# Evolutionary Tradeoffs in Antibiotic Resistance

> **NIH NIH R35** · HARVARD MEDICAL SCHOOL · 2020 · $11,966

## Abstract

Antibiotic resistance emerges when a mutation in a bacterium causes a previously inhibitory concentration of a
compound to become survivable. Through the accumulation of mutations conferring varying increases in
resistance, already many easy-to-treat infections have become nearly incurable, and are spreading in part
anthropogenically. Resistance provides an almost ideal model system for the study of microbial evolution; fitness
can be well defined, imposed selective pressures can be readily tuned, and can emerge either spontaneously or
by horizontal gene transfer. The classical model of resistance evolution, that a resistant mutant has a fitness
advantage in the presence of antibiotic use, and so spreads in the population to near-fixation, captures the rise
of antibiotic resistance, but on closer inspection fails to explain several critical features of resistance. First,
antibiotic resistance rarely reaches fixation in clinical populations; more importantly, sensitivity is higher than
the population-genetic models would predict. Second, antibiotic resistance was present, and likely common, in
clinical infections before the human use of antibiotics even began. Third, despite the widespread prevalence of
antibiotic-producing bacteria in the environment, these same bacteria remain surrounded by sensitive
neighbors. For these reasons, we hypothesize that the existing model of resistance evolution is incomplete, and
in particular that there exist evolutionary factors in the environment which have a potentially countervailing
effect on resistance evolution of similar or greater magnitude to the human use of antibiotics. Here, we will
combine evolution experiments in model systems with computational modeling and database mining of
sequence data to study the constraints on the evolution of resistance, focusing on two key areas: the role of spatial
structure in the evolution of resistance, and the role of selfish genetic elements including phages and parasitic
plasmids. We expect to uncover the evolutionary mechanisms behind the emergence, spread, and limitation of
antibiotic resistance.

## Key facts

- **NIH application ID:** 10103937
- **Project number:** 3R35GM133700-01S1
- **Recipient organization:** HARVARD MEDICAL SCHOOL
- **Principal Investigator:** Michael Baym
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $11,966
- **Award type:** 3
- **Project period:** 2020-05-01 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10103937

## Citation

> US National Institutes of Health, RePORTER application 10103937, Evolutionary Tradeoffs in Antibiotic Resistance (3R35GM133700-01S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10103937. Licensed CC0.

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