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