Conferences & Workshops

CCMI’s CRISPR Screening Workshop
December 6, 2017
12:30 PM – 6:00 PM
Sanford Consortium for Regenerative Medicine
See here for more details

CCMI’s ENSEMBL Tutorial
January 11, 2018
8:30 AM – 4:30 PM
UC San Diego, MET Bldg, 141
See here for more details

SDCSB’s Quarterly Systems-to-Synthesis Meeting (S2S Spring 2018)
Thursday, May 17, 2018
1:30 PM – 6:00 PM
UC San Diego, MET Bldg, 141-143
Registration begins spring 2018

 

Weekly Events

Genetics, Bioinformatics and Systems Biology Colloquium (2017-18)

qBio Seminar Series (2017-18)

Systems Biology Career Development Seminars (Spring 2018)

Monthly Events

San Diego Bioinformatics Users Series (SDBUS)

Systems Biology Club

Environment-Genome Interactions

Investigators: Elizabeth Winzeler, Sumit Chanda, Trey Ideker, Hannah Carter, Jason Kreisberg

Over time, many chemotherapeutic agents lose their efficacy as their targets – whether an infectious agent or a cancer – become resistant. The CDC estimated in 2013 that 23,000 Americans die every year due to antibiotic resistant pathogens. Millions of lives were lost in the 1990s because malaria parasites acquired resistance to the most commonly used antimalarial therapy. Drug resistance is a major reason why metastatic disease is nearly always incurable. While one strategy for combating drug resistance is to create new classes of chemotherapies, an alternative approach is to tailor therapies for metastatic or recurrent disease based on whether known resistance alleles (both copy number and single nucleotide variants) are present. Prior knowledge of likely resistance mechanisms can be used to suppress the development of resistant clones by inhibiting multiple pathways simultaneously – a method that has been used to treat HIV infection and cure tuberculosis and HCV.  Before this vision of personalized medicine can be realized though, a more complete understanding of how cells respond to environmental stresses is needed.

In this project, we will study the relationship between genes and chemicals in a variety of experimentally tractable systems in order to better define the resistome – the collection of resistance genes, their precursors and their interactions. Part of this project will focus on the model organism Saccharomyces cerevisiae as it is a highly genetically amendable organism for the interrogation of biological systems. Both the Winzeler lab and the Ideker lab have used yeast extensively to study gene function and genetic variation (Winzeler et al., Science 1998 and Winzeler et al., Science 1999) and to screen gene-gene, gene-environment, or protein-protein interactions (Guenole et al., Mol Cell 2013). As many molecular pathways are well preserved between humans and yeast, quantitative phenotypic data from screens in yeast can be used to infer highly informative network maps of molecular interactions in human cells. To maximize the amount of biological information from a given screen, it is desirable to maximize the test space (number of genes/gene pairs/conditions) and to record multiple orthogonal/conditional phenotypes (Califano et al., Nat Genet 2012 and Dutkowski et al., Nat Biotechnol 2013). This project will use in vitro evolution and sequencing of evolved strains for a diverse set of small molecule inhibitors with a relationship to drug resistance and human cancer to create inter and intramolecular interaction maps. In addition, we will create multidimensional, orthogonal phenotype maps of resistome-drug interactions in yeast using our previously described ultra-high-density screening approach (Bean et al., PLoS One 2014) combined with our new massively parallel time-lapse imaging system to rapidly and automatically generate growth profiles. The major difference between these approaches is that the former identifies specific base changes within the map that are critical for resistance, while the latter considers gene dosage and is relevant to understanding structural rearrangements that develop after cells are exposed to small molecules.  
 
 
 
 
 
 

 
 


 
 

It is now becoming clear that when it exists, knowledge of genetic interactions can be used to predict response to chemotherapy. One recent example found that mutation in RAD50 conferred dramatic sensitivity to combined therapy with topoisomerase and checkpoint inhibitors. Expanded knowledge of similar synthetic lethal and other genetic interactions, discovered in high-throughput yeast screening and validated in human studies, could be used to identify patients likely (or unlikely) to respond to currently-available therapies. In this way, a comprehensive model of drug-gene interaction built upon maps of genetic interactions derived from systematic experimentation, could transform the measured copy number and somatic mutation profile of a tumor into a biomarker of drug response. Therefore, we are also seeking to translate the chemical-genetic maps generated above into a framework for general prediction of such interactions. Such knowledge could have a dramatic clinical impact, as currently there is a notable lack of predictive biomarkers in clinical oncology, a problem that will continue to grow as more targeted therapeutic options become available. In particular it has been proposed that the ability to reliably predict drug resistance before it occurs could inform rational combination therapies designed to prevent the emergence drug resistance.

Recent SDCSB Publications by these Investigators:

  1. Shen, JP, Zhao, D, Sasik, R, Luebeck, J, Birmingham, A, Bojorquez-Gomez, A et al.. Combinatorial CRISPR-Cas9 screens for de novo mapping of genetic interactions. Nat. Methods. 2017;14 (6):573-576. doi: 10.1038/nmeth.4225. PubMed PMID:28319113 PubMed Central PMC5449203.
  2. Soonthornvacharin, S, Rodriguez-Frandsen, A, Zhou, Y, Galvez, F, Huffmaster, NJ, Tripathi, S et al.. Systems-based analysis of RIG-I-dependent signalling identifies KHSRP as an inhibitor of RIG-I receptor activation. Nat Microbiol. 2017;2 :17022. doi: 10.1038/nmicrobiol.2017.22. PubMed PMID:28248290 PubMed Central PMC5338947.
  3. Carter, H, Marty, R, Hofree, M, Gross, AM, Jensen, J, Fisch, KM et al.. Interaction Landscape of Inherited Polymorphisms with Somatic Events in Cancer. Cancer Discov. 2017;7 (4):410-423. doi: 10.1158/2159-8290.CD-16-1045. PubMed PMID:28188128 PubMed Central PMC5460679.
  4. Cowell, AN, Loy, DE, Sundararaman, SA, Valdivia, H, Fisch, K, Lescano, AG et al.. Selective Whole-Genome Amplification Is a Robust Method That Enables Scalable Whole-Genome Sequencing of Plasmodium vivax from Unprocessed Clinical Samples. MBio. 2017;8 (1):. doi: 10.1128/mBio.02257-16. PubMed PMID:28174312 PubMed Central PMC5296604.
  5. Kramer, MH, Farré, JC, Mitra, K, Yu, MK, Ono, K, Demchak, B et al.. Active Interaction Mapping Reveals the Hierarchical Organization of Autophagy. Mol. Cell. 2017;65 (4):761-774.e5. doi: 10.1016/j.molcel.2016.12.024. PubMed PMID:28132844 PubMed Central PMC5439305.
  6. Saito, R, Rocanin-Arjo, A, You, YH, Darshi, M, Van Espen, B, Miyamoto, S et al.. Systems biology analysis reveals role of MDM2 in diabetic nephropathy. JCI Insight. 2016;1 (17):e87877. doi: 10.1172/jci.insight.87877. PubMed PMID:27777973 PubMed Central PMC5070958.
  7. Srivas, R, Shen, JP, Yang, CC, Sun, SM, Li, J, Gross, AM et al.. A Network of Conserved Synthetic Lethal Interactions for Exploration of Precision Cancer Therapy. Mol. Cell. 2016;63 (3):514-25. doi: 10.1016/j.molcel.2016.06.022. PubMed PMID:27453043 PubMed Central PMC5209245.
  8. Engin, HB, Kreisberg, JF, Carter, H. Structure-Based Analysis Reveals Cancer Missense Mutations Target Protein Interaction Interfaces. PLoS ONE. 2016;11 (4):e0152929. doi: 10.1371/journal.pone.0152929. PubMed PMID:27043210 PubMed Central PMC4820104.
  9. Yu, MK, Kramer, M, Dutkowski, J, Srivas, R, Licon, K, Kreisberg, J et al.. Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems. Cell Syst. 2016;2 (2):77-88. doi: 10.1016/j.cels.2016.02.003. PubMed PMID:26949740 PubMed Central PMC4772745.
  10. Carvunis, AR, Wang, T, Skola, D, Yu, A, Chen, J, Kreisberg, JF et al.. Evidence for a common evolutionary rate in metazoan transcriptional networks. Elife. 2015;4 :. doi: 10.7554/eLife.11615. PubMed PMID:26682651 PubMed Central PMC4764585.
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