Byramjee-Jeejeebhoy Government Medical College-Johns Hopkins University Clinical Research Site (BJGMC-JHU CRS), Pune, India.
Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil.
Front Immunol. 2021 Feb 22;12:631165. doi: 10.3389/fimmu.2021.631165. eCollection 2021.
Transcriptomic signatures for tuberculosis (TB) have been proposed and represent a promising diagnostic tool. Data remain limited in persons with advanced HIV. We enrolled 30 patients with advanced HIV (CD4 <100 cells/mm) in India; 16 with active TB and 14 without. Whole-blood RNA sequencing was performed; these data were merged with a publicly available dataset from Uganda ( = 33; 18 with TB and 15 without). Transcriptomic profiling and machine learning algorithms identified an optimal gene signature for TB classification. Receiver operating characteristic analysis was used to assess performance. Among 565 differentially expressed genes identified for TB, 40 were shared across India and Uganda cohorts. Common upregulated pathways reflect Toll-like receptor cascades and neutrophil degranulation. The machine-learning decision-tree algorithm selected gene expression values from and as most informative for TB classification. The signature accurately classified TB in discovery cohorts (India AUC 0.95 and Uganda AUC 1.0; < 0.001); accuracy was fair in external validation cohorts. Expression values of and genes in peripheral blood compose a biosignature that accurately classified TB status among patients with advanced HIV in two geographically distinct cohorts. The functional analysis suggests pathways previously reported in TB pathogenesis.
转录组特征已被提出可作为一种有前途的诊断工具，用于结核病（TB）。但在晚期 HIV 患者中的数据仍然有限。我们在印度招募了 30 名晚期 HIV（CD4 <100 个细胞/mm）患者；其中 16 名患有活动性结核病，14 名没有。进行了全血 RNA 测序；这些数据与乌干达的公开数据集（= 33；18 名患有结核病，15 名没有）合并。转录组分析和机器学习算法确定了用于结核病分类的最佳基因特征。使用接收者操作特征分析来评估性能。在为结核病确定的 565 个差异表达基因中，有 40 个在印度和乌干达队列中共享。常见的上调途径反映了 Toll 样受体级联和嗜中性粒细胞脱颗粒。机器学习决策树算法选择了 和 作为用于 TB 分类的最有信息的基因表达值。该特征在发现队列中准确地分类了结核病（印度 AUC 为 0.95，乌干达 AUC 为 1.0；<0.001）；在外部验证队列中的准确性是合理的。外周血中的 和 基因的表达值组成了一个生物标志物，可在两个地理位置不同的队列中准确分类晚期 HIV 患者的结核病状态。功能分析表明了以前报道的结核病发病机制中的途径。
