# Deciphering the Heterogeneous Response to Influenza by a Multi-Scale Systems Approach

> **NIH NIH R01** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2024 · $637,256

## Abstract

Project Summary
Seasonal influenza epidemics, caused by influenza A and B viruses, result in 3–5 million severe cases and
300,000–500,000 deaths globally each year - especially in high-risk groups such as young children, pregnant
women, obese individuals, individuals with a compromised immune system, and indigenous populations. The
burden of influenza can vary widely between seasons, in part due to characteristics of the circulating viruses,
the existing immunity in the population, and the effectiveness of seasonal influenza vaccines against the
circulating virus strains. Disease morbidity and mortality increase when a new influenza strain reasserts or
jumps the host and becomes capable of infecting humans. In this case, there is no (or minimal) pre-existing
antibody-mediated immunity to the new viral strain at the population level, leading to millions of infections and
a rapid global spread of the virus. In the absence of antibodies, the severity of the disease can be ameliorated
by broadly cross-reactive cellular immunity. However, the precise mechanism of how immune cells mediate
recovery in some individuals, but not others is far from clear. NIAID has made significant investments in the
generation of data to improve our understanding of infectious diseases, their progression, risk, and severity as
well as treatment and prevention. Not only subject of specific programs, such as CEIRS (Centers of Excellence
for Influenza Research and Surveillance) and the ongoing efforts in CIVICs (Collaborative Influenza Vaccine
Innovation Centers), but in particular, omics-related programs have generated high-throughput genomic,
proteomic, and integrated "omic" data sets, and provided other related resources to the scientific community to
promote basic and applied research in infectious diseases. We will make use of these open access datasets
and resources available via the Bioinformatics Resource Centers (BRCs) in this application. In particular, we
will utilize immune epitope, viral sequence and antiviral drug information from the Influenza Research
Database (IRD) and combine these data with other public information from studies of human cohorts infected
with the influenza virus. Single-cell data will provide sufficient cellular detail and will serve as “scaffold” in the
case that only bulk data is available. In our view, a comprehensive and truly predictive model of these complex
relationships can only be achieved through the systematic, integrative, and multi-dimensional OMICS approach
that we offer. Host response to vaccination and to influenza infection is the result of complex traits that involve
a combination of host factors along with entire networks of transcripts, proteins, glycans and metabolites.
Together these responses impact cellular, tissue, and whole organism behaviors. Thus, the host responses to
vaccination and infection are an emergent property of molecular networks. The goal of this integrated systems
biology approach is to understand me...

## Key facts

- **NIH application ID:** 10873103
- **Project number:** 5R01AI170112-03
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** CHRISTIAN FORST
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $637,256
- **Award type:** 5
- **Project period:** 2022-07-14 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10873103

## Citation

> US National Institutes of Health, RePORTER application 10873103, Deciphering the Heterogeneous Response to Influenza by a Multi-Scale Systems Approach (5R01AI170112-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10873103. Licensed CC0.

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