A multiscale mechanistic model of human dendritic cells for in-silico investigation of immune responses and novel therapeutics discovery

被引:2
|
作者
Aghamiri, Sara Sadat [1 ]
Puniya, Bhanwar Lal [1 ]
Amin, Rada [1 ]
Helikar, Tomas [1 ]
机构
[1] Univ Nebraska Lincoln, Dept Biochem, Lincoln, NE 68588 USA
来源
FRONTIERS IN IMMUNOLOGY | 2023年 / 14卷
关键词
systems immunology; predictive modeling; In-Silico experiments; antigen-presenting cell; dendritic cell; immunology highlights; ACTIVATING RECEPTOR CD226; ANTIGEN-PRESENTING CELLS; DYNAMIC APPROACH; T-CELLS; INNATE; SIGNALS; TUMOR; NEUTROPHILS; PATHWAYS; HMGB1;
D O I
10.3389/fimmu.2023.1112985
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
Dendritic cells (DCs) are professional antigen-presenting cells (APCs) with the unique ability to mediate inflammatory responses of the immune system. Given the critical role of DCs in shaping immunity, they present an attractive avenue as a therapeutic target to program the immune system and reverse immune disease disorders. To ensure appropriate immune response, DCs utilize intricate and complex molecular and cellular interactions that converge into a seamless phenotype. Computational models open novel frontiers in research by integrating large-scale interaction to interrogate the influence of complex biological behavior across scales. The ability to model large biological networks will likely pave the way to understanding any complex system in more approachable ways. We developed a logical and predictive model of DC function that integrates the heterogeneity of DCs population, APC function, and cell-cell interaction, spanning molecular to population levels. Our logical model consists of 281 components that connect environmental stimuli with various layers of the cell compartments, including the plasma membrane, cytoplasm, and nucleus to represent the dynamic processes within and outside the DC, such as signaling pathways and cell-cell interactions. We also provided three sample use cases to apply the model in the context of studying cell dynamics and disease environments. First, we characterized the DC response to Sars-CoV-2 and influenza co-infection by in-silico experiments and analyzed the activity level of 107 molecules that play a role in this co-infection. The second example presents simulations to predict the crosstalk between DCs and T cells in a cancer microenvironment. Finally, for the third example, we used the Kyoto Encyclopedia of Genes and Genomes enrichment analysis against the model's components to identify 45 diseases and 24 molecular pathways that the DC model can address. This study presents a resource to decode the complex dynamics underlying DC-derived APC communication and provides a platform for researchers to perform in-silico experiments on human DC for vaccine design, drug discovery, and immunotherapies.
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页数:15
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