Examining B-cell dynamics and responsiveness in different inflammatory milieus using an agent-based model.
Examining B-cell dynamics and responsiveness in different inflammatory milieus using an agent-based model.
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DOI：
10.1371/journal.pcbi.1011776
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发表时间：
2024-01
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
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B细胞是免疫系统的重要组成部分，通过产生抗原特异性抗体以及幼稚和记忆B细胞的吞噬功能来中和感染因子。然而，B细胞反应可能会受到改变整体炎症环境的各种条件的影响，这些条件可能是由于脓毒症中所见的大量急性损伤，或者由于产生低水平的闷烧背景炎症，如糖尿病，肥胖症或高龄。这种由炎性细胞因子白细胞介素-6（IL-6）和肿瘤坏死因子-α（TNF-α）介导的B细胞功能障碍增加了晚期脓毒症患者对医院感染的易感性，并增加了慢性疾病患者复发感染（如SARS-CoV-2）的发生率或严重程度。我们建议，模拟B细胞动力学可以帮助调查他们的反应，不同水平和模式的全身炎症。基于B细胞免疫剂的模型（BCIABM）是通过整合关于幼稚B细胞、短寿命浆细胞、长寿命浆细胞、记忆B细胞和调节B细胞沿着它们的各种分化途径和细胞因子/介质的知识而开发的。校准BCIABM以反映响应以下的生理行为：1）预期通过产生有效免疫记忆导致免疫致敏的轻度抗原刺激，和2）代表脓毒症期间观察到的急性实质性炎症的重度抗原激发，先前在脓毒症患者B细胞行为研究中记录。校准后，BCIABM用于模拟在低慢性背景炎症状态下对重复抗原刺激的B细胞反应，实施为低背景水平的IL-6和TNF-α，通常见于糖尿病、肥胖或高龄患者。通过与已知此类合并症发生率较高的COVID-19感染的退伍军人管理局（VA）患者队列进行比较，对免疫应答水平进行了评估和验证。BCIABM成功地再现了预期的轻度抗原暴露的免疫记忆的适当发展，以及脓毒症患者中观察到的免疫麻痹。然后，模拟实验显示，随着背景慢性炎症水平的增加，B细胞反应性显著降低，重现了VA人群中观察到的不同COVID-19感染数据。BCIABM被证明可用于动态地代表B细胞功能的已知机制，并在一系列不同的抗原暴露和炎症状态下再现免疫记忆反应。这些结果阐明了先前的研究，这些研究通过假定一种已建立和保守的机制来解释广泛的表型表现中的B细胞功能障碍，从而证明了B细胞反应和背景炎症之间存在类似的负相关性。在这项工作中，我们提出了一个B细胞免疫记忆形成的计算模型，这种现象允许人类对他们以前遇到的病原体产生免疫力。计算模型被开发为基于代理的模型，其中细胞被单独表示，并响应来自环境和其他细胞的刺激来执行其细胞功能和动作。我们在脓毒症的背景下研究了免疫记忆形成的过程，脓毒症是一种高度炎症性的疾病，可发生在严重的损伤，疾病或创伤后。然后，我们使用该模型来解释最近讨论COVID-19重复感染影响的研究结果;特别是，我们注意到，所引用的研究是在相对狭窄的人群中进行的，即那些在退伍军人事务部（VA）寻求治疗的人群医院，他们会经历高于正常水平的背景炎症。我们使用该模型来证明这种背景炎症可以损害对COVID-19感染的记忆形成过程，并解释VA人群中再感染严重程度增加的原因。
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. 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 in response 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. 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. 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. In this work, we present a computational model of immune memory formation in B-cells, the phenomenon that allows a human being to develop immunity against pathogens they have previously encountered. The computational model was developed as an agent-based model, in which cells are represented individually and perform their cellular functions and actions in response to stimuli form the environment and other cells. We examine the process of immune memory formation in the context of sepsis, a highly inflammatory condition that can occur after serious injuries, diseases, or trauma. We then use this model to offer an explanation for recent findings discussing the impact of repeated COVID-19 infection; specifically, we note that the referenced study was performed in a relatively narrow population, those that sought care at a Veterans Affairs (VA) hospital, that would experience higher than normal levels of background inflammation. We use the model to demonstrate that this background inflammation can impair the process of memory formation in response to a COVID-19 infection and posit one explanation for increasing severity of reinfections in the VA population.
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DOI：
10.1084/jem.20092210
发表时间：
2010-05-10
期刊：
The Journal of experimental medicine
影响因子：
--
作者：
Allende ML;Tuymetova G;Lee BG;Bonifacino E;Wu YP;Proia RL
通讯作者：
Proia RL
影响因子：
82.9
作者：
Bowe, Benjamin;Xie, Yan;Al-Aly, Ziyad
通讯作者：
Al-Aly, Ziyad
影响因子：
120.7
作者：
Boomer, Jonathan S.;To, Kathleen;Chang, Kathy C.;Takasu, Osamu;Osborne, Dale F.;Walton, Andrew H.;Bricker, Traci L.;Jarman, Stephen D., II;Kreisel, Daniel;Krupnick, Alexander S.;Srivastava, Anil;Swanson, Paul E.;Green, Jonathan M.;Hotchkiss, Richard S.
通讯作者：
Hotchkiss, Richard S.
影响因子：
7.3
作者：
Sjaastad FV;Condotta SA;Kotov JA;Pape KA;Dail C;Danahy DB;Kucaba TA;Tygrett LT;Murphy KA;Cabrera-Perez J;Waldschmidt TJ;Badovinac VP;Griffith TS
通讯作者：
Griffith TS
影响因子：
7.3
作者：
Boldison J;Wong FS
通讯作者：
Wong FS