# Improved understanding of TB transmission by accounting for within-host heterogeneity of M. tuberculosis: A population-based molecular epidemiology study in a high HIV prevalent setting

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA-IRVINE · 2022 · $624,222

## Abstract

ABSTRACT
 In high tuberculosis (TB) incidence settings, individuals with TB are often infected with multiple strains
of M. tuberculosis complex (Mtbc). Despite this known fact, current TB transmission studies largely ignore
within-host Mtbc heterogeneity. We believe that accounting for within-host Mtbc heterogeneity will reduce
sampling bias and significantly improve the understanding of TB transmission dynamics in households and in
community gathering places. For example, TB transmission studies in high TB incidence settings have found
that the majority of TB cases occurring concurrently within the same household have non-matching molecular
fingerprints. This finding has led to the conclusion that the majority of household TB cases were acquired
outside of the household. However, none of those studies have appropriately accounted for within-host
heterogeneity, which could have led to missed detection of Mtbc genetic clusters within the household. In
addition, despite numerous TB transmission studies, factors that predict TB transmission remain poorly
understood. Accounting for within-host Mtbc heterogeneity could improve the detection of pathogen and host
related factors that affect transmission. Together, improved understanding of these areas could lead to more
accurate identification of transmission networks and disease hotspots, which can then guide interventions to
interrupt TB transmission. Moreover, the proposed research could instigate significant changes in the practice
of future TB transmission studies by evaluating the impact of accounting for within-host Mtbc heterogeneity on
TB transmission inference.
 The proposed research will address the current gaps in knowledge by incorporating two novel methods
for detecting within-host Mtbc heterogeneity: 1) we will conduct whole-genome sequencing (WGS) on early
primary culture samples to detect heterogeneous Mtbc strains; and 2) we will perform targeted amplicon-based
sequencing of 150 genetic loci important for phylogenetic and resistance prediction. We will use advanced
bioinformatic methods to integrate these sources of data on Mtbc heterogeneity. The proposed research will
also utilize community-based door-to-door active case finding to minimize sampling bias. These methods will
be applied to achieve 2 specific aims: 1) to determine the impact of accounting for within-host heterogeneity of
Mtbc strains on inference in a population-based TB transmission study; and 2) to determine more accurately
the proportion of household TB cases that are attributable to transmission within the household by conducting
a prospective household contact study. We will also determine pathogen and host factors that predict individual
and population-level transmission. This project will generate important scientific knowledge of TB transmission
and factors that affect transmission, and findings will inform and guide targeted interventions to combat TB
epidemics by interrupting the transmission network in local ...

## Key facts

- **NIH application ID:** 10327709
- **Project number:** 5R01AI147336-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA-IRVINE
- **Principal Investigator:** Sanghyuk Sam Shin
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $624,222
- **Award type:** 5
- **Project period:** 2020-02-07 → 2025-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10327709

## Citation

> US National Institutes of Health, RePORTER application 10327709, Improved understanding of TB transmission by accounting for within-host heterogeneity of M. tuberculosis: A population-based molecular epidemiology study in a high HIV prevalent setting (5R01AI147336-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10327709. Licensed CC0.

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