ORIGINAL RESEARCH published: 09 June 2022 doi: 10.3389/fmicb.2022.897161 Evaluation of Fourier Transform Infrared Spectroscopy as a First-Line Typing Tool for the Identification of Edited by: Extended-Spectrum β-Lactamase- Pablo Power, Universidad de Buenos Aires, Producing Klebsiella pneumoniae Outbreaks in the Hospital Setting Argentina Reviewed by: Angela Novais, University of Porto, Jun Hao Wang-Wang 1, 2†, Antoni E. Bordoy 1, 3†, Elisa Martró 1, 2, 3, 4, Portugal María Dolores Quesada 1, 2, 4, María Pérez-Vázquez 5, 6, Mercedes Guerrero-Murillo 7, 8, Yanyan Hu, Zhejiang University, China Andrea Tiburcio 1, Marina Navarro 1, Laia Castellà 9, Nieves Sopena 10, Irma Casas 11, Verónica Saludes 1, 2,3, 4‡, Montserrat Giménez 1, 4‡ and Pere-Joan Cardona 1, 2, 3, 12*‡ *Correspondence: Pere-Joan Cardona 1 Microbiology Department, Laboratori Clínic Metropolitana Nord, Germans Trias i Pujol University Hospital, Badalona, Spain, pcardonai.germanstrias@gencat.cat 2 Genetics and Microbiology Department, Universitat Autònoma de Barcelona, Bellaterra, Spain, 3 Germans Trias i Pujol These authors have contributed † Research Institute (IGTP), Badalona, Spain, 4 Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública equally to this work and share first (CIBERESP), Instituto de Salud Carlos III (ISCIII), Madrid, Spain, 5 Reference and Research Laboratory for Antibiotic authorship Resistance and Health Care Infections, National Centre for Microbiology, Instituto de Salud Carlos III (ISCIII), Madrid, Spain, 6 Centro de Investigación Biomédica en Red en Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III (ISCIII), ‡ These authors have contributed Madrid, Spain, 7 Clinical Genomics Research Unit, Germans Trias i Pujol Research Institute (IGTP), Can Ruti Campus, equally to this work and share senior Badalona, Spain, 8 Clinical Genomics Unit, Clinical Genetics Service, Laboratori Clínic Metropolitana Nord, Germans Trias authorship i Pujol University Hospital, Can Ruti Campus, Badalona, Spain, 9 Enfermería Control de Infección, Dirección Enfermería, Germans Trias i Pujol University Hospital, Badalona, Spain, 10 Infectious Diseases Department, Germans Trias i Pujol Specialty section: University Hospital, Badalona, Spain, 11 Preventive Medicine Department, Germans Trias i Pujol University Hospital, Badalona, This article was submitted to Spain, 12 Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III Antimicrobials, Resistance and (ISCIII), Madrid, Spain Chemotherapy, a section of the journal Frontiers in Microbiology Early detection of pathogen cross-transmission events and environmental reservoirs is Received: 15 March 2022 needed to control derived nosocomial outbreaks. Whole-genome sequencing (WGS) is Accepted: 19 May 2022 Published: 09 June 2022 considered the gold standard for outbreak confirmation, but, in most cases, it is time- Citation: consuming and has elevated costs. Consequently, the timely incorporation of WGS results Wang-Wang JH, Bordoy AE, to conventional epidemiology (CE) investigations for rapid outbreak detection is scarce. Martró E, Quesada MD, Fourier transform infrared spectroscopy (FTIR) is a rapid technique that establishes Pérez-Vázquez M, Guerrero-Murillo M, Tiburcio A, similarity among bacteria based on the comparison of infrared light absorption patterns Navarro M, Castellà L, Sopena N, of bacterial polysaccharides and has been used as a typing tool in recent studies. The Casas I, Saludes V, Giménez M and Cardona P-J (2022) Evaluation of aim of the present study was to evaluate the performance of the FTIR as a first-line typing Fourier Transform Infrared tool for the identification of extended-spectrum β-lactamase-producing Klebsiella Spectroscopy as a First-Line Typing pneumoniae (ESBL-Kp) outbreaks in the hospital setting in comparison with CE Tool for the Identification of Extended-Spectrum β-Lactamase- investigations using WGS as the gold standard method. Sixty-three isolates of ESBL-Kp Producing Klebsiella pneumoniae collected from 2018 to 2021 and classified according to CE were typed by both FTIR and Outbreaks in the Hospital Setting. Front. Microbiol. 13:897161. WGS. Concordance was measured using the Adjusted Rand index (AR) and the Adjusted doi: 10.3389/fmicb.2022.897161 Wallace coefficient (AW) for both CE and FTIR clustering considering WGS as the reference Frontiers in Microbiology | www.frontiersin.org 1 June 2022 | Volume 13 | Article 897161 Wang-Wang et al. ESBL-Kp Outbreak Identification by FTIR method. Both AR and AW were significantly higher for FTIR clustering than CE clustering (0.475 vs. 0.134, p = 0.01, and 0.521 vs. 0.134, p = 0.009, respectively). Accordingly, FTIR inferred more true clustering relationships than CE (38/42 vs. 24/42, p = 0.001). However, a similar proportion of genomic singletons was detected by both FTIR and CE (13/21 vs. 12/21, p = 1). This study demonstrates the utility of the FTIR method as a quick, low-cost, first-line tool for the detection of ESBL-Kp outbreaks, while WGS analyses are being performed for outbreak confirmation and isolate characterization. Thus, clinical microbiology laboratories would benefit from integrating the FTIR method into CE investigations for infection control measures in the hospital setting. Keywords: cluster analysis, Fourier transform infrared spectroscopy, FTIR, Klebsiella pneumoniae, outbreak, whole-genome sequencing, conventional epidemiology, nosocomial infection INTRODUCTION Besides, bioinformatics analyses of WGS data require large computational resources to be processed, and the obtained results Nosocomial infections cause a high impact on the healthcare often require expert interpretation. Thus, WGS is difficult to system due to an increase in antimicrobials use, patient morbidity implement in the routine clinical practice in most clinical and mortality, and hospitalization length. Nosocomial infection microbiology laboratories. Therefore, the implementation of quicker prevalence has ranged from 5% to 10% in recent years (World and easier to use approaches for bacterial typing could facilitate Health Organization, 2016, 2021). It is well established that the task of outbreak identification by CE teams in the hospital setting. early detection of pathogen cross-transmission events along Fourier transform infrared (FTIR) spectroscopy is a phenotypic with a quick implementation of correct infection control measures technique introduced in the 1950s based on the comparison is required to prevent the spread of nosocomial infections of infrared light absorption patterns after the application of (Brouqui et al., 2017; Haque et al., 2018). The bacterial species the Fourier transform (Naumann et al., 1991; Griffiths and de most frequently involved in nosocomial infections are Haseth, 2007). Such patterns vary according to the functional Enterobacter spp., Staphylococcus spp., Klebsiella spp., groups present in biomolecules such as carbohydrates, lipids, Acinetobacter spp., Pseudomonas spp., and Enterococcus spp., and proteins. The polysaccharide region (1,200–900 cm−1) which are also known as the ESKAPE pathogens and usually contributes more significantly than others to the spectral exhibit multidrug resistance and virulence (Santajit and differences observed between strains from the same species Indrawattana, 2016; Antimicrobial Resistance Collaborators, (Helm et al., 1991; Naumann et al., 1991). Based on the 2022). Among them, Klebsiella pneumoniae is one of the polysaccharide region, recent studies have demonstrated that microorganisms with the highest burden in healthcare settings the most interesting wavelength of the infrared spectrum for (Santajit and Indrawattana, 2016). Specifically, in 2020, the bacterial typing is 1,300–800 cm−1 (Martak et al., 2019; Vogt reported rate of ESBL-Kp in Spain was 26.8% (European Centre et al., 2019). FTIR spectroscopy is used by the IR Biotyper for Disease Prevention and Control, 2020) and it was 21.5% system (Bruker GmbH, Leipzig, Germany), which offers a rapid in our hospital. and low-cost typing technique (both sample processing and For successful nosocomial outbreak control, it is necessary to amount and number of reagents are minimal) with a short identify as soon as possible any potential transmission routes turn-around time (3–4 h). Similarity between transformed spectra and any likely associated environmental reservoirs through is established by multivariate data analysis of spectral peaks, conventional epidemiology (CE) investigations that must and relationships between isolates can then be identified be confirmed later by bacterial typing. Pulsed-field gel (Quintelas et al., 2018; Burckhardt et al., 2019; Martak et al., electrophoresis (PFGE) has been widely used as the gold standard 2019; Novais et al., 2019; Rakovitsky et al., 2020). Thus, FTIR molecular method for outbreak identification based on bacterial could be an alternative method for the quick identification of typing (Peacock et al., 2002). However, over the last decade, nosocomial outbreaks. However, the few studies performed so whole-genome sequencing (WGS) has become the gold standard far regarding the utility of FTIR toward ESBL-Kp outbreak method due to its higher discriminatory power and increased detection have focused on the comparison of FTIR typing accessibility (Bertelli and Greub, 2013; European Centre for Disease results to genomic analysis, but none of them have compared Prevention and Control, 2018). The potential of WGS for establishing FTIR to CE, although CE constitutes the most frequently used precise relationships between bacterial isolates makes this technique outbreak investigation tool in many healthcare settings. The the ideal tool for outbreak investigation in scenarios where other aim of the present study was to evaluate the performance of routinely used methods lack sensitivity (Peacock et al., 2018; the IR Biotyper as a first-line bacterial typing tool for the European Centre for Disease Prevention and Control, 2019). identification of ESBL-Kp outbreaks in the hospital setting in Nevertheless, WGS-based laboratory methods and bioinformatics comparison with CE investigations using WGS as the gold analyses require skilled staff and are laborious and time-consuming. standard method. Frontiers in Microbiology | www.frontiersin.org 2 June 2022 | Volume 13 | Article 897161 Wang-Wang et al. ESBL-Kp Outbreak Identification by FTIR MATERIALS AND METHODS water in a 1.5-ml suspension vial with metal beads (Bruker GmbH, Leipzig, Germany) and homogenized through vortex. Study Design Then, 50 μl of 70% (v/v) ethanol was added in each vial and A retrospective and cross-sectional study was performed in a suspensions were homogenized again. Subsequently, 15 μl of tertiary care hospital. This center is the general hospital for each suspension vial was placed in quadruplicate on a 96-spot 200,000 inhabitants and the reference hospital for 1,200,000 silicon plate (Bruker GmbH, Leipzig, Germany). As quality inhabitants of the Northern Metropolitan Area of Barcelona control, 12 μl of two infrared test standards (IRTS 1 and IRTS (Catalonia, Spain). 2, each standard containing an Escherichia coli strain with a The study dataset comprised 63 ESBL-Kp isolates collected specific spectrum) was placed in duplicate on the same silicon from patients admitted to the reference hospital (n = 61) and plate. The plate was dried for 30 min at 37°C before introducing from the environment (n = 2) from July 2018 to October, 2021. it into the Biotyper system. Among them, 40 isolates belonged to epidemiological outbreaks identified by CE during the study period by the hospital Spectrum Analysis of FTIR Data infection prevention and control team. The CE criterion used For FTIR analysis, manufacturer’s recommendations were in the present study to declare a nosocomial outbreak followed. Briefly, all samples were tested in quadruplicate using (epidemiological cluster) was the detection of two or more the default analysis settings (wavelength region 1,300–800 cm−1) nosocomial ESBL-Kp infections (detected after the first 48 h and OPUS 8.2.28 software (Bruker GmbH, Leipzig, Germany). of hospital admission which were not present at the time of Samples that did not meet the quality criteria were excluded. admission; Horan et al., 2008) in the same hospital unit/ward The OPUS software generates an average spectrum using the in less than a month (Centers for Disease Control and Prevention, four obtained spectra of each replicate for each sample. It 2012). Additionally, in critical units (cardiac care unit, intensive then analyzes the similarity between the average spectra from care unit, neonatal intensive care unit, and both SARS-CoV-2 different samples and generates a dendrogram based on intensive care units) nosocomial ESBL-Kp colonization cases hierarchical cluster analysis (HCA) using Euclidian metric were also considered for outbreak declaration. The date of distance and average linkage method. The FTIR software suggests outbreak closure was set when no ESBL-Kp isolates were a cutoff value for clustering based on Simpson’s Index of identified in the same hospital unit/ward for 3 months since Diversity (Hunter and Gaston, 1988). For K. pneumoniae, the last identification. The remaining 23 isolates were randomly manufacturer recommends using a cutoff ranging from 0.20 selected among cases that did not meet the nosocomial outbreak to 0.25, with an initial validation to determine the best value criteria and therefore were considered epidemiological singletons. using a molecular method as gold standard. Accordingly, a ESBL-Kp isolates were pseudonymized using a consecutive subset of the study isolates comprising the first 17 isolates number in order to maintain patient confidentiality. All ESBL-Kp with available WGS results (12 forming a genomic cluster plus isolates were obtained for routine clinical diagnosis purpose five singletons) was used to determine an initial cutoff. The and no additional sampling was required; therefore, informed obtained cutoff was then validated using all 63 isolates by consent was waived. maximizing the overall congruence between FTIR and WGS results. When two or more isolates had a FTIR spectrum Routine Microbiological Diagnostics and distance less or equal to the designated cutoff value, they were Antimicrobial Susceptibility Testing considered to belong to the same FTIR cluster, and isolates All isolates were firstly identified at the species level by matrix- that did not meet this criterion were considered FTIR singletons. assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS, Bruker Daltonik GmbH, Sample Preparation for WGS Analysis Bremen, Germany). Antibiotic susceptibility was interpreted All ESBL-Kp samples were cultured in Mueller–Hinton agar (Becton following European Committee on Antimicrobial Susceptibility Dickinson GmbH, Heidelberg, Germany) overnight at 37°C. Then, Testing (EUCAST) criteria (The European Committee on a single colony was subcultured in thioglycolate broth (bioMérieux Antimicrobial Susceptibility Testing, 2021). All isolates were SA, Marcy-l’Étoile, France) overnight at 37°C with agitation. DNA subsequently conserved in storage media at −80°C until was extracted from 1.5 ml of grown thioglycolate broth using the further analyses. DNeasy Ultra Clean™ Microbial kit (Qiagen GmbH, Hilden, Germany) following manufacture’s recommendations. Nextera XT Sample Preparation for FTIR Analysis DNA Library Preparation Kit (Illumina Inc., San Diego, All ESBL-Kp isolates were cultured in BD MacConkey II agar United States) was used for dual-indexed library preparation medium (Becton Dickinson GmbH, Heidelberg, Germany) for according to the manufacturer’s instructions, and paired-end 24 h at 37°C. Then, all bacterial isolates were subcultured in sequencing was performed using the Illumina MiSeq platform Mueller-Hinton agar medium from a single colony (Becton (Illumina Inc., San Diego, United States). Dickinson GmbH, Heidelberg, Germany) for another 24 h at 37°C before testing. For bacterial suspension preparation, the Bioinformatic Analyses of WGS Data modified H2O-EtOH protocol was used (Hu et al., 2021); a For WGS clustering analysis of ESBL-Kp isolates, two approaches loopful of bacterial cells was suspended in 50 μl of deionized were combined to increase the discriminatory power of WGS Frontiers in Microbiology | www.frontiersin.org 3 June 2022 | Volume 13 | Article 897161 Wang-Wang et al. ESBL-Kp Outbreak Identification by FTIR analysis as previously described (Raro et al., 2020): (i) core Besides, the proportion of isolates correctly clustered by genome multilocus sequence typing (cgMLST) of 2,358 either FTIR or CE was calculated using WGS as the reference chromosomal target genes and (ii) phylogenetic analysis of method. Proportions were compared using the Fisher’s exact core-genome single nucleotide polymorphisms (cgSNP). A test. Statistical significance was set at p < 0.05. genomic cluster of ESBL-Kp isolates by WGS was defined when the following two conditions were met based on an internal validation according to recorded epidemiological data: (i) ≥2 RESULTS cases of the same sequence type (ST) with a genetic distance of ≤15 alleles in the cgMLST analysis and (ii) a common Conventional Epidemiology Identified monophyletic origin in the phylogenetic tree supported by a Eight Outbreaks bootstrap value ≥70% with a genetic distance of ≤17 SNP CE investigations detected eight nosocomial outbreaks over provided by cgSNP analysis. Isolates that did not meet these the study period, which accounted for 40 out of 63 studied criteria were classified as genomic singletons. Each genomic isolates. Specifically, the outbreaks were located at the CCU cluster and singleton were considered a different ESBL-Kp (outbreak A, n = 4), the OW (outbreak B, n = 2), the NPHD introduction event into the hospital. (outbreak C, n = 3; and outbreak H, n = 4), the ICU (outbreak cgMLST analysis was performed with Ridom SeqSphere+ D, n = 9; and outbreak E, n = 8), the SICU 1 (outbreak F, n = 4), software 5.1.0 (Ridom GmbH, Münster, Germany; Jünemann and the SICU 2 (outbreak G, n = 6). According to the used et al., 2013) using assembly de novo settings with SPAdes 3.11.1 CE criterion, the remaining 23 isolates presented no evident (Bankevich et al., 2012) and the parameter “pairwise ignoring epidemiological links among them or the isolates related to missing values.” From this analysis, ST data were obtained, outbreaks A-H. Distribution for all isolates according to source and a minimum spanning tree was built to visualize allele and ward where they were detected can be found in distance between isolates. Supplementary Table 1; Supplementary Text 1. Prokka v.1.14 (Seemann, 2014) was used to annotate de novo assemblies, and these annotated assemblies were used as input for Roary v3.13.0 (Page et al., 2015). An alignment of WGS Detected 32 Different Introductions 3,524 core genes (present in >99% of isolates) comprising of ESBL-Kp 3,481,593 bp was generated. Variable positions of the alignment The cgMLST allele distance among the 63 isolates is depicted were extracted (78,071 SNPs), and a maximum-likelihood in a minimum spanning tree shown in Figure 1A. Additionally, phylogenetic tree of SNPs was constructed using RAxML v7.0.4 the phylogenetic tree of a cgSNP alignment of 78,071 high- (Stamatakis, 2006) with a general time-reversible (GTR) model quality sites is shown in Figure 1B. In both Figure 1 panels, and a gamma correction for site rate variation. Node support the clusters that meet the combined cgMLST and cgSNP criteria was assessed through bootstrapping with 100 replicates. are identified. Additionally, resistance analysis was performed using According to WGS clustering results, sequenced isolates KmerResistance 2.2 online tool with default parameters (Clausen represented at least 32 different introductions of ESBL-Kp into et al., 2016, 2018; Center for Genomic Epidemiology, 2021). the hospital over the study period; 11 different clusters (namely Those genes with “template_ID” lower than 98%, “template_ I–XI, 42 isolates), plus 21 singletons were identified (Figure 1; coverage” lower than 100% and a “depth” that did not reach Supplementary Table 1). WGS analysis confirmed two monoclonal 40X were filtered out. outbreaks (B and C) and six polyclonal outbreaks (A, D, E, F, G, and H) caused by 10 different genomic clusters (I–IV and VI–XI) plus nine singletons (Table 1). The isolates were assigned Concordance Between Clustering to 17 different ST by cgMLST; among them, 41 out of 63 isolates Methodologies (65.1%) belonged to high-risk clones ST147, ST307, and ST392, To assess the concordance of the clustering results obtained being ST307 the most prevalent one (22/63, 34.9%; Figures 1A, by both the CE investigations and FTIR vs. WGS as the reference 2A). Genomic clusters I, III, V, X, and XI belonged to different method, the Adjusted Rand index (AR) and the Adjusted Wallace STs, but genomic clusters II, IV, VI–IX belonged to ST307. Mean coefficient (AW) were calculated with 95% CI using an online allele distances between isolates of a genomic cluster ranged from tool (Comparing Partitions, 2011). AR compares the overall 1 to 9.5 (Supplementary Text 2). Six out of the 11 genomic congruence between two typing methods (Santos and Embrechts, clusters included isolates from more than one hospital location 2009), while AW compares the agreement of two typing methods over the study period (Figure 2B). Cluster I isolates were the considering one of them as the reference method (Severiano most spread in the hospital, affecting up to six different wards/ et al., 2011). AR and AW values may range from 0 to 1, where units. The analysis of antibiotic resistance from WGS data confirmed 0 means agreement expected by chance and 1 means perfect the presence of at least one ESBL-resistance gene in all cases, correlation between both methods (Severiano et al., 2011; being CTX-M-15 the most frequently detected ESBL, present in Hoffman et al., 2015). Additionally, p values comparing the 95% (60/63) of the isolates (data not shown). AW and AR values for (i) CE vs. WGS clustering and (ii) As for the comparison between WGS and CE results, outbreaks FTIR vs. WGS clustering were obtained according to the jackknife detected by CE included two or more isolates that were confirmed pseudo-values resampling method (Comparing Partitions, 2011). by WGS to belong to the same genomic cluster in 24 out of Frontiers in Microbiology | www.frontiersin.org 4 June 2022 | Volume 13 | Article 897161 Wang-Wang et al. ESBL-Kp Outbreak Identification by FTIR A B FIGURE 1 | (A) Minimum spanning tree of extended-spectrum β-lactamase-producing Klebsiella pneumoniae (ESBL-Kp) isolates (N = 63) showing identified clusters (roman numerals and encircled dotted lines) and allele distance between strains based on cgMLST analysis of 2,358 genes. Each sequence type (ST) assigned is indicated with a different color. Branch lengths are not to scale. (B) Maximum-likelihood phylogenetic tree obtained from cgSNP analysis of ESBL-Kp isolates showing identified clusters (roman numerals and color legend). Hospital locations are also indicated (CCU, cardiac care unit; CD, cardiology department; (Continued) Frontiers in Microbiology | www.frontiersin.org 5 June 2022 | Volume 13 | Article 897161 Wang-Wang et al. ESBL-Kp Outbreak Identification by FTIR FIGURE 1 | GD, geriatric department; ICU, intensive care unit; IDD, infectious disease department; ND, neurology department; NICU, neonatal intensive care unit; NPHD, nephrology department; OHD, oncohematology department; OW, obstetric ward; SD, surgery department; SICU 1, SARS-CoV-2 intensive care unit 1; and SICU 2, SARS-CoV-2 intensive care unit 2). Reference: NTUH-K2044 (accession no. NC_012731.1). The tree scale represents the number of substitutions per variable site. All nodes corresponding to each individual genomic cluster were supported with bootstrap values ≥70%. TABLE 1 | Contingency table comparing epidemiological clustering (A–H) vs. WGS clustering (I–XI) as the reference method. WGS clustering I II III IV V VI VII VIII IX X XI S Total Epidemiological A 2 2 4 clustering 2 2 B C 3 3 D 1 1 2 2 3 9 E 1 3 4 8 F 2 1 1 4 G 1 4 1 6 H 2 1 1 4 S 4 2 2 1 2 12 23 Total 16 4 2 2 2 2 2 2 3 4 3 21 63 S, Singleton. 42 (57.1%) cases. Moreover, 11 out of 23 (47.8%) isolates not in three instances (clusters I, X, and XI; Figure 3; Table 2). identified as belonging to any outbreak according to CE formed All this translates into an AR and AW values of 0.475 (95% or were part of five out of the six genomic clusters detected CI, 0.262–0.691) and 0.521 (95% CI, 0.377–0.665), respectively, in more than one hospital location (I, II, V, VIII, and XI; for FTIR clustering considering WGS as the reference method. Figure 2B). Finally, CE correctly identified 12 out of 21 (57.1%) In order to assess the value of FTIR over CE investigations genomic singletons (Table 1). in the hospital setting, AR and AW values were also calculated for CE clustering considering WGS as the reference method: AR was 0.134 (95% CI, 0.000–0.278) and AW was 0.134 (95% FTIR Is a Useful First-Line Tool for CI, 0.000–0.281). Both AR and AW were significantly higher Detection of ESBL-Kp Outbreaks for FTIR clustering than for CE clustering (0.475 vs. 0.134, For FTIR clustering, an optimal cutoff of 0.240 was obtained p = 0.01, and 0.521 vs. 0.134, p = 0.009, respectively). To further after an initial validation (n = 17) which coincided with the evaluate the benefit of enhancing CE investigations using FTIR, best cutoff value for the whole study dataset (N = 63; we analyzed which of those isolates that were part of a genomic Supplementary Figure 1). Among all 63 ESBL-Kp isolates cluster (n = 42) were grouped by either FTIR or CE. FTIR tested, FTIR detected 10 clusters (namely 1–10, including 47 clusters included two or more isolates that WGS confirmed isolates) and 16 singletons. The two largest clusters were cluster to belong to the same genomic cluster in 38 out of 42 (90.5%) 4 (n = 14, 22.2%) and cluster 1 (n = 13, 20.6%; Figure 3; Table 2). cases. Besides, 14 of 23 (45.7%) isolates not identified as Regarding the isolates initially classified into eight outbreaks belonging to any outbreak according to CE investigations were by CE (n = 40), FTIR classified two outbreaks as monoclonal grouped into FTIR clusters detected in more than one hospital (B and C) and six as polyclonal (A, D, E, F, G, and H), location (1, 2, 4, 5, 6, and 10), whereas FTIR correctly identified similarly to WGS results (Supplementary Table 1). However, 13 out of 21 (61.9%) genomic singletons (Table 2). Therefore, FTIR detected less ESBL-Kp clusters and singletons than WGS FTIR significantly inferred more true genomic relationships (8 vs. 10 and 7 vs. 9, respectively; Figure 3; than CE (38/42 vs. 24/42, p = 0.001). However, a similar Supplementary Table 1). Additionally, and in consonance with proportion of genomic singletons was detected by both FTIR WGS, FTIR detected six clusters (1, 2, 4, 5, 6, and 10) affecting and CE (13/21 vs. 12/21, p = 1). Importantly, FTIR was able more than one hospital location. However, four (1, 4, 6, and to detect a genomic relationship for isolates belonging to 10) out of these six clusters were not fully concordant with genomic clusters IV, V, VIII, and XI, which could be classified WGS clustering (Table 2; Supplementary Text 3). In fact, as “hidden outbreaks” because CE research was unable to when considering all studied isolates (N = 63), the concordance establish any epidemiological link between them. between FTIR and WGS was as follows: FTIR grouped in the same cluster singletons or isolates of different genomic clusters in three occasions (clusters 3, 4, and 10), a perfect agreement DISCUSSION was observed between FTIR and genomic clusters in three cases (cluster 2/II, 5/V, and 7/IX), and finally, WGS grouped This study demonstrates the utility of the FTIR method as a in the same genomic cluster isolates of different FTIR clusters rapid first-line laboratory tool for ESBL-Kp outbreak detection, Frontiers in Microbiology | www.frontiersin.org 6 June 2022 | Volume 13 | Article 897161 Wang-Wang et al. ESBL-Kp Outbreak Identification by FTIR A B FIGURE 2 | (A) Distribution of isolates according to their sequence type (ST). Genomic clusters of isolates for each ST are indicated in colors. NA, not assigned. (B) Distribution of isolates according to WGS clustering results. Hospital locations affected by each genomic clustering category are indicated in colors. while confirmatory results are being generated by WGS. FTIR et al., 2022). However, although some of those studies use inferred more true genomic relationships than CE. Moreover, WGS-based analyses, most of them rely on PFGE and MLST FTIR detected four hidden outbreaks identified by WGS. Thus, as the reference molecular method and their isolates are often FTIR adds valuable information that can be incorporated into closely related, belonging to just one or a reduced number of CE investigations and does so in a timely manner. outbreaks. Additionally, none of the previous studies compares A great performance of the FTIR method regarding the FTIR vs. CE, being CE the most frequently used outbreak investigation of bacterial clusters has been suggested by a few investigation tool in the healthcare setting. In the present study, studies performed so far. These evaluations have been carried a lower concordance between FTIR and WGS clustering methods out for Klebsiella pneumoniae, Pseudomonas aeruginosa, (AR = 0.475, AW = 0.521) was obtained for K. pneumoniae isolates Enterobacter cloacae, and Acinetobacter baumannii (Dinkelacker than in the previously published studies where AR ranged et al., 2018, Martak et al., 2019; Vogt et al., 2019; Rakovitsky from 0.743 to 1.0 and AW ranged from 0.708 to 1.0 (Dinkelacker et al., 2020; Hu et al., 2021, Lombardo et al., 2021). Some of et al., 2018; Martak et al., 2019; Rakovitsky et al., 2020; Hu the previous studies employ a workflow different from the et al., 2021). Two methodological strengths of the present commercial one using the attenuated total reflection mode compared to previous studies may explain the lower concordance accessory of the FTIR instrument (Silva et al., 2020; Novais results observed here. Firstly, we analyzed isolates collected Frontiers in Microbiology | www.frontiersin.org 7 June 2022 | Volume 13 | Article 897161 Wang-Wang et al. ESBL-Kp Outbreak Identification by FTIR FIGURE 3 | Dendrogram obtained by clustering the Fourier transform infrared spectroscopy (FTIR) spectra of 63 extended-spectrum β-lactamase-producing K. pneumoniae isolates. The vertical dashed line indicates the used cutoff value (0.240). Resulting FTIR clusters (1–10) are shadowed in gray. Corresponding sequence type (ST), WGS cluster, and epidemiological outbreak are indicated for each isolate. S, singleton. Frontiers in Microbiology | www.frontiersin.org 8 June 2022 | Volume 13 | Article 897161 Wang-Wang et al. ESBL-Kp Outbreak Identification by FTIR TABLE 2 | Contingency table comparing FTIR clustering (1–10) vs. WGS clustering (I–XI) as the reference method. WGS clustering I II III IV V VI VII VIII IX X XI S Total FTIR clustering 1 13 13 2 4 4 3 2 1 3 4 1 2 2 2 2 5 14 5 2 2 6 2 2 7 3 3 8 2 2 9 2 2 10 2 2 S 2 1 13 16 Total 16 4 2 2 2 2 2 2 3 4 3 21 63 S, Singleton. from more than one hospital location, from different patients than CE clustering. Accordingly, FTIR inferred more true and over a long period of time (3 years), while previous studies genomic relationships than epidemiological criteria, while a analyzed isolates belonging to a single hospital location and similar proportion of genomic singletons was detected by both collected less than 1 year apart. Secondly, we combined cgMLST FTIR and epidemiological criteria. In an epidemiologically and cgSNP phylogenetic analysis in order to improve the suspected outbreak scenario, a rapid result is required to confirm resolution of the WGS-based reference method, as previously whether isolates are closely related or not. The detection of described (Raro et al., 2020). Such combination allowed the related isolates involves the implementation of the most identification of the best allele and SNP cutoffs for cluster appropriate infection control measures as soon as possible. definition of our WGS data. It is important to note that a Therefore, although FTIR clustered 38.1% of genomic singletons, universal genomic cutoff value to determine whether different in the clinical practice, misdiagnosis of singletons as related isolates belong to an outbreak does not currently exist (López isolates by FTIR clustering might be desirable over the opposite et al., 2021). Less than 15 alleles of distance in pairwise because it promotes the implementation of outbreak control comparisons between isolates has been suggested for outbreak measures to limit the potential spread of the outbreak, while delimitation by cgMLST (Lepuschitz et al., 2019; Miro et al., confirmation by WGS is being performed. Contrarily to WGS, 2020). However, this cutoff may not always be reliable as genetic the IR Biotyper is a rapid (3–4 h), low-cost method with a variability depends on the time elapsed among the collection simple sample preparation that can be easily implemented in dates of isolates. Similarly, if non-identical sequences share a the everyday routine of a microbiology laboratory after the monophyletic origin, the identification of the isolates that belong overnight culture of the isolate of interest. to an outbreak may be difficult, as a defined SNP cutoff has Importantly, FTIR was able to detect a relationship for not yet been established. It should also be pointed out that isolates belonging to four clusters hidden to the CE investigations each laboratory must internally validate its FTIR cutoff for because the isolates were obtained from different hospital wards/ clustering analysis with a reference method, given that the units and/or differed in collection date by more than one manufacturer has an unofficial range of cutoff values for month, evidencing the difficulty to establish an epidemiological K. pneumoniae (0.20–0.25) based on information gathered from link among them. Thus, we suggest that a prospective surveillance internal validations of different laboratories (Rakovitsky et al., approach should be carried out in order to identify all potential 2020). The cutoff applied in the present study after internal hidden epidemiological clusters of isolates from patients or validation (0.240) was similar to those used by Hu et al. (0.221) environmental reservoirs; this could be easily implemented and Martak et al. (0.220; Martak et al., 2019; Hu et al., 2021). using FTIR as a first-line surveillance tool, while WGS could Genome composition of K. pneumoniae clones may slightly be performed for clustering confirmation as well as for genomic vary over time and from patient to patient, generating phenotypic characterization of circulating isolates at the level of ST, differences that are not detected by FTIR. As recommended resistome, and virulome. As other studies describe, high-risk by the manufacturer, only the default wavelength region, which clones ST147, ST307, and ST392 are frequently associated comprises the polysaccharide region, was analyzed by FTIR with ESBL production and detected in healthcare settings in in the present study; thus, other existing differences/similarities Spain (D’Apolito et al., 2020; Peirano et al., 2020). Particularly, at the level of proteins or fatty acids could not be detected. in the present study these high-risk clones represented more Nevertheless, and despite the expected lower discriminatory than 60% of studied isolates, being ST307 the most prevalent power of FTIR for clustering identification than WGS, when one (34.9%). It is also important to remark that previous the clustering performances of CE and FTIR were compared, studies have demonstrated that outbreak delimitation based both AR and AW were significantly higher for FTIR clustering on ST determination might not be adequate (Zhou et al., Frontiers in Microbiology | www.frontiersin.org 9 June 2022 | Volume 13 | Article 897161 Wang-Wang et al. ESBL-Kp Outbreak Identification by FTIR 2017; Gona et al., 2020). In this sense, the detection of different DATA AVAILABILITY STATEMENT genomic clusters within ST307 in our geographical region (Figure 2A) probably indicates multiple introductions of ST307 The data for this study have been deposited in the European into the hospital setting. Nucleotide Archive (ENA) at EMBL-EBI under accession The use of the FTIR in the clinical microbiology practice number PRJEB51676. has potential limitations. Firstly, FTIR spectra analysis does not provide species identification. In a hypothetical situation of an outbreak, an accidentally mixed culture for any of the ETHICS STATEMENT isolates could potentially lead to its misclassification as a non-related strain. This limitation can be easily resolved by Ethical review and approval was not required for the confirming species identification using MALDI-TOF MS (or study on human participants in accordance with the local legislation other bacterial identification techniques). Secondly, the FTIR and institutional requirements. Written informed consent for technique requires a standardization of the culture medium participation was not required for this study in accordance with and incubation times of the samples (Dinkelacker et al., 2018; the national legislation and the institutional requirements. Quintelas et al., 2018; Hu et al., 2021); the use of different medium or incubation times may affect the growth and the cell-wall composition and, therefore, the spectra quality and AUTHOR CONTRIBUTIONS subsequent analysis. Our study also has several limitations. Firstly, not all ESBL-Kp P-JC conceived the study. EM, VS, MG, and P-JC designed the isolates detected in the hospital over the study period could study. LC, NS, IC, and MG performed the epidemiological analyses. be included in this study, concealing the detection of any JW-W, MQ, AT, and MN performed the routine microbiological further potential outbreak relationships or the detection of analyses. JW-W, AT, and MN performed the FTIR experiments. environmental reservoirs. Secondly, only one biological replicate JW-W and VS performed the WGS experiments. JW-W, AB, from each isolate was analyzed; thus, the inherent biological MP-V, MG-M, and VS performed the bioinformatics analyses. variability could not be taken into account. Finally, although JW-W, AB, and VS analyzed and interpreted the data and wrote the FTIR operated under standard laboratory conditions, the first draft of the manuscript. AB created the figures and temperature and hygrometry were not controlled. Since FTIR performed the statistical analyses. JW-W, AB, EM, MP-V, VS, spectroscopy techniques are known to be very sensible to MG, and P-JC reviewed and edited the manuscript. All authors variations in these two physical properties, it is not known contributed to the article and approved the submitted version. to what extent their fluctuations might have affected the results of this study. ACKNOWLEDGMENTS We thank the Germans Trias i Pujol Research Institute (IGTP) CONCLUSION Translational Genomics Core Facility and staff for their contribution to this publication. We also thank CERCA Programme/ FTIR is a rapid, low-cost, and easy-to-use phenotypic method Government of Catalonia for their support to the IGTP. that could be used in the clinical microbiology laboratory as first-line tool for the rapid identification of ESBL-Kp outbreaks, SUPPLEMENTARY MATERIAL while the WGS analyses are being performed for confirmation and further characterization. Thus, integrating FTIR results The Supplementary Material for this article can be found online into epidemiological investigations may add valuable information at: https://www.frontiersin.org/articles/10.3389/fmicb.2022.897161/ for infection control in the hospital setting. full#supplementary-material Burckhardt, I., Sebastian, K., Mauder, N., Kostrzewa, M., Burckhardt, F., and REFERENCES Zimmermann, S. (2019). 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