Impact of Natural Infection on the Baseline Immune States in Humans

  • Funded by National Institutes of Health (NIH)
  • Total publications:0 publications

Grant number: 1F30AI194770-01

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Key facts

  • Disease

    Unspecified
  • Start & end year

    2025
    2028
  • Known Financial Commitments (USD)

    $34,558
  • Funder

    National Institutes of Health (NIH)
  • Principal Investigator

    Yona Lei
  • Research Location

    United States of America
  • Lead Research Institution

    YALE UNIVERSITY
  • Research Priority Alignment

    N/A
  • Research Category

    Pathogen: natural history, transmission and diagnostics

  • Research Subcategory

    Immunity

  • Special Interest Tags

    N/A

  • Study Type

    Clinical

  • Clinical Trial Details

    Not applicable

  • Broad Policy Alignment

    Pending

  • Age Group

    Unspecified

  • Vulnerable Population

    Unspecified

  • Occupations of Interest

    Unspecified

Abstract

Influenza is a year-round public health burden, causing millions of severe illnesses and hundreds of thousands of respiratory deaths globally. A key challenge in developing more effective vaccines lies in the inherent variability of the human immune system, as vaccine responses are highly variable across individuals, with many failing to develop adequate protective immunity. Low vaccine responsiveness has been associated with specific pre- vaccination baseline immune states. The baseline immune state of an individual determines their immune function and response. We and others have linked inter-individual variations in vaccination outcomes to molecular and cellular immune components that encode the baseline state. Our group previously showed that high vaccine responsiveness is associated with a "naturally adjuvanted" baseline state characterized by enhanced innate immune response potential, a finding supported by corresponding differences in stimulation responses of immune cells from high and low vaccine responders in vitro. We also found that clinically healthy males recovered from mild COVID-19 exhibited a more "poised" baseline state and stronger immune responses to subsequent influenza vaccination. These studies suggest that variations in baseline immune states contribute to heterogenous responses to vaccination, and that prior exposures may establish new baseline states that impact future responses in an antigen-agnostic manner. It remains unclear how infection alters an individual's baseline state over time, how these changes vary across individuals, and if they have functional consequences. Using longitudinal samples from a household cohort that allows control for environmental confounders, and using influenza infection as a model, my proposal aims to address these gaps to better understand the functional impact of infection on baseline immune states. Given the antigen-nonspecific nature of innate immune cells, understanding how infection impacts their function is a key to revealing potential underlying mechanisms. I hypothesize that influenza infection induces durable antigen-agnostic transcriptional and epigenetic changes that give rise to enhanced innate immune response potential. Aim 1 will assess the impact of infection on baseline immune states and innate cell response capacity. Through single-cell multimodal immune profiling, I will assess infection-induced transcriptional and epigenetic changes in peripheral immune cells. Using the same samples, I will examine innate response capacity to in vitro stimulation. Aim 2 will elucidate how infection-induced durable changes mechanistically drive innate cell responses to stimulation. I will implement a causal network inference approach to infer immune determinants of response capacity, followed by experimental validation to establish causality. This work will advance our understanding of infection-induced antigen-agnostic immune reprogramming, potentially revealing key drivers of human immune variation and strategies to modulate baseline states for improving vaccination outcome. Rigorous scientific training will be guided by mentors with experimental and computational expertise, complemented by longitudinal clinical and professional skill development.