Transcriptional regulation landscape in health and disease

Transcription factors (TFs) control gene expression by binding to highly specific DNA sequences in gene regulatory regions. This TF binding is central to control myriad biological processes. Indeed, transcriptional dysregulation has been associated with many diseases such as autoimmune diseases and cancer. In this thesis, I studied the transcriptional regulation of cytokines and gene transcriptional dysregulation in cancer. Cytokines are small proteins produced by immune cells that play a key role in the development of the immune system and response to pathogens and inflammation. I mined three decades of research and developed a user-friendly database, CytReg, containing 843 human and 647 mouse interactions between TFs and cytokines. I analyzed CytReg and integrated it with phenotypic and functional datasets to provide novel insights into the general principles that govern cytokine regulation. I also predicted novel cytokine promoter-TF interactions based on cytokine co-expression patterns and motif analysis, and studied the association of cytokine transcriptional dysregulation with disease. Transcriptional dysregulation can be caused by single nucleotide variants (SNVs) affecting TF binding sites (TFBS). Therefore, I created a database of altered TFBS (aTFBS-DB) by calculating the effect (gain/loss) of all possible SNVs across the human genome for 741 TFs. I showed how the probabilities to gain or disrupt TFBSs in regulatory regions differ between the major TF families, and that cis-eQTL SNVs are more likely to perturb TFBSs than common SNVs in the human population. To further study the effect of somatic SNVs in TFBS, I used the aTFBS-DB to develop TF-aware burden test (TFABT), a novel algorithm to predict cancer driver SNVs in gene promoters. I applied the TFABT to the Pan-Cancer Analysis of Whole Genomes (PCAWG) cohort and identified 2,555 candidate driver SNVs across 20 cancer types. Further, I characterized these cancer drivers using functional and biophysical assay data from three cancer cell lines, demonstrating that most SNVs alter transcriptional activity and differentially recruit cofactors. Taken together, these studies can be used as a blueprint to study transcriptional mechanisms in specific cellular processes (i.e. cytokine expression) and the effect of transcriptional dysregulation in disease (i.e. cancer)

Uncovering human transcription factor interactions associated with genetic variants, novel DNA motifs, and repetitive elements using enhanced yeast one-hybrid assays

Identifying transcription factor (TF) binding to noncoding variants, uncharacterized DNA motifs, and repetitive genomic elements has been difficult due to technical and computational challenges. Indeed, current experimental methods such as chromatin immunoprecipitation are capable of only testing one TF at a time and motif prediction algorithms often lead to false positive and false negative predictions. Here, we address these limitations by developing two approaches based on enhanced yeast one-hybrid assays. The first approach allows to interrogate the binding of >1,000 human TFs to single nucleotide variant alleles, short insertions and deletions (indels), and novel DNA motifs; while the second approach allows for the identification of TFs that bind to repetitive DNA elements. Using the former approach, we identified gain of TF interactions to a GG→AA mutation in the TERT promoter and an 18 bp indel in the TAL1 super-enhancer, both of which are associated with cancer, and identified the TFs that bind to three uncharacterized DNA motifs identified by the ENCODE Project in footprinting assays. Using the latter approach, we detected the binding of 75 TFs to the highly repetitive Alu elements. We anticipate that these approaches will expand our capabilities to study genetic variation and under-characterized genomic regions.https://doi.org/10.1101/459305First author draf

Global landscape of mouse and human cytokine transcriptional regulation

Cytokines are cell-to-cell signaling proteins that play a central role in immune development, pathogen responses, and diseases. Cytokines are highly regulated at the transcriptional level by combinations of transcription factors (TFs) that recruit cofactors and the transcriptional machinery. Here, we mined through three decades of studies to generate a comprehensive database, CytReg, reporting 843 and 647 interactions between TFs and cytokine genes, in human and mouse respectively. By integrating CytReg with other functional datasets, we determined general principles governing the transcriptional regulation of cytokine genes. In particular, we show a correlation between TF connectivity and immune phenotype and disease, we discuss the balance between tissue-specific and pathogen-activated TFs regulating each cytokine gene, and cooperativity and plasticity in cytokine regulation. We also illustrate the use of our database as a blueprint to predict TF–disease associations and identify potential TF–cytokine regulatory axes in autoimmune diseases. Finally, we discuss research biases in cytokine regulation studies, and use CytReg to predict novel interactions based on co-expression and motif analyses which we further validated experimentally. Overall, this resource provides a framework for the rational design of future cytokine gene regulation studies.National Institutes of Health (NIH) [R00 GM114296 and R35 GM128625 to J.I.F.B., 5T32HL007501-34 to J.A.S.]; National Science Foundation [NSF-REU BIO-1659605 to M.M.]. Funding for open access charge: NIH [R35 GM128625]. (R00 GM114296 - National Institutes of Health (NIH); R35 GM128625 - National Institutes of Health (NIH); 5T32HL007501-34 - National Institutes of Health (NIH); NSF-REU BIO-1659605 - National Science Foundation; R35 GM128625 - NIH)Published versio

Widespread perturbation of ETS factor binding sites in cancer.

Although \u3e90% of somatic mutations reside in non-coding regions, few have been reported as cancer drivers. To predict driver non-coding variants (NCVs), we present a transcription factor (TF)-aware burden test based on a model of coherent TF function in promoters. We apply this test to NCVs from the Pan-Cancer Analysis of Whole Genomes cohort and predict 2555 driver NCVs in the promoters of 813 genes across 20 cancer types. These genes are enriched in cancer-related gene ontologies, essential genes, and genes associated with cancer prognosis. We find that 765 candidate driver NCVs alter transcriptional activity, 510 lead to differential binding of TF-cofactor regulatory complexes, and that they primarily impact the binding of ETS factors. Finally, we show that different NCVs within a promoter often affect transcriptional activity through shared mechanisms. Our integrated computational and experimental approach shows that cancer NCVs are widespread and that ETS factors are commonly disrupted

Identification of Single Nucleotide Non-coding Driver Mutations in Cancer

Recent whole-genome sequencing studies have identified millions of somatic variants present in tumor samples. Most of these variants reside in non-coding regions of the genome potentially affecting transcriptional and post-transcriptional gene regulation. Although a few hallmark examples of driver mutations in non-coding regions have been reported, the functional role of the vast majority of somatic non-coding variants remains to be determined. This is because the few driver variants in each sample must be distinguished from the thousands of passenger variants and because the logic of regulatory element function has not yet been fully elucidated. Thus, variants prioritized based on mutational burden and location within regulatory elements need to be validated experimentally. This is generally achieved by combining assays that measure physical binding, such as chromatin immunoprecipitation, with those that determine regulatory activity, such as luciferase reporter assays. Here, we present an overview of in silico approaches used to prioritize somatic non-coding variants and the experimental methods used for functional validation and characterization

Differential recognition of mycobacterium tuberculosis-specific epitopes as a function of tuberculosis disease history

RATIONALE: Individuals with a history of tuberculosis (TB) disease are at elevated risk of disease recurrence. The underlying cause is not known, but one explanation is that previous disease results in less-effective immunity against Mycobacterium tuberculosis (Mtb).OBJECTIVES: We hypothesized that the repertoire of Mtb-derived epitopes recognized by T cells from individuals with latent Mtb infection differs as a function of previous diagnosis of active TB disease.METHODS: T-cell responses to peptide pools in samples collected from an adult screening and an adolescent validation cohort were measured by IFN-γ enzyme-linked immunospot assay or intracellular cytokine staining.MEASUREMENTS AND MAIN RESULTS: We identified a set of "type 2" T-cell epitopes that were recognized at 10-fold-lower levels in Mtb-infected individuals with a history of TB disease less than 6 years ago than in those without previous TB. By contrast, "type 1" epitopes were recognized equally well in individuals with or without previous TB. The differential epitope recognition was not due to differences in HLA class II binding, memory phenotypes, or gene expression in the responding T cells. Instead, "TB disease history-sensitive" type 2 epitopes were significantly (P &lt; 0.0001) more homologous to sequences from bacteria found in the human microbiome than type 1 epitopes.CONCLUSIONS: Preferential loss of T-cell reactivity to Mtb epitopes that are homologous to bacteria in the microbiome in persons with previous TB disease may reflect long-term effects of antibiotic TB treatment on the microbiome.</p

Microbiota epitope similarity either dampens or enhances the immunogenicity of disease-associated antigenic epitopes

<div><p>The microbiome influences adaptive immunity and molecular mimicry influences T cell reactivity. Here, we evaluated whether the sequence similarity of various antigens to the microbiota dampens or increases immunogenicity of T cell epitopes. Sets of epitopes and control sequences derived from 38 antigenic categories (infectious pathogens, allergens, autoantigens) were retrieved from the Immune Epitope Database (IEDB). Their similarity to microbiome sequences was calculated using the BLOSUM62 matrix. We found that sequence similarity was associated with either dampened (tolerogenic; e.g. most allergens) or increased (inflammatory; e.g. Dengue and West Nile viruses) likelihood of a peptide being immunogenic as a function of epitope source category. Ten-fold cross-validation and validation using sets of manually curated epitopes and non-epitopes derived from allergens were used to confirm these initial observations. Furthermore, the genus from which the microbiome homologous sequences were derived influenced whether a tolerogenic versus inflammatory modulatory effect was observed, with <i>Fusobacterium</i> most associated with inflammatory influences and <i>Bacteroides</i> most associated with tolerogenic influences. We validated these effects using PBMCs stimulated with various sets of microbiome peptides. “Tolerogenic” microbiome peptides elicited IL-10 production, “inflammatory” peptides elicited mixed IL-10/IFNγ production, while microbiome epitopes homologous to self were completely unreactive for both cytokines. We also tested the sequence similarity of cockroach epitopes to specific microbiome sequences derived from households of cockroach allergic individuals and non-allergic controls. Microbiomes from cockroach allergic households were less likely to contain sequences homologous to previously defined cockroach allergens. These results are compatible with the hypothesis that microbiome sequences may contribute to the tolerization of T cells for allergen epitopes, and lack of these sequences might conversely be associated with increased likelihood of T cell reactivity against the cockroach epitopes. Taken together this study suggests that microbiome sequence similarity influences immune reactivity to homologous epitopes encoded by pathogens, allergens and auto-antigens.</p></div

Microbiome peptides homologous to dominant epitopes are associated with constitutive IL-10 production.

<p>(A) Combined responses to microbiome derived epitopes for the inflammatory (blue) and tolerogenic (red). (B, C) Combined response to self, pathogen and microbiome derived epitopes for (B) IL-10 and (C) IFNγ. Response is expressed as fold above background. Each dot represents one donor/category combination. IFNγ (dots) and IL-10 (squares). Median ± interquartile range is shown. Two-tailed Mann-Whitney, *, p≤0.05, **, p≤0.01.</p

List of epitopes and non-epitopes per category.

<p>List of epitopes and non-epitopes per category.</p