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

Yes / Oxidative genome damage is an unavoidable consequence of cellular metabolism. It arises at gene regulatory elements by epigenetic demethylation during transcriptional activation1,2. Here we show that promoters are protected from oxidative damage via a process mediated by the nuclear mitotic apparatus protein NuMA (also known as NUMA1). NuMA exhibits genomic occupancy approximately 100 bp around transcription start sites. It binds the initiating form of RNA polymerase II, pause-release factors and single-strand break repair (SSBR) components such as TDP1. The binding is increased on chromatin following oxidative damage, and TDP1 enrichment at damaged chromatin is facilitated by NuMA. Depletion of NuMA increases oxidative damage at promoters. NuMA promotes transcription by limiting the polyADP-ribosylation of RNA polymerase II, increasing its availability and release from pausing at promoters. Metabolic labelling of nascent RNA identifies genes that depend on NuMA for transcription including immediate-early response genes. Complementation of NuMA-deficient cells with a mutant that mediates binding to SSBR, or a mitotic separation-of-function mutant, restores SSBR defects. These findings underscore the importance of oxidative DNA damage repair at gene regulatory elements and describe a process that fulfils this function.

Network growing mechanisms are used to construct random networks that have structural behaviors similar to existing networks such as genetic networks, in efforts of understanding the evolution of complex topologies. Popular mechanisms, such as preferential attachment, are capable of preserving network features such as the degree distribution. However, little is known about such randomly grown structures regarding robustness to disturbances (e.g., edge deletions). Moreover, preferential attachment does not target optimizing the network's functionality, such as information flow. Here, we consider a network to be optimal if it's natural functionality is relatively high in addition to possessing some degree of robustness to disturbances. Specifically, a robust network would continue to (1) transmit information, (2) preserve it's connectivity and (3) preserve internal clusters post failures. In efforts to pinpoint features that would possibly replace or collaborate with the degree of a node as criteria for preferential attachment, we present a case study on both; undirected and directed networks. For undirected networks, we make a case study on wireless sensor networks in which we outline a strategy using Support Vector Regression. For Directed networks, we formulate an Integer Linear Program to gauge the exact transcriptional regulatory network optimal structures, from there on we can identify variations in structural features post optimization.

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.

Elevated levels of circulating asymmetric and symmetric dimethylarginines (ADMA and SDMA) predict and potentially contribute to end organ damage in cardiovascular diseases. Alanine-glyoxylate aminotransferase 2 (AGXT2) regulates systemic levels of ADMA and SDMA, and also of beta-aminoisobutyric acid (BAIB)-a modulator of lipid metabolism. We identified a putative binding site for hepatic nuclear factor 4 α (HNF4α) in AGXT2 promoter sequence. In a luciferase reporter assay we found a 75% decrease in activity of Agxt2 core promoter after disruption of the HNF4α binding site. Direct binding of HNF4α to Agxt2 promoter was confirmed by chromatin immunoprecipitation assay. siRNA-mediated knockdown of Hnf4a led to an almost 50% reduction in Agxt2 mRNA levels in Hepa 1–6 cells. Liver-specific Hnf4a knockout mice exhibited a 90% decrease in liver Agxt2 expression and activity, and elevated plasma levels of ADMA, SDMA and BAIB, compared to wild-type littermates. Thus we identified HNF4α as a major regulator of Agxt2 expression. Considering a strong association between human HNF4A polymorphisms and increased risk of type 2 diabetes our current findings suggest that downregulation of AGXT2 and subsequent impairment in metabolism of dimethylarginines and BAIB caused by HNF4α deficiency might contribute to development of cardiovascular complications in diabetic patients.

L'une des caractéristiques majeures des microorganismes, et donc des bactéries, est qu'ils sont en contact direct avec l'environnement et doivent donc percevoir et répondre rapidement à ses variations. Pour cela, plusieurs niveaux de contrôle existent, et récemment le rôle des ARN non codants régulateurs, ou riborégulateurs, a été mis en lumière comme un mécanisme de contrôle peu couteux et rapide pour la cellule. Chez le phytopathogène Agrobacterium fabrum (anciennement appelé Agrobaterium tumefaciens), la virulence est principalement régulée au niveau transcriptionnel par le système à deux composants VirANirG. L'implication des riborégulateurs dans la virulence d'A.fabrum est encore mal connue et ces travaux de thèse ont eu pour objectif de déterminer l'implication de riborégulateurs dans le cycle infectieux de cette bactérie.Pour cela, nous avons identifié l'ensemble des transcrits d 'A. fabrum C58 en combinant l'utilisation de plusieurs méthodes d'analyses globales et nous avons étudié la fonction de différents candidats transcrits à partir du plasmide Ti (plasmide de virulence). Des souches modifiées dans la production des riborégulateurs candidats ont été construites, Jeurs ARNm cibles ont été prédits et validés, et des tests phénotypiques, en particulier des tests de virulence, ont été réalisés. Ainsi, le séquençage des transcrits de petite taille a permis d'identifier plus d'un millier de riborégulateurs potentiels dont plusieurs sont exprimés à partir de régions en relation avec le cycle infectieux. Nous avons validé 4 de ces petits transcrits comme étant des riborégulateurs puisqu'ils sont de petite taille, non traduits en protéine et fortement structurés (RNAI 111, RNA1083, RNA1059 et RNA1051) . Plus particulièrement, nous avons montré que le riborégulateur RNAI 111 était nécessaire pour la virulence d'A.fabrum C58, et que son action semblait se faire au travers du contrôle post-transcriptionnel de gènes impliqués dans les fonctions de virulence et de transfert du plasmide Ti. Un rôle plus modéré du riborégulateur RNA1083 dans le contrôle du cycle infectieux a également été observé, potentiellement au travers de la modulation de la mobilité et du transfert conjugatif du plasmide Ti. D'autre part, nous avons mis en évidence deux autres riborégulateurs, RNA1059 et RNA1051, qui sont impliqués dans le contrôle du maintien du plasmide Ti via une implication dans la réplication du plasmide (RNA 1059) et via une implication dans un nouveau system de toxine-antitoxine présent sur le plasmide Ti (RNA1051). Ainsi à partir d'une analyse globale nous avons mis en évidence le rôle des riboregulateurs dans les systèmes de mise en place de l'infection bactérienne , soit via le contrôle de facteurs de virulence, soit via le contrôle de la persistance du plasmide responsable de la virulence / One of the main characteristics of microorganisms, including bacteria., is their direct interaction with their environment. They thus need to perceive and quickly answer to its variations. Several steps of control exist, and recently the role of regulatory non-coding RNA, or riboregulator, was highlighted as a fast and economic mechanism of regulation. In the phytopathogen Agrobacterium fabrum (previously named Agrobacterium tumefaciens), the virulence is mainly controlled transcriptionally by the two components system VirANirG. The implication of riboregulators in the virulence of this bacterium is still unknown . The objectives of this thesis were to identify A .fabrum riboregulators and to determine their involvement in the infectious cycle of the bacteria. To this end, we identified small transcripts of A . fabrum C58 strain by combining several global analyses, and we studied the function of different candidates transcribed from the Ti plasmid (the virulence plasmid). Strains modified in the production of these candidates were constructed, their mRNA targets were predicted and validated, and phenotypic analyses -especially virulence tests­ were realized.Thereby, small transcript deep-sequencing allowed the identification of a thousand potential riboregulators, some of them being transcribed from regions related to the infectious cycle. We validated 4 of these transcripts as riboregulators according to their small size, their strong secondary structure and their non-translation into protein (RNAIOS I, RNA1059, RNA1083 and RNAl ll l). In particular, we showed that RNA 1111 was necessary for the virulence of A. fabrum C58, and that it seems to act through the posttranscriptional control of genes implicated in virulence functions and in Ti plasmid conjugation. A more moderated role of RNA 1083 was also observed, potentially by the modulation of the bacterial mobility and of the plasmid conjugation. Furthermore, we highlighted two riboregulators, RNA1059 and RNA1051, involved in the control of the Ti plasmid persistence, through their implication in the replication of the plasmid (RNA1059) and in a toxin-antitoxin system present on the Ti plasmid (RNA1051) .Thus, from a global analysis, we brought out the role of riboregulators in the control of several steps of the infectious cycle of A. fabrum C58, through the control of virulence factors, or through the contrai of the persistence of the main actor of the virulence, the Ti plasmid

Elevated levels of circulating asymmetric and symmetric dimethylarginines (ADMA and SDMA) predict and potentially contribute to end organ damage in cardiovascular diseases. Alanine-glyoxylate aminotransferase 2 (AGXT2) regulates systemic levels of ADMA and SDMA, and also of beta-aminoisobutyric acid (BAIB)-a modulator of lipid metabolism. We identified a putative binding site for hepatic nuclear factor 4 α (HNF4α) in AGXT2 promoter sequence. In a luciferase reporter assay we found a 75% decrease in activity of Agxt2 core promoter after disruption of the HNF4α binding site. Direct binding of HNF4α to Agxt2 promoter was confirmed by chromatin immunoprecipitation assay. siRNA-mediated knockdown of Hnf4a led to an almost 50% reduction in Agxt2 mRNA levels in Hepa 1–6 cells. Liver-specific Hnf4a knockout mice exhibited a 90% decrease in liver Agxt2 expression and activity, and elevated plasma levels of ADMA, SDMA and BAIB, compared to wild-type littermates. Thus we identified HNF4α as a major regulator of Agxt2 expression. Considering a strong association between human HNF4A polymorphisms and increased risk of type 2 diabetes our current findings suggest that downregulation of AGXT2 and subsequent impairment in metabolism of dimethylarginines and BAIB caused by HNF4α deficiency might contribute to development of cardiovascular complications in diabetic patients.