3.2 FTIR-S analysis
After analyzing different spectral ranges, infrared absorbance in the
lipid region (1400-1500 and 2800-3000 cm-1) showed the
highest discriminatory power for the correct classification of P.
aeruginosa outbreak strains. HCA using Euclidean distance and Ward
linkage differentiated the outbreak strains (ST175) from control
strains, which belonged to different STs such as ST227, ST253, ST381,
ST557 and ST885 (Figure 3), with similar discriminatory power to PFGE
classification. In this case, the cut-off dendrogram distance for
outbreak and controls differentiation was 0.350. For isolate
differentiation, the automated cut-off distance assigned by IR Biotyper
was 0.062. The two strains belonging to the same patient were clustered
together with lower distance between them. Using PCA analysis in both IR
Biotyper software and Clover MS Data Analysis software, ST175 strains
were clustered together, distinctly from other STs (Figure 4A and 4B).
The specific differences in infrared spectra absorbances among ST175 and
other STs were found in 1445 cm-1 (Figure 5A), 2925
and 2955 cm-1 (Figure 5B). The three subgroups inside
the outbreak observed by WGS were not discriminated by HCA (Figure 3).
In the PCA scatter plot (Figure 4C and 4D), although WGS Group 2 was
partially separated from other groups, Groups 1 and 3 overlapped. Using
PLS-DA and RF algorithm (Figure S4), it was also showed that Group 2
formed a distinct cluster, and the k-fold cross validation obtained for
differentiation of the three groups were 88.9% and 97.8%, respectively
(Table S4).
DISCUSSION
The implementation of MALDI-TOF MS and FTIR-S technology, combined with
Machine Learning algorithms, allowed the correct classification of the
MDR P. aeruginosa isolates causing a nosocomial outbreak in
2013-2014: 97.0% and 100% of the isolates were correctly classified by
the algorithms applied to the protein peaks by MALDI-TOF MS with
intensities above the established threshold (0.1) or to the specific
biomarker peaks found with this technology, respectively. These results
showed that MALDI-TOF MS yields a discrimination power similar to PFGE,
the reference method for bacterial typing. Besides, the same protein
spectra were further classified according to the information provided by
WGS analysis. In this case, PLS-DA and SVM algorithms allowed a good
classification of the P. aeruginosa isolates specifically
correlated with the outbreak (Group 1) and showed that Group 2 was
closely related with the outbreak strains, as the genomic analysis
pointed out, and Group 3 and the control group were clearly unrelated to
the outbreak. The validation of the classification models, carried out
with 32 P. aeruginosa isolates characterized by ASO-PCR, yielded
90.0% correct classification of their protein spectra. Only three
isolates were misclassified using the developed models, which indicate
that the methodology described in this study may be applied as a rapid
screening method when an outbreak is suspected. The implementation of
FTIR-S technology showed the same discrimination power as PFGE to
differentiate P. aeruginosa outbreak isolates. However, when the
same level of classification provided by WGS technology was attempted
with FTIR-S, the control group and outbreak Group 2 were clearly defined
but Groups 1 and 3 overlapped. However, FTIR-S provided complementary
information to the classification obtained with MALDI-TOF MS spectra by
showing correct classification of the different STs analyzed.
Previous studies have demonstrated that specific biomarker peaks present
in the protein spectra obtained by MALDI-TOF MS could be used for the
monitoring of P. aeruginosa sequence types. Cabrolier et al.
described a specific peak at 7359 m/z that, combined with the
absence of peaks at 7329 and 12154 m/z were specific of P.
aeruginosa ST175 (Cabrolier et al.,
2015). These results were further confirmed by Mulet et al., who also
found the peak at 7359 m/z as a biomarker for P.
aeruginosa ST175 and described another peak at 6911 m/z as
specific for this ST (Mulet et al.,
2021). In the present study, a peak at 6915 m/z has been
described as a biomarker of the P. aeruginosa outbreak strains
belonging to the same sequence type. Although the difference of only 4m/z between both peaks fall within the accepted margin of error
of the applied pipeline (± 4.5 m/z ) and no other marker peak has
been found in the area, further analysis is needed to confirm that both
studies refer the same peak. Finally, the other two biomarker peaks
described for the P. aeruginosa outbreak strains (5169 and 7236m/z ) have never been described before and, therefore, they might
be outbreak-specific markers.
The reproducibility of this MALDI-TOF MS based method for the rapid
detection of a P. aeruginosa outbreak was evaluated. With the
exception of the peak at 5740 m/z , whose inter-day CV was just
below 30.0%, the peaks located between 2000 and 7000 m/z -where
the most common bacterial proteins locate-, showed CV values ranging
between 10.0% and 15.0% and have been considered as acceptable in
previous studies (R. T. K. Pang et al.,
2004). The specific biomarker peaks that allowed the differentiation of
the outbreak isolates showed an average intra-day CV of 13.6% (range
between 7.0 and 20.5%). Again, these values can be considered as
acceptable. Only the biomarker peak at 6915 m/z showed an
inter-day CV variability CV of 32.5% (Figure S3D). Although this value
is above the established limit (20.0%), the peak was always detected inP. aeruginosa outbreak strains regardless its intensity.
Therefore, the presence of the 6915 m/z peak can be reliably
correlated with the outbreak along with the detection of the other two
biomarker peaks at 5169 and 7236 m/z .
In recent years, FTIR-S has emerged as a reliable technology for
outbreak analysis in clinical microbiology laboratories
(Quintelas, Ferreira, Lopes, & Sousa,
2018). The simplicity and low costs of the sample preparation procedure
and interpretation of results allows the follow-up of nosocomial
outbreaks in real time since the turnaround time for the analysis of 30
isolates with this technology is approximately 3 hours. Although the
most studied microorganism so far is Klebsiella pneumoniae(Rakovitsky et al., 2020), FTIR-S has
been evaluated for typing other bacterial species such asSalmonella (Cordovana et al.,
2021), or Streptococcus pneumoniae(Burckhardt et al., 2019).
In our study, FTIR-S was able to discriminate P. aeruginosaoutbreak isolates from non-outbreak isolates at the same level than
PFGE, either by HCA with a cut-off score of 0.350 or PCA (Figure 3 and
Figure 4). In addition, the two strains isolated from the same patient
(numbers 139 and 143) were clustered together with very low distance,
which indicates that this method recognizes them as the same strain and
is reproducible (Figure 3). For ST differentiation, the lipid region
(1400-1500 and 2800-3000 cm-1) showed the highest
discriminatory power and allowed the correct classification of P.
aeruginosa outbreak strains according to PFGE results. However, when
this classification was compared with WGS information, the three
outbreak subgroups were not clearly differentiated by FTIR-S by applying
HCA. Similarly, the implementation of PCA algorithm did not cluster the
three groups of isolates separately, although Group 2 isolates were
almost grouped apart (Figures 4C and 4D). When using PLS-DA and RF
analyses for differentiation of these groups, it was also observed that
Group 2 was clearly separated from the other outbreak groups according
to WGS and the biggest differences among them were found in the 800-1600
cm-1 region (Figure S5). It is important to note that
WGS groups are based on SNP distance, and maybe these differences are
not expressed phenotypically, and thus, FTIR-S clusters may not reflect
differences detected by WGS. At the moment of writing, only one previous
study has evaluated P. aeruginosa typing by FTIR-S in comparison
to PFGE and MLST results (Martak et al.,
2019). The authors showed the reliability of the method for
differentiation of STs with an optimal cut-off distance between 0.184
and 0.374. Besides, these results can be obtained in a turnaround time
of 3h, a great advantage over PFGE.
One of the limitations of this study is the small number of strains
available, but it is important to acknowledge that bacterial outbreaks
usually involve a limited number of patients if they are well contained,
thus making larger number of samples unavailable for research purposes.
The other limitation would be the limited number of isolates
characterized by WGS. But, due to the costs of this technique, a more
affordable approach (ASO-PCR) was carried out to classify the rest of
the isolates.
However, despite these drawbacks, the results of this study showed that
rapid diagnostic methods such as MALDI-TOF MS and FTIR-S may represent
fast alternatives to conventional strategies -based on DNA sequencing-
for real-time monitoring of nosocomial outbreaks, providing
complementary information for the prompt characterization of the
suspected isolates in a cost-efficient way. Although confirmation of the
outbreak strains may request further analysis by WGS, the implementation
and further validation of these rapid typing methods could help to
reduce the number of isolates that require confirmation by expensive
tests available to a limited number of microbiology laboratories.