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Staphylococcus saprophyticus is a primary cause of community-acquired urinary tract infections (UTIs) in young women. S. saprophyticus colonizes humans and animals but basic features of its molecular epidemiology are undetermined. We conducted a phylogenomic analysis of 321 S. saprophyticus isolates collected from human UTIs worldwide during 1997–2017 and 232 isolates from human UTIs and the pig-processing chain in a confined region during 2016–2017. We found epidemiologic and genomic evidence that the meat-production chain is a major source of S. saprophyticus causing human UTIs; human microbiota is another possible origin. Pathogenic S. saprophyticus belonged to 2 lineages with distinctive genetic features that are globally and locally disseminated. Pangenome-wide approaches identified a strong association between pathogenicity and antimicrobial resistance, phages, platelet binding proteins, and an increased recombination rate. Our study provides insight into the origin, transmission, and population structure of pathogenic S. saprophyticus and identifies putative new virulence factors.

Staphylococcus saprophyticus is the cause of uncomplicated urinary tract infection (UTI) in 10%–20% of young women (1). Despite a greater successful treatment rate, S. saprophyticus UTI has a higher recurrent infection frequency than Escherichia coli UTI (2). Rare complications of S. saprophyticus UTI include acute pyelonephritis, nephrolithiasis, and endocarditis (3).

S. saprophyticus frequently colonizes humans and can be found in the gastrointestinal tract, vagina, and perineum (4). S. saprophyticus also is part of the gut and rectal flora of livestock, including pigs and cattle, and a frequent contaminant of meat and fermented food products (1). S. saprophyticus also has been recovered from polluted aquatic environments (5).

The reservoirs of S. saprophyticus causing UTI in humans are believed to be endogenous, but evidence is lacking. Moreover, given the frequent bacterial contamination of meat through the meat-processing chain, meat and food are speculated to be sources of human gut colonization and human S. saprophyticus infection (6). Some studies have reported high genetic diversity among isolates from human infections, food products, and other sources (5,7,8). However, previous studies were performed with a limited number of isolates, which prevented the description of the global and local molecular epidemiology of S. saprophyticus.

We used phenotypic, genomic, and pangenome-wide association study (pan-GWAS) approaches to characterize S. saprophyticus both globally and locally. In addition, we identified adaptive features that drive S. saprophyticus evolution, defined the S. saprophyticus population structure, investigated dissemination routes, and identified new pathogenicity factors.

The human isolates used in our study were recovered as part of routine clinical diagnostic testing; thus, ethics approval and informed consent were not required. All data were handled anonymously. Sample collection was in accordance with the European Parliament and Council decision for the epidemiologic surveillance and control of communicable disease through the European Antimicrobial Resistance Surveillance Network (https://www.ecdc.europa.eu/en/activities/surveillance/EARS-Net/Pages/index.aspx). Slaughterhouse samples were part of the routine control practices for evaluation of good hygiene practices and programs to assure meat safety (European Parliament and Council regulation no. 853/2004).

The global S. saprophyticus collection we used included 299 isolates from humans collected in 7 countries during 1997–2017: 286 from UTIs, 12 from invasive disease, and 1 from colonization (Appendix 1 Table 1). We also analyzed the genomes of S. saprophyticus for 38 isolates from 5 other countries: 35 isolates from human UTIs (8), 2 from human hand swabs (8), an isolate from Byzantine Troy (8), and ATCC 15305 (9), a previously investigated human UTI-causing isolate.

The local collection included isolates collected in Lisbon, Portugal, during 2016–2017: 128 human UTI isolates collected in 3 hospitals and 104 slaughterhouse isolates collected from equipment, pork samples, workers’ hands, and a pig’s rectum. In addition, we included 5 isolates from animals and 12 isolates from food used in other studies (8) (Appendix 2).

Figure 1

Figure 1. Maximum-likelihood tree of Staphylococcus saprophyticusisolates recovered from human infections and colonization globally, 1997–2017. The tree was constructed by using 9,134 SNPs without recombination. Among analyzed isolates, 321 were...

Figure 4

Figure 4. Growth rate of Staphylococcus saprophyticusclonal lineages in tryptic soy broth (TSB) and in different concentrations of female sex hormones. All assays were performed in triplicate and each experiment...

We performed whole-genome sequencing (WGS) on MiSeq (Illumina, https://www.illumina.com) and MinIon nanopore (Oxford Nanopore, https://nanoporetech.com) platforms, as described (10) (Appendix 2). We separately analyzed global population and local epidemiology of S. saprophyticus and their phylogeny by using single-nucleotide polymorphisms (SNPs). We identified SNPs by mapping the draft genomes to a reference genome, S. saprophyticus ATCC 15305 (GenBank accession no. AP008934.1) by using the web-based CSI phylogeny version 1.4 (11) with the default parameter, but we disabled the minimum distance between SNPs in the parameter. We used Gubbins version 2.3.4 (12) with default parameters to concatenate SNPs and remove recombination regions. We reconstructed the phylogenies by using RAxML version 8.2.4 (https://github.com/stamatak/standard-RAxML) and generalized time-reversible nucleotide substitution with gamma correction model with 100 bootstrap value. We visualized the maximum-likelihood trees by using Interactive Tree of Life (ihttps://itol.embl.de) (Figures 1–3; Appendix 2 Figure 3). Recombination to mutation (r/m) ratios detected by using Gubbins were calculated as the average r/m of isolates in the entire collection and separately for each lineage by using as reference closed genomes of KS40 for lineage G and KS160 for lineage S, both obtained on the MinIon platform (Oxford Nanopore).

We used Prokka version 1.13 (https://vicbioinformatics.com/software.prokka.shtml) to annotate genomes and defined the pangenome by using 85% blastp (https://blast.ncbi.nlm.nih.gov/Blast.cgi) identity in Roary version 3.12 (http://sanger-pathogens.github.io/Roary). We performed GWAS by using Scoary version 1.6.16 (13) to identify genes associated with lineages and considered Bonferroni corrected p1) statistically significant; we identified genes associated with epidemiologic groups and considered Benjamini Hochberg corrected and pairwise comparison p90% nucleotide identity and >60% coverage to be present.

We used Prism 6.0 (GraphPad, https://www.graphpad.com) to compare the means of 2 groups. We used a 2-tailed unpaired Mann-Whitney test or χ2 test for comparison and considered p

Figure 5. Genetic determinants that contribute to the distinction of clonal lineages and lifestyle of Staphylococcus saprophyticus. The graph displays determinants that contribute (A) and mediate (B) adaptation of ...

To further compare the 2 lineages, we constructed the pangenome of the 338 human S. saprophyticus genomes and identified 10,222 genes with 85% blastp clustering by using Roary. Among these, 8,351 genes, present in