SMB2023 FollowMonday during the "CT01" time block. Room assignment: Cartoon Room 1 (#3145) in The Ohio Union.
University of Idaho
"Regulation of CD8+ T cells may explain age dependent immune response to influenza infection"
Influenza viral infection is known to have more serious consequences on elderly populations. Previous modeling efforts for influenza infection have found differences in the immune response dynamics to influenza infection between young and aged mice. A better understanding of the immunological mechanisms by which aging leads to discrepant immune responses may inform treatment strategies. One possible explanation for these differences may be a difference between ages in the intensity of the activation of CD8+ T cell proliferation by the presence of virus. In order to further investigate this proposed mechanism and the difference in immune response dynamics, we consider several ordinary differential equation models describing the dynamics of CD8+ T cell and viral titer count. We apply these models to existing experimental data of viral titer and CD8+ T cell counts collected periodically over a period of 19 days from mice populations infected with influenza A/Peurto Rico/8/34 (H1N1). The models we consider are fit to our data by the differential evolution method for global optimization. After fitting the models, we use Akaike information criterion with small sample corrections in order to identify the model which best represents the considered data. Our chosen model differs from previously considered models by the inclusion of viral regulation of CD8+ T cells. We perform identifiability analysis of the selected model by considering loss profiles across the parameter search range. We identify that relationships between model parameters present challenges for model identifiability. We find that when clearance rate of virus by T cells is assumed to differ between populations then our model predicts two key differences in immune response dynamics. First, it predicts delayed proliferation response for the younger mice. Second, it predicts higher CD8+ T cell regulation by virus for the younger mice.
Additional authors: Dr. Esteban A. Hernandez-Vargas, University of Idaho, Department of Mathematics and Statistical Science
University of Vermont Larner College of Medicine
"Examining B-cell dynamics and responsiveness in different inflammatory milieus using an agent-based model"
Introduction: B-cells are essential components of the immune system that neutralize infectious agents through the generation of antigen-specific antibodies and through the phagocytic functions of naïve and memory B-cells. However, the B-cell response can become compromised by a variety of conditions that alter the overall inflammatory milieu, be that due to substantial, acute insults as seen in sepsis, or due to those that produce low-level, smoldering background inflammation such as diabetes, obesity, or advanced age. This B-cell dysfunction, mediated by the inflammatory cytokines Interleukin-6 (IL-6) and Tumor Necrosis Factor-alpha (TNF-α), increases the susceptibility of late-stage sepsis patients to nosocomial infections and increases the incidence or severity of recurrent infections, such as SARS-CoV-2, in those with chronic conditions. We propose that modeling B-cell dynamics can aid the investigation of their responses to different levels and patterns of systemic inflammation. Methods: The B-cell Immunity Agent-based Model (BCIABM) was developed by integrating knowledge regarding naïve B-cells, short-lived plasma cells, long-lived plasma cells, memory B-cells, and regulatory B-cells, along with their various differentiation pathways and cytokines/mediators. The BCIABM was calibrated to reflect physiologic behaviors to: 1) mild antigen stimuli expected to result in immune sensitization through the generation of effective immune memory, and 2) severe antigen challenges representing the acute substantial inflammation seen during sepsis, previously documented in studies on B-cell behavior in septic patients. Once calibrated, the BCIABM was used to simulate the B-cell response to repeat antigen stimuli during states of low, chronic background inflammation, implemented as low background levels of IL-6 and TNF-α often seen in patients with conditions such as diabetes, obesity, or advanced age. The levels of immune responsiveness were evaluated and validated by comparing to a Veteran’s Administration (VA) patient cohort with COVID-19 infection known to have a higher incidence of such comorbidities. Results: The BCIABM was successfully able to reproduce the expected appropriate development of immune memory to mild antigen exposure, as well as the immunoparalysis seen in septic patients. Simulation experiments then revealed significantly decreased B-cell responsiveness as levels of background chronic inflammation increased, reproducing the different COVID-19 infection data seen in a VA population. Conclusion: The BCIABM proved useful in dynamically representing known mechanisms of B-cell function and reproduced immune memory responses across a range of different antigen exposures and inflammatory statuses. These results elucidate previous studies demonstrating a similar negative correlation between the B-cell response and background inflammation by positing an established and conserved mechanism that explains B-cell dysfunction across a wide range of phenotypic presentations.
Additional authors: Gary An, Department of Surgery, University of Vermont Larner College of Medicine; Chase Cockrell, Department of Surgery, University of Vermont Larner College of Medicine
Quiyana M. Murphy
"Understanding Neutrophil Dynamics during COVID-19 Infection"
Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) results in varied clinical outcomes, with virus-induced chronic inflammation and tissue injury being associated with enhanced disease pathogenesis. To determine the role of tissue damage on immune populations recruitment and function, a mathematical model of innate immunity following SARS-CoV-2 infection has been proposed. The model was fit to published longitudinal immune marker data from patients with mild and severe COVID-19 disease and key parameters were estimated for each clinical outcome. Analytical, bifurcation, and numerical investigations were conducted to determine the effect of parameters and initial conditions on long-term dynamics. The results were used to suggest changes needed to achieve immune resolution.
Additional authors: Stanca M. Ciupe, Virginia Tech Department of Mathematics