and pathway crosstalk will be finally unleashed. / Ph. D.

Transcription factors (TFs) have been a focal point of molecular biology research for decades, with evolving methodologies offering progressively deeper insights into their critical roles in gene regulation. Recent advancements in experimental and computational techniques have significantly enhanced our understanding of TF functionality, yet this depth of knowledge varies widely across the spectrum of known TFs — from extensively characterized ones with quantitative binding affinity data to those scarcely studied or understood. In this work, we systematically carried out binding and expression experiments on all Escherichia coli TFs using a standardized computational pipeline to identify direct and indirect regulatory targets. We further leveraged our binding data to develop a novel biophysically motivated neural network capable of predicting TF-DNA binding affinity from DNA sequence. This approach allowed us to design binding sites with specified affinities, including those stronger than any sequence observed in nature, which we validate experimentally using an in vitro binding assay. We further optimized this assay to provide insight into complex TF binding regimes, where chemical signals can modulate TF binding affinity. Finally, we demonstrate the utility of systematically mapping TF binding sites through a case study on a previously thought dormant TF acquired from viral infection, revealing an unexpected phenotype where it can hijack the host cell. This work not only offers broad insights into the determinants of TF binding and regulation, but also provides a means to predictively engineer binding sites with desired affinity, while demonstrating the power of efficient data processing in uncovering intricate biological processes. / 2025-05-23T00:00:00Z

Computational biology is an interdisciplinary field that focuses on developing mathematical models and algorithms to interpret biological data so as to understand biological problems. With current high-throughput technology development, different types of biological data can be measured in a large scale, which calls for more sophisticated computational methods to analyze and interpret the data. In this dissertation research work, we propose novel methods to integrate, model and analyze multiple biological data, including microarray gene expression data, protein-DNA interaction data and protein-protein interaction data. These methods will help improve our understanding of biological systems. First, we propose a knowledge-guided multi-scale independent component analysis (ICA) method for biomarker identification on time course microarray data. Guided by a knowledge gene pool related to a specific disease under study, the method can determine disease relevant biological components from ICA modes and then identify biologically meaningful markers related to the specific disease. We have applied the proposed method to yeast cell cycle microarray data and Rsf-1-induced ovarian cancer microarray data. The results show that our knowledge-guided ICA approach can extract biologically meaningful regulatory modes and outperform several baseline methods for biomarker identification. Second, we propose a novel method for transcriptional regulatory network identification by integrating gene expression data and protein-DNA binding data. The approach is built upon a multi-level analysis strategy designed for suppressing false positive predictions. With this strategy, a regulatory module becomes increasingly significant as more relevant gene sets are formed at finer levels. At each level, a two-stage support vector regression (SVR) method is utilized to reduce false positive predictions by integrating binding motif information and gene expression data; a significance analysis procedure is followed to assess the significance of each regulatory module. The resulting performance on simulation data and yeast cell cycle data shows that the multi-level SVR approach outperforms other existing methods in the identification of both regulators and their target genes. We have further applied the proposed method to breast cancer cell line data to identify condition-specific regulatory modules associated with estrogen treatment. Experimental results show that our method can identify biologically meaningful regulatory modules related to estrogen signaling and action in breast cancer. Third, we propose a bootstrapping Markov Random Filed (MRF)-based method for subnetwork identification on microarray data by incorporating protein-protein interaction data. Methodologically, an MRF-based network score is first derived by considering the dependency among genes to increase the chance of selecting hub genes. A modified simulated annealing search algorithm is then utilized to find the optimal/suboptimal subnetworks with maximal network score. A bootstrapping scheme is finally implemented to generate confident subnetworks. Experimentally, we have compared the proposed method with other existing methods, and the resulting performance on simulation data shows that the bootstrapping MRF-based method outperforms other methods in identifying ground truth subnetwork and hub genes. We have then applied our method to breast cancer data to identify significant subnetworks associated with drug resistance. The identified subnetworks not only show good reproducibility across different data sets, but indicate several pathways and biological functions potentially associated with the development of breast cancer and drug resistance. In addition, we propose to develop network-constrained support vector machines (SVM) for cancer classification and prediction, by taking into account the network structure to construct classification hyperplanes. The simulation study demonstrates the effectiveness of our proposed method. The study on the real microarray data sets shows that our network-constrained SVM, together with the bootstrapping MRF-based subnetwork identification approach, can achieve better classification performance compared with conventional biomarker selection approaches and SVMs. We believe that the research presented in this dissertation not only provides novel and effective methods to model and analyze different types of biological data, the extensive experiments on several real microarray data sets and results also show the potential to improve the understanding of biological mechanisms related to cancers by generating novel hypotheses for further study. / Ph. D.

Le système musculo-squelettique se compose des muscles, du squelette et du tissu conjonctif qui comprend, entre autres, les tendons et le tissu conjonctif musculaire. Le tissu conjonctif musculaire contribue à l'élasticité et à la rigidité des muscles, alors que les tendons transmettent les forces musculaires à l'os nécessaires aux mouvements du corps. Contrairement au muscle et au squelette, la mise en place et la formation du tissu conjonctif restent à ce jour peu étudiées. Afin d'identifier les mécanismes moléculaires sous-jacents à la formation du tissu conjonctif au cours du développement du membre, cinq facteurs de transcription à doigt de zinc ont été examinés : OSR1, OSR2, EGR1, KLF2 et KLF4. Ces facteurs de transcription sont exprimés dans différents sous-compartiments du système musculo-squelettique et leur surexpression influence la différentiation des cellules mésenchymateuses du membre. Afin d'élucider leurs rôles au niveau de la régulation génique, plusieurs stratégies à haut-débit (RNA-seq, ChIP-seq) ont été mises en place. Ces stratégies ont permis : (i) d'identifier que les facteurs de transcription partagent des fonctions régulatrices communes liées à la transduction du signal, à la communication cellulaire et à l'adhésion cellulaire ; (ii) de révéler que les gènes différentiellement exprimés étaient enrichis pour des signatures d'activation et de répression chromatiniennes, suggérant qu'ils sont dynamiquement régulés ; (iii) de distinguer les gènes cibles directs des cibles indirectes. Ces résultats fournissent ainsi une base pour des travaux futurs visant à mieux comprendre l'inter-connectivité entre les différents composants de l'appareil locomoteur. / The musculoskeletal system is composed of muscles, skeletal elements and connective tissues such as tendon and muscle connective tissue. Muscle connective tissue contributes to the elasticity and rigidity of muscles, while tendons transmit forces generated by muscles to the bone to allow body motion. In contrast to muscle and skeleton, connective tissue patterning and formation remain poorly investigated. In order to identify molecular mechanisms underlying connective tissue formation during limb development, five zinc-finger transcription factors were investigated: OSR1, OSR2, EGR1, KLF2 and KLF4. These transcription factors are expressed in distinct subcompartments of the musculoskeletal system and influence the differentiation of limb mesenchymal cells upon overexpression. To further investigate their roles at the molecular level, several genome-wide strategies (RNA-seq, ChIP-seq) were employed. These strategies enabled: (i) to identify that the transcription factors share common regulatory functions and positively regulate biological processes related to signal transduction, cell communication and biological adhesion; (ii) to reveal that the differentially expressed genes were enriched for both active and repressive chromatin signatures at their promoters, suggesting that they are dynamically regulated; (iii) to distinguish between indirect and direct target genes. Altogether, these results provide a framework for future investigations to better understand the interconnectivity between components of the musculoskeletal system.

Squamous cell carcinoma (SCC) and adenocarcinoma (ADC) are the most common histological types of cervical cancer (CC). The worse prognosis of ADC cases highlights the need for better molecular characterization regarding differences between these CC types. RNA-Seq analysis of seven SCC and three ADC human papillomavirus 16-positive samples and the comparison with public data from non-tumoral human papillomavirus-negative cervical tissue samples revealed pathways exclusive to each histological type, such as the epithelial maintenance in SCC and the maturity-onset diabetes of the young (MODY) pathway in ADC. The transcriptional regulatory network analysis of cervical SCC samples unveiled a set of six transcription factor (TF) genes with the potential to positively regulate long non-coding RNA genes DSG1-AS1, CALML3-AS1, IGFL2-AS1, and TINCR. Additional analysis revealed a set of MODY TFs regulated in the sequence predicted to be repressed bymiR-96-5p ormiR-28-3p in ADC. These microRNAs were previously described to target LINC02381, which was predicted to be positively regulated by two MODY TFs upregulated in cervical ADC. Therefore, we hypothesize LINC02381might act by decreasing the levels ofmiR-96-5p andmiR-28-3p, promoting the MODY activation in cervical ADC. The novel TF networks here described should be explored for the development of more efficient diagnostic tools.

Les cellules souches somatiques présentent habituellement un comportement très différent des cellules souches pluripotentes. Les bases moléculaires de l’auto-renouvellement des cellules souches embryonnaires ont été récemment déchiffrées grâce à la facilité avec laquelle nous pouvons maintenant les purifier et les maintenir en culture durant de longues périodes de temps. Par contre, il en va tout autrement pour les cellules souches hématopoïétiques. Dans le but d’en apprendre davantage sur le fonctionnement moléculaire de l’auto-renouvellement des cellules souches hématopoïétiques, j’ai d’abord conçu une nouvelle méthode de criblage gain-de-fonction qui répond aux caprices particuliers de ces cellules. Partant d’une liste de plus de 700 facteurs nucléaires et facteurs de division asymétrique candidats, j’ai identifié 24 nouveaux facteurs qui augmentent l’activité des cellules souches hématopoïétiques lorsqu’ils sont surexprimés. J’ai par la suite démontré que neuf de ces facteurs agissent de manière extrinsèque aux cellules souches hématopoïétiques, c’est-à-dire que l’effet provient des cellules nourricières modifiées en co-culture. J’ai également mis à jour un nouveau réseau de régulation de transcription qui implique cinq des facteurs identifiés, c’est-à-dire PRDM16, SPI1, KLF10, FOS et TFEC. Ce réseau ressemble étrangement à celui soutenant l’ostéoclastogénèse. Ces résultats soulèvent l’hypothèse selon laquelle les ostéoclastes pourraient aussi faire partie de la niche fonctionnelle des cellules souches hématopoïétiques dans la moelle osseuse. De plus, j’ai identifié un second réseau de régulation impliquant SOX4, SMARCC1 et plusieurs facteurs identifiés précédemment dans le laboratoire, c’est-à-dire BMI1, MSI2 et KDM5B. D’autre part, plusieurs indices accumulés tendent à démontrer qu’il existe des différences fondamentales entre le fonctionnement des cellules souches hématopoïétiques murines et humaines. / Somatic stem cells usually exhibit a very different behavior compared to pluripotent stem cells. The molecular basis of embryonic stem cell self-renewal was recently decrypted by the relative straightforwardness with which we can now purify and maintain these cells in culture for long periods of time. However, this is not the case with hematopoietic stem cells. In order to elucidate the molecular mechanisms of hematopoietic stem cell self-renewal, I developed a novel gain-of-function screening strategy, which bypasses some constraints found with these cells. Starting from a list of more than 700 candidate nuclear factors and asymmetric division factors, I have identified 24 new factors that increase hematopoietic stem cell activity when overexpressed. I have also found that nine of these factors act extrinsically to hematopoietic stem cells, i.e., the effect comes from the engineered feeder cells in co-culture. Moreover, I have revealed a new transcriptional regulatory network including five of the factors identified, i.e., PRDM16, SPI1, KLF10, FOS and TFEC. This network is particularly similar to that involved in osteoclastogenesis. These results raise the hypothesis that osteoclasts might also be part of the functional hematopoietic stem cell niche in the bone marrow. Furthermore, I have identified a second regulatory network involving SOX4, SMARCC1 and several factors previously identified in the laboratory, i.e., BMI1, MSI2 and KDM5B. Besides, several lines of evidence tend to show that there are fundamental differences between mouse and human hematopoietic stem cells.

Embryonic stem cells (ESCs) are characterized by their unlimited capacity for self-renewal and the ability to contribute to every lineage of the developing embryo. The promoters of developmentally regulated loci within these cells are marked by coincident epigenetic modifications of gene activation and repression, termed bivalent domains. Trithorax group (TrxG) and Polycomb Group (PcG) proteins respectively place these epigenetic marks on chromatin and extensively colocalize with Oct4 in ESCs. Although it appears that these cells are poised and ready for differentiation, the switch that permits this transition is critically held in check. The derepression of bivalent domains upon knockdown of Oct4 or PcG underscores their respective roles in maintaining the pluripotent state through epigenetic regulation of chromatin structure. The mechanisms that facilitate the recruitment and retention of Oct4, TrxG, and PcG proteins at developmentally regulated loci to maintain the pluripotent state, however, remain unknown. Oct4 may function as either a transcriptional activator or repressor. Prevailing thought holds that both of these activities are required to maintain the pluripotent state through activation of genes implicated in pluripotency and cell-cycle control with concomitant repression of genes required for differentiation and lineage-specific differentiation. More recent evidence however, suggests that the activator function of Oct4 may play a more critical role in maintaining the pluripotent state (Hammachi et al., 2012). The purpose of the studies described in this dissertation was to clarify the underlying mechanisms by which Oct4 functions in transcriptional activation and repression. By so doing, we wished to contextualize its role in pluripotent cells, and to provide insight into how changes in Oct4 function might account for its ability to facilitate cell fate transitions. As a result of our studies we find that Oct4 function is dependent upon post-translational modifications (PTMs). We find through a combination of experimental approaches, including genome-wide microarray analysis, bioinformatics, chromatin immunoprecipitation, functional molecular, and biochemical analyses, that in the pluripotent state Oct4, Akt, and Hmgb2 participate in a regulatory feedback loop. Akt-mediated phosphorylation of Oct4 facilitates interaction with PcG recruiter Hmgb2. Consequently, Hmgb2 functions as a context dependent modulator of Akt and Oct4 function, promoting transcriptional poise at Oct4 bound loci. Sumoylation of Oct4 is then required to maintain Hmgb2 enrichment at repressed loci and to transmit the H3K27me3 mark in daughter progeny. The expression of Oct4 phosphorylation mutants however, leads to Akt inactivation and initiates the DNA Damage Checkpoint response. Our results suggest that this may subsequently facilitate chromatin reorganization and cell fate transitions. In summary, our results suggest that controlled modulation of Oct4, Akt, and Hmgb2 function is required to maintain pluripotency and for the faithful induction of transcriptional programs required for lineage specific differentiation.

Les cellules souches somatiques présentent habituellement un comportement très différent des cellules souches pluripotentes. Les bases moléculaires de l’auto-renouvellement des cellules souches embryonnaires ont été récemment déchiffrées grâce à la facilité avec laquelle nous pouvons maintenant les purifier et les maintenir en culture durant de longues périodes de temps. Par contre, il en va tout autrement pour les cellules souches hématopoïétiques. Dans le but d’en apprendre davantage sur le fonctionnement moléculaire de l’auto-renouvellement des cellules souches hématopoïétiques, j’ai d’abord conçu une nouvelle méthode de criblage gain-de-fonction qui répond aux caprices particuliers de ces cellules. Partant d’une liste de plus de 700 facteurs nucléaires et facteurs de division asymétrique candidats, j’ai identifié 24 nouveaux facteurs qui augmentent l’activité des cellules souches hématopoïétiques lorsqu’ils sont surexprimés. J’ai par la suite démontré que neuf de ces facteurs agissent de manière extrinsèque aux cellules souches hématopoïétiques, c’est-à-dire que l’effet provient des cellules nourricières modifiées en co-culture. J’ai également mis à jour un nouveau réseau de régulation de transcription qui implique cinq des facteurs identifiés, c’est-à-dire PRDM16, SPI1, KLF10, FOS et TFEC. Ce réseau ressemble étrangement à celui soutenant l’ostéoclastogénèse. Ces résultats soulèvent l’hypothèse selon laquelle les ostéoclastes pourraient aussi faire partie de la niche fonctionnelle des cellules souches hématopoïétiques dans la moelle osseuse. De plus, j’ai identifié un second réseau de régulation impliquant SOX4, SMARCC1 et plusieurs facteurs identifiés précédemment dans le laboratoire, c’est-à-dire BMI1, MSI2 et KDM5B. D’autre part, plusieurs indices accumulés tendent à démontrer qu’il existe des différences fondamentales entre le fonctionnement des cellules souches hématopoïétiques murines et humaines. / Somatic stem cells usually exhibit a very different behavior compared to pluripotent stem cells. The molecular basis of embryonic stem cell self-renewal was recently decrypted by the relative straightforwardness with which we can now purify and maintain these cells in culture for long periods of time. However, this is not the case with hematopoietic stem cells. In order to elucidate the molecular mechanisms of hematopoietic stem cell self-renewal, I developed a novel gain-of-function screening strategy, which bypasses some constraints found with these cells. Starting from a list of more than 700 candidate nuclear factors and asymmetric division factors, I have identified 24 new factors that increase hematopoietic stem cell activity when overexpressed. I have also found that nine of these factors act extrinsically to hematopoietic stem cells, i.e., the effect comes from the engineered feeder cells in co-culture. Moreover, I have revealed a new transcriptional regulatory network including five of the factors identified, i.e., PRDM16, SPI1, KLF10, FOS and TFEC. This network is particularly similar to that involved in osteoclastogenesis. These results raise the hypothesis that osteoclasts might also be part of the functional hematopoietic stem cell niche in the bone marrow. Furthermore, I have identified a second regulatory network involving SOX4, SMARCC1 and several factors previously identified in the laboratory, i.e., BMI1, MSI2 and KDM5B. Besides, several lines of evidence tend to show that there are fundamental differences between mouse and human hematopoietic stem cells.

Embryonic stem cells (ESCs) are characterized by their unlimited capacity for self-renewal and the ability to contribute to every lineage of the developing embryo. The promoters of developmentally regulated loci within these cells are marked by coincident epigenetic modifications of gene activation and repression, termed bivalent domains. Trithorax group (TrxG) and Polycomb Group (PcG) proteins respectively place these epigenetic marks on chromatin and extensively colocalize with Oct4 in ESCs. Although it appears that these cells are poised and ready for differentiation, the switch that permits this transition is critically held in check. The derepression of bivalent domains upon knockdown of Oct4 or PcG underscores their respective roles in maintaining the pluripotent state through epigenetic regulation of chromatin structure. The mechanisms that facilitate the recruitment and retention of Oct4, TrxG, and PcG proteins at developmentally regulated loci to maintain the pluripotent state, however, remain unknown. Oct4 may function as either a transcriptional activator or repressor. Prevailing thought holds that both of these activities are required to maintain the pluripotent state through activation of genes implicated in pluripotency and cell-cycle control with concomitant repression of genes required for differentiation and lineage-specific differentiation. More recent evidence however, suggests that the activator function of Oct4 may play a more critical role in maintaining the pluripotent state (Hammachi et al., 2012). The purpose of the studies described in this dissertation was to clarify the underlying mechanisms by which Oct4 functions in transcriptional activation and repression. By so doing, we wished to contextualize its role in pluripotent cells, and to provide insight into how changes in Oct4 function might account for its ability to facilitate cell fate transitions. As a result of our studies we find that Oct4 function is dependent upon post-translational modifications (PTMs). We find through a combination of experimental approaches, including genome-wide microarray analysis, bioinformatics, chromatin immunoprecipitation, functional molecular, and biochemical analyses, that in the pluripotent state Oct4, Akt, and Hmgb2 participate in a regulatory feedback loop. Akt-mediated phosphorylation of Oct4 facilitates interaction with PcG recruiter Hmgb2. Consequently, Hmgb2 functions as a context dependent modulator of Akt and Oct4 function, promoting transcriptional poise at Oct4 bound loci. Sumoylation of Oct4 is then required to maintain Hmgb2 enrichment at repressed loci and to transmit the H3K27me3 mark in daughter progeny. The expression of Oct4 phosphorylation mutants however, leads to Akt inactivation and initiates the DNA Damage Checkpoint response. Our results suggest that this may subsequently facilitate chromatin reorganization and cell fate transitions. In summary, our results suggest that controlled modulation of Oct4, Akt, and Hmgb2 function is required to maintain pluripotency and for the faithful induction of transcriptional programs required for lineage specific differentiation.