An increase in erythromycin resistance in methicillin-susceptible Staphylococcus aureus from blood correlates with the use of macrolide/lincosamide/streptogramin antibiotics. EARS-Net Spain (2004–2020)

Frontiers in Microbiology 01 frontiersin.org An increase in erythromycin resistance in methicillin-susceptible Staphylococcus aureus from blood correlates with the use of macrolide/lincosamide/ streptogramin antibiotics. EARS-Net Spain (2004–2020) AchrafElMammery 1,2†, EvaRamírezdeArellano 1,3†, JavierE.Cañada-García 1,2,3†, EmiliaCercenado 4,5, LauraVillar-Gómara 6, VerónicaCasquero-García 1, SilviaGarcía-Cobos 1, JoséAntonioLepe 3,7, EnriqueRuizdeGopeguiBordes 3,8, JorgeCalvo-Montes 3,9, NievesLarrosaEscartín 3,10, RafaelCantón 3,11, MaríaPérez-Vázquez 1,3†, BelénAracil 1,3†, JesúsOteo-Iglesias 1,3*† and on behalf of the Spanish EARS-Net Group 1 Laboratorio de Referencia e Investigación en Resistencia a Antibióticos e Infecciones Relacionadas con la Asistencia Sanitaria, Centro Nacional de Microbiología, Instituto de Salud Carlos III, Madrid, Spain, 2 Escuela Internacional de Doctorado, Ciencias Biomédicas y Salud Pública- IMIENS (UNED), Madrid, Spain, 3 CIBER de Enfermedades Infecciosas (CIBERINFEC), Instituto Salud Carlos III, Madrid, Spain, 4 Servicio de Microbiología, Hospital General Universitario Gregorio Marañón, Madrid, Spain, 5 CIBER de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain, 6 Agencia Española de Medicamentos y Productos Sanitarios (AEMPS), Madrid, Spain, 7 Servicio de Microbiología, Hospital Universitario Virgen del Rocío, Sevilla, Spain, 8 Servicio de Microbiología, Hospital Universitario Son Espases, Palma de Mallorca, Spain, 9 Servicio de Microbiología, Hospital Universitario Marqués de Valdecilla-IDIVAL, Santander, Spain, 10 Servicio de Microbiología, Hospital Universitario Vall d’Hebron, Barcelona, Spain, 11 Servicio de Microbiología, Hospital Universitario Ramón y Cajal, Madrid, Spain Objectives: To describe and analyse erythromycin resistance trends in blood isolates of Staphylococcus aureus (EARS-Net Spain, 2004–2020) and the association of these trends with the consumption of macrolide, lincosamide, and streptogramin B (MLSB) antibiotics. To assess molecular changes that could be involved in erythromycin resistance trends by whole genome analysis of representative isolates. Materials and methods: We collected antibiotic susceptibility data for all firstblood S. aureus isolates in patients from 47 Spanish hospitals according to EARS-Net criteria. MLSB antibiotic consumption was obtained from the Spanish Agency for Medicines and Medical Devices (2008–2020). We sequenced 137 representative isolates for core genome multilocus sequence typing, resistome and virulome analysis. Results: For the 36,612 invasive S. aureus isolates, methicillin resistance decreased from 26.4% in 2004 to 22.4% in 2020. Erythromycin resistance in methicillin- OPEN ACCESS EDITED BY Kristina Kadlec, Independent Researcher, Wunstorf, Germany REVIEWED BY Juan-ignacio Alós, Getafe University Hospital, Spain Meiji Soe Aung, Sapporo Medical University, Japan Etinosa Igbinosa, University of Benin, Nigeria *CORRESPONDENCE Jesús Oteo-Iglesias [email protected] †These authors have contributed equally to this work RECEIVED 10 May 2023 ACCEPTED 08 September 2023 PUBLISHED 26 September 2023 CITATION El Mammery A, Ramírez de Arellano E, Cañada-García JE, Cercenado E, Villar-Gómara L, Casquero-García V, García-Cobos S, Lepe JA, Ruiz de Gopegui Bordes E, Calvo-Montes J, Larrosa Escartín N, Cantón R, Pérez-Vázquez M, Aracil B and Oteo-Iglesias J (2023) An increase in erythromycin resistance in methicillinsusceptible Staphylococcus aureus from blood correlates with the use of macrolide/ lincosamide/streptogramin antibiotics. EARS-Net Spain (2004–2020). Front. Microbiol. 14:1220286. doi: 10.3389/fmicb.2023.1220286 COPYRIGHT © 2023 El Mammery, Ramírez de Arellano, Cañada-García, Cercenado, Villar-Gómara, Casquero-García, García-Cobos, Lepe, Ruiz de Gopegui Bordes, Calvo-Montes, Larrosa Escartín, Cantón, Pérez-Vázquez, Aracil and Oteo-Iglesias. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. TYPE Original Research PUBLISHED 26 September 2023 DOI 10.3389/fmicb.2023.1220286 El Mammery et al. 10.3389/fmicb.2023.1220286 Frontiers in Microbiology 02 frontiersin.org susceptible S. aureus (MSSA) increased from 13.6% in 2004 to 28.9% in 2020 (p < 0.001); however, it decreased from 68.7 to 61.8% (p < 0.0001) in methicillinresistant S. aureus (MRSA). Total consumption of MLSB antibiotics increased from 2.72 defined daily doses per 1,000 inhabitants per day (DID) in 2014 to 3.24 DID in 2016. By WGS, the macrolide resistance genes detected were erm (59.8%), msrA (46%), and mphC (45.2%). The erm genes were more prevalent in MSSA (44/57, 77.2%) than in MRSA (38/80, 47.5%). Most of the erm genes identified in MSSA after 2013 differed from the predominant ermC gene (17/22, 77.3%), largely because ermT was significantly associated with MSSA after 2013 (11/29, 37.9%). All 13 ermT isolates in this study, except one, belonged to ST398 and came from 10 hospitals and six Spanish provinces. Conclusion: The significant increase in erythromycin resistance in blood MSSA correlated with the consumption of the MLSB antibiotics in Spain. These preliminary data seem support the hypothesis that the human ST398 MSSA clade with ermT-mediated resistance to erythromycin may beinvolved in this trend. KEYWORDS Staphylococcus aureus, macrolides, ST398, ermT, EARS-Net, antibiotic resistance, antibiotic consumption Introduction The ever-increasing prevalence of antibiotic resistance in bacteria is a serious concern that requires an international approach to management. Hence, the World Health Organization (WHO) and the European Commission both recognize the importance of understanding the emergence and determinants of resistance and the need for control strategies. In Europe, the European Antimicrobial Resistance Surveillance Network (EARS-Net) has collected antimicrobial susceptibility data for isolates from routine blood and cerebrospinal fluid cultures since 1988 (Gagliotti etal., 2011). Funded by the European Commission, EARS-Net is a European network of national surveillance systems coordinated by the European Centre for Disease Prevention and Control (ECDC), whose goal is to collect comparable, reliable data to identify variations in antimicrobial resistance over time and space, providing a basis for infection prevention and control programs (ECDC, 2022). An EARS-Net indicator organism is Staphylococcus aureus, which is a pathogen of major clinical importance for nosocomial infections. Since the introduction of antibiotics in clinical practice, S. aureus has progressively developed resistance to the most frequently used antibiotics. It is noteworthy that the first clinical isolate of methicillinresistant S. aureus (MRSA) was reported in 1961, just 1 year after the launch of methicillin (Jevons, 1961). WHO considers MRSA the most important Gram-positive bacterial strain (Priority 2/High) for research and the development of new antibiotics (Tacconelli etal., 2018). Macrolides, lincosamides, and streptogramin B (MLS B ) are alternative antibiotics used to treat severe staphylococcal infections, mainly in penicillin-allergic patients, along with vancomycin and the combination of linezolid and rifampicin (Ji and Xu, 2021). Macrolide resistance emerges quickly and persists, even after a short course of therapy (Malhotra-Kumar etal., 2009; Van Heirstraeten etal., 2012). Total MLS B consumption did not change significantly in Europe from 2007 to 2017 (Adriaenssens etal., 2021); however, the consumption of long-acting macrolides increased, and seasonal variation was high, suggesting that MLS B antibiotics were prescribed inappropriately in many countries (Adriaenssens etal., 2021). Staphylococcal resistance to macrolides includes target-site modification by methylation, such as erythromycin ribosome methylase (erm) genes; antibiotic efflux pumps, such as ABC-F proteins (msr genes) and major facilitator superfamily transporters (mef genes); and drug inactivation, such as phosphotransferases (mph genes) and esterases (ere genes) (Feßler etal., 2018; Lade etal., 2022). The erm gene products methylate specific targets in the 23S rRNA, preventing the antibiotic from binding to its ribosomal target (di Bonaventura etal., 2019). This is the most widespread mechanism of resistance to macrolides, lincosamides and streptogramin B cross-resistance (MLSB phenotype). The most common erm genes in S. aureus are erm(C) and erm(A), which can beeither constitutive or inducible (Fyfe etal., 2016; di Bonaventura etal., 2019). The goals of this study were (i) to describe and analyse erythromycin resistance trends in the blood isolates of S. aureus collected by EARS-Net Spain (2004–2020), (ii) to compare erythromycin resistance with trends in the consumption of the MLS B antibiotic family (J01F family), and (iii) to characterize antibiotic resistance genes and the prevalence of resistant clones by whole genome sequencing (WGS) in a representative sample of S. aureus isolates. Materials and methods Antibiotic resistance Forty-seven Spanish EARS-Net hospitals collected antibiotic susceptibility data for all S. aureus isolated from the first blood collected from each patient from 2004 to 2020. Using EARS-Net criteria, weselected hospitals that were distributed evenly across the country (TESSy-The European Surveillance System, 2022). These hospitals serve ~13.5 million people, ~32% of the total Spanish El Mammery et al. 10.3389/fmicb.2023.1220286 Frontiers in Microbiology 03 frontiersin.org population, and are representative of 15 of the 17 Spanish autonomous communities. Each laboratory identified the isolates and tested their susceptibilities according to standard microbiological procedures using commercial microdilution broth assays (Gagliotti etal., 2011, 2021). Results were interpreted according to EUCAST criteria. 1 A quality assurance exercise (UK National External Quality Assessment Scheme) was performed annually to ensure comparable results among the hospital laboratories. All the S. aureus isolates included in EARS-Net have information on susceptibility to methicillin (the only mandatory indicator), but the number of isolates tested for other antibiotics may vary (Table1). Antibiotic consumption Community consumption of the MLS B antibiotic family (WHO code J01F) according to the public health prescriptions for the period 2012–2020 was provided by the Spanish National Action Plan on Antimicrobial Resistance (PRAN), coordinated by the Spanish Agency for Medicines and Medical Devices (AEMPS) from the Ministry of Health, and was obtained from the database of retail pharmacy sales from National Health System prescriptions (covering near 100% of the Spanish population). In addition, the community use of MLS B antibiotics from private health prescriptions was 1 https://www.eucast.org/clinical_breakpoints obtained from market research companies provided by the PRAN for the period 2012–2020. MLS B hospital dispensing data were also available from public hospital pharmacies and market research companies for private hospital pharmacies, both provided by the PRAN. The consumption data were tabulated, and the number of units was converted into defined daily doses (DDD) of active drug ingredients according to WHO methodology (WHO Collaborating Centre for Drug Statistics Methodology, 2023). The number of DDD per 1,000 inhabitants per day (DID) was calculated for each active drug ingredient. WGS of Staphylococcus aureus isolates To study the population structure and macrolide resistance genes in S. aureus by WGS, weselected a total of 137 isolates sent to the S. aureus Reference Laboratory of the Spanish National Microbiology Centre according to the following characteristics: 46 MRSA isolated before 2013, 34 MRSA isolated after 2013, 28 MSSA isolated before 2013, and 29 MSSA isolates after 2013. These isolates were selected to have a broad geographic representation, and to represent MSSA and MRSA from periods before and after the start of the trend change in erythromycin resistance detected by EARS-Net at MSSA in 2013. These isolates, which were isolated from blood (41, 29.9%), skin (35, 25.5%), the respiratory tract (34, 24.8%), and other samples (27, 19.7%), came from a total of 71 healthcare centers in 30 Spanish provinces. TABLE1 Evolution of antibiotic resistance of blood isolates of Staphylococcus aureus in according to EARS-Net (Spain, 2004–2020). Antibiotics Year (n) Methicillin*Erythromycin*Ciprofloxacin*Clindamycin*Gentamicin* 2004 (1,604) 26.4 (423/1,604) 31.2 (496/1,592) 27.7 (348/1,259) 13.4 (88/657) 8.5 (134/1,586) 2005 (1,388) 27.3 (379/1,388) 31.3 (429/1,371) 27.3 (265/973) 12.5 (124/998) 6.9 (89/1,292) 2006 (1,620) 25.0 (405/1,620) 27.6 (437/1,584) 27.0 (303/1,125) 11.0 (143/1,303) 6.7 (101/1,520) 2007 (1,730) 25.5 (441/1,730) 26.0 (446/1,718) 27.0 (355/1,317) 8.7 (130/1,495) 5.7 (94/1,648) 2008 (1,756) 26.6 (467/1,756) 29.6 (505/1,708) 30.6 (376/1,229) 12.2 (181/1,487) 6.9 (108/1,567) 2009 (1,835) 25.9 (475/1,835) 28.2 (511/1,815) 32.1 (380/1,184) 10.3 (153/1,491) 7.4 (128/1,740) 2010 (2,115) 25.2 (533/2,115) 28.3 (599/2,115) 30.9 (421/1,365) 12.0 (227/1,897) 6.1 (125/2,052) 2011 (2,147) 22.6 (485/2,147) 24.9 (528/2,121) 28.4 (429/1,513) 10.2 (216/2,115) 5.2 (110/2,123) 2012 (2,194) 25.0 (549/2,194) 27.2 (584/2,148) 29.2 (508/1,742) 8.2 (179/2,183) 5.2 (114/2,211) 2013 (2,113) 23.4 (519/2,113) 34.4 (658/1,913) 24.5 (428/1,751) 8.9 (177/1,990) 3.8 (81/2,113) 2014 (2,235) 23.2 (463/2,235) 35.4 (699/1,976) 25.1 (433/1,726) 8.7 (173/1,994) 3.8 (80/2,105) 2015 (2,290) 26.7 (561/2,290) 39.6 (886/2,238) 31.2 (557/1,786) 12.5 (263/2,110) 4.7 (107/2,283) 2016 (2,270) 25.4 (611/2,270) 35.9 (845/2,168) 27.8 (501/1,805) 11.4 (225/1,978) 3.3 (75/2,269) 2017 (2,206) 26.3 (580/2,206) 41.6 (787/1,894) 30.5 (485/1,593) 8.3 (145/1,753) 5.6 (108/1,939) 2018 (3,189) 23.5 (749/3,189) 39.9 (1,248/3,128) 26.8 (801/2,989) 15.0 (394/2,625) 4.2 (126/3,022) 2019 (2,847) 20.8 (592/2,847) 39.8 (1,074/2,699) 22.8 (486/2,135) 15.5 (325/2,098) 4.5 (104/2,315) 2020 (3,073) 22.4 (688/3,073) 36.1 (1,082/2,998) 26.1 (612/2,346) 18.4 (533/2,896) 5 (128/2,556) Total (36,612) 24.4 (8,920/36,612) 32.7(11,814/35,186) 27.6 (7,688/27,838) 11.8 (3,676/31,070) 5.5 (1888/34,328) X2 for trend (P) 23.20 (<0.0001) 292.5 (<0.0001) 10.45 (0.001) 78.43 (<0.0001) 66.34 (<0.0001) *The resistance results to the different antibiotics tested are expressed as percentages; the number of resistant isolates/number of total isolates tested for that antibiotic is shown in parentheses. El Mammery et al. 10.3389/fmicb.2023.1220286 Frontiers in Microbiology 04 frontiersin.org Genomic library preparation and sequence analysis Genomic library preparation and sequence analysis were conducted as described (Pérez-Vázquez etal., 2019). Raw sequence data were submitted to the European Nucleotide Archive (PRJEB61102). The quality of the short reads was assessed using FASTQC, and they were assembled into contigs with Unicycler 0.4.8 (Wick etal., 2017). The quality of the assembly was assessed with QUAST.2 Prokka v1.14-beta (Seemann, 2014) was used for automatic de novo assembly annotation. Phylogenetic analyses Sequence types (STs) were calculated according to the multilocus sequence typing (MLST) scheme of the Public databases for molecular typing and microbial genome diversity (PubMLST) 3 using Ariba v2.6.2 (Hunt etal., 2017). A simple diversity index (SDI; Gastmeier etal., 2006) was applied to analyze population diversity. Core genome MLST (cgMLST), consisting of 1861 targets for S. aureus provided by SeqSphere+3.5.0 (Ridom, Münster, Germany), was performed with the 137 sequenced isolates. Additionally, ST398 isolates from this study were analyzed using cgMLST together with a collection of 239S. aureus of this ST downloaded from the NCBI database using “chromosome” and “complete” as filtering criteria for assembly level and the absence of mec genes as the genotypic criterion. Analysis of antimicrobial resistance and virulence genes Antibiotic resistance genes were analyzed by Ariba v2-6.2 using the CARD database4 and ResFinder (CGE server5). Virulence genes were analyzed with the previous methodology using the database Virulencefinder_db.6 Statistical analysis The significance of the trends in macrolide resistance was calculated by the χ 2 test for trend. Trends in J01F antibiotic family consumption were examined by simple linear regression analysis. The strength of the association between MLS B antibiotic use and erythromycin resistance was determined by linear regression analysis (Cuevas etal., 2011). The resistance proportion was transformed to the natural logarithm of the odds of resistance. The log of the odds of resistance (as the dependent variable) was expressed as a simple linear function of the independent variable (antibiotic use) (Cuevas etal., 2011). Macrolide resistance for 2013–2020 was correlated with 2 http://quast.bioinf.spbau.ru/, accessed on 3 March 2023. 3 https://pubmlst.org/organisms/staphylococcus-aureus 4 https://card.mcmaster.ca, accessed on 3 December 2022. 5 https://cge.cbs.dtu.dk, accessed on 3 December 2022. 6 https://bitbucket.org/genomicepidemiology/virulencefinder_db/src/ master/, version 2022-12-02. antibiotic use in the prior year. Fisher’s exact test was used to compare the prevalence of resistance or virulence genes by groups. p values < 0.05 were considered statistically significant. Statistical analyses were performed using GraphPad Prism software v.7.02 (GraphPad Software Inc., San Diego, CA, UnitedStates). Results Sentinel hospitals, patients, and isolates Most of the participating hospitals (29, 61.7%) were tertiary university hospitals with >500 beds, and 9 (19.1%) had >1,000 beds; however, three primary (6.4%) and 15 secondary (31.9%) hospitals also participated. Data on 36,612 consecutive S. aureus blood infections, corresponding to the same number of patients, were collected between 2004 and 2020; 23,887 patients (65.2%) were males, and 34,146 (93.3%) were adults (>14 years). The blood cultures were obtained from patients in internal medicine (45.9%), the emergency room (28.2%), intensive care units (8.7%), surgery (5.9%), pediatrics (3.1%), and other departments (8.2%). Antibiotic resistance trends in blood isolates of Staphylococcus aureus Global resistance rates to methicillin, erythromycin, ciprofloxacin, gentamicin, and clindamycin were 24.4% (36,612 isolates analyzed), 32.7% (35,186 analyzed), 27.6% (27,838 analyzed), 11.8% (31,070 analyzed), and 5.5% (34,328 analyzed), respectively. Methicillin resistance decreased from 26.4% in 2004 to 22.4% in 2022 (Table1) (p < 0.0001). There was a similar decrease in resistance to other antibiotics, including ciprofloxacin (from 27.7% in 2004 to 26.1% in 2020, p = 0.001) and gentamicin (from 8.5% in 2004 to 5.5% in 2020, p < 0.0001) (Table1). For MLS B antibiotics monitored in EARS-Net, erythromycin resistance increased from 31.2% in 2004 to 36.1% in 2020 (p < 0.0001; the highest value of the series was 41.6% in 2017), and clindamycin resistance increased from 13.4% in 2004 to 18.4% in 2020 (the highest value) (p < 0.0001) (Table1). There was greater resistance to ciprofloxacin, gentamicin, erythromycin, and clindamycin in MRSA isolates (89.5, 13.9, 66, and 27.1%, respectively) than in MSSA isolates (7.2, 3.1, 21.9, and 7.4%, respectively) (p < 0.001). However, between 2004 and 2020, MRSA isolates showed a significant decrease in ciprofloxacin resistance, from 91.6 to 74.2% (χ 2 for trend = 76.7, p < 0.0001), in gentamicin resistance, from 22.1 to 15.3% (χ2 for trend = 21.9, p < 0.0001), in erythromycin resistance, from 68.7 to 61.8% (χ2 for trend = 25.7, p < 0.0001), and in clindamycin resistance from 42.8 to 25.6% (χ 2 for trend = 26.79, p < 0.0001) (Figure1). In contrast, MSSA isolates showed an increase in erythromycin resistance from 13.6% in 2004 to 28.9% in 2020 (χ 2 for trend = 747.9, p < 0.0001), with the peak in 2019 at 35.5%. This trend was also seen for clindamycin resistance, from 3.7% in 2004 to 14.4% in 2020 (χ2 for trend = 208.9, p < 0.0001), and, although more moderate, for ciprofloxacin resistance, from 4.8% in 2004 to 7.2% in 2020 (χ 2 for trend = 14.6; p = 0.0001). No significant variations over time in gentamicin resistance in MSSA isolates were identified (Figure1). El Mammery et al. 10.3389/fmicb.2023.1220286 Frontiers in Microbiology 05 frontiersin.org Macrolides, lincosamides, and streptogramins consumption Community use of MLS B antibiotics (WHO code J01F), including both public and private health prescriptions, increased in Spain from 2.61 DIDs in 2012 to 3.22 DIDs in 2016 (22.9%, r 2 = 0.91, p = 0.012). It stabilized at around ~3.10 DIDs in 2017–2018 and decreased to 2.80 DIDs in 2019 prior to the COVID-19 pandemic in 2020, the year with the lowest use (2.04 DID) (Table 2). Nosocomial consumption of MLSB between 2012 and 2020 (Table2) increased from 0.11 DIDs (2012) to 0.14 DIDs (2019) (27.3%, r2 = 0.84, p = 0.006) with a peak (0.17 DIDs) in 2020 likely due the pandemic and the use of macrolides in admitted COVID-19 patients. The total consumption of MLS B antibiotics (community and hospital consumption) increased between 2012 (2.72 DID) and 2016 (3.34 DID) (22.8%, r 2 = 0.91, p = 0.01) stabilizing between 2017 and 2018 at ~3.23 DID (Table2). Correlation of erythromycin resistance in MSSA with total use of J01F antibiotics The rates of erythromycin resistance in MSSA from 2013 to 2020 correlated with the total use of the J01F family of antibiotics (r 2 = 0.55, p = 0.04) in the previous year (2012–2019) (Figure2). However, when the resistance data for 2016 (representing atypical outlier data of unknown origin) were removed from the series, the correlation improved (r2 = 0.85, p = 0.003) (Figure2). A B FIGURE1 Antibiotic resistance trends in MRSA (A) and MSSA (B) blood isolates. (A) Shows significant decrease in resistance to ciprofloxacin, gentamicin, erythromycin and clindamycin; while (B) displays significant increase in resistance to erythromycin, clindamycin and ciprofloxacin. El Mammery et al. 10.3389/fmicb.2023.1220286 Frontiers in Microbiology 06 frontiersin.org Resistance genes in erythromycin-resistant Staphylococcus aureus isolates by WGS WGS of the 137 isolates identified erm genes in 83 (60.6%) isolates, a msr(A) gene in 63 (46%), and an mph(C) gene in 62 (45.2%). Five erm genes were identified, including erm(C) (49, 35.8%), erm(A) (21, 15.3%), erm(T) (13, 9.5%), erm(B) (one, 0.7%), and erm(X) (one, 0.7%); two isolates had two erm genes (Supplementary Table S1). A total of 61 isolates (44.5%) had both msr(A) and mph(C) genes, and 9 (6.6%) also carried an erm gene (Table3). Of the 73 erythromycinresistant isolates that had only erm genes, 19 (26%) showed a constitutive expression (84.2% had the erm(C) gene), and 54 (74%) were inducible (48.1% had the erm(C) gene). This predominance of constitutive erm(C) genes was statistically significant (p = 0.01). The erm genes were more prevalent in MSSA (45/57, 78.9%) than in MRSA (38/80, 47.5%) (p = 0.0007), whereas msr(A) predominated in MRSA (50/80, 62.5%) vs. MSSA (13/57, 22.8%) (p < 0.0001) (Table3). There was no significant difference in macrolide resistance genes present in the isolates collected before or after 2013, although the erm genes were more prevalent after 2013 (41/63; 65.1%) than before 2013 (42/74, 56.7%). For the MSSA isolates collected after 2013, the group showing the most EARS-Net-based increase in erythromycin resistance, erm gene prevalence was 75.9% of (22/29), similar to MSSA strains isolated before 2013 (23/28, 82.1%) (Table3). However, after 2013, the erm genes in MSSA were mostly different from the predominant erm(C) and included 11 strains with erm(T) and six with erm(A) (17/22, 77.3%) (Table3; Supplementary Table S1). In fact, the erm(T) gene was significantly associated with MSSA isolated after 2013 (11/29, 37.9%), whereas it was identified in only two of the other isolates (2/108, 1.8%) (p < 0.0001). All 13 isolates with erm(T), except one, belonged to ST398 and were collected from 10 hospitals in six different Spanish provinces. The sequence of the genetic environment of erm(T) genes identified the rep13 gene, which is involved in plasmid replication. Nine isolates (6.6%) carried resistance genes for lincosamides (Supplementary Table S1), including lincosamide nucleotidyltransferases (lnu) genes in six isolates [five lnu(A) and one lnu(B)], genes encoding the ABC-F proteins vga and lsa in one isolate each, and the cfr methylase gene in two isolates. One isolate had both lnu(B) and lsa (Table3). All MRSA had the mecA gene, and 80% (64/80) of MRSA and 29.8% (17/57) of MSSA had resistance genes to at least one of the three main aminoglycosides (gentamicin, tobramycin, or amikacin) TABLE2 Community and nosocomial consumption of macrolide, lincosamine, and streptogramin antibiotics group (WHO code J01F). Year Community consumption from public health prescriptions Community use from health insurances prescriptions Community consumption from private health prescriptions Total community consumption Nosocomial consumption Total consumption 2012 1.72 0.17 0.72 2.61 0.11 2.72 2013 1.76 0.18 0.75 2.69 0.10 2.79 2014 1.86 0.18 0.78 2.82 0.10 2.92 2015 2.14 0.20 0.87 3.21 0.11 3.32 2016 2.14 0.19 0.89 3.22 0.12 3.34 2017 2.10 0.18 0.82 3.10 0.13 3.23 2018 2.10 0.18 0.80 3.08 0.14 3.22 2019 1.93 0.16 0.71 2.80 0.14 2.94 2020 1.42 0.10 0.52 2.04 0.17 2.21 Expressed in DDD per 1,000 inhabitants per day (DIDs). A B FIGURE2 Occurrence of erythromycin resistance in S. aureus causing blood infections (years 2013–2020), plotted against total use of family J01F antibiotics (2012–2019) in Spain with 95% confidence intervals (A). In (B), the outlier erythromycin resistance data of 2016 (consumption of 2015) is excluded. Consumption is expressed in DIDs, DDDs/1000 inhabitants/day. Log odds is the natural logarithm of the OR. El Mammery et al. 10.3389/fmicb.2023.1220286 Frontiers in Microbiology 07 frontiersin.org (Supplementary Table S1). The predominant genes encoding resistance to aminoglycosides were the aph (3′)-IIIa gene encoding a phosphotransferase with resistance to amikacin (60/137, 43.8%), the aadD1 gene encoding a nucleotidyl transferase with resistance to tobramycin (45/137, 32.8%), and the aac (6′)-Ie/aph (2″)-Ia encoding a two-domain acetyltransferase/phosphotransferase enzyme with resistance to the three aminoglycosides (12/137, 8.8%). The aac (6′)- Ie/aph (2″)-Ia gene was observed in 10% (8/80) of MRSA and 7% (4/57) of MSSA. No changes over time were observed for aminoglycoside resistance genes. We identified mutations in the genes encoding topoisomerase II and/or topoisomerase IV in 53.3% (73/137) of the isolates, 78.7% (63/80) were in MRSA, and 17.5% (10/57) were in MSSA. Frequent mutations were S84L in gyrA (71/73, 97.3% of all isolates with mutations in this gene) and S80F in parC (95.9%) (Supplementary Table S1). Fifteen isolates had more than two mutations in topoisomerase II/IV genes. Phylogenetic analysis of erythromycin-resistant Staphylococcus aureus isolates by WGS The 137 erythromycin-resistant S. aureus studied by MLST were grouped into 30 STs with an SDI of 21.9 and a mean of 4.6 isolates per ST (range = 1–45). The most prevalent STs (≥5 isolates) were ST125 TABLE3 Distribution of macrolide and lincosamide resistance genes and main sequence types in S. aureus according to the groups studied. Isolates (n) erm genes (%)* msr (A) gene (%) mph(C) gene (%) cfr gene (%) lnu genes (%) vga genes (%) Total STs (SDI) ST125 (%) ST5 (%) ST398 (%) ST1 (%) ST30 (%) ST8 Total (137) 83 (60.6%) (49 erm(C)) 63 (46%) 62 (45.2%) 2 (1.5%) 6 (4.4%) 1 (0.7%) 30 (21.9%) 45 (32.8%) 16 (11.7%) 14 (10.2%) 11 (8%) 8 (5.8%) 8 (5.8%) MRSA (80) 38 (47.5%) (32 erm(C)) 50 (62.5%) 50 (62.5%) 0 4 (5%) 1 (1.2%) 15 (18.7%) 40 (50%) 9 (5.6%) 3 (3.7%) 8 (10%) 0 7 (8.8%) MSSA (57) 45 (78.9%) (17 erm(A)) 13 (22.8%) 12 (21%) 2 (3.5%) 2 (3.5%) 0 21 (36.8%) 5 (8.8%) 7 (12.3%) 11 (19.3%) 3 (5.3%) 8 (14%) 1 (1.8%) Before 2013 (74) 42 (56.7%) (27 erm(C)) 38 (51.3%) 37 (50%) 0 3 (4.1%) 1 (1.4%) 23 (31%) 31 (41.9%) 7 (9.5%) 1 (1.3%) 3 (4%) 5 (6.8%) 7 (9.5%) After 2013 (63) 41 (65.1%) (22 erm(C)) 25 (39.7%) 25 (39.7%) 2 (3.2%) 3 (4.8%) 0 17 (27%) 14 (22.2%) 9 (14.3%) 13 (20.6%) 8 (12.7%) 3 (4.8%) 1 (1.6%) MRSA before 2013 (46) 19 (41.3%) (16 erm(C)) 33 (71.7%) 32 (69.6%) 0 2 (4.3%) 1 (2.2%) 11 (24%) 27 (58.7%) 3 (6.5%) 0 2 (4.3%) 0 7 (15.2%) MSSA before 2013 (28) 23 (82.1%) (11 erm(A)) 5 (17.9%) 5 (17.9%) 0 1 (3.6%) 0 16 (57.1%) 4 (14.3%) 4 (14.3%) 1 (3.6%) 1 (3.6%) 5 (17.9%) 0 MRSA after 2013 (34) 19 (55.9%) (16 erm(C)) 17 (50%) 18 (52.9%) 0 2 (5.9%) 0 8 (23.5%) 13 (38.2%) 6 (17.6%) 3 (8.8%) 6 (17.6%) 0 0 MSSA after 2013 (29) 22 (75.9%) (11 erm(T)) 8 (27.6%) 7 (24.1%) 2 (6.9%) 1 (3.4%) 0 13 (44.8%) 1 (3.4%) 3 (10.3%) 10 (34.5%) 2 (6.9%) 3 (10.3%) 1 (3.4%) *The most frequent erm gene by group is detailed in brackets. El Mammery et al. 10.3389/fmicb.2023.1220286 Frontiers in Microbiology 08 frontiersin.org (45, 32.8%), ST5 (16, 11.7%), ST398 (14, 10.2%), ST1 (11, 8%), ST30 (8, 5.8%), and ST8 (8, 5.8%), accounting for 74.4% of all STs (Supplementary Table S1). Genome assemblies of 134S. aureus isolates were analyzed using a gene-by-gene approach, and the allelic distance from cgMLST was visualized in a minimum spanning tree (Figure3). All isolates differed by an average of 880 alleles (range = 0–1779), and the average allelic difference between ST125 isolates from this study was 60 (range = 0–121). ST5 isolates differed by an average of 188 alleles (range = 38–339); in ST398 isolates, it was 133 alleles (range = 1–265); in ST1 isolates, it was 126 alleles (range = 0–253); in ST30 isolates it was 213 alleles (range = 134–237); and in ST8 isolates, it was 155 alleles (range = 61–250). ST125 was more prevalent in MRSA than in MSSA isolates (40/80 vs. 5/57, respectively, p < 0.0001); however, ST30 (0/80 vs. 8/57, respectively; p = 0.0007) and ST398 (3/80 vs. 11/57, respectively; p = 0.004) were more prevalent in MSSA (Figure3). There were no differences in the distribution of STs in isolates before or after 2013, except for ST8, which was more prevalent in isolates prior to 2013 (7/74 vs. 1/63, p = 0.047), and ST398, which was more prevalent in isolates after 2013 (1/74 vs. 13/63, p = 0.0003). Regarding to erythromycin-resistant mechanism, ST1, ST30 and ST398 were more frequent in erm isolates (10/82, 12.2%; 8/82, 9.6%; and 13/82, 15.8%; respectively) than in msr(A) isolates (1/63, 1.6%; 0/63; and 1/63, 1.6%; respectively) (p < 0.05). The prevalence of ST398/erm(T) in MSSA strains isolated after 2013 (10/29, 34.5%) was much higher compared to the other isolates (2/108, 1.8%) (p < 0.0001) (Figure3). The comparative analysis of ST398/erm(T) isolates included in this study with a collection S. aureus ST398 isolates from NCBI database showed that the most frequent mechanism of macrolide resistance was erm gene production, being erm(C) most frequent one followed by erm(A), erm(B) and erm(T). All MSSA isolates with erm(T) are grouped, MSSA erm(T) isolates from this study are grouped in a cluster together with animal-independent ST398 MSSA isolates reported in NewYork (Uhlemann etal., 2012) (Figure4). Virulence genes in erythromycin-resistant Staphylococcus aureus isolates identified by WGS The most common virulence genes (detected in ≥14 isolates) in the 137 isolates were the alpha-toxin gene hla (136, 99.3%), complement inhibitor gene scn (107, 78.1%), staphylokinase gene sak (90, 65.7%), chemotaxis inhibitory protein gene chp (87, 63.5%), betatoxin gene hlb (32, 23.4%), hyaluronidase gene hysA (20, 14.6%), enterotoxin gene sea (15, 10.9%), and toxic shock syndrome toxin 1 gene tsst-1 (14, 10.2%) (Supplementary Table S1). Other important virulence genes in S. aureus, such as Panton- Valentine leucocidin (PVL) genes lukS and lukF, and exfoliative toxin A gene eta were detected in eight (5.8%; 75% of them belonging to FIGURE3 Population structure of Staphylococcus aureus isolates from this study: minimum-spanning tree. Distances shown are based on cgMLST of 1861 genes using the parameter “pairwise ignoring missing values.” Fill colors in each circle indicate MSSA and MRSA and the year of isolation, color of the dashed line in circles indicates macrolide resistance mechanism type. El Mammery et al. 10.3389/fmicb.2023.1220286 Frontiers in Microbiology 09 frontiersin.org ST8) and six (4.4, 66.7% belonging to ST15) isolates, respectively (Supplementary Table S1). Wefound no pattern of virulence genes associated with the different groups, except for the presence of the lukF/luKS genes in the MRSA isolates. Of the genes mentioned above, the ST398/erm(T) isolates only presented homogeneously the hla (100%), chp (91.7%) and scn (91.7%) genes. Discussion In addition to methicillin resistance, the cross-resistance to MLS B antibiotics among S. aureus strains, as well as the rapid transmission of resistance genes, is a major concern for the future efficacy of antibiotic therapy. Specifically, the extensive use of MLSB antibiotics against Gram-positive bacteria is of concern because macrolideresistant MRSA strains are believed to bea major cause of clinical infections (Miklasińska-Majdanik, 2021), and they are associated with increased mortality rates (Bishr et al., 2021). In our study, erythromycin resistance in MRSA was approximately three times more common than in MSSA. However, our major concern that led to this research was the significant increase in erythromycin-resistant MSSA blood isolates that were not observed in MRSA. The main strengths of this study were the analysis of bacteraemia caused by S. aureus, representing a broad national caseload over FIGURE4 Population structure of ST398 Staphylococcus aureus in comparison with other publicly available whole genomes of the same ST. Minimum spanning tree, distance based in a cgMLST scheme of 1861 genes. Fill colors in each circle indicate MSSA and MRSA from this study or from other origins. Blue dashed line around circles remark isolates with erm(T) genes and green dashed line represent isolate NC_017631.1 (Uhlemann etal., 2012). Gray shadows represent groups of strains; a threshold of 5 alleles was applied.