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Article

Genomic Analysis of MRSA for Evaluating Local Transmission Dynamics in Geriatric Long-Term Care Facilities in Japan

by
Takayuki Suzuki
1,
Teppei Sasahara
1,2,*,
Shinya Watanabe
2,
Koki Kosami
3,
Dai Akine
1,4,
Yumi Kinoshita
5,
Longzhu Cui
2 and
Shuji Hatakeyama
1,6
1
Division of Infectious Diseases, Department of Infection and Immunity, Jichi Medical University, Yakushiji 3311-1, Shimotsuke 329-0498, Tochigi, Japan
2
Division of Bacteriology, Department of Infection and Immunity, Jichi Medical University, Yakushiji 3311-1, Shimotsuke 329-0498, Tochigi, Japan
3
Division of Public Health, Center for Community Medicine, Jichi Medical University, Yakushiji 3311-1, Shimotsuke 329-0498, Tochigi, Japan
4
Department of Infectious Diseases, Ibaraki Prefectural Central Hospital, Koibuchi 6528, Kasama 309-1793, Ibaraki, Japan
5
Clinical Microbiology Laboratory, Jichi Medical University Hospital, Yakushiji 3311-1, Shimotsuke 329-0498, Tochigi, Japan
6
Division of General Medicine, Center for Community Medicine, Jichi Medical University, Yakushiji 3311-1, Shimotsuke 329-0498, Tochigi, Japan
*
Author to whom correspondence should be addressed.
Antibiotics 2025, 14(9), 874; https://doi.org/10.3390/antibiotics14090874
Submission received: 8 July 2025 / Revised: 28 August 2025 / Accepted: 29 August 2025 / Published: 30 August 2025
(This article belongs to the Special Issue Molecular Epidemiology and Antibiotic Resistance in MRSA: Recent Advances in Surveillance and Control)
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Abstract

Background/Objectives: Methicillin-resistant Staphylococcus aureus (MRSA) colonization in geriatric long-term care facilities (LTCFs) is a global concern. However, the transmission dynamics of MRSA among LTCF residents in Japan remain largely unknown. Methods: Whole-genome sequencing was conducted on 85 MRSA isolates obtained from 76 residents across 4 geriatric LTCFs in Japan. Single-nucleotide polymorphism (SNP) analysis was performed to identify the transmission dynamics, with a threshold of ≤15 pairwise core-genome SNP distances defining recent transmission clusters (genomic clusters). Antimicrobial susceptibility testing and investigation of antimicrobial resistance genes were also performed. Results: Among the 76 MRSA-carrying residents, 34 (44.7%) belonged to 14 genomic clusters, including strains from clinical specimens of 7 individuals. Three individuals acquired MRSA strains within the LTCFs, which were part of genomic clusters. Conversely, 14 residents who underwent testing immediately after admission carried MRSA strains within genomic clusters, suggesting transmission prior to their LTCF admission. MRSA isolates that were prevalent among LTCF residents were generally susceptible to trimethoprim–sulfamethoxazole but resistant to levofloxacin and clindamycin. Conclusions: Acquisition of MRSA genomic cluster strains among LTCF residents can occur both during and before admission to the facility. These findings underscore the need for measures that mitigate MRSA transmission inside and outside LTCFs.

1. Introduction

Antimicrobial resistance (AMR) poses a considerable global concern, with methicillin-resistant Staphylococcus aureus (MRSA) emerging as the second leading cause for AMR-associated mortality worldwide [1]. Residents of geriatric long-term care facilities (LTCFs) are particularly vulnerable to MRSA colonization or infection owing to prevalent risk factors, including advanced age, antimicrobial drug use, presence of wounds, nasogastric tube feeding, indwelling urinary catheter, and recent history of admission to acute care hospitals [2,3]. Several studies have documented greater incidences of MRSA colonization among residents of LTCFs compared with those in the general community and hospital settings [4,5]. Infection prevention and control (IPC) strategies have therefore become crucial for preventing MRSA transmission in LTCFs. Despite this, MRSA prevalence in geriatric LTCFs remained unchanged, even with IPC education and staff training [6]. Given that these institutions differ from acute care hospitals in terms of length of stay, required medical practice, assistance for daily living, and staff shortages, a direct application of hospital-based IPC strategies may be difficult to translate to the LTCF setting.
For the implementation of effective IPC strategies, understanding the MRSA transmission dynamics in LTCFs is essential. However, such data remain largely unknown in the context of Japan. While some studies have utilized whole-genome sequencing (WGS) to investigate MRSA transmission in LTCFs [7,8], there may be inherent differences in the circumstances of LTCF residents in Japan, such as the long-term care insurance system, provided services [9], and world-renowned aging populations, compared with those in previous studies [10]. Furthermore, the epidemiology of prevalent MRSA is subject to regional variation [3]. Previously, we investigated MRSA carriage among the residents of four LTCFs in Japan, revealing that MRSA carriage tended to be higher among those with shorter lengths of stay, although the difference was not statistically significant [11]. In this study, we aimed to determine the genomic epidemiology of MRSA transmission among residents in geriatric LTCFs in Japan by performing WGS to analyze the MRSA strains collected in the previous study.

2. Results

2.1. Genetic Features of MRSA Isolates

We discovered extensive genetic diversity among the isolates, representing multiple multi-locus sequence types (MLSTs), SCCmec, and spa types with varying proportions (Table 1). For LTCFs A, B, and D, dominant isolates with specific spa types were observed, revealing the predominance of community-associated MRSA (CA-MRSA) clones, such as ST1-SCCmec IV (ST1-IV), ST8-IV, ST22-IV, ST45-V, and ST72-IV. Conversely, healthcare-associated MRSA (HA-MRSA) clones, including ST5-II and ST764-II, and their single-locus variants were predominant in LTFC C. Notably, despite the adjacent location and comparable patient characteristics between LTCFs A and C, the most prevalent clones in each location varied.

2.2. SNP Analysis and Identification of Genomic Clusters

The presence of prevalent spa types within each LTCF suggests potential genomic clusters identified through single-nucleotide polymorphism (SNP) analysis. Excluding the single-strain CC22, we found that 34 (44.7%) MRSA-carrying residents of geriatric LTCFs belonged to 14 genomic clusters and that every LTCF had at least one genomic cluster (Figure 1A–D and Figure 2). Isolates of these clusters were detected from the clinical specimens, in addition to the nasal swabs, of seven individuals. Among the residents associated with genomic clusters, three residents (D-2, D-4, and D-11) acquired these strains within LTCF D. In contrast, 14 residents harbored strains prior to admission owing to positive initial testing results within 0.5 months of admission. While a cutoff value of 15 core-genome SNPs (cgSNPs) did not reveal any transmissions between LTCFs, JMUB7474 and JMUB7486, two strains that originated from residents of LTCFs A and C, respectively, were closely related, with a 33-cgSNP difference (Figure 2A). The former strain was isolated shortly after the individual’s admission, whereas the latter was detected 1.5 months later.
Moreover, to evaluate the temporal dynamics of MRSA carriage among LTCF residents, we reviewed alterations in the carrier status of residents who underwent initial testing on admission and a second test >2 months later (Figure 3). Among the 65 residents who met these criteria, 55 (84.6%) showed unchanged carrier status. Of the 14 MRSA carriers at admission, 7 (4.6%) became non-carriers at the second test, whereas 3 (2.9%) acquired genomic cluster strains following admission. However, before the results of SNP analysis were available, confirming whether the acquired MRSA strains originated within their LTCFs was not possible, as two of them were already carriers of other MRSA strains at the time of admission.
Pan-GWAS revealed that none of the 6667 potential coding sequences exhibited a significantly higher prevalence in genomic cluster strains.

2.3. Antimicrobial Susceptibility and Genetic AMR Profile

All isolates were susceptible to vancomycin, teicoplanin, daptomycin, linezolid, and mupirocin, and 84 (98.8%) were susceptible to trimethoprim–sulfamethoxazole. Conversely, 83 (97.6%) and 76 (89.4%) isolates were resistant to levofloxacin and clindamycin, respectively. Although 62 (72.9%) isolates were susceptible to minocycline, only 2 (28.6%) isolates in LTCF C were susceptible to minocycline. All minocycline-resistant isolates in LTCF C were ST764-II, and all ST764-II isolates in other LTCFs were also resistant to minocycline (Table S1).
The molecular basis for levofloxacin resistance was determined to be the presence of at least one gyrA point mutation (S84L, S85P, E88G, or E88K) in 98.8% (82/83) of the isolates. Furthermore, all clindamycin-resistant isolates had either erm (A) or erm (C). All tetracycline-nonsusceptible isolates carried tet (M), which encodes a ribosomal protection protein (Table S2).

2.4. Virulence Gene Profile

Overall, the number of toxic shock syndrome toxin-1 (tst-1) positive isolates was two (2.4%, Table S3). In CC1, all ST1-IVa and ST2725-IVa isolates had sea, she, sek, seq, sak, and scn, excluding JMUB7519 (ST1-IVa). In CC5, all ST764-II isolates (n = 11) carried seb, seg, sei, sem, sen, seo, seu, sak, and scn. In CC8, there were only two USA300 clones (ST8-IVa with sek, seq, and lukF/S-PV).

3. Discussion

In this study, we elucidated the transmission dynamics of MRSA among residents in geriatric LTCFs in Japan, an aspect that was unclear in our previous study. Multiple putative transmission events were observed within each LTCF, with 34 (44.7%) MRSA-carrying residents sharing 14 individual genomic cluster strains. Although most residents showed no changes in carrier status, three residents acquired MRSA strains during LTCF admission. In addition, genomic cluster strains were discovered from the clinical specimens of seven individuals.
Our analysis showed that most genomic cluster strains among LTCF residents were likely acquired prior to admission. Owing to the absence of personal information, such as prior hospitalization, contact with healthcare environments, or household contact screening, it was not possible to determine the exact location of transmission. However, considering that most LTCFs cater to individuals requiring post-hospitalization rehabilitation therapy or daily living support beyond their home settings, hospitals emerge as the most likely source of external transmissions. This is consistent with existing evidence citing acute care hospitals as the most common source of inter-facility transmission [7]. However, outpatient care, including rehabilitation, can be provided as home-based services in Japan, allowing another avenue for transmissions to occur. Future research is required, as there is insufficient data on the spread of MRSA in patients undergoing outpatient care. Furthermore, to prevent MRSA transmission among LTCF residents, interventions inside and outside these facilities are paramount. Recent randomized controlled trials have shown that decolonizing MRSA carriers after hospital discharge and implementing universal decolonization among LTCF residents result in reduced MRSA infections and infection-related hospital transfers, respectively [12,13]. Therefore, the combined implementation of IPC strategies and decolonization approaches is crucial, particularly in areas where MRSA transmission is a clear concern. Our results provide molecular evidence for the necessity of interventions to reduce MRSA colonization among LTCF residents.
Further SNP analysis revealed that MRSA transmission among residents can cause both colonization and clinical infections. Of note, despite the prevalence of CA-MRSA clones, isolates prevalent within LTCFs displayed resistance to non-β-lactam agents, such as levofloxacin and clindamycin. Although CA-MRSA strains have exhibited resistance to multiple non-β-lactam agents in some cases [14], this specific drug susceptibility profile in LTCF residents remained unclear. Our findings suggest that most of the prevalent MRSA strains were susceptible to trimethoprim–sulfamethoxazole. However, considering the high prevalence of renin–angiotensin system inhibitor (RASi) usage for hypertension among LTCF residents [15], cautious administration is warranted, as concurrent trimethoprim–sulfamethoxazole and RASi use has been associated with an increased risk of sudden mortality [16]. This underscores the balance of minimizing MRSA transmission among LTCF residents with consideration of other comorbidities.
Similarities and differences were observed in the characteristics of MRSA strains across LTCFs. Notably, despite the geographical proximity and shared hospital transfers, LTCFs A and C showed significant variation in the prevalence of specific ST-SCCmec types. This is particularly interesting as LTCF C is designed to assist with activities of daily living, whereas LTCF A prioritizes rehabilitation for the return of individuals to their homes. Such differences may reflect unique resident profiles, including the levels of required medical practice, length of stay, and degrees of exposure to other residents and healthcare personnel. These factors may have influenced the acquisition of distinct clones. Another factor to consider is that strains prevalent within the region may affect each facility. In our study, ST8-IV isolates were predominant in LTCF D but not in other facilities. A nationwide analysis of MRSA isolated from bloodstream infections in Japan revealed that ST8-IV was the most prevalent strain in areas where LTCF D was located (49.0%) but accounted for only 23.2% in areas where LTCFs A, B, and C were located [17]. This suggests that both facility characteristics and local endemic strains influence MRSA prevalence among LTCF residents. Additionally, ST764-II isolates were found in three LTCFs situated in both eastern and western parts of Japan. Recently, ST764-II has been identified as a high-risk clone, with a 30-day mortality rate of 48% for bloodstream infections [18]. This clone has diverged from the New York/Japan clone (ST5-II) and is characterized by a high proportion of seb. Although ST764-II clones were reported to have spread in the Japanese LTCF environment, primarily in the Chubu region in 2019 [19], the LTCFs in our study were located in different areas, suggesting that this high-risk clone has widely disseminated across LTCFs in Japan. Given that most LTCF residents have comorbidities that could complicate their clinical course, it may be necessary to closely monitor whether this emerging clone will continue to grow.
Despite the insights offered by our study, several limitations should be acknowledged. First, valuable information, such as the residents’ age, sex, underlying medical conditions, and occupied rooms, was unavailable in our prior study. As such, we could not investigate the transmissions between residents based on potential contact patterns. Second, our sample size was relatively smaller than those of previous studies [7,8]. Third, sampling practices varied across facilities, including the frequency of screening tests, timing of initial testing, and duration of study. These differences make it difficult to encompass the temporal aspects of transmission. Fourth, only nasal samples were collected. Since MRSA clonal complexes have a predilection towards either the nares or groin [20], and the oral cavity is recognized as a significant reservoir source [21,22], samples from other body sites could reveal unidentified transmission cases. Fifth, we examined only one colony from each individual. Multiple colonies could also demonstrate unidentified transmission cases. Lastly, staff and visitor screening was not performed, and environmental samples were not collected. With these samples, a more comprehensive evaluation of the transmission pathways could be possible. However, because these limitations hindered the identification of transmission instances, overestimation is unlikely in this study. Future studies should focus on gathering detailed resident information, including prior hospitalizations and transfers between LTCFs, longitudinal sampling, and contact tracing, including household members and LTCF staff, to more definitively distinguish between MRSA acquisition sites inside and outside LTCFs. A thorough understanding of transmission dynamics could elucidate unknown targets for focused instruction of standard precautions and decolonization of populations such as outpatient rehabilitation care.

4. Materials and Methods

4.1. Bacterial Isolates and Study Facilities

WGS was performed using MRSA isolates obtained from four geriatric LTCFs—anonymized as A, B, C, and D (Table 2)—in Japan; the isolate collection has been published previously [11]. Briefly, these MRSA isolates were collected for a cohort study using Seed-Swab MRSA™ kits (Eiken Chemical Co., Ltd., Tokyo, Japan) and cultured on MRSA selective agar plates (MDRS-K™; Kyokuto Pharmaceutical Industrial Co., Ltd., Tokyo, Japan). Microbial identification was confirmed using a VITEK 2 automated system (bioMerieux, Durham, NC, USA), and oxacillin resistance was determined using the disk diffusion method. A single colony from each individual resident was investigated. LTCFs A and C were adjacent, and residents of these facilities may be transferred to the same hospital. MRSA strains were isolated between 1 August 2018 and 31 March 2020. In addition to nasal samples, MRSA isolates were collected from specimens obtained from infectious sites for diagnostic purposes during the specified period (clinical isolates). Among the 453 residents in the LTCFs, 85 MRSA isolates were obtained from 76 residents, including 9 clinical isolates obtained from 8 residents. Notably, LTCF D had the highest number of MRSA-carrying residents (n = 44).

4.2. WGS and Transmission Analysis

WGS was performed for all collected isolates to investigate MRSA transmission among LTCF residents, where SNP analysis determined the transmission events. Genomic DNA was extracted using the QIAamp DNA mini kit (Qiagen, Hilden, Germany), and paired-end libraries were prepared using Nextera XT Library Prep (Illumina, Inc., San Diego, CA, USA). Sequencing was performed with the Illumina HiSeq XTen system at Macrogen (Illumina, Inc., San Diego, CA, USA) or the Illumina MiSeq (Illumina, Inc., San Diego, CA, USA) at our university using the MiSeq Reagent Kit v.3 (Illumina, Inc., San Diego, CA, USA). The resulting data have been deposited under accession number DRA017333. The individual accession numbers of the isolates are listed in Table S4.
We performed trimming of raw reads to eliminate adapters and low-quality sequences, followed by de novo assembly using CLC Genomics Workbench version 9 (CLCbio, Qiagen, Valencia, CA, USA). We determined the MLST, SCCmec type, and spa type for each isolate using PubMLST, SCCmecFinder v1.2, and spaTyper v1.0 [23,24,25,26,27]. The core genome of each clonal complex was identified using Prokka v1.14.6 [28] and Roary v3.13.0 [29]. The pairwise cgSNP distance matrix was calculated using pairsnp v0.3.1 [30], and isolates sharing a cgSNP distance ≤15 were defined as genomic clusters, which is indicative of recent transmission [31]. The genomes AP018923.1, CP091525.1, AP020318.1, and CP083259.1 were used as references for CC1, CC5, CC8, and CC45, respectively. To determine the reference genomes, genome data of S. aureus with an assembly level of complete genome or chromosome as of 5 October 2023 were collected using NCBI genome download. The genomic data with the closest genomic distance to our strain were determined as the reference genome for each CC using Mash v2.3 [32]. Maximum-likelihood phylogenetic trees were then generated using a general time reversible model and gamma correction, followed by visualization with metadata [33,34,35].
To assess the temporal dynamics of MRSA carriage among LTCF residents, changes in the carrier status were compared between the initial test on admission and a second test >2 months later. The transition of MRSA carriage status was visually represented using a Sankey diagram created with the plotly library version 5.18.0 and Python 3.10.8. Additionally, a pan-genome-wide association study (pan-GWAS) was performed using Scoary v1.6.16 to investigate bacterial factors influencing transmission [36].

4.3. Antimicrobial Susceptibility Testing and Detecting AMR Genes

Antimicrobial susceptibility testing was performed using the BD Phoenix M50 system with the PMIC/ID-86 panel (BD Diagnostic Systems, Franklin Lakes, NJ, USA). The results were interpreted in accordance with the Clinical and Laboratory Standards Institute (CLSI) M100 33rd edition guidelines [37]. Furthermore, AMR genes, point mutations, and disinfectant-resistance genes were identified using ResFinder v4.3.3 [24,38,39]. The threshold for gene detection was set to 90% identity and 80% coverage.

4.4. Detection of Virulence Factor

Virulence genes were identified using VirulenceFinder v2.0.4 [40,41]. The threshold for gene detection was set to 90% identity and 80% coverage.

5. Conclusions

Our study showed that MRSA acquisition among residents of geriatric LTCFs can occur within the facility and prior to admission. This underscores the need for effective interventions within and outside LTCFs to prevent MRSA transmission in this cohort of individuals.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/antibiotics14090874/s1. Table S1: Antimicrobial susceptibility of isolates for each sequence-type staphylococcal cassette chromosome mec type; Table S2: Antimicrobial resistance genes and point mutations for each sequence-type staphylococcal cassette chromosome mec type; Table S3: Virulence genes for each sequence-type staphylococcal cassette chromosome mec type; Table S4: Individual accession numbers of the samples.

Author Contributions

Conceptualization, T.S. (Takayuki Suzuki), T.S. (Teppei Sasahara), L.C., and S.H.; methodology, T.S. (Takayuki Suzuki), S.W., and T.S. (Teppei Sasahara); software, T.S. (Takayuki Suzuki); validation, T.S. (Takayuki Suzuki); formal analysis, T.S. (Takayuki Suzuki) and K.K.; investigation, T.S. (Takayuki Suzuki), S.W., K.K., D.A., Y.K., L.C., and S.H.; resources, T.S. (Teppei Sasahara); data curation, T.S. (Takayuki Suzuki), T.S. (Teppei Sasahara), and K.K.; writing—original draft preparation, T.S. (Takayuki Suzuki); writing—review and editing, T. Sasahara, S.W., L.C., and S.H.; visualization, T.S. (Takayuki Suzuki); supervision, T.S. (Teppei Sasahara); project administration, T.S. (Teppei Sasahara); funding acquisition, T.S. (Teppei Sasahara), S.W., and L.C. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Japan Agency for Medical Research and Development (AMED) under grant number JP20dk0110030 to T. Sasahara; JP21fk0108497 to S.W.; and JP22ae0121045, JP21gm1610002, and JP20fk0108134 to L.C.; and by the JSPS KAKENHI (Grant Nos. 19K08960 and 22K19386 to S.W. and 20F20104 and 21K19488 to L.C.). The funding body played no part in the study design, data collection, analysis, interpretation, or paper writing.

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki. Ethical review and approval were waived by the Jichi Medical University Bioethics Committee for Medical Research, as this study solely analyzed the bacterial isolates without the use of personal information (Receipt ID: 22-123).

Informed Consent Statement

Patient consent was waived because this study only used bacterial isolates and did not use any personal information.

Data Availability Statement

The whole-genome data from this study were deposited in DDBJ/ENA/GenBank under the BioProject accession number PRJDB16917.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
MRSAMethicillin-resistant Staphylococcus aureus
LTCFsLong-term care facilities
SNPSingle-nucleotide polymorphism
AMRAntimicrobial resistance
IPCInfection prevention and control
WGSWhole-genome sequencing
CA-MRSACommunity-associated MRSA
MLSTMulti-locus sequence type
cgSNPCore-genome SNP
pan-GWASPan-genome-wide association study
CLSIClinical and Laboratory Standards Institute
RASiRenin–angiotensin system inhibitor

References

  1. Antimicrobial Resistance Collaborators Global Burden of Bacterial Antimicrobial Resistance in 2019: A Systematic Analysis. Lancet 2022, 399, 629–655. [CrossRef]
  2. Safdar, N.; Maki, D.G. The Commonality of Risk Factors for Nosocomial Colonization and Infection with Antimicrobial-Resistant Staphylococcus Aureus, Enterococcus, Gram-Negative Bacilli, Clostridium Difficile, and Candida. Ann. Intern. Med. 2002, 136, 834–844. [Google Scholar] [CrossRef]
  3. Hasanpour, A.H.; Sepidarkish, M.; Mollalo, A.; Ardekani, A.; Almukhtar, M.; Mechaal, A.; Hosseini, S.R.; Bayani, M.; Javanian, M.; Rostami, A. The Global Prevalence of Methicillin-Resistant Staphylococcus Aureus Colonization in Residents of Elderly Care Centers: A Systematic Review and Meta-Analysis. Antimicrob. Resist. Infect. Control 2023, 12, 4. [Google Scholar] [CrossRef]
  4. March, A.; Aschbacher, R.; Dhanji, H.; Livermore, D.M.; Böttcher, A.; Sleghel, F.; Maggi, S.; Noale, M.; Larcher, C.; Woodford, N. Colonization of Residents and Staff of a Long-Term-Care Facility and Adjacent Acute-Care Hospital Geriatric Unit by Multiresistant Bacteria. Clin. Microbiol. Infect. 2010, 16, 934–944. [Google Scholar] [CrossRef]
  5. Gamblin, J.; Jefferies, J.M.; Harris, S.; Ahmad, N.; Marsh, P.; Faust, S.N.; Fraser, S.; Moore, M.; Roderick, P.; Blair, I.; et al. Nasal Self-Swabbing for Estimating the Prevalence of Staphylococcus Aureus in the Community. J. Med. Microbiol. 2013, 62, 437–440. [Google Scholar] [CrossRef]
  6. Baldwin, N.S.; Gilpin, D.F.; Tunney, M.M.; Kearney, M.P.; Crymble, L.; Cardwell, C.; Hughes, C.M. Cluster Randomised Controlled Trial of an Infection Control Education and Training Intervention Programme Focusing on Meticillin-Resistant Staphylococcus Aureus in Nursing Homes for Older People. J. Hosp. Infect. 2010, 76, 36–41. [Google Scholar] [CrossRef]
  7. Chow, A.; Lim, V.W.; Khan, A.; Pettigrew, K.; Lye, D.C.B.; Kanagasabai, K.; Phua, K.; Krishnan, P.; Ang, B.; Marimuthu, K.; et al. MRSA Transmission Dynamics Among Interconnected Acute, Intermediate-Term, and Long-Term Healthcare Facilities in Singapore. Clin. Infect. Dis. 2017, 64, S76–S81. [Google Scholar] [CrossRef]
  8. Harrison, E.M.; Ludden, C.; Brodrick, H.J.; Blane, B.; Brennan, G.; Morris, D.; Coll, F.; Reuter, S.; Brown, N.M.; Holmes, M.A.; et al. Transmission of Methicillin-Resistant Staphylococcus Aureus in Long-Term Care Facilities and Their Related Healthcare Networks. Genome Med. 2016, 8, 102. [Google Scholar] [CrossRef]
  9. Yamada, M.; Arai, H. Long-Term Care System in Japan. Ann. Geriatr. Med. Res. 2020, 24, 174–180. [Google Scholar] [CrossRef]
  10. Population Estimates by Age (Five-Year Groups) and Sex-1 May 2023 (Final Estimates), 1 October 2023 (Provisional Estimates). Available online: https://www.e-stat.go.jp/en/stat-search/files?page=1&layout=datalist&toukei=00200524&tstat=000000090001&cycle=1&year=20230&month=24101210&tclass1=000001011678&result_back=1&tclass2val=0 (accessed on 2 December 2023).
  11. Sasahara, T.; Ae, R.; Yoshimura, A.; Kosami, K.; Sasaki, K.; Kimura, Y.; Akine, D.; Ogawa, M.; Hamabata, K.; Hatakeyama, S.; et al. Association between Length of Residence and Prevalence of MRSA Colonization among Residents in Geriatric Long-Term Care Facilities. BMC Geriatr. 2020, 20, 481. [Google Scholar] [CrossRef]
  12. Huang, S.S.; Singh, R.; McKinnell, J.A.; Park, S.; Gombosev, A.; Eells, S.J.; Gillen, D.L.; Kim, D.; Rashid, S.; Macias-Gil, R.; et al. Decolonization to Reduce Postdischarge Infection Risk among MRSA Carriers. N. Engl. J. Med. 2019, 380, 638–650. [Google Scholar] [CrossRef]
  13. Miller, L.G.; McKinnell, J.A.; Singh, R.D.; Gussin, G.M.; Kleinman, K.; Saavedra, R.; Mendez, J.; Catuna, T.D.; Felix, J.; Chang, J.; et al. Decolonization in Nursing Homes to Prevent Infection and Hospitalization. N. Engl. J. Med. 2023, 389, 1766–1777. [Google Scholar] [CrossRef]
  14. Diep, B.A.; Chambers, H.F.; Graber, C.J.; Szumowski, J.D.; Miller, L.G.; Han, L.L.; Chen, J.H.; Lin, F.; Lin, J.; Phan, T.H.; et al. Emergence of Multidrug-Resistant, Community-Associated, Methicillin-Resistant Staphylococcus Aureus Clone USA300 in Men Who Have Sex with Men. Ann. Intern. Med. 2008, 148, 249–257. [Google Scholar] [CrossRef]
  15. Welsh, T.; Gladman, J.; Gordon, A.L. The Treatment of Hypertension in Care Home Residents: A Systematic Review of Observational Studies. J. Am. Med. Dir. Assoc. 2014, 15, 8–16. [Google Scholar] [CrossRef]
  16. Fralick, M.; Macdonald, E.M.; Gomes, T.; Antoniou, T.; Hollands, S.; Mamdani, M.M.; Juurlink, D.N. Canadian Drug Safety and Effectiveness Research Network Co-Trimoxazole and Sudden Death in Patients Receiving Inhibitors of Renin-Angiotensin System: Population Based Study. BMJ 2014, 349, g6196. [Google Scholar] [CrossRef]
  17. Kaku, N.; Sasaki, D.; Ota, K.; Miyazaki, T.; Yanagihara, K. Changing Molecular Epidemiology and Characteristics of MRSA Isolated from Bloodstream Infections: Nationwide Surveillance in Japan in 2019. J. Antimicrob. Chemother. 2022, 77, 2130–2141. [Google Scholar] [CrossRef]
  18. Hisatsune, J.; Kutsuno, S.; Iwao, Y.; Ishida-Kuroki, K.; Yahara, K.; Kitamura, N.; Kajihara, T.; Kayama, S.; Sugawara, Y.; Kitagawa, H.; et al. Staphylococcus Aureus ST764-SCCmecII High-Risk Clone in Bloodstream Infections Revealed through National Genomic Surveillance Integrating Clinical Data. Nat. Commun. 2025, 16, 2698. [Google Scholar] [CrossRef]
  19. Kawamura, K.; Kitaoka, K.; Kimura, K.; Wachino, J.-I.; Kondo, T.; Iinuma, Y.; Murakami, N.; Fujimoto, S.; Arakawa, Y. Spread of Seb-Positive Methicillin-Resistant Staphylococcus Aureus SCCmec Type II-ST764 Among Elderly Japanese in Nonacute Care Settings. Microb. Drug Resist. 2019, 25, 915–924. [Google Scholar] [CrossRef]
  20. Chow, A.; Htun, H.L.; Hon, P.-Y.; Ang, B.; Kanagasabai, K.; Koh, J.; Holden, M.T.G.; Hsu, L.-Y. Comparative Epidemiology and Factors Associated with Major Healthcare-Associated Methicillin-Resistant Staphylococcus Aureus Clones among Interconnected Acute-, Intermediate- and Long-Term Healthcare Facilities in Singapore. Clin. Microbiol. Infect. 2020, 27, 785.e9–785.e16. [Google Scholar] [CrossRef]
  21. Kearney, A.; Kinnevey, P.; Shore, A.; Earls, M.; Poovelikunnel, T.T.; Brennan, G.; Humphreys, H.; Coleman, D.C. The Oral Cavity Revealed as a Significant Reservoir of Staphylococcus Aureus in an Acute Hospital by Extensive Patient, Healthcare Worker and Environmental Sampling. J. Hosp. Infect. 2020, 105, 389–396. [Google Scholar] [CrossRef]
  22. Kinnevey, P.M.; Kearney, A.; Shore, A.C.; Earls, M.R.; Brennan, G.; Poovelikunnel, T.T.; Humphreys, H.; Coleman, D.C. Meticillin-Resistant Staphylococcus Aureus Transmission among Healthcare Workers, Patients and the Environment in a Large Acute Hospital under Non-Outbreak Conditions Investigated Using Whole-Genome Sequencing. J. Hosp. Infect. 2021, 118, 99–107. [Google Scholar] [CrossRef]
  23. Larsen, M.V.; Cosentino, S.; Rasmussen, S.; Friis, C.; Hasman, H.; Marvig, R.L.; Jelsbak, L.; Sicheritz-Pontén, T.; Ussery, D.W.; Aarestrup, F.M.; et al. Multilocus Sequence Typing of Total-Genome-Sequenced Bacteria. J. Clin. Microbiol. 2012, 50, 1355–1361. [Google Scholar] [CrossRef]
  24. Camacho, C.; Coulouris, G.; Avagyan, V.; Ma, N.; Papadopoulos, J.; Bealer, K.; Madden, T.L. BLAST+: Architecture and Applications. BMC Bioinform. 2009, 10, 421. [Google Scholar] [CrossRef]
  25. Jolley, K.A.; Bray, J.E.; Maiden, M.C.J. Open-Access Bacterial Population Genomics: BIGSdb Software, the Pubmlst.org Website and Their Applications. Wellcome Open Res. 2018, 3, 124. [Google Scholar] [CrossRef]
  26. Kaya, H.; Hasman, H.; Larsen, J.; Stegger, M.; Johannesen, T.B.; Allesøe, R.L.; Lemvigh, C.K.; Aarestrup, F.M.; Lund, O.; Larsen, A.R. SCCmecFinder, a Web-Based Tool for Typing of Staphylococcal Cassette Chromosome Mec in Staphylococcus Aureus Using Whole-Genome Sequence Data. mSphere 2018, 3, 10–1128. [Google Scholar] [CrossRef]
  27. Bartels, M.D.; Petersen, A.; Worning, P.; Nielsen, J.B.; Larner-Svensson, H.; Johansen, H.K.; Andersen, L.P.; Jarløv, J.O.; Boye, K.; Larsen, A.R.; et al. Comparing Whole-Genome Sequencing with Sanger Sequencing for Spa Typing of Methicillin-Resistant Staphylococcus Aureus. J. Clin. Microbiol. 2014, 52, 4305–4308. [Google Scholar] [CrossRef]
  28. Seemann, T. Prokka: Rapid Prokaryotic Genome Annotation. Bioinformatics 2014, 30, 2068–2069. [Google Scholar] [CrossRef]
  29. Page, A.J.; Cummins, C.A.; Hunt, M.; Wong, V.K.; Reuter, S.; Holden, M.T.G.; Fookes, M.; Falush, D.; Keane, J.A.; Parkhill, J. Roary: Rapid Large-Scale Prokaryote Pan Genome Analysis. Bioinformatics 2015, 31, 3691–3693. [Google Scholar] [CrossRef]
  30. Tonkin-Hill, G. Pairsnp: A Set of Scripts for Calculating Pairwise SNP Distance. 2023. Available online: https://github.com/gtonkinhill/pairsnp (accessed on 2 December 2023).
  31. Coll, F.; Raven, K.E.; Knight, G.M.; Blane, B.; Harrison, E.M.; Leek, D.; Enoch, D.A.; Brown, N.M.; Parkhill, J.; Peacock, S.J. Definition of a Genetic Relatedness Cutoff to Exclude Recent Transmission of Meticillin-Resistant Staphylococcus Aureus: A Genomic Epidemiology Analysis. Lancet Microbe 2020, 1, e328–e335. [Google Scholar] [CrossRef]
  32. Ondov, B.D.; Treangen, T.J.; Melsted, P.; Mallonee, A.B.; Bergman, N.H.; Koren, S.; Phillippy, A.M. Mash: Fast Genome and Metagenome Distance Estimation Using MinHash. Genome Biol. 2016, 17, 132. [Google Scholar] [CrossRef]
  33. Minh, B.Q.; Schmidt, H.A.; Chernomor, O.; Schrempf, D.; Woodhams, M.D.; von Haeseler, A.; Lanfear, R. IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era. Mol. Biol. Evol. 2020, 37, 1530–1534. [Google Scholar] [CrossRef]
  34. Hoang, D.T.; Chernomor, O.; von Haeseler, A.; Minh, B.Q.; Vinh, L.S. UFBoot2: Improving the Ultrafast Bootstrap Approximation. Mol. Biol. Evol. 2018, 35, 518–522. [Google Scholar] [CrossRef] [PubMed]
  35. Letunic, I.; Bork, P. Interactive Tree Of Life (ITOL) v5: An Online Tool for Phylogenetic Tree Display and Annotation. Nucleic Acids Res. 2021, 49, W293–W296. [Google Scholar] [CrossRef] [PubMed]
  36. Brynildsrud, O.; Bohlin, J.; Scheffer, L.; Eldholm, V. Rapid Scoring of Genes in Microbial Pan-Genome-Wide Association Studies with Scoary. Genome Biol. 2016, 17, 238. [Google Scholar] [CrossRef] [PubMed]
  37. CLSI Supplement M100. Performance Standards for Antimicrobial Susceptibility Testing, 33rd ed.; Clinical and Laboratory Standards Institute: Wayne, PA, USA, 2023. [Google Scholar]
  38. Bortolaia, V.; Kaas, R.S.; Ruppe, E.; Roberts, M.C.; Schwarz, S.; Cattoir, V.; Philippon, A.; Allesoe, R.L.; Rebelo, A.R.; Florensa, A.F.; et al. ResFinder 4.0 for Predictions of Phenotypes from Genotypes. J. Antimicrob. Chemother. 2020, 75, 3491–3500. [Google Scholar] [CrossRef]
  39. Zankari, E.; Allesøe, R.; Joensen, K.G.; Cavaco, L.M.; Lund, O.; Aarestrup, F.M. PointFinder: A Novel Web Tool for WGS-Based Detection of Antimicrobial Resistance Associated with Chromosomal Point Mutations in Bacterial Pathogens. J. Antimicrob. Chemother. 2017, 72, 2764–2768. [Google Scholar] [CrossRef]
  40. Malberg Tetzschner, A.M.; Johnson, J.R.; Johnston, B.D.; Lund, O.; Scheutz, F. In Silico Genotyping of Escherichia Coli Isolates for Extraintestinal Virulence Genes by Use of Whole-Genome Sequencing Data. J. Clin. Microbiol. 2020, 58, 10–1128. [Google Scholar] [CrossRef]
  41. Joensen, K.G.; Scheutz, F.; Lund, O.; Hasman, H.; Kaas, R.S.; Nielsen, E.M.; Aarestrup, F.M. Real-Time Whole-Genome Sequencing for Routine Typing, Surveillance, and Outbreak Detection of Verotoxigenic Escherichia Coli. J. Clin. Microbiol. 2014, 52, 1501–1510. [Google Scholar] [CrossRef]
Antibiotics 14 00874 g001
Figure 1. Methicillin-resistant Staphylococcus aureus (MRSA) transmission dynamics in four long-term care facilities. (A–D) show MRSA genomic clusters in long-term care facilities A, B, C, and D, respectively. Each circle represents an MRSA isolate, and the strains of clonal complex (CC) 1, CC5, CC8, CC22, and CC45 are indicated using green, yellow, red, purple, and indigo colors, respectively. Isolates from clinical specimens are marked with a * in circles. Samples with a core genome single-nucleotide polymorphism (cgSNP) distance ≤15 were connected by a solid line if they originated from different hosts and by a dashed line if they originated from the same host. The numbers attached to these lines indicate the cgSNP distance. The bottom axis is the time axis, where 0 denotes the month in which this study began at each institution, and the unit of the number is measured in months (M). This figure was created in BioRender. Suzuki, T. (2025) https://BioRender.com/biq47q2 (accessed on 5 July 2025). Abbreviations: CC, clonal complex; Neg, negative.
Figure 1. Methicillin-resistant Staphylococcus aureus (MRSA) transmission dynamics in four long-term care facilities. (A–D) show MRSA genomic clusters in long-term care facilities A, B, C, and D, respectively. Each circle represents an MRSA isolate, and the strains of clonal complex (CC) 1, CC5, CC8, CC22, and CC45 are indicated using green, yellow, red, purple, and indigo colors, respectively. Isolates from clinical specimens are marked with a * in circles. Samples with a core genome single-nucleotide polymorphism (cgSNP) distance ≤15 were connected by a solid line if they originated from different hosts and by a dashed line if they originated from the same host. The numbers attached to these lines indicate the cgSNP distance. The bottom axis is the time axis, where 0 denotes the month in which this study began at each institution, and the unit of the number is measured in months (M). This figure was created in BioRender. Suzuki, T. (2025) https://BioRender.com/biq47q2 (accessed on 5 July 2025). Abbreviations: CC, clonal complex; Neg, negative.
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Figure 2. Maximum-likelihood phylogenetic trees with heatmaps based on core-genome single-nucleotide polymorphism (cgSNP) analysis. (A–D) depict clonal complexes (CC) 1, CC5, CC8, and CC45, respectively. The reference genomes of each clonal complex are shown as outgroups. To the right of the sample name, the multi-locus sequence type (MLST), facility, patient ID for this study, heatmap displaying the cgSNP distance, and clinical specimens are presented. Isolates from clinical specimens are marked with ★ (star). Abbreviations: MLST, multi-locus sequence type; cgSNP, core-genome single-nucleotide polymorphism.
Figure 2. Maximum-likelihood phylogenetic trees with heatmaps based on core-genome single-nucleotide polymorphism (cgSNP) analysis. (A–D) depict clonal complexes (CC) 1, CC5, CC8, and CC45, respectively. The reference genomes of each clonal complex are shown as outgroups. To the right of the sample name, the multi-locus sequence type (MLST), facility, patient ID for this study, heatmap displaying the cgSNP distance, and clinical specimens are presented. Isolates from clinical specimens are marked with ★ (star). Abbreviations: MLST, multi-locus sequence type; cgSNP, core-genome single-nucleotide polymorphism.
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Figure 3. Temporal changes in the methicillin-resistant Staphylococcus aureus (MRSA)-carrying state. Residents who underwent an initial test immediately after admission and a follow-up test >2 months later were included in this analysis. For each test, they were classified as MRSA non-carriers, MRSA carriers of isolates involved in genomic clusters, and MRSA carriers of isolates not involved in genomic clusters. Only three participants (4.6%) acquired genomic cluster strains after admission, whereas seven MRSA carriers became non-carriers in the second test.
Figure 3. Temporal changes in the methicillin-resistant Staphylococcus aureus (MRSA)-carrying state. Residents who underwent an initial test immediately after admission and a follow-up test >2 months later were included in this analysis. For each test, they were classified as MRSA non-carriers, MRSA carriers of isolates involved in genomic clusters, and MRSA carriers of isolates not involved in genomic clusters. Only three participants (4.6%) acquired genomic cluster strains after admission, whereas seven MRSA carriers became non-carriers in the second test.
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Table 1. Distribution of isolate types at each facility.
Table 1. Distribution of isolate types at each facility.
ABCD
CC and MLST, n (%)
CC1
ST14 (28.6%)6 (54.5%)2 (28.6%)10 (18.9%)
ST27253 (21.4%)1 (9.1%)--
ST8737---2 (3.8%)
ST8738-1 (9.1%)--
CC5
ST5-1 (9.1%)-2 (3.8%)
ST7642 (14.3%)-5 (71.4%)4 (7.5%)
ST87332 (14.3%)---
CC8
ST82 (14.3%)2 (18.2%)-27 (50.9%)
ST87361 (7.1%)---
CC22
ST22---1 (1.9%)
CC45
ST45---7 (13.2%)
SCCmec type, n (%)
II4 (28.6%)1 (9.1%)5 (71.4%)7 (13.2%)
IVa10 (71.4%)8 (72.7%)2 (28.6%)13 (24.5%)
IVc-2 (18.2%)--
IVj---27 (50.9%)
V---6 (11.3%)
spa type, n (%)
t0023 (21.4%)-3 (42.9%)1 (1.9%)
t005---1 (1.9%)
t0082 (14.3%)2 (18.2%)-2 (3.8%)
t010--1 (14.3%)-
t0451 (7.1%)--3 (5.7%)
t062---1 (1.9%)
t211---3 (5.7%)
t334---1 (1.9%)
t1062-1 (9.1%)--
t1081---6 (11.3%)
t1767---2 (3.8%)
t1781--1 (14.3%)-
t17847 (50.0%)5 (45.5%)2 (28.6%)11 (20.8%)
t2427---1 (1.9%)
t3081---2 (3.8%)
t4494-3 (27.3%)--
t5071---15 (28.3%)
t7744---1 (1.9%)
t209461 (7.1%)---
Unknown type---3 (5.7%)
Abbreviations: CC, clonal complex; MLST, multi-locus sequence type; ST, sequence type; SCCmec, staphylococcal cassette chromosome mec.
Table 2. Characteristics of participants and isolates at each facility.
Table 2. Characteristics of participants and isolates at each facility.
ABCD
Facility typeHSFHSFSNHHSF
Resident capacity, n1005060150
Region of JapanEasternEasternEasternWestern
Number of participants, n1289989135
Number of participants with at least one MRSA isolation, n1411744
Number of isolates, n1411753
Source of samples, n
Nasal swab
Clinical specimen
Airway tract secretions---5
Urine---1
Otitis externa-1--
Skin abscess-1--
Dacryocystitis-1--
Number of participants sorted by test frequency, n
1 time109901896
2 times1993039
3 times--41-
Frequency of nasal swab tests, median (IQR)1 (1–1)1 (1–1)2 (2–3)1 (1–2)
Timing of the first nasal swab test from admission, median (IQR), month2.5 (1.0–7.4)<0.5 (<0.5–4.3)7.0 (1.0–25.5)<0.5 (<0.5–<0.5)
Duration of the study, month2018207
Abbreviations: HSF, health services facility; SNH, special nursing home; MRSA, methicillin-resistant Staphylococcus aureus; IQR, interquartile range.
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MDPI and ACS Style

Suzuki, T.; Sasahara, T.; Watanabe, S.; Kosami, K.; Akine, D.; Kinoshita, Y.; Cui, L.; Hatakeyama, S. Genomic Analysis of MRSA for Evaluating Local Transmission Dynamics in Geriatric Long-Term Care Facilities in Japan. Antibiotics 2025, 14, 874. https://doi.org/10.3390/antibiotics14090874

AMA Style

Suzuki T, Sasahara T, Watanabe S, Kosami K, Akine D, Kinoshita Y, Cui L, Hatakeyama S. Genomic Analysis of MRSA for Evaluating Local Transmission Dynamics in Geriatric Long-Term Care Facilities in Japan. Antibiotics. 2025; 14(9):874. https://doi.org/10.3390/antibiotics14090874

Chicago/Turabian Style

Suzuki, Takayuki, Teppei Sasahara, Shinya Watanabe, Koki Kosami, Dai Akine, Yumi Kinoshita, Longzhu Cui, and Shuji Hatakeyama. 2025. "Genomic Analysis of MRSA for Evaluating Local Transmission Dynamics in Geriatric Long-Term Care Facilities in Japan" Antibiotics 14, no. 9: 874. https://doi.org/10.3390/antibiotics14090874

APA Style

Suzuki, T., Sasahara, T., Watanabe, S., Kosami, K., Akine, D., Kinoshita, Y., Cui, L., & Hatakeyama, S. (2025). Genomic Analysis of MRSA for Evaluating Local Transmission Dynamics in Geriatric Long-Term Care Facilities in Japan. Antibiotics, 14(9), 874. https://doi.org/10.3390/antibiotics14090874

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