Biology subjects

Aanensen, D. M.

Publications and source records attributed to Aanensen, D. M..

6 recordsLinked to original sources

bioRxiv2018

High-resolution sweep metagenomics using ultrafast read mapping and inference

Determining the composition of bacterial communities beyond the level of a genus or species is challenging because of the considerable overlap between genomes representing close relatives. Here, we present the mSWEEP method for identifying and estimating the relative abundances of bacterial lineages from plate sweeps of enrichment cultures. mSWEEP leverages biologically grouped sequence assembly databases, applying probabilistic modelling, and provides controls for false positive results. Using sequencing data from major pathogens, we demonstrate significant improvements in lineage quantification and detection accuracy. Our method facilitates investigating cultures comprising mixtures of bacteria, and opens up a new field of plate sweep metagenomics.

bioinformatics↗

bioRxiv2018

Development and worldwide use of a non-lethal and minimal population-level impact protocols for the isolation of chytrids from amphibians

Parasitic chytrid fungi have emerged as a significant threat to amphibian species worldwide, necessitating the development of techniques to isolate these pathogens into sterile culture for research purposes. However, early methods of isolating chytrids from their hosts relied on killing amphibians. We modified a pre-existing protocol for isolating chytrids from infected animals to use toe clips and biopsies from toe webbing rather than euthanizing hosts, and distributed the protocol to interested researchers worldwide as part of the BiodivERsA project RACE - here called the RML protocol. In tandem, we developed a lethal procedure for isolating chytrids from tadpole mouthparts. Reviewing a database of use a decade after their inception, we find that these methods have been widely applied across at least 5 continents, 23 countries and in 62 amphibian species, and have been successfully used to isolate chytrids in remote field locations. Isolation of chytrids by the non-lethal RML protocol occured in 18% of attempts with 207 fungal isolates and three species of chytrid being recovered. Isolation of chytrids from tadpoles occured in 43% of attempts with 334 fungal isolates of one species (Batrachochytrium dendrobatidis) being recovered. Together, these methods have resulted in a significant reduction and refinement of our use of threatened amphibian species and have improved our ability to work with this important group of emerging fungal pathogens.

ecology↗

bioRxiv2017

Rapid genome sequencing for outbreak analysis of the emerging human fungal pathogen Candida auris

BackgroundCandida auris was first described in 2009, and has since caused nosocomial outbreaks, invasive infections and fungaemia across 11 countries in five continents. An outbreak of C. auris occurred in a specialised cardiothoracic London hospital between April 2015 and November 2016, which to date has been the largest outbreak reported worldwide, involving a total of 72 patients.\n\nMethodsTo understand the epidemiology of C. auris infection within this hospital, we sequenced the genomes of outbreak isolates using Oxford Nanopore Technologies and Illumina in order to type antifungal resistance alleles and to explore the outbreak within its local and global context.\n\nFindingsPhylogenomic analysis placed the UK outbreak in the India/Pakistan clade, demonstrating an Asian origin. The outbreak showed similar diversity to that of the entire clade and limited local spatiotemporal clustering was observed. One isolate displayed resistance to both echinocandins and 5-flucytosine; the former was associated with a serine to tyrosine amino acid substitution in the gene FKS1, and the latter was associated with a phenylalanine to isoleucine substitution in the gene FUR1. These mutations are novel for this pathogen.\n\nInterpretationMultiple differential episodic selection of antifungal resistant genotypes has occurred within a genetically heterogenous population across this outbreak, creating a resilient pathogen and making it difficult to define local-scale patterns of transmission as well as implementing outbreak control measures.\n\nFundingAntimicrobial Research Collaborative, Imperial College London

epidemiology↗

bioRxiv2017

PANINI: Pangenome Neighbor Identification for Bacterial Populations

The standard workhorse for genomic analysis of the evolution of bacterial populations is phylogenetic modelling of mutations in the core genome. However, in the current era of population genomics, a notable amount of information about evolutionary and transmission processes in diverse populations can be lost unless the accessory genome is also taken into consideration. Here we introduce PANINI, a computationally scalable method for identifying the neighbours for each isolate in a data set using unsupervised machine learning with stochastic neighbour embedding. PANINI is browser-based and integrates with the Microreact platform for rapid online visualisation and exploration of both core and accessory genome evolutionary signals together with relevant epidemiological, geographic, temporal and other metadata. Several case studies with single-and multi-clone pneumococcal populations are presented to demonstrate ability to identify biologically important signals from gene content data. PANINI is available at http://panini.wgsa.net/ and code at http://gitlab.com/cgps/panini

microbiology↗

bioRxiv2017

Phandango: an interactive viewer for bacterial population genomics.

SummaryFully exploiting the wealth of data in current bacterial population genomics datasets requires synthesising and integrating different types of analysis across millions of base pairs in hundreds or thousands of isolates. Current approaches often use static representations of phylogenetic, epidemiological, statistical and evolutionary analysis results that are difficult to relate to one another. Phandango is an interactive application running in a web browser allowing fast exploration of large-scale population genomics datasets combining the output from multiple genomic analysis methods in an intuitive and interactive manner.\n\nAvailabilityPhandango is a web application freely available for use at https://jameshadfield.github.io/phandango and includes a diverse collection of datasets as examples. Source code together with a detailed wiki page is available on GitHub at https://github.com/jameshadfield/phandango\n\nContactjh22@sanger.ac.uk, sh16@sanger.ac.uk

bioinformatics↗

bioRxiv2017

Frequent recombination of pneumococcal capsule highlights future risks of emergence of novel serotypes.

Capsular diversity of Streptococcus pneumoniae constitutes a major obstacle in eliminating the pneumococcal disease. Such diversity is genetically encoded by almost 100 variants of the capsule polysaccharide locus (cps). However, the evolutionary dynamics of the capsule - the target of the currently used vaccines - remains not fully understood. Here, using genetic data from 4,469 bacterial isolates, we found cps to be an evolutionary hotspot with elevated substitution and recombination rates. These rates were a consequence of altered selection at this locus, supporting the hypothesis that the capsule has an increased potential to generate novel diversity compared to the rest of the genome. Analysis of twelve serogroups revealed their complex evolutionary history, which was principally driven by recombination with other serogroups and other streptococci. We observed significant variation in recombination rates between different serogroups. This variation could only be partially explained by the lineage-specific recombination rate, the remaining factors being likely driven by serogroup-specific ecology and epidemiology. Finally, we discovered two previously unobserved mosaic serotypes in the densely sampled collection from Mae La, Thailand, here termed 10X and 21X. Our results thus emphasise the strong adaptive potential of the bacterium by its ability to generate novel serotypes by recombination.

evolutionary biology↗