Mathematical modeling of Mycobacterium tuberculosis dissemination

结核分枝杆菌传播的数学模型

• 批准号: 10612718
• 负责人: Vitaly V. Ganusov
• 金额: $ 57.07万
• 依托单位: UNIVERSITY OF TENNESSEE KNOXVILLE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-22 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10612718
• 关键词: Adult; Advocate; Animal Model; Bacteria; Bar Codes; Blood specimen; CD4 Positive T Lymphocytes; Cells; Cessation of life; Child; Clinical; Colony-forming units; Communicable Diseases; Computational Biology; Data; Data Analyses; Deposition; Diagnosis; Dimensions; Disease; Disease Progression; Dose; Experimental Models; Focal Infection; Gene Expression Profile; Goals; Granuloma; HIV; Health; Hematogenous; Hematogenous Spread; Human; Immune; Immunity; Immunocompromised Host; Individual; Infection; Kinetics; Licensing; Lung; Lung diseases; Lung infections; Macrophage; Mediating; Messenger RNA; Methods; Micro Array Data; Modeling; Monkeys; Mus; Mycobacterium tuberculosis; Mycobacterium tuberculosis H37Rv; Pathogenesis; Pathology; Pathway interactions; Patients; Plasmids; Population; Probability; Process; Protocols documentation; Pulmonary alveolar structure; Repression; Research; Risk; Scientist; Site; Spleen; Supporting Cell; System; T cell response; Techniques; Testing; Time; Tissues; Tuberculosis; Tuberculosis Vaccines; Vaccines; Whole Blood; conventional dosing; design; experimental study; genetic signature; graph theory; human disease; improved; in vivo; innovation; insight; lung lobe; lung upper lobe; lymph nodes; mathematical model; mortality; neutrophil; novel; novel marker; pathogen; predictive signature; progression risk; vaccine development

Research Summary Tuberculosis (TB), a disease caused by the bacteria Mycobacterium tuberculosis (Mtb), remains a major infec- tious disease of humans in the world. After the initial local infection of one site in the lung Mtb somehow dissem- inates in the lung and often spreads beyond the lung. In fact, extrapulmonary TB is a hallmark of the disease in young children and immunocompromised adults that is difficult to diagnose and treat. Our understanding of Mtb dissemination, both within the lung and beyond, remains limited, however. In this proposal we assembled a team of scientists with expertise in computational biology (Ganusov, Aitchison, Duffy, Langston) and TB pathogenesis (Urdahl, Sherman, Behar) to provide quantitative understanding of mechanisms of Mtb dissemination the lung and systemically. To this end, we will be using a number of highly innovative techniques such as i) a novel animal model of TB: infection of mice with an ultra low dose (ULD, 1-3 colony forming units, CFU) of Mtb along with a set of 50 barcoded Mtb strains, ii) an Mtb strain H37Rv-pBP10 with the replication clock plasmid, allowing to estimate how quickly bacteria are eliminated in vivo, and iii) mRNA-based gene signatures predicting bacterial numbers in murine lungs and TB disease progression risk in humans. With three complementary specific aims we will pro- vide detailed, quantitative understanding of fundamental processes of how Mtb disseminates from the deposition in lung alveoli to the whole lung and systemically. In Aim 1 we will determine the pathway of Mtb dissemination within the lung using a novel model of ULD-infected mice that mimics better human infection than many other animal models. In particular, we will discriminate between alternative hypotheses of Mtb spread in the lungs such the “bubble model” (in which Mtb spreads locally between lung lobes) and the “reseeding model” (in which Mtb spreads hematogenously to different parts of the lung after disseminating systemically). In Aim 2 we will determine the contribution of different cell populations, including Mtb-specific CD4 T cell response, to kinetics of Mtb dissemination systemically in mice infected with conventional doses (CD, 150 CFU) of Mtb. To parameterize best fit models we will use data from experiments with Mtb H37Rv carrying the replication clock plasmid pBP10. Finally, in Aim 3 we will attempt to improve on our recently derived mRNA-based gene signatures predicting CFU in murine lungs using cutting-edge graph theory-based methods of data dimensionality reduction. We will also perform experiments and define a new signature predicting disseminated TB in mice, and test its accuracy using data from monkeys and humans. Taken together, by combining experimental data from highly innovative experiments involving novel techniques (ultra low dose infections, barcoded strains, replication clock plasmid, microarray-based gene signatures) we will provide a quantitative understanding of how Mtb disseminates in the lung and systemically in the body.

研究总结 结核病(TB)是由结核分枝杆菌(Mtb)引起的一种疾病，一直是影响人类健康的主要因素。 世界上最常见的人类疾病。在肺部一个部位最初局部感染后，结核分枝杆菌以某种方式剥离- 多发于肺内，常扩散至肺外。事实上，肺外结核病是这种疾病的一个标志 幼童和免疫功能低下的成年人很难诊断和治疗。我们对Mtb的理解 然而，在肺内和肺外的传播仍然有限。在这个提案中，我们组建了一个团队 在计算生物学(Ganusov、Aitchison、Duffy、Langston)和结核病发病机制方面具有专长的科学家 (Urdahl，Sherman，Behar)提供对结核分枝杆菌在肺部传播机制的定量了解 而且是系统性的。为此，我们将使用许多高度创新的技术，例如i)一种新的动物 结核病模型：用超低剂量(ULD，1-3个集落形成单位，CFU)的Mtb和一组 在50株Mtb条码菌株中，ii)一株带有复制时钟质粒的Mtb菌株H37Rv-pBP10，允许估计 细菌在体内被消除的速度，以及iii)基于mRNA的基因签名预测细菌数量 小鼠肺部和人类结核病进展风险。通过三个相辅相成的fic目标，我们将支持- 通过详细、定量地了解结核分枝杆菌如何从沉积中传播的基本过程 从肺泡到全肺和全身。在目标1中，我们将确定结核分枝杆菌的传播途径 在肺内使用一种新的ULD感染小鼠模型，该模型比其他许多模型更好地模拟人类感染 动物模型。特别是，我们将区分结核分枝杆菌在肺部传播的不同假设。 这样的“气泡模型”(结核分枝杆菌在肺叶之间局部扩散)和“补种模型”(其中 结核分枝杆菌经全身传播后，通过血液传播到肺的不同部位)。在《目标2》中我们将 确定不同细胞群，包括结核分枝杆菌特异性fic CD4T细胞反应，对 结核分枝杆菌在感染常规剂量(Cd，150CFU)的小鼠中系统性传播。进行参数化的步骤 最佳fit模型我们将使用携带复制时钟质粒pBP10的Mtb H37Rv实验数据。 最后，在目标3中，我们将尝试改进我们最近基于mrna的基因签名预测。 基于前沿图论的数据降维方法在小鼠肺部CFU研究中的应用。我们会 还进行实验和defiNe一个新的信号，预测小鼠的播散性结核病，并测试其准确性 使用来自猴子和人类的数据。总而言之，通过结合高度创新的实验数据 涉及新技术的实验(超低剂量感染、条形码菌株、复制时钟质粒、 基于微阵列的基因签名)我们将提供关于结核分枝杆菌如何在 肺脏和全身的。