Staphylococcus aureus ST764-SCCmecII high-risk clone in bloodstream infections revealed through national genomic surveillance integrating clinical data

Staphylococcus aureus ST764-SCCmecII high-risk clone in bloodstream infections revealed through national genomic surveillance integrating clinical data | 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 Article Staphylococcus aureus ST764-SCCmecII high-risk clone in bloodstream infections revealed through national genomic surveillance integrating clinical data Motoyuki Sugai, Junzo Hisatsune, Shoko Kutsuno, Yasuhisa Iwao, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4824867/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Mar, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Antimicrobial resistance is a global health concern, and methicillin-resistant Staphylococcus aureus (MRSA) is one of the highest priority organisms exhibiting this phenotype. Here, we performed a national surveillance integrating patient clinical data of S. aureus isolated from bloodstream infections. We performed genome sequencing, standardized antimicrobial susceptibility testing, and collected clinical metadata of 580 S. aureus isolates collected during 2019–2020. We focused on three predominant clonal complexes (CC1, CC5, and CC8) and assessed their microbiological and clinical significance and regional prevalence. Furthermore, we conducted a genomic comparison of the isolates of 2019–2000 with those of 1994–2000 and investigated the evolutionary trajectory of emerging clones from the three dominant clonal complexes. We revealed that the emerging MRSA ST764-SCC mec II showed the highest mortality rate within 30 days of hospitalization. This high-risk clone diverged from the New York/Japan clone (ST5-SCC mec II), which was inferred to have undergone repeated infections with phages carrying superantigen toxin genes and acquired antimicrobial resistance genes via mobile genetic elements, leading to its emergence around 1994. Overall, we provide a blueprint for a national genomic surveillance study that integrates clinical data and enables identification and evolutionary characterization of a high-risk clone. Biological sciences/Microbiology/Bacteriology Health sciences/Diseases/Infectious diseases/Bacterial infection Staphylococcus aureus bloodstream infection genomic surveillance mortality antimicrobial susceptibility Japan Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Antimicrobial resistance (AMR) is one of the greatest threats to human health, necessitating effective surveillance. Staphylococcus aureus is an important antimicrobial-resistant bacterium. Its spread in healthcare and community settings and in the livestock industry is a global challenge because S. aureus causes diverse pathologies, from skin and soft tissue infections to systemic invasive diseases, such as pneumonia, septicemia, infective endocarditis, osteomyelitis, toxic shock syndrome, and food poisoning through its enterotoxin 1 . Bloodstream infection (BSI) caused by S. aureus is a severe condition with an increased mortality risk 2,3 . An estimated 119,247 S. aureus BSIs with 19,832 associated deaths occurred in the United States 4 in 2017. The number of BSI deaths attributed to S. aureus in Japan was 17,412 in 2011 and 17,157 in 2017, wherein 5,924 (34%) deaths were attributed to methicillin-resistant S. aureus (MRSA) in 2011, which significantly decreased to 4,224 (24.6%) in 2017 5 . The ST5-Staphylococcal Cassette Chromosome mec (SCC mec ) II is present in the New York/Japan (N/J) clone, the most common clone responsible for healthcare-associated MRSA (HA-MRSA) BSIs in Japan, although infections by this clone have recently been declining 6,7,8 . ST764-SCC mec II, belonging to clonal complex (CC) 5, was first isolated from the blood cultures of patients with invasive infections in Japan in 2005 and was reported as a hybrid variant of ST5-SCC mec II and community-acquired MRSA (CA-MRSA) with acquired virulence genes 9 . Recently, ST764-SCC mec II has been isolated from invasive infections in Asia, including Thailand and China 10,11 . In contrast, the proportion of CA-MRSA-SCC mec IV has increased in isolates from invasive infections of MRSA BSIs 8,12 and other SCC mec IV MRSA clones (ST1-SCC mec IV or ST8-SCC mec IV) 8,13 . However, CA-MRSA SCC mec IVa USA300 lineages (including USA300-LV, a Latin American variant), which are predominant in nosocomial settings in North and South America and cause severe infections with high mortality rates, are still not common in Japan 8 . The ST8-SCC mec IVl with the CA-MRSA genotype, represented by the MRSA/J clone, was first isolated in Japan from the bullous impetigo of a child in 2003 14 , and one case of death caused by a strongly invasive pathotype was reported in 2012 15 . During the same period, we reported a case of systemically disseminated MRSA/J infection 16,17 . ST2725-SCC mec IV, belonging to CC1, which has never been reported outside Japan, was recently isolated from the blood cultures of patients in Japanese regional hospitals 18,19 . In recent years, various MRSA clones isolated from BSIs have emerged in Japan. However, studies on the molecular epidemiology and genomic characterization of MRSA isolates from BSIs are limited to certain healthcare facilities or regions. A pioneering national genomic surveillance study in Philippines included S. aureus , although it was limited by inconsistencies in the antimicrobial panels that were tested at sentinel sites, thus highlighting the importance of standardized phenotypic antimicrobial susceptibility testing 20 . Here, we conducted a large-scale national genomic surveillance of S. aureus isolated from patients with BSI during 2019–2020, performed antimicrobial susceptibility testing using the same panel in the same reference laboratory, collected clinical metadata of these S. aureus isolates, and investigated the clinical risk of the clones. We also conducted genomic comparisons with isolates collected during 1994–2000 to delineate the evolutionary trajectories of BSI-derived S. aureus clones. By combining the national genome sequencing data with the standardized antimicrobial susceptibility testing results and clinical metadata, this report highlighted dynamic changes in MRSA in 26 years (1994–2020) in Japan and the emergence of ST764-SCC mec II with high mortality risk associated with BSI. RESULTS Phylogenetic relationship and geographic distribution of 580 S. aureus isolated from blood during 2019–2020 Population structure analysis of S. aureus isolated from BSIs during 2019–2020 using Bayesian hierarchical clustering of the core genome alignment showed 13 distinct sequence clusters (SCs) ranging in size from seven to 155 genomes (Fig. 1 a and Supplementary Table 1). Phylogenetic analyses revealed three dominant SCs, namely SC1, SC3, and SC8, comprising 155 (26.7% of the total population), 127 (21.9% of the total population), and 73 genomes (12.6% of the total population), respectively (Fig. 1 a, pie chart 1). We performed in silico multilocus sequence typing to identify the sequence types (STs) that comprised the SC. The CCs of these STs were examined using the eBURST analysis. SC1 comprised CC1, which included ST1 (104 genomes, 67.1% of SC1), ST2725 (28 genomes, 18.1% of SC1), and ST81 (10 genomes, 6.5% of SC1) (Supplementary Table 1). SC3 comprised CC8 and, almost exclusively, ST8 (127 genomes, 84.3% of SC3). SC8 comprised CC5, which included ST5 (73 genomes, 57.5% of SC5) and ST764 (25 genomes, 34.2% of SC5). The most dominant STs of the 580 S. aureus were ST8 (18.4%), ST1 (17.9%), and ST5 (7.2%) (Fig. 1 a, pie chart 2). BSI-derived S. aureus from 2019 to 2020 contained 343 isolates (59.1%) from western and 237 strains (40.9%) from Eastern Japan (Fig. 1 a, pie chart 3). The most dominant STs in western Japan were ST8 (23.0%), ST1 (11.9%), and ST5 (7.0%) (Fig. 1 b, pie chart 1). However, the most dominant ST in Eastern Japan were ST1 (26.6%), ST8 (11.8%), and ST188 (7.6%). Additionally, the major STs of CC8 in SC3 were ST8 (approximately 80%) and ST630 (less than 10%) in both western and Eastern Japan (Fig. 1 c, middle panel). Similarly, the major STs of CC5 in SC8 were ST5 (approximately 57%) and ST764 (approximately 34%) in both western and Eastern Japan (Fig. 1 c, right panel). In Eastern Japan, the major STs of CC1 in SC1 were ST1 (75.9%), ST81 (10.8%), and ST2725 (4.8%). In western Japan, the STs of CC1 in SC1 were ST1 (56.9%) and ST2725 (33.3%), which were significantly higher than those in Eastern Japan. The proportion of MRSA of BSI-derived S. aureus during 2019–2020 was 46.4%, mostly SCC mec IV (76.5% of total SCC mec ) (Fig. 1 a, pie chart 4). The proportions of MRSA in western and eastern Japan were 47.5 and 44.7%, respectively (Fig. 1 b, pie chart 2). SCC mec IV was the most common MRSA of BSI-derived S. aureus in both regions, followed by SCC mec II. The major STs of MRSA (n = 269) were ST1 (34.6%) and ST8 (26%) (Supplementary Fig. 1, left panel). In Eastern Japan, the major STs of MRSA were ST1 (55.7%) and ST8 (14.2%). In addition, ST81, which belongs to CC1, was detected in Eastern Japan, although in small numbers, and not in western Japan. In contrast, in western Japan, the major STs of MRSA were ST8 (33.5%) and ST1 (21.3%). The number of STs of methicillin-susceptible S. aureus (MSSA) were more diverse than those of MRSA (Supplementary Fig. 1, right panel). These results indicate that the representative CCs of BSI-derived S. aureus in Japan were CC1, CC8, and CC5 (Fig. 1 a), with ST1 and ST2725 having different distributions in the Eastern and western regions. ST764 infection is associated with high mortality Subsequently, we investigated the association between these lineages and 30-day mortality rates. Notably, the 30-day mortality rate of S. aureus BSIs caused by the ST764-SCC mec II clone was 48% (Table 1 ), which was significantly higher than the 22% of the other clones ( p = 0.0088, log-rank test) (Table 2 ). The 30-day mortality rate associated with the ST8-SCC mec I clone was also high (50%) compared to that of the other clones, although the difference was not statistically significant owing to the small sample size (n = 10). Further analysis of the ST764-SCC mec II clone revealed that age was a confounding factor significantly associated with the ST764-SCC mec II clone ( p = 0.026, Mann–Whitney U test) and 30-day mortality rate ( p < 0.0001, univariate Cox regression) with p -values < 0.05. The association between the ST764-SCC mec II clone and 30-day mortality rate remained significant even after controlling for the confounding effect of age (Table 2 ) ( p = 0.04, hazard ratio 1.86, 95% CI: 1.03–3.38). There were no confounding factors in the following clinical variables: sex, diabetes, hemodialysis, surgery within 30 days before blood culture, artificial respirator use, and atopic dermatitis. Table 1 Comparison of the 30-day mortality rate of each clone. ST type 30-day mortality (%) Total (n = 580) 134/580 (23.0) MRSA (n = 269) 71/269 (26.4) MSSA (n = 311) 63/311 (20.3) CC1: ST1-MRSA-SCC mec IV (n = 93) 20/93 (21.5) ST2725-MRSA-SCC mec IV (n = 28) 8/28 (28.6) CC5: ST5-MRSA-SCC mec II (n = 11) 1/11 (9.1) ST764-MRSA-SCC mec II (n = 25) 12/25 (48.0) CC8: USA300 (n = 4) 1/4 (25.0) MRSA/J (n = 19) 3/19 (15.8) ST8-MRSA-SCC mec I (n = 10) 5/10 (50.0) ST8-MRSA-SCC mec IVj (n = 24) 8/24 (33.3) Table 2 Comparison of clinical factors between ST764-MRSA-SCC mec II and other clones. ST764-MRSA-SCC mec II (n = 25) Others (n = 555) p -value All (n = 580) Median age (IQR) 80 (78–88) 76(64–85) 0.026 77 (65–85) Male sex (%) 16 (64%) 340 (61%) n.s. 356 (61%) Diabetes 7 (28%) 151 (27%) n.s. 158 (27%) Hemodialysis 4 (16%) 49 (9%) n.s. 53 (9%) Surgery within 30 days before blood culture 3 (12%) 48 (9%) n.s. 51 (9%) Artificial respirator use 2 (8%) 40 (7%) n.s. 42 (7%) Atopic dermatitis 0 (0%) 17 (4%) n.s. 17 (3%) Death within 30 days of hospitalization 12 (48%) 122 (22%) 0.0088 0.04 # 134 (23%) # after adjusting for age Abbreviation: IQR, interquartile range n.s.: p > 0.1 Phylogenetic relationship of BSI-derived 183 S. aureus during 1994–2000 We performed a comparative phylogenetic analysis of S. aureus isolated from BSIs during 2019–2020 and 1994–2000. We collected data from a post-marketing surveillance conducted during 1994–2000 after levofloxacin (LVFX) was released in Japan 21 . Population structure analysis of 183 BSI-derived S. aureus isolated during 1994–2000 using Bayesian hierarchical clustering of the core genome alignment showed 10 distinct SCs, ranging in size from 2 to 109 genomes (Supplementary Fig. 2 and Supplementary Table 2). Phylogenetic analysis revealed that SC1 (109 genomes, 59.6% of the total population) was dominant (Supplementary Fig. 2, pie chart 1) and comprised CC5 and almost exclusively ST5 (97 genomes, 90% of SC1, 53% of the total population). The Shannon diversity index of the STs significantly increased from 3.08399 in 1994–2020 to 4.767595 in 2019–2020. This indicates that the diversity of STs in Japan increased over the past two decades despite differences in sample sizes between 2019–2020 and 1994–2000. The proportion of MRSA in BSI-derived S. aureus in 1994–2000 was over 56%, mostly SCC mec II (50.8% of the total population) (Supplementary Fig. 2, pie chart 4). The proportion of STs of MRSA was almost exclusively ST5 (84.6%) (Supplementary Fig. 3, left upper panel). The most common STs of MSSA was ST5 (12.7%); however, the ST types of MSSA were more diverse than those of MRSA (Supplementary Fig. 3, right upper panel). The predominant CC of 183 S. aureus during 1994–2000 was the CC5 lineage, including the N/J clone (ST5-SCC mec II) (Supplementary Fig. 2). These results confirm the dynamic changes in the population structure of MRSA within 20 years when comparing the phylogenetic analysis data of the isolates of 1994–2000 to those of 2019–2020. Comparative analysis of antimicrobial resistance pattern of BSI-derived S. aureus We examined the differences in the number of antimicrobial resistant genes (ARGs) between the isolates of 2019–2020 and 1994–2000 (Supplementary Fig. 4a). The average number of ARGs in 2019–2020 was 2.8, which was 2.2 less than that in 1994–2000 (average of 5.0) (Supplementary Fig. 4a, left). The number of ARGs between the two periods (1994–2000 and 2019–2020 with an average of 7.9 and 4.6, respectively) was significantly different for MRSA but not for MSSA (an average of 1.2 in both periods) (Supplementary Fig. 4a, right panel). The results of the proportion of each ARGs revealed that the prevalence of the aminoglycoside resistance gene aadD , tetracycline resistance gene tet (M), bleomycin resistance gene bleO , fosfomycin (FOM) resistance gene fosB , and chlorhexidine resistance gene qacA decreased between the two periods in the total samples or MRSA (Supplementary Fig. 4b). In contrast, the prevalence of aminoglycoside resistant gene ant ( 9 ) -Ia , macrolide resistant genes erm (A) and erm (C), FOM resistance gene fosD , chlorhexidine resistant gene qacB , and the quinolone resistance-determining region (QRDR) mutations (GrlA/S80F/E84G and GryA/S84L/E88G) contributing to quinolone resistance in chromosomal grlA and gyrA were not significantly affected in the total samples or MRSA. Conversely, the prevalence of the aminoglycoside resistance genes aac(6’)-aph(2”) , ant ( 9 ) -Ia , erm (A), erm (C), and QRDR mutations (GrlA/S80F and GyrA/S84L) increased in isolates of 2019–2020 in MSSA (n = 311) (Supplementary Table 3). Antimicrobial susceptibility testing showed that resistance to cefazolin (CEZ), cefmetazole (CMZ), gentamicin (GM), clindamycin (CLDM), and minocycline (MINO) significantly decreased in isolates of 2019–2020 in the total samples or MRSA; however, this was not significant for resistance to arbekacin (ABK), erythromycin (EM), and LVFX (Supplementary Fig. 4c, upper and middle panels). In MSSA, the level of resistance to each antibiotic was very low; however, resistance to EM and LVFX increased slightly from 1994–2000 to 2019–2020 (Supplementary Fig. 4c, lower panel). We subsequently examined the distribution of ARGs in CC1, CC5, and CC8, the dominant clones of BSI-derived MRSA of 2019–2020 (Fig. 2 ). In CC1, the ST1-SCC mec IV and ST2725-SCC mec IV lineages contained the same number of ARGs (mean, 3.9) (Fig. 2 a, CC1). Most ST1-SCC mec IV and ST2725-SCC mec IV strains had blaZ , ant ( 9 ) -Ia , erm (A), QRDR mutations (GrlA/S80F/E84G and GyrA/S84L), and the same distribution pattern (Fig. 2 b, CC1 and Supplementary Table 3). Consistent with the presence of these ARGs and QRDR mutations in ST1-SCC mec IV and ST2725-IV, the resistance rates of these strains to oxacillin (MPIPC), EM, and LVFX were high (Fig. 2 c, upper panel). In CC5, the average number of ARGs in ST5-SCC mec II of 1994–2000 was 8.2; however, in 2019–2020 it was 7.0, which was significantly lower than that of 1994–2000 (Fig. 2 a, CC5). The average number of ARGs in ST764-SCC mec II in 2019–2020 was 6.0, which was significantly lower than that in ST5-SCC mec II. The proportions of ARGs in ST5-SCC mec II during the two periods were high for aadD , ant ( 9 ) -Ia , erm (A), bleO , and QRDR mutations (GrlA/S80F/E84K and GyrA/S84L/S85P) (Fig. 2 b, CC5 and Supplementary Table 3). In contrast, the ARGs with a decreasing trend in ST5-SCC mec II in 2019–2020 were blaZ , aac(6’)-aph(2”) , tet (M), cat, fosB , and qacA . The ARGs showing an increasing trend in ST5-SCC mec II in 2019–2020 were erm (C), fosD , and qacB . ARGs with high proportions in the ST764-SCC mec II strain included ant ( 9 ) -Ia , erm (A), and QRDR mutations (GrlA/S80Y/E84K and GyrA/S84L/E88G); particularly, ARGs with a higher proportion than those in ST5-SCC mec II were aac(6’)-aph(2”) , tet (M), fosD , and qacB . The ARGs of ST764-SCC mec II aadD , erm (C), and bleO were lower in abundance than those of ST5-SCC mec II. Resistance rates to MPIPC, CEZ, CMZ, EM, CLDM, and LVFX remained consistently high in ST5-SCC mec II during both periods (Fig. 2 c, middle panel). The resistance rates to GM and MINO were significantly lower in ST5-SCC mec II of 2019–2020 than in that of 1994–2000. In contrast, ST764-SCC mec II showed very high rates of resistance to these antibiotics, except for ABK. In CC8, the average numbers of ARGs in ST8-SCC mec IVj and ST8-SCC mec IVa during 2019–2020 were 4.8 and 4.1, respectively, which were significantly lower than those in ST8-SCC mec I (average, 6.2) (Fig. 2 a, CC8). The average number of ARGs in ST8-SCC mec IV was lower than that in ST8-SCC mec I; however, the difference was statistically significant between ST8-SCC mec IVj and ST8-SCC mec IVa. Most ST8-SCC mec I possessed blaZ , aac(6’)-aph(2”) , ant ( 9 ) -Ia , erm (A), tet (M), and QRDR mutations (GrlA/S80F and GyrA/S84L/S85P) (Fig. 2 b, CC8 and Supplementary Table 3). ARGs with high proportions in ST8-SCC mec IVj included blaZ , ant ( 9 ) -Ia , ermA , tet (M), and QRDR mutations (GrlA/S80F and GyrA/S84L); ARGs with low proportions included aac(6’)-aph(2”) . Conversely, the proportion of ant ( 9 ) -Ia , erm (A), tet (M), and QRDR mutations (GrlA/S80F and GyrA/S84L) in ST8-SCC mec IVl was lower than that of other ST8-MRSAs, whereas that of aac(6’)-aph(2”) , aadD , bleO , and qacB was higher. The prevalence of mph (C) and msr (A) in ST8-SCC mec IVa was approximately 38%, which is unique to this clone. ST8-MRSA showed varying patterns of resistance to each antibiotic depending on the SCC mec type; however, ST8-SCC mec I was more resistant than the other clones (Fig. 2 c, lower). The small number of BSI-derived USA300 isolates in Japan makes the comparative assessment difficult. Comparative analysis of virulence factor genes (VFGs) of BSI-derived S. aureus We investigated the presence of VFGs in S. aureus isolates from BSIs. The proportion of toxic shock syndrome toxin-1 gene ( tst-1 ) in 2019–2020 significantly decreased compared to that in 1994–2000. In addition, the proportion of the staphylococcal enterotoxin gene cluster EGC ( seg , sei , sem , sen , and seo ) in the total samples or MRSA of 2019–2020 was drastically reduced compared to that in 1994–2000; however, it remained approximately 30% in MSSA (Supplementary Fig. 4d). In contrast, the proportions of sea , seh , sek , and seq in the total samples or MRSA of 2019–2020 increased compared to those of 1994–2000. A few Panton-Valentine leukocidin (PVL) genes ( lukF-PV and lukS-PV ), exfoliative toxin (ET) genes ( eta , etb , and etd ), and epidermal cell differentiation inhibitor (EDIN) genes ( ednA , ednB , and ednC ) were detected during both periods (Supplementary Fig. 4d). For a human-specific immune evasion cluster, the proportion of the staphylococcal complement inhibitor gene ( scn ) and staphylokinase gene ( sak ) in the two periods remained at the same level; however, that of the chemotaxis inhibitory protein Chp gene ( chp ) was markedly decreased in the total and MRSA. Subsequently, we examined the distribution of VFGs in CC1, CC5, and CC8, the dominant clones of BSI-derived MRSA in 2019–2020 (Fig. 3 ). In CC1, most ST1-SCC mec IV and ST2725-SCC mec IV had a high prevalence of sea , seh , sek , seq , scn , and sak , with the same distribution pattern (Fig. 3 , CC1). The proportion of VFGs in ST5-SCC mec II during the two periods was high for sec , tst-1 , EGC, and sel , whereas the lower proportion for sep was 36% (Fig. 3 , CC5). The proportion of seb in ST5-SCC mec II in 1994–2000 was 12% but was not detected in 2019–2020. The proportions of seb , seg , sei , sem , sen , and seo in ST764-SCC mec II were high, whereas the proportions of sek and seq were low (40%). ST764-SCC mec II did not possess sec , tst-1 , or sel . In CC8, ST8-SCC mec I and ST8-SCC mec IVj possessed 50 and 75% of sep , respectively, with no other SE genes present (Fig. 3 , CC8). The proportion of chp in the ST8-SCC mec IVj and IVl clones was significantly lower than that in the other clones. ST8-SCC mec IVl contained high proportions of sec , tst-1 , and sel (90%). In addition, ST8-SCC mec IVl possessed 90% sel and 40% se1 and ednA . ST8-SCC mec IVa had sek , seq , and PVL genes ( lukF-PV and lukS-PV ) belonging to the USA300 lineage. Evolutionary origins and population dynamics of three CC lineages of blood-derived S. aureus in Japan To provide a historical perspective on the emergence of the three dominant CC lineages of S. aureus causing bacteremia in Japan, we constructed time-calibrated phylogenies of CC1 (ST1 and ST2725), CC5 (ST5 and ST764), and CC8 (MRSA/J and USA300) using Bayesian coalescent analysis implemented in the BEAST software. Each phylogenetic tree was calibrated using the isolation dates of the strains, which ranged from 1997 to 2020 (CC1) and 1982 to 2020 (CC5 and CC8), and public collection isolates with identifiable isolation dates (Supplementary Fig. 5 and Supplementary Table 4). Root-to-tip regression analyses revealed a positive correlation between genetic distance and sampling date for each lineage (Supplementary Fig. 6). Given the presence of a temporal structure in each dataset, we performed dated coalescent phylogenetic analysis. Consequently, we estimated the time to the most recent common ancestor (tMRCA) of the ST1-SCC mec IV and ST2725-SCC mec IV lineages to be approximately 1998 (95% highest posterior density [HPD] intervals: 1995–2002) (Fig. 4 a). The results of this analysis indicated that the ancestor of ST1-MRSA- t 1784 acquired SCC mec IV, Tn 554 harboring ant ( 9 ) -Ia , and erm (A) 22 after 1965 (95% HPD intervals: 1958–1974). ST1-MRSA- t 1784 emerged around 1998 (95% HPD intervals: 1995–2002) and started to circulate in Japan after 2000. Six years later, around 2004 (95% HPD intervals: 2001–2006), the ST2725-SCC mec IV- t 1784 lineage diverged from ST1 and spread primarily to western Japan (Fig. 4 a square light blue and Fig. 1 c bar graph in the left panel). However, the ST1- t 127 lineage diverged in 1953 and acquired the genomic island νSa4, carrying the seb and fusidic acid resistance gene fusC -harboring SCC fus 23 by phage infections somewhere up to 1970. Through a timescale phylogenetic analysis, we first estimated the age of SCC fus acquisition in the ST1- t 127 lineage. Reconstruction of the effective population size over time showed that the effective population size of CC1 remained steady until approximately 2000 but sharply rose after 2005, following the emergence of many lineages (Fig. 4 a, lower panel). In CC8, USA300 isolated from BSIs in Japan belonged to the USA300-NAE (North American Epidemic) lineage 24 and appeared around 1989 (95% HPD intervals: 1986–1992) (Fig. 4 b). This is consistent with the previously reported data 24 . The trajectory of the emergence of ST8-SCC mec IVl (MRSA/J) (Fig. 4 b, square light blue) was somewhere between 1957 and 1986, when it acquired SCC mec IVl and νSa4 (carrying sec , tst-1 , and sel ) by phage infections (Fig. 4 b, light blue of index No. 2 and 5, and Supplementary Fig. 7a) and plasmids carrying se1 (Fig. 4 b, light blue of index No. 5, and Supplementary Fig. 7b), emerging around 1986 (95% HPD intervals: 1983–1989) contemporaneously and in parallel with the USA300-NAE lineage (SCC mec IVa-arginine catabolic mobile element [ACME] type I). In addition, the ST8-SCC mec I and ST8-SCC mec IVj lineages emerged around 1979 (95% HPD interval: 1976–1982) and 1988 (95% HPD interval: 1985–1991), respectively. Some strains of the ST8-SCC mec I and ST8-SCC mec IVj lineages were estimated to have acquired tet (M)-harboring Tn 916 -like transposable units in the late 1980s and 1990s (Fig. 4 b and Supplementary Fig. 7c). These multiple ST8 lineages emerged one after another during the 1980s–1990s, and the effective population size of CC8 increased in the late 1990s-2000s (Fig. 4 b, lower). In CC5, we estimated the tMRCA of the ST5-SCC mec II (N/J clone) and ST764-SCC mec II lineages to be around 1959 (95%HPD interval: 1956–1962) (Fig. 4 c). Our genome sequence comparison inferred the trajectory of the emergence of the ST764-SCC mec II clone from the N/J clone based on recombination events (index No. 5 in Fig. 4 c and Supplementary Fig. 8). At the first step, somewhere between 1928 and 1959, SCC mec II, genomic island νSa3 (carrying sec , tst-1 , and sel ) (Fig. 4 c and Supplementary Fig. 8a) and tet (M)-harboring Tn 916 -like transposable unit (Supplementary Fig. 8b) were acquired by phage infection or transposition, respectively, resulting in the emergence of ST5-SCC mec II (N/J clone). In the second step, after 1974 (95%HPD interval: 1971–1976), N/J clones acquired the genomic island νSa4 carrying seb by phage infection and emerged as a seb -positive-N/J lineage, which evolved into the ST764-SCC mec II lineage (Fig. 4 c and Supplementary Fig. 8c). At the third step, genomic island νSa3 (carrying sec , tst-1 , and sel ) was replaced by a completely different phage, followed by a recombination event at the same location at the νSa3 of N/J clone, loosing blaZ (Fig. 4 c and Supplementary Fig. 8a). Finally, approximately 20 years after the emergence of the N/J clone, around 1979 (95% HPD intervals: 1977–1982), the ST764-SCC mec II lineage diverged from ST5 and spread throughout Japan after 1994. One sub-lineage of ST764-SCC mec II acquired the genomic island νSa1 carrying sek and seq by phage infection somewhere between 1998–2001 (Fig. 4 c and Supplementary Fig. 8d). Another ST764-SCC mec II sub-lineage acquired ACME type II somewhere between 1996 and 1999 (Fig. 4 c and Supplementary Fig. 8e). However, the ST5-SCC mec I ancestor diverged from the N/J lineage around 1928, and somewhere between 1932–1967, the ST5-SCC mec I lineage emerged by acquiring SCC mec I and νSa4 (carrying sec , sek , and seq ) by phage infections but did not spread as far in Japan (Supplementary Tables 1 and 2). ST5-SCC mec IV followed a different phylogenetic trajectory from the clones described above, emerging somewhere between 1966 and 1994 with the acquisition of SCC mec I, which was also not widespread in Japan (Fig. 4 c). The effective population size of CC5 increased in a staircase-like manner during the emergence of ST5-SCC mec II and ST764-SCC mec II (Fig. 4 c, lower panel). DISCUSSION More than 80 years since the antimicrobial-resistant S. aureus (including MRSA) was first recognized, MRSA has rapidly evolved following the widespread use of antimicrobials and has been reported to have a remarkable potential to spread globally 25 . Here, we used 580 S. aureus genomes from the late 2010s and 183 genomes from the late 1990s, derived from BSIs, to determine the evolutionary history and geographical distribution of Japanese BSI-derived S. aureus , focusing on three CC lineages. We previously reported that the prevalence of MRSA in inpatients decreased from 40.3 to 35.1% between 2011 and 2019, according to data from the national phenotypic antimicrobial surveillance (Japan Nosocomial Infections Surveillance, JANIS), and the analysis of resistance profiles indicated that isolates resistant to three antimicrobials (OXA, EM, and LVFX) increased, whereas those resistant to six antimicrobials (OXA, GM, EM, CLDM, MINO, and LVFX) decreased 26 . A comparison of the resistance profiles and genome data of BSI-derived isolates in a single university hospital between 2011 and 2019 revealed that increased resistance to the three drugs corresponded to CC8-SCC mec IV. Here, we compared BSI-derived MRSA isolates of 1994–2000 with those of 2019–2020, wherein the proportion of isolates resistant to the six drugs decreased from 33.7 to 10.8%, whereas the proportion of isolates resistant to three drugs sharply increased from 1 to 41.6%, confirming the change in the multidrug-resistant phenotype of MRSA (Supplementary Table 5 and Supplementary Fig. 9). Additionally, our genome analysis indicated that ST5-SCC mec II (N/J clone) showed significant reduction in the proportion of isolates resistant to the six drugs, whereas ST764-SCC mec II appeared as a new clone resistant to the six drugs during 2019–2020. Conversely, 96.4% (109/112 strains) of the increased proportion of isolates resistant to the three drugs belonged to the CC1-SCC mec IV lineage, which was different from the results of the previous study in a single university hospital. The reason for this difference is that the single university hospital was located in western Japan, where CC8-SCC mec IV is more prevalent (Supplementary Fig. 1); however, in the nationwide surveillance of BSI-derived S. aureus used this study, the predominant CC1-SCC mec IV had a significant impact on the resistance to the three antimicrobials. ST1-SCC mec IV and ST2725-SCC mec IV had extremely high rates of erm (A) and QRDR mutations, and these genes contributed significantly to resistance to the three antimicrobials (Fig. 2 b, CC1). This drastic change in MRSA clones reflected not only the resistance gene repertoire but also the virulence gene repertoire of MRSA (Fig. 3 , Supplementary Fig. 1). A significant decrease in the proportion of tst-1 and EGC ( seg , sei , sem , sen , and seo ) and an increase in the proportion of sea , seh , sek , and seq can be attributed to an increase in ST1-SCC mec IV and ST8-SCC mec IV and a decrease in ST5-SCC mec II. Our timescale phylogenetic and population dynamics analysis showed that ST5-SCC mec II emerged around 1960 and reigned as a representative of HA-MRSA in Japan until 2000. However, after 2000, it was replaced by some sublineages of CC1-SCC mec IV and CC8-SCC mec IV, which are representative clones of CA-MRSA. This was further supported by several previous epidemiological studies 8,12,27 . Regional differences in the distribution of CC1 were observed: ST1 and ST81 tended to be slightly more common in Eastern Japan, whereas ST2725-SCC mec IV circulated slightly more frequently in western Japan (Fig. 1 c). These STs-SCC mec IV possessed almost identical patterns of ARGs, VFGs, and antimicrobial resistance patterns (Fig. 2 b, c, and Fig. 3 ). Notably, the interval between the emergence of ST1-SCC mec IV and ST2725-SCC mec IV was very short, only approximately five years, as revealed for the first time in our study (Fig. 4 a). Most ST1-SCC mec IV- t 1784 isolates in Japan belong to a different lineage from the PVL-negative ST1-SCC mec IV- t 127, which is widespread in Europe 28 . Moreover, some subclades of European ST1-SCC mec IV- t 127 have SCC mec fus carrying the fusidic acid resistance gene fusC but not ST1-SCC mec IV- t 1784. The Japanese fusC -positive ST1-MSSA- t 127 strain is rare and mecA negative (Supplementary Table 2). ST1-SCC mec IV- t 1284 is a more modern lineage than European ST1- t 127, according to timescale phylogenetic analysis. Japanese ST2725-SCC mec IV- t 1784 has been detected in both inpatients and outpatients, suggesting that it has spread to hospitals and community 19 . To the best of our knowledge, the ST2725-SCC mec IV- t 1784 clone has not been reported overseas and is a relatively young community-acquired MRSA unique to Japan. In contrast to CC1, CC5 was distributed nationally, without an eastern-western regional bias. Although CC5 in Japan has been replaced, its proportion is still high, ranking third (approximately 12%) after CC1 and CC8. The dominant CC5, ST5-SCC mec II (the N/J clone), has spread 29 and circulated in medical centers throughout Japan since its emergence in 1959. Over a period of approximately 35 years, our time-calibrated phylogenic analysis inferred that the ST5-SCC mec II lineage underwent repeated acquisition or shedding via superantigen toxin-harboring phage infection and antimicrobial resistant gene-carrying transposons, leading to the emergence of the ST764-SCC mec II lineage around 1994. Furthermore, herein, we found that several strains of ST764-SCC mec II were independently acquired by phage infection carrying sek / seq or ACME-II and the ACME-related cassette JR1 (cJR1) around 2000 (Fig. 4 c, 3 blue bar). ACME-II was first identified in S. epidermidis 30 . Urushinbara et al. reported several variants of the ACME-II-SCC mec composite island (ACME-II-SCC mec -CI) in ST764, isolated from Hokkaido, Japan 31 . Therefore, we presume that the ST764-SCC mec II lineage isolated in Japan is becoming increasingly diverse. ST764-ACME-SCC mec -CI may be distributed throughout Northern-Eastern Japan, particularly in Hokkaido. We demonstrated that ST764-SCC mec II was the highest-risk clone, with statistical significance, according to the risk assessment of the 30-day mortality rate (Table 1 ). The spread of seb -positive ST764-SCC mec II clones in long-term care facilities in Japan was recently reported by Kawamura 32 . In our study, 72.4% (21/29) of the registered ST764-SCC mec II cases were ST764-SCC mec II- t 002 (Fig. 4 c, 4 ocher bar). In China, ST764-SCC mec II- t 1084, a lineage similar to the Japanese ST764-SCC mec II- t 002, has recently been reported to increase as a hypervirulent clone 11 . Our study indicates that age is a confounding factor significantly associated with ST764 and 30-day mortality. This is in agreement with a previous observational study demonstrating the spread of ST764 in Japanese LTCF residents with multiple comorbidities and increased susceptibility to infections 32 . The association between ST764 and 30-day mortality rate remained significant, even after controlling for the confounding effect of age. A previous regional surveillance study demonstrated that all MRSA-SCC mec II isolates from patients with pneumonia belonged to ST764 33 . Our recent epidemiological risk assessment study of isolates from patients admitted to the intensive care unit 34 and isolates from BSIs demonstrated that the primary focus of ST764 was pneumonia. Furthermore, ST764 was the only lineage that emerged and increased in number within 20 years and was resistant to six antimicrobials: OXA, GM, EM, CLDM, MINO, and LVFX. Overall, these results strongly suggest that ST764 is an emerging highest-risk MRSA clone with multiple antimicrobial resistances that causes BSI. Intriguingly, comparison of the complete genome sequences of ST764 with its ancestral lineage ST5 revealed frequent detection of a large chromosomal inversion (24% in 25 complete genomes of ST764 compared to the reference genome N315 in this study vs. 3.4% in 147 of ST5 in the NCBI public database) (Supplementary Fig. 10 and Supplementary Table 6). In prokaryotes, large chromosomal inversions are associated with phenotypic changes in bacterial virulence through phase variation 35,36,37 , thereby contributing to bacterial survival strategies and defining evolutionary trajectories. ST764 may have been in the middle of an evolutionary process. CC8 tends to be slightly more common in western Japan; however, it has spread almost nationwide, and when considering the MRSA surveillance results of previous research 8,13 , it is reconfirmed that CC8 circulates in community 8 and hospital settings. Most CC8 isolates from BSIs in 2019–2020 were ST8-SCC mec IV. We revealed through timescale phylogenic analysis that ST8-SCC mec I and multiple ST8-SCC mec IV clones emerged around the same period in the 1980s. In recent years, the USA300 lineage, which is increasingly found in dermatology clinics in Japan, has been isolated from BSIs in healthcare settings at a low rate 8 . All the detected USA300 strains belonged to the USA300-NA lineage. We first revealed that the emergence of MRSA/J clone associated with invasive infections occurred in Japan, almost at the same period as USA300-NA. This study has some limitations. First, we performed in-depth genomic analysis of only three major clonal lineages CC1, CC5, and CC8 but not that of minor lineages. Second, clinical data for isolates in the period 1994–2000 were not available. Therefore, we were unable to compare the clinical significance of ST764-SCC mec II with ST5-SCC mec II (the N/J clone), which was dominant in 1994–2000 and the ancestor clone of ST764-SCC mec II. Third, we were not able to collect isolates from the Tohoku-region in eastern Japan and the Shikoku-region in western Japan, leaving a possibility of bias. Despite these limitations, this is the first study revealing that MRSA populations causing BSIs in Japan are primarily composed of three clonally expanded CC lineages, each emerging and spreading at distinct times and places. This study further identified ST764-SCC mec II- t 1084 as a clone associated with a high mortality rate and characterized its evolutionary trajectory. In summary, our study provides a blueprint for national genomic surveillance that integrates clinical data and enables identification and evolutionary characterization of a high-risk clone. METHODS Study design We recruited 63 Japanese medical institutions through the National Hospital Organization. S. aureus isolates were detected in two or more blood samples obtained at the same time collected at each hospital between April 2019 and July 2020. In total, 798 isolates were collected from 55 hospitals by the Antimicrobial Resistance Research Center (AMR-RC) of the National Institute of Infectious Diseases (NIID). Of the 798 strains collected, eight S. argenteus and one S. epidermidis isolates were re-identified by MALDI biotyper (Bruker Daltonics, Billerica, MA) at the AMR-RC-NIID and excluded from subsequent analyses. Patient information used in this study included age, sex, race, date of admission, underlying medical condition, presence or absence of diabetes, presence or absence of dialysis, medical history, surgical history (30 days to time of blood culture), presence or absence of injection drug use, presence or absence of HIV infection, history of influenza (within 2 weeks), infection site, date of discharge or death, and 30-day mortality rate (from the time of blood culture collection). Although two sets of blood culture tests are usually performed, for samples with positive strains in both vials, the S. aureus isolate from the first vial was used for whole-genome sequencing and integrated analysis with clinical data, and the isolate from the second vial was excluded. Only first-time isolates from the same patient were used, and S. aureus re-isolated multiple times were excluded. Ultimately, of the 789 S. aureus strains, 209 strains were excluded, and 580 isolates were analyzed. For comparative analysis of BSI-derived S. aureus isolates from decades ago, we used a nationwide collection (also known as the YK-Collection) of clinical isolates from 1994 to 2016 in a previous study 21 , which was stored in the Japanese AMR bank ( https://jarbb.jp/en/about/ ) at NIID-AMR-RC. We randomly selected 183 BSI-derived S. aureus isolates from 1994 to 2000. All isolates were stored at − 80 ºC in preservation medium supplemented with 30% (vol/vol) glycerol and cultured at 37 ºC in tryptone soy broth. The following data regarding S. aureus isolates were collected from participating medical institutions: day of isolation in blood culture, day of admission, infected nest, underlying disease, and day of discharge or death. Ethics approval This study was approved by the Medical Research Ethics Committee of the National Institute of Infectious Diseases (approval no. 1251). Although approval was granted for the overall study, each participating hospital obtained approval from the respective ethical approval committee. Regarding informed consent, an opt-out approach (a method where research information is disclosed on websites, providing research subjects the opportunity to decline participation) was implemented at each participating medical institution. All S. aureus isolates were anonymized and individually numbered when isolated from blood cultures. All data and isolates were fully anonymized before being sent to AMR-RC-NIID. DNA extraction and whole-genome sequencing All isolates were subcultured from glycerol stocks onto tryptic soy agar at 37 ºC for overnight. A single colony was picked up and cultured at 37 ºC in TSB for 12 h. Genomic DNA was extracted from the liquid culture using lysostaphin (FUJIFILM Wako Pure Chemical Corp., Osaka, Japan) and Agencourt AMPure XP (Beckman Coulter Inc., Brea, CA, USA), according to the manufacturer’s instructions. Short-read DNA libraries were prepared for Illumina sequencing using the Enzymatic 5X WGS Fragmentation Mix, 5X WGS Ligase Mix (BioStream Corp, Tokyo, Japan), and the automated NGS preparation system Biomek i7 Workstation (Beckman Coulter Inc., Brea, CA, USA). Short-read Illumina sequencing was performed on the Illumina HiSeq X FIVE platform to generate 150-bp paired-end reads at Macrogen Japan Corp., Tokyo, Japan, and on Illumina MiSeq to generate 300-bp paired-end reads at the NIID-AMR-RC according to the manufacturer’s instructions. For long-read sequencing, genomic DNA from each strain was purified using the Monarch HMW DNA Extraction Kit for Tissue (#T3060; New England BioLabs, Ipswich, MA, USA) following the manufacturer’s instructions. A long-read DNA library was prepared using the SQK-RBK004 Rapid Barcoding Kit [Oxford Nanopore Technologies (ONT), Oxford, UK], and barcoding was performed on GridION (ONT, Oxford, UK) using MinKNOW v21.05.25 and an FLO-MIN106 flow cell (ONT, Oxford, UK). De novo assembly and annotation Quality control of the raw sequenced reads was performed using FastQC v0.11.5 ( https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ ). Illumina reads were assembled into contigs using Shovill v1.0.9 pipeline (available at https://github.com/tseemann/shovill ) with the option –trim to produce high quality draft genomes. The Shovill program performs subsampling of read depth down to 150X, trimming adapters, correcting sequencing errors, and assembling using SPAdes v3.15.5 38 . ONT reads were trimmed using FiltLong ( https://github.com/rrwick/Filtlong ), assembled with trimmed long reads using Flye v2.9.1 39 , and polished with trimmed Illumina reads using Pilon v1.24 40 . Quality of the assembled genome sequences was assessed using QUAST v4.0 41 and CheckM v1.1.0 42 . We performed the taxonomy check of each genome using the dfast_qc v0.4.2 ( https://github.com/nigyta/dfast_qc ) and confirmed S. aureus . Genome annotation of all the isolates was performed using DFAST-core v1.2.16 43 . In silico sequence typing and detection of virulence factor and antimicrobial resistance genes The ST of each isolate was defined using mlst v2.22.1 ( https://github.com/tseemann/mlst ), which extracts seven housekeeping genes ( arcC , aroE , glpF , gmk , pta , tpi , and yqiL ) from the sequence contigs and matches them against characterized STs in the S. aureus PubMLST database ( https://pubmlst.org/organisms/staphylococcus-aureus/ ). PHYLOViZ v2.0 44 with the geoBURST Full MST algorithm was used to determine CC in the MLST database (as of July 2022). The presence of virulence factors and antimicrobial resistance genes was detected by ABRicate v1.0.1 ( https://github.com/tseemann/abricate ) with ResFinder 45 database 2022-06-09 and VFDB 46 database 2022-06-09 and custom virulence factor database for S. aureus 47 with 90% identity and 90% query coverage cutoffs. PointFinder v4.1.11 48 was used to identify antibiotic resistance genes encoded by chromosomal mutations with 90% identity and 90% query coverage cutoffs. SCC mec and spa typing was performed using the web-based SCC mec Finder 49 and spaTyper v0.2.1 ( https://github.com/HCGB-IGTP/spaTyper ) with default settings. GenomeMatcher v3.0.2 50 was used for chromosome sequence comparison and visualization. Phylogenetic and clustering analysis A phylogenetic tree of the core genome alignment was constructed using the kSNP3.0 51 algorithm without reference genomes. Genetic population structure analysis was performed by partitioning the isolates into SCs of genetically similar individuals, using the Bayesian hierarchical clustering program FastBAPS package v1.0.8 52 in R v4.2.2 (R Core Team. 2019. R: Language and environment for statistical computing; available at https://www.R-project.org/ ). A maximum-likelihood (ML) phylogenetic tree was constructed using RAxML-NG v1.0.1 53 with the best model inferred by ModelTest-NG v0.1.7 54 and 100 bootstrap replicates. The ML trees were rooted at the midpoint and visualized using Figtree v1.4.4 (available at http://tree.bio.ed.ac.uk/software/figtree ). Time-calibrated phylogeny and population demographic analysis To perform temporal analysis of the CC1, CC5, and CC8 lineages individually using our collection and NCBI public database (Supplementary Table 4), snippy v4.6.0 (available at https://github.com/tseemann/snippy ) was used to perform reference-based mapping and identify SNPs for CC1, CC5, and CC8 with snippy-multi script and default parameters; MSSA476 (Assembly accession no. BX571857), N315 (accession no. BA000018), and JH4899 (accession no. AP014921) were used as references for CC1, CC5, and CC8, respectively. The Core SNP aligned sequence files of each CC were generated using snippy-core and snippy-clean_full_aln scripts. Recombination-free aligned sequences were generated using Gubbins v3.2.0 55 for each SC lineage. ML trees were checked using a Figtree (Supplementary Fig. 5). To reduce the computational intensity, we constructed dated phylogenies from randomly sampled datasets, each of which includes 150–190 isolates. We then investigated the temporal signals in the ML trees for each SC lineage using TempEst 56 to assess the linear relationship between the root-to-tip distance and year of isolation. A time-calibrated phylogenetic tree and effective population size over time (including the age of the most recent common ancestor [MRCA]) were estimated using BEAST v2.6.7, a coalescent Bayesian skyline tree prior and a strict molecular clock model 57 . A general time reversible substitution model with a gamma distribution for site rate heterogeneity was used, and a Markov chain Monte Carlo analysis with a length of 500 million steps was run for various numbers of coalescent intervals of the Bayesian skyline (known as dimensions in the model). The effective sample size for all parameters was > 200, and sufficient chain mixing was confirmed using Tracer v1.7.1 58 . The log-combiner program in the BEAST package was subsequently used to combine the results of these runs. The final tree was output and annotated using FigTree v1.4.4. Antimicrobial susceptibility testing The minimum inhibitory concentrations of ampicillin (ABPC), oxacillin (MPIPC), CEZ, CMZ, imipenem (IPM), ampicillin/sulbactam (ABPC/SBT), GM, ABK, EM, CLDM, MINO, LVFX, vancomycin (VCM), teicoplanin (TEIC), daptomycin (DAP), sulfamethoxazole/trimethoprim (ST), FOM, rifampicin (RFP), linezolid (LZD), mupirocin (MUP), cefoxitin (CFX), and I-CLDM were determined via broth microdilution testing using a MicroScan Pos series panel for MicroScan WalkAway96 System (Beckman Coulter Inc., Brea, CA, USA) according to the manufacturer’s instructions. Antimicrobial susceptibility was measured according to the CLSI guidelines (Thirty-First Edition: M100) and EUCAST v11.0 (CLSI. Performance Standards for Antimicrobial Susceptibility Testing— Thirty-First Edition: M100. 2021; EUCAST. Breakpoint Tables for Interpretation of MICs and Zone Diameters, Version 11.0. 2021.). Statistical analysis Statistical analyses were performed using R program v4.0.3 (R Core Team. 2019. R: Language and environment for statistical computing. ( https://www.R-project.org/ ) and JMP version 13.2.1 (SAS Institute, Cary, NC, USA). The diversity parameters of ST types were determined with the Shannon diversity index using R. Statistical analyses of the number of ARGs were conducted using the ggplot2 package ver. 3.3.6 59 in R. Survival and regression analyses were conducted using JMP. Data availability The metadata for each of the 580 S. aureus isolated in 2019–2020, including MLST, SC, minimum inhibitory concentration, clinical and geographical information, and genetic polymorphisms, are summarized in Supplementary Table 1. The raw sequences obtained in this study have been deposited in GenBank/EMBL/DDBJ under the BioProject accession number PRJDB15501 and Sequence Read Archive (SRA) accession numbers DRR456294–DRR457056 (Supplementary Tables 1 and 2). Supplementary Tables 4 and 6 lists the accession numbers from the NCBI database used in this study. Six supplementary tables and 10 supplementary figures are available in the online version of this article. Declarations Data availability The metadata for each of the 580 S. aureus isolated in 2019–2020, including MLST, SC, minimum inhibitory concentration, clinical and geographical information, and genetic polymorphisms, are summarized in Supplementary Table 1. The raw sequences obtained in this study have been deposited in GenBank/EMBL/DDBJ under the BioProject accession number PRJDB15501 and Sequence Read Archive (SRA) accession numbers DRR456294–DRR457056 (Supplementary Tables 1 and 2). Supplementary Table 4 and 6 lists the accession numbers from the NCBI database used in this study. Six supplementary tables and 10 supplementary figures are available in the online version of this article. ACKNOWLEDGMENTS We are grateful to all the hospitals participating in JARBS-SA. We are grateful to Yumiko Hosaka for discussions, and the following staff for technical contributions to this project: Eiko Anzai, Takahisa Ishizuka, Mayumi Sasada, Koichi Shimakawa, Sayoko Kawakami, Yoshie Taki, Satoyo Wakai, Sadao Aoki, Mikako Nakazawa, Emi Fujimura, Noriko Sakamoto, Elahi Shaheem, and Chika Arai. This work was supported by the Research Program on Emerging and Reemerging Infectious Diseases of the Japan Agency for Medical Research and Development (AMED) under grant number 21fk0108604. AUTHOR CONTRIBUTIONS MS (Sugai) conceptualized this study. MS (Sugai), MS, JH (Hisatsune), TK (Kajihara), HK (Kitagawa), HK (Ohge), TM (Mizukami), TT (Takahashi), and FK (Kawano) designed this study. TM, TT, FK and JARBS-SA Consortium contributed collecting the isolates and clinical data. SK (Kutsuno), YI (Iwao), KI (Ishida-Kuroki) were involved in species identification. JH, YS (Sugawara) contributed to genome sequencing. JH, TK, and KY (Yahara) analyzed the data. JH contributed to the information processing and database construction. JH was the major contributor to the writing of the manuscript and MS, KY, and SK (Kayama) significantly contributed to edit the manuscript. All authors have read and approved the final manuscript. COMPETING INTERESTS The authors declare no competing interests. ORCID FOR CORRESPONDING AUTHORS Motoyuki Sugai 0000-0001-9252-7739 Juzo Hisatsune 0000-0001-9330-4203 *JARBS-SA CONSORTIUM Yu Tsunashima *1 , Takahiro Fujita *2 , Katsushi Kanno *3 , Takeo Endo *4 , Yukari Kato *5 , Takao Yokoe *6 , Hiroshi Mizukoshi *7 , Isamu Kamimaki *8 , Michiyo Misawa *9 , Yumi Suzuki * 10, Shuichi Otawa *11 , Yumiko Owatari *12 , Osamu Okamura *13 , Katsuhiro Kuwahara *14 , Yoshinori Inoue *15 , Sumiyo Nishihara *16 , Kazuya Takahashi *17 , Hitoshi Inoue *18 , Tatsuo Kato *19 , Naoko Maeda *20 , Naoki Takayama *21 , Kazuko Shiozawa *22 , Yuta Hayashi *23 , Shimoeda Hirokazu *24 , Mariko Ueda *24 , Toshio Makie *25 , Kenji Yamamoto *26 , Koichi Nitta *27 , Toshio Saito *28 , Sami Fujihara *29 , Kazutaka Yassuda *30 , Shinji Tamaki *31 , Shu Sugitani *32 , Katsuyuki Tomita *33 , Masami Watanabe *34 , Toshikazu Ikeda *35 , Takashi Saito *36 , Yutaka Fujiwara *37 , Masanobu Shigeta *38 , Ayumi Maeoka *39 , Kozue Miyazaki *40 , Yusuke Mimura *41 , Yutaka Sato *42 , Akari Goto *43 , Takafumi Okada *44 , Hitomi Kawamura *45 , Kazutoshi Hiyama *46 , Kentaro Wakamatsu *47 , Toshitaka Muto *48 , Eriko Shigyo *49 , Haruka Ejima *50 , Tomoyuki Mizukami *51 , Toru Yamanaka *52 , Kazuyoshi Nakamura *53 , Narihiko Kubo * 54, Tomoku Ichimiya *55 , Yukihiro Zaizen *56 , Yuji Hamaguchi *57 , Chiharu Kuriwaki *58 , Shinji Aratake *59 , Tomoko Yuda *60 , Sachiko Hara *61 , Takuji Tsuchiya *62 , Kiyoshi Okita *63 *1 National Hospital Organization Hokkaido Medical Center, 2* National Hospital Organization Hokkaido Cancer Center, *3 National Hospital Organization Kasumigaura Medical Center, *4 National Hospital Organization Mito Medical Center, *5 National Hospital Organization Takasaki General Medical Center, *6 National Hospital Organization Shibukawa Medical Center, *7 National Hospital Organization Nishisaitama-chuo National Hospital, *8 National Hospital Organization Saitama Hospital, *9 National Hospital Organization Chiba Medical Center, *10 National Hospital Organization Shimoshizu Hospital, *11 National Hospital Organization Tokyo National Hospital, *12 National Hospital Organization Kanagawa Hospital, *13 National Hospital Organization Niigata National Hospital, *14 National Hospital Organization Nishiniigata Chuo Hospital, *15 National Hospital Organization Hokuriku Hospital, *16 National Hospital Organization Kanazawa Medical Center, *17 National Hospital Organization Iou National Hospital, *18 National Hospital Organization Tsuruga Medical Center, *19 National Hospital Organization National Hospital Organization Nagara Medical Center, *20 National Hospital Organization Shizuoka Medical Center, *21 National Hospital Organization Tenryu Hospital, *22 National Hospital Organization Toyohashi Medical Center, *23 National Hospital Organization Higashinagoya National Hospital, *24 National Hospital Organization National Mie Hospital, *25 National Hospital Organization Suzuka National Hospital, *26 National Hospital Organization Utano National Hospital, *27 National Hospital Organization Maizuru Medical Center, *28 National Hospital Organization Osaka Toneyama Medical Center, *29 National Hospital Organization Hyogo-chuo National Hospital, *30 National Hospital Organization Kobe Medical Center, *31 National Hospital Organization Nara Medical Center, *32 National Hospital Organization Tottori Medical Center, *33 National Hospital Organization Yonago Medical Center, *34 National Hospital Organization Hamada Medical Center, *35 National Hospital Organization Matsue Medical Center, *36 National Hospital Organization Okayama Medical Center, *37 National Hospital Organization Minami-Okayama Medical Center, *38 National Hospital Organization Kure Medical Center and Chugoku Cancer Center, *39 National Hospital Organization Fukuyama Medical Center, *40 National Hospital Organization Higashihiroshima Medical Center, *41 National Hospital Organization Yamaguchi-Ube Medical Center, *42 National Hospital Organization Kanmon Medical Center, *43 National Hospital Organization Tokushima Hospital, *44 National Hospital Organization Shikoku Medical Center for Children and Adults, *45 National Hospital Organization Kochi Hospital, *46 National Hospital Organization Fukuokahigashi Medical Center, *47 National Hospital Organization Omuta National Hospital, *48 National Hospital Organization Kokura Medical Center, *49 National Hospital Organization Saga Hospital, *50 National Hospital Organization Nagasaki Medical Center, *51 National Hospital Organization Kumamoto Medical Center, *52 National Hospital Organization Kumamotominami Hospital, *53 National Hospital Organization Kumamoto Saishun Medical Center, *54 National Hospital Organization Beppu Medical Center, *55 National Hospital Organization Oita Medical Center, *56 National Hospital Organization Nishi-Beppu National Hospital, *57 National Hospital Organization Miyazaki Higashi Hospital, *58 National Hospital Organization Kagoshima Medical Center, *59 National Hospital Organization Ibusuki Medical Center, *60 National Hospital Organization Iwaki Hospital, *61 National Hospital Organization Kurihama Medical and Addiction Center, *62 National Hospital Organization Higashi-Nagano Hospital, *63 National Hospital Organization Kamo Psychiatric Medical Center References Turner NA et al (2019) Methicillin-resistant Staphylococcus aureus : an overview of basic and clinical research. 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Syst Biol 67:901–904. https://doi.org/10.1093/sysbio/syy032 Wickham H (2016) ggplot2: Elegant Graphics for Data Analysis. Springer-, New York. https://doi.org/doi:10.1007/978-0-387-98141-3 Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryTables.xlsx Supplementary Table 1 - 6 SupplementaryFig.110.pdf Supplementary Figure 1-10 SupplementaryMaterialLegends.docx Cite Share Download PDF Status: Published Journal Publication published 19 Mar, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4824867","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":334119520,"identity":"f62ef2af-e286-4013-bd78-0e5a0d694076","order_by":0,"name":"Motoyuki 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Center","correspondingAuthor":false,"prefix":"","firstName":"Fumio","middleName":"","lastName":"Kawano","suffix":""}],"badges":[],"createdAt":"2024-07-30 01:25:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4824867/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4824867/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-025-57575-2","type":"published","date":"2025-03-19T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61809842,"identity":"924671a8-9e00-4730-8ddb-0bb5530f4fec","added_by":"auto","created_at":"2024-08-05 20:16:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":122935,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePopulation structure and geographic distribution of BSI-derived \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eS. aureus\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e during 2019–2020. a.\u003c/strong\u003eMidpoint-rooted maximum likelihood (ML) tree showing the phylogenetic structure of 580 \u003cem\u003eS. aureus\u003c/em\u003e isolates during 2019–2020. The scale bar represents the number of nucleotide substitutions per site. Matrix shows the metadata; 1, Bayesian analysis of population structure (BAPS) sequence cluster (SC) number identified by FastBAPS; 2, Sequence type (ST) number of each \u003cem\u003eS. aureus\u003c/em\u003eisolate; 3, indicates the eastern or western Japan where \u003cem\u003eS. aureus\u003c/em\u003e was isolated; 4, indicates the SCC\u003cem\u003emec\u003c/em\u003e type of MRSA. Pie graph shows the proportion of the metadata 1–4. ST with less than 3 isolates included in “other.” \u003cstrong\u003eb.\u003c/strong\u003e Genotypic distribution in eastern or western Japan. ST under top 10 included in “other.” \u003cstrong\u003ec.\u003c/strong\u003eComparison of geographic proportions of STs are shown in SC1, SC3, and SC8. The color tone is shown in the same pattern in Fig. 1. Metadata information of 580 \u003cem\u003eS. aureus\u003c/em\u003e isolated in 2019–2020 is summarized in Supplementary Table 1.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4824867/v1/513e3e24bd3e65fcb4ffbec3.png"},{"id":61809844,"identity":"2a0bec68-2f45-460e-904d-6fed57034b95","added_by":"auto","created_at":"2024-08-05 20:16:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":124063,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of ARG distribution and antimicrobial resistance levels in BSI-derived CC1-, CC5-, and CC8-MRSA lineages during 1994–2000 and 2019–2020.\u003c/strong\u003e \u003cstrong\u003ea.\u003c/strong\u003e The number of ARGs per strain is shown but duplicate ARG genes are not counted. * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01. \u003cstrong\u003eb.\u003c/strong\u003e Presence heatmap of major ARGs and quinolone resistance QRDR mutations identified. The numbers in the heatmap columns are shown as percentages. \u003cstrong\u003ec.\u003c/strong\u003eAntimicrobial susceptibility profiles of BSI-derived MRSA. The radar chart shows the resistance rates of isolates to MPIPC, CEZ, CMZ, GM, ABK, EM, CLDM, MINO, and LVFX in 1994\u003cstrong\u003e–\u003c/strong\u003e2000 and 2019\u003cstrong\u003e–\u003c/strong\u003e2020.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4824867/v1/999083b94e779c77588d0961.png"},{"id":61809840,"identity":"c5c31d2e-85bf-4899-adfa-43b1170ed05e","added_by":"auto","created_at":"2024-08-05 20:16:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":86582,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of VFG distribution in BSI-derived CC1-, CC5-, and CC8-MRSA lineages during 1994–2000 and 2019–2020.\u003c/strong\u003e The proportion of major VFGs of each MRSA type is shown in a heatmap. The numbers in the heatmap columns are shown as percentages.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4824867/v1/3ff2d1ebd4c2852987ccd07c.png"},{"id":61809843,"identity":"10fa49f6-e4cb-45b7-8b26-3672d6aaf3d8","added_by":"auto","created_at":"2024-08-05 20:16:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":105830,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBayesian phylogeny and population dynamics of (a) sequence cluster 1, (b) 3, and (c) 8.\u003c/strong\u003e Upper panel indicates the Bayesian maximum clade credibility time-calibrated phylogenies based on non-recombining regions of the core genome. Arrow nodes indicate the divergence date (median estimate with 95% highest posterior). Blue horizontal bars at each node represent 95% confidence intervals. Lower panel, Bayesian skyline plots showing changes in effective population size over time. Median is represented by a black line, and 95% confidence intervals are in blue. For index, \u003cem\u003espa\u003c/em\u003e typing was included in the metadata. In index No. 5, \u003cem\u003eant(9)-Ia\u003c/em\u003e and \u003cem\u003eerm\u003c/em\u003e(A) harbored in Tn\u003cem\u003e554\u003c/em\u003e are indicated in red. \u003cstrong\u003eb.\u003c/strong\u003e MRSA/J acquired SCC\u003cem\u003emec\u003c/em\u003eIVl, genomic island vSa4, and \u003cem\u003ese1\u003c/em\u003e-harboring plasmid somewhere between 1957\u003cstrong\u003e \u003c/strong\u003eand\u003cstrong\u003e \u003c/strong\u003e1986. \u003cem\u003esec\u003c/em\u003e, \u003cem\u003etst-1,\u003c/em\u003e and \u003cem\u003esel\u003c/em\u003e on vSa4 and \u003cem\u003ese1\u003c/em\u003e on the plasmid are indicated in index No. 5 in blue (see also Supplementary Fig. 7a, b). Some strains of ST8-SCC\u003cem\u003emec\u003c/em\u003eIVj and ST8-SCC\u003cem\u003emec\u003c/em\u003eI acquired \u003cem\u003etet\u003c/em\u003e(M)-harboring Tn\u003cem\u003e916\u003c/em\u003e-like transposable unit somewhere between the late 1980s and 1990s. \u003cem\u003etet\u003c/em\u003e(M) is indicated in index No. 5 in blue (see also Supplementary Fig. 7c). \u003cstrong\u003ec.\u003c/strong\u003e In the process leading to the emergence of ST5-SCC\u003cem\u003emec\u003c/em\u003eII (N/J clone) and ST764-SCC\u003cem\u003emec\u003c/em\u003eII, it is estimated that SCC\u003cem\u003emec\u003c/em\u003eII, genomic islands, and transposons were acquired stepwise. Related genes are indicated in index No. 5 in blue (see also Supplementary Table 8).\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4824867/v1/f2d3b7331c9c2672b09f0e62.png"},{"id":78875388,"identity":"9a1354f0-e7cf-4b07-ab67-65242d5ca75c","added_by":"auto","created_at":"2025-03-20 07:08:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2021747,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4824867/v1/60acf750-c637-4171-ab3d-3a75de37cd43.pdf"},{"id":61809847,"identity":"51fe2733-2769-4378-a3cb-aec264c41157","added_by":"auto","created_at":"2024-08-05 20:16:57","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":446059,"visible":true,"origin":"","legend":"Supplementary Table 1 - 6","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4824867/v1/e245217caa2c96eaaec75e88.xlsx"},{"id":61809845,"identity":"52800390-605c-44e4-b593-5b8f9ec41381","added_by":"auto","created_at":"2024-08-05 20:16:57","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":8644583,"visible":true,"origin":"","legend":"Supplementary Figure 1-10","description":"","filename":"SupplementaryFig.110.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4824867/v1/186a11c71ce23824aa60ca23.pdf"},{"id":61811252,"identity":"e9944f59-4280-40a5-994b-23c3335c4e7b","added_by":"auto","created_at":"2024-08-05 20:24:57","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17660,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialLegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-4824867/v1/eb9f36c47790168450b5829f.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Staphylococcus aureus ST764-SCCmecII high-risk clone in bloodstream infections revealed through national genomic surveillance integrating clinical data","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAntimicrobial resistance (AMR) is one of the greatest threats to human health, necessitating effective surveillance. \u003cem\u003eStaphylococcus aureus\u003c/em\u003e is an important antimicrobial-resistant bacterium. Its spread in healthcare and community settings and in the livestock industry is a global challenge because \u003cem\u003eS. aureus\u003c/em\u003e causes diverse pathologies, from skin and soft tissue infections to systemic invasive diseases, such as pneumonia, septicemia, infective endocarditis, osteomyelitis, toxic shock syndrome, and food poisoning through its enterotoxin\u003csup\u003e1\u003c/sup\u003e. Bloodstream infection (BSI) caused by \u003cem\u003eS. aureus\u003c/em\u003e is a severe condition with an increased mortality risk\u003csup\u003e2,3\u003c/sup\u003e. An estimated 119,247 \u003cem\u003eS. aureus\u003c/em\u003e BSIs with 19,832 associated deaths occurred in the United States\u003csup\u003e4\u003c/sup\u003e in 2017. The number of BSI deaths attributed to \u003cem\u003eS. aureus\u003c/em\u003e in Japan was 17,412 in 2011 and 17,157 in 2017, wherein 5,924 (34%) deaths were attributed to methicillin-resistant \u003cem\u003eS. aureus\u003c/em\u003e (MRSA) in 2011, which significantly decreased to 4,224 (24.6%) in 2017\u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe ST5-Staphylococcal Cassette Chromosome \u003cem\u003emec\u003c/em\u003e (SCC\u003cem\u003emec\u003c/em\u003e) II is present in the New York/Japan (N/J) clone, the most common clone responsible for healthcare-associated MRSA (HA-MRSA) BSIs in Japan, although infections by this clone have recently been declining\u003csup\u003e6,7,8\u003c/sup\u003e. ST764-SCC\u003cem\u003emec\u003c/em\u003eII, belonging to clonal complex (CC) 5, was first isolated from the blood cultures of patients with invasive infections in Japan in 2005 and was reported as a hybrid variant of ST5-SCC\u003cem\u003emec\u003c/em\u003eII and community-acquired MRSA (CA-MRSA) with acquired virulence genes\u003csup\u003e9\u003c/sup\u003e. Recently, ST764-SCC\u003cem\u003emec\u003c/em\u003eII has been isolated from invasive infections in Asia, including Thailand and China\u003csup\u003e10,11\u003c/sup\u003e. In contrast, the proportion of CA-MRSA-SCC\u003cem\u003emec\u003c/em\u003eIV has increased in isolates from invasive infections of MRSA BSIs\u003csup\u003e8,12\u003c/sup\u003e and other SCC\u003cem\u003emec\u003c/em\u003eIV MRSA clones (ST1-SCC\u003cem\u003emec\u003c/em\u003eIV or ST8-SCC\u003cem\u003emec\u003c/em\u003eIV)\u003csup\u003e8,13\u003c/sup\u003e. However, CA-MRSA SCC\u003cem\u003emec\u003c/em\u003eIVa USA300 lineages (including USA300-LV, a Latin American variant), which are predominant in nosocomial settings in North and South America and cause severe infections with high mortality rates, are still not common in Japan\u003csup\u003e8\u003c/sup\u003e. The ST8-SCC\u003cem\u003emec\u003c/em\u003eIVl with the CA-MRSA genotype, represented by the MRSA/J clone, was first isolated in Japan from the bullous impetigo of a child in 2003\u003csup\u003e14\u003c/sup\u003e, and one case of death caused by a strongly invasive pathotype was reported in 2012\u003csup\u003e15\u003c/sup\u003e. During the same period, we reported a case of systemically disseminated MRSA/J infection\u003csup\u003e16,17\u003c/sup\u003e. ST2725-SCC\u003cem\u003emec\u003c/em\u003eIV, belonging to CC1, which has never been reported outside Japan, was recently isolated from the blood cultures of patients in Japanese regional hospitals\u003csup\u003e18,19\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn recent years, various MRSA clones isolated from BSIs have emerged in Japan. However, studies on the molecular epidemiology and genomic characterization of MRSA isolates from BSIs are limited to certain healthcare facilities or regions. A pioneering national genomic surveillance study in Philippines included \u003cem\u003eS. aureus\u003c/em\u003e, although it was limited by inconsistencies in the antimicrobial panels that were tested at sentinel sites, thus highlighting the importance of standardized phenotypic antimicrobial susceptibility testing\u003csup\u003e20\u003c/sup\u003e. Here, we conducted a large-scale national genomic surveillance of \u003cem\u003eS. aureus\u003c/em\u003e isolated from patients with BSI during 2019\u0026ndash;2020, performed antimicrobial susceptibility testing using the same panel in the same reference laboratory, collected clinical metadata of these \u003cem\u003eS. aureus\u003c/em\u003e isolates, and investigated the clinical risk of the clones. We also conducted genomic comparisons with isolates collected during 1994\u0026ndash;2000 to delineate the evolutionary trajectories of BSI-derived \u003cem\u003eS. aureus\u003c/em\u003e clones.\u003c/p\u003e \u003cp\u003eBy combining the national genome sequencing data with the standardized antimicrobial susceptibility testing results and clinical metadata, this report highlighted dynamic changes in MRSA in 26 years (1994\u0026ndash;2020) in Japan and the emergence of ST764-SCC\u003cem\u003emec\u003c/em\u003eII with high mortality risk associated with BSI.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003ePhylogenetic relationship and geographic distribution of 580 S. aureus isolated from blood during 2019\u0026ndash;2020\u003c/h2\u003e\n \u003cp\u003ePopulation structure analysis of \u003cem\u003eS. aureus\u003c/em\u003e isolated from BSIs during 2019\u0026ndash;2020 using Bayesian hierarchical clustering of the core genome alignment showed 13 distinct sequence clusters (SCs) ranging in size from seven to 155 genomes (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea and Supplementary Table 1). Phylogenetic analyses revealed three dominant SCs, namely SC1, SC3, and SC8, comprising 155 (26.7% of the total population), 127 (21.9% of the total population), and 73 genomes (12.6% of the total population), respectively (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea, pie chart 1). We performed \u003cem\u003ein silico\u003c/em\u003e multilocus sequence typing to identify the sequence types (STs) that comprised the SC. The CCs of these STs were examined using the eBURST analysis. SC1 comprised CC1, which included ST1 (104 genomes, 67.1% of SC1), ST2725 (28 genomes, 18.1% of SC1), and ST81 (10 genomes, 6.5% of SC1) (Supplementary Table 1). SC3 comprised CC8 and, almost exclusively, ST8 (127 genomes, 84.3% of SC3). SC8 comprised CC5, which included ST5 (73 genomes, 57.5% of SC5) and ST764 (25 genomes, 34.2% of SC5). The most dominant STs of the 580 \u003cem\u003eS. aureus\u003c/em\u003e were ST8 (18.4%), ST1 (17.9%), and ST5 (7.2%) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea, pie chart 2). BSI-derived \u003cem\u003eS. aureus\u003c/em\u003e from 2019 to 2020 contained 343 isolates (59.1%) from western and 237 strains (40.9%) from Eastern Japan (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea, pie chart 3). The most dominant STs in western Japan were ST8 (23.0%), ST1 (11.9%), and ST5 (7.0%) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb, pie chart 1). However, the most dominant ST in Eastern Japan were ST1 (26.6%), ST8 (11.8%), and ST188 (7.6%). Additionally, the major STs of CC8 in SC3 were ST8 (approximately 80%) and ST630 (less than 10%) in both western and Eastern Japan (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ec, middle panel). Similarly, the major STs of CC5 in SC8 were ST5 (approximately 57%) and ST764 (approximately 34%) in both western and Eastern Japan (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ec, right panel). In Eastern Japan, the major STs of CC1 in SC1 were ST1 (75.9%), ST81 (10.8%), and ST2725 (4.8%). In western Japan, the STs of CC1 in SC1 were ST1 (56.9%) and ST2725 (33.3%), which were significantly higher than those in Eastern Japan. The proportion of MRSA of BSI-derived \u003cem\u003eS. aureus\u003c/em\u003e during 2019\u0026ndash;2020 was 46.4%, mostly SCC\u003cem\u003emec\u003c/em\u003eIV (76.5% of total SCC\u003cem\u003emec\u003c/em\u003e) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea, pie chart 4). The proportions of MRSA in western and eastern Japan were 47.5 and 44.7%, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb, pie chart 2). SCC\u003cem\u003emec\u003c/em\u003eIV was the most common MRSA of BSI-derived \u003cem\u003eS. aureus\u003c/em\u003e in both regions, followed by SCC\u003cem\u003emec\u003c/em\u003eII. The major STs of MRSA (n\u0026thinsp;=\u0026thinsp;269) were ST1 (34.6%) and ST8 (26%) (Supplementary Fig. 1, left panel). In Eastern Japan, the major STs of MRSA were ST1 (55.7%) and ST8 (14.2%). In addition, ST81, which belongs to CC1, was detected in Eastern Japan, although in small numbers, and not in western Japan. In contrast, in western Japan, the major STs of MRSA were ST8 (33.5%) and ST1 (21.3%). The number of STs of methicillin-susceptible \u003cem\u003eS. aureus\u003c/em\u003e (MSSA) were more diverse than those of MRSA (Supplementary Fig. 1, right panel). These results indicate that the representative CCs of BSI-derived \u003cem\u003eS. aureus\u003c/em\u003e in Japan were CC1, CC8, and CC5 (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea), with ST1 and ST2725 having different distributions in the Eastern and western regions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eST764 infection is associated with high mortality\u003c/h2\u003e\n \u003cp\u003eSubsequently, we investigated the association between these lineages and 30-day mortality rates. Notably, the 30-day mortality rate of \u003cem\u003eS. aureus\u003c/em\u003e BSIs caused by the ST764-SCC\u003cem\u003emec\u003c/em\u003eII clone was 48% (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), which was significantly higher than the 22% of the other clones (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0088, log-rank test) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The 30-day mortality rate associated with the ST8-SCC\u003cem\u003emec\u003c/em\u003eI clone was also high (50%) compared to that of the other clones, although the difference was not statistically significant owing to the small sample size (n\u0026thinsp;=\u0026thinsp;10). Further analysis of the ST764-SCC\u003cem\u003emec\u003c/em\u003eII clone revealed that age was a confounding factor significantly associated with the ST764-SCC\u003cem\u003emec\u003c/em\u003eII clone (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026, Mann\u0026ndash;Whitney U test) and 30-day mortality rate (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, univariate Cox regression) with \u003cem\u003ep\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The association between the ST764-SCC\u003cem\u003emec\u003c/em\u003eII clone and 30-day mortality rate remained significant even after controlling for the confounding effect of age (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04, hazard ratio 1.86, 95% CI: 1.03\u0026ndash;3.38). There were no confounding factors in the following clinical variables: sex, diabetes, hemodialysis, surgery within 30 days before blood culture, artificial respirator use, and atopic dermatitis.\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of the 30-day mortality rate of each clone.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eST type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e30-day mortality (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;580)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e134/580 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMRSA (n\u0026thinsp;=\u0026thinsp;269)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71/269 (26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSSA (n\u0026thinsp;=\u0026thinsp;311)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63/311 (20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC1:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eST1-MRSA-SCC\u003cem\u003emec\u003c/em\u003eIV (n\u0026thinsp;=\u0026thinsp;93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20/93 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eST2725-MRSA-SCC\u003cem\u003emec\u003c/em\u003eIV (n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8/28 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC5:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eST5-MRSA-SCC\u003cem\u003emec\u003c/em\u003eII (n\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1/11 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eST764-MRSA-SCC\u003cem\u003emec\u003c/em\u003eII (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12/25 (48.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC8:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSA300 (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1/4 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMRSA/J (n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3/19 (15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eST8-MRSA-SCC\u003cem\u003emec\u003c/em\u003eI (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5/10 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eST8-MRSA-SCC\u003cem\u003emec\u003c/em\u003eIVj (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8/24 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of clinical factors between ST764-MRSA-SCC\u003cem\u003emec\u003c/em\u003eII and other clones.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eST764-MRSA-SCC\u003cem\u003emec\u003c/em\u003eII\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;555)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;580)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian age (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80 (78\u0026ndash;88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76(64\u0026ndash;85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77 (65\u0026ndash;85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale sex (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e340 (61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e356 (61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e158 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHemodialysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurgery within 30 days before blood culture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArtificial respirator use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAtopic dermatitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.s.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeath within 30 days of hospitalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0088 0.04\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003csup\u003e#\u003c/sup\u003e after adjusting for age\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eAbbreviation:\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eIQR, interquartile range\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003en.s.: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.1\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003ePhylogenetic relationship of BSI-derived 183 S. aureus during 1994\u0026ndash;2000\u003c/h2\u003e\n \u003cp\u003eWe performed a comparative phylogenetic analysis of \u003cem\u003eS. aureus\u003c/em\u003e isolated from BSIs during 2019\u0026ndash;2020 and 1994\u0026ndash;2000. We collected data from a post-marketing surveillance conducted during 1994\u0026ndash;2000 after levofloxacin (LVFX) was released in Japan\u003csup\u003e21\u003c/sup\u003e. Population structure analysis of 183 BSI-derived \u003cem\u003eS. aureus\u003c/em\u003e isolated during 1994\u0026ndash;2000 using Bayesian hierarchical clustering of the core genome alignment showed 10 distinct SCs, ranging in size from 2 to 109 genomes (Supplementary Fig.\u0026nbsp;2 and Supplementary Table\u0026nbsp;2). Phylogenetic analysis revealed that SC1 (109 genomes, 59.6% of the total population) was dominant (Supplementary Fig.\u0026nbsp;2, pie chart 1) and comprised CC5 and almost exclusively ST5 (97 genomes, 90% of SC1, 53% of the total population). The Shannon diversity index of the STs significantly increased from 3.08399 in 1994\u0026ndash;2020 to 4.767595 in 2019\u0026ndash;2020. This indicates that the diversity of STs in Japan increased over the past two decades despite differences in sample sizes between 2019\u0026ndash;2020 and 1994\u0026ndash;2000. The proportion of MRSA in BSI-derived \u003cem\u003eS. aureus\u003c/em\u003e in 1994\u0026ndash;2000 was over 56%, mostly SCC\u003cem\u003emec\u003c/em\u003eII (50.8% of the total population) (Supplementary Fig.\u0026nbsp;2, pie chart 4). The proportion of STs of MRSA was almost exclusively ST5 (84.6%) (Supplementary Fig.\u0026nbsp;3, left upper panel). The most common STs of MSSA was ST5 (12.7%); however, the ST types of MSSA were more diverse than those of MRSA (Supplementary Fig.\u0026nbsp;3, right upper panel). The predominant CC of 183 \u003cem\u003eS. aureus\u003c/em\u003e during 1994\u0026ndash;2000 was the CC5 lineage, including the N/J clone (ST5-SCC\u003cem\u003emec\u003c/em\u003eII) (Supplementary Fig.\u0026nbsp;2). These results confirm the dynamic changes in the population structure of MRSA within 20 years when comparing the phylogenetic analysis data of the isolates of 1994\u0026ndash;2000 to those of 2019\u0026ndash;2020.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eComparative analysis of antimicrobial resistance pattern of BSI-derived S. aureus\u003c/h2\u003e\n \u003cp\u003eWe examined the differences in the number of antimicrobial resistant genes (ARGs) between the isolates of 2019\u0026ndash;2020 and 1994\u0026ndash;2000 (Supplementary Fig.\u0026nbsp;4a). The average number of ARGs in 2019\u0026ndash;2020 was 2.8, which was 2.2 less than that in 1994\u0026ndash;2000 (average of 5.0) (Supplementary Fig.\u0026nbsp;4a, left). The number of ARGs between the two periods (1994\u0026ndash;2000 and 2019\u0026ndash;2020 with an average of 7.9 and 4.6, respectively) was significantly different for MRSA but not for MSSA (an average of 1.2 in both periods) (Supplementary Fig.\u0026nbsp;4a, right panel). The results of the proportion of each ARGs revealed that the prevalence of the aminoglycoside resistance gene \u003cem\u003eaadD\u003c/em\u003e, tetracycline resistance gene \u003cem\u003etet\u003c/em\u003e(M), bleomycin resistance gene \u003cem\u003ebleO\u003c/em\u003e, fosfomycin (FOM) resistance gene \u003cem\u003efosB\u003c/em\u003e, and chlorhexidine resistance gene \u003cem\u003eqacA\u003c/em\u003e decreased between the two periods in the total samples or MRSA (Supplementary Fig.\u0026nbsp;4b). In contrast, the prevalence of aminoglycoside resistant gene \u003cem\u003eant\u003c/em\u003e(\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003cem\u003e-Ia\u003c/em\u003e, macrolide resistant genes \u003cem\u003eerm\u003c/em\u003e(A) and \u003cem\u003eerm\u003c/em\u003e(C), FOM resistance gene \u003cem\u003efosD\u003c/em\u003e, chlorhexidine resistant gene \u003cem\u003eqacB\u003c/em\u003e, and the quinolone resistance-determining region (QRDR) mutations (GrlA/S80F/E84G and GryA/S84L/E88G) contributing to quinolone resistance in chromosomal \u003cem\u003egrlA\u003c/em\u003e and \u003cem\u003egyrA\u003c/em\u003e were not significantly affected in the total samples or MRSA. Conversely, the prevalence of the aminoglycoside resistance genes \u003cem\u003eaac(6\u0026rsquo;)-aph(2\u0026rdquo;)\u003c/em\u003e, \u003cem\u003eant\u003c/em\u003e(\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003cem\u003e-Ia\u003c/em\u003e, \u003cem\u003eerm\u003c/em\u003e(A), \u003cem\u003eerm\u003c/em\u003e(C), and QRDR mutations (GrlA/S80F and GyrA/S84L) increased in isolates of 2019\u0026ndash;2020 in MSSA (n\u0026thinsp;=\u0026thinsp;311) (Supplementary Table\u0026nbsp;3). Antimicrobial susceptibility testing showed that resistance to cefazolin (CEZ), cefmetazole (CMZ), gentamicin (GM), clindamycin (CLDM), and minocycline (MINO) significantly decreased in isolates of 2019\u0026ndash;2020 in the total samples or MRSA; however, this was not significant for resistance to arbekacin (ABK), erythromycin (EM), and LVFX (Supplementary Fig.\u0026nbsp;4c, upper and middle panels). In MSSA, the level of resistance to each antibiotic was very low; however, resistance to EM and LVFX increased slightly from 1994\u0026ndash;2000 to 2019\u0026ndash;2020 (Supplementary Fig.\u0026nbsp;4c, lower panel). We subsequently examined the distribution of ARGs in CC1, CC5, and CC8, the dominant clones of BSI-derived MRSA of 2019\u0026ndash;2020 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). In CC1, the ST1-SCC\u003cem\u003emec\u003c/em\u003eIV and ST2725-SCC\u003cem\u003emec\u003c/em\u003eIV lineages contained the same number of ARGs (mean, 3.9) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea, CC1). Most ST1-SCC\u003cem\u003emec\u003c/em\u003eIV and ST2725-SCC\u003cem\u003emec\u003c/em\u003eIV strains had \u003cem\u003eblaZ\u003c/em\u003e, \u003cem\u003eant\u003c/em\u003e(\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003cem\u003e-Ia\u003c/em\u003e, \u003cem\u003eerm\u003c/em\u003e(A), QRDR mutations (GrlA/S80F/E84G and GyrA/S84L), and the same distribution pattern (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb, CC1 and Supplementary Table\u0026nbsp;3). Consistent with the presence of these ARGs and QRDR mutations in ST1-SCC\u003cem\u003emec\u003c/em\u003eIV and ST2725-IV, the resistance rates of these strains to oxacillin (MPIPC), EM, and LVFX were high (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec, upper panel). In CC5, the average number of ARGs in ST5-SCC\u003cem\u003emec\u003c/em\u003eII of 1994\u0026ndash;2000 was 8.2; however, in 2019\u0026ndash;2020 it was 7.0, which was significantly lower than that of 1994\u0026ndash;2000 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea, CC5). The average number of ARGs in ST764-SCC\u003cem\u003emec\u003c/em\u003eII in 2019\u0026ndash;2020 was 6.0, which was significantly lower than that in ST5-SCC\u003cem\u003emec\u003c/em\u003eII. The proportions of ARGs in ST5-SCC\u003cem\u003emec\u003c/em\u003eII during the two periods were high for \u003cem\u003eaadD\u003c/em\u003e, \u003cem\u003eant\u003c/em\u003e(\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003cem\u003e-Ia\u003c/em\u003e, \u003cem\u003eerm\u003c/em\u003e(A), \u003cem\u003ebleO\u003c/em\u003e, and QRDR mutations (GrlA/S80F/E84K and GyrA/S84L/S85P) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb, CC5 and Supplementary Table\u0026nbsp;3). In contrast, the ARGs with a decreasing trend in ST5-SCC\u003cem\u003emec\u003c/em\u003eII in 2019\u0026ndash;2020 were \u003cem\u003eblaZ\u003c/em\u003e, \u003cem\u003eaac(6\u0026rsquo;)-aph(2\u0026rdquo;)\u003c/em\u003e, \u003cem\u003etet\u003c/em\u003e(M), cat, \u003cem\u003efosB\u003c/em\u003e, and \u003cem\u003eqacA\u003c/em\u003e. The ARGs showing an increasing trend in ST5-SCC\u003cem\u003emec\u003c/em\u003eII in 2019\u0026ndash;2020 were \u003cem\u003eerm\u003c/em\u003e(C), \u003cem\u003efosD\u003c/em\u003e, and \u003cem\u003eqacB\u003c/em\u003e. ARGs with high proportions in the ST764-SCC\u003cem\u003emec\u003c/em\u003eII strain included \u003cem\u003eant\u003c/em\u003e(\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003cem\u003e-Ia\u003c/em\u003e, \u003cem\u003eerm\u003c/em\u003e(A), and QRDR mutations (GrlA/S80Y/E84K and GyrA/S84L/E88G); particularly, ARGs with a higher proportion than those in ST5-SCC\u003cem\u003emec\u003c/em\u003eII were \u003cem\u003eaac(6\u0026rsquo;)-aph(2\u0026rdquo;)\u003c/em\u003e, \u003cem\u003etet\u003c/em\u003e(M), \u003cem\u003efosD\u003c/em\u003e, and \u003cem\u003eqacB\u003c/em\u003e. The ARGs of ST764-SCC\u003cem\u003emec\u003c/em\u003eII \u003cem\u003eaadD\u003c/em\u003e, \u003cem\u003eerm\u003c/em\u003e(C), and \u003cem\u003ebleO\u003c/em\u003e were lower in abundance than those of ST5-SCC\u003cem\u003emec\u003c/em\u003eII. Resistance rates to MPIPC, CEZ, CMZ, EM, CLDM, and LVFX remained consistently high in ST5-SCC\u003cem\u003emec\u003c/em\u003eII during both periods (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec, middle panel). The resistance rates to GM and MINO were significantly lower in ST5-SCC\u003cem\u003emec\u003c/em\u003eII of 2019\u0026ndash;2020 than in that of 1994\u0026ndash;2000. In contrast, ST764-SCC\u003cem\u003emec\u003c/em\u003eII showed very high rates of resistance to these antibiotics, except for ABK. In CC8, the average numbers of ARGs in ST8-SCC\u003cem\u003emec\u003c/em\u003eIVj and ST8-SCC\u003cem\u003emec\u003c/em\u003eIVa during 2019\u0026ndash;2020 were 4.8 and 4.1, respectively, which were significantly lower than those in ST8-SCC\u003cem\u003emec\u003c/em\u003eI (average, 6.2) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea, CC8). The average number of ARGs in ST8-SCC\u003cem\u003emec\u003c/em\u003eIV was lower than that in ST8-SCC\u003cem\u003emec\u003c/em\u003eI; however, the difference was statistically significant between ST8-SCC\u003cem\u003emec\u003c/em\u003eIVj and ST8-SCC\u003cem\u003emec\u003c/em\u003eIVa. Most ST8-SCC\u003cem\u003emec\u003c/em\u003eI possessed \u003cem\u003eblaZ\u003c/em\u003e, \u003cem\u003eaac(6\u0026rsquo;)-aph(2\u0026rdquo;)\u003c/em\u003e, \u003cem\u003eant\u003c/em\u003e(\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003cem\u003e-Ia\u003c/em\u003e, \u003cem\u003eerm\u003c/em\u003e(A), \u003cem\u003etet\u003c/em\u003e(M), and QRDR mutations (GrlA/S80F and GyrA/S84L/S85P) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb, CC8 and Supplementary Table\u0026nbsp;3). ARGs with high proportions in ST8-SCC\u003cem\u003emec\u003c/em\u003eIVj included \u003cem\u003eblaZ\u003c/em\u003e, \u003cem\u003eant\u003c/em\u003e(\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003cem\u003e-Ia\u003c/em\u003e, \u003cem\u003eermA\u003c/em\u003e, \u003cem\u003etet\u003c/em\u003e(M), and QRDR mutations (GrlA/S80F and GyrA/S84L); ARGs with low proportions included \u003cem\u003eaac(6\u0026rsquo;)-aph(2\u0026rdquo;)\u003c/em\u003e. Conversely, the proportion of \u003cem\u003eant\u003c/em\u003e(\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003cem\u003e-Ia\u003c/em\u003e, \u003cem\u003eerm\u003c/em\u003e(A), \u003cem\u003etet\u003c/em\u003e(M), and QRDR mutations (GrlA/S80F and GyrA/S84L) in ST8-SCC\u003cem\u003emec\u003c/em\u003eIVl was lower than that of other ST8-MRSAs, whereas that of \u003cem\u003eaac(6\u0026rsquo;)-aph(2\u0026rdquo;)\u003c/em\u003e, \u003cem\u003eaadD\u003c/em\u003e, \u003cem\u003ebleO\u003c/em\u003e, and \u003cem\u003eqacB\u003c/em\u003e was higher. The prevalence of \u003cem\u003emph\u003c/em\u003e(C) and \u003cem\u003emsr\u003c/em\u003e(A) in ST8-SCC\u003cem\u003emec\u003c/em\u003eIVa was approximately 38%, which is unique to this clone. ST8-MRSA showed varying patterns of resistance to each antibiotic depending on the SCC\u003cem\u003emec\u003c/em\u003e type; however, ST8-SCC\u003cem\u003emec\u003c/em\u003eI was more resistant than the other clones (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec, lower). The small number of BSI-derived USA300 isolates in Japan makes the comparative assessment difficult.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eComparative analysis of virulence factor genes (VFGs) of BSI-derived S. aureus\u003c/h2\u003e\n \u003cp\u003eWe investigated the presence of VFGs in \u003cem\u003eS. aureus\u003c/em\u003e isolates from BSIs. The proportion of toxic shock syndrome toxin-1 gene (\u003cem\u003etst-1\u003c/em\u003e) in 2019\u0026ndash;2020 significantly decreased compared to that in 1994\u0026ndash;2000. In addition, the proportion of the staphylococcal enterotoxin gene cluster EGC (\u003cem\u003eseg\u003c/em\u003e, \u003cem\u003esei\u003c/em\u003e, \u003cem\u003esem\u003c/em\u003e, \u003cem\u003esen\u003c/em\u003e, and \u003cem\u003eseo\u003c/em\u003e) in the total samples or MRSA of 2019\u0026ndash;2020 was drastically reduced compared to that in 1994\u0026ndash;2000; however, it remained approximately 30% in MSSA (Supplementary Fig.\u0026nbsp;4d). In contrast, the proportions of \u003cem\u003esea\u003c/em\u003e, \u003cem\u003eseh\u003c/em\u003e, \u003cem\u003esek\u003c/em\u003e, and \u003cem\u003eseq\u003c/em\u003e in the total samples or MRSA of 2019\u0026ndash;2020 increased compared to those of 1994\u0026ndash;2000. A few Panton-Valentine leukocidin (PVL) genes (\u003cem\u003elukF-PV\u003c/em\u003e and \u003cem\u003elukS-PV\u003c/em\u003e), exfoliative toxin (ET) genes (\u003cem\u003eeta\u003c/em\u003e, \u003cem\u003eetb\u003c/em\u003e, and \u003cem\u003eetd\u003c/em\u003e), and epidermal cell differentiation inhibitor (EDIN) genes (\u003cem\u003eednA\u003c/em\u003e, \u003cem\u003eednB\u003c/em\u003e, and \u003cem\u003eednC\u003c/em\u003e) were detected during both periods (Supplementary Fig.\u0026nbsp;4d). For a human-specific immune evasion cluster, the proportion of the staphylococcal complement inhibitor gene (\u003cem\u003escn\u003c/em\u003e) and staphylokinase gene (\u003cem\u003esak\u003c/em\u003e) in the two periods remained at the same level; however, that of the chemotaxis inhibitory protein Chp gene (\u003cem\u003echp\u003c/em\u003e) was markedly decreased in the total and MRSA.\u003c/p\u003e\n \u003cp\u003eSubsequently, we examined the distribution of VFGs in CC1, CC5, and CC8, the dominant clones of BSI-derived MRSA in 2019\u0026ndash;2020 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). In CC1, most ST1-SCC\u003cem\u003emec\u003c/em\u003eIV and ST2725-SCC\u003cem\u003emec\u003c/em\u003eIV had a high prevalence of \u003cem\u003esea\u003c/em\u003e, \u003cem\u003eseh\u003c/em\u003e, \u003cem\u003esek\u003c/em\u003e, \u003cem\u003eseq\u003c/em\u003e, \u003cem\u003escn\u003c/em\u003e, and \u003cem\u003esak\u003c/em\u003e, with the same distribution pattern (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, CC1). The proportion of VFGs in ST5-SCC\u003cem\u003emec\u003c/em\u003eII during the two periods was high for \u003cem\u003esec\u003c/em\u003e, \u003cem\u003etst-1\u003c/em\u003e, EGC, and \u003cem\u003esel\u003c/em\u003e, whereas the lower proportion for \u003cem\u003esep\u003c/em\u003e was 36% (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, CC5). The proportion of \u003cem\u003eseb\u003c/em\u003e in ST5-SCC\u003cem\u003emec\u003c/em\u003eII in 1994\u0026ndash;2000 was 12% but was not detected in 2019\u0026ndash;2020. The proportions of \u003cem\u003eseb\u003c/em\u003e, \u003cem\u003eseg\u003c/em\u003e, \u003cem\u003esei\u003c/em\u003e, \u003cem\u003esem\u003c/em\u003e, \u003cem\u003esen\u003c/em\u003e, and \u003cem\u003eseo\u003c/em\u003e in ST764-SCC\u003cem\u003emec\u003c/em\u003eII were high, whereas the proportions of \u003cem\u003esek\u003c/em\u003e and \u003cem\u003eseq\u003c/em\u003e were low (40%). ST764-SCC\u003cem\u003emec\u003c/em\u003eII did not possess \u003cem\u003esec\u003c/em\u003e, \u003cem\u003etst-1\u003c/em\u003e, or \u003cem\u003esel\u003c/em\u003e. In CC8, ST8-SCC\u003cem\u003emec\u003c/em\u003eI and ST8-SCC\u003cem\u003emec\u003c/em\u003eIVj possessed 50 and 75% of \u003cem\u003esep\u003c/em\u003e, respectively, with no other SE genes present (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, CC8). The proportion of \u003cem\u003echp\u003c/em\u003e in the ST8-SCC\u003cem\u003emec\u003c/em\u003eIVj and IVl clones was significantly lower than that in the other clones. ST8-SCC\u003cem\u003emec\u003c/em\u003eIVl contained high proportions of \u003cem\u003esec\u003c/em\u003e, \u003cem\u003etst-1\u003c/em\u003e, and \u003cem\u003esel\u003c/em\u003e (90%). In addition, ST8-SCC\u003cem\u003emec\u003c/em\u003eIVl possessed 90% \u003cem\u003esel\u003c/em\u003e and 40% \u003cem\u003ese1\u003c/em\u003e and \u003cem\u003eednA\u003c/em\u003e. ST8-SCC\u003cem\u003emec\u003c/em\u003eIVa had \u003cem\u003esek\u003c/em\u003e, \u003cem\u003eseq\u003c/em\u003e, and PVL genes (\u003cem\u003elukF-PV\u003c/em\u003e and \u003cem\u003elukS-PV\u003c/em\u003e) belonging to the USA300 lineage.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eEvolutionary origins and population dynamics of three CC lineages of blood-derived S. aureus in Japan\u003c/h2\u003e\n \u003cp\u003eTo provide a historical perspective on the emergence of the three dominant CC lineages of \u003cem\u003eS. aureus\u003c/em\u003e causing bacteremia in Japan, we constructed time-calibrated phylogenies of CC1 (ST1 and ST2725), CC5 (ST5 and ST764), and CC8 (MRSA/J and USA300) using Bayesian coalescent analysis implemented in the BEAST software. Each phylogenetic tree was calibrated using the isolation dates of the strains, which ranged from 1997 to 2020 (CC1) and 1982 to 2020 (CC5 and CC8), and public collection isolates with identifiable isolation dates (Supplementary Fig.\u0026nbsp;5 and Supplementary Table\u0026nbsp;4). Root-to-tip regression analyses revealed a positive correlation between genetic distance and sampling date for each lineage (Supplementary Fig.\u0026nbsp;6). Given the presence of a temporal structure in each dataset, we performed dated coalescent phylogenetic analysis. Consequently, we estimated the time to the most recent common ancestor (tMRCA) of the ST1-SCC\u003cem\u003emec\u003c/em\u003eIV and ST2725-SCC\u003cem\u003emec\u003c/em\u003eIV lineages to be approximately 1998 (95% highest posterior density [HPD] intervals: 1995\u0026ndash;2002) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea). The results of this analysis indicated that the ancestor of ST1-MRSA-\u003cem\u003et\u003c/em\u003e1784 acquired SCC\u003cem\u003emec\u003c/em\u003eIV, Tn\u003cem\u003e554\u003c/em\u003e harboring \u003cem\u003eant\u003c/em\u003e(\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003cem\u003e-Ia\u003c/em\u003e, and \u003cem\u003eerm\u003c/em\u003e(A)\u003csup\u003e22\u003c/sup\u003e after 1965 (95% HPD intervals: 1958\u0026ndash;1974). ST1-MRSA-\u003cem\u003et\u003c/em\u003e1784 emerged around 1998 (95% HPD intervals: 1995\u0026ndash;2002) and started to circulate in Japan after 2000. Six years later, around 2004 (95% HPD intervals: 2001\u0026ndash;2006), the ST2725-SCC\u003cem\u003emec\u003c/em\u003eIV-\u003cem\u003et\u003c/em\u003e1784 lineage diverged from ST1 and spread primarily to western Japan (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea square light blue and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ec bar graph in the left panel). However, the ST1-\u003cem\u003et\u003c/em\u003e127 lineage diverged in 1953 and acquired the genomic island \u0026nu;Sa4, carrying the \u003cem\u003eseb\u003c/em\u003e and fusidic acid resistance gene \u003cem\u003efusC\u003c/em\u003e-harboring SCC\u003cem\u003efus\u003c/em\u003e\u003csup\u003e23\u003c/sup\u003e by phage infections somewhere up to 1970. Through a timescale phylogenetic analysis, we first estimated the age of SCC\u003cem\u003efus\u003c/em\u003e acquisition in the ST1-\u003cem\u003et\u003c/em\u003e127 lineage. Reconstruction of the effective population size over time showed that the effective population size of CC1 remained steady until approximately 2000 but sharply rose after 2005, following the emergence of many lineages (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea, lower panel).\u003c/p\u003e\n \u003cp\u003eIn CC8, USA300 isolated from BSIs in Japan belonged to the USA300-NAE (North American Epidemic) lineage\u003csup\u003e24\u003c/sup\u003e and appeared around 1989 (95% HPD intervals: 1986\u0026ndash;1992) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb). This is consistent with the previously reported data\u003csup\u003e24\u003c/sup\u003e. The trajectory of the emergence of ST8-SCC\u003cem\u003emec\u003c/em\u003eIVl (MRSA/J) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb, square light blue) was somewhere between 1957 and 1986, when it acquired SCC\u003cem\u003emec\u003c/em\u003eIVl and \u0026nu;Sa4 (carrying \u003cem\u003esec\u003c/em\u003e, \u003cem\u003etst-1\u003c/em\u003e, and \u003cem\u003esel\u003c/em\u003e) by phage infections (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb, light blue of index No. 2 and 5, and Supplementary Fig.\u0026nbsp;7a) and plasmids carrying \u003cem\u003ese1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb, light blue of index No. 5, and Supplementary Fig.\u0026nbsp;7b), emerging around 1986 (95% HPD intervals: 1983\u0026ndash;1989) contemporaneously and in parallel with the USA300-NAE lineage (SCC\u003cem\u003emec\u003c/em\u003eIVa-arginine catabolic mobile element [ACME] type I). In addition, the ST8-SCC\u003cem\u003emec\u003c/em\u003eI and ST8-SCC\u003cem\u003emec\u003c/em\u003eIVj lineages emerged around 1979 (95% HPD interval: 1976\u0026ndash;1982) and 1988 (95% HPD interval: 1985\u0026ndash;1991), respectively. Some strains of the ST8-SCC\u003cem\u003emec\u003c/em\u003eI and ST8-SCC\u003cem\u003emec\u003c/em\u003eIVj lineages were estimated to have acquired \u003cem\u003etet\u003c/em\u003e(M)-harboring Tn\u003cem\u003e916\u003c/em\u003e-like transposable units in the late 1980s and 1990s (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb and Supplementary Fig.\u0026nbsp;7c). These multiple ST8 lineages emerged one after another during the 1980s\u0026ndash;1990s, and the effective population size of CC8 increased in the late 1990s-2000s (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb, lower).\u003c/p\u003e\n \u003cp\u003eIn CC5, we estimated the tMRCA of the ST5-SCC\u003cem\u003emec\u003c/em\u003eII (N/J clone) and ST764-SCC\u003cem\u003emec\u003c/em\u003eII lineages to be around 1959 (95%HPD interval: 1956\u0026ndash;1962) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec). Our genome sequence comparison inferred the trajectory of the emergence of the ST764-SCC\u003cem\u003emec\u003c/em\u003eII clone from the N/J clone based on recombination events (index No. 5 in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec and Supplementary Fig.\u0026nbsp;8). At the first step, somewhere between 1928 and 1959, SCC\u003cem\u003emec\u003c/em\u003eII, genomic island \u0026nu;Sa3 (carrying \u003cem\u003esec\u003c/em\u003e, \u003cem\u003etst-1\u003c/em\u003e, and \u003cem\u003esel\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec and Supplementary Fig.\u0026nbsp;8a) and \u003cem\u003etet\u003c/em\u003e(M)-harboring Tn\u003cem\u003e916\u003c/em\u003e-like transposable unit (Supplementary Fig.\u0026nbsp;8b) were acquired by phage infection or transposition, respectively, resulting in the emergence of ST5-SCC\u003cem\u003emec\u003c/em\u003eII (N/J clone). In the second step, after 1974 (95%HPD interval: 1971\u0026ndash;1976), N/J clones acquired the genomic island \u0026nu;Sa4 carrying \u003cem\u003eseb\u003c/em\u003e by phage infection and emerged as a \u003cem\u003eseb\u003c/em\u003e-positive-N/J lineage, which evolved into the ST764-SCC\u003cem\u003emec\u003c/em\u003eII lineage (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec and Supplementary Fig.\u0026nbsp;8c). At the third step, genomic island \u0026nu;Sa3 (carrying \u003cem\u003esec\u003c/em\u003e, \u003cem\u003etst-1\u003c/em\u003e, and \u003cem\u003esel\u003c/em\u003e) was replaced by a completely different phage, followed by a recombination event at the same location at the \u0026nu;Sa3 of N/J clone, loosing \u003cem\u003eblaZ\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec and Supplementary Fig.\u0026nbsp;8a). Finally, approximately 20 years after the emergence of the N/J clone, around 1979 (95% HPD intervals: 1977\u0026ndash;1982), the ST764-SCC\u003cem\u003emec\u003c/em\u003eII lineage diverged from ST5 and spread throughout Japan after 1994. One sub-lineage of ST764-SCC\u003cem\u003emec\u003c/em\u003eII acquired the genomic island \u0026nu;Sa1 carrying \u003cem\u003esek\u003c/em\u003e and \u003cem\u003eseq\u003c/em\u003e by phage infection somewhere between 1998\u0026ndash;2001 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec and Supplementary Fig.\u0026nbsp;8d). Another ST764-SCC\u003cem\u003emec\u003c/em\u003eII sub-lineage acquired ACME type II somewhere between 1996 and 1999 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec and Supplementary Fig.\u0026nbsp;8e). However, the ST5-SCC\u003cem\u003emec\u003c/em\u003eI ancestor diverged from the N/J lineage around 1928, and somewhere between 1932\u0026ndash;1967, the ST5-SCC\u003cem\u003emec\u003c/em\u003eI lineage emerged by acquiring SCC\u003cem\u003emec\u003c/em\u003eI and \u0026nu;Sa4 (carrying \u003cem\u003esec\u003c/em\u003e, \u003cem\u003esek\u003c/em\u003e, and \u003cem\u003eseq\u003c/em\u003e) by phage infections but did not spread as far in Japan (Supplementary Tables\u0026nbsp;1 and 2). ST5-SCC\u003cem\u003emec\u003c/em\u003eIV followed a different phylogenetic trajectory from the clones described above, emerging somewhere between 1966 and 1994 with the acquisition of SCC\u003cem\u003emec\u003c/em\u003eI, which was also not widespread in Japan (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec). The effective population size of CC5 increased in a staircase-like manner during the emergence of ST5-SCC\u003cem\u003emec\u003c/em\u003eII and ST764-SCC\u003cem\u003emec\u003c/em\u003eII (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec, lower panel).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eMore than 80 years since the antimicrobial-resistant \u003cem\u003eS. aureus\u003c/em\u003e (including MRSA) was first recognized, MRSA has rapidly evolved following the widespread use of antimicrobials and has been reported to have a remarkable potential to spread globally\u003csup\u003e25\u003c/sup\u003e. Here, we used 580 \u003cem\u003eS. aureus\u003c/em\u003e genomes from the late 2010s and 183 genomes from the late 1990s, derived from BSIs, to determine the evolutionary history and geographical distribution of Japanese BSI-derived \u003cem\u003eS. aureus\u003c/em\u003e, focusing on three CC lineages. We previously reported that the prevalence of MRSA in inpatients decreased from 40.3 to 35.1% between 2011 and 2019, according to data from the national phenotypic antimicrobial surveillance (Japan Nosocomial Infections Surveillance, JANIS), and the analysis of resistance profiles indicated that isolates resistant to three antimicrobials (OXA, EM, and LVFX) increased, whereas those resistant to six antimicrobials (OXA, GM, EM, CLDM, MINO, and LVFX) decreased\u003csup\u003e26\u003c/sup\u003e. A comparison of the resistance profiles and genome data of BSI-derived isolates in a single university hospital between 2011 and 2019 revealed that increased resistance to the three drugs corresponded to CC8-SCC\u003cem\u003emec\u003c/em\u003eIV. Here, we compared BSI-derived MRSA isolates of 1994\u0026ndash;2000 with those of 2019\u0026ndash;2020, wherein the proportion of isolates resistant to the six drugs decreased from 33.7 to 10.8%, whereas the proportion of isolates resistant to three drugs sharply increased from 1 to 41.6%, confirming the change in the multidrug-resistant phenotype of MRSA (Supplementary Table\u0026nbsp;5 and Supplementary Fig.\u0026nbsp;9). Additionally, our genome analysis indicated that ST5-SCC\u003cem\u003emec\u003c/em\u003eII (N/J clone) showed significant reduction in the proportion of isolates resistant to the six drugs, whereas ST764-SCC\u003cem\u003emec\u003c/em\u003eII appeared as a new clone resistant to the six drugs during 2019\u0026ndash;2020. Conversely, 96.4% (109/112 strains) of the increased proportion of isolates resistant to the three drugs belonged to the CC1-SCC\u003cem\u003emec\u003c/em\u003eIV lineage, which was different from the results of the previous study in a single university hospital. The reason for this difference is that the single university hospital was located in western Japan, where CC8-SCC\u003cem\u003emec\u003c/em\u003eIV is more prevalent (Supplementary Fig.\u0026nbsp;1); however, in the nationwide surveillance of BSI-derived \u003cem\u003eS. aureus\u003c/em\u003e used this study, the predominant CC1-SCC\u003cem\u003emec\u003c/em\u003eIV had a significant impact on the resistance to the three antimicrobials. ST1-SCC\u003cem\u003emec\u003c/em\u003eIV and ST2725-SCC\u003cem\u003emec\u003c/em\u003eIV had extremely high rates of \u003cem\u003eerm\u003c/em\u003e(A) and QRDR mutations, and these genes contributed significantly to resistance to the three antimicrobials (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, CC1). This drastic change in MRSA clones reflected not only the resistance gene repertoire but also the virulence gene repertoire of MRSA (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary Fig.\u0026nbsp;1). A significant decrease in the proportion of \u003cem\u003etst-1\u003c/em\u003e and EGC (\u003cem\u003eseg\u003c/em\u003e, \u003cem\u003esei\u003c/em\u003e, \u003cem\u003esem\u003c/em\u003e, \u003cem\u003esen\u003c/em\u003e, and \u003cem\u003eseo\u003c/em\u003e) and an increase in the proportion of \u003cem\u003esea\u003c/em\u003e, \u003cem\u003eseh\u003c/em\u003e, \u003cem\u003esek\u003c/em\u003e, and \u003cem\u003eseq\u003c/em\u003e can be attributed to an increase in ST1-SCC\u003cem\u003emec\u003c/em\u003eIV and ST8-SCC\u003cem\u003emec\u003c/em\u003eIV and a decrease in ST5-SCC\u003cem\u003emec\u003c/em\u003eII.\u003c/p\u003e \u003cp\u003eOur timescale phylogenetic and population dynamics analysis showed that ST5-SCC\u003cem\u003emec\u003c/em\u003eII emerged around 1960 and reigned as a representative of HA-MRSA in Japan until 2000. However, after 2000, it was replaced by some sublineages of CC1-SCC\u003cem\u003emec\u003c/em\u003eIV and CC8-SCC\u003cem\u003emec\u003c/em\u003eIV, which are representative clones of CA-MRSA. This was further supported by several previous epidemiological studies\u003csup\u003e8,12,27\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRegional differences in the distribution of CC1 were observed: ST1 and ST81 tended to be slightly more common in Eastern Japan, whereas ST2725-SCC\u003cem\u003emec\u003c/em\u003eIV circulated slightly more frequently in western Japan (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). These STs-SCC\u003cem\u003emec\u003c/em\u003eIV possessed almost identical patterns of ARGs, VFGs, and antimicrobial resistance patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, c, and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Notably, the interval between the emergence of ST1-SCC\u003cem\u003emec\u003c/em\u003eIV and ST2725-SCC\u003cem\u003emec\u003c/em\u003eIV was very short, only approximately five years, as revealed for the first time in our study (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Most ST1-SCC\u003cem\u003emec\u003c/em\u003eIV-\u003cem\u003et\u003c/em\u003e1784 isolates in Japan belong to a different lineage from the PVL-negative ST1-SCC\u003cem\u003emec\u003c/em\u003eIV-\u003cem\u003et\u003c/em\u003e127, which is widespread in Europe\u003csup\u003e28\u003c/sup\u003e. Moreover, some subclades of European ST1-SCC\u003cem\u003emec\u003c/em\u003eIV-\u003cem\u003et\u003c/em\u003e127 have SCC\u003cem\u003emec\u003c/em\u003e\u003csub\u003e\u003cem\u003efus\u003c/em\u003e\u003c/sub\u003e carrying the fusidic acid resistance gene \u003cem\u003efusC\u003c/em\u003e but not ST1-SCC\u003cem\u003emec\u003c/em\u003eIV-\u003cem\u003et\u003c/em\u003e1784. The Japanese \u003cem\u003efusC\u003c/em\u003e-positive ST1-MSSA-\u003cem\u003et\u003c/em\u003e127 strain is rare and \u003cem\u003emecA\u003c/em\u003e negative (Supplementary Table\u0026nbsp;2). ST1-SCC\u003cem\u003emec\u003c/em\u003eIV-\u003cem\u003et\u003c/em\u003e1284 is a more modern lineage than European ST1-\u003cem\u003et\u003c/em\u003e127, according to timescale phylogenetic analysis.\u003c/p\u003e \u003cp\u003eJapanese ST2725-SCC\u003cem\u003emec\u003c/em\u003eIV-\u003cem\u003et\u003c/em\u003e1784 has been detected in both inpatients and outpatients, suggesting that it has spread to hospitals and community\u003csup\u003e19\u003c/sup\u003e. To the best of our knowledge, the ST2725-SCC\u003cem\u003emec\u003c/em\u003eIV-\u003cem\u003et\u003c/em\u003e1784 clone has not been reported overseas and is a relatively young community-acquired MRSA unique to Japan. In contrast to CC1, CC5 was distributed nationally, without an eastern-western regional bias. Although CC5 in Japan has been replaced, its proportion is still high, ranking third (approximately 12%) after CC1 and CC8. The dominant CC5, ST5-SCC\u003cem\u003emec\u003c/em\u003eII (the N/J clone), has spread\u003csup\u003e29\u003c/sup\u003e and circulated in medical centers throughout Japan since its emergence in 1959. Over a period of approximately 35 years, our time-calibrated phylogenic analysis inferred that the ST5-SCC\u003cem\u003emec\u003c/em\u003eII lineage underwent repeated acquisition or shedding via superantigen toxin-harboring phage infection and antimicrobial resistant gene-carrying transposons, leading to the emergence of the ST764-SCC\u003cem\u003emec\u003c/em\u003eII lineage around 1994. Furthermore, herein, we found that several strains of ST764-SCC\u003cem\u003emec\u003c/em\u003eII were independently acquired by phage infection carrying \u003cem\u003esek\u003c/em\u003e/\u003cem\u003eseq\u003c/em\u003e or ACME-II and the ACME-related cassette JR1 (cJR1) around 2000 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e blue bar). ACME-II was first identified in \u003cem\u003eS. epidermidis\u003c/em\u003e\u003csup\u003e30\u003c/sup\u003e. Urushinbara et al. reported several variants of the ACME-II-SCC\u003cem\u003emec\u003c/em\u003e composite island (ACME-II-SCC\u003cem\u003emec\u003c/em\u003e-CI) in ST764, isolated from Hokkaido, Japan\u003csup\u003e31\u003c/sup\u003e. Therefore, we presume that the ST764-SCC\u003cem\u003emec\u003c/em\u003eII lineage isolated in Japan is becoming increasingly diverse. ST764-ACME-SCC\u003cem\u003emec\u003c/em\u003e-CI may be distributed throughout Northern-Eastern Japan, particularly in Hokkaido.\u003c/p\u003e \u003cp\u003eWe demonstrated that ST764-SCC\u003cem\u003emec\u003c/em\u003eII was the highest-risk clone, with statistical significance, according to the risk assessment of the 30-day mortality rate (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The spread of \u003cem\u003eseb\u003c/em\u003e-positive ST764-SCC\u003cem\u003emec\u003c/em\u003eII clones in long-term care facilities in Japan was recently reported by Kawamura\u003csup\u003e32\u003c/sup\u003e. In our study, 72.4% (21/29) of the registered ST764-SCC\u003cem\u003emec\u003c/em\u003eII cases were ST764-SCC\u003cem\u003emec\u003c/em\u003eII-\u003cem\u003et\u003c/em\u003e002 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e ocher bar). In China, ST764-SCC\u003cem\u003emec\u003c/em\u003eII-\u003cem\u003et\u003c/em\u003e1084, a lineage similar to the Japanese ST764-SCC\u003cem\u003emec\u003c/em\u003eII-\u003cem\u003et\u003c/em\u003e002, has recently been reported to increase as a hypervirulent clone\u003csup\u003e11\u003c/sup\u003e. Our study indicates that age is a confounding factor significantly associated with ST764 and 30-day mortality. This is in agreement with a previous observational study demonstrating the spread of ST764 in Japanese LTCF residents with multiple comorbidities and increased susceptibility to infections\u003csup\u003e32\u003c/sup\u003e. The association between ST764 and 30-day mortality rate remained significant, even after controlling for the confounding effect of age. A previous regional surveillance study demonstrated that all MRSA-SCC\u003cem\u003emec\u003c/em\u003eII isolates from patients with pneumonia belonged to ST764\u003csup\u003e33\u003c/sup\u003e. Our recent epidemiological risk assessment study of isolates from patients admitted to the intensive care unit\u003csup\u003e34\u003c/sup\u003e and isolates from BSIs demonstrated that the primary focus of ST764 was pneumonia. Furthermore, ST764 was the only lineage that emerged and increased in number within 20 years and was resistant to six antimicrobials: OXA, GM, EM, CLDM, MINO, and LVFX. Overall, these results strongly suggest that ST764 is an emerging highest-risk MRSA clone with multiple antimicrobial resistances that causes BSI.\u003c/p\u003e \u003cp\u003eIntriguingly, comparison of the complete genome sequences of ST764 with its ancestral lineage ST5 revealed frequent detection of a large chromosomal inversion (24% in 25 complete genomes of ST764 compared to the reference genome N315 in this study vs. 3.4% in 147 of ST5 in the NCBI public database) (Supplementary Fig.\u0026nbsp;10 and Supplementary Table\u0026nbsp;6). In prokaryotes, large chromosomal inversions are associated with phenotypic changes in bacterial virulence through phase variation\u003csup\u003e35,36,37\u003c/sup\u003e, thereby contributing to bacterial survival strategies and defining evolutionary trajectories. ST764 may have been in the middle of an evolutionary process. CC8 tends to be slightly more common in western Japan; however, it has spread almost nationwide, and when considering the MRSA surveillance results of previous research\u003csup\u003e8,13\u003c/sup\u003e, it is reconfirmed that CC8 circulates in community\u003csup\u003e8\u003c/sup\u003e and hospital settings. Most CC8 isolates from BSIs in 2019\u0026ndash;2020 were ST8-SCC\u003cem\u003emec\u003c/em\u003eIV. We revealed through timescale phylogenic analysis that ST8-SCC\u003cem\u003emec\u003c/em\u003eI and multiple ST8-SCC\u003cem\u003emec\u003c/em\u003eIV clones emerged around the same period in the 1980s. In recent years, the USA300 lineage, which is increasingly found in dermatology clinics in Japan, has been isolated from BSIs in healthcare settings at a low rate\u003csup\u003e8\u003c/sup\u003e. All the detected USA300 strains belonged to the USA300-NA lineage. We first revealed that the emergence of MRSA/J clone associated with invasive infections occurred in Japan, almost at the same period as USA300-NA.\u003c/p\u003e \u003cp\u003eThis study has some limitations. First, we performed in-depth genomic analysis of only three major clonal lineages CC1, CC5, and CC8 but not that of minor lineages. Second, clinical data for isolates in the period 1994\u0026ndash;2000 were not available. Therefore, we were unable to compare the clinical significance of ST764-SCC\u003cem\u003emec\u003c/em\u003eII with ST5-SCC\u003cem\u003emec\u003c/em\u003eII (the N/J clone), which was dominant in 1994\u0026ndash;2000 and the ancestor clone of ST764-SCC\u003cem\u003emec\u003c/em\u003eII. Third, we were not able to collect isolates from the Tohoku-region in eastern Japan and the Shikoku-region in western Japan, leaving a possibility of bias.\u003c/p\u003e \u003cp\u003eDespite these limitations, this is the first study revealing that MRSA populations causing BSIs in Japan are primarily composed of three clonally expanded CC lineages, each emerging and spreading at distinct times and places. This study further identified ST764-SCC\u003cem\u003emec\u003c/em\u003eII-\u003cem\u003et\u003c/em\u003e1084 as a clone associated with a high mortality rate and characterized its evolutionary trajectory. In summary, our study provides a blueprint for national genomic surveillance that integrates clinical data and enables identification and evolutionary characterization of a high-risk clone.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eWe recruited 63 Japanese medical institutions through the National Hospital Organization. \u003cem\u003eS. aureus\u003c/em\u003e isolates were detected in two or more blood samples obtained at the same time collected at each hospital between April 2019 and July 2020. In total, 798 isolates were collected from 55 hospitals by the Antimicrobial Resistance Research Center (AMR-RC) of the National Institute of Infectious Diseases (NIID). Of the 798 strains collected, eight \u003cem\u003eS. argenteus\u003c/em\u003e and one \u003cem\u003eS. epidermidis\u003c/em\u003e isolates were re-identified by MALDI biotyper (Bruker Daltonics, Billerica, MA) at the AMR-RC-NIID and excluded from subsequent analyses. Patient information used in this study included age, sex, race, date of admission, underlying medical condition, presence or absence of diabetes, presence or absence of dialysis, medical history, surgical history (30 days to time of blood culture), presence or absence of injection drug use, presence or absence of HIV infection, history of influenza (within 2 weeks), infection site, date of discharge or death, and 30-day mortality rate (from the time of blood culture collection). Although two sets of blood culture tests are usually performed, for samples with positive strains in both vials, the \u003cem\u003eS. aureus\u003c/em\u003e isolate from the first vial was used for whole-genome sequencing and integrated analysis with clinical data, and the isolate from the second vial was excluded. Only first-time isolates from the same patient were used, and \u003cem\u003eS. aureus\u003c/em\u003e re-isolated multiple times were excluded. Ultimately, of the 789 \u003cem\u003eS. aureus\u003c/em\u003e strains, 209 strains were excluded, and 580 isolates were analyzed. For comparative analysis of BSI-derived \u003cem\u003eS. aureus\u003c/em\u003e isolates from decades ago, we used a nationwide collection (also known as the YK-Collection) of clinical isolates from 1994 to 2016 in a previous study\u003csup\u003e21\u003c/sup\u003e, which was stored in the Japanese AMR bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://jarbb.jp/en/about/\u003c/span\u003e\u003cspan address=\"https://jarbb.jp/en/about/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) at NIID-AMR-RC. We randomly selected 183 BSI-derived \u003cem\u003eS. aureus\u003c/em\u003e isolates from 1994 to 2000. All isolates were stored at \u0026minus;\u0026thinsp;80 \u0026ordm;C in preservation medium supplemented with 30% (vol/vol) glycerol and cultured at 37 \u0026ordm;C in tryptone soy broth. The following data regarding \u003cem\u003eS. aureus\u003c/em\u003e isolates were collected from participating medical institutions: day of isolation in blood culture, day of admission, infected nest, underlying disease, and day of discharge or death.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEthics approval\u003c/h2\u003e \u003cp\u003eThis study was approved by the Medical Research Ethics Committee of the National Institute of Infectious Diseases (approval no. 1251). Although approval was granted for the overall study, each participating hospital obtained approval from the respective ethical approval committee. Regarding informed consent, an opt-out approach (a method where research information is disclosed on websites, providing research subjects the opportunity to decline participation) was implemented at each participating medical institution. All \u003cem\u003eS. aureus\u003c/em\u003e isolates were anonymized and individually numbered when isolated from blood cultures. All data and isolates were fully anonymized before being sent to AMR-RC-NIID.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction and whole-genome sequencing\u003c/h2\u003e \u003cp\u003eAll isolates were subcultured from glycerol stocks onto tryptic soy agar at 37 \u0026ordm;C for overnight. A single colony was picked up and cultured at 37 \u0026ordm;C in TSB for 12 h. Genomic DNA was extracted from the liquid culture using lysostaphin (FUJIFILM Wako Pure Chemical Corp., Osaka, Japan) and Agencourt AMPure XP (Beckman Coulter Inc., Brea, CA, USA), according to the manufacturer\u0026rsquo;s instructions. Short-read DNA libraries were prepared for Illumina sequencing using the Enzymatic 5X WGS Fragmentation Mix, 5X WGS Ligase Mix (BioStream Corp, Tokyo, Japan), and the automated NGS preparation system Biomek i7 Workstation (Beckman Coulter Inc., Brea, CA, USA). Short-read Illumina sequencing was performed on the Illumina HiSeq X FIVE platform to generate 150-bp paired-end reads at Macrogen Japan Corp., Tokyo, Japan, and on Illumina MiSeq to generate 300-bp paired-end reads at the NIID-AMR-RC according to the manufacturer\u0026rsquo;s instructions. For long-read sequencing, genomic DNA from each strain was purified using the Monarch HMW DNA Extraction Kit for Tissue (#T3060; New England BioLabs, Ipswich, MA, USA) following the manufacturer\u0026rsquo;s instructions. A long-read DNA library was prepared using the SQK-RBK004 Rapid Barcoding Kit [Oxford Nanopore Technologies (ONT), Oxford, UK], and barcoding was performed on GridION (ONT, Oxford, UK) using MinKNOW v21.05.25 and an FLO-MIN106 flow cell (ONT, Oxford, UK).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDe novo assembly and annotation\u003c/h2\u003e \u003cp\u003eQuality control of the raw sequenced reads was performed using FastQC v0.11.5 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/\u003c/span\u003e\u003cspan address=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Illumina reads were assembled into contigs using Shovill v1.0.9 pipeline (available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/tseemann/shovill\u003c/span\u003e\u003cspan address=\"https://github.com/tseemann/shovill\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with the option \u0026ndash;trim to produce high quality draft genomes. The Shovill program performs subsampling of read depth down to 150X, trimming adapters, correcting sequencing errors, and assembling using SPAdes v3.15.5\u003csup\u003e38\u003c/sup\u003e. ONT reads were trimmed using FiltLong (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/rrwick/Filtlong\u003c/span\u003e\u003cspan address=\"https://github.com/rrwick/Filtlong\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), assembled with trimmed long reads using Flye v2.9.1\u003csup\u003e39\u003c/sup\u003e, and polished with trimmed Illumina reads using Pilon v1.24\u003csup\u003e40\u003c/sup\u003e. Quality of the assembled genome sequences was assessed using QUAST v4.0\u003csup\u003e41\u003c/sup\u003e and CheckM v1.1.0\u003csup\u003e42\u003c/sup\u003e. We performed the taxonomy check of each genome using the dfast_qc v0.4.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/nigyta/dfast_qc\u003c/span\u003e\u003cspan address=\"https://github.com/nigyta/dfast_qc\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and confirmed \u003cem\u003eS. aureus\u003c/em\u003e. Genome annotation of all the isolates was performed using DFAST-core v1.2.16\u003csup\u003e43\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eIn silico sequence typing and detection of virulence factor and antimicrobial resistance genes\u003c/h2\u003e \u003cp\u003eThe ST of each isolate was defined using mlst v2.22.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/tseemann/mlst\u003c/span\u003e\u003cspan address=\"https://github.com/tseemann/mlst\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which extracts seven housekeeping genes (\u003cem\u003earcC\u003c/em\u003e, \u003cem\u003earoE\u003c/em\u003e, \u003cem\u003eglpF\u003c/em\u003e, \u003cem\u003egmk\u003c/em\u003e, \u003cem\u003epta\u003c/em\u003e, \u003cem\u003etpi\u003c/em\u003e, and \u003cem\u003eyqiL\u003c/em\u003e) from the sequence contigs and matches them against characterized STs in the \u003cem\u003eS. aureus\u003c/em\u003e PubMLST database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmlst.org/organisms/staphylococcus-aureus/\u003c/span\u003e\u003cspan address=\"https://pubmlst.org/organisms/staphylococcus-aureus/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). PHYLOViZ v2.0\u003csup\u003e44\u003c/sup\u003e with the geoBURST Full MST algorithm was used to determine CC in the MLST database (as of July 2022). The presence of virulence factors and antimicrobial resistance genes was detected by ABRicate v1.0.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/tseemann/abricate\u003c/span\u003e\u003cspan address=\"https://github.com/tseemann/abricate\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with ResFinder\u003csup\u003e45\u003c/sup\u003e database 2022-06-09 and VFDB\u003csup\u003e46\u003c/sup\u003e database 2022-06-09 and custom virulence factor database for \u003cem\u003eS. aureus\u003c/em\u003e\u003csup\u003e47\u003c/sup\u003e with 90% identity and 90% query coverage cutoffs. PointFinder v4.1.11\u003csup\u003e48\u003c/sup\u003e was used to identify antibiotic resistance genes encoded by chromosomal mutations with 90% identity and 90% query coverage cutoffs. SCC\u003cem\u003emec\u003c/em\u003e and \u003cem\u003espa\u003c/em\u003e typing was performed using the web-based SCC\u003cem\u003emec\u003c/em\u003eFinder\u003csup\u003e49\u003c/sup\u003e and spaTyper v0.2.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/HCGB-IGTP/spaTyper\u003c/span\u003e\u003cspan address=\"https://github.com/HCGB-IGTP/spaTyper\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with default settings. GenomeMatcher v3.0.2\u003csup\u003e50\u003c/sup\u003e was used for chromosome sequence comparison and visualization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePhylogenetic and clustering analysis\u003c/h2\u003e \u003cp\u003eA phylogenetic tree of the core genome alignment was constructed using the kSNP3.0\u003csup\u003e51\u003c/sup\u003e algorithm without reference genomes. Genetic population structure analysis was performed by partitioning the isolates into SCs of genetically similar individuals, using the Bayesian hierarchical clustering program FastBAPS package v1.0.8\u003csup\u003e52\u003c/sup\u003e in R v4.2.2 (R Core Team. 2019. R: Language and environment for statistical computing; available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A maximum-likelihood (ML) phylogenetic tree was constructed using RAxML-NG v1.0.1\u003csup\u003e53\u003c/sup\u003e with the best model inferred by ModelTest-NG v0.1.7\u003csup\u003e54\u003c/sup\u003e and 100 bootstrap replicates. The ML trees were rooted at the midpoint and visualized using Figtree v1.4.4 (available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tree.bio.ed.ac.uk/software/figtree\u003c/span\u003e\u003cspan address=\"http://tree.bio.ed.ac.uk/software/figtree\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eTime-calibrated phylogeny and population demographic analysis\u003c/h2\u003e \u003cp\u003eTo perform temporal analysis of the CC1, CC5, and CC8 lineages individually using our collection and NCBI public database (Supplementary Table\u0026nbsp;4), snippy v4.6.0 (available at \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) was used to perform reference-based mapping and identify SNPs for CC1, CC5, and CC8 with \u003cem\u003esnippy-multi\u003c/em\u003e script and default parameters; MSSA476 (Assembly accession no. BX571857), N315 (accession no. BA000018), and JH4899 (accession no. AP014921) were used as references for CC1, CC5, and CC8, respectively. The Core SNP aligned sequence files of each CC were generated using \u003cem\u003esnippy-core\u003c/em\u003e and \u003cem\u003esnippy-clean_full_aln\u003c/em\u003e scripts. Recombination-free aligned sequences were generated using Gubbins v3.2.0\u003csup\u003e55\u003c/sup\u003e for each SC lineage. ML trees were checked using a Figtree (Supplementary Fig.\u0026nbsp;5). To reduce the computational intensity, we constructed dated phylogenies from randomly sampled datasets, each of which includes 150\u0026ndash;190 isolates. We then investigated the temporal signals in the ML trees for each SC lineage using TempEst\u003csup\u003e56\u003c/sup\u003e to assess the linear relationship between the root-to-tip distance and year of isolation. A time-calibrated phylogenetic tree and effective population size over time (including the age of the most recent common ancestor [MRCA]) were estimated using BEAST v2.6.7, a coalescent Bayesian skyline tree prior and a strict molecular clock model\u003csup\u003e57\u003c/sup\u003e. A general time reversible substitution model with a gamma distribution for site rate heterogeneity was used, and a Markov chain Monte Carlo analysis with a length of 500\u0026nbsp;million steps was run for various numbers of coalescent intervals of the Bayesian skyline (known as dimensions in the model). The effective sample size for all parameters was \u0026gt;\u0026thinsp;200, and sufficient chain mixing was confirmed using Tracer v1.7.1\u003csup\u003e58\u003c/sup\u003e. The log-combiner program in the BEAST package was subsequently used to combine the results of these runs. The final tree was output and annotated using FigTree v1.4.4.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eAntimicrobial susceptibility testing\u003c/h2\u003e \u003cp\u003eThe minimum inhibitory concentrations of ampicillin (ABPC), oxacillin (MPIPC), CEZ, CMZ, imipenem (IPM), ampicillin/sulbactam (ABPC/SBT), GM, ABK, EM, CLDM, MINO, LVFX, vancomycin (VCM), teicoplanin (TEIC), daptomycin (DAP), sulfamethoxazole/trimethoprim (ST), FOM, rifampicin (RFP), linezolid (LZD), mupirocin (MUP), cefoxitin (CFX), and I-CLDM were determined via broth microdilution testing using a MicroScan Pos series panel for MicroScan WalkAway96 System (Beckman Coulter Inc., Brea, CA, USA) according to the manufacturer\u0026rsquo;s instructions. Antimicrobial susceptibility was measured according to the CLSI guidelines (Thirty-First Edition: M100) and EUCAST v11.0 (CLSI. Performance Standards for Antimicrobial Susceptibility Testing\u0026mdash; Thirty-First Edition: M100. 2021; EUCAST. Breakpoint Tables for Interpretation of MICs and Zone Diameters, Version 11.0. 2021.).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using R program v4.0.3 (R Core Team. 2019. R: Language and environment for statistical computing. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and JMP version 13.2.1 (SAS Institute, Cary, NC, USA). The diversity parameters of ST types were determined with the Shannon diversity index using R. Statistical analyses of the number of ARGs were conducted using the ggplot2 package ver. 3.3.6\u003csup\u003e59\u003c/sup\u003e in R. Survival and regression analyses were conducted using JMP.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe metadata for each of the 580 \u003cem\u003eS. aureus\u003c/em\u003e isolated in 2019\u0026ndash;2020, including MLST, SC, minimum inhibitory concentration, clinical and geographical information, and genetic polymorphisms, are summarized in Supplementary Table\u0026nbsp;1. The raw sequences obtained in this study have been deposited in GenBank/EMBL/DDBJ under the BioProject accession number PRJDB15501 and Sequence Read Archive (SRA) accession numbers DRR456294\u0026ndash;DRR457056 (Supplementary Tables\u0026nbsp;1 and 2). Supplementary Tables\u0026nbsp;4 and 6 lists the accession numbers from the NCBI database used in this study. Six supplementary tables and 10 supplementary figures are available in the online version of this article.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData availability\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe metadata for each of the 580 \u003cem\u003eS. aureus\u003c/em\u003e isolated in 2019–2020, including MLST, SC, minimum inhibitory concentration, clinical and geographical information, and genetic polymorphisms, are summarized in Supplementary Table 1. The raw sequences obtained in this study have been deposited in GenBank/EMBL/DDBJ under the BioProject accession number PRJDB15501 and Sequence Read Archive (SRA) accession numbers DRR456294–DRR457056 (Supplementary Tables 1 and 2). Supplementary Table 4 and 6 lists the accession numbers from the NCBI database used in this study. Six supplementary tables and 10 supplementary figures are available in the online version of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;We are grateful to all the hospitals participating in JARBS-SA. We are grateful to Yumiko Hosaka for discussions, and the following staff for technical contributions to this project: Eiko Anzai, Takahisa Ishizuka, Mayumi Sasada, Koichi Shimakawa, Sayoko Kawakami, Yoshie Taki, Satoyo Wakai, Sadao Aoki, Mikako Nakazawa,\u0026nbsp;Emi Fujimura,\u0026nbsp;Noriko Sakamoto, Elahi Shaheem, and Chika Arai. This work was supported by the Research Program on Emerging and Reemerging Infectious Diseases of the Japan Agency for Medical Research and Development (AMED) under grant number 21fk0108604.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMS (Sugai) conceptualized this study. MS (Sugai), MS, JH (Hisatsune), TK\u0026nbsp;(Kajihara), HK (Kitagawa), HK (Ohge), TM (Mizukami), TT (Takahashi), and FK (Kawano) designed this study. TM, TT, FK and JARBS-SA Consortium contributed collecting the isolates and clinical data. SK (Kutsuno), YI (Iwao), KI (Ishida-Kuroki) were involved in species identification. JH, YS (Sugawara) contributed to genome sequencing. JH, TK, and KY (Yahara) analyzed the data. JH contributed to the information processing and database construction. JH was the major contributor to the writing of the manuscript and MS, KY, and SK (Kayama) significantly contributed to edit the manuscript. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eORCID FOR CORRESPONDING AUTHORS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMotoyuki Sugai\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;0000-0001-9252-7739\u003c/p\u003e\n\u003cp\u003eJuzo Hisatsune \u0026nbsp;0000-0001-9330-4203\u003c/p\u003e\n\u003cp\u003e \u003cstrong\u003e*JARBS-SA CONSORTIUM\u003c/strong\u003e \u003cp\u003eYu Tsunashima\u003csup\u003e*1\u003c/sup\u003e, Takahiro Fujita\u003csup\u003e*2\u003c/sup\u003e, Katsushi Kanno\u003csup\u003e*3\u003c/sup\u003e, Takeo Endo\u003csup\u003e*4\u003c/sup\u003e, Yukari Kato\u003csup\u003e*5\u003c/sup\u003e, Takao Yokoe\u003csup\u003e*6\u003c/sup\u003e, Hiroshi Mizukoshi\u003csup\u003e*7\u003c/sup\u003e, Isamu Kamimaki\u003csup\u003e*8\u003c/sup\u003e, Michiyo Misawa\u003csup\u003e*9\u003c/sup\u003e, Yumi Suzuki\u003csup\u003e*\u003c/sup\u003e10, Shuichi Otawa\u003csup\u003e*11\u003c/sup\u003e, Yumiko Owatari\u003csup\u003e*12\u003c/sup\u003e, Osamu Okamura\u003csup\u003e*13\u003c/sup\u003e, Katsuhiro Kuwahara\u003csup\u003e*14\u003c/sup\u003e, Yoshinori Inoue\u003csup\u003e*15\u003c/sup\u003e, Sumiyo Nishihara\u003csup\u003e*16\u003c/sup\u003e, Kazuya Takahashi\u003csup\u003e*17\u003c/sup\u003e, Hitoshi Inoue\u003csup\u003e*18\u003c/sup\u003e, Tatsuo Kato\u003csup\u003e*19\u003c/sup\u003e, Naoko Maeda\u003csup\u003e*20\u003c/sup\u003e, Naoki Takayama\u003csup\u003e*21\u003c/sup\u003e, Kazuko Shiozawa\u003csup\u003e*22\u003c/sup\u003e, Yuta Hayashi\u003csup\u003e*23\u003c/sup\u003e, Shimoeda Hirokazu\u003csup\u003e*24\u003c/sup\u003e, Mariko Ueda\u003csup\u003e*24\u003c/sup\u003e, Toshio Makie\u003csup\u003e*25\u003c/sup\u003e, Kenji Yamamoto\u003csup\u003e*26\u003c/sup\u003e, Koichi Nitta\u003csup\u003e*27\u003c/sup\u003e, Toshio Saito\u003csup\u003e*28\u003c/sup\u003e, Sami Fujihara\u003csup\u003e*29\u003c/sup\u003e, Kazutaka Yassuda\u003csup\u003e*30\u003c/sup\u003e, Shinji Tamaki\u003csup\u003e*31\u003c/sup\u003e, Shu Sugitani\u003csup\u003e*32\u003c/sup\u003e, Katsuyuki Tomita\u003csup\u003e*33\u003c/sup\u003e, Masami Watanabe\u003csup\u003e*34\u003c/sup\u003e, Toshikazu Ikeda\u003csup\u003e*35\u003c/sup\u003e, Takashi Saito\u003csup\u003e*36\u003c/sup\u003e, Yutaka Fujiwara\u003csup\u003e*37\u003c/sup\u003e, Masanobu Shigeta\u003csup\u003e*38\u003c/sup\u003e, Ayumi Maeoka\u003csup\u003e*39\u003c/sup\u003e, Kozue Miyazaki\u003csup\u003e*40\u003c/sup\u003e, Yusuke Mimura\u003csup\u003e*41\u003c/sup\u003e, Yutaka Sato\u003csup\u003e*42\u003c/sup\u003e, Akari Goto\u003csup\u003e*43\u003c/sup\u003e, Takafumi Okada\u003csup\u003e*44\u003c/sup\u003e, Hitomi Kawamura\u003csup\u003e*45\u003c/sup\u003e, Kazutoshi Hiyama\u003csup\u003e*46\u003c/sup\u003e, Kentaro Wakamatsu\u003csup\u003e*47\u003c/sup\u003e, Toshitaka Muto\u003csup\u003e*48\u003c/sup\u003e, Eriko Shigyo\u003csup\u003e*49\u003c/sup\u003e, Haruka Ejima\u003csup\u003e*50\u003c/sup\u003e, Tomoyuki Mizukami\u003csup\u003e*51\u003c/sup\u003e, Toru Yamanaka\u003csup\u003e*52\u003c/sup\u003e, Kazuyoshi Nakamura\u003csup\u003e*53\u003c/sup\u003e, Narihiko Kubo\u003csup\u003e*\u003c/sup\u003e54, Tomoku Ichimiya\u003csup\u003e*55\u003c/sup\u003e, Yukihiro Zaizen\u003csup\u003e*56\u003c/sup\u003e, Yuji Hamaguchi\u003csup\u003e*57\u003c/sup\u003e, Chiharu Kuriwaki\u003csup\u003e*58\u003c/sup\u003e, Shinji Aratake\u003csup\u003e*59\u003c/sup\u003e, Tomoko Yuda\u003csup\u003e*60\u003c/sup\u003e, Sachiko Hara\u003csup\u003e*61\u003c/sup\u003e, Takuji Tsuchiya\u003csup\u003e*62\u003c/sup\u003e, Kiyoshi Okita\u003csup\u003e*63\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003csup\u003e*1\u003c/sup\u003e National Hospital Organization Hokkaido Medical Center, \u003csup\u003e2*\u003c/sup\u003e National Hospital Organization Hokkaido Cancer Center, \u003csup\u003e*3\u003c/sup\u003eNational Hospital Organization Kasumigaura Medical Center, \u003csup\u003e*4\u003c/sup\u003eNational Hospital Organization Mito Medical Center, \u003csup\u003e*5\u003c/sup\u003eNational Hospital Organization Takasaki General Medical Center, \u003csup\u003e*6\u003c/sup\u003eNational Hospital Organization Shibukawa Medical Center, \u003csup\u003e*7\u003c/sup\u003eNational Hospital Organization Nishisaitama-chuo National Hospital, \u003csup\u003e*8\u003c/sup\u003eNational Hospital Organization Saitama Hospital, \u003csup\u003e*9\u003c/sup\u003eNational Hospital Organization Chiba Medical Center, \u003csup\u003e*10\u003c/sup\u003eNational Hospital Organization Shimoshizu Hospital, \u003csup\u003e*11\u003c/sup\u003eNational Hospital Organization Tokyo National Hospital, \u003csup\u003e*12\u003c/sup\u003eNational Hospital Organization Kanagawa Hospital, \u003csup\u003e*13\u003c/sup\u003eNational Hospital Organization Niigata National Hospital, \u003csup\u003e*14\u003c/sup\u003eNational Hospital Organization Nishiniigata Chuo Hospital, \u003csup\u003e*15\u003c/sup\u003eNational Hospital Organization Hokuriku Hospital, \u003csup\u003e*16\u003c/sup\u003eNational Hospital Organization Kanazawa Medical Center, \u003csup\u003e*17\u003c/sup\u003eNational Hospital Organization Iou National Hospital, \u003csup\u003e*18\u003c/sup\u003eNational Hospital Organization Tsuruga Medical Center, \u003csup\u003e*19\u003c/sup\u003eNational Hospital Organization National Hospital Organization Nagara Medical Center, \u003csup\u003e*20\u003c/sup\u003eNational Hospital Organization Shizuoka Medical Center, \u003csup\u003e*21\u003c/sup\u003eNational Hospital Organization Tenryu Hospital, \u003csup\u003e*22\u003c/sup\u003eNational Hospital Organization Toyohashi Medical Center, \u003csup\u003e*23\u003c/sup\u003eNational Hospital Organization Higashinagoya National Hospital, \u003csup\u003e*24\u003c/sup\u003eNational Hospital Organization National Mie Hospital, \u003csup\u003e*25\u003c/sup\u003eNational Hospital Organization Suzuka National Hospital, \u003csup\u003e*26\u003c/sup\u003eNational Hospital Organization Utano National Hospital, \u003csup\u003e*27\u003c/sup\u003eNational Hospital Organization Maizuru Medical Center, \u003csup\u003e*28\u003c/sup\u003eNational Hospital Organization Osaka Toneyama Medical Center, \u003csup\u003e*29\u003c/sup\u003eNational Hospital Organization Hyogo-chuo National Hospital, \u003csup\u003e*30\u003c/sup\u003eNational Hospital Organization Kobe Medical Center, \u003csup\u003e*31\u003c/sup\u003eNational Hospital Organization Nara Medical Center, \u003csup\u003e*32\u003c/sup\u003eNational Hospital Organization Tottori Medical Center, \u003csup\u003e*33\u003c/sup\u003eNational Hospital Organization Yonago Medical Center, \u003csup\u003e*34\u003c/sup\u003eNational Hospital Organization Hamada Medical Center, \u003csup\u003e*35\u003c/sup\u003eNational Hospital Organization Matsue Medical Center, \u003csup\u003e*36\u003c/sup\u003eNational Hospital Organization Okayama Medical Center, \u003csup\u003e*37\u003c/sup\u003eNational Hospital Organization Minami-Okayama Medical Center, \u003csup\u003e*38\u003c/sup\u003eNational Hospital Organization Kure Medical Center and Chugoku Cancer Center, \u003csup\u003e*39\u003c/sup\u003eNational Hospital Organization Fukuyama Medical Center, \u003csup\u003e*40\u003c/sup\u003eNational Hospital Organization Higashihiroshima Medical Center, \u003csup\u003e*41\u003c/sup\u003eNational Hospital Organization Yamaguchi-Ube Medical Center, \u003csup\u003e*42\u003c/sup\u003eNational Hospital Organization Kanmon Medical Center, \u003csup\u003e*43\u003c/sup\u003eNational Hospital Organization Tokushima Hospital, \u003csup\u003e*44\u003c/sup\u003eNational Hospital Organization Shikoku Medical Center for Children and Adults, \u003csup\u003e*45\u003c/sup\u003eNational Hospital Organization Kochi Hospital, \u003csup\u003e*46\u003c/sup\u003eNational Hospital Organization Fukuokahigashi Medical Center, \u003csup\u003e*47\u003c/sup\u003eNational Hospital Organization Omuta National Hospital, \u003csup\u003e*48\u003c/sup\u003eNational Hospital Organization Kokura Medical Center, \u003csup\u003e*49\u003c/sup\u003eNational Hospital Organization Saga Hospital, \u003csup\u003e*50\u003c/sup\u003eNational Hospital Organization Nagasaki Medical Center, \u003csup\u003e*51\u003c/sup\u003eNational Hospital Organization Kumamoto Medical Center, \u003csup\u003e*52\u003c/sup\u003eNational Hospital Organization Kumamotominami Hospital, \u003csup\u003e*53\u003c/sup\u003eNational Hospital Organization Kumamoto Saishun Medical Center, \u003csup\u003e*54\u003c/sup\u003eNational Hospital Organization Beppu Medical Center, \u003csup\u003e*55\u003c/sup\u003eNational Hospital Organization Oita Medical Center, \u003csup\u003e*56\u003c/sup\u003eNational Hospital Organization Nishi-Beppu National Hospital, \u003csup\u003e*57\u003c/sup\u003eNational Hospital Organization Miyazaki Higashi Hospital, \u003csup\u003e*58\u003c/sup\u003eNational Hospital Organization Kagoshima Medical Center, \u003csup\u003e*59\u003c/sup\u003eNational Hospital Organization Ibusuki Medical Center, \u003csup\u003e*60\u003c/sup\u003eNational Hospital Organization Iwaki Hospital, \u003csup\u003e*61\u003c/sup\u003eNational Hospital Organization Kurihama Medical and Addiction Center, \u003csup\u003e*62\u003c/sup\u003eNational Hospital Organization Higashi-Nagano Hospital, \u003csup\u003e*63\u003c/sup\u003eNational Hospital Organization Kamo Psychiatric Medical Center\u003c/p\u003e "},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTurner NA et al (2019) Methicillin-resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e: an overview of basic and clinical research. 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Springer-, New York. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi:10.1007/978-0-387-98141-3\u003c/span\u003e\u003cspan address=\"doi:10.1007/978-0-387-98141-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Staphylococcus aureus, bloodstream infection, genomic surveillance, mortality, antimicrobial susceptibility, Japan","lastPublishedDoi":"10.21203/rs.3.rs-4824867/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4824867/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAntimicrobial resistance is a global health concern, and methicillin-resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (MRSA) is one of the highest priority organisms exhibiting this phenotype. Here, we performed a national surveillance integrating patient clinical data of \u003cem\u003eS. aureus\u003c/em\u003e isolated from bloodstream infections. We performed genome sequencing, standardized antimicrobial susceptibility testing, and collected clinical metadata of 580 \u003cem\u003eS. aureus\u003c/em\u003e isolates collected during 2019\u0026ndash;2020. We focused on three predominant clonal complexes (CC1, CC5, and CC8) and assessed their microbiological and clinical significance and regional prevalence. Furthermore, we conducted a genomic comparison of the isolates of 2019\u0026ndash;2000 with those of 1994\u0026ndash;2000 and investigated the evolutionary trajectory of emerging clones from the three dominant clonal complexes. We revealed that the emerging MRSA ST764-SCC\u003cem\u003emec\u003c/em\u003eII showed the highest mortality rate within 30 days of hospitalization. This high-risk clone diverged from the New York/Japan clone (ST5-SCC\u003cem\u003emec\u003c/em\u003eII), which was inferred to have undergone repeated infections with phages carrying superantigen toxin genes and acquired antimicrobial resistance genes via mobile genetic elements, leading to its emergence around 1994. Overall, we provide a blueprint for a national genomic surveillance study that integrates clinical data and enables identification and evolutionary characterization of a high-risk clone.\u003c/p\u003e","manuscriptTitle":"Staphylococcus aureus ST764-SCCmecII high-risk clone in bloodstream infections revealed through national genomic surveillance integrating clinical data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-05 20:16:52","doi":"10.21203/rs.3.rs-4824867/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2c3a1f36-7a56-4a76-8250-ccf60d9233a3","owner":[],"postedDate":"August 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":35381764,"name":"Biological sciences/Microbiology/Bacteriology"},{"id":35381765,"name":"Health sciences/Diseases/Infectious diseases/Bacterial infection"}],"tags":[],"updatedAt":"2025-03-20T07:08:03+00:00","versionOfRecord":{"articleIdentity":"rs-4824867","link":"https://doi.org/10.1038/s41467-025-57575-2","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2025-03-19 04:00:00","publishedOnDateReadable":"March 19th, 2025"},"versionCreatedAt":"2024-08-05 20:16:52","video":"","vorDoi":"10.1038/s41467-025-57575-2","vorDoiUrl":"https://doi.org/10.1038/s41467-025-57575-2","workflowStages":[]},"version":"v1","identity":"rs-4824867","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4824867","identity":"rs-4824867","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}