# Systems Genetics of Tuberculosis

> **NIH NIH P01** · UNIV OF MASSACHUSETTS MED SCH WORCESTER · 2024 · $2,817,044

## Abstract

Systems Genetics of Tuberculosis. Overall Program
Abstract:
Mycobacterium tuberculosis (Mtb) infection outcomes are highly variable. Most individuals
contain the infection and remain asymptomatic for a lifetime. A fraction of those infected develop
disease; and even among these patients, the timing, location, and presentation of the pathology
is remarkably diverse. This variability is also evident in the efficacy of both chemotherapy and
vaccination. While this heterogeneity represents a great challenge for TB control efforts, the
biological determinants of Mtb infection outcome have been difficult to define due to the
complexity of contributing factors. Both human and bacterial populations are genetically and
phenotypically diverse, and interactions between this genetic complexity and a variety of
environmental factors ultimately determines clinical course. To overcome this complexity, we
leveraged new mammalian and bacterial genetic resources to create a model system that can
be used to study the effect of each of these variables in isolation and in combination. Host
diversity is incorporated using mice from the Collaborative Cross (CC) and Diversity Outbred
(DO) resources, newly generated reference panels that reflect the diversity of an outbred
population. Bacterial variation is incorporated using large panels of Mtb strains that reflect both
naturally- and experimentally-generated diversity. Controlled interventions, such as vaccination,
can be overlaid on this host- pathogen diversity. Using this highly-tractable system, our program
discovered that the combinatorial complexity of these interactions converge on a discrete
number of biological pathways that influence outcome. Supported by the cutting-edge mouse
and human genetic, genomic, and analytical resources provided by the Cores, our SGTB
program will now focus on parallel studies in this model system and human clinical samples to
identify and dissect the pathways that influence outcome. This structure will ensure that
mechanistic mouse studies are linked to relevant human phenotypes. Ultimately, these insights
will be leveraged to develop more precise correlates of risk, more specific diagnostics based on
clinical phenotypes, and new strategies for the optimization and preclinical development of
vaccines.

## Key facts

- **NIH application ID:** 10861324
- **Project number:** 1P01AI181898-01
- **Recipient organization:** UNIV OF MASSACHUSETTS MED SCH WORCESTER
- **Principal Investigator:** CHRISTOPHER M SASSETTI
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $2,817,044
- **Award type:** 1
- **Project period:** 2024-08-20 → 2029-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10861324

## Citation

> US National Institutes of Health, RePORTER application 10861324, Systems Genetics of Tuberculosis (1P01AI181898-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10861324. Licensed CC0.

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