Computational and Empirical Studies Predict Mycobacterium tuberculosis-Specific T Cells as a Biomarker for Infection Outcome.
Computational and Empirical Studies Predict Mycobacterium tuberculosis-Specific T Cells as a Biomarker for Infection Outcome.
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DOI：
10.1371/journal.pcbi.1004804
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发表时间：
2016-04
影响因子：
4.3
通讯作者：
Kirschner DE
中科院分区：
文献类型：
--
作者：
Marino S;Gideon HP;Gong C;Mankad S;McCrone JT;Lin PL;Linderman JJ;Flynn JL;Kirschner DE
识别结核病的生物标志物是开发感染结果和保护的免疫学相关因素的持续挑战。生物标志物的发现对于帮助设计和测试新的治疗方法和疫苗也是必要的。为了有效地预测包括结核病在内的任何疾病感染进展的生物标志物，需要大量的实验数据来达到统计效力并做出准确的预测。我们采取了双管齐下的方法，使用实验和计算建模来解决这个问题。我们首先从28只感染了低剂量结核分枝杆菌的非人灵长类动物（NHP）身上收集了200份血液样本，历时2年。我们从每个样本中确定T细胞及其产生的细胞因子（单个和多个）以及猴子的状态和感染进展数据。使用机器学习技术来查询实验NHP数据集，但没有识别任何潜在的结核病生物标志物。同时，我们使用我们广泛的新型NHP数据集来建立和校准一个多器官计算模型，该模型结合了单个肉芽肿范围内感染部位（例如肺）发生的情况，以及可以在猴子和人类中跟踪的血液水平读数。然后，我们生成了一个与淋巴结和血液动力学耦合的大型计算机肉芽肿数据库，并开发了一个计算机工具，将肉芽肿水平的结果扩展到全宿主规模，以确定最能预测结核分枝杆菌（Mtb）感染结果的工具。通过对血液测量的分析，确定了感染后不同时间点效应T细胞表型的mtb特异性频率，作为感染结果的有希望的指标。我们强调，将湿实验室和计算方法相结合，有望加速结核病生物标志物的发现。结核病（TB）是一种因吸入结核分枝杆菌感染而引起的疾病。并非所有感染结核细菌的人都会生病。因此，已将两种结核病相关疾病分类为：潜伏性结核病感染（未患病但仍携带细菌）和活动性结核病。如果治疗不当，活动性结核病可能是致命的。全世界每年有近130万人死于结核病，2013年新增感染病例约860万。目前还没有有效的结核病疫苗，而且用多种抗生素治疗感染需要很长时间（6-9个月），不遵守规定是出现耐药菌株的一个主要因素。开发有效疫苗和可能更短的治疗方案的关键一步是能够识别与预后和感染进展相关的生物标志物（类似于胆固醇水平是心脏健康的衡量标准）。在这项研究中，我们展示了如何将计算机建模、统计和数学与来自非人类灵长类动物研究的数据集结合起来，可以加速生物标志物的发现，并提供了一种新的方法来确定在临床实践中有用的保护相关因素，特别是在结核病最普遍的发展中国家。
Identifying biomarkers for tuberculosis (TB) is an ongoing challenge in developing immunological correlates of infection outcome and protection. Biomarker discovery is also necessary for aiding design and testing of new treatments and vaccines. To effectively predict biomarkers for infection progression in any disease, including TB, large amounts of experimental data are required to reach statistical power and make accurate predictions. We took a two-pronged approach using both experimental and computational modeling to address this problem. We first collected 200 blood samples over a 2- year period from 28 non-human primates (NHP) infected with a low dose of Mycobacterium tuberculosis. We identified T cells and the cytokines that they were producing (single and multiple) from each sample along with monkey status and infection progression data. Machine learning techniques were used to interrogate the experimental NHP datasets without identifying any potential TB biomarker. In parallel, we used our extensive novel NHP datasets to build and calibrate a multi-organ computational model that combines what is occurring at the site of infection (e.g., lung) at a single granuloma scale with blood level readouts that can be tracked in monkeys and humans. We then generated a large in silico repository of in silico granulomas coupled to lymph node and blood dynamics and developed an in silico tool to scale granuloma level results to a full host scale to identify what best predicts Mycobacterium tuberculosis (Mtb) infection outcomes. The analysis of in silico blood measures identifies Mtb-specific frequencies of effector T cell phenotypes at various time points post infection as promising indicators of infection outcome. We emphasize that pairing wetlab and computational approaches holds great promise to accelerate TB biomarker discovery. Tuberculosis (TB) is a disease that is caused by infection after inhaling the bacterium Mycobacterium tuberculosis. Not everyone infected with TB bacteria becomes sick. As a result, two TB-related conditions have been categorized: latent TB infection (not sick but still harboring the bacteria) and active TB disease. If not treated properly, active TB disease can be fatal. Almost 1.3 million die of TB worldwide each year, with ~8,6 million new infections in 2013. No effective vaccine is available to protect against TB and treatment of infection with multiple antibiotics is lengthy (6–9 months), with non-compliance being a major factor for the emergence of drug-resistant strains. A key step in developing effective vaccines and possibly shorter treatment regimens is the ability to identify biomarkers that correlate prognosis and progression to infection (similar to how cholesterol levels are a measure of heart health). In this study we show how pairing computer modeling, statistics and mathematics with datasets derived from non-human primate studies can accelerate biomarker discovery, and offer a new approach to identifying correlates of protection that will be useful in clinical practice, particularly in developing countries where TB is most prevalent.