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

基本信息

批准号：
10320904
负责人：
GILLIAN L BEAMER
金额：
$ 65.93万
依托单位：
TUFTS UNIVERSITY BOSTON
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-01-15 至 2022-06-14
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10320904
关键词：
AIDS/HIV problem Address Adult Aerosols Alleles Animal Model Bacillus Biological Markers Blood Categories Classification Clinical Consensus Data Databases Diagnosis Diagnostic Diagnostic tests Disease Disease Outcome Dose Genetic Variation Genotype Granuloma Harvest Heterozygote Human Image Image Analysis Inbred Strain Individual Infection Intervention Knowledge Lung Malaria Malignant Neoplasms 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 base biomarker signature human pathogen improved individual patient learning algorithm model development novel diagnostics novel marker outcome prediction pathogen predictive marker predictive modeling prognostic protein biomarkers public health intervention response supervised learning survival outcome tool transmission process tuberculosis diagnostics

项目摘要

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).

项目概要/摘要结核病 (TB) 是由易感人群中的传染性病原体结核分枝杆菌 (M.tb) 引起的个人，但我们还无法对易患肺结核病的人与易患肺结核病的人的结果进行分类或预测那些容易产生抵抗力的人。在某种程度上，这反映了有关基因型的知识差距可能会增加易感性，以及经过验证的疾病相关性（例如肺蛋白生物标志物的血清）测量单独或组合签名。我们通过使用 Diversity Outbred (DO) 小鼠来解决这些知识差距，具有丰富遗传多样性和杂合性的群体，例如人类。而且，和人类一样， DO 小鼠的低剂量 M.tb 感染会产生一系列结果，从高度易感到高度易感耐药性和许多中间结果。在本提案中，我们使用 DO 群体来： 1) 识别和测试基因型（等位基因和统计显着位点）预测诊断等结果的能力类别（类）； 2) 识别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（class）； 3) 识别和测试血清生物标志物（蛋白质）特征可以在疾病出现临床表现之前的 3 周窗口内预测疾病的发作。表现最好的签名将使用人类样本进行测试。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（有助于诊断）以及基因型和血清蛋白特征（对于预后有用）。