Antimicrobial resistance (AMR) is one of the major threats to human and animal health worldwide, yet few high-throughput tools exist to analyse and predict the resistance of a bacterial isolate from sequencing data. Here we present a new tool, ARIBA, that identifies AMR-associated genes and single nucleotide polymorphisms directly from short reads, and generates detailed and customizable output. The accuracy and advantages of ARIBA over other tools are demonstrated on three datasets from Gram-positive and Gram-negative bacteria, with ARIBA outperforming existing methods.

Gonorrhoea and MDR Neisseria gonorrhoeae remain public health concerns globally. Enhanced, quality-assured, gonococcal antimicrobial resistance (AMR) surveillance is essential worldwide. The WHO global Gonococcal Antimicrobial Surveillance Programme (GASP) was relaunched in 2009. We describe the phenotypic, genetic and reference genome characteristics of the 2016 WHO gonococcal reference strains intended for quality assurance in the WHO global GASP, other GASPs, diagnostics and research worldwide. The 2016 WHO reference strains (n = 14) constitute the eight 2008 WHO reference strains and six novel strains. The novel strains represent low-level to high-level cephalosporin resistance, high-level azithromycin resistance and a porA mutant. All strains were comprehensively characterized for antibiogram (n = 23), serovar, prolyliminopeptidase, plasmid types, molecular AMR determinants, N. gonorrhoeae multiantigen sequence typing STs and MLST STs. Complete reference genomes were produced using single-molecule PacBio sequencing. The reference strains represented all available phenotypes, susceptible and resistant, to antimicrobials previously and currently used or considered for future use in gonorrhoea treatment. All corresponding resistance genotypes and molecular epidemiological types were described. Fully characterized, annotated and finished references genomes (n = 14) were presented. The 2016 WHO gonococcal reference strains are intended for internal and external quality assurance and quality control in laboratory investigations, particularly in the WHO global GASP and other GASPs, but also in phenotypic (e.g. culture, species determination) and molecular diagnostics, molecular AMR detection, molecular epidemiology and as fully characterized, annotated and finished reference genomes in WGS analysis, transcriptomics, proteomics and other molecular technologies and data analysis.

Human tuberculosis (TB) is caused by members of the Mycobacterium tuberculosis complex (MTBC). The MTBC comprises several human-adapted lineages known as M. tuberculosis sensu stricto, as well as two lineages (L5 and L6) traditionally referred to as Mycobacterium africanum . Strains of L5 and L6 are largely limited to West Africa for reasons unknown, and little is known of their genomic diversity, phylogeography and evolution. Here, we analysed the genomes of 350 L5 and 320 L6 strains, isolated from patients from 21 African countries, plus 5 related genomes that had not been classified into any of the known MTBC lineages. Our population genomic and phylogeographical analyses showed that the unclassified genomes belonged to a new group that we propose to name MTBC lineage 9 (L9). While the most likely ancestral distribution of L9 was predicted to be East Africa, the most likely ancestral distribution for both L5 and L6 was the Eastern part of West Africa. Moreover, we found important differences between L5 and L6 strains with respect to their phylogeographical substructure and genetic diversity. Finally, we could not confirm the previous association of drug-resistance markers with lineage and sublineages. Instead, our results indicate that the association of drug resistance with lineage is most likely driven by sample bias or geography. In conclusion, our study sheds new light onto the genomic diversity and evolutionary history of M. africanum , and highlights the need to consider the particularities of each MTBC lineage for understanding the ecology and epidemiology of TB in Africa and globally.

As whole-genome sequencing capacity becomes increasingly decentralized, there is a growing opportunity for collaboration and the sharing of surveillance data within and between countries to inform typhoid control policies. This vision requires free, community-driven tools that facilitate access to genomic data for public health on a global scale. Here we present the Pathogenwatch scheme for Salmonella enterica serovar Typhi (S. Typhi), a web application enabling the rapid identification of genomic markers of antimicrobial resistance (AMR) and contextualization with public genomic data. We show that the clustering of S. Typhi genomes in Pathogenwatch is comparable to established bioinformatics methods, and that genomic predictions of AMR are highly concordant with phenotypic susceptibility data. We demonstrate the public health utility of Pathogenwatch with examples selected from >4,300 public genomes available in the application. Pathogenwatch provides an intuitive entry point to monitor of the emergence and spread of S. Typhi high risk clones.