Uncovering mechanisms of transcriptional regulations by systematic mining of cis regulatory elements with gene expression profiles

<p>Abstract</p> <p>Background</p> <p>Contrary to the traditional biology approach, where the expression patterns of a handful of genes are studied at a time, microarray experiments enable biologists to study the expression patterns of many genes simultaneously from gene expression profile data and decipher the underlying hidden biological mechanism from the observed gene expression changes. While the statistical significance of the gene expression data can be deduced by various methods, the biological interpretation of the data presents a challenge.</p> <p>Results</p> <p>A method, called CisTransMine, is proposed to help infer the underlying biological mechanisms for the observed gene expression changes in microarray experiments. Specifically, this method will predict potential cis-regulatory elements in promoter regions which could regulate gene expression changes. This approach builds on the MotifADE method published in 2004 and extends it with two modifications: up-regulated genes and down-regulated genes are tested separately and in addition, tests have been implemented to identify combinations of transcription factors that work synergistically. The method has been applied to a genome wide expression dataset intended to study myogenesis in a mouse C2C12 cell differentiation model. The results shown here both confirm the prior biological knowledge and facilitate the discovery of new biological insights.</p> <p>Conclusion</p> <p>The results validate that the CisTransMine approach is a robust method to uncover the hidden transcriptional regulatory mechanisms that can facilitate the discovery of mechanisms of transcriptional regulation.</p

β-cells cis-regulatory networks and type 1 diabetes

[eng] Type 1 Diabetes (T1D) is a ­cell­targeted autoimmune disease, leading to a reduction in pancreatic ­cell mass that renders patients insulin­dependent for life. In early stages of the disease, cells from the immune system infiltrate pancreatic islets in a process called insulitis. During this stage, a cross­talk is established between cells in the pancreatic islets and the infiltrating immune cells, mediated by the release of cytokines and chemokines. Studying the gene regulatory networks driving cell responses during insulitis, will allow us to pinpoint key gene pathways leading to ­cell loss­of­function and apoptosis, and also to understand the role cells have in their own demise. In the present thesis, we used two different cytokine cocktails, IFN­ and IFN­ + IL­1, to model early and late insulitis, respectively. After exposing cells and pancreatic islets to such proinflammatory cytokines, we characterized the changes in their chromatin landscape, gene networks and protein profiles. Using both models, we observed dramatic chromatin remodeling in terms of accessibility and/or H3K27ac histone modification enrichment, coupled with up­regulation of the nearby genes and increased abundance of the corresponding protein. Mining gene regulatory networks of ­cells exposed to IFN­ revealed two potential therapeutic interventions which were able to reduce interferon signature in cells: 1) Inhibition of bromodomain proteins, which resulted in a down­regulation of IFN­­induced HLA­I and CXCL10 expression; 2) Baricitnib, a JAK1/2 inhibitor, which was able to reduce both IFN­­induced HLA­I and CXCL10 expression levels and cell apoptosis. In cells exposed to IFN­ + IL­1, we were able to identify a subset of novel regulatory elements uncovered upon the exposure, which we named Induced Regulatory Elements (IREs). Such regions were enriched for T1D­associated risk variants, suggesting that cells might carry a portion of T1D genetic risk. Interestingly, we identified two T1D lead variants overlapping IREs, in which the risk allele modulated the IRE enhancer activity, exposing a potential T1D mechanism acting through cells. To facilitate the access to these genomic data, together with other datasets relevant for the pancreatic islet community, we developed the Islet Regulome Browser (http://www.isletregulome.org/), a free web application that allows exploration and integration of pancreatic islet genomic data

Statistical Methods in Integrative Genomics

Statistical methods in integrative genomics aim to answer important biology questions by jointly analyzing multiple types of genomic data (vertical integration) or aggregating the same type of data across multiple studies (horizontal integration). In this article, we introduce different types of genomic data and data resources, and then review statistical methods of integrative genomics, with emphasis on the motivation and rationale of these methods. We conclude with some summary points and future research directions

Activation of a Metabolic Gene Regulatory Network Downstream of mTOR Complex 1

Aberrant activation of the mammalian target of rapamycin complex 1 (mTORC1) is a common molecular event in a variety of pathological settings, including genetic tumor syndromes, cancer, and obesity. However, the cell-intrinsic consequences of mTORC1 activation remain poorly defined. Through a combination of unbiased genomic, metabolomic, and bioinformatic approaches, we demonstrate that mTORC1 activation is sufficient to stimulate specific metabolic pathways, including glycolysis, the oxidative arm of the pentose phosphate pathway, and de novo lipid biosynthesis. This is achieved through the activation of a transcriptional program affecting metabolic gene targets of hypoxia-inducible factor (HIF1α) and sterol regulatory element-binding protein (SREBP1 and SREBP2). We find that SREBP1 and 2 promote proliferation downstream of mTORC1, and the activation of these transcription factors is mediated by S6K1. Therefore, in addition to promoting protein synthesis, mTORC1 activates specific bioenergetic and anabolic cellular processes that are likely to contribute to human physiology and disease

Network Medicine in the Age of Biomedical Big Data

Network medicine is an emerging area of research dealing with molecular and genetic interactions, network biomarkers of disease, and therapeutic target discovery. Large-scale biomedical data generation offers a unique opportunity to assess the effect and impact of cellular heterogeneity and environmental perturbations on the observed phenotype. Marrying the two, network medicine with biomedical data provides a framework to build meaningful models and extract impactful results at a network level. In this review, we survey existing network types and biomedical data sources. More importantly, we delve into ways in which the network medicine approach, aided by phenotype-specific biomedical data, can be gainfully applied. We provide three paradigms, mainly dealing with three major biological network archetypes: protein-protein interaction, expression-based, and gene regulatory networks. For each of these paradigms, we discuss a broad overview of philosophies under which various network methods work. We also provide a few examples in each paradigm as a test case of its successful application. Finally, we delineate several opportunities and challenges in the field of network medicine. We hope this review provides a lexicon for researchers from biological sciences and network theory to come on the same page to work on research areas that require interdisciplinary expertise. Taken together, the understanding gained from combining biomedical data with networks can be useful for characterizing disease etiologies and identifying therapeutic targets, which, in turn, will lead to better preventive medicine with translational impact on personalized healthcare

Role of Cis-regulatory Elements in Transcriptional Regulation: From Evolution to 4D Interactions

Transcriptional regulation is the principal mechanism in establishing cell-type specific gene activity by exploring an almost infinite space of different combinations of regulatory elements, transcription factors with high precision. Recent efforts have mapped thousands of candidate regulatory elements, of which a great portion is cell-type specific yet it is still unclear as to what fraction of these elements is functional, what genes these elements regulate, or how they are established in a cell-type specific manner. In this dissertation, I will discuss methods and approaches I developed to better understand the role of regulatory elements and transcription factors in gene expression regulation. First, by comparing the transcriptome and chromatin landscape between mouse and human innate immune cells I showed specific gene expression programs are regulated by highly conserved regulatory elements that contain a set of constrained sequence motifs, which can successfully classify gene-induction in both species. Next, using chromatin interactions I accurately defined functional enhancers and their target genes. This fine mapping dramatically improved the prediction of transcriptional changes. Finally, we built a supervised learning approach to detect the short DNA sequences motifs that regulate the activation of regulatory elements following LPS stimulation. This approach detected several transcription factors to be critical in remodeling the epigenetic landscape both across time and individuals. Overall this thesis addresses several important aspects of cis-regulatory elements in transcriptional regulation and started to derive principles and models of gene-expression regulation that address the fundamental question: “How do cis-regulatory elements drive cell-type-specific transcription?

Identifying gene regulatory networks common to multiple plant stress responses

Stress responses in plants can be defined as a change that affects the homeostasis of pathways, resulting in a phenotype that may or may not be visible to the human eye, affecting the fitness of the plant. Crosstalk is believed to be the shared components of pathways of networks, and is widespread in plants, as shown by examples of crosstalk between transcriptional regulation pathways, and hormone signalling. Crosstalk between stress responses is believed to exist, particularly crosstalk within the responses to biotic stress, and within the responses to abiotic stress. Certain hormone pathways are known to be involved in the crosstalk between the responses to both biotic and abiotic stresses, and can confer immunity or tolerance of Arabidopsis thaliana to these stresses. Transcriptional regulation has also been identified as an important factor in controlling tolerance and resistance to stresses. In this thesis, networks of regulation mediating the response tomultiple stresses are studied. Firstly, co-regulation was predicted for genes differentially expressed in two or more stresses by development of a novel multi-clustering approach, Wigwams Identifies Genes Working Across Multiple Stresses (Wigwams). This approach finds groups of genes whose expression is correlated within stresses, but also identifies a strong statistical link between subsets of stresses. Wigwams identifies the known co-expression of genes encoding enzymes of metabolic and flavonoid biosynthesis pathways, and predicts novels clusters of co-expressed genes. By hypothesising that by being coexpressed could also infer that the genes are co-regulated, promoter motif analysis and modelling provides information for potential upstream regulators. The context-free regulation of groups of co-expressed genes, or potential regulons, was explored using models generated by modelling techniques, in order to generate a quantitative model of transcriptional regulation during the response to B. cinerea, P. syringae pv. tomato DC3000 and senescence. This model was subsequently validated and extended by experimental techniques, using Yeast 1-Hybrid to investigate the protein-DNA interactions, and also microarrays. Analysis of mutants and plants overexpressing a predicted regulator, Rap2.6L, by gene expression analysis identified a number of potential regulon members as downstream targets. Rap2.6L was identified as an indirect regulator of the transcription factor members of three potential regulons co-expressed in the stresses B. cinerea, P. syringae pv. tomato DC3000 and long day senescence, allowing the confirmation of a predicted gene regulatory network operating in multiple stress responses