IISc Mathematics Initiative, Indian Institute of Science, Bangalore 560012, India.
Department of Biochemistry, Indian Institute of Science, Bangalore 560012, India.
EBioMedicine. 2021 May;67:103352. doi: 10.1016/j.ebiom.2021.103352. Epub 2021 Apr 24.
Precise differential diagnosis between acute viral and bacterial infections is important to enable appropriate therapy, avoid unnecessary antibiotic prescriptions and optimize the use of hospital resources. A systems view of host response to infections provides opportunities for discovering sensitive and robust molecular diagnostics.
We combine blood transcriptomes from six independent datasets (n = 756) with a knowledge-based human protein-protein interaction network, identifies subnetworks capturing host response to each infection class, and derives common response cores separately for viral and bacterial infections. We subject the subnetworks to a series of computational filters to identify a parsimonious gene panel and a standalone diagnostic score that can be applied to individual samples. We rigorously validate the panel and the diagnostic score in a wide range of publicly available datasets and in a newly developed Bangalore-Viral Bacterial (BL-VB) cohort.
We discover a 10-gene blood-based biomarker panel (Panel-VB) that demonstrates high predictive performance to distinguish viral from bacterial infections, with a weighted mean AUROC of 0.97 (95% CI: 0.96-0.99) in eleven independent datasets (n = 898). We devise a new stand-alone patient-wise score (VB) based on the panel, which shows high diagnostic accuracy with a weighted mean AUROC of 0.94 (95% CI 0.91-0.98) in 2996 patient samples from 56 public datasets from 19 different countries. Further, we evaluate VB in a newly generated South Indian (BL-VB, n = 56) cohort and find 97% accuracy in the confirmed cases of viral and bacterial infections. We find that VB is (a) capable of accurately identifying the infection class in culture-negative indeterminate cases, (b) reflects recovery status, and (c) is applicable across different age groups, covering a wide spectrum of acute bacterial and viral infections, including uncharacterized pathogens. We tested our VB score on publicly available COVID-19 data and find that our score detected viral infection in patient samples.
Our results point to the promise of VB as a diagnostic test for precise diagnosis of acute infections and monitoring recovery status. We expect that it will provide clinical decision support for antibiotic prescriptions and thereby aid in antibiotic stewardship efforts.
Grand Challenges India, Biotechnology Industry Research Assistance Council (BIRAC), Department of Biotechnology, Govt. of India.
准确区分急性病毒感染和细菌感染对于实施恰当的治疗、避免不必要的抗生素处方以及优化医院资源的使用非常重要。从宿主对感染的反应的系统角度来看，为发现敏感且稳健的分子诊断方法提供了机会。
我们结合了来自六个独立数据集（n=756）的血液转录组数据和基于知识的人类蛋白质-蛋白质相互作用网络，确定了捕获每种感染类型的宿主反应的子网，并分别为病毒和细菌感染推导出共同的反应核心。我们对子网进行了一系列计算筛选，以识别简洁的基因面板和可应用于单个样本的独立诊断评分。我们在广泛的公开数据集和新开发的班加罗尔病毒细菌（BL-VB）队列中对面板和诊断评分进行了严格的验证。
我们发现了一个基于血液的 10 基因生物标志物面板（Panel-VB），可用于区分病毒和细菌感染，在 11 个独立数据集（n=898）中具有加权平均 AUROC 为 0.97（95%CI：0.96-0.99）。我们设计了一个基于该面板的新的独立患者评分（VB），在来自 19 个不同国家的 56 个公共数据集的 2996 个患者样本中，具有加权平均 AUROC 为 0.94（95%CI 0.91-0.98）的高诊断准确性。此外，我们在新生成的印度南部（BL-VB，n=56）队列中评估了 VB，发现病毒和细菌感染的确诊病例准确率为 97%。我们发现 VB 能够：（a）准确识别培养阴性不确定病例的感染类型；（b）反映恢复状态；（c）适用于不同年龄组，涵盖广泛的急性细菌和病毒感染，包括未鉴定的病原体。我们在公开的 COVID-19 数据上测试了我们的 VB 评分，发现我们的评分在患者样本中检测到了病毒感染。
我们的研究结果表明 VB 作为一种用于急性感染的精确诊断和监测恢复状态的诊断测试具有广阔的应用前景。我们期望它将为抗生素处方提供临床决策支持，从而有助于抗生素管理工作。
印度大挑战、生物技术产业研究援助理事会（BIRAC）、印度生物技术部、印度政府。
