# Metaproteomics analysis of Gargling samples from CoviD-19 infected patients
# Live Resources
Ihling et al have published a protein MS-based ‘proof-of-principle’ method to detect SARS-CoV-2 virus proteins from gargle samples from COVID-19 patients (see here). In the original manuscript, the authors detected peptides from SARS-CoV-2 virus proteins and present evidence for their spectral annotation.
We were interested in exploring the possibility of presence of microorganisms in the samples from the original manuscript. For this, Peter Thuy-Boun (Wolan Lab, Scripps Institute) searched the two RAW files using COMPIL 2.0 against a comprehensive 113 million protein sequences and roughly 4.8 billion unique tryptic peptide sequences database library. The peptides identified through this approach were subjected to Unipept 4.3 analysis to detect the most abundant genera and species present in the sample. Five most significant genera/species were used along with the RAW files and COVID-19 protein database as inputs for a Galaxy workflow to a) search the datasets; b) detect microbial peptides and determine the taxonomy associated with the peptides using Unipept; c) validation of peptide spectral matches by using PepQuery and determining the number of valid peptides corresponding to microbial taxonomic units.
The analysis of the gargling solutions using COMPIL 2.0 and Unipept (both outside of Galaxy) and using SearchGUI/PeptideShaker, Unipept and PepQuery (all within Galaxy workflows) resulted in detection of three opportunistic pathogens - Stenotrophomonas maltophilia and Streptococcus pneumoniae.
The Galaxy workflow includes RAW data conversion to MGF and mzML format. The MGF files are searched against the combined database of Human Uniprot proteome, contaminant proteins and SARS-Cov-2 proteins database using X!tandem, MSGF+, OMSSA search algorithms with SearchGUI and FDR and protein grouping using PeptideShaker. The unique peptides were searched with Unipept to obtain the taxa and functional annotation and to confirm the presence of metaproteomes. The detected peptides were later subjected to analysis by PepQuery and Lorikeet to ascertain the quality of peptide identification.
|Number of validated peptides|
Stenotrophomonas maltophilia is another emerging multi-drug resistant opportunistic pathogen originating in hospital settings, especially among immunocompromised hosts. S. maltophilia has shown to cause latent pulmonary infection in immunocompromised patients and its colonization rates in cystic fibrosis patients have been increasing. Lastly, Streptococcus pneumoniae is a commensal microorganism that colonizes the upper respiratory tract in healthy individuals. However, in susceptible individuals with weaker immune systems, such as the elderly, the bacterium may become pathogenic and spread to other locations to cause disease.
Clinical studies have started identifying possible co-infecting bacteria in COVID-19 patients. Amongst these S. pneumoniae has been shown to be the most common, although we have not come across any studies that indicated that S. maltophila detected as co-infecting pathogens. Our analysis raises the possibility that S. maltophila could be present as a co-infection in the COVID-19 patients. Both of these bacteria are known to be nosocomial, opportunistic pathogens in immuno-compromised patients and are difficult to treat because of its multi-drug resistance.
Interestingly, the PepQuery analysis indicates that the spectral evidence for the detection of the peptides from these organisms is quite solid. We plan to perform Lorikeet analysis to ascertain the spectral evidence. We have also contacted the authors of the original manuscript and reported our findings and have discussed the possibility of using new gargling solutions samples to detect the presence of any cohabitating emerging pathogens in COVID-19 patients using mass spectrometry based metaproteomics analysis.