Biology subjects

Heinz, E.

Publications and source records attributed to Heinz, E..

4 recordsLinked to original sources

bioRxiv2018

Widespread cytoplasmic polyadenylation programs asymmetry in the germline and early embryo

BACKGROUNDThe program of embryonic development is launched by selective activation of a silent maternal transcriptome. In Caenorhabditis elegans, nuclei of the adult germline are responsible for the synthesis of at least two distinct mRNA populations; those required for housekeeping functions, and those that program the oocyte-to-embryo transition. We mapped this separation by changes to the length-distribution of poly(A)-tails that depend on GLD-2 mediated cytoplasmic polyadenylation and its regulators genome-wide.\n\nRESULTSMore than 1000 targets of cytoplasmic polyadenylation were identified by differential polyadenylation. Amongst mRNA with the greatest dependence on GLD-2 were those encoding RNA binding proteins with known roles in spatiotemporal patterning such as mex-5 and pos-1. In General, the 3 UTR of GLD-2 targets were longer, contained cytosine-patches, and were enriched for non-standard polyadenylation-motifs. To identify the deadenylase that initiated transcript silencing, we depleted the known deadenylases in the gld-2(0) mutant background. Only the loss of CCF-1 suppressed the short-tailed phenotype of GLD-2 targets suggesting that in addition to its general role in RNA turnover, this is the major deadenylase for regulatory silencing of maternal mRNA. Analysis of poly(A)-tail length-change in the embryo lacking specific RNA-binding proteins revealed new candidates for asymmetric expression in the first embryonic divisions.\n\nCONCLUSIONThe concerted action of RNA binding proteins exquisitely regulates GLD-2 activity in space and time. We present our data as interactive web resources for a model where GLD-2 mediated cytoplasmic polyadenylation regulates target mRNA at each stage of worm germline and early embryonic development.

developmental biology↗

bioRxiv2018

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

The ability to distinguish between pathogens 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 program was demonstrated using 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.\n\nDATA SUMMARYO_LISource code for rPinecone is available on GitHub under the open source licence GNU GPL 3; (url: https://github.com/alexwailan/rpinecone).\nC_LIO_LINewick format files for both phylogenetic trees have been deposited in Figshare; (url: https://doi.org/10.6084/m9.figshare.7022558)\nC_LIO_LIGeographical analysis of the S. Typhi Dataset using Microreact is available at https://microreact.org/project/r1IqkrN1X.\nC_LIO_LIAccession numbers, meta data and sample lineage results of both datasets used in this paper are listed in the supplementary tables.\nC_LI\n\nI/We confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. {boxtimes}\n\nIMPACT STATEMENTWhole genome sequence data from bacterial pathogens is increasingly used in the epidemiological investigation of infectious disease, both in outbreak and endemic situations. However, distinguishing bacterial species which are both very similar and which are likely to come from a small geographical and temporal range presents a major technical challenge for epidemiologists. rPinecone was designed to address this challenge and utilises phylogenetic data to define lineages within bacterial populations that have limited variation. This approach is therefore of great interest to epidemiologists as it adds a further level of clarity above and beyond that which is offered by existing approaches which have not been designed to consider bacterial isolates containing variation that only transiently exist, but which is epidemiologically informative. rPinecone has the flexibility to be applied to multiple pathogens and has direct application for investigations of clinical outbreaks and endemic disease to understand transmission dynamics or geographical hotspots of disease.

genomics↗

bioRxiv2018

Emergence of carbapenem, beta-lactamase inhibitor and cefoxitin resistant lineages from a background of ESBL-producing Klebsiella pneumoniae and K. quasipneumoniae highlights different evolutionary mechanisms

Klebsiella pneumoniae is recognised as a major threat to public health, with increasing emergence of multidrug-resistant lineages including strains resistant to all available antibiotics. We present an in-depth analysis of 178 extended-spectrum beta-lactamase (ESBL)-producing Klebsiella strains, with a high background diversity and two dominant lineages, as well as several equally resistant lineages with less prevalence. Neither the overall resistance profile nor the virulence factors explain the prevalence of some lineages; we observe several putative hypervirulence factors across the population, including a reduced virulence plasmid, but this does not correlate with expansion of one or few highly virulent and resistant lineages. Phenotypic analysis of the profiles of resistance traits shows that the vast majority of the phenotypic resistance profiles can be explained by detailed genetic analyses. The main discrepancies are observed for beta-lactams combined with beta-lactamase inhibitors, where most, but not all, resistant strains carry a carbapenemase or ampC. Complete genomes for six selected strains, including three of the 21 carbapenem-resistant ones, are reported, which give detailed insights into the early evolution of the bla-NDM-1 enzyme, a carbapenemase that was first reported in 2009 and is now globally distributed. Whole-genome based high-resolution analyses of the dominant lineages suggests a very dynamic picture of gene transfer and selection, with phenotypic changes due to plasmid acquisition and chromosomal changes, and emphasize the need to monitor the bacteria at high resolution to understand the rise of high-risk clones, which cannot be explained by obvious differences in resistance profiles or virulence factors.\n\nImportanceCarbapenem-resistant and extended-spectrum beta-lactamase (ESBL) carrying Enterobacteriaceae were recently highlighted as critical priority fo the development of new treatments by the WHO. Klebsiella pneumoniae is a member of the Enterobacteriaceae and has seen a dramatic rise in clinical relevance due to its uncanny ability to accumulate multidrug-resistance plasmids. We present a detailed analysis of a set of ESBL-resistant K. pneumoniae clinical isolates, and our high-resolution whole-genome sequence analyses highlight that acquisition of drug resistances is not a one-way street in K. pneumoniae, but a highly dynamic process of gain and loss, and that the most successful lineages in the clinic are not necessarily the most resistant or most virulent ones. Analysis of the virulence potential also shows that these strains harbour some, but not all, hallmarks of hypervirulent strains, emphasizing that it is not a clear distinction between hypervirulent and other strains, but equally in flux.

genomics↗

bioRxiv2018

Kaptive Web: user-friendly capsule and lipopolysaccharide serotype prediction for Klebsiella genomes

As whole genome sequencing becomes an established component of the microbiologists toolbox, it is imperative that researchers, clinical microbiologists and public health professionals have access to genomic analysis tools for rapid extraction of epidemiologically and clinically relevant information. For the gram-negative hospital pathogens such as Klebsiella pneumoniae, initial efforts have focused on detection and surveillance of antimicrobial resistance genes and clones. However, with the resurgence of interest in alternative infection control strategies targeting Klebsiella surface polysaccharides, the ability to extract information about these antigens is increasingly important.\n\nHere we present Kaptive Web, an online tool for rapid typing of Klebsiella K and O loci, which encode the polysaccharide capsule and lipopolysaccharide O antigen, respectively. Kaptive Web enables users to upload and analyse genome assemblies in a web browser. Results can be downloaded in tabular format or explored in detail via the graphical interface, making it accessible for users at all levels of computational expertise.\n\nWe demonstrate Kaptive Webs utility by analysis of >500 K. pneumoniae genomes. We identify extensive K and O locus diversity among 201 genomes belonging to the carbapenemase- associated clonal group 258 (25 K and six O loci). Characterisation of a further 309 genomes indicates that such diversity is common among the multi-drug resistant clones and that these loci represent useful epidemiological markers for strain subtyping. These findings reinforce the need for rapid, reliable and accessible typing methods such as Kaptive Web.\n\nKaptive Web is available for use at kaptive.holtlab.net and source code is available at github.com/kelwyres/Kaptive-Web.

microbiology↗