Samart Kewalin, Buskirk Landon, Tonielli Amy, Krishnan Arjun, Ravi Janani
Computational Bioscience Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Department of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
bioRxiv. 2025 Jun 2:2025.06.02.657296. doi: 10.1101/2025.06.02.657296.
Tuberculosis (TB) remains the second leading cause of infectious disease mortality worldwide, killing over one million people annually. Rising antibiotic resistance has created an urgent need for host-directed therapeutics (HDTs) - preferably by repurposing existing approved drugs - that modulate host immune responses rather than directly targeting the pathogen. Repurposed therapeutics have been successfully identified for cancer and COVID-19 by finding drugs that reverse disease gene expression patterns (an approach called 'connectivity scoring'), but this approach remains largely unexplored for bacterial infections like TB. The application of transcriptome-based methods to TB faces significant challenges, including dataset heterogeneity across transcriptomics platforms and biological conditions, uncertainty about optimal scoring methods, and lack of systematic approaches to identify robust disease signatures. Here, we developed an integrative computational workflow combining multiple connectivity scoring methods with consensus disease signature construction and used it to systematically identify FDA-approved drugs as promising TB host-directed therapeutics. Our framework integrates six complementary connectivity methods and constructs weighted consensus signatures from 21 TB gene expression datasets spanning microarray and RNA-seq platforms, diverse cell types, and infection conditions. Our approach prioritized 140 high-confidence drug candidates that consistently reverse TB-associated gene expression changes, successfully recovering known HDTs, including statins (atorvastatin, lovastatin, fluvastatin) and vitamin D receptor agonists (calcitriol). We identified promising novel candidates such as niclosamide and tamoxifen, both recently validated in experimental TB models, and revealed enrichment for therapeutically relevant mechanisms, e.g., cholesterol metabolism inhibition and immune modulation pathways. Network analysis of disease-drug interactions identified 10 key bridging genes (including MYD88, RELA, and CXCR2) that represent potential novel druggable targets for TB host-directed therapy. This work establishes transcriptome-based connectivity mapping as a viable approach for systematic HDT discovery in bacterial infections and provides a robust computational framework applicable to other infectious diseases. Our findings offer immediate opportunities for experimental validation of prioritized drug candidates and mechanistic investigation of identified druggable targets in TB pathogenesis.
结核病（TB）仍是全球传染病死亡的第二大主要原因，每年导致超过100万人死亡。抗生素耐药性的不断上升迫切需要宿主导向疗法（HDTs）——最好是通过重新利用现有的已批准药物——来调节宿主免疫反应，而不是直接针对病原体。通过寻找能够逆转疾病基因表达模式的药物（一种称为“连通性评分”的方法），已经成功地为癌症和新冠肺炎确定了重新利用的疗法，但这种方法在结核病等细菌感染方面在很大程度上仍未得到探索。将基于转录组的方法应用于结核病面临重大挑战，包括转录组学平台和生物学条件之间的数据集异质性、最佳评分方法的不确定性，以及缺乏识别稳健疾病特征的系统方法。在这里，我们开发了一种综合计算工作流程，将多种连通性评分方法与共识疾病特征构建相结合，并使用它来系统地识别美国食品药品监督管理局（FDA）批准的药物，作为有前景的结核病宿主导向疗法。我们的框架整合了六种互补的连通性方法，并从21个结核病基因表达数据集中构建加权共识特征，这些数据集涵盖微阵列和RNA测序平台、不同的细胞类型以及感染条件。我们的方法对140种高可信度药物候选物进行了优先排序，这些候选物能够持续逆转与结核病相关的基因表达变化，成功找回了已知的HDTs，包括他汀类药物（阿托伐他汀、洛伐他汀、氟伐他汀）和维生素D受体激动剂（骨化三醇）。我们确定了有前景的新型候选物，如氯硝柳胺和他莫昔芬，这两种药物最近在实验性结核病模型中得到了验证，并揭示了治疗相关机制的富集，例如胆固醇代谢抑制和免疫调节途径。疾病-药物相互作用的网络分析确定了10个关键的桥接基因（包括MYD88、RELA和CXCR2），它们代表了结核病宿主导向治疗潜在的新型可成药靶点。这项工作确立了基于转录组的连通性图谱作为在细菌感染中系统发现HDT的可行方法，并提供了一个适用于其他传染病的强大计算框架。我们的发现为优先药物候选物的实验验证以及结核病发病机制中已确定的可成药靶点的机制研究提供了即时机会。
