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High-level resistance and treatment failures with ceftriaxone and azithromycin, the first-line agents for gonorrhoea treatment are reported and antimicrobial-resistant Neisseria gonorrhoeae is an urgent public health threat. Our aims were to determine antimicrobial resistance rates, resistance determinants and phylogeny of N. gonorrhoeae in Ireland, 2014-2016. Overall, 609 isolates from four University Hospitals were tested for susceptibility to extended-spectrum cephalosporins (ESCs) and azithromycin by the MIC Test Strips. Forty-three isolates were whole-genome sequenced based on elevated MICs. The resistance rate to ceftriaxone, cefixime, cefotaxime and azithromycin was 0, 1, 2.1 and 19%, respectively. Seven high-level azithromycin-resistant (HLAzi-R) isolates were identified, all susceptible to ceftriaxone. Mosaic penA alleles XXXIV, X and non-mosaic XIII, and G120K plus A121N/D/G (PorB1b), H105Y (MtrR) and A deletion (mtrR promoter) mutations, were associated with elevated ESC MICs. A2059G and C2611T mutations in 23S rRNA were associated with HLAzi-R and azithromycin MICs of 4-32 mg/L, respectively. The 43 whole-genome sequenced isolates belonged to 31 NG-MAST STs. All HLAzi-R isolates belonged to MLST ST1580 and some clonal clustering was observed; however, the isolates differed significantly from the published HLAzi-R isolates from the ongoing UK outbreak. There is good correlation between previously described genetic antimicrobial resistance determinants and phenotypic susceptibility categories for ESCs and azithromycin in N. gonorrhoeae. This work highlights the advantages and potential of whole-genome sequencing to be applied at scale in the surveillance of antibiotic resistant strains of N. gonorrhoeae, both locally and internationally.

Abstract Today, thanks to advances in cloud computing, it is possible for small teams of software developers to produce internet-scale products, a feat that was previously the preserve of large organizations. Herein, we describe how these advances in software engineering can be made more readily available to bioinformaticians. In the same way that cloud computing has democratized access to distributed systems engineering for generalist software engineers, access to scalable and reproducible bioinformatic engineering can be democratized for generalist bioinformaticians and biologists. We present solutions, based on our own efforts, to achieve this goal.

The way in which computer code is perceived and used in biological research has been a source of some controversy and confusion, and has resulted in sub-optimal outcomes related to reproducibility, scalability and productivity. We suggest that the confusion is due in part to a misunderstanding of the function of code when applied to the life sciences. Code has many roles, and in this paper we present a three-dimensional taxonomy to classify those roles and map them specifically to the life sciences. We identify a “sweet spot” in the taxonomy—a convergence where bioinformaticians should concentrate their efforts in order to derive the most value from the time they spend using code. We suggest the use of the “inverse Conway maneuver” to shape a research team so as to allow dedicated software engineers to interface with researchers working in this “sweet spot.” We conclude that in order to address current issues in the use of software in life science research such as reproducibility and scalability, the field must reevaluate its relationship with software engineering, and adapt its research structures to overcome current issues in bioinformatics such as reproducibility, scalability and productivity.

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