Systems biology approach to redefine susceptibility testing and treatment of AMR pathogens in the context of host immunity

Title: SYSTEMS BIOLOGY APPROACH TO REDEFINE SUSCEPTIBILITY TESTING AND TREATMENT OF MDR PATHOGENS IN THE CONTEXT OF HOST IMMUNITY
Awardee Organization: UNIVERSITY OF CALIFORNIA SAN DIEGO
Contact PI / Project Leader: PALSSON, BERNHARD O
Project Number: 1U01AI124316-01

Abstract:

For something as complex and multifaceted as bacterial antibiotic resistance (AR), our drug evaluation paradigm is strikingly narrow and homogenous: MIC/MBC testing in standardized bacteriologic media. We have shown that this drug evaluation paradigm is inadequate, even misleading, as changes in the media conditions of the procedure lead to dramatically different results. A more holistic definition of antibiotic therapy that centers on understanding antibiotic activity in synergy with host innate immune factors such as cationic antimicrobial peptides (AMPs), serum and phagocytic cells (e.g. neutrophils) reveals therapeutic options unrecognized in standard testing. The proposed U01 program represents a groundbreaking approach to use systems biology approaches and inform more effective antibiotic utilization in the context of host innate immunity. We propose to: 1) build an iterative systems biology workflow that integrates multiple experimental and computational approaches to give a comprehensive assessment of AR; and 2) apply this workflow to high priority pathogens to systematically elucidate AR mechanisms and their condition­dependency. The iterative workflow includes: (i) omics and physiological data generation. Clinically isolated strains of the selected pathogens will be grown under conventional testing (bacteriologic media) and more physiologic conditions (tissue culture media, serum, and in presence of AMPs and neutrophils) to probe for advantageous gain of activity. The omics data types collected are: DNA resequencing, RNAseq, and metabolomics. (ii) Bioinformatics and data modeling analysis involves three approaches: big data analysis for data set dimensionality and coarse grained variable dependencies assessment, genome­scale modeling for mechanistic elucidation and analysis, and machine learning that uses AR­related metadata to classify the overall biological functions. This analysis will lead to understanding of AR mechanisms. (iii) Multi­scale validation from animal models, to laboratory evolution, to cytology, to gene expression alteration, to structural protein analysis of putative targets. The validation thus ranges from host behavior to atomistic detail of ligand­target interactions. The iterative loop then closes, comparing computational prediction to experimental outcomes. False­negative and false­positive predictions are then algorithmically analyzed by a hypothesis generating family of algorithms that then makes suggestions about what conditions to use in the next iteration of the loop. The pathogens that we will focus on are methicillin­resistant ​Staphylococcus aureus ​(MRSA), the carbapenem­resistant Enterobacteriaceae (CRE) Klebsiella ​pneumoniae ​and ​Acinetobacter baumannii,​ and Pseudomonas aeruginosa​. The team of investigators has made the foundational observations and led the development of the technologies on which the iterative workflow is based. A multi­ and genome­scale methods of systems biology fulfills requirements of RFA­AI­14­064 to which it responds.