Mathematical modeling of Mycobacterium tuberculosis dissemination

结核分枝杆菌传播的数学模型

• 批准号: 10364119
• 负责人: Vitaly V. Ganusov
• 金额: $ 59.16万
• 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 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10364119
• 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; Lung; Lung diseases; Lung infections; Mediating; Messenger RNA; Methods; Modeling; Monkeys; Mus; Mycobacterium tuberculosis; Mycobacterium tuberculosis H37Rv; Pathogenesis; Pathology; Pathway interactions; Patients; Plasmids; Population; Probability; Process; Protocols documentation; Pulmonary Tuberculosis; Pulmonary alveolar structure; Research; Risk; Scientist; Site; Spleen; Supporting Cell; System; T cell response; Techniques; Testing; Time; Tissues; Tuberculosis; Tuberculosis Vaccines; Vaccines; Whole Blood; base; design; experimental study; genetic signature; graph theory; human disease; improved; in vivo; innovation; insight; lung lobe; lung upper lobe; lymph nodes; macrophage; mathematical model; mortality; neutrophil; novel; novel marker; pathogen; predictive signature; 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）是一种由结核分枝杆菌（Mtb）引起的疾病，仍然是一种主要的传染病。 世界上人类的一种严重疾病，在肺部某一部位最初感染后，结核分枝杆菌以某种方式传播。 事实上，肺外结核病是该疾病的一个标志。 难以诊断和治疗的幼儿和免疫功能低下的成年人。 然而，在这项提议中，我们组建了一个团队。 拥有计算生物学专业知识的科学家（Ganusov、Aitchison、Duffy、Langston）和结核病发病机制 （Urdahl、Sherman、Behar）提供对 Mtb 肺部传播机制的定量理解 为此，我们将系统地使用许多高度创新的技术，例如 i) 一种新颖的动物。 结核病模型：用超低剂量（ULD，1-3 菌落形成单位，CFU）的 Mtb 和一组小鼠感染小鼠 50 个带有条形码的 Mtb 菌株，ii) 带有复制时钟质粒的 Mtb 菌株 H37Rv-pBP10，可以估计 细菌在体内被消除的速度有多快，以及 iii) 基于 mRNA 的基因特征预测体内细菌数量 通过三个互补的具体目标，我们将促进小鼠肺部和人类结核病进展风险。 详细、定量地了解 Mtb 如何从沉积物中传播的基本过程 在目标 1 中，我们将确定 Mtb 传播的途径。 使用一种新型 ULD 感染小鼠模型在肺内进行研究，该模型比许多其他模型更好地模拟人类感染 特别是，我们将区分结核分枝杆菌在肺部传播的替代假设。 例如“气泡模型”（其中 Mtb 在肺叶之间局部传播）和“重新播种模型”（其中 Mtb 在全身传播后通过血行传播到肺部的不同部位）。 确定不同细胞群（包括 Mtb 特异性 CD4 T 细胞反应）对动力学的贡献 感染常规剂量（CD，150 CFU） Mtb 的小鼠体内 Mtb 的全身传播 参数化。 为了建立最佳拟合模型，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，我们将尝试改进最近导出的基于 mRNA 的基因特征预测 我们将使用基于图论的尖端数据降维方法计算小鼠肺部的 CFU。 还进行实验并定义预测小鼠播散性结核病的新特征，并测试其准确性 通过结合高度创新的实验数据，综合使用来自猴子和人类的数据。 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、 基于微阵列的基因特征）我们将定量了解 Mtb 如何在 肺部和全身。