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

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.

Molecular regulation of PPARγ/RXRα signaling by the novel cofactor ZFP407

Cofactors interacting with PPARγ can regulate adipogenesis and adipocyte metabolism by modulating the transcriptional activity and selectivity of PPARγ signaling. ZFP407 was previously demonstrated to regulate PPARγ target genes such as GLUT4, and its overexpression improved glucose homeostasis in mice. Here, using a series of molecular assays, including protein-interaction studies, mutagenesis, and ChIP-seq, ZFP407 was found to interact with the PPARγ/RXRα protein complex in the nucleus of adipocytes. Consistent with this observation, ZFP407 ChIP-seq peaks significantly overlapped with PPARγ ChIP-seq peaks, with more than half of ZFP407 peaks overlapping with PPARγ peaks. Transcription factor binding motifs enriched in these overlapping sites included CTCF, RARα/RXRγ, TP73, and ELK1, which regulate cellular development and function within adipocytes. Site-directed mutagenesis of frequent PPARγ phosphorylation or SUMOylation sites did not prevent its regulation by ZFP407, while mutagenesis of ZFP407 domains potentially necessary for RXR and PPARγ binding abrogated any impact of ZFP407 on PPARγ activity. These data suggest that ZFP407 controls the activity of PPARγ, but does so independently of post-translational modifications, likely by direct binding, establishing ZFP407 as a newly identified PPARγ cofactor. In addition, ZFP407 ChIP-seq analyses identified regions that did not overlap with PPARγ peaks. These non-overlapping peaks were significantly enriched for the transcription factor binding motifs of TBX19, PAX8, HSF4, and ZKSCAN3, which may contribute to the PPARγ-independent functions of ZFP407 in adipocytes and other cell types.

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.

Widespread variation in molecular interactions and regulatory properties among transcription factor isoforms

Most human Transcription factors (TFs) genes encode multiple protein isoforms differing in DNA binding domains, effector domains, or other protein regions. The global extent to which this results in functional differences between isoforms remains unknown. Here, we systematically compared 693 isoforms of 246 TF genes, assessing DNA binding, protein binding, transcriptional activation, subcellular localization, and condensate formation. Relative to reference isoforms, two-thirds of alternative TF isoforms exhibit differences in one or more molecular activities, which often could not be predicted from sequence. We observed two primary categories of alternative TF isoforms: "rewirers" and "negative regulators", both of which were associated with differentiation and cancer. Our results support a model wherein the relative expression levels of, and interactions involving, TF isoforms add an understudied layer of complexity to gene regulatory networks, demonstrating the importance of isoform-aware characterization of TF functions and providing a rich resource for further studies.

Speos: an ensemble graph representation learning framework to predict core gene candidates for complex diseases

Understanding phenotype-to-genotype relationships is a grand challenge of 21st century biology with translational implications. The recently proposed "omnigenic" model postulates that effects of genetic variation on traits are mediated by core-genes and -proteins whose activities mechanistically influence the phenotype, whereas peripheral genes encode a regulatory network that indirectly affects phenotypes via core gene products. Here, we develop a positive-unlabeled graph representation-learning ensemble-approach based on a nested cross-validation to predict core-like genes for diverse diseases using Mendelian disorder genes for training. Employing mouse knockout phenotypes for external validations, we demonstrate that core-like genes display several key properties of core genes: Mouse knockouts of genes corresponding to our most confident predictions give rise to relevant mouse phenotypes at rates on par with the Mendelian disorder genes, and all candidates exhibit core gene properties like transcriptional deregulation in disease and loss-of-function intolerance. Moreover, as predicted for core genes, our candidates are enriched for drug targets and druggable proteins. In contrast to Mendelian disorder genes the new core-like genes are enriched for druggable yet untargeted gene products, which are therefore attractive targets for drug development. Interpretation of the underlying deep learning model suggests plausible explanations for our core gene predictions in form of molecular mechanisms and physical interactions. Our results demonstrate the potential of graph representation learning for the interpretation of biological complexity and pave the way for studying core gene properties and future drug development.

Paired yeast one-hybrid assays to detect DNA-binding cooperativity and antagonism across transcription factors

Cooperativity and antagonism between transcription factors (TFs) can drastically modify their binding to regulatory DNA elements. While mapping these relationships between TFs is important for understanding their context-specific functions, existing approaches either rely on DNA binding motif predictions, interrogate one TF at a time, or study individual TFs in parallel. Here, we introduce paired yeast one-hybrid (pY1H) assays to detect cooperativity and antagonism across hundreds of TF-pairs at DNA regions of interest. We provide evidence that a wide variety of TFs are subject to modulation by other TFs in a DNA region-specific manner. We also demonstrate that TF-TF relationships are often affected by alternative isoform usage and identify cooperativity and antagonism between human TFs and viral proteins from human papillomaviruses, Epstein-Barr virus, and other viruses. Altogether, pY1H assays provide a broadly applicable framework to study how different functional relationships affect protein occupancy at regulatory DNA regions.

AD-Syn-Net: systematic identification of Alzheimer's disease-associated mutation and co-mutation vulnerabilities via deep learning

Alzheimer's disease (AD) is one of the most challenging neurodegenerative diseases because of its complicated and progressive mechanisms, and multiple risk factors. Increasing research evidence demonstrates that genetics may be a key factor responsible for the occurrence of the disease. Although previous reports identified quite a few AD-associated genes, they were mostly limited owing to patient sample size and selection bias. There is a lack of comprehensive research aimed to identify AD-associated risk mutations systematically. To address this challenge, we hereby construct a large-scale AD mutation and co-mutation framework ('AD-Syn-Net'), and propose deep learning models named Deep-SMCI and Deep-CMCI configured with fully connected layers that are capable of predicting cognitive impairment of subjects effectively based on genetic mutation and co-mutation profiles. Next, we apply the customized frameworks to data sets to evaluate the importance scores of the mutations and identified mutation effectors and co-mutation combination vulnerabilities contributing to cognitive impairment. Furthermore, we evaluate the influence of mutation pairs on the network architecture to dissect the genetic organization of AD and identify novel co-mutations that could be responsible for dementia, laying a solid foundation for proposing future targeted therapy for AD precision medicine. Our deep learning model codes are available open access here: https://github.com/Pan-Bio/AD-mutation-effectors.

Gene-environment interactions and their impact on human health

The molecular processes underlying human health and disease are highly complex. Often, genetic and environmental factors contribute to a given disease or phenotype in a non-additive manner, yielding a gene-environment (G × E) interaction. In this work, we broadly review current knowledge on the impact of gene-environment interactions on human health. We first explain the independent impact of genetic variation and the environment. We next detail well-established G × E interactions that impact human health involving environmental toxicants, pollution, viruses, and sex chromosome composition. We conclude with possibilities and challenges for studying G × E interactions.

A Transcriptional Cofactor Regulatory Network for the C. elegans Intestine

Chromatin modifiers and transcriptional cofactors (collectively referred to as CFs) work with DNA-binding transcription factors (TFs) to regulate gene expression. In multicellular eukaryotes, distinct tissues each execute their own gene expression program for accurate differentiation and subsequent functionality. While the function of TFs in differential gene expression has been studied in detail in many systems, the contribution of CFs has remained less explored. Here we uncovered the contributions of CFs to gene regulation in the Caenorhabditis elegans intestine. We first annotated 366 CFs encoded by the C. elegans genome and assembled a library of 335 RNAi clones. Using this library, we analyzed the effects of individually depleting these CFs on the expression of 19 fluorescent transcriptional reporters in the intestine and identified 216 regulatory interactions. We found that different CFs interact specifically with different promoters, and that both essential and intestinally expressed CFs exhibit the highest proportion of interactions. We did not find all members of CF complexes acting on the same set of reporters but instead found diversity in the promoter targets of each complex component. Finally, we found that previously identified activation mechanisms for the acdh-1 promoter use different CFs and TFs. Overall, we demonstrate that CFs function specifically rather than ubiquitously at intestinal promoters and provide an RNAi resource for reverse genetic screens.

Gain-of-Function Variomics and Multi-omics Network Biology for Precision Medicine

Traditionally, disease causal mutations were thought to disrupt gene function. However, it becomes more clear that many deleterious mutations could exhibit a "gain-of-function" (GOF) behavior. Systematic investigation of such mutations has been lacking and largely overlooked. Advances in next-generation sequencing have identified thousands of genomic variants that perturb the normal functions of proteins, further contributing to diverse phenotypic consequences in disease. Elucidating the functional pathways rewired by GOF mutations will be crucial for prioritizing disease-causing variants and their resultant therapeutic liabilities. In distinct cell types (with varying genotypes), precise signal transduction controls cell decision, including gene regulation and phenotypic output. When signal transduction goes awry due to GOF mutations, it would give rise to various disease types. Quantitative and molecular understanding of network perturbations by GOF mutations may provide explanations for 'missing heritability" in previous genome-wide association studies. We envision that it will be instrumental to push current paradigm toward a thorough functional and quantitative modeling of all GOF mutations and their mechanistic molecular events involved in disease development and progression. Many fundamental questions pertaining to genotype-phenotype relationships remain unresolved. For example, which GOF mutations are key for gene regulation and cellular decisions? What are the GOF mechanisms at various regulation levels? How do interaction networks undergo rewiring upon GOF mutations? Is it possible to leverage GOF mutations to reprogram signal transduction in cells, aiming to cure disease? To begin to address these questions, we will cover a wide range of topics regarding GOF disease mutations and their characterization by multi-omic networks. We highlight the fundamental function of GOF mutations and discuss the potential mechanistic effects in the context of signaling networks. We also discuss advances in bioinformatic and computational resources, which will dramatically help with studies on the functional and phenotypic consequences of GOF mutations.

Enhanced Yeast One-Hybrid Assays to Study Protein-DNA Interactions

The specificity in gene regulation is controlled by interactions between transcription factors (TFs) and genomic DNA regions such as promoters and enhancers. Enhanced yeast one-hybrid (eY1H) assays are among the methods used for high-throughput detection of transcription factor-DNA interactions. Here, we describe the procedure for screening interactions between DNA regions of interest ("DNA-baits") and an array of transcription factors ("TF-preys"), after DNA-bait and TF-prey yeast strains have been generated. Using a high-density array robotic platform, this method can be used to screen interactions between multiple DNA regions and >1000 TFs within a single experiment.

Bioinformatic Evaluation of Features on Cis-regulatory Elements at 6q25.1

Eukaryotic non-coding regulatory features contribute significantly to cellular plasticity which on aberration leads to cellular malignancy. Enhancers are cis-regulatory elements that contribute to the development of resistance to endocrine therapy in estrogen receptor (ER)-positive breast cancer leading to poor clinical outcome. ER is vital for therapeutic targets in ER-positive breast cancer. Here, we review and report the different regulatory features present on ER with the objective to delineate potential mechanisms which may contribute to development of resistance. The UCSC Genome Browser, data mining, and bioinformatics tools were used to review enhancers, transcription factors (TFs), histone marks, long non-coding RNAs (lncRNAs), and variants residing in the non-coding region of the ER gene. We report 7 enhancers, 3 of which were rich in TF-binding sites and histone marks in a cell line-specific manner. Furthermore, some enhancers contain estrogen resistance variants and sites for lncRNA. Our review speculates putative models suggesting potential aberrations in gene regulation and expression if these regulatory landscapes and assemblies are altered. This review gives an interesting perspective in designing integrated in vitro studies including non-coding elements to study development of endocrine resistance in ER-positive breast cancer.

The pZRS non-coding regulatory mutation resulting in triphalangeal thumb-polysyndactyly syndrome changes the pattern of local interactions

Herein, we report on a large Polish family presenting with a classical triphalangeal thumb-polysyndactyly syndrome (TPT-PS). This rare congenital limb anomaly is generally caused by microduplications encompassing the Sonic Hedgehog (SHH) limb enhancer, termed the zone of polarizing activity (ZPA) regulatory sequence (ZRS). Recently, a pathogenic variant in the pre-ZRS (pZRS), a conserved sequence located near the ZRS, has been described in a TPT-PS Dutch family. We performed targeted ZRS sequencing, array comparative genomic hybridization, and whole-exome sequencing. Next, we sequenced the recently described pZRS region. Finally, we performed a circular chromatin conformation capture-sequencing (4C-seq) assay on skin fibroblasts of one affected family member and control samples to examine potential alterations in the SHH regulatory domain and functionally characterize the identified variant. We found that all affected individuals shared a recently identified pathogenic point mutation in the pZRS region: NC_000007.14:g.156792782C>G (GRCh38/hg38), which is the same as in the Dutch family. The results of 4C-seq experiments revealed increased interactions within the whole SHH regulatory domain (SHH-LMBR1 TAD) in the patient compared to controls. Our study expands the number of TPT-PS families carrying a pathogenic alteration of the pZRS and underlines the importance of routine pZRS sequencing in the genetic diagnostics of patients with TPT-PS or similar phenotypes. The pathogenic mutation causative for TPT-PS in our patient gave rise to increased interactions within the SHH regulatory domain in yet unknown mechanism.

A network-based method for predicting disease-associated enhancers

Background

Enhancers regulate transcription of target genes, causing a change in expression level. Thus, the aberrant activity of enhancers can lead to diseases. To date, a large number of enhancers have been identified, yet a small portion of them have been found to be associated with diseases. This raises a pressing need to develop computational methods to predict associations between diseases and enhancers.

Results

In this study, we assumed that enhancers sharing target genes could be associated with similar diseases to predict the association. Thus, we built an enhancer functional interaction network by connecting enhancers significantly sharing target genes, then developed a network diffusion method RWDisEnh, based on a random walk with restart algorithm, on networks of diseases and enhancers to globally measure the degree of the association between diseases and enhancers. RWDisEnh performed best when the disease similarities are integrated with the enhancer functional interaction network by known disease-enhancer associations in the form of a heterogeneous network of diseases and enhancers. It was also superior to another network diffusion method, i.e., PageRank with Priors, and a neighborhood-based one, i.e., MaxLink, which simply chooses the closest neighbors of known disease-associated enhancers. Finally, we showed that RWDisEnh could predict novel enhancers, which are either directly or indirectly associated with diseases.

Conclusions

Taken together, RWDisEnh could be a potential method for predicting disease-enhancer associations.

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.

Large-scale data analysis for robotic yeast one-hybrid platforms and multi-disciplinary studies using GateMultiplex

BACKGROUND

Yeast one-hybrid (Y1H) is a common technique for identifying DNA-protein interactions, and robotic platforms have been developed for high-throughput analyses to unravel the gene regulatory networks in many organisms. Use of these high-throughput techniques has led to the generation of increasingly large datasets, and several software packages have been developed to analyze such data. We previously established the currently most efficient Y1H system, meiosis-directed Y1H; however, the available software tools were not designed for processing the additional parameters suggested by meiosis-directed Y1H to avoid false positives and required programming skills for operation.

RESULTS

We developed a new tool named GateMultiplex with high computing performance using C++. GateMultiplex incorporated a graphical user interface (GUI), which allows the operation without any programming skills. Flexible parameter options were designed for multiple experimental purposes to enable the application of GateMultiplex even beyond Y1H platforms. We further demonstrated the data analysis from other three fields using GateMultiplex, the identification of lead compounds in preclinical cancer drug discovery, the crop line selection in precision agriculture, and the ocean pollution detection from deep-sea fishery.

CONCLUSIONS

The user-friendly GUI, fast C++ computing speed, flexible parameter setting, and applicability of GateMultiplex facilitate the feasibility of large-scale data analysis in life science fields.

Mutation Edgotype Drives Fitness Effect in Human

Missense mutations are known to perturb protein-protein interaction networks (known as interactome networks) in different ways. However, it remains unknown how different interactome perturbation patterns (“edgotypes”) impact organismal fitness. Here, we estimate the fitness effect of missense mutations with different interactome perturbation patterns in human, by calculating the fractions of neutral and deleterious mutations that do not disrupt PPIs (“quasi-wild-type”), or disrupt PPIs either by disrupting the binding interface (“edgetic”) or by disrupting overall protein stability (“quasi-null”). We first map pathogenic mutations and common non-pathogenic mutations onto homology-based three-dimensional structural models of proteins and protein-protein interactions in human. Next, we perform structure-based calculations to classify each mutation as either quasi-wild-type, edgetic, or quasi-null. Using our predicted as well as experimentally determined interactome perturbation patterns, we estimate that >∼40% of quasi-wild-type mutations are effectively neutral and the remaining are mostly mildly deleterious, that >∼75% of edgetic mutations are only mildly deleterious, and that up to ∼75% of quasi-null mutations may be strongly detrimental. These estimates are the first such estimates of fitness effect for different network perturbation patterns in any interactome. Our results suggest that while mutations that do not disrupt the interactome tend to be effectively neutral, the majority of human PPIs are under strong purifying selection and the stability of most human proteins is essential to human life.

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 (Sil^ThPOK). In the present study, we describe a new autoregulatory loop whereby ThPOK binds to the Sil^ThPOK 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 (T_reg) cell gene expression signature. This promotes selective differentiation of naive CD4 T cells into GITR^loPD-1^loCD25^lo (Triple^lo) T_reg 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.

Molecular Regulation of the Melatonin Biosynthesis Pathway in Unipolar and Bipolar Depression

Melatonin is a neurohormone that maintains the circadian rhythms of the body. By regulating the secretion of other hormones and neurotransmitters, it acts as a pleiotropic modulator that affects, for example, reproductive, immune, cardiovascular, sleep, and wake systems and mood. Thus, synthetic melatonin has become an essential component in the treatment of depressive disorders. Although we know the pathway of melatonin action in the brain, we lack comprehensive cross-sectional studies on the periphery of depressed patients. This study aimed to comprehensively analyze the differences between healthy control subjects (n = 84) and unipolar and bipolar depression patients (n = 94), including an analysis of the melatonin pathway at the level of the genes and serum biomarkers. An innovative approach is a pilot study based on gene expression profiling carried out on clinical and cell culture models using agomelatine and melatonin. We confirmed the melatonin biosynthesis pathway's molecular regulation dysfunctions, with a specific pattern for unipolar and bipolar depression, at the AANAT gene, its polymorphisms (rs8150 and rs3760138), and examined the serum biomarkers (serotonin, AANAT, ASMT, and melatonin). The biological pathway analysis uncovered pathways and genes that were uniquely altered after agomelatine treatment in a clinical model and melatonin treatment in a cell culture model. In both models, we confirmed the immunomodulatory effect of melatonin agents in depression.

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.

On the prediction of DNA-binding preferences of C2H2-ZF domains using structural models: application on human CTCF

Cis2-His2 zinc finger (C2H2-ZF) proteins are the largest family of transcription factors in human and higher metazoans. To date, the DNA-binding preferences of many members of this family remain unknown. We have developed a computational method to predict their DNA-binding preferences. We have computed theoretical position weight matrices (PWMs) of proteins composed by C2H2-ZF domains, with the only requirement of an input structure. We have predicted more than two-third of a single zinc-finger domain binding site for about 70% variants of Zif268, a classical member of this family. We have successfully matched between 60 and 90% of the binding-site motif of examples of proteins composed by three C2H2-ZF domains in JASPAR, a standard database of PWMs. The tests are used as a proof of the capacity to scan a DNA fragment and find the potential binding sites of transcription-factors formed by C2H2-ZF domains. As an example, we have tested the approach to predict the DNA-binding preferences of the human chromatin binding factor CTCF. We offer a server to model the structure of a zinc-finger protein and predict its PWM.

e-MutPath: computational modeling reveals the functional landscape of genetic mutations rewiring interactome networks

Understanding the functional impact of cancer somatic mutations represents a critical knowledge gap for implementing precision oncology. It has been increasingly appreciated that the interaction profile mediated by a genomic mutation provides a fundamental link between genotype and phenotype. However, specific effects on biological signaling networks for the majority of mutations are largely unknown by experimental approaches. To resolve this challenge, we developed e-MutPath (edgetic Mutation-mediated Pathway perturbations), a network-based computational method to identify candidate ‘edgetic’ mutations that perturb functional pathways. e-MutPath identifies informative paths that could be used to distinguish disease risk factors from neutral elements and to stratify disease subtypes with clinical relevance. The predicted targets are enriched in cancer vulnerability genes, known drug targets but depleted for proteins associated with side effects, demonstrating the power of network-based strategies to investigate the functional impact and perturbation profiles of genomic mutations. Together, e-MutPath represents a robust computational tool to systematically assign functions to genetic mutations, especially in the context of their specific pathway perturbation effect.

Parallel functional testing identifies enhancers active in early postnatal mouse brain

Enhancers are cis-regulatory elements that play critical regulatory roles in modulating developmental transcription programs and driving cell-type-specific and context-dependent gene expression in the brain. The development of massively parallel reporter assays (MPRAs) has enabled high-throughput functional screening of candidate DNA sequences for enhancer activity. Tissue-specific screening of in vivo enhancer function at scale has the potential to greatly expand our understanding of the role of non-coding sequences in development, evolution, and disease. Here, we adapted a self-transcribing regulatory element MPRA strategy for delivery to early postnatal mouse brain via recombinant adeno-associated virus (rAAV). We identified and validated putative enhancers capable of driving reporter gene expression in mouse forebrain, including regulatory elements within an intronic CACNA1C linkage disequilibrium block associated with risk in neuropsychiatric disorder genetic studies. Paired screening and single enhancer in vivo functional testing, as we show here, represents a powerful approach towards characterizing regulatory activity of enhancers and understanding how enhancer sequences organize gene expression in the brain.

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.

A review on kinases phosphorylating the carboxyl-terminal domain of RNA polymerase II-Biological functions and inhibitors

RNA polymerase II (RNA Pol II) plays a major role in gene transcription for eukaryote. One of the major modes of regulation in eukaryotes is the phosphorylation of the carboxyl-terminal domain (CTD) of RNA Pol II. The current study found that the phosphorylation of Ser2, Ser5, Ser7, Thr4 and Tyr1 among the heptapeptide repeats of CTD plays a key role in the transcription process. We therefore review the biological functions and inhibitors of kinases that phosphorylate these amino acid residues including transcriptional cyclin-dependent protein kinases (CDKs), bromodomain-containing protein 4 (BRD4), Polo-like kinases 3 (Plk3) and Abelson murine leukemia viral oncogene 1 and 2 (c-Abl1/2).

Chromatin Environment and Cellular Context Specify Compensatory Activity of Paralogous MEF2 Transcription Factors

Compensation among paralogous transcription factors (TFs) confers genetic robustness of cellular processes, but how TFs dynamically respond to paralog depletion on a genome-wide scale in vivo remains incompletely understood. Using single and double conditional knockout of myocyte enhancer factor 2 (MEF2) family TFs in granule neurons of the mouse cerebellum, we find that MEF2A and MEF2D play functionally redundant roles in cerebellar-dependent motor learning. Although both TFs are highly expressed in granule neurons, transcriptomic analyses show MEF2D is the predominant genomic regulator of gene expression in vivo. Strikingly, genome-wide occupancy analyses reveal upon depletion of MEF2D, MEF2A occupancy robustly increases at a subset of sites normally bound to MEF2D. Importantly, sites experiencing compensatory MEF2A occupancy are concentrated within open chromatin and undergo functional compensation for genomic activation and gene expression. Finally, motor activity induces a switch from non-compensatory to compensatory MEF2-dependent gene regulation. These studies uncover genome-wide functional interdependency between paralogous TFs in the brain.

Majidi et al. study how transcription factors respond to paralog depletion by conditionally depleting MEF2A and MEF2D in mouse cerebellum. Depletion of MEF2D induces functionally compensatory genomic occupancy by MEF2A. Compensation occurs within accessible chromatin in a context-dependent manner. This study explores the interdependency between paralogous transcription factors.

Genome-wide pathogenesis interpretation using a heat diffusion-based systems genetics method and implications for gene function annotation

Background

Genetics is best dedicated to interpreting pathogenesis and revealing gene functions. The past decade has witnessed unprecedented progress in genetics, particularly in genome‐wide identification of disorder variants through Genome‐Wide Association Studies (GWAS) and Phenome‐Wide Association Studies (PheWAS). However, it is still a great challenge to use GWAS/PheWAS‐derived data to elucidate pathogenesis.

Methods

In this study, we used HotNet2, a heat diffusion‐based systems genetics algorithm, to calculate the networks for disease genes obtained from GWAS and PheWAS, with an attempt to get deeper insights into disease pathogenesis at a molecular level.

Results

Through HotNet2 calculation, significant networks for 202 (for GWAS) and 167 (for PheWAS) types of diseases were identified and evaluated, respectively. The GWAS‐derived disease networks exhibit a stronger biomedical relevance than PheWAS counterparts. Therefore, the GWAS‐derived networks were used for pathogenesis interpretation by integrating the accumulated biomedical information. As a result, the pathogenesis for 64 diseases was elucidated in terms of mutation‐caused abnormal transcriptional regulation, and 47 diseases were preliminarily interpreted in terms of mutation‐caused varied protein‐protein interactions. In addition, 3,802 genes (including 46 function‐unknown genes) were assigned with new functions by disease network information, some of which were validated through mice gene knockout experiments.

Conclusions

Systems genetics algorithm HotNet2 can efficiently establish genotype‐phenotype links at the level of biological networks. Compared with original GWAS/PheWAS results, HotNet2‐calculated disease‐gene associations have stronger biomedical significance, hence provide better interpretations for the pathogenesis of genome‐wide variants, and offer new insights into gene functions as well. These results are also helpful in drug development.

A Boolean Logical model for Reprogramming of Testes-derived male Germline Stem Cells into Germline pluripotent stem cells

BACKGROUND AND OBJECTIVE

Male germline stem (GS) cells are responsible for the maintenance of spermatogenesis throughout the adult life of males. Upon appropriate in vitro culture conditions, these GS cells can undergo reprogramming to become germline pluripotent stem (GPS) cells with the loss of spermatogenic potential. In recent years, voluminous data of gene transcripts in GS and GPS cells have become available. However, the mechanism of reprogramming of GS cells into GPS cells remains elusive. This study was designed to develop a Boolean logical model of gene regulatory network (GRN) that might be involved in the reprogramming of GS cells into GPS cells.

METHODS

The gene expression profile of GS and GPS cells (GSE ID: GSE11274 and GSE74151) were analyzed using R Bioconductor to identify differentially expressed genes (DEGs) and were functionally annotated with DAVID server. Potential pluripotent genes among the DEGs were then predicted using a combination of machine learning [Support Vector Machine (SVM)] and BLAST search. Protein isoforms were identified by pattern matching with UniProt database with in-house scripts written in C++. Both linear and non-linear interaction maps were generated using the STRING server. CellNet is used to study the relationship of GRNs between the GS and GPS cells. Finally, the GRNs involving all the genes from integrated methods and literature was constructed and qualitative modelling for reprogramming of GS to GPS cells were done by considering the discrete, asynchronous, multivalued logical formalism using the GINsim modeling and simulation tool.

RESULTS

Through the use of machine learning and logical modeling, the present study identified 3585 DEGs and 221 novel pluripotent genes including Tet1, Cdh1, Tfap2c, Etv4, Etv5, Prdm14, and Prdm10 in GPS cells. Pathway analysis revealed that important signaling pathways such as core pluripotency network, PI3K-Akt, WNT, GDNF and BMP4 signalling pathways were important for the reprogramming of GS cells to GPS cells. On the other hand, CellNet analysis of GRNs of GS and GPS cells revealed that GS cells were similar to gonads whereas GPS cells were similar to ESCs in gene expression profile. A logical regulatory model was developed, which showed that TGFβ negatively regulated the reprogramming of the GS to GPS cells, as confirmed by perturbations studies.

CONCLUSION

The study identified novel pluripotent genes involved in the reprogramming of GS cells into GPS cells. A multivalued logical model of cellular reprogramming is proposed, which suggests that reprogramming of GS cells to GPS cells involves signalling pathways namely LIF, GDNF, BMP4, and TGFβ along with some novel pluripotency genes.

Gene regulatory landscape of the sonic hedgehog locus in embryonic development

The organs of vertebrate species display a wide variety of morphology. A remaining challenge in evolutionary developmental biology is to elucidate how vertebrate lineages acquire distinct morphological features. Developmental programs are driven by spatiotemporal regulation of gene expression controlled by hundreds of thousands of cis-regulatory elements. Changes in the regulatory elements caused by the introduction of genetic variants can confer regulatory innovation that may underlie morphological novelties. Recent advances in sequencing technology have revealed a number of potential regulatory variants that can alter gene expression patterns. However, a limited number of studies demonstrate causal dependence between genetic and morphological changes. Regulation of Shh expression is a good model to understand how multiple regulatory elements organize tissue-specific gene expression patterns. This model also provides insights into how evolution of molecular traits, such as gene regulatory networks, lead to phenotypic novelty.

AnimalTFDB 3.0: a comprehensive resource for annotation and prediction of animal transcription factors

The Animal Transcription Factor DataBase (AnimalTFDB) is a resource aimed to provide the most comprehensive and accurate information for animal transcription factors (TFs) and cofactors. The AnimalTFDB has been maintained and updated for seven years and we will continue to improve it. Recently, we updated the AnimalTFDB to version 3.0 (http://bioinfo.life.hust.edu.cn/AnimalTFDB/) with more data and functions to improve it. AnimalTFDB contains 125,135 TF genes and 80,060 transcription cofactor genes from 97 animal genomes. Besides the expansion in data quantity, some new features and functions have been added. These new features are: (i) more accurate TF family assignment rules; (ii) classification of transcription cofactors; (iii) TF binding sites information; (iv) the GWAS phenotype related information of human TFs; (v) TF expressions in 22 animal species; (vi) a TF binding site prediction tool to identify potential binding TFs for nucleotide sequences; (vii) a separate human TF database web interface (HumanTFDB) was designed for better utilizing the human TFs. The new version of AnimalTFDB provides a comprehensive annotation and classification of TFs and cofactors, and will be a useful resource for studies of TF and transcription regulation.

Locally acting transcription factors regulate p53-dependent cis-regulatory element activity

The master tumor suppressor p53 controls transcription of a wide-ranging gene network involved in apoptosis, cell cycle arrest, DNA damage repair, and senescence. Recent studies revealed pervasive binding of p53 to cis-regulatory elements (CREs), which are non-coding segments of DNA that spatially and temporally control transcription through the combinatorial binding of local transcription factors. Although the role of p53 as a strong trans-activator of gene expression is well known, the co-regulatory factors and local sequences acting at p53-bound CREs are comparatively understudied. We designed and executed a massively parallel reporter assay (MPRA) to investigate the effect of transcription factor binding motifs and local sequence context on p53-bound CRE activity. Our data indicate that p53-bound CREs are both positively and negatively affected by alterations in local sequence context and changes to co-regulatory TF motifs. Our data suggest p53 has the flexibility to cooperate with a variety of transcription factors in order to regulate CRE activity. By utilizing different sets of co-factors across CREs, we hypothesize that global p53 activity is guarded against loss of any one regulatory partner, allowing for dynamic and redundant control of p53-mediated transcription.

Loss-of-function tolerance of enhancers in the human genome

Previous studies have surveyed the potential impact of loss-of-function (LoF) variants and identified LoF-tolerant protein-coding genes. However, the tolerance of human genomes to losing enhancers has not yet been evaluated. Here we present the catalog of LoF-tolerant enhancers using structural variants from whole-genome sequences. Using a conservative approach, we estimate that individual human genomes possess at least 28 LoF-tolerant enhancers on average. We assessed the properties of LoF-tolerant enhancers in a unified regulatory network constructed by integrating tissue-specific enhancers and gene-gene interactions. We find that LoF-tolerant enhancers tend to be more tissue-specific and regulate fewer and more dispensable genes relative to other enhancers. They are enriched in immune-related cells while enhancers with low LoF-tolerance are enriched in kidney and brain/neuronal stem cells. We developed a supervised learning approach to predict the LoF-tolerance of all enhancers, which achieved an area under the receiver operating characteristics curve (AUROC) of 98%. We predict 3,519 more enhancers would be likely tolerant to LoF and 129 enhancers that would have low LoF-tolerance. Our predictions are supported by a known set of disease enhancers and novel deletions from PacBio sequencing. The LoF-tolerance scores provided here will serve as an important reference for disease studies.

Enhancers are elements where transcription factors bind and regulate the expression of protein-coding genes. Although multiple previous studies have focused on which genes can tolerate loss-of-function (LoF), none has systematically evaluated the tolerance of all enhancers in the human genome to LoF. Individual studies have shown a broad range of phenotypic effects of enhancer LoF. The phenotypic effects of enhancer LoF likely fall into a spectrum where deletion of LoF-tolerant enhancers would not elicit substantial phenotypic impact, while some enhancers are likely to cause fitness defects when deleted. Here we report a systematic computational approach that uses machine learning and properties of enhancers in a unified human regulatory network with tissue-specific annotations to predict the LoF-tolerance of all enhancers identified in the human genome. The LoF-tolerance scores of enhancers provided in this study can significantly facilitate the interpretation and prioritization of non-coding sequence variants for disease and functional studies.

Pathway perturbations in signaling networks: Linking genotype to phenotype

Genes and gene products interact with each other to form signal transduction networks in the cell. The interactome networks are under intricate regulation in physiological conditions, but could go awry upon genome instability caused by genetic mutations. In the past decade with next-generation sequencing technologies, an increasing number of genomic mutations have been identified in a variety of disease patients and healthy individuals. As functional and systematic studies on these mutations leap forward, they begin to reveal insights into cellular homeostasis and disease mechanisms. In this review, we discuss recent advances in the field of network biology and signaling pathway perturbations upon genomic changes, and highlight the success of various omics datasets in unraveling genotype-to-phenotype relationships.

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.

Noncoding mutations target cis-regulatory elements of the FOXA1 plexus in prostate cancer

Prostate cancer is the second most commonly diagnosed malignancy among men worldwide. Recurrently mutated in primary and metastatic prostate tumors, FOXA1 encodes a pioneer transcription factor involved in disease onset and progression through both androgen receptor-dependent and androgen receptor-independent mechanisms. Despite its oncogenic properties however, the regulation of FOXA1 expression remains unknown. Here, we identify a set of six cis-regulatory elements in the FOXA1 regulatory plexus harboring somatic single-nucleotide variants in primary prostate tumors. We find that deletion and repression of these cis-regulatory elements significantly decreases FOXA1 expression and prostate cancer cell growth. Six of the ten single-nucleotide variants mapping to FOXA1 regulatory plexus significantly alter the transactivation potential of cis-regulatory elements by modulating the binding of transcription factors. Collectively, our results identify cis-regulatory elements within the FOXA1 plexus mutated in primary prostate tumors as potential targets for therapeutic intervention.

FOXA1 pioneer transcription factor is recurrently mutated in primary and metastatic prostate tumors. Here, authors identify a set of six cis-regulatory elements in the FOXA1 regulatory plexus harboring somatic SNVs in primary prostate tumors and characterize their role in regulating FOXA1 expression and prostate cancer cell growth.

Extensive disruption of protein interactions by genetic variants across the allele frequency spectrum in human populations

Each human genome carries tens of thousands of coding variants. The extent to which this variation is functional and the mechanisms by which they exert their influence remains largely unexplored. To address this gap, we leverage the ExAC database of 60,706 human exomes to investigate experimentally the impact of 2009 missense single nucleotide variants (SNVs) across 2185 protein-protein interactions, generating interaction profiles for 4797 SNV-interaction pairs, of which 421 SNVs segregate at > 1% allele frequency in human populations. We find that interaction-disruptive SNVs are prevalent at both rare and common allele frequencies. Furthermore, these results suggest that 10.5% of missense variants carried per individual are disruptive, a higher proportion than previously reported; this indicates that each individual's genetic makeup may be significantly more complex than expected. Finally, we demonstrate that candidate disease-associated mutations can be identified through shared interaction perturbations between variants of interest and known disease mutations.

TAGOOS: genome-wide supervised learning of non-coding loci associated to complex phenotypes

Genome-wide association studies (GWAS) associate single nucleotide polymorphisms (SNPs) to complex phenotypes. Most human SNPs fall in non-coding regions and are likely regulatory SNPs, but linkage disequilibrium (LD) blocks make it difficult to distinguish functional SNPs. Therefore, putative functional SNPs are usually annotated with molecular markers of gene regulatory regions and prioritized with dedicated prediction tools. We integrated associated SNPs, LD blocks and regulatory features into a supervised model called TAGOOS (TAG SNP bOOSting) and computed scores genome-wide. The TAGOOS scores enriched and prioritized unseen associated SNPs with an odds ratio of 4.3 and 3.5 and an area under the curve (AUC) of 0.65 and 0.6 for intronic and intergenic regions, respectively. The TAGOOS score was correlated with the maximal significance of associated SNPs and expression quantitative trait loci (eQTLs) and with the number of biological samples annotated for key regulatory features. Analysis of loci and regions associated to cleft lip and human adult height phenotypes recovered known functional loci and predicted new functional loci enriched in transcriptions factors related to the phenotypes. In conclusion, we trained a supervised model based on associated SNPs to prioritize putative functional regions. The TAGOOS scores, annotations and UCSC genome tracks are available here: https://tagoos.readthedocs.io.

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.

Programmable RNA-Guided RNA Effector Proteins Built from Human Parts

Epitranscriptomic regulation controls information flow through the central dogma and provides unique opportunities for manipulating cells at the RNA level. However, both fundamental studies and potential translational applications are impeded by a lack of methods to target specific RNAs with effector proteins. Here, we present CRISPR-Cas-inspired RNA targeting system (CIRTS), a protein engineering strategy for constructing programmable RNA control elements. We show that CIRTS is a simple and generalizable approach to deliver a range of effector proteins, including nucleases, degradation machinery, translational activators, and base editors to target transcripts. We further demonstrate that CIRTS is not only smaller than naturally occurring CRISPR-Cas programmable RNA binding systems but can also be built entirely from human protein parts. CIRTS provides a platform to probe fundamental RNA regulatory processes, and the human-derived nature of CIRTS provides a potential strategy to avoid immune issues when applied to epitranscriptome-modulating therapies.

Functional Conservation of a Developmental Switch in Mammals since the Jurassic Age

ThPOK is a "master regulator" of T lymphocyte lineage choice, whose presence or absence is sufficient to dictate development to the CD4 or CD8 lineages, respectively. Induction of ThPOK is transcriptionally regulated, via a lineage-specific silencer element, SilThPOK. Here, we take advantage of the available genome sequence data as well as site-specific gene targeting technology, to evaluate the functional conservation of ThPOK regulation across mammalian evolution, and assess the importance of motif grammar (order and orientation of TF binding sites) on SilThPOK function in vivo. We make three important points: First, the SilThPOK is present in marsupial and placental mammals, but is not found in available genome assemblies of nonmammalian vertebrates, indicating that it arose after divergence of mammals from other vertebrates. Secondly, by replacing the murine SilThPOK in situ with its marsupial equivalent using a knockin approach, we demonstrate that the marsupial SilThPOK supports correct CD4 T lymphocyte lineage-specification in mice. To our knowledge, this is the first in vivo demonstration of functional equivalency for a silencer element between marsupial and placental mammals using a definitive knockin approach. Finally, we show that alteration of the position/orientation of a highly conserved region within the murine SilThPOK is sufficient to destroy silencer activity in vivo, demonstrating that motif grammar of this "solid" synteny block is critical for silencer function. Dependence of SilThPOK function on motif grammar conserved since the mid-Jurassic age, 165 Ma, suggests that the SilThPOK operates as a silenceosome, by analogy with the previously proposed enhanceosome model.

A novel synthetic-genetic-array-based yeast one-hybrid system for high discovery rate and short processing time

Eukaryotic gene expression is often tightly regulated by interactions between transcription factors (TFs) and their DNA cis targets. Yeast one-hybrid (Y1H) is one of the most extensively used methods to discover these interactions. We developed a high-throughput meiosis-directed yeast one-hybrid system using the Magic Markers of the synthetic genetic array analysis. The system has a transcription factor–DNA interaction discovery rate twice as high as the conventional diploid-mating approach and a processing time nearly one-tenth of the haploid-transformation method. The system also offers the highest accuracy in identifying TF–DNA interactions that can be authenticated in vivo by chromatin immunoprecipitation. With these unique features, this meiosis-directed Y1H system is particularly suited for constructing novel and comprehensive genome-scale gene regulatory networks for various organisms.

Gain-of-Function Mutations: An Emerging Advantage for Cancer Biology

Advances in next-generation sequencing have identified thousands of genomic variants that perturb the normal functions of proteins, further contributing to diverse phenotypic consequences in cancer. Elucidating the functional pathways altered by loss-of-function (LOF) or gain-of-function (GOF) mutations will be crucial for prioritizing cancer-causing variants and their resultant therapeutic liabilities. In this review, we highlight the fundamental function of GOF mutations and discuss the potential mechanistic effects in the context of signaling networks. We also summarize advances in experimental and computational resources, which will dramatically help with studies on the functional and phenotypic consequences of mutations. Together, systematic investigations of the function of GOF mutations will provide an important missing piece for cancer biology and precision therapy.

Options and Considerations When Using a Yeast One-Hybrid System

Comprehensive mapping of protein-DNA interactions is essential to uncover the mechanisms involved in gene regulation. However, the data generated has been sparse given the number of regulatory elements and transcription factors (TFs) encoded in the genomes of metazoan organisms. Yeast one-hybrid (Y1H) assays provide a powerful "DNA-centered" method, complementary to "TF-centered" methods such as chromatin immunoprecipitation, to identify the TFs that can bind a DNA sequence of interest. Here, we present different technical variations that should be considered when using a Y1H system, including the type of DNA sequence to test, source of TF clones, as well as types of vectors and screening format. Finally, we discuss limitations of the assay and future challenges.

High-Throughput Yeast One-Hybrid Screens Using a Cell Surface gLUC Reporter

Gene-centered yeast one-hybrid (Y1H) screens using arrayed genome-wide transcription factor (TF) clone collections provide a simple and effective strategy to identify TF-promoter interactions using a DNA fragment as bait. In an effort to improve the assay we recently developed a Y1H system that uses a cell surface Gaussia luciferase reporter (gLUC59). Compared to other available methods, this luciferase-based strategy requires a shorter processing time, enhances the throughput and improves result analysis of gene-centered Y1H screens. Here, we described the procedure to perform high-throughput screens using this novel strategy, which involves a protocol for mating two haploid yeast strains carrying an arrayed TF clone collection and a promoter::gLUC59 reporter, respectively, and a protocol for analyzing gLUC59 activity in the resulting diploid cells. © 2019 by John Wiley & Sons, Inc.

Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences

Identifying the sets of transcription factors (TFs) that regulate each human gene is a daunting task that requires integrating numerous experimental and computational approaches. One such method is the yeast one-hybrid (Y1H) assay, in which interactions between TFs and DNA regions are tested in the milieu of the yeast nucleus using reporter genes. Y1H assays involve two components: a 'DNA-bait' (e.g., promoters, enhancers, silencers, etc.) and a 'TF-prey,' which can be screened for reporter gene activation. Most published protocols for performing Y1H screens are based on transforming TF-prey libraries or arrays into DNA-bait yeast strains. Here, we describe a pipeline, called enhanced Y1H (eY1H) assays, where TF-DNA interactions are interrogated by mating DNA-bait strains with an arrayed collection of TF-prey strains using a high density array (HDA) robotic platform that allows screening in a 1,536 colony format. This allows for a dramatic increase in throughput (60 DNA-bait sequences against >1,000 TFs takes two weeks per researcher) and reproducibility. We illustrate the different types of expected results by testing human promoter sequences against an array of 1,086 human TFs, as well as examples of issues that can arise during screens and how to troubleshoot them.

Trinucleotide Base Pair Stacking Free Energy for Understanding TF-DNA Recognition and the Functions of SNPs

Single nucleotide polymorphisms (SNPs) affect base pair stacking, which is the primary factor for maintaining the stability of DNA. However, the mechanism of how SNPs lead to phenotype variations is still unclear. In this work, we connected SNPs and base pair stacking by a 3-mer base pair stacking free energy matrix. The SNPs with large base pair stacking free energy differences led to phenotype variations. A molecular dynamics (MD) simulation was then applied. Our results showed that base pair stacking played an important role in the transcription factor (TF)-DNA interaction. Changes in DNA structure mainly originate from TF-DNA interactions, and with the increased base pair stacking free energy, the structure of DNA approaches its free type, although its binding affinity was increased by the SNP. In addition, quantitative models using base pair stacking features revealed that base pair stacking can be used to predict TF binding specificity. As such, our work combined knowledge from bioinformatics and structural biology and provided a new understanding of the relationship between SNPs and phenotype variations. The 3-mer base pair stacking free energy matrix is useful in high-throughput screening of SNPs and predicting TF-DNA binding affinity.

Pituitary Transcription Factor Mutations Leading to Hypopituitarism

Congenital pituitary hormone deficiency is a disabling condition. It is part of a spectrum of disorders including craniofacial midline developmental defects ranging from holoprosencephaly through septo-optic dysplasia to combined and isolated pituitary hormone deficiency. The first genes discovered in the human disease were based on mouse models of dwarfism due to mutations in transcription factor genes. High-throughput DNA sequencing technologies enabled clinicians and researchers to find novel genetic causes of hypopituitarism for the more than three quarters of patients without a known genetic diagnosis to date. Transcription factor (TF) genes are at the forefront of the functional analysis of novel variants of unknown significance due to the relative ease in in vitro testing in a research lab. Genetic testing in hypopituitarism is of high importance to the individual and their family to predict phenotype composition, disease progression and to avoid life-threatening complications such as secondary adrenal insufficiency.This chapter aims to highlight our current understanding about (1) the contribution of TF genes to pituitary development (2) the diversity of inheritance and phenotype features in combined and select isolated pituitary hormone deficiency and (3) provide an initial assessment on how to approach variants of unknown significance in human hypopituitarism. Our better understanding on how transcription factor gene variants lead to hypopituitarism is a meaningful step to plan advanced therapies to specific genetic changes in the future.

The transcription factor POU3F2 regulates a gene coexpression network in brain tissue from patients with psychiatric disorders

Schizophrenia and bipolar disorder are complex psychiatric diseases with risks contributed by multiple genes. Dysregulation of gene expression has been implicated in these disorders, but little is known about such dysregulation in the human brain. We analyzed three transcriptome datasets from 394 postmortem brain tissue samples from patients with schizophrenia or bipolar disorder or from healthy control individuals without a known history of psychiatric disease. We built genome-wide coexpression networks that included microRNAs (miRNAs). We identified a coexpression network module that was differentially expressed in the brain tissue from patients compared to healthy control individuals. This module contained genes that were principally involved in glial and neural cell genesis and glial cell differentiation, and included schizophrenia risk genes carrying rare variants. This module included five miRNAs and 545 mRNAs, with six transcription factors serving as hub genes in this module. We found that the most connected transcription factor gene POU3F2, also identified on a genome-wide association study for bipolar disorder, could regulate the miRNA hsa-miR-320e and other putative target mRNAs. These regulatory relationships were replicated using PsychENCODE/BrainGVEX datasets and validated by knockdown and overexpression experiments in SH-SY5Y cells and human neural progenitor cells in vitro. Thus, we identified a brain gene expression module that was enriched for rare coding variants in genes associated with schizophrenia and that contained the putative bipolar disorder risk gene POU3F2 The transcription factor POU3F2 may be a key regulator of gene expression in this disease-associated gene coexpression module.

Extracting Complementary Insights from Molecular Phenotypes for Prioritization of Disease-Associated Mutations

Rapid advances in next-generation sequencing technology have resulted in an explosion of whole-exome/genome sequencing data, providing an unprecedented opportunity to identify disease- and trait-associated variants in humans on a large scale. To date, the long-standing paradigm has leveraged fitness-based approximations to translate this ever-expanding sequencing data into causal insights in disease. However, while this approach robustly identifies variants under evolutionary constraint, it fails to provide molecular insights. Moreover, complex disease phenomena often violate standard assumptions of a direct organismal phenotype to overall fitness effect relationship. Here we discuss the potential of a molecular phenotype-oriented paradigm to uniquely identify candidate disease-causing mutations from the human genetic background. By providing a direct connection between single nucleotide mutations and observable organismal and cellular phenotypes associated with disease, we suggest that molecular phenotypes can readily incorporate alongside established fitness-based methodologies to provide complementary insights to the functional impact of human mutations. Lastly, we discuss how integrated approaches between molecular phenotypes and fitness-based perspectives facilitate new insights into the molecular mechanisms underlying disease-associated mutations while also providing a platform for improved interpretation of epistasis in human disease.

LHX2 Mediates the FGF-to-SHH Regulatory Loop during Limb Development

During limb development, fibroblast growth factors (Fgfs) govern proximal⁻distal outgrowth and patterning. FGFs also synchronize developmental patterning between the proximal⁻distal and anterior⁻posterior axes by maintaining Sonic hedgehog (Shh) expression in cells of the zone of polarizing activity (ZPA) in the distal posterior mesoderm. Shh, in turn, maintains Fgfs in the apical ectodermal ridge (AER) that caps the distal tip of the limb bud. Crosstalk between Fgf and Shh signaling is critical for patterned limb development, but the mechanisms underlying this feedback loop are not well-characterized. Implantation of Fgf beads in the proximal posterior limb bud can maintain SHH expression in the former ZPA domain (evident 3 h after application), while prolonged exposure (24 h) can induce SHH outside of this domain. Although temporally and spatially disparate, comparative analysis of transcriptome data from these different populations accentuated genes involved in SHH regulation. Comparative analysis identified 25 candidates common to both treatments, with eight linked to SHH expression or function. Furthermore, we demonstrated that LHX2, a LIM Homeodomain transcription factor, is an intermediate in the FGF-mediated regulation of SHH. Our data suggest that LHX2 acts as a competency factor maintaining distal posterior SHH expression subjacent to the AER.