Rohith Srivas

Stanford University, Palo Alto, California, United States

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Publications (9)80.54 Total impact

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    ABSTRACT: 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.Oncogene advance online publication, 2 March 2015; doi:10.1038/onc.2015.22.
    Oncogene 03/2015; DOI:10.1038/onc.2015.22 · 8.56 Impact Factor
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    ABSTRACT: Mec1-Ddc2 (ATR-ATRIP) controls the DNA damage checkpoint and shows differential cell-cycle regula-tion in yeast. To find regulators of Mec1-Ddc2, we ex-ploited a mec1 mutant that retains catalytic activity in G2 and recruitment to stalled replication forks, but which is compromised for the intra-S phase check-point. Two screens, one for spontaneous survivors and an E-MAP screen for synthetic growth effects, identified loss of PP4 phosphatase, pph3D and psy2D, as the strongest suppressors of mec1-100 lethality on HU. Restored Rad53 phosphorylation ac-counts for part, but not all, of the pph3D-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. INTRODUCTION
    Molecular Cell 01/2015; 57(2). DOI:10.1016/j.molcel.2014.11.016 · 14.46 Impact Factor
  • Molecular Cancer Research 11/2014; 12(11 Supplement):B34-B34. DOI:10.1158/1557-3125.MODORG-B34 · 4.50 Impact Factor
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    ABSTRACT: 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.
    Cell Reports 12/2013; DOI:10.1016/j.celrep.2013.11.035 · 7.21 Impact Factor
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    ABSTRACT: 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.
    Molecular cell 12/2012; DOI:10.1016/j.molcel.2012.11.023 · 14.46 Impact Factor
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    ABSTRACT: 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.
    Journal of Proteome Research 08/2012; 11(10):4983-91. DOI:10.1021/pr3005524 · 5.00 Impact Factor
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    ABSTRACT: 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.
    Nature Protocol 08/2011; 6(9):1308-23. DOI:10.1038/nprot.2011.368 · 8.36 Impact Factor
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    ABSTRACT: 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.
    PLoS Genetics 12/2009; 5(12):e1000782. DOI:10.1371/journal.pgen.1000782 · 8.17 Impact Factor
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    ABSTRACT: 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.
    Proceedings of the National Academy of Sciences 02/2007; 104(6):1777-82. DOI:10.1073/pnas.0610772104 · 9.81 Impact Factor

Publication Stats

706 Citations
80.54 Total Impact Points


  • 2015
    • Stanford University
      • Department of Genetics
      Palo Alto, California, United States
  • 2007–2015
    • University of California, San Diego
      • • Department of Medicine
      • • Department of Bioengineering
      San Diego, California, United States