- Volume 3, Issue 10, 2017

The need for novel antibiotics in an era where antimicrobial resistance is on the rise, and the number of new approved antimicrobial drugs reaching the market is declining, is evident. The underused potential of post-translationally modified peptides for clinical use makes this class of peptides interesting candidates. In this study, we made use of the vast amounts of available genomic data and screened all publicly available prokaryotic genomes (~3000) to identify 394 novel head-to-tail cyclized antimicrobial peptides. To verify these in silico results, we isolated and characterized a novel antimicrobial peptide from Bacillus pumilus that we named pumilarin. Pumilarin was demonstrated to have a circular structure and showed antimicrobial activity against several indicator strains, including pathogens.

Pseudomonas aeruginosa is a ubiquitous opportunistic pathogen with several clones being frequently associated with outbreaks in hospital settings. ST395 is among these so-called ‘international’ clones. We aimed here to define the biological features that could have helped the implantation and spread of the clone ST395 in hospital settings. The complete genome of a multidrug resistant index isolate (DHS01) of a large hospital outbreak was analysed. We identified DHS01-specific genetic elements, among which were identified those shared with a panel of six independent ST395 isolates responsible for outbreaks in other hospitals. DHS01 has the fifth largest chromosome of the species (7.1 Mbp), with most of its 1555 accessory genes borne by either genomic islands (GIs, n=48) or integrative and conjugative elements (ICEs, n=5). DHS01 is multidrug resistant mostly due to chromosomal mutations. It displayed signatures of adaptation to chronic infection in part due to the loss of a 131 kbp chromosomal fragment. Four GIs were specific to the clone ST395 and contained genes involved in metabolism (GI-4), in virulence (GI-6) and in resistance to copper (GI-7). GI-7 harboured an array of six copper transporters and was shared with non-pathogenic Pseudomonas sp. retrieved from copper-contaminated environments. Copper resistance was confirmed phenotypically in all other ST395 isolates and possibly accounted for the spreading capability of the clone in hospital outbreaks, where water networks have been incriminated. This suggests that genes transferred from copper-polluted environments may have favoured the implantation and spread of the international clone P. aeruginosa ST395 in hospital settings.

Yersinia pseudotuberculosis is a Gram-negative intestinal pathogen of humans and has been responsible for several nationwide gastrointestinal outbreaks. Large-scale population genomic studies have been performed on the other human pathogenic species of the genus Yersinia, Yersinia pestis and Yersinia enterocolitica allowing a high-resolution understanding of the ecology, evolution and dissemination of these pathogens. However, to date no purpose-designed large-scale global population genomic analysis of Y. pseudotuberculosis has been performed. Here we present analyses of the genomes of 134 strains of Y. pseudotuberculosis isolated from around the world, from multiple ecosystems since the 1960s. Our data display a phylogeographic split within the population, with an Asian ancestry and subsequent dispersal of successful clonal lineages into Europe and the rest of the world. These lineages can be differentiated by CRISPR cluster arrays, and we show that the lineages are limited with respect to inter-lineage genetic exchange. This restriction of genetic exchange maintains the discrete lineage structure in the population despite co-existence of lineages for thousands of years in multiple countries. Our data highlights how CRISPR can be informative of the evolutionary trajectory of bacterial lineages, and merits further study across bacteria.

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.

Salmonella enterica and Escherichia coli are bacterial species that colonize different animal hosts with sub-types that can cause life-threatening infections in humans. Source attribution of zoonoses is an important goal for infection control as is identification of isolates in reservoir hosts that represent a threat to human health. In this study, host specificity and zoonotic potential were predicted using machine learning in which Support Vector Machine (SVM) classifiers were built based on predicted proteins from whole genome sequences. Analysis of over 1000 S. enterica genomes allowed the correct prediction (67 –90 % accuracy) of the source host for S. Typhimurium isolates and the same classifier could then differentiate the source host for alternative serovars such as S. Dublin. A key finding from both phylogeny and SVM methods was that the majority of isolates were assigned to host-specific sub-clusters and had high host-specific SVM scores. Moreover, only a minor subset of isolates had high probability scores for multiple hosts, indicating generalists with genetic content that may facilitate transition between hosts. The same approach correctly identified human versus bovine E. coli isolates (83 % accuracy) and the potential of the classifier to predict a zoonotic threat was demonstrated using E. coli O157. This research indicates marked host restriction for both S. enterica and E. coli, with only limited isolate subsets exhibiting host promiscuity by gene content. Machine learning can be successfully applied to interrogate source attribution of bacterial isolates and has the capacity to predict zoonotic potential.

Illumina sequencing platforms have enabled widespread bacterial whole genome sequencing. While Illumina data is appropriate for many analyses, its short read length limits its ability to resolve genomic structure. This has major implications for tracking the spread of mobile genetic elements, including those which carry antimicrobial resistance determinants. Fully resolving a bacterial genome requires long-read sequencing such as those generated by Oxford Nanopore Technologies (ONT) platforms. Here we describe our use of the ONT MinION to sequence 12 isolates of Klebsiella pneumoniae on a single flow cell. We assembled each genome using a combination of ONT reads and previously available Illumina reads, and little to no manual intervention was needed to achieve fully resolved assemblies using the Unicycler hybrid assembler. Assembling only ONT reads with Canu was less effective, resulting in fewer resolved genomes and higher error rates even following error correction with Nanopolish. We demonstrate that multiplexed ONT sequencing is a valuable tool for high-throughput bacterial genome finishing. Specifically, we advocate the use of Illumina sequencing as a first analysis step, followed by ONT reads as needed to resolve genomic structure.