Draft Genome Sequence of NDM-5-Producing Escherichia coli Sequence Type 648 and Genetic Context of blaNDM-5 in Australia

We report here the draft genome sequence of uropathogenic Escherichia\ua0coli sequence type 648 (ST648) possessing blaNDM-5 from a 55-year-old female in Australia with a history of travel to India. The plasmid-mediated blaNDM-5 was in a genetic context nearly identical to that of the GenBank entry of an IncX3 blaNDM-5 plasmid previously reported from India (Klebsiella\ua0pneumoniae MGR-K194)

rPinecone : Define sub-lineages of a clonal expansion via a phylogenetic tree

The ability to distinguish different circulating pathogen clones from each other is a fundamental requirement to understand the epidemiology of infectious diseases. Phylogenetic analysis of genomic data can provide a powerful platform to identify lineages within bacterial populations, and thus inform outbreak investigation and transmission dynamics. However, resolving differences between pathogens associated with low-variant (LV) populations carrying low median pairwise single nucleotide variant (SNV) distances remains a major challenge. Here we present rPinecone, an R package designed to define sub-lineages within closely related LV populations. rPinecone uses a root-to-tip directional approach to define sub-lineages within a phylogenetic tree according to SNV distance from the ancestral node. The utility of this software was demonstrated using both simulated outbreaks and real genomic data of two LV populations: a hospital outbreak of methicillin-resistant Staphylococcus aureus and endemic Salmonella Typhi from rural Cambodia. rPinecone identified the transmission branches of the hospital outbreak and geographically confined lineages in Cambodia. Sub-lineages identified by rPinecone in both analyses were phylogenetically robust. It is anticipated that rPinecone can be used to discriminate between lineages of bacteria from LV populations where other methods fail, enabling a deeper understanding of infectious disease epidemiology for public health purposes.Peer reviewe

PlasmidTron: assembling the cause of phenotypes and genotypes from NGS data.

Increasingly rich metadata are now being linked to samples that have been whole-genome sequenced. However, much of this information is ignored. This is because linking this metadata to genes, or regions of the genome, usually relies on knowing the gene sequence(s) responsible for the particular trait being measured and looking for its presence or absence in that genome. Examples of this would be the spread of antimicrobial resistance genes carried on mobile genetic elements (MGEs). However, although it is possible to routinely identify the resistance gene, identifying the unknown MGE upon which it is carried can be much more difficult if the starting point is short-read whole-genome sequence data. The reason for this is that MGEs are often full of repeats and so assemble poorly, leading to fragmented consensus sequences. Since mobile DNA, which can carry many clinically and ecologically important genes, has a different evolutionary history from the host, its distribution across the host population will, by definition, be independent of the host phylogeny. It is possible to use this phenomenon in a genome-wide association study to identify both the genes associated with the specific trait and also the DNA linked to that gene, for example the flanking sequence of the plasmid vector on which it is encoded, which follows the same patterns of distribution as the marker gene/sequence itself. We present PlasmidTron, which utilizes the phenotypic data normally available in bacterial population studies, such as antibiograms, virulence factors, or geographical information, to identify traits that are likely to be present on DNA that can randomly reassort across defined bacterial populations. It is also possible to use this methodology to associate unknown genes/sequences (e.g. plasmid backbones) with a specific molecular signature or marker (e.g. resistance gene presence or absence) using PlasmidTron. PlasmidTron uses a k-mer-based approach to identify reads associated with a phylogenetically unlinked phenotype. These reads are then assembled de novo to produce contigs in a fast and scalable-to-large manner. PlasmidTron is written in Python 3 and is available under the open source licence GNU GPL3 from https://github.com/sanger-pathogens/plasmidtron

PlasmidTron: assembling the cause of phenotypes from NGS data

AbstractWhen defining bacterial populations through whole genome sequencing (WGS) the samples often have detailed associated metadata that relate to disease severity, antimicrobial resistance, or even rare biochemical traits. When comparing these bacterial populations, it is apparent that some of these phenotypes do not follow the phylogeny of the host i.e. they are genetically unlinked to the evolutionary history of the host bacterium. One possible explanation for this phenomenon is that the genes are moving independently between hosts and are likely associated with mobile genetic elements (MGE). However, identifying the element that is associated with these traits can be complex if the starting point is short read WGS data. With the increased use of next generation WGS in routine diagnostics, surveillance and epidemiology a vast amount of short read data is available and these types of associations are relatively unexplored. One way to address this would be to perform assembly de novo of the whole genome read data, including its MGEs. However, MGEs are often full of repeats and can lead to fragmented consensus sequences. Deciding which sequence is part of the chromosome, and which is part of a MGE can be ambiguous. We present PlasmidTron, which utilises the phenotypic data normally available in bacterial population studies, such as antibiograms, virulence factors, or geographic information, to identify sequences that are likely to represent MGEs linked to the phenotype. Given a set of reads, categorised into cases (showing the phenotype) and controls (phylogenetically related but phenotypically negative), PlasmidTron can be used to assemble de novo reads from each sample linked by a phenotype. A k-mer based analysis is performed to identify reads associated with a phylogenetically unlinked phenotype. These reads are then assembled de novo to produce contigs. By utilising k-mers and only assembling a fraction of the raw reads, the method is fast and scalable to large datasets. This approach has been tested on plasmids, because of their contribution to important pathogen associated traits, such as AMR, hence the name, but there is no reason why this approach cannot be utilized for any MGE that can move independently through a bacterial population. PlasmidTron is written in Python 3 and available under the open source licence GNU GPL3 from https://github.com/sanger-pathogens/plasmidtron.DATA SUMMARYSource code for PlasmidTron is available from Github under the open source licence GNU GPL 3; (url - https://goo.gl/ot6rT5)Simulated raw reads files have been deposited in Figshare; (url - https://doi.org/10.6084/m9.figshare.5406355.vl)Salmonella enterica serovar Weltevreden strain VNS10259 is available from GenBank; accession number GCA_001409135.Salmonella enterica serovar Typhi strain BL60006 is available from GenBank; accession number GCA_900185485.Accession numbers for all of the Illumina datasets used in this paper are listed in the supplementary tables.I/We confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. ⊠IMPACT STATEMENTPlasmidTron utilises the phenotypic data normally available in bacterial population studies, such as antibiograms, virulence factors, or geographic information, to identify sequences that are likely to represent MGEs linked to the phenotype.</jats:sec

Global prevalence of carbapenem resistance in neutropenic patients and association with mortality and carbapenem use: systematic review and meta-analysis

Background: Carbapenem-resistant Gram-negative bacteria are recognized as a cause of difficult-to-treat infections associated with high mortality. Objectives: To perform a systematic review of currently available data on distribution, characteristics and outcome associated with carbapenem-resistant bloodstream infections in adult neutropenic patients. Methods: Included studies were identified through Medline, Embase and Cochrane databases between January 1995 and April 2016. Random effect meta-analysis was used to quantify the association between carbapenem resistance and mortality and between carbapenem exposure and resistance. Results: A total of 30 studies from 21 countries were included. Overall carbapenem resistance varied from 2% to 53% (median 9%) among studies. Infections due to carbapenem-resistant Pseudomonas spp. were reported in 18 (60%) studies showing high median resistance rates (44% of all carbapenem-resistant Gram-negatives and 19% of Pseudomonas isolates). Resistance of Enterobacteriaceae was less commonly reported and bloodstream infections due to carbapenem-resistant Klebsiella spp. were mainly documented from endemic areas (Greece, Italy, Israel). Carbapenem resistance in Acinetobacter spp. was reported in 9 (30%) studies (median resistance 58% of Acinetobacter isolates). Mortality rates ranged from 33% to 71% (median 50%) in patients with carbapenem-resistant infections. Carbapenemresistance appeared to correlate with mortality (OR 4.89, 95% CI 3.30-7.26) and previous exposure to carbapenems (OR 4.63, 95% CI 3.08-6.96). Conclusions: Carbapenem resistance represents a threat to neutropenic patients. In this group, resistance is likely promoted by previous carbapenem use and leads to high mortality rates. The knowledge of resistance patterns is crucial and can direct clinicians in the use of alternatives to carbapenem-based regimens

Mechanisms involved in acquisition of blaNDM genes by IncA/C2 and IncFIIY plasmids

blaNDM genes confer carbapenem resistance and have been identified on transferable plasmids belonging to different incompatibility (Inc) groups. Here we present the complete sequences of four plasmids carrying a blaNDM gene, pKP1-NDM-1, pEC2-NDM-3, pECL3-NDM-1, and pEC4-NDM-6, from four clinical samples originating from four different patients. Different plasmids carry segments that align to different parts of the blaNDM region found on Acinetobacter plasmids. pKP1-NDM-1 and pEC2-NDM-3, from Klebsiella pneumoniae and Escherichia coli, respectively, were identified as type 1 IncA/C2 plasmids with almost identical backbones. Different regions carrying blaNDM are inserted in different locations in the antibiotic resistance island known as ARI-A, and ISCR1 may have been involved in the acquisition of blaNDM-3 by pEC2-NDM-3. pECL3-NDM-1 and pEC4-NDM-6, from Enterobacter cloacae and E. coli, respectively, have similar IncFIIY backbones, but different regions carrying blaNDM are found in different locations. Tn3-derived inverted-repeat transposable elements (TIME) appear to have been involved in the acquisition of blaNDM-6 by pEC4-NDM-6 and the rmtC 16S rRNA methylase gene by IncFIIY plasmids. Characterization of these plasmids further demonstrates that even very closely related plasmids may have acquired blaNDM genes by different mechanisms. These findings also illustrate the complex relationships between antimicrobial resistance genes, transposable elements, and plasmids and provide insights into the possible routes for transmission of blaNDM genes among species of the Enterobacteriaceae family

Species identification within Acinetobacter calcoaceticus-baumannii complex using MALDI-TOF MS

Acinetobacter baumannii, one of the more clinically relevant species in the Acinetobacter genus is well known to be multi-drug resistant and associated with bacteremia, urinary tract infection, pneumonia, wound infection and meningitis. However, it cannot be differentiated from closely related species such as Acinetobacter calcoaceticus, Acinetobacter pittii and Acinetobacter nosocomialis by most phenotypic tests and can only be differentiated by specific, time consuming genotypic tests with very limited use in clinical microbiological laboratories. As a result, these species are grouped into the A. calcoaceticus-. A. baumannii (Acb) complex. Herein we investigated the mass spectra of 73 Acinetobacter spp., representing ten different species, using an AB SCIEX 5800 MALDI-TOF MS to differentiate members of the Acinetobacter genus, including the species of the Acb complex. RpoB gene sequencing, 16S rRNA sequencing, and gyrB multiplex PCR were also evaluated as orthogonal methods to identify the organisms used in this study. We found that whilst 16S rRNA and rpoB gene sequencing could not differentiate A. pittii or A. calcoaceticus, they can be differentiated using gyrB multiplex PCR and MALDI-TOF MS. All ten Acinetobacter species investigated could be differentiated by their MALDI-TOF mass spectra

Contrasting patterns of longitudinal population dynamics and antimicrobial resistance mechanisms in two priority bacterial pathogens over 7 years in a single center

Abstract: Background: Two of the most important pathogens contributing to the global rise in antimicrobial resistance (AMR) are Klebsiella pneumoniae and Enterobacter cloacae. Despite this, most of our knowledge about the changing patterns of disease caused by these two pathogens is based on studies with limited timeframes that provide few insights into their population dynamics or the dynamics in AMR elements that they can carry. Results: We investigate the population dynamics of two priority AMR pathogens over 7 years between 2007 and 2012 in a major UK hospital, spanning changes made to UK national antimicrobial prescribing policy in 2007. Between 2006 and 2012, K. pneumoniae showed epidemiological cycles of multi-drug-resistant (MDR) lineages being replaced approximately every 2 years. This contrasted E. cloacae where there was no temporally changing pattern, but a continuous presence of the mixed population. Conclusions: The differing patterns of clonal replacement and acquisition of mobile elements shows that the flux in the K. pneumoniae population was linked to the introduction of globally recognized MDR clones carrying drug resistance markers on mobile elements. However, E. cloacae carries a chromosomally encoded ampC conferring resistance to front-line treatments and shows that MDR plasmid acquisition in E. cloacae was not indicative of success in the hospital. This led to markedly different dynamics in the AMR populations of these two pathogens and shows that the mechanism of the resistance and its location in the genome or mobile elements is crucial to predict population dynamics of opportunistic pathogens in clinical settings