Identification of Antibiotic Resistance Proteins via MiCId's Augmented Workflow. A Mass Spectrometry-Based Proteomics Approach.
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Alves G, Ogurtsov A, Karlsson R, Jaén-Luchoro D, Piñeiro-Iglesias B, Salvà-Serra F, Andersson B, Moore ERB, Yu YK
J. Am. Soc. Mass Spectrom. 33 (6) 917-931 [2022-06-01; online 2022-05-02]
Fast and accurate identifications of pathogenic bacteria along with their associated antibiotic resistance proteins are of paramount importance for patient treatments and public health. To meet this goal from the mass spectrometry aspect, we have augmented the previously published Microorganism Classification and Identification (MiCId) workflow for this capability. To evaluate the performance of this augmented workflow, we have used MS/MS datafiles from samples of 10 antibiotic resistance bacterial strains belonging to three different species: Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa. The evaluation shows that MiCId's workflow has a sensitivity value around 85% (with a lower bound at about 72%) and a precision greater than 95% in identifying antibiotic resistance proteins. In addition to having high sensitivity and precision, MiCId's workflow is fast and portable, making it a valuable tool for rapid identifications of bacteria as well as detection of their antibiotic resistance proteins. It performs microorganismal identifications, protein identifications, sample biomass estimates, and antibiotic resistance protein identifications in 6-17 min per MS/MS sample using computing resources that are available in most desktop and laptop computers. We have also demonstrated other use of MiCId's workflow. Using MS/MS data sets from samples of two bacterial clonal isolates, one being antibiotic-sensitive while the other being multidrug-resistant, we applied MiCId's workflow to investigate possible mechanisms of antibiotic resistance in these pathogenic bacteria; the results showed that MiCId's conclusions agree with the published study. The new version of MiCId (v.07.01.2021) is freely available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html.
Clinical Genomics Gothenburg [Collaborative]
Glycoproteomics and MS Proteomics [Service]
PubMed 35500907
DOI 10.1021/jasms.1c00347
Crossref 10.1021/jasms.1c00347