Computational protocol: Antimicrobial resistance in Staphylococcus pseudintermedius and the molecular epidemiology of methicillin-resistant S. pseudintermedius in small animals in Finland

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Protocol publication

[…] Susceptibility data for clinical S. pseudintermedius isolates (screening specimens excluded) were analysed using WHONET (v. 5.6, WHO). Non-susceptibility percentages, including resistant and intermediate isolates, with 95% CIs, were calculated and presented separately for MRSP, methicillin-susceptible S. pseudintermedius (MSSP) and all S. pseudintermedius isolates. CLSI breakpoints were used,, except for fusidic acid, for which a non-susceptibility breakpoint of ≤23 mm was used. Yearly trends for non-susceptibility percentages were plotted and trends were investigated using a Cochran–Armitage trend test for each antimicrobial. The statistical difference in non-susceptibilities between MRSP and MSSP was investigated using Pearson’s χ test based on the WHONET output. P values <0.05 were considered statistically significant. Analyses were performed with the SAS® System for Windows (v. 9.3, USA). To calculate the number of MDR isolates (resistance to at least three antimicrobial classes), macrolide and lincosamide resistance was pooled due to common MLSB resistance (macrolide, lincosamide and streptogramin B).The proportions of MRSP in clinical and screening specimens were calculated for dogs and cats in order to derive crude prevalence estimates for MRSP in dogs and cats seeking veterinary care, from which microbiological specimens are obtained, and in high-risk populations, respectively. Patients in the latter populations have previously identified risk factors for MRSP, such as frequent antimicrobial exposure, chronic or intermittent infection, such as pyoderma, surgical site infection or previous exposure to MRSP (either in hospital or family). Screening is targeted at these patients.To compare the genetic relatedness of STs, the allele sequences for each ST were added back-to-back (ack, cpn60, fdh, pta, purA, sar, tuf). The resulting sequences were aligned and a phylogenetic tree was inferred by the Bayesian Markov Chain Monte Carlo (MCMC) method implemented in BEAST (v. 1.7.2). Each run was continued until the effective sample size (ESS) was >200. Posterior probabilities were calculated with a 10% burn-in and values >0.7 were considered significant. Results were visualized in FigTree (v. 1.40). Additionally, goeBURST (v 1.2.1) software was used for population structure analysis of STs. Analysis was conducted at double- and triple-locus variant levels. Single- and double-locus variants of previously described clonal complexes (CCs) were assigned to that CC. The number of isolates per ST or CC per year was calculated based on the specimen collection date.For analysis of predictors for MRSP, data were analysed separately for clinical S. pseudintermedius isolates and screening specimens by logistic regression with MRSP as the outcome variable. As data from the MRSP outbreak at the Veterinary Teaching Hospital of the University of Helsinki (VTH) in 2010–11 were likely to skew the results, data for this period were omitted. Due to the low number of cats in the data (n = 18 for clinical specimens and zero positive out of 145 for screening specimens), these, as well as specimens from unknown species (n = 11), were omitted from the analyses. ORs with 95% CI and P values were calculated for each variable. Variables with a P value ≤0.2 in the univariable analysis were included in the multivariable analysis. Multivariable logistic regression was performed using a backward step (Wald) method. P values <0.05 were considered statistically significant in the final model. Analyses were performed using SPSS v. 24 (IBM Inc.). […]

Pipeline specifications

Software tools BEAST, FigTree, BURST
Applications Phylogenetics, WGS analysis
Organisms Staphylococcus pseudintermedius, Canis lupus familiaris, Homo sapiens
Chemicals Methicillin