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.

研究摘要 结核病（TB）是由细菌结核分枝杆菌（MTB）引起的疾病，仍然是主要的信息 世界上人类的疾病。在最初对肺MTB中一个部位的局部感染后，以某种方式弥补了 肺部的污染物经常散布在肺部之外。实际上，肺外结核病是该病的标志 幼儿和免疫受损的成年人很难诊断和治疗。我们对MTB的理解 但是，无论是在肺部及以后的传播，都仍然有限。在这个建议中，我们组建了一个团队 具有计算生物学专业知识的科学家（Ganusov，Aitchison，Duffy，Langston）和TB发病机理 （乌达尔，谢尔曼，贝加尔）为MTB传播机制提供定量理解 并系统地。为此，我们将使用许多高度创新的技术，例如i）一种新型动物 TB的模型：MTB的超低剂量（ULD，1-3个菌落形成单位，CFU）的感染 在50个条形码的MTB菌株中，II）具有复制时钟质粒的MTB菌株H37RV-PBP10，可以估算 在体内消除细菌的速度和iii）基于mRNA的基因特征预测细菌数量 人类的鼠肺和结核病疾病进展风险。有了三个完成者的特定目标，我们将 视频详细的，定量理解MTB如何从沉积中传播的基本过程 在AIM 1中，我们将确定MTB传播的途径 在肺中使用一种新型的ULD感染小鼠模型，该模型比许多其他模仿人类感染更好的人类感染 动物模型。特别是，我们将区分MTB的替代假设 这样的“气泡模型”（MTB在肺部爱之间局部传播）和“恢复模型”（其中 全身传播后，MTB将血源性扩散到肺的不同部位）。在目标2中，我们将 确定不同细胞群体的贡献，包括MTB特异性CD4 T细胞反应，对动力学的动力学 MTB在感染MTB的常规剂量（CD，150 CFU）的小鼠中系统地传播。参数化 最佳模型我们将使用带有MTB H37RV的实验数据的数据，带有复制时钟质粒PBP10。 最后，在AIM 3中，我们将尝试改进我们最近基于mRNA的基因特征的预测 鼠肺中的CFU使用基于尖端图理论的数据维度降低方法。我们将 还执行实验并定义一个新的签名，以预测小鼠的传播结核，并测试其精度 使用来自猴子和人类的数据。通过结合来自高度创新的实验数据来在一起 涉及新技术的实验（超低剂量感染，条形码菌株，复制钟质粒， 基于微阵列的基因特征）我们将对MTB如何在 肺部并系统地在体内。