# Emerging novel mechanisms of antibiotic resistance in the prevalent foodborne pathogen, Salmonella

> **NIH NIH R01** · STANFORD UNIVERSITY · 2022 · $394,250

## Abstract

PROJECT ABSTRACT
Salmonella (non-typhi) causes 1.2M illnesses and 23K hospitalizations yearly; 100K of these infections are
antibiotic-resistant. The steady emergence of antibiotic resistance suggests that Salmonella are actively evolving
to evade antibiotics, but our knowledge of how Salmonella become resistant to certain antibiotics is extremely
limited. Here we propose to elucidate how Salmonella acquires antibiotic resistance, as a critical first step in
developing effective methods to prevent and treat this prevalent disease. Salmonella is an ideal pathogen to
study for two reasons: first, Salmonella is highly experimentally tractable in vitro; second, findings from studying
Salmonella are likely applicable to other closely related Gram-negative enteric pathogens (Shigella, Escherichia
coli, etc.). Leveraging a committed and experienced multidisciplinary team of experts in microbial genomics tool
development (Bhatt) and Salmonella biology (Monack), and supported by experts in statistical genetics, clinical
microbiology, antibiotic resistance, adaptive laboratory evolution and emerging bacterial pathogens, we will
identify and validate new Salmonella genes that are critical for resistance to the three most important antibiotics
used to treat Salmonella: ceftriaxone, ciprofloxacin, and azithromycin. To achieve this goal, we have (1) collected
a large set of sequenced pathogen isolates and matched phenotype data, and (2) developed and validated a
computational method to comprehensively identify mutations associated with antibiotic resistance. Specifically,
we will use existing adaptive laboratory evolution data from experiments on Gram-negative enteric pathogens;
we will also access a unique CDC dataset of 1,579 Salmonella strains and antibiotic sensitivity phenotypes.
Methodologically, we have developed and validated an innovative new computational tool, called mustache,
which identifies a prevalent but often overlooked class of mutations using sequencing data. Mustache identifies
the location of insertion sequences/transposases, or so-called “jumping genes”, using short-read DNA
sequencing information. When combined with existing high-throughput genomic analysis methods to identify
point mutations and small insertions/deletions, our tool generates a comprehensive list of mutations associated
with antibiotic resistance. By applying our tool to data from existing adaptive laboratory evolution experiments
(Aim 1) and the CDC Salmonella dataset (Aim 2), we anticipate identifying many novel genes that are associated
with antibiotic resistance. We will also perform an orthogonal, high-throughput antibiotic resistance screen in two
transposon-mutagenized libraries of Salmonella to identify antibiotic-resistance genes in vitro (Aim 3). We
anticipate that the results from all three aims will converge upon important and exciting new antibiotic targets in
Salmonella as well as enteric Gram-negative pathogens, more broadly. These results will inform the
...

## Key facts

- **NIH application ID:** 10458558
- **Project number:** 5R01AI148623-04
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Ami Siddharth Bhatt
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $394,250
- **Award type:** 5
- **Project period:** 2019-09-23 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10458558

## Citation

> US National Institutes of Health, RePORTER application 10458558, Emerging novel mechanisms of antibiotic resistance in the prevalent foodborne pathogen, Salmonella (5R01AI148623-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10458558. Licensed CC0.

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