Biology subjects

Luhmann, N.

Publications and source records attributed to Luhmann, N..

4 recordsLinked to original sources

bioRxiv2021

Role of the mobilome in the global dissemination of the carbapenem resistance gene blaNDM

The mobile resistance gene blaNDM encodes the NDM enzyme which hydrolyses carbapenems, a class of antibiotics used to treat some of the most severe bacterial infections. blaNDM is globally distributed across a variety of Gram-negative bacteria on multiple plasmids, typically located within a highly recombining and transposon-rich genomic region. This complexity means the dynamics underlying the dissemination of blaNDM remain poorly resolved. In this work, we compile a dataset of over 6000 bacterial genomes harbouring the blaNDM gene including 104 newly generated PacBio hybrid assemblies from clinical and livestock associated isolates across China. We develop a novel computational approach to track structural variants surrounding blaNDM in bacterial genomes. This allows us to identify the prevalent genomic contexts of blaNDM and reconstruct the key mobile genetic elements and events in its global spread. We estimate that blaNDM emerged on a Tn125 transposon before 1985 but only reached a global prevalence around a decade after its first recorded observation in 2005. We find that the Tn125 transposon played an important role in early plasmid-mediated jumps of blaNDM but was overtaken by other elements in recent years including IS26-flanked pseudo-composite transposons and Tn3000. Lastly, we observe a notable correlation between plasmid backbones bearing blaNDM and the sampling location of isolates. This observation suggests that the dissemination of resistance genes is mainly driven by successive between-plasmid transposon jumps, with plasmid exchange much more restricted due to the adaptation of plasmids to specific bacterial hosts.

microbiology↗

bioRxiv2020

BlastFrost: Fast querying of 100,000s of bacterial genomes in Bifrost graphs

BlastFrost is a highly efficient method for querying 100,000s of genome assemblies. It builds on Bifrost, a recently developed dynamic data structure for compacted and colored de Bruijn graphs from bacterial genomes. BlastFrost queries a Bifrost data structure for sequences of interest, and extracts local subgraphs, thereby enabling the efficient identification of the presence or absence of individual genes or single nucleotide sequence variants. Here we describe the algorithms and implementation of BlastFrost. We also present two exemplar practical applications. In the first, we determined the presence of the individual genes within the SPI-2 Salmonella pathogenicity island within a collection of 926 representative genomes in minutes. In the second application, we determined the existence of known single nucleotide polymorphisms associated with fluoroquinolone resistance in the genes gyrA, gyrB and parE among 190, 209 Salmonella genomes. BlastFrost is available for download at https://github.com/nluhmann/BlastFrost.

bioinformatics↗

bioRxiv2017

GrapeTree: Visualization of core genomic relationships among 100,000 bacterial pathogens

O_LICurrent methods struggle to reconstruct and visualise the genomic relationships of [≥]100,000 bacterial genomes.\nC_LIO_LIGrapeTree facilitates the analyses of allelic profiles from 10,000s of core genomes within a web browser window.\nC_LIO_LIGrapeTree implements a novel minimum spanning tree algorithm to reconstruct genetic relationships despite missing data together with a static \"GrapeTree Layout\" algorithm to render interactive visualisations of large trees.\nC_LIO_LIGrapeTree is a stand-along package for investigating Newick trees plus associated metadata and is also integrated into EnteroBase to facilitate cutting edge navigation of genomic relationships among >160,000 genomes from bacterial pathogens.\nC_LIO_LIThe GrapeTree package was released under the GPL v3.0 Licence.\nC_LI

bioinformatics↗

bioRxiv2017

Accurate Reconstruction of Microbial Strains Using Representative Reference Genomes

Exploring the genetic diversity of microbes within the environment through metagenomic sequencing first requires classifying these reads into taxonomic groups. Current methods compare these sequencing data with existing biased and limited reference databases. Several recent evaluation studies demonstrate that current methods either lack sufficient sensitivity for species-level assignments or suffer from false positives, overestimating the number of species in the metagenome. Both are especially problematic for the identification of low-abundance microbial species, e. g. detecting pathogens in ancient metagenomic samples. We present a new method, SPARSE, which improves taxonomic assignments of metagenomic reads. SPARSE balances existing biased reference databases by grouping reference genomes into similarity-based hierarchical clusters, implemented as an efficient incremental data structure. SPARSE assigns reads to these clusters using a probabilistic model, which specifically penalizes non-specific mappings of reads from unknown sources and hence reduces false-positive assignments. Our evaluation on simulated datasets from two recent evaluation studies demonstrated the improved precision of SPARSE in comparison to other methods for species-level classification. In a third simulation, our method successfully differentiated multiple co-existing Escherichia coli strains from the same sample. In real archaeological datasets, SPARSE identified ancient pathogens with[≤] 0.02% abundance, consistent with published findings that required additional sequencing data. In these datasets, other methods either missed targeted pathogens or reported non-existent ones. SPARSE and all evaluation scripts are available at https://github.com/zheminzhou/SPARSE.

bioinformatics↗