Abstract
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.
【초록키워드】 COVID-19, Treatment, coronavirus, mass spectrometry, SARS-COV-2 infection, Diagnosis, SARS-CoV-2 coronavirus, clinical samples, pathogen, Early detection, Patient, Pathogens, platform, COVID-19 patients, Analysis, Streptococcus pneumoniae, COVID-19 patient, Pseudomonas, focus, data set, data sets, gargling solutions, characterization, individual, Nasopharyngeal samples, Acinetobacter, positive sample, multidrug-resistant, Lactobacillus rhamnosus, clinical sample, affected, detect, collected, COVID-19 negative, infected with SARS-CoV-2, Pseudomona, 【제목키워드】 microorganism, Sample, Potential, Presence,