MSACL 2018 US Abstract

Topic: Microbiology/Virology

Resistome Profiling in Gram Negative Bacteria to Identify Selection-Averse Antimicrobial Drug Targets

Anaamika Campeau (Presenter)
University of California, San Diego

Bio: I am originally from Moorpark, CA. After completing my undergraduate work at UC Berkeley and a Master's degree at the University of Southern California, I entered the Biomedical Sciences Doctoral Program at UCSD, where I am currently in my second year. I work in the lab of Dr. David Gonzalez, using quantitative, mass spectrometry-based proteomic methods to study host-pathogen interactions and antibiotic resistance.

Authorship: Anaamika Campeau, MS (1), Adam Feist, PhD (2), Bernhard Palsson, PhD (2), David Gonzalez, PhD (2) (3)
(1) University of California, San Diego Department of Pharmacology (2) University of California, San Diego Jacobs School of Engineering (3) University of California, San Diego Skagg's School of Pharmacy and Pharmaceutical Sciences

Short Abstract

The advent of Gram negative bacterial strains increasingly resistant to standard regimens of antibiotics represents an imminent public health crisis. Given the long-term unsustainability of traditional antimicrobial drug development, novel, systems-based strategies that combat bacterial infection from multiple angles are needed. Here, we performed adaptive laboratory evolution (ALE) on a model Gram negative, E. coli, and on a closely-related human pathogen, K. pneumoniae. Following the evolution of these strains, we performed whole genome sequencing and quantitative mass spectrometry-based proteomic profiling of evolved strains. This work will ultimately lead to the functional characterization of new, selection-averse drug targets.

Long Abstract

Introduction

While antibiotic resistance is often studied in the context of the mutational analysis of singular drug targets, antibiotic resistance is, in fact, a system-wide achievement. Previous studies have shown that antibiotic resistance in E. coli confers collateral sensitivity to an array of antibiotics from various classes with disparate mechanisms of action1. Resistance to polymyxins, a class of antibiotics that target the outer membrane of gram negative bacteria, leads to collateral sensitivity to rifampin, chloramphenicol, and ampicillin, for example2,3. Given the idiosyncratic nature of collateral sensitivity profiles in polymyxin-resistant bacteria and the dire need for alternate strategies for combatting resistance to antibiotics, our overall short-term goal was to profile collateral vulnerabilities secondary to polymyxin resistance. Specifically, we sought to evaluate the genome and proteome of progressive polymyxin resistance in lab-evolved strains of a well-characterized model organism, Escherichia coli, and a closely-related, clinically important gram negative bacterium, Klebsiella pneumoniae to elucidate the system-wide events necessary for polymyxin resistance, collateral sensitivity, and cross-resistance to other antibiotics. Using this strategy, we identified novel, targetable collateral vulnerabilities associated with polymyxin resistance. Our long-term goal in this study is to reverse polymyxin resistance by knocking down identified collateral vulnerabilities accrued during the acquisition of polymyxin resistance.

Methods

We directed evolution of the E. coli substrain MG1655 to increasing levels of colistin sulfate using a robotic adaptive laboratory evolution (ALE) culturing system. Samples were collected and sequenced regularly, and biomass from four time points were collected for quantitative proteomic analysis. Quantitation was performed using tandem mass tags on an Orbitrap Fusion Mass Spectrometer. Spectra were matched using Proteome Discoverer, and analysis was performed using various bioinformatics tools, including Blast, String, and EcoCyc4-6. We then directed evolution of K. pneumoniae strain K1100 to increasing levels of colistin sulfate as above. Collateral hits identified through mass spectrometry were prioritized based on functional significance and homology to other known, functionally important proteins. When no such annotation or homology existed, results were prioritized by statistical parameters accounting for magnitude of fold change and reproducibility across replicates7.

Results

Progressive polymyxin resistance in E. coli and K. pnuemoniae is accompanied by a stable, global shift in expressed proteins. Collateral events associated with polymyxin resistance include a preponderance of multidrug efflux pumps, biofilm-associated proteins, and stress-related proteins. These findings were recapitulated in both E. coli and K. pneumoniae strains.

Conclusions & Discussion

Antibiotic resistance presents a growing problem with a global scope. Here, we show that proteomic investigations of laboratory-evolved strains of important human pathogens can uncover new drug targets to combat antibiotic resistance. While further validation is required, these findings represent possible selection-averse antimicrobial drug targets.


References & Acknowledgements:

1. M. Baym, L.K. Stone, R. Kishony, Multidrug evolutionary strategies to reverse antibiotic resistance, Science, (2016); 351, 6268, review.

2. Suzuki S, Horinouchi T, Furusawa C. Prediction of antibiotic resistance by gene expression profiles. Nature Communications. 2014;5:5792.

3. M. Yoshida, S. Reyes, S. Tsuda, et al. Time-programmable drug dosing allows the manipulation, suppression and reversal of antibiotic drug resistance in vitro. Nature Communications., (2017); 8, 15589.

4. NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Research. 2016;44(Database issue):D7-D19. doi:10.1093/nar/gkv1290.

5. Jensen, et al. STRING 8--a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res. 2009 Jan;37(Database issue):D412-6.

6. Keseler, I.M., et al
EcoCyc: fusing model organism databases with systems biology. Nucleic Acids Research 41:D605-612 2013.

7. Xiao Y, et al. A novel significance score for gene selection and ranking. Bioinformatics. 2014 Mar 15;30(6):801-7. doi: 10.1093/bioinformatics/btr671. Epub 2012 Feb 9.


Financial Disclosure

DescriptionY/NSource
GrantsyesMicrobiome and Microbial Sciences Initiative Graduate Research Fellowship
Salaryno
Board Memberno
Stockno
Expensesno

IP Royalty: no

Planning to mention or discuss specific products or technology of the company(ies) listed above:

no