Concurrent Clonal Expansion of Community-Associated Methicillin-resistant Staphylococcus aureus (MRSA) Clones in a Tertiary Hospital

Concurrent Clonal Expansion of Community-Associated Methicillin-resistant Staphylococcus aureus (MRSA) Clones in a Tertiary Hospital | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Concurrent Clonal Expansion of Community-Associated Methicillin-resistant Staphylococcus aureus (MRSA) Clones in a Tertiary Hospital Sharif Hala, Omniya Fallatah, Wesam Bahaitham, Mohammed Malaikah, and 17 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3788315/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Methicillin-resistant Staphylococcus aureus (MRSA) poses a significant public health threat due to its ability to cause a range of diseases in humans and its resistance to multiple classes of antimicrobials. Community-associated MRSA (CA-MRSA) strains, originating in the community, are increasingly known to underlie hospital infections. However, the dynamics of CA-MRSA clones in hospital settings are not well-characterized. Methods In this study, we conducted a genomic survey of a systematic cross-sectional collection of MRSA isolated over one and a half years in a major tertiary hospital in Jeddah, southwest Saudi Arabia. We subjected 194 isolates recovered from different body sites of 175 patients over two years to whole-genome sequencing and integrated the genomic data with detailed clinical information from electronic health record (EHR) data. We employed a broad range of genomics and text and data mining approaches to decipher the dynamics of MRSA clones, including resistance and virulence mechanisms, and the clinical manifestation of MRSA infections. Results Our results revealed a diverse clonal population underlying the population diversity in the hospital, with six dominant sequence types (STs) concurrently expanding over the past six decades. The major clones in the population harbored hallmarks of CA-MRSA, belonging to ST5 (n = 33), ST672 (n = 36), ST97 (n = 14), ST6 (n = 15), ST88 (n = 19), and ST8 (n = 27). The PVL locus was found in 60% of the ST8 strains and three strains of ST97 and ST88. Phylodynamic analysis showed that ST97, ST6, and ST672 formed more recently than other clones over the past two decades. ST97 carriage was significantly linked to in-hospital mortality and the diagnosis of sepsis. We characterized multiple cases of cross-resistance and showed diverse symptoms associated with colonization/infection by each ST. We further identified the emergence of antimicrobial resistance determinants within each clone and found evidence of the sharing of plasmids carrying antimicrobial resistance genes across multiple MRSA lineages. Conclusion Altogether, the study presents an in-depth analysis of the complex dynamics of MRSA, reflecting the concurrent emergence of multiple clones in a single hospital and highlighting the multiple introductions of CA-MRSA strains into the hospital. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Staphylococcus aureus , a Gram-positive opportunistic pathogen, is frequently found in the skin, nasopharynx, and gastrointestinal tract [ 1 ]. Up to 30% of the human population is estimated to asymptomatically host the bacterium [ 2 ]. While many S. aureus carriers remain asymptomatic, colonization often precedes infections that can result in severe and life-threatening diseases. Moreover, multidrug resistant and methicillin-resistant S. aureus (MRSA) has emerged over the past few decades as a significant public health threat, and caused higher mortality rates, or therapeutic failure [ 3 , 4 ]. Surveillance systems help guide empirical therapy and improve the understanding of MRSA isolates in different regions, which is essential for effective treatment and control efforts. MRSA has traditionally been considered a health care-associated pathogen in patients with established risk factors. Over recent decades, multiple MRSA strains have been reported in patients without established risk factors in the form of community-associated MRSA (CA-MRSA) [ 5 ]. Genomic epidemiology, based on whole genome sequencing, has played a crucial role in enhancing our understanding of the epidemiology and characteristics of community-acquired MRSA[ 6 ]. Despite some of the well-characterized exclusive virulence factors, SCC mec locus types and distinctive geographical distributions between HA-MRSA and CA-MRSA strains, the epidemiological distinction between CA-MRSA and HA-MRSA has become blurred in recent years. This changing epidemiology has been attributed to the transmission of CA-MRSA strains in the community and their subsequent spread to hospitals [ 7 ] [ 8 , 9 ]. The routes of the introduction, evolution, and dynamics of CA-MRSA clones in hospital settings are yet to be fully elucidated. A hub for tourism, mass gatherings, and high population diversity, the Gulf Cooperation Council (GCC) region, and in particular, the Kingdom of Saudi Arabia (KSA), serve as settings in which the dissemination of CA-MRSA is facilitated [ 10 ]. Isolated reports throughout the GCC have found a high prevalence of MRSA among S. aureus isolates, averaging about 25–35% [ 10 ]. In Saudi Arabia and Kuwait, MRSA infections contribute significantly to the burden of health care delivery with increasing occurrence of CA-MRSA lineages causing nosocomial infections [ 11 , 12 ]. A recent work showed the presence of a high degree of diversity and an emergence of both pandemic and rare MRSA strains among isolates obtained from a major hospital setting in Riyadh, Saudi Arabia [ 12 ]. Further work also showed that MRSA colonizing health care workers at the facility were of similar population structure as those identified in patients [ 13 ]. Despite these insights, the population genomics and dynamics of MRSA strains in hospital settings were not systematically analyzed, as previous studies predominantly employed typing methods based on the variation in a few genes and therefore had a low resolution. This study aims to address the abovementioned knowledge gap by obtaining in-depth insights into the evolution and genomic epidemiology of a systematic collection of MRSA strains in a major tertiary hospital in the KSA. We employed a broad range population genomic and data mining methods to analyze the whole genome sequencing data and integrate the genomic data with the hospital and clinical record for the patients. Our results indicate a diverse population with dominant clones with hallmarks of CA-MRSA, which have acquired multiple resistance determinants and virulence factors on epidemiological time scale. The clones exhibit distinctive clinical manifestations and mortality, pointing to the diversity of evolutionary trajectories of CA-MRSA strains in hospital settings. Methods Collection description We collected 194 S. aureus isolates, presumed to be MRSA, along with their epidemiological information for 175 patients between 2019 and 2021 as part of the hospital surveillance initiative. The isolates originated from King Abdulaziz Medical City Jeddah (KAMC-J) and the Saudi Ministry of National Guard Health Affairs (MNGHA) in Jeddah, Saudi Arabia. The medical city is home to a tertiary care hospital with over 600 beds, including adult intensive care units (ICU) designed for both medical and surgical patients. The hospital serves as the primary point of admissions for millions of pilgrims to and from the city of Makkah during the annual Hajj and Umrah, mass-gathering events, during their journeys [ 14 ]. In the years 2020 and 2021, 185 and 183 cases were reported in the hospital, respectively. Around one-fourth of cases were colonization, and the rest were infections, with hospital-acquired infections accounting for one-fourth of infection sources. The Ministry of National Guard Health Affairs (MNGHA) provided ethical approval for the study under the number RJ17-023-J. We extracted various epidemiological data points from patients clinically diagnosed with MRSA colonization/infection from the electronic health record (EHR) data. The clinical metadata included demographic details such as age, gender, length of hospitalization, admission and discharge dates, sample collection date, ward of admission, sampling date, patient status (treatment outcome), and the type of sample based on the body colonization site. The cause of in-hospital death was not recorded. The clinical metadata for each patient is provided in Supplemental Table S1 . Drug susceptibility testing Patient specimens were obtained from diverse sources following cultivation on MacConkey agar (Saudi-prepared media laboratory, SPML, Saudi Arabia). Strains were identified using the VITEK MS automated mass spectrometry microbial identification system (bioMérieux, France). To confirm the strain identification, we applied the Sanger method with Applied Biosystems technology to sequence the conserved 16S rRNA gene(s) of individual isolates. The VITEK2 rapid identification system by bioMérieux, France, was used for antimicrobial susceptibility testing (AST). Short-read sequencing Single colony boiling and the GenElute Bacterial Genomic DNA Kit (Sigma-Aldrich, Germany) were used for DNA extraction. Subsequently, we fragmented the extracted DNA through sonication to produce fragments within the 300–500 bp range (Covaris, USA). We prepared libraries manually and with automated methods using the QIAseq FX DNA Library Kit (Qiagen, Germany) with BioMek FXP liquid handling automation (Beckman Corporation, USA). The NovaSeq platform (Illumina, USA) was used for sequencing, yielding 150 bp paired-end reads. The fastqc package (v0.1.3) in R was employed for quality control of the reads. De novo assembly was carried out using Unicycler (v0.5.0) ( https://github.com/rrwick/Unicycler#install-from-source ) with default parameters [ 15 ]. Contigs shorter than 200 bps were excluded from subsequent analysis. We characterized strains through SCCmec typing using staphopia-sccmec ( www.github.com/staphopia/staphopia-sccmec ) [ 16 ], spa typing with spatyper-0.3.3 ( https://github.com/mjsull/spa_typing ), and agr typing with agrvate ( www.github.com/VishnuRaghuram94/AgrVATE ) [ 17 ]. We detected virulence factors and resistance genes using srst2 v0.2.0 ( www.github.com/katholt/srst2 ) on Plasmidfinder (v2.1.1) [ 18 ]VFDB (v6.0) [ 19 ], and ResFinder (v4.1) [ 20 ] and CARD (v3.2.8) [ 21 ] databases, with a 90% identity threshold for identifying genes. Furthermore, assemblies were annotated with Prokka [ 21 ], and the pangenome of plasmids was constructed using Panaroo [ 22 ]. For the construction of the phylogenetic tree, short reads were mapped against the reference Staphylococcus aureus subsp. aureus NCTC 8325 genome (accession number: GCF_000013425.1) using the Snippy pipeline ( https://github.com/tseemann/snippy ). We calculated pair-wise SNP distances for the mapped genomes using the ape package (v5.7.1) in R [ 23 ]. Visualization of the tree and associated metadata was performed using the ggtree package (v3.8.2) [ 24 ]in R. Clones within the population were identified using hierarchical Bayesian clustering (BAPS) with the implemented package in R, i.e., rheirBAPS ( www.github.com/gtonkinhill/rhierbaps ), by taking the first level of clonal inference [ 25 , 26 ]. The BAPS analysis revealed nine clones, four of which (BAPS1, BAPS2, BAPS3, BAPS4, BAPS5, and BAPS8) were found to have more than ten representative isolates. The list of corresponding clones for each genome is provided in Supplemental Table S1 . Genome-wide association studies (GWAS) analysis were conducted on both accessory genes (Panaroo output) and SNPs using Scoary (1.6.16) ( www.github.com/AdmiralenOla/Scoary ) [ 27 ]. Variants with a p-value < 0.05 were filtered for the naïve, Bonferroni-corrected, best pairwise comparison, and worst pairwise comparison, with the latter two corresponding to the highest (lowest) possible number of supporting pairs and the lowest (highest) possible number of opposing pairs ( www.github.com/AdmiralenOla/Scoary ). Transmission analysis and phylodynamic analysis We conducted phylodynamic analysis on the four dominant clones. To achieve this, we first mapped the short reads for the strains in each clone against the concatenated assembly of the strains from the same clone that had the best assembly statistics, i.e., the highest N50. We then subjected the alignment to Gubbins (v.3.3.1) [ 28 ] with five iterations to remove hypervariable regions. We used the BEAST (v2) package to reconstruct the dated evolutionary tree for each clone [ 29 ]. We applied the strict clock time model with a constant population and ran Markov chain Monte Carlo (MCMC) simulations for 10^8 iterations for sequence types (STs), confirming convergence by checking the effective sample size (ESS) to be above 200 for key parameters, such as clock rate and the age of the root. After leaving out 10% of the iterations as burn-in, we aggregated the trees with the TreeAnnotator application as part of the BEAST package and visualized the tree with the ggtree package (v3.8.2) in R [ 24 ]. Contextualization of the population diversity We integrated the genomes with the genomic database from the Pathogen Detection database ( https://www.ncbi.nlm.nih.gov/pathogens/ ) to provide population genomic context for the collection under study. Therefore, we extracted the epidemiological SNP clusters on 06/09/2023, encompassing the strains under study, from the Pathogen Detection database ( www.ncbi.nlm.nih.gov/pathogens ). These SNP clusters consisted of strains with a maximum pairwise SNP distance of 50 SNPs. This cut-off is fixed and hence cannot be changed. Plasmidome analysis and long-read sequencing We prepared and multiplexed libraries for the selected isolates using 96-plex Rabid Barcoding Kits. We then loaded the libraries into MinION flow cells (Oxford Nanopore Technologies). The sequencing continued for 48 hours following the manufacturer's protocol. Subsequently, we performed a hybrid assembly employing Unicycler, utilizing the conservative option to recover long assemblies. The assembled contigs that contained the resistance gene and origin of replications underwent thorough screening for the presence of complete copies of origin of replications, virulence factor genes, and antimicrobial resistance genes within the plasmids using BLAST with the aforementioned databases of known genes. We visualized the plasmids with Proksee portal ( www.proksee.ca ) [ 30 ]. Diagnostic term text analysis We analyzed diagnostic documentation for individual patients to identify pertinent diagnostic terminology and symptoms. Due to the absence or incompleteness of standardized medical codes, such as the ICD-10, within patient records, we used a natural language processing approach to extract medically relevant terms. First, the diagnostic notes underwent a text processing pipeline developed with the tidytext package (v0.4.1) in R. The pipeline removed special characters and common, inconsequential words (stop words). We then used three biomedical-specific corpus libraries, i.e., BC5CDR, en_core_sci_lg, and ner_bionlp13cg_md, to extract terms related to diseases and symptoms in the named entity recognition part of the pipeline. The BC5CDR corpus, specifically designed for disease name entity recognition, comprises 1500 PubMed articles annotated with 5818 diseases and 3116 chemical-disease interactions [ 31 ]. Finally, the en_core_sci_lg and ner_bionlp13cg_md libraries encompass 600k general medical terms and genetic expressions for 16 types of cancer, respectively [ 32 ]. The list of extracted terms associated with each patient is provided in Supplemental Table S1 . We calculated term-weighting schemes based on frequency–inverse document frequency (tf-idf) values to assess the significance of the extracted terms for patients carrying different sequence types (STs). To this end, individual text corpora were created for each ST, which included diagnostic notes pertaining to patients with the same ST. For each term, we computed the tf–idf values, which correspond to the frequency of a term within the document (textual corpus for each ST) offset by the number of documents (number of textual corpora for each ST) containing the term. This metric serves as a proxy for the importance or overrepresentation of a specific term in the notes related to a particular ST. Integration of genomic data with clinical metadata We dissected the link between in-hospital mortality and colonization/infection by different STs by computing the odds ratio of death using the odds-ratio function from the Epitools package (v0.5.10.1). The results were further confirmed by a survival analysis Cox regression model with the coxph function from the survival package in R, using the following formula: $$Surv(Age, InHospital death) \sim ST$$ We computed the hazard ratio (HR) associated with the carriage of a major ST compared to the rest of the population. A HR 1 indicates an increased hazard of death. We validated the odds-ratio calculations through additional analysis using binary logistic regression model. In this analysis, the mortality outcome served as the binary response variable. We assessed the significance of resistance while accounting for potential confounding factors, including sequence type (ST), age, gender and body site of infection, using the following formula: $$InHospital death \sim ST+age+gender+body site$$ Here, ST, gender, and body site were considered categorical (nominal) independent variables and were consequently factorized. STs with fewer than ten representative genomes were grouped together. The regression coefficient (β) for the in-hospital death parameter was extracted for each ST. This coefficient represented the alteration in log odds of death associated with colonization by colonization with each ST. Results • Population diversity reveals the presence of multiple clones Our collection comprised 194 strains isolated over a two-year period in hospitals. Our analysis reveals a diverse population, as reflected by the number of STs (n = 43), spa (n = 23), agr (n = 23), and SCCmec (n = 24) types. The population was found to be non-typable for ST, spa, and agr in 9%, 2%, and 1% of the strains, respectively. The concordance level between ST and agr and spa typing was high, in contrast to the SCCmec typing of the STs, which showed variation in the locus belonging to the same STs (Fig. 1 ). We identified six dominant clones represented by more than ten strains. These clones correspond to the six STs in the population: BAPS1/ST5 (n = 33), BAPS2/ST672 (n = 36), BAPS3/ST97 (n = 14), BAPS4/ST6 (n = 15), BAPS5/ST88 (n = 19), and BAPS8/ST8 (n = 27) (Fig. 1 ). Except for ST672, other clones were recently reported as CA-MRSA strains in hospital settings in Riyadh with microarrays. [ 12 , 13 ]. In line with this, a community mode of infection acquisition was confirmed for 25% up to 75% across all dominant clones (Figure S1 A). Typing based on the agr gene, a key regulatory element in the virulence mechanism, revealed the presence of three types of gp genes: gpI (in BAPS2/ST672, BAPS3/ST97, BAPS4/ST6, and BAPS8/ST8 clones), gpII (in BAPS1/ST5 clone), and gpIII (in BAPS5/ST88) types (one isolate contained gpV type), pointing to the diversity of virulence mechanisms for the major clones. Moreover, we conducted typing based on the variation of the spa gene, which encodes the Staphylococcus protein A triggering B cell proliferation. [ 33 ]. The spa gene is commonly used for epidemiological surveillance of S. aureus because of its high variation, offering a higher discrimination typing compared to MLST and agr methods [ 34 ]. The analysis identified a total of 63 different known spa types in 188 genomes (Fig. 1 , Supplementary Table S1 ). Six types (t690 (n = 12), t304 (n = 14), t008 (n = 18), t311 (n = 18), t3841 (n = 31)) were the most prevalent in the population and together were detected in 51.5% (100/194) of the genomes (Fig. 1 ). Among these types, t311, t3841, t304, t690, and t008 were the most prevalent in BAPS1/ST5 (n = 18/33), BAPS2/ST672 (n = 29/36), BAPS4/ST6 (n = 14/15), BAPS5/ST88 (n = 12/19), and BAPS8/ST8 (n = 18/19) clones, respectively. The spa types t008, t304, and t311 were reported predominantly from Europe, America, and to a lesser extent in Africa. [ 34 ]. However, t690, t3841 were not reported as prevalent types before and appear to be exclusive to the current collection. The BAPS3/ST97 group consisted of six spa types, including t521 (n = 2/14), t359 (n = 3/14), t2770 (n = 2/14), t267 (n = 4/14), t2297 (n = 2/14) and t1544 (n = 1/14), showing the high level of genomic dynamics of the clone. The contextualization of the genomes with the global genomic collection of S. aureus identified a low level of mixing with the global genomic collection, with only two clusters (SNP clusters) with external genomes. This included the grouping of ST8 strains with a clinical sample from Denmark (SNP cluster PDS000144949.1) and two strains of ST1535, which belonged to a cluster (PDS000069007.8) comprising clinical strains from Denmark and the Netherlands, as well as two environmental samples from meat retailers from unpublished genomes in Saudi Arabia, indicating a link with non-clinical settings. These findings suggest the occurrence of community-associated clones, some of which were specific to the study setting and had links with the environment. • SCCmec typing indicated the presence of highly dynamics locus SCCmec typing indicated the presence of a highly dynamic locus. Our analysis showed mec genes in all strains, although not all genes were found in the context of known SCCmec elements. Among the strains, 67 exhibited a complete match to one of the known SCCmec elements (Fig. 1 , Figure S2). For the rest, a partial match to an element with a sequence (Hamming) distance of > 0 and < 5 was observed (Figure S2). All strains were found to contain the mecA gene, although 25 harbored divergent copies of the gene not situated in an SCCmec element. For the copies of mecA in an SCCmec element, a strong association with resistance to beta-lactams of oxacillin and cephalosporins was identified (GWAS p-value < 0.05 after accounting for lineage effect). HA-MRSA is recognized to carry SCCmec types I, II, or III, while CA-MRSA carries smaller SCCmec types IV, V, or VI. [ 35 ]. In our collection, except for one ST8 sample that carried an SCCmec type III locus, the other strains harbored SCCmec loci associated with CA-MRSA strains (Figure S2). The loss of SCCmec occurred in 22 lineages across the phylogenetic tree, including two times within the BAPS1/ST5 clones and three times in BAPS2/ST672, BAPS5/ST88, and BAPS8/ST88 groups (Figure S2). Besides the loss of SCCmec elements, switching between SCCmec types was detected within identified clones. Specifically, we observed a switching in SCCmec types within the BAPS2/ST672 clone, shifting from type V/VII to type IVd. Similarly, the BAPS1/ST5 clone exhibited a switch between types V/VII and type VI (observed in two genomes). The BAPS8/ST88 clone included switches between SCCmec types, changing from type VI to type IIIa, and types V/VII were observed in four genomes. The observation demonstrates significant dynamics of SCCmec on epidemiological time scales. This finding aligns with the reported high mobility of the CA-MRSA-associated SCCmec types of IV and V, especially the type IV element, which is recognized to be transferred to methicillin-susceptible backgrounds frequently. [ 36 , 37 ]. • Presence of hallmark genes for CA-MRSA virulence across independent lineages In addition to the SCCmec types, the clones in the population included multiple virulence genes associated with CA-MRSA strains (Fig. 1 ). Most notably, multiple strains contained PVL genes, lukF -PV and lukS -PV, encoding a two-component S. aureus pore-forming protein well-characterized as causative of community-acquired invasive diseases. [ 38 ]. The PVL genes were found in 31 genomes, which included multiple lineages: 60% (12/20) of strains in BAPS8/ST8 clones harbored the PVL locus, while these genes occurred less frequently in other major clones, i.e., one genome each in BAPS3/ST97 and BAPS5/ST88. In other minor clones, the locus was detected in the clones of ST80 (n = 4), ST22 (n = 3), and ST30 (n = 3), all of which were described as CA-MRSA strains (Fig. 1 , Supplementary Table S1 ). Another virulence factor for CA-MRSA strains is the Toxic Shock Syndrome Toxin ( tsst ) gene, encoding a toxin associated with toxic shock syndrome. [ 39 ]. The gene was observed in 15 genomes in our collection, belonging to ST22 (n = 8), ST361 (n = 3), and one genome from each of ST8, ST30, and ST672. For the three strains characterized by BAPS9/ST22/t223, BAPS9/ST22/t223, and ST30/t233, the gene co-occurred with PVL genes within the same genomes. This co-occurrence renders these strains clinically significant, exhibiting a dual virulence phenotype. The results show the independent occurrence of some known hallmark genes for CA-MRSA in the collection. • Distinctive pattern of virulence gene across the clones As the virulence mechanisms of CA-MRSA are not fully characterized, we screened the collection for the presence of other virulence genes, specifically focusing on 29 genes that were variably present in the major clones and the background population (Fig. 2 , Supplementary Table S1 ). For a set of secretion protein genes, including essC and esxBC, toxin genes like esxD, eap/map, and adhesion genes such as set22 and esaD, the major clones more frequently contained the virulence genes compared to the background population, indicating the potential higher virulence of these clones (p-value from ANOVA test < 0.05) (Fig. 2 A). Moreover, the screening revealed that some clones contained more genes encoding superantigens (SAg) [ 39 , 40 ], which are potent immunostimulatory exotoxins causing T cell proliferation and cytokine release, than the rest of the population (p-value from ANOVA test < 0.05). The gene list included selq (55%) and selk (55%) in BAPS8/ST8, set21 in all BAPS5/ST88, set16 in all BAPS2/ST672 (Fig. 2 A). Some BAPS groups contained virulence factors that were significantly more frequent than other clones, i.e., adhesion clf gene in BAPS3/ST97 (100% of strains), adhesion cna (100% of strains), and enterotoxin sea gene (93% of strains) in BAPS4/ST6, hyaluronidase hysA, and human-specific immune evasion cluster chp gene (84% of strains) in BAPS5/ST88, and adhesion sdrD (82%) in BAPS8/ST8 (Fig. 2 A). Although the specific role of these genes in virulence requires further functional analysis, the variation of the virulence gene profiles across major clones points to the complex and diverse virulence mechanisms in CA-MRSA. • Population has high resistance level somewhat reflecting the prescriptions The population exhibits a high resistance level against beta-lactams such as penicillin and second-generation cephalosporins, as well as fusidic acid, with resistance levels of 98%, 90%, 90%, and 71% for benzylpenicillin, oxacillin, cefoxitin, and fusidic acid, respectively (Fig. 1 , Fig. 2 B). The resistance levels for fluoroquinolones were moderate (moxifloxacin 31% and levofloxacin 46%). For aminoglycosides (tobramycin and gentamicin), macrolides (erythromycin and clindamycin), tetracycline, and trimethoprim, low resistance levels of 11%, 13%, 23%, 9%, 15%, and 20%, respectively, were observed. No resistance to vancomycin, linezolid, or tigecycline was detectable in the collection. Moderate to strong cross-resistance was prevalent for fluoroquinolones, fusidic acid, and beta-lactams (see Fig. 1 ), while for macrolides, tetracycline, and trimethoprim, there was a weaker correlation of resistance with other groups. This pattern somewhat reflects the prescription pattern in the window of six months before sampling: the most prescribed antimicrobial classes in the six months prior to admission and at the time of diagnosis were vancomycin, beta-lactams (piperacillin/tazobactam and carbapenems) (43% of prescriptions), fluoroquinolones (46% of prescriptions), and fusidic acids (37% of prescriptions), while tetracyclines and aminoglycosides were less commonly prescribed. Vancomycin was still the most used antimicrobial at the time of diagnosis and following antimicrobial susceptibility, although no resistance had yet arisen (Supplemental Table S1 ). For other antimicrobials, resistance levels varied across the clones: BAPS3/ST97 strains exhibited higher resistance to aminoglycosides (gentamicin and tobramycin), while for fluoroquinolones, the ST672 and ST6 clones were found to be more resistant compared to the other major clones (Fig. 2 B) (p-value from proportion test < 0.05). Some level of clindamycin resistance was observed for all major clones except BAPS4/ST6 and BAPS3/ST97 (Fig. 2 B). For tetracycline, BAPS5/ST88 and BAPS1/ST5 were found to be relatively more resistant. • Resistance genes profiles differ across clones with ST8 and ST5 harboring more resistance genes The collection exhibited a diverse array of antimicrobial resistance genes for two databases of known resistance genes, with varying average counts observed across major BAPS clones: BAPS8/ST8 (ResFinder: 6.75, CARD: 6.1), BAPS4/ST6 (ResFinder: 5.13, CARD: 4.0), BAPS2/ST672 (ResFinder: 5.41, CARD: 4.61), BAPS5/ST88 (ResFinder: 5.84, CARD: 4.61), BAPS1/ST5 (ResFinder: 6.67, CARD: 3.84), and BAPS3/ST97 (ResFinder: 5.79, CARD: 5.48) (Fig. 2 C). The BAPS8/ST8 group strain had higher resistance genes except BAPS1/ST5 (p-value from one-sided Wilcoxon signed-rank test < 0.05). On the next level, BAPS3/ST97 and BAPS5/ST88 were more resistant than BAPS4/ST6 and BAPS2/ST672, although they had comparable resistant gene counts as the other minor STs altogether (Fig. 2 C). Both ST8 and ST5 lineages belong to the significant clonal complexes of S. aureus (CC5 and CC8), associated with rapid dissemination of methicillin and multidrug resistance in both hospital and community settings [ 3 , 41 ]. The distribution of resistance genes revealed a consistent presence of tetracycline resistance gene blaZ across all clones, highlighting a higher count of macrolide resistance genes msrA and mphC in BAPS8/ST8 and BAPS1/ST5 (Fig. 1 , Fig. 2 C). Further exploration into genes and mutations strongly associated with resistance demonstrated variable presence across major clones. The mecA gene as mentioned above strongly linked with beta-lactam resistance and was found lost in some lineages. The bifunctional enzyme aacA-aphD gene (tetracycline efflux pump gene tetK ), significantly linked with aminoglycoside resistance genes, emerged frequently in the BAPS3/ST97 (BAPS5/ST88) clone (GWAS p-value < 0.05 after accounting for lineage effect). Macrolide resistance genes msrA and ermC underlie higher resistance in BAPS8/ST8 and BAPS1/ST5, respectively, and emerged across multiple lineages (GWAS p-value < 0.05) (Fig. 1 ). The fusc gene, linked with fusidic acid resistance, was prevalent in all major clones except BAPS4/ST6. Among the resistance mutations for fluoroquinolones, the S84L mutation in gyrA was found significantly linked with resistance phenotypes for moxifloxacin and levofloxacin (GWAS p-value < 0.05). The gene diversity highlights variety of emerging resistance trends, occurring both among different clones and more recently within individual clones. • Similar plasmids carrying antimicrobial resistance genes are shared between lineages To contextualize the resistance genes, we conducted long-read sequencing for strains associated with bloodstream infections. We retrieved full genome sequence of plasmids from on isolate (ID00734) (plasmid 1) belonging to BAPS5/ST88 from bloodstream infection and one from (ID00777) (plasmid 2) BAPS8/ST8 (Fig. 3 A). Both strains contained a mosaic plasmid of size 28K. The search for similar plasmid backbones in the database did not pinpoint any previously reported backbone. The highest coverage of 69% was found with a plasmid with accession CP094779.1 recovered from the non-human sources [ 42 ]. Plasmid ID00734 is predominantly limited to the BAPS5/ST88 clone and occurred in 6 strains and one isolate and one strain from untyped ST (Fig. 3 A). The plasmid is a mosaic plasmid with a replicon reported in previous clinical isolates consisting of a blaZ and blaI genes surrounded by mobile genetic elements. The same plasmid also accommodates the macrolide resistance gene of msr(A) (Fig. 3 B). Three aminoglycoside resistance genes of aph(A), sat(A) and aad(K) were linked with an IS6 element. The plasmid also contained a cadC gene for cadmium resistance. The size and lack of conjugative transfer genes suggest that the plasmid belongs to low-copy non-conjugative theta replicating plasmids [ 43 ]. The plasmid from BAPS8/ ST8 isolate somewhat resembled the plasmid from BAPS5/ST88 (76% sequence identity) in that it has similar size and the same bla cassette. However, the plasmid lacks msr(A) and a tet(K) gene linked with IS6 plasmid has replaced the aminoglycoside carrying plasmid. Moreover, the plasmid has a broader phylogenetic distribution and occurred in strains from ST67, ST1482 and ST1 strains in which more than 95% of the plasmid contigs were covered by the reads of these strains (Fig. 3 A). The sharing of the plasmids and variation in the arrangement of antimicrobial resistance genes suggest a high rate of horizontal gene transfer occurring at plasmid and gene levels. • Concurrent expansion of clones over the past few decades, with steady population increase The comparison of dynamics among the clones within the hospitals also indicates marked differences between them. The BAPS1/ST5, BAPS5/ST88, and BAPS8/ST8, belonging to globally known circulating clones, emerged 30 to 50 years ago, while the other three BAPS groups emerged more recently (Fig. 4 ). The higher clock rate for BAPS3/ST97 and BAPS4/ST6 points to recent expansions of these clones, indicating that the emerging mutations have not yet been purged by selection pressure (Fig. 4 ). The population dynamics of the clones in the hospital indicate an increase in the effective population size for BAPS/ST15, BAPS2/ST672, BAPS3/ST97, and BAPS4/ST6 over the last decade, despite a slight recent decrease (Fig. 5 ). For the more long-standing BAPS5/ST88 and BAPS8/ST8 clones, the populations remained constant after an initial increase (Fig. 5 ). Further examination of the Bayesian phylodynamic trees reveals signatures of multiple independent introductions of the clones from the community into the hospital wards (Fig. 6 ). For the strains with no evidence of community acquisition routes for the infection, transmission appeared to have occurred uniformly across the hospital wards. Specifically, for BAPS2/ST672, five incidents of potential recent transmission (i.e., divergence in the past one year) involving patient pairs ID00707/D00696, ID00647/D00642, ID00792/D00791, ID00624/D00765 were observed. For BAPS5/ST88, two incidents (ID00832/D00828, ID00646/D00718), for BAPS8/ST8, one incident (ID00819/D00783), for BAPS1/ST5, one incident (ID00726/D00727), and for BAPS4/ST6 (ID00774/D00721) were observed. Except for two incidents, the rest of the transmission involved the same body sites, and only two occurred between patients with the same admission ward (Fig. 6 ). On one occasion, i.e., BAPS4/ST6 (ID00774/D00721), the sampling times for the patients involved in transmission were five months apart, showing the persistence of the clone in the hospital environment. Furthermore, for two patients in BAPS1/ST5 and BAPS8/ST8, distinct strains were found to colonize the same patients, suggesting repeated mixed infections [ 44 ]. Within-patient diversity indicated seven incidents in the BAPS groups (the shaded boxes in Figure), three of which in BAPS2/ST672, BAPS3/ST97, and BAPS4/ST6 happened between strains in the blood and other sites, pointing to the invasiveness of the strains. Altogether, these findings indicate the multiple introductions of strains into the hospital and the circulation of the strains across wards and the same patient's sides happening on epidemiological time scales. • Diverse clinical manifestation amongst the major clones We finally examined the clinical features and manifestations of the strains to dissect the differences in virulence among the major clones. All major clones were extracted from bodily fluids, including nasal secretions, sputum, and wound exudate (fluid discharged from a wound). Except for BAPS8/ST8, other STs contained strains from bloodstream infections. Bloodstream infection did not occur in the rest of the population (Figure S1 B). Except for BAPS8/ST8, other clones included bloodstream invasive strains. While clones exhibited a uniform distribution across ages, the hospital duration for the colonized patients showed slight variations among the major clones (Figure S1 C). Patients with BAPS3/ST97 and BAPS/ST88 showed a significantly longer hospitalization period (p-value from Wilcoxon signed-rank test < 0.05) (Figure S1 D). The integration of in-hospital morality data and the colonized ST confirmed a hazard ratio greater than one corresponding to the increased hazard of death for all major clones compared with the other clones in the population. However, only for BAPS3/ST97 was a significantly higher death odds-ratio than one confirmed (Figure S3A) (95% CI greater than one). After accounting for the impact of demographic factors and other comorbidities, only the colonization by BAPS3/ST97 strains was found significantly linked with in-hospital mortality (Figure S4B). We further linked the colonization of STs with the importance of the terms extracted from the diagnostic notes for the patients. Figure S4 shows the top ten important terms for the corpus for each ST, which are highly variable across corpora for different STs. The terms include comorbidities, e.g., cancers related terms of "carcinoma," "glioblastoma," and "neuroblastoma" for BAPS2/ST672, BAPS3/ST97, and BAPS5/ST88, as well as infection types (Figure S5). These might be due to the presence of long-stay patients and frequent visitors to the clinic who are immunocompromised. For patients with BAPS3/ST97, the bloodstream infection term of "sepsis" was found to be the most important. For BAPS5/ST88, similar terms, i.e., "septic" and "shock," still ranked among the top ten most important terms. These critical clinical circumstances for patients carrying ST97 and ST88 are in concordance with the higher in-hospital mortality for these patients. Underscoring the clinical significance of these two clones (see Discussion), the evidence also shows the broad infection sites and patients’ circumstances linked with the infection/colonization of each of CA-MRSA clone. Discussion In this study, we employed a population-level surveillance approach to decipher the genomic epidemiology, population dynamics, and evolution of a systematic collection of S. aureus from a single hospital. The depth and breadth of the sampling from various body sites and hospital wards, as well as the availability of detailed clinical data, allowed us to obtain a comprehensive image of the evolving clones in a comparative genomic framework. Our results demonstrate a diverse population with high dynamics in the acquisition of antimicrobial resistance and virulence genes for CA-MRSA clones, which coexist in the hospital. We observe multiple colonies of CA-MRSA in competition and each clone to have acquired peculiar virulence and antimicrobial resistance genes and were associated with hospital associated bacteremia, as reported before [ 45 , 46 ]. In some clones, virulence genes, resistance genes and plasmids were acquired over few years across different lineages. Further we provide evidence for horizontal gene transfer through sharing of plasmids carrying resistance genes between distinct clones, although MRSA is not a recombinogenic strain. Virulence of CA-MRSA is a complex trait, which implicate multiple genes and pathways, not fully characterized [ 9 , 47 , 48 ]. The diversity of underlying genes in our collection point to multiple evolutionary trajectories, involving transfer of virulence and resistance genes of CA-MRSA in response to various antimicrobial treatments and infection types. This study provides the first large-scale whole genome sequencing insights genomic study for MRSA in Saudi, while previous studies were either examined few strains or employed low resolution genetic typing methods. Some of the major reported clones, i.e. ST5 and ST8, are globally known circulating clones, while others, i.e. ST97 and ST672 are less well-characterized [ 9 , 49 ]. ST97, which showed strong link with in-hospital mortality, potential introduction from nasal into the bloodstream, and link with sepsis, was previously reported from infections in outbreaks and appear to be an emerging clone of zoonotic origin. The clone emerged in the pig and independently acquired virulence and resistance determinants over time [ 50 , 51 ]. The clinical manifestation of ST97, in addition to specific virulence factors, suggest that the clone has the potential to be become a dominant global clone in human infections. Despite significant insights that our study provided, we highlight few limitations. First the sampling covered one and a half years of evolution of MRSA, and therefore long-term dynamics of the clones count not be examined. MRSA clones are known to compete, and dominant lineages may change over few years. This clonal replacement may involve the two strains belonging to the same clone but harboring different SCCmec, as reported for ST5 SCCmec type I by the ST5-SCCmec type II [ 52 ]. A longer window will also allow deciphering the evolutionary advantage of the virulence and resistance genes exclusive to each clone and determining whether these factors enable the clone to outcompete other clones. Furthermore, despite the detailed available clinical data, some key information about the cause of death could not be retrieved from the patients’ history. This complicated establishing a definitive link between the colonization by STs and odds of death attributable to the CA-MRSA infection. This limitation of EHR data is recognized as the data is primarily produced for clinical purposes and not research and therefore patient’s data might not be always complete [ 53 ]. However, the insight from the study still demonstrates the value of integrating detailed clinical data, including diagnostic notes. This necessitates the integrity and completeness in clinical metadata in future studies to attain a comprehensive picture of the evolution of MRSA strains in hospitals. Conclusions The co-existence of multiple clones with contrasting dynamics and underlying genetic biomarkers of resistance may reflect a flexible and plastic genome for CA-MRSA, allowing rapid evolution and adaptation to hospital environments. Our study underscores the need for continuous, long-term genomic surveillance to understand the epidemiological features of the clones and design targeted preventive strategies and measures based on the pathogenicity and resistance abilities of the clones. Since, the hospital has been at the heart of a major highway linking Mekkah, where more than 2M Muslim pilgrims pass throughout the year, the city may serve as a melting pot for the transmission of emerging and well-established virulent MRSA strains. Thus, setting up a continuous monitoring of MRSA strains is crucial to further understand and to early detect novel resistance and virulence strains. Declarations Availability of Data and Materials Genomic sequencing data was deposited in the European Nucleotide Archive (ENA) and GeneBank under the study accession PRJEB60942 and PRJNA954771, respectively. The metadata for the strains are described in Supplemental Table S1. Declaration of conflict of interest The authors declare no conflict of interest. Funding Declaration DM and GZ are supported by KAUST baseline (BAS/1/1108-01-01). SH, HA, MB are funded by KAIMRC, MNGHA grant (NRJ21J/290/11 ) . Acknowledgment This work was supported partially by national infectious diseases initiative at KACST. Authors contribution SH and DM conceptualized the study and designed experiments. SH and DM wrote and edited the paper. SH, OF, WB, MM, HA, JH and GZ conducted research and collected data. MA, SZ, AAMB, MHHA, LHHA, HARU, AFM, AAAA, DA, MK, MB, MAS, and AA contributed to sequencing and collection curations. HARU, AFM, AAAA, AAAB, DA, MK, MB, MAS, and AA, provided lab and administration support during the study provided lab and administration support. DM supervised and managed the study and provided overall guidance. All authors have read and approved the final manuscript. References Howden BP, et al. Staphylococcus aureus host interactions and adaptation. Nat Rev Microbiol. Jun 2023;21(6):380–95. 10.1038/s41579-023-00852-y . Sakr A, Bregeon F, Mege JL, Rolain JM, Blin O. 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Center","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"Abdullah Ahmad","lastName":"Alamri","suffix":""},{"id":265085707,"identity":"0e34eb58-7d32-43d3-84a3-9241260eb5a8","order_by":14,"name":"Abdulaziz Atef Adel Abdulaziz","email":"","orcid":"","institution":"King Abdullah International Medical Research Center","correspondingAuthor":false,"prefix":"","firstName":"Abdulaziz","middleName":"Atef Adel","lastName":"Abdulaziz","suffix":""},{"id":265085708,"identity":"fa3ebabb-4029-4bf9-b33e-2b0c12b879e6","order_by":15,"name":"Doaa Aboalola","email":"","orcid":"","institution":"King Abdullah International Medical Research Center","correspondingAuthor":false,"prefix":"","firstName":"Doaa","middleName":"","lastName":"Aboalola","suffix":""},{"id":265085709,"identity":"5bb9b0a2-143c-4805-8b35-dc69ee94be71","order_by":16,"name":"Mai Kaaki","email":"","orcid":"","institution":"King Abdullah International Medical Research Center","correspondingAuthor":false,"prefix":"","firstName":"Mai","middleName":"","lastName":"Kaaki","suffix":""},{"id":265085710,"identity":"9f88fb91-dedc-4f23-bd37-396c65be1e26","order_by":17,"name":"Mohammed Bosaeed","email":"","orcid":"","institution":"King Abdullah International Medical Research Center","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Bosaeed","suffix":""},{"id":265085711,"identity":"17354b3e-efe2-411e-93a7-68c401fb47db","order_by":18,"name":"Majed Al Shamrani","email":"","orcid":"","institution":"King Saud bin Abdulaziz University-Health Sciences, Ministry of National Guard-Health Affairs (MNGHA)","correspondingAuthor":false,"prefix":"","firstName":"Majed","middleName":"Al","lastName":"Shamrani","suffix":""},{"id":265085712,"identity":"71a5588b-439e-4788-966b-21f883a0c873","order_by":19,"name":"Abdulfattah Alamri","email":"","orcid":"","institution":"King Abdullah International Medical Research Center","correspondingAuthor":false,"prefix":"","firstName":"Abdulfattah","middleName":"","lastName":"Alamri","suffix":""},{"id":265085713,"identity":"04205006-eb48-4667-93d1-31493f52f2e9","order_by":20,"name":"Danesh Moradigaravand","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABW0lEQVRIie2Rv0vDQBTHXwjYJWnXlEDzL1wopBV//SEuDQdxSXQQQkDBTnEJ7dpC9W+IS3FMCZjlbNZzSxenCrqURip4VSo9UOkomA8cPN7dhy/vHUBBwV8kYkdof9ZKa9WVvFW5tbFClqW4gfLVlf2flXJyP5rkt3BYUeMJzV73Tyo9/Jg9XVsaStMInt0YtMtoXamSY1yXCTj9jlVvmh283aNWQ78a2npIMQi9cQyItNYVFNmGKvjghAQMxQxEBCQyVHnoCSEVQZR9pgCvpNNGnn8opRlTLpBGkpkqD7yDMI1BfGOK1s04hdoGG3apSIbSmscIJQFLadtmGGEQBaYA5VKqdFpXZV9x+oF02jTbCdKTwK0O7izcpxiNgvGRhCiXUk5t/SX3d52uVLp5mC/OUC0uDZXpOd7rpKNJNnd3alqXSwHuR9geeJbblb55v8bi9+uCgoKC/8k7fwmDfXOEDPwAAAAASUVORK5CYII=","orcid":"","institution":"King Abdullah University of Science and Technology (KAUST)","correspondingAuthor":true,"prefix":"","firstName":"Danesh","middleName":"","lastName":"Moradigaravand","suffix":""}],"badges":[],"createdAt":"2023-12-21 18:15:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3788315/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3788315/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49245292,"identity":"5605ec69-eb0e-4a50-83f1-69a5de529d06","added_by":"auto","created_at":"2024-01-05 20:14:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1000914,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhylogenetic distribution of the isolates included in the study. \u003c/strong\u003eThe tree is neighbor-joining tree reconstructed from the core genome variants. The red labels underneath the ARG panel correspond to their respective antimicrobial class. Resistance genes and mutations are those significantly linked with their phenotypes, as per GWAS results. The terms \u0026lt;\u0026lt;AMG\u0026gt;\u0026gt;,\u0026lt;\u0026lt;TET\u0026gt;\u0026gt;, \u0026lt;\u0026lt;MAC\u0026gt;\u0026gt;, \u0026lt;\u0026lt;FUS\u0026gt;\u0026gt;, \u0026lt;\u0026lt;BLM\u0026gt;\u0026gt; and \u0026lt;\u0026lt;FQO\u0026gt;\u0026gt; refer to aminoglycoside, tetracycline, macrolide, fusdic acid, beta-lactam and fluroquinolone, respectively. The virulence genes are key genes for CA-MRSA.\u003c/p\u003e","description":"","filename":"floatimage19.png","url":"https://assets-eu.researchsquare.com/files/rs-3788315/v1/7a73f4d92c7577c77503246f.png"},{"id":49245246,"identity":"3fd4dd16-4737-4532-9a6d-3f584dde6180","added_by":"auto","created_at":"2024-01-05 20:06:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":511372,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAntibiogram and virulence and resistance genes profiles for for the major STs and other minor STs, grouped together.\u003c/strong\u003e A) Virulence gene profiles For virulence genes, we shortlisted the genes that were significantly variable across the clones (pvalue Chi-square test \u0026lt; 0.05). For the full list of genes refer to Supplemental Table S1. B) Resistance frequency for different antimicrobials. We grouped susceptible and intermediate phenotypes together. Antimicrobials for the same class are shown in the first four blue boxes, which correspond to beta-lactams, aminoglycoside, fluroquinolone and macrolides. C) Resistance gene profile from two databases CARD and ResFinder for the clones. D) Count of resistance genes from two databases in the clones.\u003c/p\u003e","description":"","filename":"floatimage22.png","url":"https://assets-eu.researchsquare.com/files/rs-3788315/v1/6d1f15ba3e7bc6a2d6b3a63d.png"},{"id":49245245,"identity":"159281d6-7b06-4b6d-9cf1-953173a4653e","added_by":"auto","created_at":"2024-01-05 20:06:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1018969,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePlasmid maps for three plasmids for the bloodstream infections isolates. \u003c/strong\u003eA) Phylogenetic distribution of the plasmids in the population. Numbers correspond to the percentage of mapping (coverage) of the short reads after mapping to the plasmids. B) The genomic map of the plasmid. The outer red ring shows the identical regions in the plasmid 2 to plasmid 1.\u003c/p\u003e","description":"","filename":"floatimage31.png","url":"https://assets-eu.researchsquare.com/files/rs-3788315/v1/749060cd430daafc19104d24.png"},{"id":49245243,"identity":"136dd0f4-f5cd-461f-8fe8-96b951e20e7c","added_by":"auto","created_at":"2024-01-05 20:06:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":243893,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe comparative analysis of the dynamics of the MRSA clones identified in the hospital. \u003c/strong\u003eEach bar corresponds to one BAPS group. The STs designate the most common ST in each BAPS group. Error bars correspond to 95% HPD, the interval that contains 95% of the mass of the posterior distribution around the center of the distribution. The age of the most recent common ancestor (MRCA) is in years. The unit for clock rate is the changes per site per year.\u003c/p\u003e","description":"","filename":"Screenshot20240105at3.04.41PM.png","url":"https://assets-eu.researchsquare.com/files/rs-3788315/v1/952aeff90d04513726ac568c.png"},{"id":49245248,"identity":"be554b34-3683-4ad4-82de-efaa759e9126","added_by":"auto","created_at":"2024-01-05 20:06:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":347648,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe skyline plot of the populations of the clones circulating in the hospital. \u003c/strong\u003eThe shaded region corresponds to the 95% Confidence Interval (CI).\u003c/p\u003e","description":"","filename":"Screenshot20240105at3.04.54PM.png","url":"https://assets-eu.researchsquare.com/files/rs-3788315/v1/88933a55d42c5aa29beaa79e.png"},{"id":49245293,"identity":"26cb9f40-d195-4533-b339-107954763847","added_by":"auto","created_at":"2024-01-05 20:14:55","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":603116,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDynamics of the clones in the hospital wards. \u003c/strong\u003eThe trees are Bayesian tree with the horizontal bars corresponding to the 95% Highest Posterior Density (HPD). Wards that have less than representative samples are not shown. The shaded boxes show strains from the same patients. Infection was designated as community-acquired if the diagnosis occurred within 48 hours of patient’s admission to the hospital.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3788315/v1/bb20079f2b02262bbce9b802.jpeg"},{"id":49337595,"identity":"7735f9a0-7a48-4dbe-ae53-3cebcdf1e3ea","added_by":"auto","created_at":"2024-01-09 00:22:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2392790,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3788315/v1/4236e018-f199-4e3d-aee7-bfc9624d9e49.pdf"},{"id":49245249,"identity":"fdfecc1c-3efd-4914-a954-95e18a03aadf","added_by":"auto","created_at":"2024-01-05 20:06:55","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3110590,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-3788315/v1/8ce4277d6ae6b5c9c7b01a41.docx"},{"id":49245250,"identity":"d0c9d638-2da5-4e5c-b276-2799899e91cf","added_by":"auto","created_at":"2024-01-05 20:06:55","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":105163,"visible":true,"origin":"","legend":"","description":"","filename":"SupplumentalTableS1.csv","url":"https://assets-eu.researchsquare.com/files/rs-3788315/v1/a8ad8f3902641e6508fe5ce1.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eConcurrent Clonal Expansion of Community-Associated Methicillin-resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (MRSA) Clones in a Tertiary Hospital\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, a Gram-positive opportunistic pathogen, is frequently found in the skin, nasopharynx, and gastrointestinal tract [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Up to 30% of the human population is estimated to asymptomatically host the bacterium [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. While many \u003cem\u003eS. aureus\u003c/em\u003e carriers remain asymptomatic, colonization often precedes infections that can result in severe and life-threatening diseases. Moreover, multidrug resistant and methicillin-resistant \u003cem\u003eS. aureus\u003c/em\u003e (MRSA) has emerged over the past few decades as a significant public health threat, and caused higher mortality rates, or therapeutic failure [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Surveillance systems help guide empirical therapy and improve the understanding of MRSA isolates in different regions, which is essential for effective treatment and control efforts.\u003c/p\u003e \u003cp\u003eMRSA has traditionally been considered a health care-associated pathogen in patients with established risk factors. Over recent decades, multiple MRSA strains have been reported in patients without established risk factors in the form of community-associated MRSA (CA-MRSA) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Genomic epidemiology, based on whole genome sequencing, has played a crucial role in enhancing our understanding of the epidemiology and characteristics of community-acquired MRSA[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Despite some of the well-characterized exclusive virulence factors, SCC\u003cem\u003emec\u003c/em\u003e locus types and distinctive geographical distributions between HA-MRSA and CA-MRSA strains, the epidemiological distinction between CA-MRSA and HA-MRSA has become blurred in recent years. This changing epidemiology has been attributed to the transmission of CA-MRSA strains in the community and their subsequent spread to hospitals [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The routes of the introduction, evolution, and dynamics of CA-MRSA clones in hospital settings are yet to be fully elucidated.\u003c/p\u003e \u003cp\u003eA hub for tourism, mass gatherings, and high population diversity, the Gulf Cooperation Council (GCC) region, and in particular, the Kingdom of Saudi Arabia (KSA), serve as settings in which the dissemination of CA-MRSA is facilitated [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Isolated reports throughout the GCC have found a high prevalence of MRSA among \u003cem\u003eS. aureus\u003c/em\u003e isolates, averaging about 25\u0026ndash;35% [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In Saudi Arabia and Kuwait, MRSA infections contribute significantly to the burden of health care delivery with increasing occurrence of CA-MRSA lineages causing nosocomial infections [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. A recent work showed the presence of a high degree of diversity and an emergence of both pandemic and rare MRSA strains among isolates obtained from a major hospital setting in Riyadh, Saudi Arabia [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Further work also showed that MRSA colonizing health care workers at the facility were of similar population structure as those identified in patients [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Despite these insights, the population genomics and dynamics of MRSA strains in hospital settings were not systematically analyzed, as previous studies predominantly employed typing methods based on the variation in a few genes and therefore had a low resolution.\u003c/p\u003e \u003cp\u003eThis study aims to address the abovementioned knowledge gap by obtaining in-depth insights into the evolution and genomic epidemiology of a systematic collection of MRSA strains in a major tertiary hospital in the KSA. We employed a broad range population genomic and data mining methods to analyze the whole genome sequencing data and integrate the genomic data with the hospital and clinical record for the patients. Our results indicate a diverse population with dominant clones with hallmarks of CA-MRSA, which have acquired multiple resistance determinants and virulence factors on epidemiological time scale. The clones exhibit distinctive clinical manifestations and mortality, pointing to the diversity of evolutionary trajectories of CA-MRSA strains in hospital settings.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCollection description\u003c/h2\u003e \u003cp\u003eWe collected 194 S. aureus isolates, presumed to be MRSA, along with their epidemiological information for 175 patients between 2019 and 2021 as part of the hospital surveillance initiative. The isolates originated from King Abdulaziz Medical City Jeddah (KAMC-J) and the Saudi Ministry of National Guard Health Affairs (MNGHA) in Jeddah, Saudi Arabia. The medical city is home to a tertiary care hospital with over 600 beds, including adult intensive care units (ICU) designed for both medical and surgical patients. The hospital serves as the primary point of admissions for millions of pilgrims to and from the city of Makkah during the annual Hajj and Umrah, mass-gathering events, during their journeys [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In the years 2020 and 2021, 185 and 183 cases were reported in the hospital, respectively. Around one-fourth of cases were colonization, and the rest were infections, with hospital-acquired infections accounting for one-fourth of infection sources. The Ministry of National Guard Health Affairs (MNGHA) provided ethical approval for the study under the number RJ17-023-J.\u003c/p\u003e \u003cp\u003eWe extracted various epidemiological data points from patients clinically diagnosed with MRSA colonization/infection from the electronic health record (EHR) data. The clinical metadata included demographic details such as age, gender, length of hospitalization, admission and discharge dates, sample collection date, ward of admission, sampling date, patient status (treatment outcome), and the type of sample based on the body colonization site. The cause of in-hospital death was not recorded. The clinical metadata for each patient is provided in Supplemental Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDrug susceptibility testing\u003c/h2\u003e \u003cp\u003ePatient specimens were obtained from diverse sources following cultivation on MacConkey agar (Saudi-prepared media laboratory, SPML, Saudi Arabia). Strains were identified using the VITEK MS automated mass spectrometry microbial identification system (bioM\u0026eacute;rieux, France). To confirm the strain identification, we applied the Sanger method with Applied Biosystems technology to sequence the conserved 16S rRNA gene(s) of individual isolates. The VITEK2 rapid identification system by bioM\u0026eacute;rieux, France, was used for antimicrobial susceptibility testing (AST).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eShort-read sequencing\u003c/h2\u003e \u003cp\u003eSingle colony boiling and the GenElute Bacterial Genomic DNA Kit (Sigma-Aldrich, Germany) were used for DNA extraction. Subsequently, we fragmented the extracted DNA through sonication to produce fragments within the 300\u0026ndash;500 bp range (Covaris, USA). We prepared libraries manually and with automated methods using the QIAseq FX DNA Library Kit (Qiagen, Germany) with BioMek FXP liquid handling automation (Beckman Corporation, USA). The NovaSeq platform (Illumina, USA) was used for sequencing, yielding 150 bp paired-end reads.\u003c/p\u003e \u003cp\u003eThe fastqc package (v0.1.3) in R was employed for quality control of the reads. De novo assembly was carried out using Unicycler (v0.5.0) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/rrwick/Unicycler#install-from-source\u003c/span\u003e\u003cspan address=\"https://github.com/rrwick/Unicycler#install-from-source\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with default parameters [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Contigs shorter than 200 bps were excluded from subsequent analysis. We characterized strains through SCCmec typing using staphopia-sccmec (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://github.com/rrwick/Unicycler#install-from-source\" target=\"_blank\"\u003ewww.github.com/staphopia/staphopia-sccmec\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.github.com/staphopia/staphopia-sccmec\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], spa typing with spatyper-0.3.3 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/mjsull/spa_typing\u003c/span\u003e\u003cspan address=\"https://github.com/mjsull/spa_typing\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and agr typing with agrvate (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://github.com/rrwick/Unicycler#install-from-source\" target=\"_blank\"\u003ewww.github.com/VishnuRaghuram94/AgrVATE\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.github.com/VishnuRaghuram94/AgrVATE\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. We detected virulence factors and resistance genes using srst2 v0.2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://github.com/rrwick/Unicycler#install-from-source\" target=\"_blank\"\u003ewww.github.com/katholt/srst2\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.github.com/katholt/srst2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) on Plasmidfinder (v2.1.1) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]VFDB (v6.0) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and ResFinder (v4.1) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and CARD (v3.2.8) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] databases, with a 90% identity threshold for identifying genes. Furthermore, assemblies were annotated with Prokka [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and the pangenome of plasmids was constructed using Panaroo [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor the construction of the phylogenetic tree, short reads were mapped against the reference Staphylococcus aureus subsp. aureus NCTC 8325 genome (accession number: GCF_000013425.1) using the Snippy pipeline (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/tseemann/snippy\u003c/span\u003e\u003cspan address=\"https://github.com/tseemann/snippy\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We calculated pair-wise SNP distances for the mapped genomes using the ape package (v5.7.1) in R [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Visualization of the tree and associated metadata was performed using the ggtree package (v3.8.2) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]in R. Clones within the population were identified using hierarchical Bayesian clustering (BAPS) with the implemented package in R, i.e., rheirBAPS (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://github.com/rrwick/Unicycler#install-from-source\" target=\"_blank\"\u003ewww.github.com/gtonkinhill/rhierbaps\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.github.com/gtonkinhill/rhierbaps\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), by taking the first level of clonal inference [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The BAPS analysis revealed nine clones, four of which (BAPS1, BAPS2, BAPS3, BAPS4, BAPS5, and BAPS8) were found to have more than ten representative isolates. The list of corresponding clones for each genome is provided in Supplemental Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eGenome-wide association studies (GWAS) analysis were conducted on both accessory genes (Panaroo output) and SNPs using Scoary (1.6.16) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://github.com/rrwick/Unicycler#install-from-source\" target=\"_blank\"\u003ewww.github.com/AdmiralenOla/Scoary\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.github.com/AdmiralenOla/Scoary\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Variants with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were filtered for the na\u0026iuml;ve, Bonferroni-corrected, best pairwise comparison, and worst pairwise comparison, with the latter two corresponding to the highest (lowest) possible number of supporting pairs and the lowest (highest) possible number of opposing pairs (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://github.com/rrwick/Unicycler#install-from-source\" target=\"_blank\"\u003ewww.github.com/AdmiralenOla/Scoary\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.github.com/AdmiralenOla/Scoary\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eTransmission analysis and phylodynamic analysis\u003c/h2\u003e \u003cp\u003eWe conducted phylodynamic analysis on the four dominant clones. To achieve this, we first mapped the short reads for the strains in each clone against the concatenated assembly of the strains from the same clone that had the best assembly statistics, i.e., the highest N50. We then subjected the alignment to Gubbins (v.3.3.1) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] with five iterations to remove hypervariable regions.\u003c/p\u003e \u003cp\u003eWe used the BEAST (v2) package to reconstruct the dated evolutionary tree for each clone [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. We applied the strict clock time model with a constant population and ran Markov chain Monte Carlo (MCMC) simulations for 10^8 iterations for sequence types (STs), confirming convergence by checking the effective sample size (ESS) to be above 200 for key parameters, such as clock rate and the age of the root. After leaving out 10% of the iterations as burn-in, we aggregated the trees with the TreeAnnotator application as part of the BEAST package and visualized the tree with the ggtree package (v3.8.2) in R [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eContextualization of the population diversity\u003c/h2\u003e \u003cp\u003eWe integrated the genomes with the genomic database from the Pathogen Detection database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/pathogens/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/pathogens/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to provide population genomic context for the collection under study. Therefore, we extracted the epidemiological SNP clusters on 06/09/2023, encompassing the strains under study, from the Pathogen Detection database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://github.com/rrwick/Unicycler#install-from-source\" target=\"_blank\"\u003ewww.ncbi.nlm.nih.gov/pathogens\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/pathogens\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These SNP clusters consisted of strains with a maximum pairwise SNP distance of 50 SNPs. This cut-off is fixed and hence cannot be changed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePlasmidome analysis and long-read sequencing\u003c/h2\u003e \u003cp\u003eWe prepared and multiplexed libraries for the selected isolates using 96-plex Rabid Barcoding Kits. We then loaded the libraries into MinION flow cells (Oxford Nanopore Technologies). The sequencing continued for 48 hours following the manufacturer's protocol. Subsequently, we performed a hybrid assembly employing Unicycler, utilizing the conservative option to recover long assemblies. The assembled contigs that contained the resistance gene and origin of replications underwent thorough screening for the presence of complete copies of origin of replications, virulence factor genes, and antimicrobial resistance genes within the plasmids using BLAST with the aforementioned databases of known genes. We visualized the plasmids with Proksee portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://github.com/rrwick/Unicycler#install-from-source\" target=\"_blank\"\u003ewww.proksee.ca\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.proksee.ca\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic term text analysis\u003c/h2\u003e \u003cp\u003eWe analyzed diagnostic documentation for individual patients to identify pertinent diagnostic terminology and symptoms. Due to the absence or incompleteness of standardized medical codes, such as the ICD-10, within patient records, we used a natural language processing approach to extract medically relevant terms. First, the diagnostic notes underwent a text processing pipeline developed with the tidytext package (v0.4.1) in R. The pipeline removed special characters and common, inconsequential words (stop words). We then used three biomedical-specific corpus libraries, i.e., BC5CDR, en_core_sci_lg, and ner_bionlp13cg_md, to extract terms related to diseases and symptoms in the named entity recognition part of the pipeline. The BC5CDR corpus, specifically designed for disease name entity recognition, comprises 1500 PubMed articles annotated with 5818 diseases and 3116 chemical-disease interactions [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Finally, the en_core_sci_lg and ner_bionlp13cg_md libraries encompass 600k general medical terms and genetic expressions for 16 types of cancer, respectively [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The list of extracted terms associated with each patient is provided in Supplemental Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eWe calculated term-weighting schemes based on frequency\u0026ndash;inverse document frequency (tf-idf) values to assess the significance of the extracted terms for patients carrying different sequence types (STs). To this end, individual text corpora were created for each ST, which included diagnostic notes pertaining to patients with the same ST. For each term, we computed the tf\u0026ndash;idf values, which correspond to the frequency of a term within the document (textual corpus for each ST) offset by the number of documents (number of textual corpora for each ST) containing the term. This metric serves as a proxy for the importance or overrepresentation of a specific term in the notes related to a particular ST.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eIntegration of genomic data with clinical metadata\u003c/h2\u003e \u003cp\u003eWe dissected the link between in-hospital mortality and colonization/infection by different STs by computing the odds ratio of death using the odds-ratio function from the Epitools package (v0.5.10.1). The results were further confirmed by a survival analysis Cox regression model with the coxph function from the survival package in R, using the following formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$Surv(Age, InHospital death) \\sim ST$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWe computed the hazard ratio (HR) associated with the carriage of a major ST compared to the rest of the population. A HR\u0026thinsp;\u0026lt;\u0026thinsp;1 implies reduced hazard of death for carrying a ST while a HR\u0026thinsp;\u0026gt;\u0026thinsp;1 indicates an increased hazard of death.\u003c/p\u003e \u003cp\u003eWe validated the odds-ratio calculations through additional analysis using binary logistic regression model. In this analysis, the mortality outcome served as the binary response variable. We assessed the significance of resistance while accounting for potential confounding factors, including sequence type (ST), age, gender and body site of infection, using the following formula:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$InHospital death \\sim ST+age+gender+body site$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere, ST, gender, and body site were considered categorical (nominal) independent variables and were consequently factorized. STs with fewer than ten representative genomes were grouped together. The regression coefficient (β) for the in-hospital death parameter was extracted for each ST. This coefficient represented the alteration in log odds of death associated with colonization by colonization with each ST.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e\u0026bull; Population diversity reveals the presence of multiple clones\u003c/h2\u003e \u003cp\u003eOur collection comprised 194 strains isolated over a two-year period in hospitals. Our analysis reveals a diverse population, as reflected by the number of STs (n\u0026thinsp;=\u0026thinsp;43), \u003cem\u003espa\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;23), \u003cem\u003eagr\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;23), and SCCmec (n\u0026thinsp;=\u0026thinsp;24) types. The population was found to be non-typable for ST, spa, and \u003cem\u003eagr\u003c/em\u003e in 9%, 2%, and 1% of the strains, respectively. The concordance level between ST and \u003cem\u003eagr\u003c/em\u003e and spa typing was high, in contrast to the SCCmec typing of the STs, which showed variation in the locus belonging to the same STs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We identified six dominant clones represented by more than ten strains. These clones correspond to the six STs in the population: BAPS1/ST5 (n\u0026thinsp;=\u0026thinsp;33), BAPS2/ST672 (n\u0026thinsp;=\u0026thinsp;36), BAPS3/ST97 (n\u0026thinsp;=\u0026thinsp;14), BAPS4/ST6 (n\u0026thinsp;=\u0026thinsp;15), BAPS5/ST88 (n\u0026thinsp;=\u0026thinsp;19), and BAPS8/ST8 (n\u0026thinsp;=\u0026thinsp;27) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Except for ST672, other clones were recently reported as CA-MRSA strains in hospital settings in Riyadh with microarrays. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In line with this, a community mode of infection acquisition was confirmed for 25% up to 75% across all dominant clones (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). Typing based on the \u003cem\u003eagr\u003c/em\u003e gene, a key regulatory element in the virulence mechanism, revealed the presence of three types of gp genes: gpI (in BAPS2/ST672, BAPS3/ST97, BAPS4/ST6, and BAPS8/ST8 clones), gpII (in BAPS1/ST5 clone), and gpIII (in BAPS5/ST88) types (one isolate contained gpV type), pointing to the diversity of virulence mechanisms for the major clones. Moreover, we conducted typing based on the variation of the spa gene, which encodes the Staphylococcus protein A triggering B cell proliferation. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The spa gene is commonly used for epidemiological surveillance of \u003cem\u003eS. aureus\u003c/em\u003e because of its high variation, offering a higher discrimination typing compared to MLST and \u003cem\u003eagr\u003c/em\u003e methods [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The analysis identified a total of 63 different known spa types in 188 genomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Six types (t690 (n\u0026thinsp;=\u0026thinsp;12), t304 (n\u0026thinsp;=\u0026thinsp;14), t008 (n\u0026thinsp;=\u0026thinsp;18), t311 (n\u0026thinsp;=\u0026thinsp;18), t3841 (n\u0026thinsp;=\u0026thinsp;31)) were the most prevalent in the population and together were detected in 51.5% (100/194) of the genomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among these types, t311, t3841, t304, t690, and t008 were the most prevalent in BAPS1/ST5 (n\u0026thinsp;=\u0026thinsp;18/33), BAPS2/ST672 (n\u0026thinsp;=\u0026thinsp;29/36), BAPS4/ST6 (n\u0026thinsp;=\u0026thinsp;14/15), BAPS5/ST88 (n\u0026thinsp;=\u0026thinsp;12/19), and BAPS8/ST8 (n\u0026thinsp;=\u0026thinsp;18/19) clones, respectively. The spa types t008, t304, and t311 were reported predominantly from Europe, America, and to a lesser extent in Africa. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, t690, t3841 were not reported as prevalent types before and appear to be exclusive to the current collection. The BAPS3/ST97 group consisted of six spa types, including t521 (n\u0026thinsp;=\u0026thinsp;2/14), t359 (n\u0026thinsp;=\u0026thinsp;3/14), t2770 (n\u0026thinsp;=\u0026thinsp;2/14), t267 (n\u0026thinsp;=\u0026thinsp;4/14), t2297 (n\u0026thinsp;=\u0026thinsp;2/14) and t1544 (n\u0026thinsp;=\u0026thinsp;1/14), showing the high level of genomic dynamics of the clone. The contextualization of the genomes with the global genomic collection of S. aureus identified a low level of mixing with the global genomic collection, with only two clusters (SNP clusters) with external genomes. This included the grouping of ST8 strains with a clinical sample from Denmark (SNP cluster PDS000144949.1) and two strains of ST1535, which belonged to a cluster (PDS000069007.8) comprising clinical strains from Denmark and the Netherlands, as well as two environmental samples from meat retailers from unpublished genomes in Saudi Arabia, indicating a link with non-clinical settings. These findings suggest the occurrence of community-associated clones, some of which were specific to the study setting and had links with the environment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e\u0026bull; SCCmec typing indicated the presence of highly dynamics locus\u003c/h2\u003e \u003cp\u003eSCCmec typing indicated the presence of a highly dynamic locus. Our analysis showed mec genes in all strains, although not all genes were found in the context of known SCCmec elements. Among the strains, 67 exhibited a complete match to one of the known SCCmec elements (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Figure S2). For the rest, a partial match to an element with a sequence (Hamming) distance of \u0026gt;\u0026thinsp;0 and \u0026lt;\u0026thinsp;5 was observed (Figure S2). All strains were found to contain the \u003cem\u003emecA\u003c/em\u003e gene, although 25 harbored divergent copies of the gene not situated in an SCCmec element. For the copies of \u003cem\u003emecA\u003c/em\u003e in an SCCmec element, a strong association with resistance to beta-lactams of oxacillin and cephalosporins was identified (GWAS p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 after accounting for lineage effect). HA-MRSA is recognized to carry SCCmec types I, II, or III, while CA-MRSA carries smaller SCCmec types IV, V, or VI. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In our collection, except for one ST8 sample that carried an SCCmec type III locus, the other strains harbored SCCmec loci associated with CA-MRSA strains (Figure S2). The loss of SCCmec occurred in 22 lineages across the phylogenetic tree, including two times within the BAPS1/ST5 clones and three times in BAPS2/ST672, BAPS5/ST88, and BAPS8/ST88 groups (Figure S2). Besides the loss of SCCmec elements, switching between SCCmec types was detected within identified clones. Specifically, we observed a switching in SCCmec types within the BAPS2/ST672 clone, shifting from type V/VII to type IVd. Similarly, the BAPS1/ST5 clone exhibited a switch between types V/VII and type VI (observed in two genomes). The BAPS8/ST88 clone included switches between SCCmec types, changing from type VI to type IIIa, and types V/VII were observed in four genomes. The observation demonstrates significant dynamics of SCCmec on epidemiological time scales. This finding aligns with the reported high mobility of the CA-MRSA-associated SCCmec types of IV and V, especially the type IV element, which is recognized to be transferred to methicillin-susceptible backgrounds frequently. [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e\u0026bull; Presence of hallmark genes for CA-MRSA virulence across independent lineages\u003c/h2\u003e \u003cp\u003eIn addition to the SCCmec types, the clones in the population included multiple virulence genes associated with CA-MRSA strains (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Most notably, multiple strains contained PVL genes, \u003cem\u003elukF\u003c/em\u003e-PV and \u003cem\u003elukS\u003c/em\u003e-PV, encoding a two-component S. aureus pore-forming protein well-characterized as causative of community-acquired invasive diseases. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The PVL genes were found in 31 genomes, which included multiple lineages: 60% (12/20) of strains in BAPS8/ST8 clones harbored the PVL locus, while these genes occurred less frequently in other major clones, i.e., one genome each in BAPS3/ST97 and BAPS5/ST88. In other minor clones, the locus was detected in the clones of ST80 (n\u0026thinsp;=\u0026thinsp;4), ST22 (n\u0026thinsp;=\u0026thinsp;3), and ST30 (n\u0026thinsp;=\u0026thinsp;3), all of which were described as CA-MRSA strains (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Another virulence factor for CA-MRSA strains is the Toxic Shock Syndrome Toxin (\u003cem\u003etsst\u003c/em\u003e) gene, encoding a toxin associated with toxic shock syndrome. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The gene was observed in 15 genomes in our collection, belonging to ST22 (n\u0026thinsp;=\u0026thinsp;8), ST361 (n\u0026thinsp;=\u0026thinsp;3), and one genome from each of ST8, ST30, and ST672. For the three strains characterized by BAPS9/ST22/t223, BAPS9/ST22/t223, and ST30/t233, the gene co-occurred with PVL genes within the same genomes. This co-occurrence renders these strains clinically significant, exhibiting a dual virulence phenotype. The results show the independent occurrence of some known hallmark genes for CA-MRSA in the collection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e\u0026bull; Distinctive pattern of virulence gene across the clones\u003c/h2\u003e \u003cp\u003eAs the virulence mechanisms of CA-MRSA are not fully characterized, we screened the collection for the presence of other virulence genes, specifically focusing on 29 genes that were variably present in the major clones and the background population (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). For a set of secretion protein genes, including essC and esxBC, toxin genes like esxD, eap/map, and adhesion genes such as set22 and esaD, the major clones more frequently contained the virulence genes compared to the background population, indicating the potential higher virulence of these clones (p-value from ANOVA test\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Moreover, the screening revealed that some clones contained more genes encoding superantigens (SAg) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], which are potent immunostimulatory exotoxins causing T cell proliferation and cytokine release, than the rest of the population (p-value from ANOVA test\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The gene list included selq (55%) and selk (55%) in BAPS8/ST8, set21 in all BAPS5/ST88, set16 in all BAPS2/ST672 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Some BAPS groups contained virulence factors that were significantly more frequent than other clones, i.e., adhesion clf gene in BAPS3/ST97 (100% of strains), adhesion cna (100% of strains), and enterotoxin sea gene (93% of strains) in BAPS4/ST6, hyaluronidase hysA, and human-specific immune evasion cluster chp gene (84% of strains) in BAPS5/ST88, and adhesion sdrD (82%) in BAPS8/ST8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Although the specific role of these genes in virulence requires further functional analysis, the variation of the virulence gene profiles across major clones points to the complex and diverse virulence mechanisms in CA-MRSA.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e\u0026bull; Population has high resistance level somewhat reflecting the prescriptions\u003c/h2\u003e \u003cp\u003eThe population exhibits a high resistance level against beta-lactams such as penicillin and second-generation cephalosporins, as well as fusidic acid, with resistance levels of 98%, 90%, 90%, and 71% for benzylpenicillin, oxacillin, cefoxitin, and fusidic acid, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The resistance levels for fluoroquinolones were moderate (moxifloxacin 31% and levofloxacin 46%). For aminoglycosides (tobramycin and gentamicin), macrolides (erythromycin and clindamycin), tetracycline, and trimethoprim, low resistance levels of 11%, 13%, 23%, 9%, 15%, and 20%, respectively, were observed. No resistance to vancomycin, linezolid, or tigecycline was detectable in the collection. Moderate to strong cross-resistance was prevalent for fluoroquinolones, fusidic acid, and beta-lactams (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), while for macrolides, tetracycline, and trimethoprim, there was a weaker correlation of resistance with other groups. This pattern somewhat reflects the prescription pattern in the window of six months before sampling: the most prescribed antimicrobial classes in the six months prior to admission and at the time of diagnosis were vancomycin, beta-lactams (piperacillin/tazobactam and carbapenems) (43% of prescriptions), fluoroquinolones (46% of prescriptions), and fusidic acids (37% of prescriptions), while tetracyclines and aminoglycosides were less commonly prescribed. Vancomycin was still the most used antimicrobial at the time of diagnosis and following antimicrobial susceptibility, although no resistance had yet arisen (Supplemental Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). For other antimicrobials, resistance levels varied across the clones: BAPS3/ST97 strains exhibited higher resistance to aminoglycosides (gentamicin and tobramycin), while for fluoroquinolones, the ST672 and ST6 clones were found to be more resistant compared to the other major clones (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) (p-value from proportion test\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Some level of clindamycin resistance was observed for all major clones except BAPS4/ST6 and BAPS3/ST97 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). For tetracycline, BAPS5/ST88 and BAPS1/ST5 were found to be relatively more resistant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e\u0026bull; Resistance genes profiles differ across clones with ST8 and ST5 harboring more resistance genes\u003c/h2\u003e \u003cp\u003eThe collection exhibited a diverse array of antimicrobial resistance genes for two databases of known resistance genes, with varying average counts observed across major BAPS clones: BAPS8/ST8 (ResFinder: 6.75, CARD: 6.1), BAPS4/ST6 (ResFinder: 5.13, CARD: 4.0), BAPS2/ST672 (ResFinder: 5.41, CARD: 4.61), BAPS5/ST88 (ResFinder: 5.84, CARD: 4.61), BAPS1/ST5 (ResFinder: 6.67, CARD: 3.84), and BAPS3/ST97 (ResFinder: 5.79, CARD: 5.48) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The BAPS8/ST8 group strain had higher resistance genes except BAPS1/ST5 (p-value from one-sided Wilcoxon signed-rank test\u0026thinsp;\u0026lt;\u0026thinsp;0.05). On the next level, BAPS3/ST97 and BAPS5/ST88 were more resistant than BAPS4/ST6 and BAPS2/ST672, although they had comparable resistant gene counts as the other minor STs altogether (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Both ST8 and ST5 lineages belong to the significant clonal complexes of \u003cem\u003eS. aureus\u003c/em\u003e (CC5 and CC8), associated with rapid dissemination of methicillin and multidrug resistance in both hospital and community settings [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The distribution of resistance genes revealed a consistent presence of tetracycline resistance gene \u003cem\u003eblaZ\u003c/em\u003e across all clones, highlighting a higher count of macrolide resistance genes \u003cem\u003emsrA\u003c/em\u003e and \u003cem\u003emphC\u003c/em\u003e in BAPS8/ST8 and BAPS1/ST5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Further exploration into genes and mutations strongly associated with resistance demonstrated variable presence across major clones. The \u003cem\u003emecA\u003c/em\u003e gene as mentioned above strongly linked with beta-lactam resistance and was found lost in some lineages. The bifunctional enzyme \u003cem\u003eaacA-aphD\u003c/em\u003e gene (tetracycline efflux pump gene \u003cem\u003etetK\u003c/em\u003e), significantly linked with aminoglycoside resistance genes, emerged frequently in the BAPS3/ST97 (BAPS5/ST88) clone (GWAS p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 after accounting for lineage effect). Macrolide resistance genes \u003cem\u003emsrA\u003c/em\u003e and \u003cem\u003eermC\u003c/em\u003e underlie higher resistance in BAPS8/ST8 and BAPS1/ST5, respectively, and emerged across multiple lineages (GWAS p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The fusc gene, linked with fusidic acid resistance, was prevalent in all major clones except BAPS4/ST6. Among the resistance mutations for fluoroquinolones, the S84L mutation in \u003cem\u003egyrA\u003c/em\u003e was found significantly linked with resistance phenotypes for moxifloxacin and levofloxacin (GWAS p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The gene diversity highlights variety of emerging resistance trends, occurring both among different clones and more recently within individual clones.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e\u0026bull; Similar plasmids carrying antimicrobial resistance genes are shared between lineages\u003c/h2\u003e \u003cp\u003eTo contextualize the resistance genes, we conducted long-read sequencing for strains associated with bloodstream infections. We retrieved full genome sequence of plasmids from on isolate (ID00734) (plasmid 1) belonging to BAPS5/ST88 from bloodstream infection and one from (ID00777) (plasmid 2) BAPS8/ST8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Both strains contained a mosaic plasmid of size 28K. The search for similar plasmid backbones in the database did not pinpoint any previously reported backbone. The highest coverage of 69% was found with a plasmid with accession CP094779.1 recovered from the non-human sources [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Plasmid ID00734 is predominantly limited to the BAPS5/ST88 clone and occurred in 6 strains and one isolate and one strain from untyped ST (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The plasmid is a mosaic plasmid with a replicon reported in previous clinical isolates consisting of a \u003cem\u003eblaZ\u003c/em\u003e and \u003cem\u003eblaI\u003c/em\u003e genes surrounded by mobile genetic elements. The same plasmid also accommodates the macrolide resistance gene of \u003cem\u003emsr(A)\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Three aminoglycoside resistance genes of \u003cem\u003eaph(A), sat(A)\u003c/em\u003e and \u003cem\u003eaad(K)\u003c/em\u003e were linked with an IS6 element. The plasmid also contained a \u003cem\u003ecadC\u003c/em\u003e gene for cadmium resistance. The size and lack of conjugative transfer genes suggest that the plasmid belongs to low-copy non-conjugative theta replicating plasmids [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The plasmid from BAPS8/ ST8 isolate somewhat resembled the plasmid from BAPS5/ST88 (76% sequence identity) in that it has similar size and the same \u003cem\u003ebla\u003c/em\u003e cassette. However, the plasmid lacks \u003cem\u003emsr(A)\u003c/em\u003e and a \u003cem\u003etet(K)\u003c/em\u003e gene linked with IS6 plasmid has replaced the aminoglycoside carrying plasmid. Moreover, the plasmid has a broader phylogenetic distribution and occurred in strains from ST67, ST1482 and ST1 strains in which more than 95% of the plasmid contigs were covered by the reads of these strains (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The sharing of the plasmids and variation in the arrangement of antimicrobial resistance genes suggest a high rate of horizontal gene transfer occurring at plasmid and gene levels.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e\u0026bull; Concurrent expansion of clones over the past few decades, with steady population increase\u003c/h2\u003e \u003cp\u003eThe comparison of dynamics among the clones within the hospitals also indicates marked differences between them. The BAPS1/ST5, BAPS5/ST88, and BAPS8/ST8, belonging to globally known circulating clones, emerged 30 to 50 years ago, while the other three BAPS groups emerged more recently (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The higher clock rate for BAPS3/ST97 and BAPS4/ST6 points to recent expansions of these clones, indicating that the emerging mutations have not yet been purged by selection pressure (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The population dynamics of the clones in the hospital indicate an increase in the effective population size for BAPS/ST15, BAPS2/ST672, BAPS3/ST97, and BAPS4/ST6 over the last decade, despite a slight recent decrease (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003e). For the more long-standing BAPS5/ST88 and BAPS8/ST8 clones, the populations remained constant after an initial increase (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Further examination of the Bayesian phylodynamic trees reveals signatures of multiple independent introductions of the clones from the community into the hospital wards (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003e). For the strains with no evidence of community acquisition routes for the infection, transmission appeared to have occurred uniformly across the hospital wards. Specifically, for BAPS2/ST672, five incidents of potential recent transmission (i.e., divergence in the past one year) involving patient pairs ID00707/D00696, ID00647/D00642, ID00792/D00791, ID00624/D00765 were observed. For BAPS5/ST88, two incidents (ID00832/D00828, ID00646/D00718), for BAPS8/ST8, one incident (ID00819/D00783), for BAPS1/ST5, one incident (ID00726/D00727), and for BAPS4/ST6 (ID00774/D00721) were observed. Except for two incidents, the rest of the transmission involved the same body sites, and only two occurred between patients with the same admission ward (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003e). On one occasion, i.e., BAPS4/ST6 (ID00774/D00721), the sampling times for the patients involved in transmission were five months apart, showing the persistence of the clone in the hospital environment. Furthermore, for two patients in BAPS1/ST5 and BAPS8/ST8, distinct strains were found to colonize the same patients, suggesting repeated mixed infections [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Within-patient diversity indicated seven incidents in the BAPS groups (the shaded boxes in Figure), three of which in BAPS2/ST672, BAPS3/ST97, and BAPS4/ST6 happened between strains in the blood and other sites, pointing to the invasiveness of the strains. Altogether, these findings indicate the multiple introductions of strains into the hospital and the circulation of the strains across wards and the same patient's sides happening on epidemiological time scales.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e\u0026bull; Diverse clinical manifestation amongst the major clones\u003c/h2\u003e \u003cp\u003eWe finally examined the clinical features and manifestations of the strains to dissect the differences in virulence among the major clones. All major clones were extracted from bodily fluids, including nasal secretions, sputum, and wound exudate (fluid discharged from a wound). Except for BAPS8/ST8, other STs contained strains from bloodstream infections. Bloodstream infection did not occur in the rest of the population (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB). Except for BAPS8/ST8, other clones included bloodstream invasive strains. While clones exhibited a uniform distribution across ages, the hospital duration for the colonized patients showed slight variations among the major clones (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC). Patients with BAPS3/ST97 and BAPS/ST88 showed a significantly longer hospitalization period (p-value from Wilcoxon signed-rank test\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eD). The integration of in-hospital morality data and the colonized ST confirmed a hazard ratio greater than one corresponding to the increased hazard of death for all major clones compared with the other clones in the population. However, only for BAPS3/ST97 was a significantly higher death odds-ratio than one confirmed (Figure S3A) (95% CI greater than one). After accounting for the impact of demographic factors and other comorbidities, only the colonization by BAPS3/ST97 strains was found significantly linked with in-hospital mortality (Figure S4B). We further linked the colonization of STs with the importance of the terms extracted from the diagnostic notes for the patients. Figure S4 shows the top ten important terms for the corpus for each ST, which are highly variable across corpora for different STs. The terms include comorbidities, e.g., cancers related terms of \"carcinoma,\" \"glioblastoma,\" and \"neuroblastoma\" for BAPS2/ST672, BAPS3/ST97, and BAPS5/ST88, as well as infection types (Figure S5). These might be due to the presence of long-stay patients and frequent visitors to the clinic who are immunocompromised. For patients with BAPS3/ST97, the bloodstream infection term of \"sepsis\" was found to be the most important. For BAPS5/ST88, similar terms, i.e., \"septic\" and \"shock,\" still ranked among the top ten most important terms. These critical clinical circumstances for patients carrying ST97 and ST88 are in concordance with the higher in-hospital mortality for these patients. Underscoring the clinical significance of these two clones (see Discussion), the evidence also shows the broad infection sites and patients\u0026rsquo; circumstances linked with the infection/colonization of each of CA-MRSA clone.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we employed a population-level surveillance approach to decipher the genomic epidemiology, population dynamics, and evolution of a systematic collection of \u003cem\u003eS. aureus\u003c/em\u003e from a single hospital. The depth and breadth of the sampling from various body sites and hospital wards, as well as the availability of detailed clinical data, allowed us to obtain a comprehensive image of the evolving clones in a comparative genomic framework. Our results demonstrate a diverse population with high dynamics in the acquisition of antimicrobial resistance and virulence genes for CA-MRSA clones, which coexist in the hospital.\u003c/p\u003e \u003cp\u003eWe observe multiple colonies of CA-MRSA in competition and each clone to have acquired peculiar virulence and antimicrobial resistance genes and were associated with hospital associated bacteremia, as reported before [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In some clones, virulence genes, resistance genes and plasmids were acquired over few years across different lineages. Further we provide evidence for horizontal gene transfer through sharing of plasmids carrying resistance genes between distinct clones, although MRSA is not a recombinogenic strain. Virulence of CA-MRSA is a complex trait, which implicate multiple genes and pathways, not fully characterized [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The diversity of underlying genes in our collection point to multiple evolutionary trajectories, involving transfer of virulence and resistance genes of CA-MRSA in response to various antimicrobial treatments and infection types.\u003c/p\u003e \u003cp\u003eThis study provides the first large-scale whole genome sequencing insights genomic study for MRSA in Saudi, while previous studies were either examined few strains or employed low resolution genetic typing methods. Some of the major reported clones, i.e. ST5 and ST8, are globally known circulating clones, while others, i.e. ST97 and ST672 are less well-characterized [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. ST97, which showed strong link with in-hospital mortality, potential introduction from nasal into the bloodstream, and link with sepsis, was previously reported from infections in outbreaks and appear to be an emerging clone of zoonotic origin. The clone emerged in the pig and independently acquired virulence and resistance determinants over time [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The clinical manifestation of ST97, in addition to specific virulence factors, suggest that the clone has the potential to be become a dominant global clone in human infections.\u003c/p\u003e \u003cp\u003eDespite significant insights that our study provided, we highlight few limitations. First the sampling covered one and a half years of evolution of MRSA, and therefore long-term dynamics of the clones count not be examined. MRSA clones are known to compete, and dominant lineages may change over few years. This clonal replacement may involve the two strains belonging to the same clone but harboring different SCCmec, as reported for ST5 SCCmec type I by the ST5-SCCmec type II [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. A longer window will also allow deciphering the evolutionary advantage of the virulence and resistance genes exclusive to each clone and determining whether these factors enable the clone to outcompete other clones. Furthermore, despite the detailed available clinical data, some key information about the cause of death could not be retrieved from the patients\u0026rsquo; history. This complicated establishing a definitive link between the colonization by STs and odds of death attributable to the CA-MRSA infection. This limitation of EHR data is recognized as the data is primarily produced for clinical purposes and not research and therefore patient\u0026rsquo;s data might not be always complete [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. However, the insight from the study still demonstrates the value of integrating detailed clinical data, including diagnostic notes. This necessitates the integrity and completeness in clinical metadata in future studies to attain a comprehensive picture of the evolution of MRSA strains in hospitals.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe co-existence of multiple clones with contrasting dynamics and underlying genetic biomarkers of resistance may reflect a flexible and plastic genome for CA-MRSA, allowing rapid evolution and adaptation to hospital environments. Our study underscores the need for continuous, long-term genomic surveillance to understand the epidemiological features of the clones and design targeted preventive strategies and measures based on the pathogenicity and resistance abilities of the clones. Since, the hospital has been at the heart of a major highway linking Mekkah, where more than 2M Muslim pilgrims pass throughout the year, the city may serve as a melting pot for the transmission of emerging and well-established virulent MRSA strains. Thus, setting up a continuous monitoring of MRSA strains is crucial to further understand and to early detect novel resistance and virulence strains.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomic sequencing data was deposited in the European Nucleotide Archive (ENA) and GeneBank under the study accession \u003ca href=\"https://urldefense.com/v3/__https:/ksauhsedu-my.sharepoint.com/:f:/g/personal/ansarihi_kaimrc_edu_sa/EjUzPBRd7BtNrp1tOyhPPrUBMLzOUEYiv6ROF6-b4pmwxA?e=pGwYV2__;!!Nmw4Hv0!yhOzYpQD6kRFOIFkq_D1dNOxqmOnB7soZeGzf1nk3wTuQb7ZeiyNZz8zuz1PtpcpsMzuiet4fKkBI5IX1BAiESnEUQX7bg5vEVI$\" target=\"_blank\"\u003ePRJEB60942\u003c/a\u003e and PRJNA954771, respectively. The metadata for the strains are described in Supplemental Table S1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of conflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDM and GZ are supported by KAUST baseline (BAS/1/1108-01-01). SH, HA, MB are funded by KAIMRC, MNGHA grant (NRJ21J/290/11\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported partially by national infectious diseases initiative at KACST.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSH and DM conceptualized the study and designed experiments. SH and DM wrote and edited the paper. SH, OF, WB, MM, HA, JH and GZ conducted research and collected data. MA, SZ, AAMB, MHHA, LHHA, HARU, AFM, AAAA, DA, MK, MB, MAS, and AA contributed to sequencing and collection curations. HARU, AFM, AAAA, AAAB, DA, MK, MB, MAS, and AA, provided lab and administration support during the study provided lab and administration support. DM supervised and managed the study and provided overall guidance. All authors have read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHowden BP, et al. Staphylococcus aureus host interactions and adaptation. Nat Rev Microbiol. 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Jul 2016;363(14). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/femsle/fnw137\u003c/span\u003e\u003cspan address=\"10.1093/femsle/fnw137\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRezel-Potts E, Gulliford M. Electronic Health Records and Antimicrobial Stewardship Research: a Narrative Review. Curr Epidemiol Rep, pp. 1\u0026ndash;10, Jul 21 2022, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s40471-021-00278-1\u003c/span\u003e\u003cspan address=\"10.1007/s40471-021-00278-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Figure S4","content":"\u003cp\u003eFigure S4 is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3788315/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3788315/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMethicillin-resistant Staphylococcus aureus (MRSA) poses a significant public health threat due to its ability to cause a range of diseases in humans and its resistance to multiple classes of antimicrobials. Community-associated MRSA (CA-MRSA) strains, originating in the community, are increasingly known to underlie hospital infections. However, the dynamics of CA-MRSA clones in hospital settings are not well-characterized.\u003c/p\u003e \u003cp\u003eMethods\u003c/p\u003e \u003cp\u003eIn this study, we conducted a genomic survey of a systematic cross-sectional collection of MRSA isolated over one and a half years in a major tertiary hospital in Jeddah, southwest Saudi Arabia. We subjected 194 isolates recovered from different body sites of 175 patients over two years to whole-genome sequencing and integrated the genomic data with detailed clinical information from electronic health record (EHR) data. We employed a broad range of genomics and text and data mining approaches to decipher the dynamics of MRSA clones, including resistance and virulence mechanisms, and the clinical manifestation of MRSA infections.\u003c/p\u003e \u003cp\u003eResults\u003c/p\u003e \u003cp\u003eOur results revealed a diverse clonal population underlying the population diversity in the hospital, with six dominant sequence types (STs) concurrently expanding over the past six decades. The major clones in the population harbored hallmarks of CA-MRSA, belonging to ST5 (n\u0026thinsp;=\u0026thinsp;33), ST672 (n\u0026thinsp;=\u0026thinsp;36), ST97 (n\u0026thinsp;=\u0026thinsp;14), ST6 (n\u0026thinsp;=\u0026thinsp;15), ST88 (n\u0026thinsp;=\u0026thinsp;19), and ST8 (n\u0026thinsp;=\u0026thinsp;27). The PVL locus was found in 60% of the ST8 strains and three strains of ST97 and ST88. Phylodynamic analysis showed that ST97, ST6, and ST672 formed more recently than other clones over the past two decades. ST97 carriage was significantly linked to in-hospital mortality and the diagnosis of sepsis. We characterized multiple cases of cross-resistance and showed diverse symptoms associated with colonization/infection by each ST. We further identified the emergence of antimicrobial resistance determinants within each clone and found evidence of the sharing of plasmids carrying antimicrobial resistance genes across multiple MRSA lineages.\u003c/p\u003e \u003cp\u003eConclusion\u003c/p\u003e \u003cp\u003eAltogether, the study presents an in-depth analysis of the complex dynamics of MRSA, reflecting the concurrent emergence of multiple clones in a single hospital and highlighting the multiple introductions of CA-MRSA strains into the hospital.\u003c/p\u003e","manuscriptTitle":"Concurrent Clonal Expansion of Community-Associated Methicillin-resistant Staphylococcus aureus (MRSA) Clones in a Tertiary Hospital","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-05 20:06:50","doi":"10.21203/rs.3.rs-3788315/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bb39ba75-881e-4362-a0c8-c9ce18a38acf","owner":[],"postedDate":"January 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-09T00:14:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-05 20:06:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3788315","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3788315","identity":"rs-3788315","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}