Chronic bacterial infections are inherently resistant to treatment. This is true even if organisms are antibiotic-sensitive, and high concentrations of drugs reach infection sites. The infections that afflict patients with the genetic disease cystic fibrosis (CF) are a prime example. Once infection is established it cannot be eradicated, and lung dysfunction caused by chronic infections claim the lives of the vast majority patients. Importantly, the mechanisms producing treatment resistance in CF and other chronic infections are poorly understood. Here we exploit the infrastructure of an ongoing clinical trial, and new findings about genetic diversity within infecting P. aeruginosa populations as tools to study treatment resistance. In preliminary studies, we found that infecting P. aeruginosa strains evolve to produce genetically diverse (but clonally-related) bacterial populations. We found that the relative abundance of subpopulations change as patients are treated with antibiotics. This is important because subpopulations that increase in abundance during treatment possess phenotypes that enable them to resist treatment in vivo, while subpopulations that decrease lack resistance functions. Studying these subpopulations could identify the mechanisms used by bacteria to withstand antibiotics in chronic human infections.
Aim 1. Identify P. aeruginosa subpopulations and tolerance phenotypes linked to in vivo resistance. We will identify and bank variant subpopulations that show sensitivity and resistance to antibiotic treatment in vivo. We will use the sensitive and resistant subpopulations to test hypotheses that link treatment resistance to antibiotic tolerance mechanisms. We will also use an unbiased approach to identify phenotypic differences in sensitive and resistant subpopulations.
Aim 2. How do sensitive and resistant subpopulations differ in protein expression? To identify candidate bacterial functions that may produce in vivo resistance, we will compare the proteomic profiles of sensitive and resistant subpopulations. This work will identify proteins and regulatory pathways associated with treatment resistance across multiple patients, a key first step in finding bacterial functions that mediate resistance in chronic infections.
Aim 3. Which P. aeruginosa proteins likely treatment resistance in vivo? We will use several analyses to determine which differentially expressed P. aeruginosa proteins are most likely to contribute to treatment resistance in vivo. We will prioritize proteins exhibiting parallel changes in multiple patients, and those showing changes consistent with the tolerance phenotypes. We will then use engineered P. aeruginosa strains to link these proteins to tolerance mechanisms. Finally, we will use multiple reaction monitoring mass spectrometry (MRM) to determine which bacterial proteins show consistent expression changes in sputum from CF patients.

Public Health Relevance

Chronic bacterial infections are inherently resistant to treatment. This is true even if organisms are antibiotic-sensitive, and high concentrations of drugs reach infection sites. This study investagates mechanisms of treatment resistance in chronic bacterial infections in order to find new therapeutic strategies.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Project (R01)
Project #
5R01AI101307-03
Application #
8685119
Study Section
Drug Discovery and Mechanisms of Antimicrobial Resistance Study Section (DDR)
Program Officer
Taylor, Christopher E,
Project Start
2012-07-01
Project End
2017-06-30
Budget Start
2014-07-01
Budget End
2015-06-30
Support Year
3
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of Washington
Department
Microbiology/Immun/Virology
Type
Schools of Medicine
DUNS #
City
Seattle
State
WA
Country
United States
Zip Code
98195
Schweppe, Devin K; Chavez, Juan D; Bruce, James E (2016) XLmap: an R package to visualize and score protein structure models based on sites of protein cross-linking. Bioinformatics 32:306-8
Chavez, Juan D; Schweppe, Devin K; Eng, Jimmy K et al. (2016) In Vivo Conformational Dynamics of Hsp90 and Its Interactors. Cell Chem Biol 23:716-26
Goss, Christopher H; VanDevanter, Donald R (2016) CFTR modulators and pregnancy: Our work has only just begun. J Cyst Fibros 15:6-7
Heltshe, Sonya L; Goss, Christopher H (2016) Optimising treatment of CF pulmonary exacerbation: a tough nut to crack. Thorax 71:101-2
Schweppe, Devin K; Zheng, Chunxiang; Chavez, Juan D et al. (2016) XLinkDB 2.0: integrated, large-scale structural analysis of protein crosslinking data. Bioinformatics 32:2716-8
Crull, Mathew R; Ramos, Kathleen J; Caldwell, Ellen et al. (2016) Change in Pseudomonas aeruginosa prevalence in cystic fibrosis adults over time. BMC Pulm Med 16:176
Ramos, Kathleen J; Quon, Bradley S; Psoter, Kevin J et al. (2016) Predictors of non-referral of patients with cystic fibrosisfor lung transplant evaluation in the United States. J Cyst Fibros 15:196-203
Goss, Christopher H (2016) With Every Upside, There Is a Downside: Chest Radiation and Survivors of Childhood Cancers. Ann Am Thorac Soc 13:1448-9
Wu, Xia; Chavez, Juan D; Schweppe, Devin K et al. (2016) In vivo protein interaction network analysis reveals porin-localized antibiotic inactivation in Acinetobacter baumannii strain AB5075. Nat Commun 7:13414
Schweppe, Devin K; Chavez, Juan D; Navare, Arti T et al. (2016) Spectral Library Searching To Identify Cross-Linked Peptides. J Proteome Res 15:1725-31

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