The genomic sequence of Escherichia coli appeared in 1997. Since 1998, this R01 program has systematically built genome-scale models of E. coli, culminating with models of Metabolism and protein Expression (ME models) that compute up to 80% of the proteome by mass in rapidly growing cells [1]. The models generated under this program have deepened our understanding of how to read genomes, computationally model the physiological processes they encode, and to guide design of interventions. These genome-scale models have enabled hundreds, possibly over a thousand, systems biology studies of bacteria. Half of the unmodeled proteome represents stress functions that provide responses to oxidative, thermal, and acid stresses. The human immune system uses these stresses to eradicate pathogens, but no mechanistic model can currently compute how these stresses perturb key cellular processes on a genome-scale. Therefore, new modeling methods are needed. Our preliminary data strongly indicate that the underlying molecular mechanisms can be modeled by extending the ME modeling approach. Our laboratory has also developed Adaptive Laboratory Evolution (ALE) technology capable of generating hundreds of evolved strains and high precision DNA assembly protocols to find all mutations occurring during ALE. We can thus computationally model, experimentally evolve, molecularly profile and mechanistically determine the genetic basis of stress tolerance. We propose an iterative three-step process that will 1) EVOLVE E. coli under various stress conditions, 2) ANALYZE the resulting phenotypes through data analytics and mechanistic modeling, and 3) VALIDATE model- driven hypotheses. This iterative workflow takes advantage of our ALE technology, extensive mutational and RNA-Seq databases with accompanying data analytics, and genome-scale modeling capabilities to elucidate cellular responses to oxidative, thermal, and acid stresses. A major outcome of the proposed program is an experimentally-validated genome-scale model that employs novel methodologies to describe stress responses, metabolism and protein expression (called the StressME model) that increase computational coverage of the E. coli proteome up to 90% of proteome mass. In particular, the three stresses that we propose to study are critical for a deep systems-level understanding of the tolerance that pathogens have against the stresses imposed by the immune system and certain types of antimicrobials. The genetic basis revealed can be compared to characteristics of wild type strains isolated from patients. Thus, these new models will facilitate future translational studies that investigate infectious disease.

Public Health Relevance

Immune systems eradicate pathogens by applying oxidative, thermal, and acid stresses. This R01 program builds genome-scale computer models of pathogen response mechanisms to these stresses in genetic and molecular detail to help develop a deeper understanding of infectious disease and its treatment. The program also uses a novel adaptive laboratory evolution platform to find key sequence mutations that tolerize pathogens to these stresses that can be compared to the mutations found in virulent strains isolated from patients.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
2R01GM057089-17A1
Application #
9597113
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Gregurick, Susan
Project Start
1998-08-01
Project End
2022-06-30
Budget Start
2018-09-01
Budget End
2019-06-30
Support Year
17
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of California, San Diego
Department
Engineering (All Types)
Type
Schools of Arts and Sciences
DUNS #
804355790
City
La Jolla
State
CA
Country
United States
Zip Code
92093
Brunk, Elizabeth; Chang, Roger L; Xia, Jing et al. (2018) Characterizing posttranslational modifications in prokaryotic metabolism using a multiscale workflow. Proc Natl Acad Sci U S A 115:11096-11101
Latif, Haythem; Federowicz, Stephen; Ebrahim, Ali et al. (2018) ChIP-exo interrogation of Crp, DNA, and RNAP holoenzyme interactions. PLoS One 13:e0197272
Brunk, Elizabeth; Sahoo, Swagatika; Zielinski, Daniel C et al. (2018) Recon3D enables a three-dimensional view of gene variation in human metabolism. Nat Biotechnol 36:272-281
Sastry, Anand; Monk, Jonathan; Tegel, Hanna et al. (2017) Machine learning in computational biology to accelerate high-throughput protein expression. Bioinformatics 33:2487-2495
Fang, Xin; Sastry, Anand; Mih, Nathan et al. (2017) Global transcriptional regulatory network for Escherichia coli robustly connects gene expression to transcription factor activities. Proc Natl Acad Sci U S A 114:10286-10291
Chen, Ke; Gao, Ye; Mih, Nathan et al. (2017) Thermosensitivity of growth is determined by chaperone-mediated proteome reallocation. Proc Natl Acad Sci U S A 114:11548-11553
Yurkovich, James T; Yang, Laurence; Palsson, Bernhard O (2017) Biomarkers are used to predict quantitative metabolite concentration profiles in human red blood cells. PLoS Comput Biol 13:e1005424
Monk, Jonathan M; Lloyd, Colton J; Brunk, Elizabeth et al. (2017) iML1515, a knowledgebase that computes Escherichia coli traits. Nat Biotechnol 35:904-908
Brunk, Elizabeth; George, Kevin W; Alonso-Gutierrez, Jorge et al. (2016) Characterizing Strain Variation in Engineered E. coli Using a Multi-Omics-Based Workflow. Cell Syst 2:335-46
Brunk, Elizabeth; Mih, Nathan; Monk, Jonathan et al. (2016) Systems biology of the structural proteome. BMC Syst Biol 10:26

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