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免疫抑制剂对 T 细胞动力学的影响:从通用的粗粒化免疫网络模型角度理解。

Effects of immunosuppressants on T-cell dynamics: Understanding from a generic coarse-grained immune network model.

机构信息

Department of Systems and Computational Biology, School of Life Sciences, University of Hyderabad, Hyderabad 500046, India.

出版信息

J Biosci. 2022;47(4). doi: 10.1007/s12038-022-00312-4.

Abstract

Long-term immunosuppressive therapy is a drug regimen often used to lower aggressive immune responses in various chronic inflammatory diseases. However, such long-term therapy leading to immune suppression may trigger other adverse reactions in the immune system. The rising concern regarding the optimal dose and duration of such treatment has motivated us to understand non-classical immunomodulatory responses induced by various immunosuppressive steroid and secosteroid drugs such as glucocorticoid and vitamin D supplements. The immunomodulatory actions of such immunosuppressants (that govern the adaptive immune response) are often mediated through their characteristic control over CD4+ T-cells involving pro- and antiinflammatory T-cells. Several early studies attempted to decode temporal and dose-dependent behaviors of such pro- and anti-inflammatory T-cells using the chemical dynamics approach. We first summarize these early works. Then, we develop a minimal coarse-grained kinetic network model to capture the commonality in their immunomodulatory functions. This generic model successfully reproduces the characteristic dynamical features, including the clinical latency period in long-term T-cell dynamics. The temporal behavior of T-cells is found to be sensitive to specific rate parameters and doses of immunosuppressants. The steady-state analysis reflects the transition from an early classified weakly regulated (autoimmune-prone) immune state to a strongly regulated state (immunocompromised state), separated by an intervening state of moderate/balanced regulation. An optimal dose and duration are essential in rescuing balanced immune regulation. This review elucidates how developing a simple generic coarse-grained immune network model may provide immense information that helps diagnose inefficacy in adaptive immune function before and after administering immunosuppressants such as glucocorticoid or vitamin D.

摘要

长期免疫抑制疗法是一种常用于降低各种慢性炎症性疾病中侵袭性免疫反应的药物方案。然而,这种导致免疫抑制的长期治疗可能会引发免疫系统的其他不良反应。对于这种治疗的最佳剂量和持续时间的日益关注,促使我们了解各种免疫抑制类固醇和甾体药物(如糖皮质激素和维生素 D 补充剂)引起的非经典免疫调节反应。这些免疫抑制剂(调节适应性免疫反应)的免疫调节作用通常通过其对涉及促炎和抗炎 T 细胞的 CD4+T 细胞的特征性控制来介导。几项早期研究试图使用化学动力学方法解码这些促炎和抗炎 T 细胞的时间和剂量依赖性行为。我们首先总结这些早期工作。然后,我们开发了一个最小的粗粒度动力学网络模型来捕捉它们在免疫调节功能中的共性。这个通用模型成功地再现了特征性的动力学特征,包括长期 T 细胞动力学中的临床潜伏期。T 细胞的时间行为被发现对特定的速率参数和免疫抑制剂的剂量敏感。稳态分析反映了从早期分类的弱调节(自身免疫倾向)免疫状态到强调节状态(免疫功能低下状态)的转变,其间存在中等/平衡调节的过渡状态。在恢复平衡免疫调节方面,最佳剂量和持续时间至关重要。这篇综述阐明了如何开发一个简单的通用粗粒度免疫网络模型可以提供大量信息,有助于在给予糖皮质激素或维生素 D 等免疫抑制剂前后诊断适应性免疫功能的无效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/437c/9734612/fb5ff01fc3c0/12038_2022_312_Fig1_HTML.jpg

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