Abstract
Background
Streptococcus dysgalactiae subsp. equisimilis (SDSE) is an emerging cause of invasive infection, particularly in aging populations. We investigated the clinical and genomic characteristics of SDSE bacteremia at a Japanese medical center serving a large older-adult population.
Method
We retrospectively analyzed 73 consecutive episodes of SDSE bacteremia, including 63 initial and 10 recurrent episodes, from April 2020 through September 2024. Clinical features were compared with those of Streptococcus pyogenes (GAS) and Streptococcus agalactiae (GBS) bacteremia. Whole-genome sequencing was performed to assess strain relatedness, emm types, sequence types (STs), and virulence gene profiles.
Results
The median age of patients with initial SDSE bacteremia was 86.0 years, the highest among 10 major bloodstream pathogens evaluated, predominantly affecting super-elderly patients compared with GAS and GBS. Lower-extremity skin and soft tissue infections were the most common portal of entry. Among 10 recurrent episodes, 5 were caused by strains with STs and emm types identical to those of the initial isolates, consistent with probable relapse; the remaining episodes involved different genotypes, suggesting reinfection. The all-cause 30-day mortality rate was 4.8%. Phylogenetic analysis demonstrated genetic diversity, including 15 emm types and 14 STs. No specific lineage or virulence gene was significantly associated with clinical outcomes.
Conclusions
In our cohort, SDSE bacteremia predominantly affected super-elderly adults and frequently arose from lower-extremity portals of entry. Recurrent episodes involved both probable relapse and potential reinfection. These findings suggest that localized interventions, such as skin care and edema management, may be important adjuncts to antimicrobial therapy for preventing recurrence.
Keywords: bacteremia, molecular epidemiology, older adults, skin and soft tissue infection, Streptococcus dysgalactiae subsp. equisimilis
SDSE bacteremia primarily affects super-elderly adults, often entering through lower-extremity skin and soft tissue infections. Genomic analysis shows recurrence may represent relapse or reinfection. Prevention requires not only antibiotics but also skin care and edema management in aging populations.
Graphical Abstract
Graphical Abstract.
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Streptococcus dysgalactiae subsp. equisimilis (SDSE), a member of the pyogenic streptococci, was historically considered less pathogenic than Lancefield group A and group B. However, since its designation as a distinct taxon in 1996 [1], SDSE is now recognized as a significant human pathogen. The SDSE isolates commonly express Lancefield group G, C, A, or L antigens and typically exhibit robust β-hemolysis [2–4]. As a commensal organism, SDSE colonizes the skin, gastrointestinal tract, and pharynx in humans [5]. It is now well established as a cause of a broad range of infections, particularly skin and soft tissue infections (SSTIs), such as cellulitis and necrotizing fasciitis [5–7]. Furthermore, recurrent infection, particularly following SSTIs, has emerged as a notable clinical feature of SDSE disease [8, 9]. Although SDSE was previously regarded primarily as an opportunistic pathogen, its clinical significance has been substantially reevaluated in recent years.
Streptococcus. dysgalactiae subsp. equisimilis shares significant pathogenic similarities with Streptococcus pyogenes (GAS), possessing highly conserved virulence factors critical for invasive infections. These include streptolysin O (slo), streptokinase A (ska), and the multifunctional surface M protein (emm), which mediates immune evasion and host cell adhesion [10–12]. However, unlike GAS, SDSE does not uniformly harbor a full complement of superantigen genes; while certain genes such as the streptococcal pyrogenic exotoxin G (speG) are present in some strains, their precise contribution to human disease remains poorly understood.
In recent years, the isolation of SDSE from sterile sites, particularly blood cultures, has increased substantially [13]. This trend has been especially pronounced in developed countries with rapidly aging populations, where SDSE is increasingly recognized as an important cause of invasive infections, including bacteremia and sepsis [10, 11].
In this study, we aimed to characterize the clinical and genomic features of SDSE strains isolated from patients with bacteremia at a medical center caring for a high proportion of older adults. The primary objective was to define the clinical features of SDSE bacteremia in older adults. Furthermore, we performed whole-genome sequencing (WGS) to assess genetic concordance among recurrent cases and to investigate potential associations between genetic lineages, including sequence types (STs) and emm types, and specific clinical manifestations or virulence genes, such as speG.
METHODS
Study Population and Clinical Data Collection
This study included consecutive patients with SDSE bacteremia identified from blood cultures at Fujita Health University Okazaki Medical Center, a 400-bed academic secondary care hospital between April 2020 and September 2024. For these included patients, all available episodes of bacteremia, including both the initial onset and subsequent recurrent episodes, were evaluated. In this study, a recurrent episode was defined as a subsequent positive blood culture for SDSE occurring at least 30 days after the initial positive culture, following clinical improvement and the completion of initial antimicrobial therapy. To evaluate the age distribution characteristics of SDSE within the institutional context, data on the 10 most frequently isolated bacterial species from blood cultures during the same period were extracted (Figure 1). For clinical comparison, 14 patients with GAS and 53 patients with Streptococcus agalactiae (GBS) bacteremia identified during the same period were also included.
Figure 1.
Age distribution of the top 10 microbial species isolated from blood cultures. Violin plots superimposed with box plots illustrate the age distribution for each microbial species. The internal box plots indicate the median (horizontal line) and the interquartile range (box boundaries), with whiskers extending to data points within 1.5 times the interquartile range. Numbers in parentheses indicate the number of cases (N). To specifically characterize the age profile of SDSE, pairwise comparisons were performed between the SDSE group and each of the other 10 species using the 2-tailed Mann–Whitney U test. To control the family-wise error rate, all P values were subsequently adjusted using the Holm-Bonferroni method (Padj). Abbreviations: CNS, coagulase-negative staphylococci; GBS, group B Streptococcus; SDSE, Streptococcus dysgalactiae subsp. equisimilis.
Retrospectively collected electronic medical record data included age, sex, comorbidities assessed by the Charlson Comorbidity Index, activities of daily living status (defined by the need for care or assistance) [14], residence prior to hospitalization, vital signs at the time of blood culture collection, primary source of bacteremia, duration of antimicrobial therapy, length of hospital stay, and all-cause 30-day mortality defined as death within 30 days after the initial blood culture collection. “Primary bacteremia” was defined as bacteremia without an identifiable source of infection.
Blood Culture Protocol
Blood cultures were processed using the BACTEC FX system (Becton, Dickinson and Company, Tokyo, Japan), and culture bottles (BACTEC Plus Aerobic and Lytic Anaerobic) were incubated at 35–37°C for up to 7 days. Isolates were cryopreserved at −80°C.
DNA Extraction and WGS
The isolates were subcultured on blood agar plates (Tryptic Soy Blood Agar with Sheep Blood No. 2; Kyokuto Pharmaceutical Industrial, Tokyo, Japan) under aerobic conditions. Genomic DNA was extracted using the Gentra Puregene Yeast/Bact Kit (Qiagen, Venlo, Netherlands) in combination with EZ Beads (AMR, Tokyo, Japan) via mechanical cell wall disruption. Only DNA samples with concentrations ≥ 1 ng/µL were subjected to WGS.
Whole-genome sequencing was performed on the NextSeq 2000 platform (Illumina, San Diego, CA, United States) using a paired-end sequencing strategy (2 × 150 bp). De novo genome assembly was conducted using SPAdes (version 3.13.1.18). The resulting assemblies were evaluated for genome size, and suspected multi-bacterial assemblies were filtered and separated based on contig size prior to further analysis.
Phylogenetic and Molecular Analysis
A molecular phylogenetic tree was reconstructed based on core genome single nucleotide polymorphisms (SNPs). Single nucleotide polymorphism detection was performed using the SNIPPY pipeline implemented in GIGAdoc [15]. Based on the core SNP alignment file (core.aln) generated by SNIPPY, a phylogenetic tree was constructed using the approximately maximum likelihood method implemented in FastTree (version 2.1.11) [16]. The General Time Reversible model was applied for phylogenetic analysis. Branch reliability was evaluated using Shimodaira-Hasegawa-like (SH-like) local support values with 1000 resamplings.
To characterize the pathogenic potential of the isolates, targeted screening for 11 virulence genes (sagA, slo, ska, skc, nga, hylD, speG, lmb, gapC, scpA, sicG) was performed using the Basic Local Alignment Search Tool (BLAST, version 2.13.0) against the assembled genomes. The presence of each gene was defined as ≥95% sequence identity and ≥95% query coverage relative to the corresponding reference sequence.
Antimicrobial Susceptibility and Therapy
Antimicrobial susceptibility testing was performed using a commercial broth microdilution panel (Dry Plate Eiken; Eiken Chemical Co., Ltd., Tokyo, Japan) according to the manufacturer's instructions. The results were interpreted according to the Clinical and Laboratory Standards Institute M100 guidelines (30th Edition, 2020). Isolates categorized as intermediate or resistant were grouped together and defined as non-susceptible.
Antimicrobial therapy was categorized into empiric and definitive therapy. Empiric therapy was defined as the antimicrobial agents administered before the pathogen identification. Definitive therapy was defined as the agent administered for the longest duration after identification. In cases where multiple agents were administered for an equal duration, the final agent used to complete the therapy was selected.
To avoid the overrepresentation of data, analyses of both antimicrobial susceptibility and antimicrobial therapy were restricted to the initial bacteremia episode for each patient.
Statistical Analysis
All continuous variables evaluated in this study exhibited non-normal distributions; therefore, they were expressed as medians and interquartile ranges (IQRs), and nonparametric statistical tests were employed throughout.
To characterize the age distribution of SDSE, the isolation age was compared with those of other major bacterial species. To this end, 2-group comparisons were performed between the SDSE group and each of the other 10 bacterial species, resulting in 10 pre-specified comparisons. The Mann–Whitney U test was used for each comparison, and the Holm–Bonferroni method was applied to adjust for multiple comparisons. The Holm method was selected because it controls the family-wise error rate with stringency comparable to the Bonferroni correction while maintaining higher statistical power.
For the comparison of patient backgrounds and clinical characteristics among the 3 independent streptococcal groups—SDSE, GAS, and GBS—the Kruskal–Wallis test was used for continuous variables, and Fisher's exact test was used for categorical variables. Furthermore, for the comparison of clinical characteristics and outcomes between the survival and mortality groups within initial SDSE episodes, the Mann–Whitney U test and Fisher's exact test were applied to continuous and categorical variables, respectively. To identify the specific categories contributing to a significant association in the contingency table, post hoc analysis was performed by calculating adjusted residuals. An absolute adjusted residual > 1.96 was considered to indicate a statistically significant deviation.
An adjusted P value (Padj) or a P value of less than .05 was considered statistically significant. All statistical analyses were performed using jamovi (version 2.6.45.0).
RESULTS
Overview of Cases and Institutional Age Distribution
Among 73 SDSE bloodstream isolates, 63 represented index initial episodes and 10 were recurrent. To maintain statistical independence, only index episodes underwent demographic and clinical comparisons. Patients aged ≥60 years represented 71.0% of all blood culture requests, closely reflecting overall hospital demographics where this age group accounted for 79.1% of total inpatient days (Supplementary Figure 1).
Comparison of Patient Age Across Bacteremia Pathogens
The age distribution of people with SDSE bacteremia was compared with that of the 10 most frequently isolated bacterial species from blood cultures (Figure 1). The median age of patients in the SDSE group was 86.0 years (IQR, 77.5–91.5), which was the highest among all species evaluated. In species-specific comparisons, patients with SDSE bacteremia were significantly older than those with the other 9 evaluated species (Padj < .05 for all comparisons), with the exception of Bacillus spp. (Padj = .089). Thus, despite the overall high proportion of older patients at this institution, SDSE bacteremia was disproportionately concentrated in an even older population compared with the other common bloodstream pathogens.
Clinical Background: Comparison of SDSE, GAS, and GBS
A comparison of clinical characteristics among 3 streptococcal species—SDSE, GAS, and GBS—revealed significant differences across several key clinical categories. Regarding patient demographics, the proportion of male patients differed significantly among the groups and was lower in the SDSE group (36.5%) than in the GBS (60.4%) and GAS (42.9%) groups (P = .039). Care-dependent individuals were most prevalent in the SDSE group (73.0%), followed by the GBS (43.1%) and GAS (35.7%) groups (P = .001).
Acquisition settings differed among groups (P < .001); healthcare-associated infections predominated in SDSE (69.8%) versus GAS (35.7%) and GBS (25.0%). Adjusted residuals indicated that SDSE was significantly associated with healthcare-associated acquisition (adjusted residual, 2.33), whereas GBS was significantly associated with community-associated acquisition (adjusted residual, 2.07). Consequently, community-care utilization was higher among patients with SDSE (42.9%; daycare service, 36.5%) than among the other groups (P < .001).
Intergroup differences occurred in solid tumor prevalence (highest in GBS, 46.2%; P = .004), diagnostic temperature (highest in GAS, median 39.4 °C; P = .005), and SSTI incidence (SDSE, 66.7%; GAS, 85.7%; GBS, 30.2%; P < .001). Conversely, age (P = .052), residential status (facility vs home; P = .536), diabetes (P = .720), SIRS score (P = .160), and all-cause 30-day mortality (P = .868) showed no significant differences (Table 1).
Table 1.
Demographic and Clinical Characteristics of Patients With Bacteremia Caused by S. pyogenes (GAS), SDSE, and S. agalactiae (GBS)
| SDSE (N = 63) |
S. pyogenes
(N = 14) |
S. agalactiae
(N = 53) |
Overall P Value | |
|---|---|---|---|---|
| Demographics | ||||
| Age (median, IQR) | 86.0 (77.5–91.5) | 79.0 (69.5–88.5) | 80.0 (74.0–87.0) | .052a |
| Male (n, %) | 23 (36.5) | 6 (42.9) | 32 (60.4) | .039b |
| Baseline functional and social status | ||||
| Acquisition setting | ||||
| Community-associated (n/N, %) | 18/63 (28.6) | 9/14 (64.3) | 37/52 (71.2) | <.001b |
| Healthcare-associated (n/N, %) | 44/63 (69.8) | 5/14 (35.7) | 13/52 (25.0) | <.001b |
| Hospital-acquired (n/N, %) | 1/63 (1.6) | 0/14 (0) | 2/52 (3.8) | .709b |
| Nursing home resident (n/N, %) | 17/63 (27.0) | 2/14 (14.3) | 10/52 (19.2) | .536b |
| Use of home/community-based care (n/N, %) | 27/63 (42.9) | 3/14 (21.4) | 4/52 (7.7) | <.001b |
| Day-care service (n/N, %) | 23/63 (36.5) | 3/14 (21.4) | 2/52 (3.8) | <.001b |
| Home-visit nursing/care (n/N, %) | 3/63 (4.8) | 0/14 (0) | 1/52 (1.9) | .765b |
| Short-stay service (n/N, %) | 1/63 (1.6) | 0/14 (0) | 1/52 (1.9) | 1.000b |
| Requiring long-term care/support (n/N, %) | 46/63 (73.0) | 5/14 (35.7) | 22/51 (43.1) | .001b |
| Comorbidities | ||||
| CCI (median, IQR) | 2 (1–3) | 1 (0–2) | 2 (1–3) | .085a |
| Diabetes mellitus (n/N, %) | 20/63 (31.7) | 6/14 (42.9) | 18/52 (34.6) | .720b |
| Liver dysfunction (n/N, %) | 3/63 (4.8) | 0/14 (0) | 1/52 (1.9) | .763b |
| Solid tumor (n/N, %) | 14/63 (22.2) | 1/14 (7.1) | 24/52 (46.2) | .004b |
| Renal dysfunction (n/N, %) | 2/63 (3.2) | 0/14 (0) | 0/52 (0) | .602b |
| Dementia (n/N, %) | 16/63 (25.4) | 3/14 (21.4) | 6/52 (11.5) | .151b |
| Hemiplegia (n/N, %) | 5/63 (7.9) | 0/14 (0) | 8/52 (15.4) | .198b |
| Congestive heart failure (n/N, %) | 11/63 (17.5) | 1/14 (7.1) | 9/52 (17.3) | .790b |
| Cerebrovascular disease (n/N, %) | 14/63 (22.2) | 1/14 (7.1) | 8/52 (15.4) | .381b |
| Clinical findings and severity | ||||
| Body temperature (°C) (median, IQR) | 38.8 (38.0–39.2) | 39.4 (38.5–40.1) | 38.3 (37.4–38.7) | .005a |
| Skin and soft tissue infection (n, %) | 42 (66.7) | 12 (85.7) | 16 (30.2) | <.001b |
| SIRS score (median, IQR) (N = 62/13/48) | 3 (2–4) | 3 (2–4) | 2 (2–3) | .160a |
| LRINEC score (median, IQR) (N = 63/12/45) | 4 (3–6) | 4 (3–10) | 4 (3–7) | .875a |
| Quick SOFA (median, IQR) (N = 62/14/49) | 1 (0–2) | 1 (1–1) | 1 (0–2) | .981a |
| Systolic blood pressure ≤ 100 mmHg (n/N, %) | 4/63 (6.3) | 0/14 (0) | 6/51 (11.8) | .401b |
| Respiratory rate ≥ 22 breaths/min (n/N, %) | 31/62 (50.0) | 10/14 (71.4) | 28/49 (57.1) | .358b |
| Altered mental status, GCS < 15 (n/N, %) | 31/63 (49.2) | 4/14 (28.6) | 18/51 (35.3) | .212b |
| Outcomes | ||||
| Length of stay, days (median, IQR) (N = 61/14/48) | 19.0 (13.0–32.0) | 13.0 (12.3–28.5) | 19.0 (12.0–36.8) | .607a |
| All-cause 30-day mortality (n/N, %) | 3/63 (4.8) | 1/14 (7.1) | 3/51 (5.9) | .868b |
Data are presented as median [interquartile range (IQR)] or n (%).
Abbreviations: CCI, Charlson Comorbidity Index; GCS, Glasgow Coma Scale; LRINEC, Laboratory Risk Indicator for Necrotizing Fasciitis; SIRS, Systemic Inflammatory Response Syndrome; SOFA, Sequential Organ Failure Assessment; SSTI, skin and soft tissue infection.
aCompared among the 3 groups using the Kruskal–Wallis test.
bCompared among the 3 groups using Fisher's exact test.
Antimicrobial Susceptibility and Therapy
The antimicrobial susceptibility profiles of the 63 initial SDSE blood isolates are summarized in Supplementary Table 1. All isolates were susceptible to penicillin G, ampicillin, ceftriaxone, meropenem, and vancomycin. Conversely, non-susceptibility (comprising intermediate and resistant phenotypes) was observed for clarithromycin (23.8%), clindamycin (23.8%), and minocycline (22.2%). Susceptibility to levofloxacin remained high at 96.8%.
Empiric therapy frequently involved β-lactam/β-lactamase inhibitors (47.6%) and third- or fourth-generation cephalosporins (22.2%) (Supplementary Table 2). Following pathogen identification, definitive therapy was de-escalated to penicillins in 33.3% of cases.
Clinical Course and Outcomes
The all-cause 30-day mortality was 4.8% (3/63 patients). Descriptively, non-survivors tended to be older (median 93.0 vs 85.0) and exhibited higher initial clinical severity, with a higher median quick Sequential Organ Failure Assessment (qSOFA) score (2.0 vs 1.0) compared to survivors. All non-survivors presented with SSTIs, and the median length of hospital stay was notably shorter in the mortality group (2.0 vs 20.0 days), reflecting early mortality (Supplementary Table 3). Among survivors, the median total duration of antimicrobial therapy was 16.0 days (IQR, 14.0–22.3). Prolonged intravenous therapy was primarily required for deep-seated infections, including infective endocarditis, pyogenic spondylitis/discitis, and iliopsoas abscesses. Detailed clinical and molecular profiles of the 3 fatal cases (all aged ≥90), which did not cluster within specific STs or emm types, are provided in Supplementary Table 4.
Source of Bacteremia
The most common index episode source was lower-limb SSTIs (52.4%), followed by primary bacteremia (33.3%) (Table 2). Overall, infections originating from the lower limbs and feet (including dorsal foot and toes) accounted for 60.3% of cases. Strikingly, all 10 recurrent episodes originated exclusively from the lower limbs (90.0%) or dorsal foot (10.0%). Primary bacteremia was more frequently observed in care-dependent patients than in independent individuals.
Table 2.
Presumed Portals of Entry in Initial and Recurrent SDSE Bacteremia
| Site of Infection | Total Initial Episode (N = 63) n (%) |
Recurrence (N = 10) n (%) |
||
|---|---|---|---|---|
| Independent (N = 17) n (%) |
Care-dependent (N = 46) n (%) |
|||
| Lower extremity | 33 (52.4) | 13 (20.6) | 20 (31.7) | 9 (90.0) |
| Dorsum of foot | 3 (4.8) | 1 (1.6) | 2 (3.2) | 1 (10.0) |
| Toe(s) | 2 (3.2) | 0 (0) | 2 (3.2) | 0 (0) |
| Buttocks | 2 (3.2) | 0 (0) | 2 (3.2) | 0 (0) |
| Upper extremity | 1 (1.6) | 0 (0) | 1 (1.6) | 0 (0) |
| Face | 1 (1.6) | 0 (0) | 1 (1.6) | 0 (0) |
| Primary bacteremia | 21 (33.3) | 3 (4.8) | 18 (28.6) | 0 (0) |
| Total | 63 (100) | 63 (100) | 10 (100) | |
Data are presented as number (%). “Primary bacteremia” indicates cases where the portal of entry could not be identified.
Molecular Epidemiological Analysis of Recurrence
The genotypes of the index and recurrent isolates were compared in 6 patients with 10 recurrent episodes (Table 3). In 4 patients (patients 2, 3, 5, and 6), STs and emm types were identical between the initial and recurrent isolates. Patient 1 had 5 recurrent episodes, with identical genotypes observed between initial and first recurrent isolates. Notably, patient 6 retained the same genotype despite an interval of 1333 days (approximately 3.5 years). Conversely, patient 4 and subsequent episodes in patient 1 exhibited different genotypes, suggesting reinfection. The median time to the first recurrence was 261.5 days (IQR, 170.8–560.0).
Table 3.
Molecular Comparison of Index and Recurrent SDSE Bacteremia Isolates
| Patient | Episode | Isolate ID | ST | emm Type | Interval From Previous Episode (Days) | SNP Distances (vs Initial) |
|---|---|---|---|---|---|---|
| 1 | Initial | FUJ02529 | 127 | stG166B.0 | - | - |
| Recurrence 1 | FUJ02617 | 127 | stG166B.0 | 35 | 12 | |
| Recurrence 2 | FUJ02619 | 127 | stG245.0 | 53 | 482 | |
| Recurrence 3 | FUJ02648 | 17 | stG6792.3 | 137 | 9,399 | |
| Recurrence 4 | FUJ02655 | 127 | stG245.0 | 250 | 479 | |
| Recurrence 5 | FUJ02658 | 271 | stG6.1 | 75 | 2,163 | |
| 2 | Initial | FUJ02650 | 148 | stG166B.0 | - | - |
| Recurrence | FUJ02660 | 148 | stG166B.0 | 335 | 38 | |
| 3 | Initial | FUJ02666 | 17 | stG6792.3 | - | - |
| Recurrence | FUJ03259 | 17 | stG6792.3 | 188 | 2 | |
| 4 | Initial | FUJ02651 | 25 | stG5420.0 | - | - |
| Recurrence | FUJ03260 | Unknown | stG245.0 | 635 | 1,803 | |
| 5 | Initial | FUJ03261 | 29 | stG485.0 | - | - |
| Recurrence | FUJ03272 | 29 | stG485.0 | 165 | 919 | |
| 6 | Initial | FUJ02526 | 15 | stG10.0 | - | - |
| Recurrence | FUJ03282 | 15 | stG10.0 | 1333 | 7 |
Abbreviations: ST, sequence type; stG, emm type designation associated with group G streptococci (Streptococcus dysgalactiae subsp. equisimilis); Unknown, ST could not be determined; -, not applicable.
Phylogenetic Analysis and Clinical Correlations
Analysis of 73 isolates identified 15 emm types and 14 STs (Figure 2), predominantly ST29, ST17, and ST127. Sequence type and emm type were strongly associated (P < .001; Cramér's V = 0.826). The superantigen speG was strictly lineage-dependent (P < .001; V = 1.000).
Figure 2.
Phylogenetic analysis of Streptococcus dysgalactiae subsp. equisimilis (SDSE) isolates from blood cultures at our institution. A molecular phylogenetic tree was constructed based on core genome single nucleotide polymorphisms (SNPs) identified from 73 clinical isolates, all from blood cultures, and 1 reference strain. Core SNPs were extracted using the SNIPPY pipeline implemented in GIGAdoc. The resulting core SNP alignment (core.aln) was used to infer an approximately maximum likelihood (ML) tree using FastTree (v2.1.11) under the general time reversible (GTR) model. The reliability of each branch was assessed using SH-like local support values based on 1000 resamples. The scale bar at the bottom left indicates the number of nucleotide substitutions per site. Metadata for each isolate are shown to the right of the tree. Columns indicate the following (from left to right): MLST: sequence type (ST) determined by multilocus sequence typing; emm type: emm gene sequence type; ●: presence of the respective gene; Re: recurrent case; SSTI: pink indicates isolates from patients with skin and soft tissue infections (SSTIs).
Identification of Common Virulence Factors
Most virulence genes (lmb, gapC, scpA, sagA, slo, ska, skc, nga, hylD) were highly conserved across all 73 isolates regardless of clinical outcome or recurrence. In contrast, speG and sicG distribution was variable.
DISCUSSION
This study integrates detailed clinical phenotyping with WGS to characterize SDSE bacteremia in a highly aged population. By combining comprehensive clinical data with molecular analysis of recurrent episodes, we demonstrate that SDSE bacteremia in this setting is associated with substantial genetic diversity and occurs predominantly in frail older adults with lower-extremity SSTIs. These findings suggest that host factor, rather than the emergence of a single dominant hypervirulent lineage, is an important determinant of disease occurrence and recurrence.
Streptococcus dysgalactiae subsp. equisimilis bacteremia in our cohort occurred at a median age of 86.0 years, exceeding reports from Sweden (75.0 years) [17] and Norway (GAS 53, GBS 64, SDSE 75 years) [18]. According to the World Health Organization [19], while life expectancy is comparable between Japan and these Northern European nations, Japan's demographic structure differs, characterized by a higher density of super-elderly individuals (≥80 years). Data from the United Nations [20] support that this difference inherently shifts the patient population baseline in Japanese clinical practice. Indeed, more than 70% of blood culture procedures at our hospital involved patients aged ≥60 years (Supplementary Figure 1). Consistent with these demographics, a recent Japanese nationwide surveillance reported a median age of 83 years for invasive SDSE infections [21]. Furthermore, our SDSE to GAS ratio (63:14) aligns with established Japanese epidemiology, where invasive SDSE infections historically outnumber invasive GAS infections [22]. Notably, our SDSE patients were older than those with other streptococcal (GAS, 79.0; GBS, 80.0) or common bacterial infections (E. coli, 81.0; K. pneumoniae, 80.0), suggesting that SDSE bacteremia is uniquely associated with the extreme end of the aging spectrum. Our 30-day mortality (4.8%) was lower than reports from Sweden (10%–12%) [17] and recent Japanese nationwide surveillance (12.2%) [21]. We hypothesize that this reflects our institutional policy of proactive, routine blood culture collection for mild or atypical symptoms, enabling the detection of less severe cases that might otherwise go undetected.
The susceptibility of super-elderly individuals to SDSE bacteremia is likely attributable to age-related skin barrier impairment [7]. Lower-limb infections accounted for 60.3% of initial episodes, predominantly (63.2%) in care-dependent patients (Table 2). This supports evidence [22] that lower limbs are a major entry portal in older individuals with hygiene and foot care challenges. Consistent with prior reports [7, 22–25], SDSE strongly associates with SSTIs in older adults, highlighting its importance in geriatric sepsis. Given the association with skin compromise and care dependency, targeted prevention is crucial. Preventive management should be stepwise, beginning with optimization of skin care and control of chronic edema and other predisposing factors. In patients with recurrent disease despite these measures, long-term antimicrobial prophylaxis (typically for up to 1 year) may be considered in accordance with current guideline recommendations [26], along with proactive dermatological interventions including meticulous skin care and the use of antiseptic washes (eg, chlorhexidine baths) [27].
The recurrence rate of SDSE bacteremia in this study was 9.5%, which falls within the previously reported range of 2%–10% [9, 12, 24, 25, 28, 29]. The median interval to recurrence was 261.5 days. Genotyping showed that 5 of the 10 recurrent episodes involved strains with STs and emm types identical to those of the corresponding index strains, supporting probable relapse, whereas the remaining episodes involved different genotypes, suggesting reinfection with distinct strains. Previous studies have similarly reported recovery of genetically identical strains in recurrent SDSE bacteremia [29], suggesting that recurrence often represents relapse caused by persistent colonization within the host rather than reinfection from external sources. Our findings suggest that recurrent SDSE bacteremia comprises both probable relapse caused by persistent colonization and reinfection with distinct strains. However, distinguishing these mechanisms remains challenging because of the limited resolution of molecular studies and interindividual differences in host susceptibility. Notably, in all recurrent episodes, the portal of entry involved the lower limbs, consistent with the findings in the corresponding index episodes. More than half of the patients with lower-limb portals of entry were care-dependent (Table 2) and predisposed to edema or chronic skin lesions. Previous studies have also reported higher recurrence rates among patients with lymphedema, skin ulcers, or chronic dermatological disorders [25, 29]. These pathophysiological changes, such as edema and impaired perfusion, may hinder the penetration of antibiotics into the target tissues [30]. The frequent involvement of lower-limb portals of entry, together with high rates of care dependency and genetically concordant isolates in probable relapse episodes, underscores the clinical importance of persistent bacterial colonization of the lower extremities, even after systemic antimicrobial therapy. Accordingly, prevention strategies should incorporate not only antimicrobial treatment but also local preventive interventions, including edema control and intensive skin care.
Streptococcus dysgalactiae subsp. equisimilis is a “typical causative microorganism” in the 2023 Duke-ISCVID IE criteria [31]. Our IE incidence (4.8%) aligns with prior estimates (3.0%–6.4%) [32, 33]. Given the aggressive nature of SDSE-associated IE [12], prompt echocardiographic evaluation remains essential.
In the present study, all SDSE isolates were uniformly susceptible to β-lactam antibiotics, and the resistance rates to macrolides and fluoroquinolones were consistent with previous epidemiological reports in Japan [11, 22, 34, 35]. Reflecting these susceptibility profiles, β-lactam therapy was highly effective in our clinical cohort, resulting in favorable clinical outcomes. These findings demonstrate that β-lactams remain reliable first-line therapeutic options for SDSE infections.
We found no significant association between specific emm types, including the stG6792 type that predominates in Japan [22], and clinical outcomes such as 30-day mortality. This finding is consistent with several previous studies [28, 36]. Although Hanada et al [34] reported significantly elevated serum creatinine and creatine kinase levels in fatal cases of invasive SDSE infection, we did not identify similar prognostic markers in our cohort, likely because of the limited number of fatal cases (N = 3). Therefore, the absence of significant associations between specific lineages or virulence genes and outcomes should be interpreted as reflecting limited statistical power rather than definitive negative evidence.
speG is a widely conserved superantigen that has the potential to contribute to severe invasive manifestations such as streptococcal toxic shock syndrome (STSS) [6, 10, 35, 37, 38]. In our cohort, we did not identify a statistically significant association between speG and clinical severity or STSS, although 1 patient with severe disease requiring limb amputation carried this gene.
This study has several limitations. First, the age-stratified analysis of bloodstream pathogen distribution was based on a microbiology laboratory database and therefore did not allow reliable differentiation between true bacteremia and blood culture contamination for all isolates. In older patients, contamination during blood collection may occur more frequently because of technical difficulties in obtaining venous access [39, 40]. Indeed, our dataset included several common skin commensals, including coagulase-negative staphylococci (n = 406) and Bacillus spp. (n = 32). Accordingly, we cannot completely exclude the possibility that such contaminants influenced the observed age distribution data. Second, because this was a single-center retrospective study conducted in Japan, the overall results may have been affected by regional demographic characteristics and institutional patient profiles. Furthermore, given Japan's uniquely aged society, the external validity of our findings beyond the Japanese setting may be limited. Therefore, the comparisons among the streptococcal species should be interpreted as descriptive observations within our specific cohort. In particular, because of the relatively small sample size, particularly for GAS bacteremia and fatal SDSE cases, several groupwise comparisons were underpowered. Consequently, the absence of statistical significance should not be interpreted as evidence of no clinically meaningful difference.
Despite limitations, consecutive patient inclusion minimized selection bias. Integrating clinical and genomic data enabled comprehensive analysis. Evaluating entry portals, care dependency, and molecular recurrence provided deep insights into SDSE epidemiology.
In conclusion, invasive SDSE predominantly affects older adults, with lower limbs serving as persistent portals driving recurrence, particularly in care-dependent patients. As populations age globally, the burden of SDSE bacteremia may increase. Although SDSE commonly colonizes the skin, it causes severe invasive disease (eg, recurrent infections, IE) upon breaching the fragile skin of older hosts. Therefore, comprehensive prevention, including meticulous skin and edema management, is essential to reduce its clinical burden.
Supplementary Material
Notes
Acknowledgments. We would like to thank the staff of the Microbiology Laboratory in the Department of Clinical Laboratory at Fujita Health University Okazaki Medical Center for their dedicated support in isolate preservation and provision. We also extend our gratitude to the Medical Affairs Division for providing essential data on the total number of inpatient days. Finally, we are deeply grateful to the other faculty members and students of the Department of Microbiology, Fujita Health University School of Medicine, for their invaluable guidance and support throughout the research and manuscript preparation.
Author contributions . T. M.: conceptualization, data curation, formal analysis, investigation, methodology, and writing—original draft; M. S.: conceptualization, data curation, validation, and writing—review and editing; T. H.: investigation and resources; Y. D.: conceptualization, resources, supervision, and writing—review and editing.
Declaration of AI and AI-assisted technologies . During the preparation of this work, the authors used NotebookLM and Gemini to generate illustrative elements, such as patient figures and medical icons, for the graphical abstract. After using this tool, the authors reviewed, assembled, and modified the visual content as needed. The authors take full responsibility for the accuracy, originality, and integrity of the final graphical abstract.
Data availability . The genomic sequences of the strains analyzed in this study are available in the DDBJ database under BioProject accession number PRJDB42266.
Ethics . The study was approved by the Institutional Review Board of Fujita Health University (HM26-218), which granted opt-out consent given the retrospective and noninvasive nature of the study.
Financial support . The study was supported by internal funding from Fujita Health University.
Contributor Information
Takahiro Matsuno, Department of Clinical Laboratory, Fujita Health University Okazaki Medical Center, Okazaki, Japan.
Masahiro Suzuki, Department of Microbiology, Fujita Health University School of Medicine, Toyoake, Japan.
Takuya Hosoda, Department of Microbiology, Fujita Health University School of Medicine, Toyoake, Japan.
Yohei Doi, Department of Microbiology, Fujita Health University School of Medicine, Toyoake, Japan; Department of Infectious Diseases, Fujita Health University School of Medicine, Toyoake, Japan; Center for Innovative Antimicrobial Therapy, Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
Supplementary Data
Supplementary materials are available at Open Forum Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.
References
- 1. Vandamme P, Pot B, Falsen E, Kersters K, Devriese LA. Taxonomic study of Lancefield streptococcal groups C, G, and L (Streptococcus dysgalactiae) and proposal of S. dysgalactiae subsp. equisimilis. subsp. nov. Int J Syst Bacteriol 1996; 46:774–81. [DOI] [PubMed] [Google Scholar]
- 2. Takahashi T, Ubukata K, Watanabe H. Invasive infection caused by Streptococcus dysgalactiae subsp. equisimilis: characteristics of strains and clinical features. J Infect Chemother 2011; 17:1–10. [DOI] [PubMed] [Google Scholar]
- 3. Broyles LN, Van Beneden C, Beall B, et al. Population-based study of invasive disease due to beta-hemolytic streptococci of groups other than A and B. Clin Infect Dis 2009; 48:706–12. [DOI] [PubMed] [Google Scholar]
- 4. Reißmann S, Friedrichs C, Rajkumari R, et al. Contribution of Streptococcus anginosus to infections caused by groups C and G streptococci, Southern India. Emerg Infect Dis 2010; 16:656–63. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Efstratiou A. Pyogenic streptococci of Lancefield groups C and G as pathogens in man. J Appl Microbiol 1997; 83(S1):72S–9S. [DOI] [PubMed] [Google Scholar]
- 6. Hashikawa S, Iinuma Y, Furushita M, et al. Characterization of group C and G streptococcal strains that cause streptococcal toxic shock syndrome. J Clin Microbiol 2004; 42:186–92. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Rantala S. Streptococcus dysgalactiae subsp. equisimilis bacteremia: an emerging infection. Eur J Clin Microbiol Infect Dis 2014; 33:1303–10. [DOI] [PubMed] [Google Scholar]
- 8. Fujita T, Yoshida H, Osaka S, et al. Comparison of characteristics of Streptococcus dysgalactiae subsp. equisimilis isolates causing repetitive vs single infections. Ann Lab Med 2019; 39:488–92. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Nevanlinna V, Huttunen R, Aittoniemi J, Luukkaala T, Rantala S. Incidence, seasonal pattern, and clinical manifestations of Streptococcus dysgalactiae subspecies equisimilis bacteremia; a population-based study. Eur J Clin Microbiol Infect Dis 2023; 42:819–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Shimomura Y, Okumura K, Murayama SY, et al. Complete genome sequencing and analysis of a Lancefield group G Streptococcus dysgalactiae subsp. equisimilis strain causing streptococcal toxic shock syndrome (STSS). BMC Genomics 2011; 12:17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Sunaoshi K, Murayama SY, Adachi K, et al. Molecular emm genotyping and antibiotic susceptibility of Streptococcus dysgalactiae subsp. equisimilis isolated from invasive and non-invasive infections. J Med Microbiol 2010; 59:82–8. [DOI] [PubMed] [Google Scholar]
- 12. Xie O, Davies MR, Tong SYC. Streptococcus dysgalactiae subsp. equisimilis infection and its intersection with Streptococcus pyogenes. Clin Microbiol Rev 2024; 37:e0017523. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. National Institute of Infectious Diseases . Streptococcal toxic shock syndrome, 2015–June 2025. Infect Agents Surveill Rep 2025; 46:175–90. Available at: https://id-info.jihs.go.jp/surveillance/iasr/index.html. Accessed 1 April 2026. [Google Scholar]
- 14. Katz S. Assessing self-maintenance: activities of daily living, mobility, and instrumental activities of daily living. J Am Geriatr Soc 1983; 31:721–7. [DOI] [PubMed] [Google Scholar]
- 15. Suzuki M. GIGAdoc. Available at: https://github.com/suzukimasahiro/gigadoc.
- 16. Poon AFY, Price MN, Dehal PS, Arkin AP. FastTree 2—approximately Maximum-likelihood trees for large alignments. PLoS One 2010; 5:e9490. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Blackberg A, Lundberg K, Svedevall S, Nilson B, Rasmussen M. Time to positivity of blood cultures in bloodstream infections with Streptococcus dysgalactiae and association with outcome. Infect Dis 2023; 55:333–9. [DOI] [PubMed] [Google Scholar]
- 18. Oppegaard O, Glambek M, Skutlaberg DH, Skrede S, Sivertsen A, Kittang BR. Streptococcus dysgalactiae bloodstream infections, Norway, 1999-2021. Emerg Infect Dis 2023; 29:260–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. World Health Organization . World health statistics 2023: monitoring health for the SDGs, sustainable development goals. Geneva: World Health Organization; 2023. [Google Scholar]
- 20. United Nations DoEaSA, Population Division . World Population Prospects 2024, Online Edition. Available at: https://population.un.org/wpp/.
- 21. Hanada S, Takata M, Morozumi M, et al. Invasive Streptococcus dysgalactiae subsp. equisimilis infections in a super-aged society: a Japanese nationwide observational study (2024-2025). BMC Infect Dis 2026; 26:1167. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Takahashi T, Sunaoshi K, Sunakawa K, Fujishima S, Watanabe H, Ubukata K. Clinical aspects of invasive infections with Streptococcus dysgalactiae ssp. equisimilis in Japan: differences with respect to Streptococcus pyogenes and Streptococcus agalactiae infections. Clin Microbiol Infect 2010; 16:1097–103. [DOI] [PubMed] [Google Scholar]
- 23. Loubinoux J, Plainvert C, Collobert G, Touak G, Bouvet A, Poyart C. Adult invasive and noninvasive infections due to Streptococcus dysgalactiae subsp. equisimilis in France from 2006 to 2010. J Clin Microbiol 2013; 51:2724–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Liao C-H, Liu L-C, Huang Y-T, Teng L-J, Hsueh P-R. Bacteremia caused by group G streptococci, Taiwan. Emerg Infect Dis 2008; 14:837–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Cohen-Poradosu R, Jaffe J, Lavi D, et al. Group G streptococcal bacteremia in Jerusalem. Emerg Infect Dis 2004; 10:1455–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Stevens DL, Bisno AL, Chambers HF, et al. Practice guidelines for the diagnosis and management of skin and soft tissue infections: 2014 update by the Infectious Diseases Society of America. Clin Infect Dis 2014; 59:147–59. [DOI] [PubMed] [Google Scholar]
- 27. Climo MW, Yokoe DS, Warren DK, et al. Effect of daily chlorhexidine bathing on hospital-acquired infection. N Engl J Med 2013; 368:533–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Rantala S, Vahakuopus S, Vuopio-Varkila J, Vuento R, Syrjanen J. Streptococcus dysgalactiae subsp. equisimilis bacteremia, Finland, 1995-2004. Emerg Infect Dis 2010; 16:843–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Trell K, Sendi P, Rasmussen M. Recurrent bacteremia with Streptococcus dysgalactiae: a case-control study. Diagn Microbiol Infect Dis 2016; 85:121–4. [DOI] [PubMed] [Google Scholar]
- 30. Ulldemolins M, Roberts JA, Rello J, Paterson DL, Lipman J. The effects of hypoalbuminaemia on optimizing antibacterial dosing in critically ill patients. Clin Pharmacokinet 2011; 50:99–110. [DOI] [PubMed] [Google Scholar]
- 31. Fowler VG, Durack DT, Selton-Suty C, et al. The 2023 Duke-international society for cardiovascular infectious diseases criteria for infective endocarditis: updating the modified Duke criteria. Clin Infect Dis 2023; 77:518–26. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Seo H, Hyun J, Kim H, et al. Risk and outcome of infective endocarditis in streptococcal bloodstream infections according to streptococcal Species. Microbiol Spectr 2023; 11:e0104923. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Chamat-Hedemand S, Dahl A, Ostergaard L, et al. Prevalence of infective endocarditis in streptococcal bloodstream infections is dependent on streptococcal Species. Circulation 2020; 142:720–30. [DOI] [PubMed] [Google Scholar]
- 34. Hanada S, Wajima T, Takata M, et al. Clinical manifestations and biomarkers to predict mortality risk in adults with invasive Streptococcus dysgalactiae subsp. equisimilis infections. Eur J Clin Microbiol Infect Dis 2024; 43:1609–19. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Wajima T, Morozumi M, Hanada S, et al. Molecular characterization of invasive Streptococcus dysgalactiae subsp. equisimilis, Japan. Emerg Infect Dis 2016; 22:247–54. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Xie O, Featherstone L, Nguyen ANT, et al. Invasive Streptococcus dysgalactiae subspecies equisimilis compared with Streptococcus pyogenes in Australia, 2011-23, and the emergence of a multi-continent stG62647 lineage: a retrospective clinical and genomic epidemiology study. Lancet Microbe 2025; 6:101182. [DOI] [PubMed] [Google Scholar]
- 37. Fraser JD, Proft T. The bacterial superantigen and superantigen-like proteins. Immunol Rev 2008; 225:226–43. [DOI] [PubMed] [Google Scholar]
- 38. Okumura K, Shimomura Y, Murayama SY, et al. Evolutionary paths of streptococcal and staphylococcal superantigens. BMC Genomics 2012; 13:404. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Dawson S. Blood culture contaminants. J Hosp Infect 2014; 87:1–10. [DOI] [PubMed] [Google Scholar]
- 40. Snyder SR, Favoretto AM, Baetz RA, et al. Effectiveness of practices to reduce blood culture contamination: a laboratory medicine best practices systematic review and meta-analysis. Clin Biochem 2012; 45:999–1011. [DOI] [PMC free article] [PubMed] [Google Scholar]
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