Potential Use of Serum Proteomics for Monitoring COVID-19 Progression to Complement RT-PCR Detection.

Abstract

RT-PCR is the primary method to diagnose COVID-19 and is also used to monitor the disease course. This approach, however, suffers from false negatives due to RNA instability and poses a high risk to medical practitioners. Here, we investigated the potential of using serum proteomics to predict viral nucleic acid positivity during COVID-19. We analyzed the proteome of 275 inactivated serum samples from 54 out of 144 COVID-19 patients and shortlisted 42 regulated proteins in the severe group and 12 in the non-severe group. Using these regulated proteins and several key clinical indexes, including days after symptoms onset, platelet counts, and magnesium, we developed two machine learning models to predict nucleic acid positivity, with an AUC of 0.94 in severe cases and 0.89 in non-severe cases, respectively. Our data suggest the potential of using a serum protein-based machine learning model to monitor COVID-19 progression, thus complementing swab RT-PCR tests. More efforts are required to promote this approach into clinical practice since mass spectrometry-based protein measurement is not currently widely accessible in clinic.

• Authors: Ying Zhang, Xue Cai, Weigang Ge, Donglian Wang, Guangjun Zhu, Liujia Qian, Nan Xiang, Liang Yue, Shuang Liang, Fangfei Zhang, Jing Wang, Kai Zhou, Yufen Zheng, Minjie Lin, Tong Sun, Ruyue Lu, Chao Zhang, Luang Xu, Yaoting Sun, Xiaoxu Zhou, Jing Yu, Mengge Lyu, Bo Shen, Hongguo Zhu, Jiaqin Xu, Yi Zhu, Tiannan Guo
• Journal: Journal of proteome research (J Proteome Res) Vol. 21 Issue 1 Pg. 90-100 (01 07 2022) ISSN: 1535-3907 [Electronic] United States
• PMID: 34783559 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)

Topics

• COVID-19
• Humans
• Proteomics
• Reverse Transcriptase Polymerase Chain Reaction
• SARS-CoV-2
• Specimen Handling