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: 10672355
• 负责人: Elke BergmannLeitner
• 金额: $ 17.17万
• 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/10672355
• 关键词: Acceleration; Animal Disease Models; Animal Model; Anti-Bacterial Agents; Antigens; Artificial Intelligence; Bacteria; Bacterial Infections; 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; Literature; Liver; Machine Learning; Maps; Maryland; Methods; Mining; Modeling; Molecular; Mouse Strains; Mus; Organ; Outcome; Output; Pathology; Phenotype; Population; Predisposition; Prognostic Marker; Public Health; Quantitative Trait Loci; Randomized; Recombinants; Reproducibility; 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; bioinformatics tool; biomarker identification; chemokine; cohort; comparative genomics; cytokine; data integration; diagnostic biomarker; disease phenotype; diversity and equity; 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.

项目概要 确定控制细胞内细菌免疫的遗传和分子机制 了解宿主与病原体之间的相互作用至关重要，并为开发治疗药物奠定基础 对策。以前为增加我们对这个主题的理解所做的尝试依赖于有针对性的 中断个体基因或分析自然群体中的自然遗传变异。在此，我们建议 采用 1) 具有预先定义的遗传变异性的动物模型，2) 最先进的免疫分析，3) 比较 基因组学，4) 计算分析以确定敏感性的免疫学和遗传基础 立克次体感染。该方法采用协作交叉（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近交系队列 资源。这种预先定义的遗传多样性显着加速了遗传决定因素的发现， 调节针对多种病原体以及其他非传染性疾病的免疫力。 CC鼠标资源 与其他动物模型不同，其高度遗传多样性可与人类相媲美。 与近交动物模型不同，每个 CC 系可重复地表现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，以确定范围 对蜱传人类病原体康氏立克次体的反应。使用立克次体感染的小鼠模型 由于对感染的易感性存在明确的表型差异，我们将首先筛选 CC 小鼠系以 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、脾脏和肝脏作为相关代表性器官的免疫分析。抄送线 然后将选择具有极端临床和免疫表型的纵向深入研究 免疫分析。这里，激活先天性和抗原特异性适应性细胞、细胞因子的频率的变化 将在整个感染和疾病过程中评估血清中的特征以及免疫细胞的抗菌活性 解决。将应用计算数据集成和生物信息学工具（机器学习）来建立 立克次体特异性免疫反应的免疫景观，以确定控制的免疫相关因素 每个 CC 系的疾病表型。拟议工作的短期影响将是识别新颖的 模拟对感染的差异免疫反应的小鼠模型。这些工具将使研究人员能够 在多样化的系统中测试疗法和/或疫苗，首次有可能预测 人类的反应。将进行计算分析以确定与相关的数量性状基因座 疾病表型和疾病特异性免疫谱。这些信息将成为未来的基础 鉴定负责细胞内细菌免疫的基因，这将具有显着的长期作用 术语对我们对多种立克次体疾病的理解的影响。