Mass spectrometry and machine learning in the identification of COVID-19 biomarkers

Author(s)

Lazari, Lucas C. ; de Oliveira, Gilberto Santos ; Macedo-Da-Silva, Janaina ; Rosa-Fernandes, Livia ; Palmisano, Giuseppe

• Document type: Journal article
• Source: FRONTIERS IN ANALYTICAL SCIENCE; v. 3, p. 15-pg., 2023-03-31.

Abstract

Identifying specific diagnostic and prognostic biological markers of COVID-19 can improve disease surveillance and therapeutic opportunities. Mass spectrometry combined with machine and deep learning techniques has been used to identify pathways that could be targeted therapeutically. Moreover, circulating biomarkers have been identified to detect individuals infected with SARS-CoV-2 and at high risk of hospitalization. In this review, we have surveyed studies that have combined mass spectrometry-based omics techniques (proteomics, lipdomics, and metabolomics) and machine learning/deep learning to understand COVID-19 pathogenesis. After a literature search, we show 42 studies that applied reproducible, accurate, and sensitive mass spectrometry-based analytical techniques and machine/deep learning methods for COVID-19 biomarker discovery and validation. We also demonstrate that multiomics data results in classification models with higher performance. Furthermore, we focus on the combination of MALDI-TOF Mass Spectrometry and machine learning as a diagnostic and prognostic tool already present in the clinics. Finally, we reiterate that despite advances in this field, more optimization in the analytical and computational parts, such as sample preparation, data acquisition, and data analysis, will improve biomarkers that can be used to obtain more accurate diagnostic and prognostic tools. (AU)

• FAPESP's process: 20/04923-0 - SARS-CoV-2 glycosylation: a blueprint structural insight for understanding COVID-19 pathogenesis
• Grantee: Giuseppe Palmisano
• Support Opportunities: Regular Research Grants
• FAPESP's process: 18/18257-1 - Multi-user equipment approved in grant 14/06863-3: HPLC system configured for analysis of carbohydrates, amino acidis, peptides and glycoproteins
• Grantee: Giuseppe Palmisano
• Support Opportunities: Multi-user Equipment Program
• FAPESP's process: 21/00140-3 - Understanding the role of protein arginylation in health and disease through analytical and biological approaches
• Grantee: Janaína Macedo da Silva
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 18/15549-1 - Post-translational modifications in Chagas Disease biological processes and diagnostics: novel methodological approaches and biological applications
• Grantee: Giuseppe Palmisano
• Support Opportunities: Research Grants - Young Investigators Grants - Phase 2
• FAPESP's process: 18/13283-4 - Discovery of biomarkers of Chagas' disease in urine using mass spectrometry techniques
• Grantee: Gilberto Santos de Oliveira
• Support Opportunities: Scholarships in Brazil - Doctorate