Human Virus Transcriptional Regulators.

Viral genomes encode transcriptional regulators that alter the expression of viral and host genes. Despite an emerging role in human diseases, a thorough annotation of human viral transcriptional regulators (vTRs) is currently lacking, limiting our understanding of their molecular features and functions. Here, we provide a comprehensive catalog of 419 vTRs belonging to 20 different virus families. Using this catalog, we characterize shared and unique cellular genes, proteins, and pathways targeted by particular vTRs and discuss the role of vTRs in human disease pathogenesis. Our study provides a unique and valuable resource for the fields of virology, genomics, and human disease genetics.

Essential role of a ThPOK autoregulatory loop in the maintenance of mature CD4+ T cell identity and function.

The transcription factor ThPOK (encoded by the Zbtb7b gene) controls homeostasis and differentiation of mature helper T cells, while opposing their differentiation to CD4+ intraepithelial lymphocytes (IELs) in the intestinal mucosa. Thus CD4 IEL differentiation requires ThPOK transcriptional repression via reactivation of the ThPOK transcriptional silencer element (SilThPOK). In the present study, we describe a new autoregulatory loop whereby ThPOK binds to the SilThPOK to maintain its own long-term expression in CD4 T cells. Disruption of this loop in vivo prevents persistent ThPOK expression, leads to genome-wide changes in chromatin accessibility and derepresses the colonic regulatory T (Treg) cell gene expression signature. This promotes selective differentiation of naive CD4 T cells into GITRloPD-1loCD25lo (Triplelo) Treg cells and conversion to CD4+ IELs in the gut, thereby providing dominant protection from colitis. Hence, the ThPOK autoregulatory loop represents a key mechanism to physiologically control ThPOK expression and T cell differentiation in the gut, with potential therapeutic relevance.

Human gene-centered transcription factor networks for enhancers and disease variants.

Gene regulatory networks (GRNs) comprising interactions between transcription factors (TFs) and regulatory loci control development and physiology. Numerous disease-associated mutations have been identified, the vast majority residing in non-coding regions of the genome. As current GRN mapping methods test one TF at a time and require the use of cells harboring the mutation(s) of interest, they are not suitable to identify TFs that bind to wild-type and mutant loci. Here, we use gene-centered yeast one-hybrid (eY1H) assays to interrogate binding of 1,086 human TFs to 246 enhancers, as well as to 109 non-coding disease mutations. We detect both loss and gain of TF interactions with mutant loci that are concordant with target gene expression changes. This work establishes eY1H assays as a powerful addition to the toolkit of mapping human GRNs and for the high-throughput characterization of genomic variants that are rapidly being identified by genome-wide association studies.

Widespread macromolecular interaction perturbations in human genetic disorders.

How disease-associated mutations impair protein activities in the context of biological networks remains mostly undetermined. Although a few renowned alleles are well characterized, functional information is missing for over 100,000 disease-associated variants. Here we functionally profile several thousand missense mutations across a spectrum of Mendelian disorders using various interaction assays. The majority of disease-associated alleles exhibit wild-type chaperone binding profiles, suggesting they preserve protein folding or stability. While common variants from healthy individuals rarely affect interactions, two-thirds of disease-associated alleles perturb protein-protein interactions, with half corresponding to "edgetic" alleles affecting only a subset of interactions while leaving most other interactions unperturbed. With transcription factors, many alleles that leave protein-protein interactions intact affect DNA binding. Different mutations in the same gene leading to different interaction profiles often result in distinct disease phenotypes. Thus disease-associated alleles that perturb distinct protein activities rather than grossly affecting folding and stability are relatively widespread.

Compendium of human transcription factor effector domains.

Transcription factors (TFs) regulate gene expression by binding to DNA sequences and modulating transcriptional activity through their effector domains. Despite the central role of effector domains in TF function, there is a current lack of a comprehensive resource and characterization of effector domains. Here, we provide a catalog of 924 effector domains across 594 human TFs. Using this catalog, we characterized the amino acid composition of effector domains, their conservation across species and across the human population, and their roles in human diseases. Furthermore, we provide a classification system for effector domains that constitutes a valuable resource and a blueprint for future experimental studies of TF effector domain function.

Viral cis-regulatory elements as sensors of cellular states and environmental cues.

To withstand a hostile cellular environment and replicate, viruses must sense, interpret, and respond to many internal and external cues. Retroviruses and DNA viruses can intercept these cues impinging on host transcription factors via cis-regulatory elements (CREs) in viral genomes, allowing them to sense and coordinate context-specific responses to varied signals. Here, we explore the characteristics of viral CREs, the classes of signals and host transcription factors that regulate them, and how this informs outcomes of viral replication, immune evasion, and latency. We propose that viral CREs constitute central hubs for signal integration from multiple pathways and that sequence variation between viral isolates can rapidly rewire sensing mechanisms, contributing to the variability observed in patient outcomes.

Identification of conserved skeletal enhancers associated with craniosynostosis risk genes.

Craniosynostosis, defined by premature fusion of one or multiple cranial sutures, is a common congenital defect affecting more than 1/2000 infants and results in restricted brain expansion. Single gene mutations account for 15-20% of cases, largely as part of a syndrome, but the majority are nonsyndromic with complex underlying genetics. We hypothesized that the two noncoding genomic regions identified by a GWAS for craniosynostosis contain distal regulatory elements for the risk genes BMPER and BMP2. To identify such regulatory elements, we surveyed conserved noncoding sequences from both risk loci for enhancer activity in transgenic Danio rerio. We identified enhancers from both regions that direct expression to skeletal tissues, consistent with the endogenous expression of bmper and bmp2. For each locus, we also found a skeletal enhancer that also contains a sequence variant associated with craniosynostosis risk. We examined the activity of each enhancer during craniofacial development and found that the BMPER-associated enhancer is active in the restricted region of cartilage closely associated with frontal bone initiation. The same enhancer is active in mouse skeletal tissues, demonstrating evolutionarily conserved activity. Using enhanced yeast one-hybrid assays, we identified transcription factors that bind each enhancer and observed differential binding between alleles, implicating multiple signaling pathways. Our findings help unveil the genetic mechanism of the two craniosynostosis risk loci. More broadly, our combined in vivo approach is applicable to many complex genetic diseases to build a link between association studies and specific genetic mechanisms.

A functional screen identifies transcriptional networks that regulate HIV-1 and HIV-2.

The molecular networks involved in the regulation of HIV replication, transcription, and latency remain incompletely defined. To expand our understanding of these networks, we performed an unbiased high-throughput yeast one-hybrid screen, which identified 42 human transcription factors and 85 total protein-DNA interactions with HIV-1 and HIV-2 long terminal repeats. We investigated a subset of these transcription factors for transcriptional activity in cell-based models of infection. KLF2 and KLF3 repressed HIV-1 and HIV-2 transcription in CD4+ T cells, whereas PLAGL1 activated transcription of HIV-2 through direct protein-DNA interactions. Using computational modeling with interacting proteins, we leveraged the results from our screen to identify putative pathways that define intrinsic transcriptional networks. Overall, we used a high-throughput functional screen, computational modeling, and biochemical assays to identify and confirm several candidate transcription factors and biochemical processes that influence HIV-1 and HIV-2 transcription and latency.

Intragenic proviral elements support transcription of defective HIV-1 proviruses.

HIV-1 establishes a persistent proviral reservoir by integrating into the genome of infected host cells. Current antiretroviral treatments do not target this persistent population of proviruses which include latently infected cells that upon treatment interruption can be reactivated to contribute to HIV-1 rebound. Deep sequencing of persistent HIV proviruses has revealed that greater than 90% of integrated HIV genomes are defective and unable to produce infectious virions. We hypothesized that intragenic elements in the HIV genome support transcription of aberrant HIV-1 RNAs from defective proviruses that lack long terminal repeats (LTRs). Using an intact provirus detection assay, we observed that resting CD4+ T cells and monocyte-derived macrophages (MDMs) are biased towards generating defective HIV-1 proviruses. Multiplex reverse transcription droplet digital PCR identified env and nef transcripts which lacked 5' untranslated regions (UTR) in acutely infected CD4+ T cells and MDMs indicating transcripts are generated that do not utilize the promoter within the LTR. 5'UTR-deficient env transcripts were also identified in a cohort of people living with HIV (PLWH) on ART, suggesting that these aberrant RNAs are produced in vivo. Using 5' rapid amplification of cDNA ends (RACE), we mapped the start site of these transcripts within the Env gene. This region bound several cellular transcription factors and functioned as a transcriptional regulatory element that could support transcription and translation of downstream HIV-1 RNAs. These studies provide mechanistic insights into how defective HIV-1 proviruses are persistently expressed to potentially drive inflammation in PLWH.

Genome-wide characterization of human minisatellite VNTRs: population-specific alleles and gene expression differences.

Variable Number Tandem Repeats (VNTRs) are tandem repeat (TR) loci that vary in copy number across a population. Using our program, VNTRseek, we analyzed human whole genome sequencing datasets from 2770 individuals in order to detect minisatellite VNTRs, i.e., those with pattern sizes ≥7 bp. We detected 35 638 VNTR loci and classified 5676 as commonly polymorphic (i.e. with non-reference alleles occurring in >5% of the population). Commonly polymorphic VNTR loci were found to be enriched in genomic regions with regulatory function, i.e. transcription start sites and enhancers. Investigation of the commonly polymorphic VNTRs in the context of population ancestry revealed that 1096 loci contained population-specific alleles and that those could be used to classify individuals into super-populations with near-perfect accuracy. Search for quantitative trait loci (eQTLs), among the VNTRs proximal to genes, indicated that in 187 genes expression differences correlated with VNTR genotype. We validated our predictions in several ways, including experimentally, through the identification of predicted alleles in long reads, and by comparisons showing consistency between sequencing platforms. This study is the most comprehensive analysis of minisatellite VNTRs in the human population to date.

Discovering human transcription factor physical interactions 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 technically and computationally challenging. Current experimental methods, such as chromatin immunoprecipitation, generally test one TF at a time, and computational motif algorithms often lead to false-positive and -negative predictions. To address these limitations, we developed an experimental approach based on enhanced yeast one-hybrid assays. The first variation of this approach interrogates the binding of >1000 human TFs to repetitive DNA elements, while the second evaluates TF binding to single nucleotide variants, short insertions and deletions (indels), and novel DNA motifs. Using this approach, we detected the binding of 75 TFs, including several nuclear hormone receptors and ETS factors, to the highly repetitive Alu elements. Further, we identified cancer-associated changes in TF binding, including gain of interactions involving ETS TFs and loss of interactions involving KLF TFs to different mutations in the TERT promoter, and gain of a MYB interaction with an 18-bp indel in the TAL1 superenhancer. Additionally, we identified TFs that bind to three uncharacterized DNA motifs identified in DNase footprinting assays. We anticipate that these enhanced yeast one-hybrid approaches will expand our capabilities to study genetic variation and undercharacterized genomic regions.

Comprehensive mapping of the human cytokine gene regulatory network.

Proper cytokine gene expression is essential in development, homeostasis and immune responses. Studies on the transcriptional control of cytokine genes have mostly focused on highly researched transcription factors (TFs) and cytokines, resulting in an incomplete portrait of cytokine gene regulation. Here, we used enhanced yeast one-hybrid (eY1H) assays to derive a comprehensive network comprising 1380 interactions between 265 TFs and 108 cytokine gene promoters. Our eY1H-derived network greatly expands the known repertoire of TF-cytokine gene interactions and the set of TFs known to regulate cytokine genes. We found an enrichment of nuclear receptors and confirmed their role in cytokine regulation in primary macrophages. Additionally, we used the eY1H-derived network as a framework to identify pairs of TFs that can be targeted with commercially-available drugs to synergistically modulate cytokine production. Finally, we integrated the eY1H data with single cell RNA-seq and phenotypic datasets to identify novel TF-cytokine regulatory axes in immune diseases and immune cell lineage development. Overall, the eY1H data provides a rich resource to study cytokine regulation in a variety of physiological and disease contexts.

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.

A gene-centered C. elegans protein-DNA interaction network provides a framework for functional predictions.

Transcription factors (TFs) play a central role in controlling spatiotemporal gene expression and the response to environmental cues. A comprehensive understanding of gene regulation requires integrating physical protein-DNA interactions (PDIs) with TF regulatory activity, expression patterns, and phenotypic data. Although great progress has been made in mapping PDIs using chromatin immunoprecipitation, these studies have only characterized ~10% of TFs in any metazoan species. The nematode C. elegans has been widely used to study gene regulation due to its compact genome with short regulatory sequences. Here, we delineated the largest gene-centered metazoan PDI network to date by examining interactions between 90% of C. elegans TFs and 15% of gene promoters. We used this network as a backbone to predict TF binding sites for 77 TFs, two-thirds of which are novel, as well as integrate gene expression, protein-protein interaction, and phenotypic data to predict regulatory and biological functions for multiple genes and TFs.

Complex expression dynamics and robustness in C. elegans insulin networks.

Gene families expand by gene duplication, and resulting paralogs diverge through mutation. Functional diversification can include neofunctionalization as well as subfunctionalization of ancestral functions. In addition, redundancy in which multiple genes fulfill overlapping functions is often maintained. Here, we use the family of 40 Caenorhabditis elegans insulins to gain insight into the balance between specificity and redundancy. The insulin/insulin-like growth factor (IIS) pathway comprises a single receptor, DAF-2. To date, no single insulin-like peptide recapitulates all DAF-2-associated phenotypes, likely due to redundancy between insulin-like genes. To provide a first-level annotation of potential patterns of redundancy, we comprehensively delineate the spatiotemporal and conditional expression of all 40 insulins in living animals. We observe extensive dynamics in expression that can explain the lack of simple patterns of pairwise redundancy. We propose a model in which gene families evolve to attain differential alliances in different tissues and in response to a range of environmental stresses.

Using networks to measure similarity between genes: association index selection.

Biological networks can be used to functionally annotate genes on the basis of interaction-profile similarities. Metrics known as association indices can be used to quantify interaction-profile similarity. We provide an overview of commonly used association indices, including the Jaccard index and the Pearson correlation coefficient, and compare their performance in different types of analyses of biological networks. We introduce the Guide for Association Index for Networks (GAIN), a web tool for calculating and comparing interaction-profile similarities and defining modules of genes with similar profiles.

Transcription factor binding to Caenorhabditis elegans first introns reveals lack of redundancy with gene promoters.

Gene expression is controlled through the binding of transcription factors (TFs) to regulatory genomic regions. First introns are longer than other introns in multiple eukaryotic species and are under selective constraint. Here we explore the importance of first introns in TF binding in the nematode Caenorhabditis elegans by combining computational predictions and experimentally derived TF-DNA interaction data. We found that first introns of C. elegans genes, particularly those for families enriched in long first introns, are more conserved in length, have more conserved predicted TF interactions and are bound by more TFs than other introns. We detected a significant positive correlation between first intron size and the number of TF interactions obtained from chromatin immunoprecipitation assays or determined by yeast one-hybrid assays. TFs that bind first introns are largely different from those binding promoters, suggesting that the different interactions are complementary rather than redundant. By combining first intron and promoter interactions, we found that genes that share a large fraction of TF interactions are more likely to be co-expressed than when only TF interactions with promoters are considered. Altogether, our data suggest that C. elegans gene regulation may be additive through the combined effects of multiple regulatory regions.

NADPH oxidase derived reactive oxygen species are involved in human neutrophil IL-1ß secretion but not in inflammasome activation.

Neutrophils are essential players in acute inflammatory responses. Upon stimulation, neutrophils activate NADPH oxidase, generating an array of reactive oxygen species (ROS). Interleukin-1 beta (IL-1ß) is a major proinflammatory cytokine synthesized as a precursor that has to be proteolytically processed to become biologically active. The role of ROS in IL-1ß processing is still controversial and has not been previously studied in neutrophils. We report here that IL-1ß processing in human neutrophils is dependent on caspase-1 and on the serine proteases elastase and/or proteinase 3. NADPH oxidase deficient neutrophils activated caspase-1 and did not exhibit differences in NALP3 expression, indicating that ROS are neither required for inflammasome activation nor for its priming, as has been reported for macrophages. Strikingly, ROS exerted opposite effects on the processing and secretion of IL-1ß; whereas ROS negatively controlled caspase-1 activity, as reported in mononuclear phagocytes, ROS were found to be necessary for the exportation of mature IL-1ß out of the cell, a role never previously described. The complex ROS-mediated regulation of neutrophil IL-1ß secretion might constitute a physiological mechanism to control IL-1ß-dependent inflammatory processes where neutrophils play a crucial role.

Semi-Automatic Detection of Errors in Genome-Scale Metabolic Models.

Background: Genome-Scale Metabolic Models (GSMMs) are used for numerous tasks requiring computational estimates of metabolic fluxes, from predicting novel drug targets to engineering microbes to produce valuable compounds. A key limiting step in most applications of GSMMs is ensuring their representation of the target organism's metabolism is complete and accurate. Identifying and visualizing errors in GSMMs is complicated by the fact that they contain thousands of densely interconnected reactions. Furthermore, many errors in GSMMs only become apparent when considering pathways of connected reactions collectively, as opposed to examining reactions individually. Results: We present Metabolic Accuracy Check and Analysis Workflow (MACAW), a collection of algorithms for detecting errors in GSMMs. The relative frequencies of errors we detect in manually curated GSMMs appear to reflect the different approaches used to curate them. Changing the method used to automatically create a GSMM from a particular organism's genome can have a larger impact on the kinds of errors in the resulting GSMM than using the same method with a different organism's genome. Our algorithms are particularly capable of identifying errors that are only apparent at the pathway level, including loops, and nontrivial cases of dead ends. Conclusions: MACAW is capable of identifying inaccuracies of varying severity in a wide range of GSMMs. Correcting these errors can measurably improve the predictive capacity of a GSMM. The relative prevalence of each type of error we identify in a large collection of GSMMs could help shape future efforts for further automation of error correction and GSMM creation.

A large-scale cancer-specific protein-DNA interaction network.

Cancer development and progression are generally associated with gene dysregulation, often resulting from changes in the transcription factor (TF) sequence or expression. Identifying key TFs involved in cancer gene regulation provides a framework for potential new therapeutics. This study presents a large-scale cancer gene TF-DNA interaction network, as well as an extensive promoter clone resource for future studies. Highly connected TFs bind to promoters of genes associated with either good or poor cancer prognosis, suggesting that strategies aimed at shifting gene expression balance between these two prognostic groups may be inherently complex. However, we identified potential for oncogene-targeted therapeutics, with half of the tested oncogenes being potentially repressed by influencing specific activators or bifunctional TFs. Finally, we investigate the role of intrinsically disordered regions within the key cancer-related TF ESR1 in DNA binding and transcriptional activity, and found that these regions can have complex trade-offs in TF function. Altogether, our study broadens our knowledge of the TFs involved in cancer gene regulation and provides a valuable resource for future studies and therapeutics.