Complete genome assemblies and antibiograms of 22 Staphylococcus capitis isolates

Abstract

Objective

Staphylococcus capitis is part of the human microbiome and an opportunistic pathogen known to cause catheter-associated bacteraemia, prosthetic joint infections, skin and wound infections, among others. Detection of S. capitis in normally sterile body sites saw an increase over the last decade in England, where a multidrug-resistant clone, NRCS-A, was widely identified in blood samples from infants in neonatal intensive care units. To address a lack of complete genomes and antibiograms of S. capitis in public databases, we performed long- and short-read whole-genome sequencing, hybrid genome assembly, and antimicrobial susceptibility testing of 22 diverse isolates.

Data description

We present complete genome assemblies of two S. capitis type strains (subspecies capitis: DSM 20326; subspecies urealyticus: DSM 6717) and 20 clinical isolates (NRCS-A: 10) from England. Each genome is accompanied by minimum inhibitory concentrations of 13 antimicrobials including vancomycin, teicoplanin, daptomycin, linezolid, and clindamycin. These 22 genomes were 2.4–2.7 Mbp in length and had a GC content of 33%. Plasmids were identified in 20 isolates. Resistance to teicoplanin, daptomycin, gentamicin, fusidic acid, rifampicin, ciprofloxacin, clindamycin, and erythromycin was seen in 1–10 isolates. Our data are a resource for future studies on genomics, evolution, and antimicrobial resistance of S. capitis.

Objective

Staphylococcus capitis, consisting of two subspecies capitis and urealyticus [1, 2], is a coagulase-negative opportunistic pathogen commonly causing late-onset sepsis (LOS) in very-low-birthweight infants in neonatal intensive care units (NICUs) and various infections in adults, such as prosthetic joint infections [3, 4]. A multidrug-resistant clone of S. capitis subsp. urealyticus, NRCS-A, has emerged as a global concern in neonatal health for its dominance in LOS, reduced susceptibility to vancomycin, enhanced biofilm-forming ability, increased disinfectant and desiccation tolerance, association with neonatal incubators, and persistence in NICUs [5,6,7]. Between 2020 and 2021, the UK Health Security Agency (UKHSA) convened a nationwide investigation into increased reporting of neonatal S. capitis bacteraemia in England [8] and requested voluntary referral of clinical isolates from diagnostic laboratories for whole-genome sequencing (WGS). Genomic epidemiological analysis of the WGS data revealed widespread presence of the NRCS-A clone in neonatal units across the country, highlighting the need to further understand genomics, antimicrobial resistance (AMR), and niche adaptation of this clone in comparison with other S. capitis subpopulations [9].

In this report, we describe complete genome assemblies and accompanying antibiograms of 22 S. capitis isolates consisting of 20 English clinical isolates and type strains of the two subspecies (capitis: DSM 20326; urealyticus: DSM 6717). The clinical isolates were selected from UKHSA’s culture collection to represent the main S. capitis subpopulations previously identified (Figure S1) [9], with 19 of these isolates recovered from normally sterile sites in patients across England. Readers are referred to Table S1 for source information of isolates. Altogether, 10 NRCS-A isolates and 12 non-NRCS-A isolates were sequenced. Our work addresses the lack of finished-grade S. capitis reference genomes and antibiograms in the National Center for Biotechnology Information (NCBI) databases, providing a bioresource for future studies on the genomics, AMR, molecular epidemiology, and evolution of S. capitis.

Data description

Each isolate was incubated on Columbia agar with horse blood (PB0122A, Thermo Scientific, UK) at 37 °C overnight for DNA extraction and antimicrobial susceptibility testing in 2021 (Batch 1, 19 isolates) or 2023 (Batch 2, three isolates) (Table S2). For Batch 1, genomic DNA of each isolate was extracted using GeneJET Genomic DNA Purification Kits (Thermo Scientific) and aliquoted. Long-read WGS was conducted with Oxford Nanopore Technologies (ONT, UK) MinION R9.4.1 flow-cells (FLO-MIN106D) and Rapid Barcoding Kits (SQK-RBK004). High-accuracy basecalling was performed with guppy (ONT). Short-read sequencing was performed on Illumina HiSeq 2500 systems at UKHSA following an in-house 2 × 101 bp protocol. For Batch 2, genomic DNA was extracted using a Wizard Genomic DNA Purification Kit (Promega, USA) and aliquoted. Long-read WGS was conducted with an ONT MinION R10.4.1 flow-cell (FLO-MIN114) and Rapid Barcoding Kit V14 (SQK-RBK114.24). Super-accuracy basecalling was performed using guppy. Short-read sequencing was performed under a 2 × 251 bp layout on Illumina NovaSeq 6000 systems at MicrobesNG (UK). Susceptibility of isolates to 13 antimicrobials was determined by the UKHSA Antimicrobial Resistance and Healthcare Associated Infections Reference Unit with gradient strips (Liofilchem, Italy, for Batch 1) or broth microdilution (EUSTAPF, Thermo Scientific, for Batch 2) following European Committee on Antimicrobial Susceptibility Testing (EUCAST) breakpoints v13.1. Inducible clindamycin resistance was sought by testing for the antagonism between erythromycin and clindamycin (D-test).

ONT reads were trimmed and filtered with fastp and nanoq [10, 11], respectively, for quality control. Illumina reads were trimmed and filtered with fastp. Hybrid assembly was performed for each genome using an ONT-reads-first strategy with Flye, Raven, and miniasm-minipolish, as implemented in Trycycler [12,13,14,15], when quality-processed ONT reads had an estimated depth of ≥ 60 folds and the assemblers produced consistent results; otherwise, Raven was used. When a genome could not be fully assembled from ONT reads, an Illumina-reads-first strategy was applied using Unicycler [16, 17]. All assemblies were polished with ONT reads using medaka (https://github.com/nanoporetech/medaka) followed by Illumina-read polishing using Polypolish and POLCA [18, 19]. Polished assemblies were then reoriented to start from dnaA (chromosomes) or repA (plasmids) genes using dnaapler [20]. Reoriented assemblies were polished with Illumina reads using Polypolish, assessed using Quast and CheckM2 [21, 22], and annotated with the NCBI Prokaryotic Genome Annotation Pipeline [23].

Twenty-two complete genome assemblies were generated, with the same GC content of 33% and lengths of 2.4–2.7 Mbp (Table S2). One to four plasmids (2.3–69 kbp) were identified in 20 isolates (NRCS-A: 9; non-NRCS-A: 11). The NRCS-A clone exhibited reduced susceptibility to teicoplanin, daptomycin, and gentamicin (Table S3). Resistance to fusidic acid (MICs: 16–32 mg/L), erythromycin (MIC > 256 mg/L), and clindamycin (MIC > 256 mg/L or inducible) was seen in 9/22 (41%), 7/22 (32%), and 5/22 (23%) of isolates, respectively, with no significant frequency difference between NRCS-A and non-NRCS-A isolates (p-value = 1, Fisher’s exact test). One NRCS-A isolate exhibited rifampicin resistance (MIC > 32 mg/L). All isolates were susceptible to vancomycin, linezolid, and quinupristin-dalfopristin, and seven non-NRCS-A isolates, including the two type strains, were susceptible to all 11 antimicrobials having EUCAST breakpoints.

Limitations

This dataset is limited to a small sample size (n = 22), which does not capture all major phenotypic and genetic variations in S. capitis. The isolates are limited to England and dominated by invasive isolates (n = 19) recovered from normally sterile body sites of humans — eight (42%) invasive isolates were collected from infants of ≤ 90 days of age, one (5%) from an infant between > 90 days and < 1 year of age, two (10%) from children between six and 11 years of age, and eight from adults (≥ 18 years of age). Moreover, antimicrobial susceptibility of all isolates in Batch 1 (n = 19) was not determined using the gold-standard method, broth microdilution, owing to technical unavailability. Future work needs to elucidate mechanisms of AMR [24] and include a wider range of isolates, such as those recovered from carriage screening, environments, animals, and other health-related samples from non-clinical settings.

Table 1 Overview of datafiles/datasets