ENVIRONMENT-GENOME INTERACTIONS The genome and molecular circuitry it encodes do not act alone, but are embedded in a web of interactions with environmental factors and stimuli such as nutrients, hormones, drugs, toxins and other chemical compounds. In this project, we will apply a multifaceted approach to systematically study the interactions between the organism and the environment, with the aim of developing large maps of gene-environment interactions and analyzing these maps to exploit fundamental principles and rules of interaction that can be used predictively. We will use two complementary network mapping approaches, one based on sequencing of resistant isolates and another based on a high-density chemogenetic screening platform. The project has three Specific Aims.
Aim 1 is based on the principle that patterns of protective mutations acquired within a genome, when the genome is exposed to a toxic small molecule growth inhibitor, can be used to simultaneously explore protein-protein interactions and study how the organism's genome interacts with this environment. Instead of using a system of knockout strains that represent the entire genome, we will allow the genome to adaptively change when exposed to a small molecule and then analyze it systematically. A total of 60 drugs will be tested in yeast in this assay. These studies will then be mirrored in a haploid human cell line, to perform small-scale whole genome sequencing studies of how human cells mutate away from cancer drugs.
Aim 2 will employ a complementary genetic mapping approach to further develop networks of genes that provide resistance to environmental perturbagens. Taking advantage of a new high-throughput 6144 colony screening format, we will screen a library of 125 chemical compounds in haploid S. cerevisiae strains that over-express or under-express complete genes, modeling the structural changes that develop in genomes exposed to environmental toxins. We will then test a subset of this interaction space among orthologous genes in human cancer cells. In the third aim, we propose to integrate the chemical-genetic interaction maps derived from evolutionary resistance (Aim 1) and genome-wide libraries (Aim 2) with a vast assortment of prior knowledge to build a cross-species model of drug-gene interaction. The goal of this model will be to learn how to predict human drug-gene interactions from diverse information including maps of drug-gene interactions in another species. Collectively, these aims will significantly advance our knowledge of the global gene networks that govern how cells become resistant to drugs. These efforts are led jointly by two pioneers in the systems biology of infectious disease: Dr. Elizabeth Winzeler, who formally joins the SDCSB faculty as of this renewal, and Dr. Sumit Chanda, a current SDCSB investigator. The project leverages the yeast genetic screening platform and computational analysis expertise established by Dr. Trey Ideker, as well as involvement from two SDCSB junior investigators, Dr. Hannah Carter and Dr. Jason Kreisberg.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Specialized Center (P50)
Project #
5P50GM085764-09
Application #
9520173
Study Section
Special Emphasis Panel (ZGM1)
Project Start
2010-09-18
Project End
Budget Start
2018-06-01
Budget End
2019-05-31
Support Year
9
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of California, San Diego
Department
Type
DUNS #
804355790
City
La Jolla
State
CA
Country
United States
Zip Code
92093
Antonova-Koch, Yevgeniya; Meister, Stephan; Abraham, Matthew et al. (2018) Open-source discovery of chemical leads for next-generation chemoprotective antimalarials. Science 362:
Zarrinpar, Amir; Chaix, Amandine; Xu, Zhenjiang Z et al. (2018) Antibiotic-induced microbiome depletion alters metabolic homeostasis by affecting gut signaling and colonic metabolism. Nat Commun 9:2872
Cowell, Annie N; Istvan, Eva S; Lukens, Amanda K et al. (2018) Mapping the malaria parasite druggable genome by using in vitro evolution and chemogenomics. Science 359:191-199
Hoeksema, Marten A; Glass, Christopher K (2018) Nature and nurture of tissue-specific macrophage phenotypes. Atherosclerosis :
Preissl, Sebastian; Fang, Rongxin; Huang, Hui et al. (2018) Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation. Nat Neurosci 21:432-439
Cowell, Annie N; Valdivia, Hugo O; Bishop, Danett K et al. (2018) Exploration of Plasmodium vivax transmission dynamics and recurrent infections in the Peruvian Amazon using whole genome sequencing. Genome Med 10:52
Link, Verena M; Duttke, Sascha H; Chun, Hyun B et al. (2018) Analysis of Genetically Diverse Macrophages Reveals Local and Domain-wide Mechanisms that Control Transcription Factor Binding and Function. Cell 173:1796-1809.e17
Zhang, Wei; Ma, Jianzhu; Ideker, Trey (2018) Classifying tumors by supervised network propagation. Bioinformatics 34:i484-i493
Xiong, Liyang; Cooper, Robert; Tsimring, Lev S (2018) Coexistence and Pattern Formation in Bacterial Mixtures with Contact-Dependent Killing. Biophys J 114:1741-1750
Cooper, Robert; Tsimring, Lev; Hasty, Jeff (2018) Microfluidics-Based Analysis of Contact-dependent Bacterial Interactions. Bio Protoc 8:

Showing the most recent 10 out of 207 publications