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

doi:10.21203/rs.3.rs-4824867/v1

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

https://doi.org/10.21203/rs.3.rs-4824867/v1

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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-SCCmecII showed the highest mortality rate within 30 days of hospitalization. This high-risk clone diverged from the New York/Japan clone (ST5-SCCmecII), 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

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 enterotoxin¹. 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 States⁴ 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 2017⁵.

The ST5-Staphylococcal Cassette Chromosome mec (SCCmec) 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-SCCmecII, 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-SCCmecII and community-acquired MRSA (CA-MRSA) with acquired virulence genes⁹. Recently, ST764-SCCmecII has been isolated from invasive infections in Asia, including Thailand and China^10,11. In contrast, the proportion of CA-MRSA-SCCmecIV has increased in isolates from invasive infections of MRSA BSIs^8,12 and other SCCmecIV MRSA clones (ST1-SCCmecIV or ST8-SCCmecIV)^8,13. However, CA-MRSA SCCmecIVa 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 Japan⁸. The ST8-SCCmecIVl with the CA-MRSA genotype, represented by the MRSA/J clone, was first isolated in Japan from the bullous impetigo of a child in 2003¹⁴, and one case of death caused by a strongly invasive pathotype was reported in 2012¹⁵. During the same period, we reported a case of systemically disseminated MRSA/J infection^16,17. ST2725-SCCmecIV, 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 testing²⁰. 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-SCCmecII with high mortality risk associated with BSI.

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. 1a 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. 1a, 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. 1a, 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. 1a, pie chart 3). The most dominant STs in western Japan were ST8 (23.0%), ST1 (11.9%), and ST5 (7.0%) (Fig. 1b, 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. 1c, 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. 1c, 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 SCCmecIV (76.5% of total SCCmec) (Fig. 1a, pie chart 4). The proportions of MRSA in western and eastern Japan were 47.5 and 44.7%, respectively (Fig. 1b, pie chart 2). SCCmecIV was the most common MRSA of BSI-derived S. aureus in both regions, followed by SCCmecII. 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. 1a), 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-SCCmecII 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-SCCmecI 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-SCCmecII clone revealed that age was a confounding factor significantly associated with the ST764-SCCmecII 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-SCCmecII 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-SCCmecIV (n = 93)	20/93 (21.5)
ST2725-MRSA-SCCmecIV (n = 28)	8/28 (28.6)
CC5:
ST5-MRSA-SCCmecII (n = 11)	1/11 (9.1)
ST764-MRSA-SCCmecII (n = 25)	12/25 (48.0)
CC8:
USA300 (n = 4)	1/4 (25.0)
MRSA/J (n = 19)	3/19 (15.8)
ST8-MRSA-SCCmecI (n = 10)	5/10 (50.0)
ST8-MRSA-SCCmecIVj (n = 24)	8/24 (33.3)

Table 2

Comparison of clinical factors between ST764-MRSA-SCCmecII and other clones.
	ST764-MRSA-SCCmecII (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 Japan²¹. 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 SCCmecII (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-SCCmecII) (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-SCCmecIV and ST2725-SCCmecIV lineages contained the same number of ARGs (mean, 3.9) (Fig. 2a, CC1). Most ST1-SCCmecIV and ST2725-SCCmecIV strains had blaZ, ant(9)-Ia, erm(A), QRDR mutations (GrlA/S80F/E84G and GyrA/S84L), and the same distribution pattern (Fig. 2b, CC1 and Supplementary Table 3). Consistent with the presence of these ARGs and QRDR mutations in ST1-SCCmecIV and ST2725-IV, the resistance rates of these strains to oxacillin (MPIPC), EM, and LVFX were high (Fig. 2c, upper panel). In CC5, the average number of ARGs in ST5-SCCmecII of 1994–2000 was 8.2; however, in 2019–2020 it was 7.0, which was significantly lower than that of 1994–2000 (Fig. 2a, CC5). The average number of ARGs in ST764-SCCmecII in 2019–2020 was 6.0, which was significantly lower than that in ST5-SCCmecII. The proportions of ARGs in ST5-SCCmecII 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. 2b, CC5 and Supplementary Table 3). In contrast, the ARGs with a decreasing trend in ST5-SCCmecII in 2019–2020 were blaZ, aac(6’)-aph(2”), tet(M), cat, fosB, and qacA. The ARGs showing an increasing trend in ST5-SCCmecII in 2019–2020 were erm(C), fosD, and qacB. ARGs with high proportions in the ST764-SCCmecII 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-SCCmecII were aac(6’)-aph(2”), tet(M), fosD, and qacB. The ARGs of ST764-SCCmecII aadD, erm(C), and bleO were lower in abundance than those of ST5-SCCmecII. Resistance rates to MPIPC, CEZ, CMZ, EM, CLDM, and LVFX remained consistently high in ST5-SCCmecII during both periods (Fig. 2c, middle panel). The resistance rates to GM and MINO were significantly lower in ST5-SCCmecII of 2019–2020 than in that of 1994–2000. In contrast, ST764-SCCmecII showed very high rates of resistance to these antibiotics, except for ABK. In CC8, the average numbers of ARGs in ST8-SCCmecIVj and ST8-SCCmecIVa during 2019–2020 were 4.8 and 4.1, respectively, which were significantly lower than those in ST8-SCCmecI (average, 6.2) (Fig. 2a, CC8). The average number of ARGs in ST8-SCCmecIV was lower than that in ST8-SCCmecI; however, the difference was statistically significant between ST8-SCCmecIVj and ST8-SCCmecIVa. Most ST8-SCCmecI possessed blaZ, aac(6’)-aph(2”), ant(9)-Ia, erm(A), tet(M), and QRDR mutations (GrlA/S80F and GyrA/S84L/S85P) (Fig. 2b, CC8 and Supplementary Table 3). ARGs with high proportions in ST8-SCCmecIVj 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-SCCmecIVl 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-SCCmecIVa was approximately 38%, which is unique to this clone. ST8-MRSA showed varying patterns of resistance to each antibiotic depending on the SCCmec type; however, ST8-SCCmecI was more resistant than the other clones (Fig. 2c, 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-SCCmecIV and ST2725-SCCmecIV 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-SCCmecII 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-SCCmecII in 1994–2000 was 12% but was not detected in 2019–2020. The proportions of seb, seg, sei, sem, sen, and seo in ST764-SCCmecII were high, whereas the proportions of sek and seq were low (40%). ST764-SCCmecII did not possess sec, tst-1, or sel. In CC8, ST8-SCCmecI and ST8-SCCmecIVj possessed 50 and 75% of sep, respectively, with no other SE genes present (Fig. 3, CC8). The proportion of chp in the ST8-SCCmecIVj and IVl clones was significantly lower than that in the other clones. ST8-SCCmecIVl contained high proportions of sec, tst-1, and sel (90%). In addition, ST8-SCCmecIVl possessed 90% sel and 40% se1 and ednA. ST8-SCCmecIVa 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-SCCmecIV and ST2725-SCCmecIV lineages to be approximately 1998 (95% highest posterior density [HPD] intervals: 1995–2002) (Fig. 4a). The results of this analysis indicated that the ancestor of ST1-MRSA-t1784 acquired SCCmecIV, Tn554 harboring ant(9)-Ia, and erm(A)²² after 1965 (95% HPD intervals: 1958–1974). ST1-MRSA-t1784 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-SCCmecIV-t1784 lineage diverged from ST1 and spread primarily to western Japan (Fig. 4a square light blue and Fig. 1c bar graph in the left panel). However, the ST1-t127 lineage diverged in 1953 and acquired the genomic island νSa4, carrying the seb and fusidic acid resistance gene fusC-harboring SCCfus²³ by phage infections somewhere up to 1970. Through a timescale phylogenetic analysis, we first estimated the age of SCCfus acquisition in the ST1-t127 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. 4a, lower panel).

In CC8, USA300 isolated from BSIs in Japan belonged to the USA300-NAE (North American Epidemic) lineage²⁴ and appeared around 1989 (95% HPD intervals: 1986–1992) (Fig. 4b). This is consistent with the previously reported data²⁴. The trajectory of the emergence of ST8-SCCmecIVl (MRSA/J) (Fig. 4b, square light blue) was somewhere between 1957 and 1986, when it acquired SCCmecIVl and νSa4 (carrying sec, tst-1, and sel) by phage infections (Fig. 4b, light blue of index No. 2 and 5, and Supplementary Fig. 7a) and plasmids carrying se1 (Fig. 4b, 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 (SCCmecIVa-arginine catabolic mobile element [ACME] type I). In addition, the ST8-SCCmecI and ST8-SCCmecIVj lineages emerged around 1979 (95% HPD interval: 1976–1982) and 1988 (95% HPD interval: 1985–1991), respectively. Some strains of the ST8-SCCmecI and ST8-SCCmecIVj lineages were estimated to have acquired tet(M)-harboring Tn916-like transposable units in the late 1980s and 1990s (Fig. 4b 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. 4b, lower).

In CC5, we estimated the tMRCA of the ST5-SCCmecII (N/J clone) and ST764-SCCmecII lineages to be around 1959 (95%HPD interval: 1956–1962) (Fig. 4c). Our genome sequence comparison inferred the trajectory of the emergence of the ST764-SCCmecII clone from the N/J clone based on recombination events (index No. 5 in Fig. 4c and Supplementary Fig. 8). At the first step, somewhere between 1928 and 1959, SCCmecII, genomic island νSa3 (carrying sec, tst-1, and sel) (Fig. 4c and Supplementary Fig. 8a) and tet(M)-harboring Tn916-like transposable unit (Supplementary Fig. 8b) were acquired by phage infection or transposition, respectively, resulting in the emergence of ST5-SCCmecII (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-SCCmecII lineage (Fig. 4c 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. 4c 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-SCCmecII lineage diverged from ST5 and spread throughout Japan after 1994. One sub-lineage of ST764-SCCmecII acquired the genomic island νSa1 carrying sek and seq by phage infection somewhere between 1998–2001 (Fig. 4c and Supplementary Fig. 8d). Another ST764-SCCmecII sub-lineage acquired ACME type II somewhere between 1996 and 1999 (Fig. 4c and Supplementary Fig. 8e). However, the ST5-SCCmecI ancestor diverged from the N/J lineage around 1928, and somewhere between 1932–1967, the ST5-SCCmecI lineage emerged by acquiring SCCmecI and νSa4 (carrying sec, sek, and seq) by phage infections but did not spread as far in Japan (Supplementary Tables 1 and 2). ST5-SCCmecIV followed a different phylogenetic trajectory from the clones described above, emerging somewhere between 1966 and 1994 with the acquisition of SCCmecI, which was also not widespread in Japan (Fig. 4c). The effective population size of CC5 increased in a staircase-like manner during the emergence of ST5-SCCmecII and ST764-SCCmecII (Fig. 4c, lower panel).

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 globally²⁵. 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) decreased²⁶. 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-SCCmecIV. 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-SCCmecII (N/J clone) showed significant reduction in the proportion of isolates resistant to the six drugs, whereas ST764-SCCmecII 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-SCCmecIV 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-SCCmecIV is more prevalent (Supplementary Fig. 1); however, in the nationwide surveillance of BSI-derived S. aureus used this study, the predominant CC1-SCCmecIV had a significant impact on the resistance to the three antimicrobials. ST1-SCCmecIV and ST2725-SCCmecIV had extremely high rates of erm(A) and QRDR mutations, and these genes contributed significantly to resistance to the three antimicrobials (Fig. 2b, 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-SCCmecIV and ST8-SCCmecIV and a decrease in ST5-SCCmecII.

Our timescale phylogenetic and population dynamics analysis showed that ST5-SCCmecII 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-SCCmecIV and CC8-SCCmecIV, 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-SCCmecIV circulated slightly more frequently in western Japan (Fig. 1c). These STs-SCCmecIV possessed almost identical patterns of ARGs, VFGs, and antimicrobial resistance patterns (Fig. 2b, c, and Fig. 3). Notably, the interval between the emergence of ST1-SCCmecIV and ST2725-SCCmecIV was very short, only approximately five years, as revealed for the first time in our study (Fig. 4a). Most ST1-SCCmecIV-t1784 isolates in Japan belong to a different lineage from the PVL-negative ST1-SCCmecIV-t127, which is widespread in Europe²⁸. Moreover, some subclades of European ST1-SCCmecIV-t127 have SCCmec_fus carrying the fusidic acid resistance gene fusC but not ST1-SCCmecIV-t1784. The Japanese fusC-positive ST1-MSSA-t127 strain is rare and mecA negative (Supplementary Table 2). ST1-SCCmecIV-t1284 is a more modern lineage than European ST1-t127, according to timescale phylogenetic analysis.

Japanese ST2725-SCCmecIV-t1784 has been detected in both inpatients and outpatients, suggesting that it has spread to hospitals and community¹⁹. To the best of our knowledge, the ST2725-SCCmecIV-t1784 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-SCCmecII (the N/J clone), has spread²⁹ 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-SCCmecII 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-SCCmecII lineage around 1994. Furthermore, herein, we found that several strains of ST764-SCCmecII were independently acquired by phage infection carrying sek/seq or ACME-II and the ACME-related cassette JR1 (cJR1) around 2000 (Fig. 4c, 3 blue bar). ACME-II was first identified in S. epidermidis³⁰. Urushinbara et al. reported several variants of the ACME-II-SCCmec composite island (ACME-II-SCCmec-CI) in ST764, isolated from Hokkaido, Japan³¹. Therefore, we presume that the ST764-SCCmecII lineage isolated in Japan is becoming increasingly diverse. ST764-ACME-SCCmec-CI may be distributed throughout Northern-Eastern Japan, particularly in Hokkaido.

We demonstrated that ST764-SCCmecII 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-SCCmecII clones in long-term care facilities in Japan was recently reported by Kawamura³². In our study, 72.4% (21/29) of the registered ST764-SCCmecII cases were ST764-SCCmecII-t002 (Fig. 4c, 4 ocher bar). In China, ST764-SCCmecII-t1084, a lineage similar to the Japanese ST764-SCCmecII-t002, has recently been reported to increase as a hypervirulent clone¹¹. 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 infections³². 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-SCCmecII isolates from patients with pneumonia belonged to ST764³³. Our recent epidemiological risk assessment study of isolates from patients admitted to the intensive care unit³⁴ 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 community⁸ and hospital settings. Most CC8 isolates from BSIs in 2019–2020 were ST8-SCCmecIV. We revealed through timescale phylogenic analysis that ST8-SCCmecI and multiple ST8-SCCmecIV 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 rate⁸. 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-SCCmecII with ST5-SCCmecII (the N/J clone), which was dominant in 1994–2000 and the ancestor clone of ST764-SCCmecII. 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-SCCmecII-t1084 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.

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 study²¹, 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 v3.15.5³⁸. ONT reads were trimmed using FiltLong (https://github.com/rrwick/Filtlong), assembled with trimmed long reads using Flye v2.9.1³⁹, and polished with trimmed Illumina reads using Pilon v1.24⁴⁰. Quality of the assembled genome sequences was assessed using QUAST v4.0⁴¹ and CheckM v1.1.0⁴². 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 v1.2.16⁴³.

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 v2.0⁴⁴ 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 ResFinder⁴⁵ database 2022-06-09 and VFDB⁴⁶ database 2022-06-09 and custom virulence factor database for S. aureus⁴⁷ with 90% identity and 90% query coverage cutoffs. PointFinder v4.1.11⁴⁸ was used to identify antibiotic resistance genes encoded by chromosomal mutations with 90% identity and 90% query coverage cutoffs. SCCmec and spa typing was performed using the web-based SCCmecFinder⁴⁹ and spaTyper v0.2.1 (https://github.com/HCGB-IGTP/spaTyper) with default settings. GenomeMatcher v3.0.2⁵⁰ was used for chromosome sequence comparison and visualization.

Phylogenetic and clustering analysis

A phylogenetic tree of the core genome alignment was constructed using the kSNP3.0⁵¹ 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 v1.0.8⁵² 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 v1.0.1⁵³ with the best model inferred by ModelTest-NG v0.1.7⁵⁴ 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 v3.2.0⁵⁵ 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 TempEst⁵⁶ 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 model⁵⁷. 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 v1.7.1⁵⁸. 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. 3.3.6⁵⁹ 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.

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, ^*3National Hospital Organization Kasumigaura Medical Center, ^*4National Hospital Organization Mito Medical Center, ^*5National Hospital Organization Takasaki General Medical Center, ^*6National Hospital Organization Shibukawa Medical Center, ^*7National Hospital Organization Nishisaitama-chuo National Hospital, ^*8National Hospital Organization Saitama Hospital, ^*9National Hospital Organization Chiba Medical Center, ^*10National Hospital Organization Shimoshizu Hospital, ^*11National Hospital Organization Tokyo National Hospital, ^*12National Hospital Organization Kanagawa Hospital, ^*13National Hospital Organization Niigata National Hospital, ^*14National Hospital Organization Nishiniigata Chuo Hospital, ^*15National Hospital Organization Hokuriku Hospital, ^*16National Hospital Organization Kanazawa Medical Center, ^*17National Hospital Organization Iou National Hospital, ^*18National Hospital Organization Tsuruga Medical Center, ^*19National Hospital Organization National Hospital Organization Nagara Medical Center, ^*20National Hospital Organization Shizuoka Medical Center, ^*21National Hospital Organization Tenryu Hospital, ^*22National Hospital Organization Toyohashi Medical Center, ^*23National Hospital Organization Higashinagoya National Hospital, ^*24National Hospital Organization National Mie Hospital, ^*25National Hospital Organization Suzuka National Hospital, ^*26National Hospital Organization Utano National Hospital, ^*27National Hospital Organization Maizuru Medical Center, ^*28National Hospital Organization Osaka Toneyama Medical Center, ^*29National Hospital Organization Hyogo-chuo National Hospital, ^*30National Hospital Organization Kobe Medical Center, ^*31National Hospital Organization Nara Medical Center, ^*32National Hospital Organization Tottori Medical Center, ^*33National Hospital Organization Yonago Medical Center, ^*34National Hospital Organization Hamada Medical Center, ^*35National Hospital Organization Matsue Medical Center, ^*36National Hospital Organization Okayama Medical Center, ^*37National Hospital Organization Minami-Okayama Medical Center, ^*38National Hospital Organization Kure Medical Center and Chugoku Cancer Center, ^*39National Hospital Organization Fukuyama Medical Center, ^*40National Hospital Organization Higashihiroshima Medical Center, ^*41National Hospital Organization Yamaguchi-Ube Medical Center, ^*42National Hospital Organization Kanmon Medical Center, ^*43National Hospital Organization Tokushima Hospital, ^*44National Hospital Organization Shikoku Medical Center for Children and Adults, ^*45National Hospital Organization Kochi Hospital, ^*46National Hospital Organization Fukuokahigashi Medical Center, ^*47National Hospital Organization Omuta National Hospital, ^*48National Hospital Organization Kokura Medical Center, ^*49National Hospital Organization Saga Hospital, ^*50National Hospital Organization Nagasaki Medical Center, ^*51National Hospital Organization Kumamoto Medical Center, ^*52National Hospital Organization Kumamotominami Hospital, ^*53National Hospital Organization Kumamoto Saishun Medical Center, ^*54National Hospital Organization Beppu Medical Center, ^*55National Hospital Organization Oita Medical Center, ^*56National Hospital Organization Nishi-Beppu National Hospital, ^*57National Hospital Organization Miyazaki Higashi Hospital, ^*58National Hospital Organization Kagoshima Medical Center, ^*59National Hospital Organization Ibusuki Medical Center, ^*60National Hospital Organization Iwaki Hospital, ^*61National Hospital Organization Kurihama Medical and Addiction Center, ^*62National Hospital Organization Higashi-Nagano Hospital, ^*63National Hospital Organization Kamo Psychiatric Medical Center

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

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Abstract

Figures

INTRODUCTION

RESULTS

Phylogenetic relationship and geographic distribution of 580 S. aureus isolated from blood during 2019–2020

ST764 infection is associated with high mortality

Phylogenetic relationship of BSI-derived 183 S. aureus during 1994–2000

Comparative analysis of antimicrobial resistance pattern of BSI-derived S. aureus

Comparative analysis of virulence factor genes (VFGs) of BSI-derived S. aureus

Evolutionary origins and population dynamics of three CC lineages of blood-derived S. aureus in Japan

DISCUSSION

METHODS

Study design

Ethics approval

DNA extraction and whole-genome sequencing

De novo assembly and annotation

In silico sequence typing and detection of virulence factor and antimicrobial resistance genes

Phylogenetic and clustering analysis

Time-calibrated phylogeny and population demographic analysis

Antimicrobial susceptibility testing

Statistical analysis

Data availability

Declarations

References

Additional Declarations

Supplementary Files

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