Examining B-cell dynamics and responsiveness in different inflammatory milieus using an agent-based model.

Area of Science:

• Immunology
• Computational Biology
• Systems Biology

Background:

• B-cells are crucial for immunity, producing antibodies and aiding in pathogen clearance.
• Inflammatory conditions like sepsis, diabetes, obesity, and advanced age compromise B-cell function.
• Dysfunctional B-cells, influenced by Interleukin-6 (IL-6) and Tumor Necrosis Factor-alpha (TNF-α), increase infection risk.

Purpose of the Study:

• To model B-cell dynamics and investigate their response to varying systemic inflammation levels.
• To understand how chronic inflammation impacts B-cell immunity and immune memory formation.

Main Methods:

• Developed the B-cell Immunity Agent-based Model (BCIABM) incorporating B-cell subtypes, differentiation, and cytokines.
• Calibrated the BCIABM to simulate responses to mild (immune memory) and severe (sepsis) antigen challenges.
• Simulated B-cell responses under chronic low-level inflammation (simulating diabetes, obesity, advanced age) and validated against COVID-19 patient data.

Main Results:

• The BCIABM accurately reproduced immune memory development and sepsis-induced immunoparalysis.
• Simulations showed significantly reduced B-cell responsiveness with increasing chronic inflammation.
• Model results aligned with observed COVID-19 infection data in a patient cohort with comorbidities.

Conclusions:

• The BCIABM effectively models B-cell dynamics and immune responses across diverse inflammatory states.
• Results support a conserved mechanism where chronic inflammation negatively correlates with B-cell responsiveness.
• This model elucidates B-cell dysfunction in various conditions, explaining altered immune presentations.