# Identifying and understanding the mechanisms for increased risk of severe infection in obesity and diabetes

> **NIH NIH U19** · STANFORD UNIVERSITY · 2024 · $312,716

## Abstract

SUMMARY 
In Project 3, we propose to develop a next-generation of hybrid experimental framework that overcomes the 
limitations of single-cohort studies by leveraging heterogeneity between datasets andaccelerates in vitro 
hypothesis testing through human organoids (in collaboration with the Technical Project). In building this hybrid 
framework, we will develop novel computational methods to predict which genes in which cell types should be 
knocked out (or knocked in) and what downstream genes and pathways will change as a result. We will 
demonstrate the successful development of this hybrid framework by identifying the mechanisms underlying the 
transcriptome signatures we have identified for predicting vaccine response to influenza and predicting the risk 
of severe outcome in patients with viral infection. In collaboration with Project 1 and 2, we will identify how 
different antibodies relate to protective and detrimental host responses to viral infections and vaccinations. To 
achieve these goals, we will create the largest bulk and single-cell transcriptome database of viral infections and 
vaccinations to date, which we estimate will include >20,000 bulk transcriptome profiles from ~100 cohorts and 
>10,000,000 single-cell RNA-seq profiles from ~2,000 samples. We will perform systems immunology analysis 
using the advanced statistical and machine learning methods and computational frameworks developed in the 
Khatri lab applied to these large amounts of bulk and single-cell transcriptome data. These computational 
frameworks will leverage biological, clinical, and technical heterogeneity in these data to identify and refine 
immune signatures (genes, proteins, cell types). Because this process typically identifies hundreds or thousands 
of genes, we will apply several methods we have developed to reduce this list of genes, including greedy forward 
search. We will also use statistical deconvolution and disease trajectory inference to reduce the number of genes, 
while still be able to identify underlying pathways, cell types, and mechanisms. Finally, we will employ systematic 
ablation-based methods to infer directional interactions between genes in immune cell types in which they occur. 
Using these inferred directed associations, we will pose hypotheses that will be investigated using organoids in 
collaboration with Dr. Satpathy and the Technical Project. We will derive a pan-virus conserved host response 
gene signature and perform a similar analysis for vaccination, obesity, and diabetes datasets to derive respective 
gene signature. We will confirm the immune cell types that preferentially express these genes and understand 
whether a change in transcriptome or a change in cell proportion or both lead to observed signatures. We will 
identify overlapping immune signatures between infection, vaccination, obesity, and diabetes, which will further 
identify detrimental and protective host responses associated with increased or decrea...

## Key facts

- **NIH application ID:** 10825317
- **Project number:** 2U19AI057229-21
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Purveshkumar Khatri
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $312,716
- **Award type:** 2
- **Project period:** 2003-09-01 → 2029-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10825317

## Citation

> US National Institutes of Health, RePORTER application 10825317, Identifying and understanding the mechanisms for increased risk of severe infection in obesity and diabetes (2U19AI057229-21). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10825317. Licensed CC0.

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