Putative Protein Biomarkers of Escherichia coli Antibiotic Multiresistance Identified by MALDI Mass Spectrometry

Abstract

The commensal bacteria Escherichia coli causes several intestinal and extra-intestinal diseases, since it has virulence factors that interfere in important cellular processes. These bacteria also have a great capacity to spread the resistance genes, sometimes to phylogenetically distant bacteria, which poses an additional threat to public health worldwide. Here, we aimed to use the analytical potential of MALDI-TOF mass spectrometry (MS) to characterize E. coli isolates and identify proteins associated closely with antibiotic resistance. Thirty strains of extended-spectrum beta-lactamase producing E. coli were sampled from various animals. The phenotypes of antibiotic resistance were determined according to Clinical and Laboratory Standards Institute (CLSI) methods, and they showed that all bacterial isolates were multi-resistant to trimethoprim-sulfamethoxazole, tetracycline, and ampicillin. To identify peptides characteristic of resistance to particular antibiotics, each strain was grown in the presence or absence of the different antibiotics, and then proteins were extracted from the cells. The protein fingerprints of the samples were determined by MALDI-TOF MS in linear mode over a mass range of 2 to 20 kDa. The spectra obtained were compared by using the ClinProTools bioinformatics software, using three machine learning classification algorithms. A putative species biomarker was also detected at a peak m/z of 4528.00.

Combination of MALDI-TOF Mass Spectrometry and Machine Learning for Rapid Antimicrobial Resistance Screening: The Case of Campylobacter spp.

“…The majority of these studies used pathogens such as Staphylococcus aureus and the βlactam antibiotic family (Sogawa et al, 2017;Wang et al, 2018;Tang et al, 2019). Therefore, there are very few published data concerning other relevant clinical or foodborne pathogens or antimicrobials such as the quinolones (e.g., ciprofloxacin) and macrolides (e.g., erythromycin and azithromycin) (Sabença et al, 2020;Sousa et al, 2020). However, macrolides and quinolones are frontline antibiotics used to treat severe infectious gastroenteritis and categorized by the World Health Organization (WHO) as critically important in human medicine (WHO, 2019).…”

Digital microbiology

“…Additional examples in the analytical step include the identification of bacterial and fungal species using matrix-assisted laser desorption/ ionization time of flight (MALDI-TOF) mass spectrometry. Machine learning based algorithms can link mass spectral profiles to specific clinical phenotypes such as antibiotic resistance [43,44]. e Surveillance of reagent lots performance with internal and external controls and automated reporting in connection to specific used lots of time [129] Imaging Are there bacteria on the microscope slide?…”

“…e Automated screening for pathogen similarities, e.g., resistance profile or automated bioinformatics [130,131] Post-analytics Highlight important data Is there a potential bacterial phenotype? e Detection of resistance by analysing MALDI-TOF spectra [43,44] Sepsis treatment What is the best treatment for the patient? e Prediction of sepsis, and best treatment, e.g., volume and antibiotics for the patient…”

Recent evolutions of machine learning applications in clinical laboratory medicine

“…The lengthy nature of conventional methods for bacterial identification and susceptibility testing purposes has resulted in the empirical use of broad-spectrum antibiotics and led to the spread of resistance [85]. Aiming at rapid and reliable detection of AMR, numerous studies have recently been reported on the use of ML algorithms in combination with the output of several analytical techniques, such as MALDI-TOF MS [89,96,[110][111][112][113][114][115][116][117][118], vibrational spectroscopy [102,103,119], whole-genome sequencing [120][121][122][123][124], a microscopy-based platform [125], and acousticenhanced flow cytometry [126].…”

“…Using an RF model on MALDI-TOF MS spectra, Huang et al [115] were able to correctly identify 93% of carbapenem-resistant and all carbapenem-sensitive Klebsiella pneumoniae isolates. Moreover, successful systems using a combination of MALDI-TOF MS data and ML algorithms were recently reported for the detection of extended-spectrum betalactamase-producing Escherichia coli (E. coli) strains [117], rapid detection of cfiA metallo-b-lactamase-producing Bacteroides fragilis strains [118], and identification of fluconazole resistance in Candida albicans strains [96].…”