# Project 1: Mechanisms of Disease Progression

> **NIH NIH U19** · SEATTLE CHILDREN'S HOSPITAL · 2020 · $1,079,976

## Abstract

Abstract – Project 1
Human Mtb infection results in a large variety of clinical outcomes, ranging from bacterial eradication, to control
and latent infection, to progression and active disease with a range of clinical phenotypes. We recently
discovered a blood transcriptional signature that predicts TB risk in Mtb-exposed individuals up to 18 months
before they exhibit clinical symptoms, a landmark contribution to the field. Still, the mechanisms that underlie
TB disease progression remain poorly understood, in large part because the key immune responses within the
human lung cannot be readily monitored. Furthermore, TB is a highly heterogeneous disease in which
individuals progress to active disease due to a variety of mechanisms. In this project, we will conduct a
comprehensive, multi-scale integration of transcriptomic, cytokine, chemokine and eicosanoid profiles from
lung and blood during Mtb infection in order to identify and model molecular mechanisms and pathways that
determine the outcome of infection. First, we will use multiple experimental strategies to recapitulate the
heterogeneity of human Mtb infection in the mouse. These include a novel “ultra low dose” (ULD) infection
model that we have pioneered in which mice are infected with 1-3 bacteria and subsequently exhibit a broad
range of outcomes, ranging from immune control to progression. We will also employ mice from the
Collaborative Cross project that have demonstrated extreme TB phenotypes and Mtb strains that span a range
of pathogenicity. Second, we will interrogate and model the host-Mtb interaction in these mouse models using
a variety of systems biology approaches in order to uncover the molecular regulators, pathways, and networks
in pulmonary innate and adaptive immune cells. We will test the predicted role of critical regulatory molecules
by genetically perturbing them in vivo and examining the impact on control of Mtb infection. We will also apply
machine-learning approaches to define multi-omic blood based signatures in mice that predict TB progression.
In our preliminary work, we have defined an early blood-based signature that predicts the late-time bacterial
burdens in ULD-infected mice. We will correlate this signature with systems-level measurements of immune
function in the lung to uncover mechanisms of Mtb control. Third, we will translate these findings to human
disease. Through the Africa Health Research Institute, we will leverage a large-scale program that will obtain
genomic sequence data as well as associated epidemiological and clinical metadata on 50,000 individuals
living in a TB-endemic region. We will conduct a candidate gene genetic association analysis to validate
regulatory molecules identified in mice to determine whether mutations in human orthologs are associated with
altered risk of TB. In addition, we will use several existing non-human primate and human datasets to refine
the blood based multi-omic progression signatures defined in mice and te...

## Key facts

- **NIH application ID:** 9878764
- **Project number:** 5U19AI135976-04
- **Recipient organization:** SEATTLE CHILDREN'S HOSPITAL
- **Principal Investigator:** ALAN A ADEREM
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,079,976
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9878764

## Citation

> US National Institutes of Health, RePORTER application 9878764, Project 1: Mechanisms of Disease Progression (5U19AI135976-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9878764. Licensed CC0.

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