Rohith Srivas

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.

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.

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 cells 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.

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.

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.

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.

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.

Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems

Summary 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.

Transcriptional repression of IFNβ1 by ATF2 confers melanoma resistance to therapy.

The resistance of melanoma to current treatment modalities represents a major obstacle for durable therapeutic response, and thus the elucidation of mechanisms of resistance is urgently needed. The crucial functions of activating transcription factor-2 (ATF2) in the development and therapeutic resistance of melanoma have been previously reported, although the precise underlying mechanisms remain unclear. Here, we report a protein kinase C-ɛ (PKCɛ)- and ATF2-mediated mechanism that facilitates resistance by transcriptionally repressing the expression of interferon-β1 (IFNβ1) and downstream type-I IFN signaling that is otherwise induced upon exposure to chemotherapy. Treatment of early-stage melanomas expressing low levels of PKCɛ with chemotherapies relieves ATF2-mediated transcriptional repression of IFNβ1, resulting in impaired S-phase progression, a senescence-like phenotype and increased cell death. This response is lost in late-stage metastatic melanomas expressing high levels of PKCɛ. Notably, nuclear ATF2 and low expression of IFNβ1 in melanoma tumor samples correlates with poor patient responsiveness to biochemotherapy or neoadjuvant IFN-α2a. Conversely, cytosolic ATF2 and induction of IFNβ1 coincides with therapeutic responsiveness. Collectively, we identify an IFNβ1-dependent, cell-autonomous mechanism that contributes to the therapeutic resistance of melanoma via the PKCɛ–ATF2 regulatory axis.