Predicting tuberculosis outcomes using genotypic and biomarker signatures

使用基因型和生物标志物特征预测结核病结果

• 批准号: 10671340
• 负责人: GILLIAN L BEAMER
• 金额: $ 80.27万
• 依托单位: TEXAS BIOMEDICAL RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10671340
• 关键词: Address; Adult; Aerosols; Alleles; Animal Model; Bacillus; Biological Markers; Blood; Categories; Classification; Clinical; Consensus; Data; Databases; Diagnosis; Diagnostic; Diagnostic tests; Disease; Disease Outcome; Dose; Euthanasia; Genetic Variation; Genotype; Granuloma; HIV/AIDS; Harvest; Heterozygote; Human; Image; Image Analysis; Inbred Strain; Individual; Infection; Intervention; Knowledge; Lung; Malaria; Malignant Neoplasms; Maps; Measures; Minority; Modeling; Morbidity - disease rate; Mus; Mycobacterium tuberculosis; Necrosis; Onset of illness; Outcome; Patient Care; Patient-Focused Outcomes; Patients; Persons; Population; Predisposition; Process; Production; Prognosis; Proteins; Pulmonary Tuberculosis; Quantitative Trait Loci; Resistance; Sampling; Serum; Serum Proteins; Testing; Time; Training; Tuberculosis; Vehicle crash; biomarker signature; biomarker validation; disease classification; human pathogen; improved; individual patient; learning algorithm; model building; model development; novel diagnostics; novel marker; outcome prediction; pathogen; predictive marker; predictive modeling; prognostic; protein biomarkers; public health intervention; response; supervised learning; tool; transmission process

PROJECT SUMMARY/ABSTRACT Tuberculosis (TB) is caused by an infectious pathogen, Mycobacterium tuberculosis (M.tb) in susceptible individuals, but we cannot yet classify or predict outcomes in those prone to pulmonary TB disease versus those prone to resistance. In part, this reflects knowledge gaps regarding genotypes that may increase susceptibility, and in validated disease correlates (e.g. serum of lung protein biomarkers) measured individually, or combined signatures. We address these knowledge gaps by using Diversity Outbred (DO) mice, a population with abundant genetic diversity and heterozygosity, like the human population. Also, like humans, a low dose M.tb infection of DO mice produces a spectrum of outcomes, from highly susceptible to highly resistant, and many intermediate outcomes. In this proposal, we use the DO population to: 1) Identify and test the capacity of genotypic (alleles and statistically significant loci) to predict outcomes such as diagnostic category (class); and 2) To identify and test lung and serum biomarker (protein) and granuloma signatures to determine diagnostic category (class); and 3) To identify and test serum biomarker (protein) signatures that can forecast disease onset, within a 3-week window before illness manifests clinically. The best performing signatures will be tested using samples from humans. Collectively, results from these studies will generate new translatable knowledge regarding correlates of pulmonary TB (useful for diagnostics), and genotypic and serum protein signatures (useful for prognostics).

项目总结/摘要 结核病（TB）是由一种传染性病原体结核分枝杆菌（M.tb）引起的， 但我们还不能对那些容易患肺结核病的人与 那些容易抵抗的人。在某种程度上，这反映了关于基因型的知识差距， 易感性，并在经验证的疾病相关性（如血清肺蛋白生物标志物）测量 单独或组合签名。我们通过使用多样性远交（DO）小鼠来解决这些知识差距， 具有丰富的遗传多样性和杂合性的群体，如人类群体。而且，和人类一样， DO小鼠的低剂量结核分枝杆菌感染产生一系列结果，从高度敏感到高度敏感， 抵抗，以及许多中间结果。在本提案中，我们使用DO群体来：1）识别和测试 基因型（等位基因和统计学显著位点）预测结果的能力， 2）鉴定和测试肺和血清生物标志物（蛋白质）和肉芽肿特征， 确定诊断类别（类别）;和3）鉴定和测试血清生物标志物（蛋白质）特征， 可以预测疾病发作，在疾病临床表现之前的3周内。表现最好的 将使用人类样本测试签名。总的来说，这些研究的结果将产生新的 关于肺结核相关性的可翻译知识（对诊断有用），以及基因型和 血清蛋白特征（对鉴别学有用）。