MS01 - IMMU-03

Immune Responses to Viral Infections and Vaccines (Part 1)

Monday, July 14 at 10:20am

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Note: this minisymposia has multiple sessions. The other session is: and Part-2.

Veronika I. Zarnitsyna (Emory University), Esteban Hernandez Vargas, University of Idaho

Description:

The immune response to vaccination arises from complex and dynamic interactions between host factors, antigen exposure, and the immune system. This mini-symposium brings together researchers leveraging mathematical modeling, data-driven analyses, and experimental immunology to quantify heterogeneity in vaccine responses and inform immunization strategies. Topics will include the impact of HLA diversity on CD8+ T cell responses, challenges in predicting immune protection in immunocompromised individuals, and the role of T cell cross-reactivity in shaping immune memory. Discussions will also address sex-based differences in lung immunity and how computational models can provide insights into immune function under varying physiological conditions. Additionally, we will explore parameter estimation techniques to optimize vaccine strategies and investigate nonlinear immune system dynamics, including stability and bifurcation behaviors in B cell responses. By integrating diverse approaches, this session aims to advance our understanding of immune variability across diverse populations and improve vaccine design.

Macauley Locke

Los Alamos National Laboratory

"Quantification of Type I Interferon Inhibition by Viral Proteins: Ebola Virus as a Case Study"

Type I interferons (IFNs) are cytokines with both antiviral properties and protective roles in innate immune responses to viral infection. They induce an antiviral cellular state and link innate and adaptive immune responses. Yet, viruses have evolved different strategies to inhibit such host responses. One of them is the existence of viral proteins which subvert type I IFN responses to allow quick and successful viral replication, thus, sustaining the infection within a host. We propose mathematical models to characterise the intra-cellular mechanisms involved in viral protein antagonism of type I IFN responses, and compare three different molecular inhibition strategies. We study the Ebola viral protein, VP35, with this mathematical approach. Approximate Bayesian computation sequential Monte Carlo, together with experimental data and the mathematical models proposed, are used to perform model calibration, as well as model selection of the different hypotheses considered. Finally, we assess if model parameters are identifiable and discuss how such identifiability can be improved with new experimental data.

Jane Marie Heffernan

York University

"COVID-19 Vaccination in HIV+ Individuals"

The immune response to vaccination is highly heterogeneous across individuals, and emerges from an intricate time-evolving interplay between humoral and cellular immune components. We have employed machine learning to study immune system heterogeneity in HIV individuals after multiple COVID-19 vaccinations. We employ a random forest (RF) approach to classify informative differences in immunogenicity between older people living with HIV (PLWH) on ART and an age-matched control group who received up to five SARS-CoV-2 vaccinations over a period of 104 weeks. An extension of our study uses supervised and unsupervised Machine Learning methods to produce physiologically accurate synthetic datasets that enable data-driven hypothesis testing and model validation. We have found that immunological variables of importance in determining different immunological outcomes from vaccination include cytokine-based features in combination with post-booster saliva IgA measures. In total, nine important features are identified from 63 possible measures included in the dataset.

Jason E. Shoemaker

University of Pittsburgh

"A More Severe Influenza Infection in Female Mice Relative to Male is Characterized by Early Viral Production and Increased Innate Immune Activity"

In humans, differences in the immune response between males and females greatly influence influenza virus infection outcomes. During the 2009 H1N1 pandemic, females were at greater risk than their male, age-matched counterparts for hospitalization and death by a ratio of nearly 3:2. The innate immune response has been implicated as a factor of these sex differences in influenza pathogenesis, with sex hormones considered an important component of innate immune regulation. Together with our collaborators at the University of Wisconsin, Madison, we have completed experiments on male and female mice infected with CA04 H1N1 influenza infection. The results of these experiments show that the female mice have increased viral production at 36 hours post infection, resulting in early and excessive innate immune activation characterized by the proinflammatory cytokine profiles. Immune cell counts show that alveolar macrophages have increased depletion in female mice, while exfiltrating macrophage cell counts are higher at 3 days post infection in female mice: both observations have been associated with increased disease severity. Finally, histopathology of the lung cells shows very few lesions in the male mice compared to female mice. These lesions are present in the alveolar region of the female mice, but not male, indicating that influenza virus penetrates more deeply in the female lungs. While the experimental data points to certain cytokine/chemokines and immune cells as potential factors influencing severity, mathematical modeling can further contribute to our understanding of increased disease severity in females by identifying sex-specific rates within the immune response to infection that differs between males and females. We are currently developing mathematical models to identify mechanism(s) responsible for the observed increases in disease severity in the female mice. We will present work recently published comparing male and female infection, discuss the caveats and challenges, and introduce solutions to these challenges going forward.

Veronika I. Zarnitsyna

Emory University School of Medicien

"Challenges in Evaluating Vaccine-Induced Protection Against Severe Disease"

Understanding the full scope of vaccine-induced protection necessitates distinguishing between a vaccine’s ability to prevent infection (first line of protection) and its capacity to mitigate disease severity (second line of protection) in those who do become infected. While much of the empirical focus has traditionally centered on vaccine effectiveness against infection, protection against progression to severe disease is equally crucial—especially for vaccine impact modeling and assessing the overall burden of a pandemic. Despite its importance, estimating this secondary layer of protection presents significant analytical challenges. Analysis of empirical data from the COVID-19 pandemic shows that estimates of vaccine effectiveness against severe disease progression can appear to rise from 0% to over 70% within months—changes unlikely to reflect true biological effects. Using mathematical modeling, we explore how such patterns can arise in settings with heterogeneous immune responses. Our findings highlight the challenges in isolating vaccine effects on disease progression and emphasize the need for refined methods that adjust for shifting risk profiles among the infected.

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Annual Meeting for the Society for Mathematical Biology, 2025.