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Signature of profiling and staging the progression of TB from infection to disease.

结核病从感染到疾病进展的特征分析和分期。

基本信息

批准号：
10214482
负责人：
William Evan Johnson
金额：
$ 20.68万
依托单位：
BOSTON UNIVERSITY MEDICAL CAMPUS
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-10 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10214482
关键词：
Biological Markers Blood specimen Cessation of life Clinical Communicable Diseases Computer software Data Data Set Development Diagnostic Disease Disease Progression Etiology Evaluation Future Gene Expression Gene Expression Profile Genes Immunologic Factors Individual Infection Language Learning Machine Learning Maps Metadata Methods Modeling Molecular Molecular Profiling Mycobacterium tuberculosis National Institute of Allergy and Infectious Disease Outcome Pathogenicity Pathway interactions Patients Performance Population Pulmonary Tuberculosis Research Research Personnel Resources Sampling Staging Technology Treatment Failure Treatment Protocols Tuberculosis Validation Visualization Visualization software Work base biomarker validation comorbidity computational platform data harmonization data tools demographics effective therapy falls genetic signature genomic biomarker interactive tool latent infection molecular modeling molecular pathology mortality multiple datasets novel pandemic disease prediction algorithm screening transcriptome sequencing treatment response treatment risk user friendly software user-friendly

项目摘要

Project Summary/Abstract Tuberculosis (TB) is the leading cause of infectious disease mortality worldwide. Nearly one-third of the world's population is infected with Mycobacterium tuberculosis (MTB). More than 10.4 million new cases of active TB disease develop annually, leading to 1.4 million deaths due to the disease each year. Despite widespread efforts to study of the etiology of disease, the development and global introduction of an effective treatment regimen, and sensitive diagnostics for identifying pulmonary TB disease, efforts to control this pandemic are falling short, largely due to a lack of a clear understanding of the pathogenic progression from MTB infection to active clinical disease. In addition, Existing gene expression studies have presented more than three dozen biomarkers to predict TB related outcomes such as identifying active TB disease, predicting risk of treatment failure, or predicting which patients will progress to active TB disease. These have been developed and refined using multiple technologies and using a diverse set of computational and machine learning prediction algorithms, but most are focused on two-class comparison (e.g. TB vs. LTBI). In this proposal, we propose to compile and harmonize dozens of existing RNA-sequencing datasets for TB outcomes. We will use these compiled data to develop a computational platform and interactive visualization tools for profiling TB signatures across all existing datasets. We plan to use this curated data and software platform to develop a more refined molecular map of progression from TB infection to active disease. Consistent with a recently presented models for TB disease development, we hypothesize that we will be able to identify gene expression patterns associated with stages on the TB disease spectrum, including: uninfected or eliminated infection, controlled or truly latent infection, future progressors or incipient disease, subclinical TB disease, and active clinical TB disease. We believe that existing gene expression data and signatures will allow us to identify distinct transcriptional profiles for each stage, and hence develop a multi-class machine learning approach for classifying patients into their corresponding stage. Overall, this proposal contributes to the field by compiling existing gene expression data and developing a wholistic map of TB progression from infection to active disease. In addition, we will provide a curated dataset and metadata in an accessible format for more than three dozen existing TB studies, and allow others to access and explore these data through a user-friendly profiling platform.

项目摘要/摘要结核病是世界范围内传染病死亡的主要原因。世界上近三分之一的人群感染结核分枝杆菌(MTB)。新增活动性结核病病例超过1040万例该病每年发展，每年导致140万人死于该病。尽管广泛存在努力研究疾病的病因学，开发并在全球推出有效的治疗方法方案和用于识别肺结核病的敏感诊断方法，控制这一大流行的努力是不足之处，主要是由于缺乏对从结核杆菌感染到活动性临床疾病。此外，现有的基因表达研究已经提出了30多个预测结核病的生物标记物相关结果，如确定活动性结核病、预测治疗失败的风险或预测患者将进展为活动性结核病。这些都是使用多个技术，并使用各种计算和机器学习预测算法，但大多数侧重于两个级别的比较(例如TB与LTBI)。在这项提案中，我们建议汇编和协调数十个现有的结核病RNA测序数据集结果。我们将使用这些汇编的数据来开发一个计算平台和交互式可视化用于分析所有现有数据集的TB签名的工具。我们计划使用这些经过整理的数据和软件开发一个更精细的从结核病感染到活动性疾病进展的分子图谱的平台。与最近提出的结核病发展模型一致，我们假设我们将能够识别与结核病病谱分期相关的基因表达模式，包括：未感染或消除感染，控制或真正潜伏感染，未来进展或早期疾病，亚临床结核病疾病和活动性临床结核病。我们相信，现有的基因表达数据和签名将使我们能够识别每个阶段的不同转录特征，从而开发出多类机器学习方法，用于将患者划分为相应的阶段。总体而言，这项建议通过汇编现有的基因表达数据并开发一种结核病从感染到活动性疾病发展的整体地图。此外，我们还将提供一个经过管理的数据集 30多项现有结核病研究的可访问格式和元数据，并允许其他人通过用户友好的分析平台访问和浏览这些数据。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Overcoming the impacts of two-step batch effect correction on gene expression estimation and inference.

DOI：
10.1093/biostatistics/kxab039
发表时间：
2023-07-14
期刊：
Biostatistics (Oxford, England)
影响因子：
0
作者：
通讯作者：

Comparison of gene set scoring methods for reproducible evaluation of multiple tuberculosis gene signatures.

用于可重复评估多个结核病基因特征的基因集评分方法的比较。

DOI：
10.1101/2023.01.19.520627
发表时间：
2023
期刊：
bioRxiv : the preprint server for biology
影响因子：
0
作者：
Wang,Xutao;VanValkenberg,Arthur;Odom-Mabey,AubreyR;Ellner,JerroldJ;Hochberg,NatashaS;Salgame,Padmini;Patil,Prasad;Johnson,WEvan
通讯作者：
Johnson,WEvan

Comparing tuberculosis gene signatures in malnourished individuals using the TBSignatureProfiler.

DOI：
10.1186/s12879-020-05598-z
发表时间：
2021-01-22
期刊：
BMC infectious diseases
影响因子：
3.7
作者：
Johnson WE;Odom A;Cintron C;Muthaiah M;Knudsen S;Joseph N;Babu S;Lakshminarayanan S;Jenkins DF;Zhao Y;Nankya E;Horsburgh CR;Roy G;Ellner J;Sarkar S;Salgame P;Hochberg NS
通讯作者：
Hochberg NS

Metagenomic profiling pipelines improve taxonomic classification for 16S amplicon sequencing data.