SUMMARY
Objectives : Evaluation of Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS) and Fourier Transform Infrared-Spectroscopy (FTIR-S) as diagnostic alternatives to DNA-based methods for the detection of Pseudomonas aeruginosasequence type (ST) 175 isolates involved in a hospital outbreak.
Methods : Twenty-seven P. aeruginosa isolates from a 2014 outbreak in the Hematology department of our hospital were previously characterized by PFGE and WGS. Besides, 8 P. aeruginosa isolates were analyzed as unrelated controls. MALDI-TOF MS spectra were acquired by applying the colony on the MALDI target plate followed by 1 µl of formic acid 100% and 1 µl of HCCA matrix. For the analysis with FTIR-S, colonies were resuspended in 70% ethanol and sterile water according to the manufacturer instructions. Spectra from both methodologies were analyzed using Clover Biosoft® software, that allowed data modelling using different algorithms and validation of the classifying models.
Results : Three outbreak-specific biomarkers were found at 5169, 6915 and 7236 m/z in MALDI-TOF MS spectra. Classification models based on these three biomarkers showed the same discrimination power displayed by PFGE. Besides, K-Nearest Neighbor algorithm allowed the discrimination of the same clusters provided by whole-genome sequencing and the validation of this model achieved 97.0% correct classification. On the other hand, FTIR-S showed a discrimination power similar to PFGE and reached correct discrimination of the different STs analyzed.
Conclusions : The combination of both technologies evaluated, paired with Machine Learning tools, may represent a powerful tool for real-time monitoring of high-risk clones and isolates involved in nosocomial outbreaks.
Key words: MALDI-TOF mass spectrometry, Fourier-Transform Infrared Spectroscopy, Pseudomonas aeruginosa , bacterial typing, Machine Learning
INTRODUCTION
Healthcare-associated infections (HAI) are becoming one of the major health concerns of the 21st century. This term groups together infections developed during and/or resulting from a hospital or nursing home stay that were not detected at the time of admission (Liu & Dickter, 2020). They represent the most frequent adverse event in healthcare settings (6.5% in acute care hospitals in the European Union and 3.2% in hospitalized patients in the United States) (Sikora & Zahra, 2021; Suetens et al., 2018). Pathogens that cause nosocomial infections can spread and cause outbreaks among inpatients and staff members, requiring control and treatment measures and ultimately increasing resource costs in hospital settings (Vincent et al., 2020). The emergence of multi-drug resistant microorganisms is another concern that arises in nosocomial outbreaks, as they pose an added complication in relation to the correct choice of antimicrobial treatment (Chhatwal et al., 2021). The control of nosocomial infection caused by multiresistant bacteria and outbreak surveillance programs should be implemented in hospitals in order to reduce mortality/morbidity, length of stay and hospital costs (Sanchez, Garcia-de-Lorenzo, Herruzo, Asensio, & Leyva, 2012).
Pseudomonas aeruginos a is one of the most frequent pathogens involved in outbreaks in hospitals and long-term care facilities (Reynolds & Kollef, 2021). This microorganism is an environmental, Gram-negative, non-fermenting bacterium that can easily become a multidrug-resistant (MDR) and extensively drug-resistant (XDR) pathogen through mutations in chromosomal genes in addition to its intrinsic resistance mechanisms (Z. Pang, Raudonis, Glick, Lin, & Cheng, 2019). Its wide range of virulence factors, such as its ability to produce biofilm and also its capacity to persist in moist environments -such as sinks and shower plates in hospitals-, coupled with its clonality and fast spread of high-risk clones, make it an ideal candidate for being the cause of nosocomial outbreaks (Del Barrio-Tofino, Lopez-Causape, & Oliver, 2020; Oliver, Mulet, Lopez-Causape, & Juan, 2015).
The reference method for outbreak characterization remains Pulse-Field Gel Electrophoresis (PFGE) but it may be insufficient for clone discrimination in some cases (Sabat et al., 2013). Multi-Locus Sequence Typing (MLST) provides complementary information to PFGE but it is still laborious and time consuming (Peacock et al., 2002). Although the implementation of novel approaches such as whole genome sequencing (WGS) would improve the follow up of clinical outbreaks by increasing the quantity and quality of the information obtained, it requires expensive and sophisticated equipment and highly skilled personnel, making it unaffordable in most clinical laboratories nowadays.
Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS) is currently implemented in most clinical microbiology laboratories for bacterial species identification (Rodriguez-Sanchez, Cercenado, Coste, & Greub, 2019). This technology has also been evaluated for purposes beyond identification, such as antimicrobial resistance detection or bacterial typing (Cabrolier, Sauget, Bertrand, & Hocquet, 2015; Gato et al., 2021; Zvezdanova et al., 2022). Therefore, MALDI-TOF MS could be a rapid and available alternative for outbreak characterization (Feucherolles et al., 2021; Oviano & Bou, 2019). In addition, Fourier Transform Infrared Spectroscopy (FTIR-S) has emerged as a promising new tool for bacterial typing (Cordovana et al., 2021). This methodology has been recently applied toSalmonella spp. and Staphylococcus pneumoniae typing ((Burckhardt et al., 2019)
In this study, we evaluated MALDI-TOF MS and FTIR-S coupled with Machine Learning classification methods for the detection and follow-up of a nosocomial outbreak caused by a P. aeruginosa high-risk clone.
MATERIALS & METHODS