Disease Model of GATA4 Mutation Reveals Transcription Factor Cooperativity in Human Cardiogenesis.

Mutation of highly conserved residues in transcription factors may affect protein-protein or protein-DNA interactions, leading to gene network dysregulation and human disease. Human mutations in GATA4, a cardiogenic transcription factor, cause cardiac septal defects and cardiomyopathy. Here, iPS-derived cardiomyocytes from subjects with a heterozygous GATA4-G296S missense mutation showed impaired contractility, calcium handling, and metabolic activity. In human cardiomyocytes, GATA4 broadly co-occupied cardiac enhancers with TBX5, another transcription factor that causes septal defects when mutated. The GATA4-G296S mutation disrupted TBX5 recruitment, particularly to cardiac super-enhancers, concomitant with dysregulation of genes related to the phenotypic abnormalities, including cardiac septation. Conversely, the GATA4-G296S mutation led to failure of GATA4 and TBX5-mediated repression at non-cardiac genes and enhanced open chromatin states at endothelial/endocardial promoters. These results reveal how disease-causing missense mutations can disrupt transcriptional cooperativity, leading to aberrant chromatin states and cellular dysfunction, including those related to morphogenetic defects.

Genetic Control of Chromatin States in Humans Involves Local and Distal Chromosomal Interactions.

Deciphering the impact of genetic variants on gene regulation is fundamental to understanding human disease. Although gene regulation often involves long-range interactions, it is unknown to what extent non-coding genetic variants influence distal molecular phenotypes. Here, we integrate chromatin profiling for three histone marks in lymphoblastoid cell lines (LCLs) from 75 sequenced individuals with LCL-specific Hi-C and ChIA-PET-based chromatin contact maps to uncover one of the largest collections of local and distal histone quantitative trait loci (hQTLs). Distal QTLs are enriched within topologically associated domains and exhibit largely concordant variation of chromatin state coordinated by proximal and distal non-coding genetic variants. Histone QTLs are enriched for common variants associated with autoimmune diseases and enable identification of putative target genes of disease-associated variants from genome-wide association studies. These analyses provide insights into how genetic variation can affect human disease phenotypes by coordinated changes in chromatin at interacting regulatory elements.

Landscape of cohesin-mediated chromatin loops in the human genome.

Physical interactions between distal regulatory elements have a key role in regulating gene expression, but the extent to which these interactions vary between cell types and contribute to cell-type-specific gene expression remains unclear. Here, to address these questions as part of phase III of the Encyclopedia of DNA Elements (ENCODE), we mapped cohesin-mediated chromatin loops, using chromatin interaction analysis by paired-end tag sequencing (ChIA-PET), and analysed gene expression in 24 diverse human cell types, including core ENCODE cell lines. Twenty-eight per cent of all chromatin loops vary across cell types; these variations modestly correlate with changes in gene expression and are effective at grouping cell types according to their tissue of origin. The connectivity of genes corresponds to different functional classes, with housekeeping genes having few contacts, and dosage-sensitive genes being more connected to enhancer elements. This atlas of chromatin loops complements the diverse maps of regulatory architecture that comprise the ENCODE Encyclopedia, and will help to support emerging analyses of genome structure and function.

A Network of Conserved Synthetic Lethal Interactions for Exploration of Precision Cancer Therapy.

An emerging therapeutic strategy for cancer is to induce selective lethality in a tumor by exploiting interactions between its driving mutations and specific drug targets. Here we use a multi-species approach to develop a resource of synthetic lethal interactions relevant to cancer therapy. First, we screen in yeast ∼169,000 potential interactions among orthologs of human tumor suppressor genes (TSG) and genes encoding drug targets across multiple genotoxic environments. Guided by the strongest signal, we evaluate thousands of TSG-drug combinations in HeLa cells, resulting in networks of conserved synthetic lethal interactions. Analysis of these networks reveals that interaction stability across environments and shared gene function increase the likelihood of observing an interaction in human cancer cells. Using these rules, we prioritize ∼10(5) human TSG-drug combinations for future follow-up. We validate interactions based on cell and/or patient survival, including topoisomerases with RAD17 and checkpoint kinases with BLM.

Yeast PP4 interacts with ATR homolog Ddc2-Mec1 and regulates checkpoint signaling.

Mec1-Ddc2 (ATR-ATRIP) controls the DNA damage checkpoint and shows differential cell-cycle regulation in yeast. To find regulators of Mec1-Ddc2, we exploited a mec1 mutant that retains catalytic activity in G2 and recruitment to stalled replication forks, but which is compromised for the intra-S phase checkpoint. Two screens, one for spontaneous survivors and an E-MAP screen for synthetic growth effects, identified loss of PP4 phosphatase, pph3Δ and psy2Δ, as the strongest suppressors of mec1-100 lethality on HU. Restored Rad53 phosphorylation accounts for part, but not all, of the pph3Δ-mediated survival. Phosphoproteomic analysis confirmed that 94% of the mec1-100-compromised targets on HU are PP4 regulated, including a phosphoacceptor site within Mec1 itself, mutation of which confers damage sensitivity. Physical interaction between Pph3 and Mec1, mediated by cofactors Psy2 and Ddc2, is shown biochemically and through FRET in subnuclear repair foci. This establishes a physical and functional Mec1-PP4 unit for regulating the checkpoint response.

Dissection of DNA damage responses using multiconditional genetic interaction maps.

To protect the genome, cells have evolved a diverse set of pathways designed to sense, signal, and repair multiple types of DNA damage. To assess the degree of coordination and crosstalk among these pathways, we systematically mapped changes in the cell's genetic network across a panel of different DNA-damaging agents, resulting in ~1,800,000 differential measurements. Each agent was associated with a distinct interaction pattern, which, unlike single-mutant phenotypes or gene expression data, has high statistical power to pinpoint the specific repair mechanisms at work. The agent-specific networks revealed roles for the histone acetyltranferase Rtt109 in the mutagenic bypass of DNA lesions and the neddylation machinery in cell-cycle regulation and genome stability, while the network induced by multiple agents implicates Irc21, an uncharacterized protein, in checkpoint control and DNA repair. Our multiconditional genetic interaction map provides a unique resource that identifies agent-specific and general DNA damage response pathways.

Dot1 promotes H2B ubiquitination by a methyltransferase-independent mechanism.

The histone methyltransferase Dot1 is conserved from yeast to human and methylates lysine 79 of histone H3 (H3K79) on the core of the nucleosome. H3K79 methylation by Dot1 affects gene expression and the response to DNA damage, and is enhanced by monoubiquitination of the C-terminus of histone H2B (H2Bub1). To gain more insight into the functions of Dot1, we generated genetic interaction maps of increased-dosage alleles of DOT1. We identified a functional relationship between increased Dot1 dosage and loss of the DUB module of the SAGA co-activator complex, which deubiquitinates H2Bub1 and thereby negatively regulates H3K79 methylation. Increased Dot1 dosage was found to promote H2Bub1 in a dose-dependent manner and this was exacerbated by the loss of SAGA-DUB activity, which also caused a negative genetic interaction. The stimulatory effect on H2B ubiquitination was mediated by the N-terminus of Dot1, independent of methyltransferase activity. Our findings show that Dot1 and H2Bub1 are subject to bi-directional crosstalk and that Dot1 possesses chromatin regulatory functions that are independent of its methyltransferase activity.

Attenuated IL-2 muteins leverage the TCR signal to enhance regulatory T cell homeostasis and response in vivo.

Interleukin-2 (IL-2), along with T-cell receptor (TCR) signaling, are required to control regulatory T cell (Treg) homeostasis and function in vivo. Due to the heightened sensitivity to IL-2, Tregs retain the ability to respond to low-dose or attenuated forms of IL-2, as currently being developed for clinical use to treat inflammatory diseases. While attenuated IL-2 increases Treg selectivity, the question remains as to whether a weakened IL-2 signal sufficiently enhances Treg suppressive function(s) toward disease modification. To understand this question, we characterized the in vivo activity and transcriptomic profiles of two different attenuated IL-2 muteins in comparison with wildtype (WT) IL-2. Our study showed that, in addition to favoring Tregs, the attenuated muteins induced disproportionately robust effects on Treg activation and conversion to effector Treg (eTreg) phenotype. Our data furthermore suggested that Tregs activated by attenuated IL-2 muteins showed reduced dependence on TCR signal, at least in part due to the enhanced ability of IL-2 muteins to amplify the TCR signal in vivo. These results point to a new paradigm wherein IL-2 influences Tregs' sensitivity to antigenic signal, and that the combination effect may be leveraged for therapeutic use of attenuated IL-2 muteins.

Quantitative proteomics reveal ATM kinase-dependent exchange in DNA damage response complexes.

ATM is a protein kinase that initiates a well-characterized signaling cascade in cells exposed to ionizing radiation (IR). However, the role for ATM in coordinating critical protein interactions and subsequent exchanges within DNA damage response (DDR) complexes is unknown. We combined SILAC-based tandem mass spectrometry and a subcellular fractionation protocol to interrogate the proteome of irradiated cells treated with or without the ATM kinase inhibitor KU55933. We developed an integrative network analysis to identify and prioritize proteins that were responsive to KU55933, specifically in chromatin, and that were also enriched for physical interactions with known DNA repair proteins. This analysis identified 53BP1 and annexin A1 (ANXA1) as strong candidates. Using fluorescence recovery after photobleaching, we found that the exchange of GFP-53BP1 in DDR complexes decreased with KU55933. Further, we found that ANXA1 knockdown sensitized cells to IR via a mechanism that was not potentiated by KU55933. Our study reveals a role for ATM kinase activity in the dynamic exchange of proteins in DDR complexes and identifies a role for ANXA1 in cellular radioprotection.

Genome-wide association data reveal a global map of genetic interactions among protein complexes.

This work demonstrates how gene association studies can be analyzed to map a global landscape of genetic interactions among protein complexes and pathways. Despite the immense potential of gene association studies, they have been challenging to analyze because most traits are complex, involving the combined effect of mutations at many different genes. Due to lack of statistical power, only the strongest single markers are typically identified. Here, we present an integrative approach that greatly increases power through marker clustering and projection of marker interactions within and across protein complexes. Applied to a recent gene association study in yeast, this approach identifies 2,023 genetic interactions which map to 208 functional interactions among protein complexes. We show that such interactions are analogous to interactions derived through reverse genetic screens and that they provide coverage in areas not yet tested by reverse genetic analysis. This work has the potential to transform gene association studies, by elevating the analysis from the level of individual markers to global maps of genetic interactions. As proof of principle, we use synthetic genetic screens to confirm numerous novel genetic interactions for the INO80 chromatin remodeling complex.

Remodeling of active endothelial enhancers is associated with aberrant gene-regulatory networks in pulmonary arterial hypertension.

Environmental and epigenetic factors often play an important role in polygenic disorders. However, how such factors affect disease-specific tissues at the molecular level remains to be understood. Here, we address this in pulmonary arterial hypertension (PAH). We obtain pulmonary arterial endothelial cells (PAECs) from lungs of patients and controls (n = 19), and perform chromatin, transcriptomic and interaction profiling. Overall, we observe extensive remodeling at active enhancers in PAH PAECs and identify hundreds of differentially active TFs, yet find very little transcriptomic changes in steady-state. We devise a disease-specific enhancer-gene regulatory network and predict that primed enhancers in PAH PAECs are activated by the differentially active TFs, resulting in an aberrant response to endothelial signals, which could lead to disturbed angiogenesis and endothelial-to-mesenchymal-transition. We validate these predictions for a selection of target genes in PAECs stimulated with TGF-ß, VEGF or serotonin. Our study highlights the role of chromatin state and enhancers in disease-relevant cell types of PAH.

Early Assessment of Molecular Progression and Response by Whole-genome Circulating Tumor DNA in Advanced Solid Tumors.

Treatment response assessment for patients with advanced solid tumors is complex and existing methods require greater precision. Current guidelines rely on imaging, which has known limitations, including the time required to show a deterministic change in target lesions. Serial changes in whole-genome (WG) circulating tumor DNA (ctDNA) were used to assess response or resistance to treatment early in the treatment course. Ninety-six patients with advanced cancer were prospectively enrolled (91 analyzed and 5 excluded), and blood was collected before and after initiation of a new, systemic treatment. Plasma cell-free DNA libraries were prepared for either WG or WG bisulfite sequencing. Longitudinal changes in the fraction of ctDNA were quantified to retrospectively identify molecular progression (MP) or major molecular response (MMR). Study endpoints were concordance with first follow-up imaging (FFUI) and stratification of progression-free survival (PFS) and overall survival (OS). Patients with MP (n = 13) had significantly shorter PFS (median 62 days vs. 310 days) and OS (255 days vs. not reached). Sensitivity for MP to identify clinical progression was 54% and specificity was 100%. MP calls were from samples taken a median of 28 days into treatment and 39 days before FFUI. Patients with MMR (n = 27) had significantly longer PFS and OS compared with those with neither call (n = 51). These results demonstrated that ctDNA changes early after treatment initiation inform response to treatment and correlate with long-term clinical outcomes. Once validated, molecular response assessment can enable early treatment change minimizing side effects and costs associated with additional cycles of ineffective treatment.

Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems.

Accurately translating genotype to phenotype requires accounting for the functional impact of genetic variation at many biological scales. Here we present a strategy for genotype-phenotype reasoning based on existing knowledge of cellular subsystems. These subsystems and their hierarchical organization are defined by the Gene Ontology or a complementary ontology inferred directly from previously published datasets. Guided by the ontology's hierarchical structure, we organize genotype data into an "ontotype," that is, a hierarchy of perturbations representing the effects of genetic variation at multiple cellular scales. The ontotype is then interpreted using logical rules generated by machine learning to predict phenotype. This approach substantially outperforms previous, non-hierarchical methods for translating yeast genotype to cell growth phenotype, and it accurately predicts the growth outcomes of two new screens of 2,503 double gene knockouts impacting DNA repair or nuclear lumen. Ontotypes also generalize to larger knockout combinations, setting the stage for interpreting the complex genetics of disease.

Chemogenetic profiling identifies RAD17 as synthetically lethal with checkpoint kinase inhibition.

Chemical inhibitors of the checkpoint kinases have shown promise in the treatment of cancer, yet their clinical utility may be limited by a lack of molecular biomarkers to identify specific patients most likely to respond to therapy. To this end, we screened 112 known tumor suppressor genes for synthetic lethal interactions with inhibitors of the CHEK1 and CHEK2 checkpoint kinases. We identified eight interactions, including the Replication Factor C (RFC)-related protein RAD17. Clonogenic assays in RAD17 knockdown cell lines identified a substantial shift in sensitivity to checkpoint kinase inhibition (3.5-fold) as compared to RAD17 wild-type. Additional evidence for this interaction was found in a large-scale functional shRNA screen of over 100 genotyped cancer cell lines, in which CHEK1/2 mutant cell lines were unexpectedly sensitive to RAD17 knockdown. This interaction was widely conserved, as we found that RAD17 interacts strongly with checkpoint kinases in the budding yeast Saccharomyces cerevisiae. In the setting of RAD17 knockdown, CHEK1/2 inhibition was found to be synergistic with inhibition of WEE1, another pharmacologically relevant checkpoint kinase. Accumulation of the DNA damage marker γH2AX following chemical inhibition or transient knockdown of CHEK1, CHEK2 or WEE1 was magnified by knockdown of RAD17. Taken together, our data suggest that CHEK1 or WEE1 inhibitors are likely to have greater clinical efficacy in tumors with RAD17 loss-of-function.

A UV-induced genetic network links the RSC complex to nucleotide excision repair and shows dose-dependent rewiring.

Efficient repair of UV-induced DNA damage requires the precise coordination of nucleotide excision repair (NER) with numerous other biological processes. To map this crosstalk, we generated a differential genetic interaction map centered on quantitative growth measurements of >45,000 double mutants before and after different doses of UV radiation. Integration of genetic data with physical interaction networks identified a global map of 89 UV-induced functional interactions among 62 protein complexes, including a number of links between the RSC complex and several NER factors. We show that RSC is recruited to both silenced and transcribed loci following UV damage where it facilitates efficient repair by promoting nucleosome remodeling. Finally, a comparison of the response to high versus low levels of UV shows that the degree of genetic rewiring correlates with dose of UV and reveals a network of dose-specific interactions. This study makes available a large resource of UV-induced interactions, and it illustrates a methodology for identifying dose-dependent interactions based on quantitative shifts in genetic networks.

Assembling global maps of cellular function through integrative analysis of physical and genetic networks.

To take full advantage of high-throughput genetic and physical interaction mapping projects, the raw interactions must first be assembled into models of cell structure and function. PanGIA (for physical and genetic interaction alignment) is a plug-in for the bioinformatics platform Cytoscape, designed to integrate physical and genetic interactions into hierarchical module maps. PanGIA identifies 'modules' as sets of proteins whose physical and genetic interaction data matches that of known protein complexes. Higher-order functional cooperativity and redundancy is identified by enrichment for genetic interactions across modules. This protocol begins with importing interaction networks into Cytoscape, followed by filtering and basic network visualization. Next, PanGIA is used to infer a set of modules and their functional inter-relationships. This module map is visualized in a number of intuitive ways, and modules are tested for functional enrichment and overlap with known complexes. The full protocol can be completed between 10 and 30 min, depending on the size of the data set being analyzed.

Global reconstruction of the human metabolic network based on genomic and bibliomic data.

Metabolism is a vital cellular process, and its malfunction is a major contributor to human disease. Metabolic networks are complex and highly interconnected, and thus systems-level computational approaches are required to elucidate and understand metabolic genotype-phenotype relationships. We have manually reconstructed the global human metabolic network based on Build 35 of the genome annotation and a comprehensive evaluation of >50 years of legacy data (i.e., bibliomic data). Herein we describe the reconstruction process and demonstrate how the resulting genome-scale (or global) network can be used (i) for the discovery of missing information, (ii) for the formulation of an in silico model, and (iii) as a structured context for analyzing high-throughput biological data sets. Our comprehensive evaluation of the literature revealed many gaps in the current understanding of human metabolism that require future experimental investigation. Mathematical analysis of network structure elucidated the implications of intracellular compartmentalization and the potential use of correlated reaction sets for alternative drug target identification. Integrated analysis of high-throughput data sets within the context of the reconstruction enabled a global assessment of functional metabolic states. These results highlight some of the applications enabled by the reconstructed human metabolic network. The establishment of this network represents an important step toward genome-scale human systems biology.