Metaproteomics Analysis of SARS-CoV-2-Infected Patient Samples Reveals Presence of Potential Coinfecting Microorganisms

In this Letter, we reanalyze published mass spectrometry data sets of clinical samples with a focus on determining the coinfection status of individuals infected with SARS-CoV-2 coronavirus. We demonstrate the use of ComPIL 2.0 software along with a metaproteomics workflow within the Galaxy platform to detect cohabitating potential pathogens in COVID-19 patients using mass spectrometry-based analysis. From a sample collected from gargling solutions, we detected Streptococcus pneumoniae (opportunistic and multidrug-resistant pathogen) and Lactobacillus rhamnosus (a probiotic component) along with SARS-Cov-2. We could also detect Pseudomonas sps. Bc-h from COVID-19 positive samples and Acinetobacter ursingii and Pseudomonas monteilii from COVID-19 negative samples collected from oro- and nasopharyngeal samples. We believe that the early detection and characterization of coinfections by using metaproteomics from COVID-19 patients will potentially impact the diagnosis and treatment of patients affected by SARS-CoV-2 infection.

Automated summary

This study reanalyzed mass spectrometry data of clinical samples to determine coinfection status in individuals infected with the SARS-CoV-2 coronavirus. The use of metaproteomics for early detection and characterization of coinfections in COVID-19 patients may impact diagnosis and treatment. Detected pathogens in COVID-19 patients included Streptococcus pneumoniae, Lactobacillus rhamnosus, Pseudomonas sps. Bc-h, Acinetobacter ursingii, and Pseudomonas monteilii.