# Project 1: Mechanisms, Dynamics, and Prediction of Heteroresistance

> **NIH NIH U19** · EMORY UNIVERSITY · 2021 · $347,887

## Abstract

ABSTRACT
Understanding antibiotic resistance mechanisms is critical to designing novel approaches and therapeutics to
combat resistant bacteria. Heteroresistance (HR) is a bacterial phenotype in which an isolate contains a
subpopulation of cells that show a substantial increase in antibiotic resistance compared to the main population.
Many species of bacteria and nearly all classes of antibiotics exhibit this form of phenotypic resistance and there
is evidence from in vitro experiments, mathematical modeling, animal infection models and clinical studies that
the resistant subpopulations can enrich during antibiotic exposure and lead to treatment failure. Recent studies
show that the resistance phenotype in HR is in the majority of cases unstable and in the absence of antibiotic
pressure it rapidly reverts to susceptibility. One major reason for the instability is the occurrence of genetically
unstable tandem gene amplifications of different types of genes that can cause resistance when present at an
increased copy number (e.g., bona fide resistance genes that are normally expressed at low levels, efflux
pumps). Due to the instability, low frequency and transient character, it is challenging to detect and study these
subpopulations and in a clinical microbiology setting this often leads to difficulties in unambiguously classifying
bacteria as susceptible or resistant, which can lead to potential treatment failures. To facilitate the improved
treatment and detection of HR infections, we need to understand in detail the underlying mechanisms and
dynamics by which the resistant sub-populations form, are maintained and recede back to their baseline
frequency in the absence of antibiotic. Specifically, we will ask: what are the key genetic, physiological and
environmental processes and signals that govern the generation of resistant sub-populations and subsequently,
if and how we may modify and interfere with them. To address these questions, we will use clinical isolates of
Enterobacteriaceae (E. coli, K. pneumoniae and Enterobacter spp) and A. baumannii.
At a basic level, this work will significantly broaden our understanding of (i) how traits exhibited by a
subpopulation of cells is generated and can influence behavior and evolution of bacterial populations, (ii) how
HR is generated by CNV, (iii) the mechanisms and dynamics of CNV and (iv) how HR may be predicted from
whole genome sequencing data. This will, in the long-term, provide us with better tools to identify HR and mitigate
its effects in clinical settings and, thereby, improve antibiotic treatment outcome.

## Key facts

- **NIH application ID:** 10170970
- **Project number:** 1U19AI158080-01
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** Dan Andersson
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $347,887
- **Award type:** 1
- **Project period:** 2021-03-05 → 2026-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10170970

## Citation

> US National Institutes of Health, RePORTER application 10170970, Project 1: Mechanisms, Dynamics, and Prediction of Heteroresistance (1U19AI158080-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10170970. Licensed CC0.

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