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

人类免疫反应的作用是在感染时清除或控制病原体。抗原提呈S对免疫反应至关重要，是病原体的多肽片段被细胞摄取并显示在细胞表面的过程。抗原提呈涉及多个尺度(遗传分子、细胞、组织和器官)的事件。简而言之，抗原提呈细胞(ARC)在感染部位承担病原体。一旦它们被吸收，它们就会在APC中被加工成多肽。然后，这些多肽与被称为主要组织相容性复合体(MHC)的蛋白质结合。这些多肽-MHC复合体(PMHC)随后被展示在APC的表面，供T细胞识别。此外，抗原递呈和识别的动力学受到其发生的更大的组织水平背景的影响，即淋巴结的结构环境，并最终受到血液和淋巴系统的外部隔室动力学的影响。要全面了解免疫反应期间抗原呈递的过程，就需要对发生在多个空间和时间尺度上的事件有一个完整的了解。数学模型是允许进行这种多尺度调查的工具。不足为奇的是，由于病原体不断地与APC相遇，作为第一道防线，许多病原体已经进化出抑制抗原提呈的方法。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，结核分枝杆菌被常驻巨噬细胞吞噬，然后复制。众所周知，为了逃避免疫监视，结核分枝杆菌会抑制宿主巨噬细胞的抗原提呈。结核分枝杆菌达到这种抑制作用的机制尚未完全阐明。我们的具体目标包括：建立数学和统计模型以预测多肽对不同MHCII分子的亲和力，特别强调多肽长度在决定亲和力中的作用；描述发生在单个APC中的加工和递呈事件；通过捕获单个淋巴结内细胞的相互作用描述抗原识别和一些下游事件；捕获体内两个血液/淋巴结室中的相关免疫动态。在多个尺度上整合这些模型将是一个关键目标，也将利用来自非人类灵长类动物和老鼠系统的数据。我们的具体目标是使用上面开发的模型来理解结核分枝杆菌感染过程中的抗原提呈，结核分枝杆菌是结核病的病原体，也是当今世界因感染性疾病而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)并快速做出反应，因此从本文描述的研究中获得的信息可以立即应用于结核分枝杆菌和其他病原体的疫苗设计。

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.