"MSM" A multi-scale approach for understanding antigen presentation in immunity

人体免疫反应在感染时起清除或控制病原体的作用。抗原呈递对免疫应答至关重要，是病原体的肽片段被细胞摄取并展示在细胞表面的过程。抗原呈递涉及多个尺度（遗传分子、细胞、组织和器官）的事件。简言之，抗原呈递细胞（ARC）在感染部位吸收病原体。一旦它们被吸收，它们就会在APC内被加工成肽。然后这些肽结合被称为主要组织相容性复合体（MHC）的蛋白质。这些肽- MHC复合物（pMHC）然后展示在APC的表面上以供T细胞识别。此外，抗原呈递和识别的动力学受到它们发生的更大组织水平背景的影响，即淋巴结的结构化环境，并最终受到血液和淋巴系统的外部隔室动力学的影响。要全面了解免疫应答过程中的抗原呈递过程，需要对多个空间和时间尺度上发生的事件有一个综合的了解。数学模型是允许这种多尺度调查的工具。毫不奇怪，由于病原体不断遇到APC作为第一道防线，许多已经进化出抑制抗原呈递的方法。一种这样的细胞内细菌病原体是结核分枝杆菌。进入肺部后，M。结核病被驻留的巨噬细胞吸收，然后复制。为了逃避免疫监视，M。已知结核病抑制其宿主巨噬细胞的抗原呈递。并对M.结核病实现这种抑制尚未完全阐明。我们的具体目标包括：建立数学和统计模型，以：预测肽对不同MHCII分子的亲和力，特别强调肽长度在确定亲和力中的作用;描述在单个APC中发生的加工和呈递事件;通过捕获单个淋巴结内细胞的相互作用来描述抗原识别和一些下游事件;在血液/淋巴结的两个隔室中捕获体内相关的免疫动力学。在多个尺度上集成模型将是一个关键目标，以及利用来自非人类灵长类动物和小鼠系统的数据。我们的具体目标是使用上述开发的模型来理解M.结核病感染是结核病的病原体，也是当今世界传染病导致死亡的主要原因。由于疫苗背后的前提是训练免疫系统识别病原体（通过抗原呈递）并快速应答，因此从本文所述的研究中获得的信息可以立即应用于M.结核病以及其他病原体。

The human immune response works to either clear or control pathogens upon infection. Antigen presentation s critical to the immune response and is the process by which peptide fragments of pathogens are taken up by cells and displayed on the cell surface. Events at multiple scales (genetic molecular, cellular, tissue, and organ) are involved in antigen presentation. Briefly, antigen-presenting cells (ARC) take up pathogens at the site of infection. Once they have been taken up, they are then processed into peptides within the APC. These peptides then bind proteins known as the major histocompatibility complex (MHC). These peptide- MHC complexes (pMHC) are then displayed on the surface of the APC for recognition by T cells. In addition, the dynamics of antigen presentation and recognition are influenced by the larger tissue-level context in which they occur, namely the structured environment of the lymph node and ultimately by external compartmental dynamics of blood and the lymphatic system. A comprehensive understanding of the process of antigen presentation during an immune response will require an integrated picture of events that are occurring over multiple spatial and time scales. Mathematical models are tools that allow for such a multiscale investigation. Not surprisingly, since pathogens meet APCs continually as a first line of defense, many have evolved ways to inhibit antigen presentation. One such intracellular bacterial pathogen is Mycobacterium tuberculosis. Upon entering the lungs, M. tuberculosis is taken up by resident macrophages and then replicates. To evade immune surveillance, M. tuberculosis is known to inhibit antigen presentation of its host macrophage. The mechanisms by which M. tuberculosis achieves this inhibition have not been completely elucidated. Our specific aims include: building mathematical and statistical models to: predict affinity of peptides for different MHCII molecules with particular emphasis on the role that peptide length plays in determining affinity; describe the processing and the presentation events occurring in a single APC; describe antigen recognition and some of the downstream events by capturing interactions of cells within a single lymph node; capture relevant immune dynamics in the body in two-compartments of blood/lymph node. Integrating the models over multiple scales will be a key goal as well as utilizing data from non-human primate and mouse systems. Our specific goal is to use the models developed above towards understanding antigen presentation during M. tuberculosis infection, the causative agent of tuberculosis, and the leading cause of death due to infectious disease in the world today. As the premise behind vaccines is to train the immune system to recognize pathogens (via antigen presentation) and to quickly respond, information gained from the studies described herein can be immediately applied to vaccine design for M. tuberculosis as well as for other pathogens.