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.
Bibliographical noteFunding Information:
We acknowledge funding for this work from the grant National Cancer Institute - Informatics Technology for Cancer Research (NCI-ITCR) grant 1U24CA199347 and National Science Foundation grant 1458524. The European Galaxy server that was used for data analysis is in part funded by Collaborative Research Centre 992 Medical Epigenetics (DFG grant SFB 992/1 2012) and German Federal Ministry of Education and Research (BMBF grants 031 A538A/A538C RBC, 031L0101 B/031L0101C de.NBI-epi, 031L0106 de.STAIR (de.NBI)).
PubMed: MeSH publication types
- Journal Article
- Research Support, Non-U.S. Gov't
- Research Support, N.I.H., Extramural
- Research Support, U.S. Gov't, Non-P.H.S.