Person: Roth, Fritz
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Roth, Fritz
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Publication Interpreting Cancer Genomes Using Systematic Host Perturbations by Tumour Virus Proteins(Nature Publishing Group, 2012) Rozenblatt-Rosen, Orit; Deo, Rahul C.; Dricot, Amélie; Askenazi, Manor; Tavares, Maria; Abderazzaq, Fieda; Byrdsong, Danielle; Correll, Mick; Fan, Changyu; Feltkamp, Mariet C.; Franchi, Rachel; Garg, Brijesh K.; Gulbahce, Natali; Hao, Tong; Korkhin, Anna; Litovchick, Larisa; Mar, Jessica C.; Pak, Theodore R.; Rabello, Sabrina; Rubio, Renee; Shen, Yun; Tasan, Murat; Wanamaker, Shelly; Roecklein-Canfield, Jennifer; Johannsen, Eric; Barabási, Albert-László; Padi, Megha; Adelmant, Guillaume; Calderwood, Michael; Rolland, Thomas; Grace, Miranda; Pevzner, Samuel; Carvunis, Anne-Ruxandra; Chen, Alyce; Cheng, Jingwei; Duarte, Melissa; Ficarro, Scott; Holthaus, Amy Marie; James, Robert; Singh, Saurav; Spangle, Jennifer; Webber, James T.; Beroukhim, Rameen; Kieff, Elliott; Cusick, Michael; Hill, David; Munger, Karl; Marto, Jarrod; Quackenbush, John; Roth, Fritz; DeCaprio, James; Vidal, MarcGenotypic differences greatly influence susceptibility and resistance to disease. Understanding genotype-phenotype relationships requires that phenotypes be viewed as manifestations of network properties, rather than simply as the result of individual genomic variations. Genome sequencing efforts have identified numerous germline mutations associated with cancer predisposition and large numbers of somatic genomic alterations. However, it remains challenging to distinguish between background, or “passenger” and causal, or “driver” cancer mutations in these datasets. Human viruses intrinsically depend on their host cell during the course of infection and can elicit pathological phenotypes similar to those arising from mutations. To test the hypothesis that genomic variations and tumour viruses may cause cancer via related mechanisms, we systematically examined host interactome and transcriptome network perturbations caused by DNA tumour virus proteins. The resulting integrated viral perturbation data reflects rewiring of the host cell networks, and highlights pathways that go awry in cancer, such as Notch signalling and apoptosis. We show that systematic analyses of host targets of viral proteins can identify cancer genes with a success rate on par with their identification through functional genomics and large-scale cataloguing of tumour mutations. Together, these complementary approaches result in increased specificity for cancer gene identification. Combining systems-level studies of pathogen-encoded gene products with genomic approaches will facilitate prioritization of cancer-causing driver genes so as to advance understanding of the genetic basis of human cancer.Publication RanBP2/Nup358 Potentiates the Translation of a Subset of mRNAs Encoding Secretory Proteins(Public Library of Science, 2013) Mahadevan, Kohila; Zhang, Hui; Akef, Abdalla; Cui, Xianying A.; Gueroussov, Serge; Cenik, Can; Roth, Fritz; Palazzo, Alexander F.In higher eukaryotes, most mRNAs that encode secreted or membrane-bound proteins contain elements that promote an alternative mRNA nuclear export (ALREX) pathway. Here we report that ALREX-promoting elements also potentiate translation in the presence of upstream nuclear factors. These RNA elements interact directly with, and likely co-evolved with, the zinc finger repeats of RanBP2/Nup358, which is present on the cytoplasmic face of the nuclear pore. Finally we show that RanBP2/Nup358 is not only required for the stimulation of translation by ALREX-promoting elements, but is also required for the efficient global synthesis of proteins targeted to the endoplasmic reticulum (ER) and likely the mitochondria. Thus upon the completion of export, mRNAs containing ALREX-elements likely interact with RanBP2/Nup358, and this step is required for the efficient translation of these mRNAs in the cytoplasm. ALREX-elements thus act as nucleotide platforms to coordinate various steps of post-transcriptional regulation for the majority of mRNAs that encode secreted proteins.Publication Viral Perturbations of Host Networks Reflect Disease Etiology(Public Library of Science, 2012) Gulbahce, Natali; Yan, Han; Dricot, Amélie; Padi, Megha; Byrdsong, Danielle; Franchi, Rachel; Lee, Deok-Sun; Rozenblatt-Rosen, Orit; Mar, Jessica C.; Calderwood, Michael; Baldwin, Amy; Zhao, Bo; Santhanam, Balaji; Braun, Pascal; Simonis, Nicolas; Huh, Kyung-Won; Hellner, Karin; Grace, Miranda; Chen, Alyce; Rubio, Renee; Marto, Jarrod; Christakis, Nicholas A.; Kieff, Elliott; Roth, Fritz; Roecklein-Canfield, Jennifer; DeCaprio, James; Cusick, Michael; Quackenbush, John; Hill, David; Münger, Karl; Vidal, Marc; Barabási, Albert-LászlóMany human diseases, arising from mutations of disease susceptibility genes (genetic diseases), are also associated with viral infections (virally implicated diseases), either in a directly causal manner or by indirect associations. Here we examine whether viral perturbations of host interactome may underlie such virally implicated disease relationships. Using as models two different human viruses, Epstein-Barr virus (EBV) and human papillomavirus (HPV), we find that host targets of viral proteins reside in network proximity to products of disease susceptibility genes. Expression changes in virally implicated disease tissues and comorbidity patterns cluster significantly in the network vicinity of viral targets. The topological proximity found between cellular targets of viral proteins and disease genes was exploited to uncover a novel pathway linking HPV to Fanconi anemia.Publication A Race through the Maze of Genomic Evidence(BioMed Central, 2008) Hughes, Timothy R; Roth, FritzPublication Chemical Substructures That Enrich for Biological Activity(Oxford University Press, 2008) Klekota, Justin; Roth, FritzMotivation: Certain chemical substructures are present in many drugs. This has led to the claim of ‘privileged’ substructures which are predisposed to bioactivity. Because bias in screening library construction could explain this phenomenon, the existence of privilege has been controversial. Results: Using diverse phenotypic assays, we defined bioactivity for multiple compound libraries. Many substructures were associated with bioactivity even after accounting for substructure prevalence in the library, thus validating the privileged substructure concept. Determinations of privilege were confirmed in independent assays and libraries. Our analysis also revealed ‘underprivileged’ substructures and ‘conditional privilege’—rules relating combinations of substructure to bioactivity. Most previously reported substructures have been flat aromatic ring systems. Although we validated such substructures, we also identified three-dimensional privileged substructures. Most privileged substructures display a wide variety of substituents suggesting an entropic mechanism of privilege. Compounds containing privileged substructures had a doubled rate of bioactivity, suggesting practical consequences for pharmaceutical discovery.Publication Q&A: Epistasis(BioMed Central, 2009) Roth, Fritz; Lipshitz, Howard D; Andrews, Brenda JPublication An en masse Phenotype and Function Prediction System for Mus musculus(BioMed Central, 2008) Taşan, Murat; Tian, Weidong; Hill, David; Gibbons, Francis D; Blake, Judith A; Roth, FritzBackground: Individual researchers are struggling to keep up with the accelerating emergence of high-throughput biological data, and to extract information that relates to their specific questions. Integration of accumulated evidence should permit researchers to form fewer - and more accurate - hypotheses for further study through experimentation. Results: Here a method previously used to predict Gene Ontology (GO) terms for Saccharomyces cerevisiae (Tian et al.: Combining guilt-by-association and guilt-by-profiling to predict Saccharomyces cerevisiae gene function. Genome Biol 2008, 9(Suppl 1):S7) is applied to predict GO terms and phenotypes for 21,603 Mus musculus genes, using a diverse collection of integrated data sources (including expression, interaction, and sequence-based data). This combined 'guilt-by-profiling' and 'guilt-by-association' approach optimizes the combination of two inference methodologies. Predictions at all levels of confidence are evaluated by examining genes not used in training, and top predictions are examined manually using available literature and knowledge base resources. Conclusion: We assigned a confidence score to each gene/term combination. The results provided high prediction performance, with nearly every GO term achieving greater than 40% precision at 1% recall. Among the 36 novel predictions for GO terms and 40 for phenotypes that were studied manually, >80% and >40%, respectively, were identified as accurate. We also illustrate that a combination of 'guilt-by-profiling' and 'guilt-by-association' outperforms either approach alone in their application to M. musculus.Publication Systematic Exploration of Synergistic Drug Pairs(Nature Publishing Group, 2011) Cokol, Murat; Tasan, Murat; Weinstein, Zohar B; Suzuki, Yo; Nergiz, Mehmet E; Costanzo, Michael; Baryshnikova, Anastasia; Giaever, Guri; Nislow, Corey; Myers, Chad L; Andrews, Brenda J; Boone, Charles; Chua, Hon Nian; Mutlu, Beste; Roth, FritzDrug synergy allows a therapeutic effect to be achieved with lower doses of component drugs. Drug synergy can result when drugs target the products of genes that act in parallel pathways (‘specific synergy’). Such cases of drug synergy should tend to correspond to synergistic genetic interaction between the corresponding target genes. Alternatively, ‘promiscuous synergy’ can arise when one drug non-specifically increases the effects of many other drugs, for example, by increased bioavailability. To assess the relative abundance of these drug synergy types, we examined 200 pairs of antifungal drugs in S. cerevisiae. We found 38 antifungal synergies, 37 of which were novel. While 14 cases of drug synergy corresponded to genetic interaction, 92% of the synergies we discovered involved only six frequently synergistic drugs. Although promiscuity of four drugs can be explained under the bioavailability model, the promiscuity of Tacrolimus and Pentamidine was completely unexpected. While many drug synergies correspond to genetic interactions, the majority of drug synergies appear to result from non-specific promiscuous synergy.Publication Interpreting Metabolomic Profiles using Unbiased Pathway Models(Public Library of Science, 2010) Hunter, Luke; Pare, Guillaume; Vasan, Ramachandran S.; Lewis, Gregory; Wang, Thomas Jue-Fuu; Chasman, Daniel; Gerszten, Robert; Deo, Rahul Chandrakant; Roth, FritzHuman disease is heterogeneous, with similar disease phenotypes resulting from distinct combinations of genetic and environmental factors. Small-molecule profiling can address disease heterogeneity by evaluating the underlying biologic state of individuals through non-invasive interrogation of plasma metabolite levels. We analyzed metabolite profiles from an oral glucose tolerance test (OGTT) in 50 individuals, 25 with normal (NGT) and 25 with impaired glucose tolerance (IGT). Our focus was to elucidate underlying biologic processes. Although we initially found little overlap between changed metabolites and preconceived definitions of metabolic pathways, the use of unbiased network approaches identified significant concerted changes. Specifically, we derived a metabolic network with edges drawn between reactant and product nodes in individual reactions and between all substrates of individual enzymes and transporters. We searched for “active modules”—regions of the metabolic network enriched for changes in metabolite levels. Active modules identified relationships among changed metabolites and highlighted the importance of specific solute carriers in metabolite profiles. Furthermore, hierarchical clustering and principal component analysis demonstrated that changed metabolites in OGTT naturally grouped according to the activities of the System A and L amino acid transporters, the osmolyte carrier SLC6A12, and the mitochondrial aspartate-glutamate transporter SLC25A13. Comparison between NGT and IGT groups supported blunted glucose- and/or insulin-stimulated activities in the IGT group. Using unbiased pathway models, we offer evidence supporting the important role of solute carriers in the physiologic response to glucose challenge and conclude that carrier activities are reflected in individual metabolite profiles of perturbation experiments. Given the involvement of transporters in human disease, metabolite profiling may contribute to improved disease classification via the interrogation of specific transporter activities.