Integration of protein context improves protein-based COVID-19 patient stratification

Jinlong Gao; Jiale He; Fangfei Zhang; Qi Xiao; Xue Cai; Xiao Yi; Siqi Zheng; Ying Zhang; Donglian Wang; Guangjun Zhu; Jing Wang; Bo Shen; Markus Ralser; Tiannan Guo; Yi Zhu

doi:10.1186/s12014-022-09370-0

Integration of protein context improves protein-based COVID-19 patient stratification

Clin Proteomics. 2022 Aug 11;19(1):31. doi: 10.1186/s12014-022-09370-0.

Authors

Jinlong Gao^#^{1

2}, Jiale He^#^{1

2}, Fangfei Zhang^#^{1

2}, Qi Xiao^{1

2}, Xue Cai^{1

2}, Xiao Yi^{1

2}, Siqi Zheng^{1

2}, Ying Zhang³, Donglian Wang³, Guangjun Zhu³, Jing Wang³, Bo Shen³, Markus Ralser^{4

5}, Tiannan Guo^{6

7}, Yi Zhu^{8

9}

Affiliations

¹ Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.
² Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China.
³ Taizhou Hospital, Wenzhou Medical University, Linhai, Zhejiang, China.
⁴ Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK.
⁵ Department of Biochemistry, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany.
⁶ Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China. guotiannan@westlake.edu.cn.
⁷ Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China. guotiannan@westlake.edu.cn.
⁸ Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China. zhuyi@westlake.edu.cn.
⁹ Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China. zhuyi@westlake.edu.cn.

^# Contributed equally.

Abstract

Background: Classification of disease severity is crucial for the management of COVID-19. Several studies have shown that individual proteins can be used to classify the severity of COVID-19. Here, we aimed to investigate whether integrating four types of protein context data, namely, protein complexes, stoichiometric ratios, pathways and network degrees will improve the severity classification of COVID-19.

Methods: We performed machine learning based on three previously published datasets. The first was a SWATH (sequential window acquisition of all theoretical fragment ion spectra) MS (mass spectrometry) based proteomic dataset. The second was a TMTpro 16plex labeled shotgun proteomics dataset. The third was a SWATH dataset of an independent patient cohort.

Results: Besides twelve proteins, machine learning also prioritized two complexes, one stoichiometric ratio, five pathways, and five network degrees, resulting a 25-feature panel. As a result, a model based on the 25 features led to effective classification of severe cases with an AUC of 0.965, outperforming the models with proteins only. Complement component C9, transthyretin (TTR) and TTR-RBP (transthyretin-retinol binding protein) complex, the stoichiometric ratio of SAA2 (serum amyloid A proteins 2)/YLPM1 (YLP Motif Containing 1), and the network degree of SIRT7 (Sirtuin 7) and A2M (alpha-2-macroglobulin) were highlighted as potential markers by this classifier. This classifier was further validated with a TMT-based proteomic data set from the same cohort (test dataset 1) and an independent SWATH-based proteomic data set from Germany (test dataset 2), reaching an AUC of 0.900 and 0.908, respectively. Machine learning models integrating protein context information achieved higher AUCs than models with only one feature type.

Conclusion: Our results show that the integration of protein context including protein complexes, stoichiometric ratios, pathways, network degrees, and proteins improves phenotype prediction.

Keywords: COVID-19; Protein complex; Proteomics; Severe cases; Stoichiometric ratio.

Abstract

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