Strategic Priming with Multiple Antigens can Yield Memory Cell Phenotypes Optimized for Infection with Mycobacterium tuberculosis: A Computational Study.
Strategic Priming with Multiple Antigens can Yield Memory Cell Phenotypes Optimized for Infection with Mycobacterium tuberculosis: A Computational Study.
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DOI：
10.3389/fmicb.2015.01477
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发表时间：
2015
影响因子：
5.2
通讯作者：
Linderman JJ
中科院分区：
文献类型：
--
作者：
Ziraldo C;Gong C;Kirschner DE;Linderman JJ
由于缺乏有效的疫苗，全世界每年新增900万结核病病例，180万人死亡。尽管许多婴儿在出生时接种了BCG（一种减毒的M。牛），这并不能防止儿童期后感染或发展结核病。预防感染或疾病所必需的免疫应答仍然是未知的，使得开发有效的结核病疫苗具有挑战性。几种新的疫苗已经准备好进行人体临床试验，但这些试验既困难又昂贵;特别具有挑战性的是确定保护所需的适当细胞反应。免疫反应的强度可能是产生成功疫苗的关键。诸如响应于不同表位组的中央记忆（CM）和效应记忆（EM）T细胞的数量的特征也与保护相关。有希望的结核病疫苗含有分枝杆菌亚单位抗原（Ag）存在于活动性和潜伏性感染。我们假设，针对不同的关键免疫显性抗原的保护可能需要一种疫苗，产生不同水平的EM和CM的每个Ag特异性记忆人口。我们创建了一个计算模型来探索EM和CM值，以及它们的比例，我们称之为内存设计空间。我们的模型捕获了淋巴结内T细胞引发的事件，并跟踪它们通过血液到外周组织的循环。我们使用该模型来测试是否可以在接种后的特定时间点在记忆设计空间内产生具有不同位置的多个Ag特异性记忆细胞群体。提升可以进一步将记忆群体转移到初始启动事件无法达到的记忆细胞比例。通过策略性地改变抗原载量、LN内细胞相互作用的性质和递送参数（例如，通过使用多亚基疫苗的免疫增强（即增强次数），我们可以产生覆盖广泛的记忆设计空间的多个Ag特异性记忆群体。给定Ag特异性记忆群体的一组所需特征，我们可以使用我们的模型作为工具来预测将产生这些群体的疫苗制剂。
Lack of an effective vaccine results in 9 million new cases of tuberculosis (TB) every year and 1.8 million deaths worldwide. Although many infants are vaccinated at birth with BCG (an attenuated M. bovis), this does not prevent infection or development of TB after childhood. Immune responses necessary for prevention of infection or disease are still unknown, making development of effective vaccines against TB challenging. Several new vaccines are ready for human clinical trials, but these trials are difficult and expensive; especially challenging is determining the appropriate cellular response necessary for protection. The magnitude of an immune response is likely key to generating a successful vaccine. Characteristics such as numbers of central memory (CM) and effector memory (EM) T cells responsive to a diverse set of epitopes are also correlated with protection. Promising vaccines against TB contain mycobacterial subunit antigens (Ag) present during both active and latent infection. We hypothesize that protection against different key immunodominant antigens could require a vaccine that produces different levels of EM and CM for each Ag-specific memory population. We created a computational model to explore EM and CM values, and their ratio, within what we term Memory Design Space. Our model captures events involved in T cell priming within lymph nodes and tracks their circulation through blood to peripheral tissues. We used the model to test whether multiple Ag-specific memory cell populations could be generated with distinct locations within Memory Design Space at a specific time point post vaccination. Boosting can further shift memory populations to memory cell ratios unreachable by initial priming events. By strategically varying antigen load, properties of cellular interactions within the LN, and delivery parameters (e.g., number of boosts) of multi-subunit vaccines, we can generate multiple Ag-specific memory populations that cover a wide range of Memory Design Space. Given a set of desired characteristics for Ag-specific memory populations, we can use our model as a tool to predict vaccine formulations that will generate those populations.