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蛋白质组学和机器学习方法揭示了一组具有药物再利用潜力的COVID-19严重程度预后标志物。

Proteomics and Machine Learning Approaches Reveal a Set of Prognostic Markers for COVID-19 Severity With Drug Repurposing Potential.

作者信息

Suvarna Kruthi, Biswas Deeptarup, Pai Medha Gayathri J, Acharjee Arup, Bankar Renuka, Palanivel Viswanthram, Salkar Akanksha, Verma Ayushi, Mukherjee Amrita, Choudhury Manisha, Ghantasala Saicharan, Ghosh Susmita, Singh Avinash, Banerjee Arghya, Badaya Apoorva, Bihani Surbhi, Loya Gaurish, Mantri Krishi, Burli Ananya, Roy Jyotirmoy, Srivastava Alisha, Agrawal Sachee, Shrivastav Om, Shastri Jayanthi, Srivastava Sanjeeva

机构信息

Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India.

Centre for Research in Nanotechnology and Sciences, Indian Institute of Technology Bombay, Mumbai, India.

出版信息

Front Physiol. 2021 Apr 27;12:652799. doi: 10.3389/fphys.2021.652799. eCollection 2021.

Abstract

The pestilential pathogen SARS-CoV-2 has led to a seemingly ceaseless pandemic of COVID-19. The healthcare sector is under a tremendous burden, thus necessitating the prognosis of COVID-19 severity. This in-depth study of plasma proteome alteration provides insights into the host physiological response towards the infection and also reveals the potential prognostic markers of the disease. Using label-free quantitative proteomics, we performed deep plasma proteome analysis in a cohort of 71 patients (20 COVID-19 negative, 18 COVID-19 non-severe, and 33 severe) to understand the disease dynamics. Of the 1200 proteins detected in the patient plasma, 38 proteins were identified to be differentially expressed between non-severe and severe groups. The altered plasma proteome revealed significant dysregulation in the pathways related to peptidase activity, regulated exocytosis, blood coagulation, complement activation, leukocyte activation involved in immune response, and response to glucocorticoid biological processes in severe cases of SARS-CoV-2 infection. Furthermore, we employed supervised machine learning (ML) approaches using a linear support vector machine model to identify the classifiers of patients with non-severe and severe COVID-19. The model used a selected panel of 20 proteins and classified the samples based on the severity with a classification accuracy of 0.84. Putative biomarkers such as angiotensinogen and SERPING1 and ML-derived classifiers including the apolipoprotein B, SERPINA3, and fibrinogen gamma chain were validated by targeted mass spectrometry-based multiple reaction monitoring (MRM) assays. We also employed an screening approach against the identified target proteins for the therapeutic management of COVID-19. We shortlisted two FDA-approved drugs, namely, selinexor and ponatinib, which showed the potential of being repurposed for COVID-19 therapeutics. Overall, this is the first most comprehensive plasma proteome investigation of COVID-19 patients from the Indian population, and provides a set of potential biomarkers for the disease severity progression and targets for therapeutic interventions.

摘要

致命病原体严重急性呼吸综合征冠状病毒2(SARS-CoV-2)引发了一场看似无休止的新型冠状病毒肺炎(COVID-19)大流行。医疗保健部门负担沉重,因此有必要对COVID-19的严重程度进行预后评估。这项对血浆蛋白质组变化的深入研究,深入了解了宿主对感染的生理反应,还揭示了该疾病潜在的预后标志物。我们使用无标记定量蛋白质组学技术,对71名患者(20名COVID-19阴性、18名COVID-19非重症和33名重症患者)进行了深度血浆蛋白质组分析,以了解疾病动态。在患者血浆中检测到的1200种蛋白质中,有38种蛋白质在非重症组和重症组之间存在差异表达。血浆蛋白质组的变化表明,在SARS-CoV-2感染重症病例中,与肽酶活性、调节性胞吐作用、血液凝固、补体激活、免疫反应中涉及的白细胞激活以及对糖皮质激素生物学过程的反应等相关途径存在显著失调。此外,我们使用线性支持向量机模型,采用监督式机器学习(ML)方法来识别非重症和重症COVID-19患者的分类器。该模型使用了一组选定的20种蛋白质,并根据严重程度对样本进行分类,分类准确率为0.84。通过基于靶向质谱的多反应监测(MRM)分析,验证了血管紧张素原和丝氨酸蛋白酶抑制剂1(SERPING1)等假定生物标志物以及包括载脂蛋白B、丝氨酸蛋白酶抑制剂A3(SERPINA3)和纤维蛋白原γ链在内的基于ML的分类器。我们还针对已鉴定的靶蛋白采用筛选方法,用于COVID-19的治疗管理。我们筛选出两种美国食品药品监督管理局(FDA)批准的药物,即塞利尼索和波纳替尼,它们显示出可重新用于COVID-19治疗的潜力。总体而言,这是对印度人群中COVID-19患者进行的首次最全面的血浆蛋白质组研究,为疾病严重程度进展提供了一组潜在生物标志物,并为治疗干预提供了靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/045b/8120435/868327b26a3f/fphys-12-652799-g001.jpg

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