Identification of Genetic Markers of Susceptibility to Intracellular Bacterial Infection Using the Collaborative Cross Mouse Model

使用协作交叉小鼠模型鉴定细胞内细菌感染易感性的遗传标记

• 批准号: 10511530
• 负责人: Elke BergmannLeitner
• 金额: $ 21.54万
• 依托单位: GENEVA FOUNDATION
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10511530
• 关键词: Animal Disease Models; Animal Model; Anti-Bacterial Agents; Antigens; Artificial Intelligence; Bacteria; Bacterial Infections; Biological Markers; Biological Models; Body Temperature; Body Weight decreased; Boutonneuse Fever; Cells; Clinical; Collaborations; Communicable Diseases; Computer Analysis; Data; Data Analyses; Disease; Disease susceptibility; Exhibits; Foundations; Frequencies; Future; Gene Targeting; Genes; Genetic; Genetic Determinism; Genetic Markers; Genetic Variation; Genome; Genomics; Health; Human; Immune; Immune response; Immune system; Immunity; Immunologics; Immunology; Immunotherapeutic agent; Inbred Mouse; Inbreeding; Individual; Infection; Interruption; Intravenous; Laboratories; Lead; Literature; Liver; Machine Learning; Maryland; Methods; Mining; Modeling; Molecular; Mouse Strains; Mus; Organ; Outcome; Output; Pathology; Phenotype; Population; Predisposition; Prognostic Marker; Public Health; Quantitative Trait Loci; Randomized; Recombinants; Research; Research Personnel; Resistance; Resistance to infection; Resolution; Resources; Rickettsia; Rickettsia Infections; Rickettsia conorii; Rocky Mountain Spotted Fever; Role; Seminal; Serum; Spleen; System; Therapeutic; Therapeutic Intervention; Tick-Borne Diseases; Time; Universities; Vaccines; Variant; Work; acute infection; adaptive immune response; base; bioinformatics tool; biomarker identification; chemokine; cohort; comparative genomics; cytokine; data integration; diagnostic biomarker; disease phenotype; genetic analysis; genetic approach; genomic locus; human disease; human model; human pathogen; insight; mouse genome; mouse model; multidisciplinary; novel; pathogen; peripheral blood; prophylactic; resilience; response; reverse genetics; targeted treatment; therapeutic development; therapeutic evaluation; tick-borne; tool; trait; transcriptomics; vaccine development

PROJECT SUMMARY Identification of the genetic and molecular mechanisms governing immunity against intracellular bacteria is imperative for understanding the host-pathogen-interplay and forms the basis for the development of therapeutic countermeasures. Previous attempts at increasing our understanding of this topic have relied on targeted interruption of individual genes or analysis natural genetic variability in natural populations. Herein, we propose to employ 1) animal models with pre-defined genetic variability, 2) cutting edge immunoprofiling, 3) comparative genomics, and 4) computational analyses to identify the immunological and genetic basis of sensitivity to Rickettsia infection. This approach employs the collaborative cross (CC) mice. This mouse resource involves a cohort of recombinant-inbred lines generated by randomizing the genetic diversity of existing inbred mouse resources. This pre-defined genetic diversity has significantly accelerated discovery of genetic determinants that regulate immunity against several pathogens as well as other non-infectious diseases. The CC mouse resource is distinct from other animal models as its high genetic diversity is comparable to that of human populations. Unlike in outbred animal models, each CC line reproducibly exhibits distinct phenotypes of disease susceptibility and immune profiles to pathogens. Our multi-disciplinary team will screen CC lines to establish the range of responses to the tick-borne human pathogen Rickettsia conorii. Using murine models of Rickettsia infection with well-established phenotypic difference in susceptibility to infection, we will screen initially CC mouse lines to encompass a detailed assessment of the disease phenotype (bacterial load, weight loss, body temperature, survival) and immunoprofiling of peripheral blood, spleen, and liver as relevant, representative organ. CC lines with extreme clinical and immunological phenotypes will then be selected for longitudinal in-depth immunoprofiling. Here, changes in the frequency of activated innate and antigen-specific adaptive cells, cytokine profiles in serum, and antibacterial activities of immune cells will be assessed throughout infection and disease resolution. Computational data integration and bioinformatics tools (machine learning) will be applied to establish the immune landscape of Rickettsia-specific immune responses to identify immune correlates that govern disease phenotype of each CC line. The short-term impact of the proposed work will be the identification of novel murine models that emulate differential immune responses to infection. These tools will enable researchers to test therapeutics and/or vaccines in a diverse system that, for the first time, has the potential to forecast responses in humans. Computational analysis will be performed to identify quantitative trait loci associated with disease phenotype and disease-specific immunoprofiles. This information will be the basis for the future identification of genes responsible for immunity against intracellular bacteria, which will have a significant long- term impact on our understanding of multiple rickettsial diseases.

项目摘要 鉴定有关针对细胞内细菌免疫力的遗传和分子机制的鉴定为 必须了解宿主 - 病原体互动的必要条件，并构成了治疗性发展的基础 对策。以前尝试提高我们对该主题的理解的尝试依赖于目标 自然种群中各个基因或分析自然遗传变异性的中断。在这里，我们建议 采用1）具有预定义遗传变异性的动物模型，2）尖端免疫植物，3）比较 基因组学和4）计算分析以确定对敏感性的免疫学和遗传基础 人力病感染。这种方法采用了协作十字（CC）小鼠。该鼠标资源涉及一个 通过随机化现有近交小鼠的遗传多样性而产生的重组染色线的队列 资源。这种预定义的遗传多样性已显着加速了遗传决定因素的发现 调节免疫力针对多种病原体以及其他非感染疾病。 CC鼠标资源 与其他动物模型不同，因为其高遗传多样性与人类种群相当。 与近代动物模型不同，每个CC线可重复显示出不同的疾病易感性表型 和对病原体的免疫特征。我们的多学科团队将筛选CC线以建立 对tick传播的人病原体立克康罗伊的反应。使用带有立克感染的鼠模型 在感染易感性的敏感性中，表型差异很大，我们最初将CC小鼠线筛选到 包括对疾病表型的详细评估（细菌负荷，体重减轻，体温， 生存）和外周血，脾脏和肝脏的免疫药物作为相关的代表性器官。 CC线 然后将选择极端的临床和免疫表型，以进行纵向深入 免疫专长。在这里，活化的先天和抗原特异性自适应细胞的频率变化，细胞因子 在整个感染和疾病中，将评估血清中血清中的特征和免疫细胞的抗菌活性 解决。计算数据集成和生物信息学工具（机器学习）将用于建立 立克特异性免疫反应的免疫景观以确定管理的免疫相关性 每条CC系的疾病表型。拟议工作的短期影响将是对新颖的识别 模仿对感染的差异免疫反应的鼠模型。这些工具将使研究人员能够 在多样化系统中测试治疗剂和/或疫苗，这首先有可能预测 人类的回应。将进行计算分析以识别与 疾病表型和疾病特异性免疫蛋白酶。这些信息将是未来的基础 鉴定负责免疫对细胞内细菌免疫的基因，该基因将具有显着的长期 术语对我们对多种立体疾病的理解的影响。