# Tracking the microbiome: purpose-built machine learning tools for tracking microbial strains over time

> **NIH NIH R21** · BRIGHAM AND WOMEN'S HOSPITAL · 2022 · $223,750

## Abstract

Summary/Abstract
Approximately 150 million people annually experience urinary tract infections (UTI), the most common cause of
which is uropathogenic Escherichia coli (UPEC). The gut is a known reservoir of UPEC, which typically reside at
low abundance, but can transcend the periurethral area to invade the bladder. While the E. coli population within
the gut can be diverse, it has been suggested that certain strains have a greater propensity to migrate and cause
infection. This may be one driving factor to explain why half of those with an acute infection have a recurrence
even after taking antibiotics that clear the first infection from the urinary tract. Being able to detect and track E.
coli strains over time would have direct clinical applications for those patients who have frequent recurrences
due to gut UPEC carriage. One such clinical application would be early detection and intervention before the
onset of infection. Unfortunately, current metagenomic algorithms are not capable of performing strain tracking
accurately enough for clinical relevance, especially for low abundance species such as E. coli. A major factor for
this lack of accuracy is that all current state-of-the-art metagenomic tools completely ignore temporal
dependence between samples. Even if it is known that multiple samples are from the same patient, current tools
analyze those samples as if they were independent. Furthermore, many metagenomic tools ignore the sequence
quality information that is provided for every nucleobase in every read. We propose to develop a more precise
strain tracking algorithm that does take this additional information into account, making the tool host-time-quality
aware. Finally, we will pilot and validate our algorithm on a clinically relevant gnotobiotic colonization model.
Specifically, humanized germ-free mice will be undergoing two rounds of E. coli challenges with therapeutic
perturbations from antibiotics or mannosides, a small molecule precision antibiotic-sparing therapeutic. We
propose the following specific aims: (1) Develop the first purpose-built computational method for tracking
bacterial strains in the microbiome over time, (2) Gnotobiotic mouse model undergoing UPEC challenges and a
therapeutic perturbation. These aims would advance the microbiome field forward allowing for the future
development of therapeutics and clinical diagnostics.

## Key facts

- **NIH application ID:** 10401922
- **Project number:** 5R21AI154075-02
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** Travis Eli Gibson
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $223,750
- **Award type:** 5
- **Project period:** 2021-05-06 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10401922

## Citation

> US National Institutes of Health, RePORTER application 10401922, Tracking the microbiome: purpose-built machine learning tools for tracking microbial strains over time (5R21AI154075-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10401922. Licensed CC0.

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