Biology subjects

Hunt, M.

Publications and source records attributed to Hunt, M..

29 records · Page 2Linked to original sources

bioRxiv2021

Deciphering Bedaquiline and Clofazimine Resistance in Tuberculosis: An Evolutionary Medicine Approach

Bedaquiline (BDQ) and clofazimine (CFZ) are core drugs for treatment of multidrug resistant tuberculosis (MDR-TB), however, our understanding of the resistance mechanisms for these drugs is sparse which is hampering rapid molecular diagnostics. To address this, we employed a unique approach using experimental evolution, protein modelling, genome sequencing, and minimum inhibitory concentration data combined with genomes from a global strain collection of over 14,151 Mycobacterium tuberculosis complex isolates and an extensive literature review. Overall, 230 genomic variants causing elevated BDQ and/or CFZ MICs could be discerned, with 201 (87.4%) variants affecting the transcriptional repressor (Rv0678) of an efflux system (mmpS5-mmpL5). Structural modelling of Rv0678 suggests four major mechanisms that confer resistance: impairment of DNA binding, reduction in protein stability, disruption of protein dimerization, and alteration in affinity for its fatty acid ligand. These modelling and experimental techniques will improve personalized medicine in an impending drug resistant era.

microbiology↗

bioRxiv2021

Exploring bacterial diversity via a curated and searchable snapshot of archived DNA sequences

The open sharing of genomic data provides an incredibly rich resource for the study of bacterial evolution and function, and even anthropogenic activities such as the widespread use of antimicrobials. Whilst these archives are rich in data, considerable processing is required before biological questions can be addressed. Here, we assembled and characterised 661,405 bacterial genomes using a uniform standardised approach, retrieved from the European Nucleotide Archive (ENA) in November of 2018. A searchable COBS index has been produced, facilitating the easy interrogation of the entire dataset for a specific gene or mutation. Additional MinHash and pp-sketch indices support genome-wide comparisons and estimations of genomic distance. An analysis on this scale revealed the uneven species composition in the ENA/public databases, with just 20 of the total 2,336 species making up 90% of the genomes. The over-represented species tend to be acute/common human pathogens. This aligns with research priorities at different levels from individuals with targeted but focused research questions, areas of focus for the funding bodies or national public health agencies, to those identified globally as priority pathogens by the WHO for their resistance to front and last line antimicrobials. Understanding the actual and potential biases in bacterial diversity depicted in this snapshot, and hence within the data being submitted to the public sequencing archives, is essential if we are to target and fill gaps in our understanding of the bacterial kingdom.

microbiology↗

bioRxiv2021

Enabling multiscale variation analysis with genome graphs

BackgroundStandard approaches to characterising genetic variation revolve around mapping reads to a reference genome and describing variants in terms of differences from the reference; this is based on the assumption that these differences will be small and provides a simple coordinate system. However this fails, and the coordinates break down, when there are diverged haplotypes at a locus (e.g. one haplotype contains a multi-kilobase deletion, a second contains a few SNPs, and a third is highly diverged with hundreds of SNPs). To handle these, we need to model genetic variation that occurs at different length-scales (SNPs to large structural variants) and that occurs on alternate backgrounds. We refer to these together as multiscale variation. ResultsWe model the genome as a directed acyclic graph consisting of successive hierarchical subgraphs ("sites") that naturally incorporate multiscale variation, and introduce an algorithm for genotyping, implemented in the software gramtools. This enables variant calling on different sequence backgrounds. In addition to producing regular VCF files, we introduce a JSON file format based on VCF, which records variant site relationships and alternate sequence backgrounds. We show two applications. First, we benchmark gramtools against existing state-of-the-art methods in joint-genotyping 17 M. tuberculosis samples at long deletions and the overlapping small variants that segregate in a cohort of 1,017 genomes. Second, in 706 African and SE Asian P. falciparum genomes, we analyse a dimorphic surface antigen gene which possesses variation on two diverged backgrounds which appeared to not recombine. This generates the first map of variation on both backgrounds, revealing patterns of recombination that were previously unknown. ConclusionsWe need new approaches to be able to jointly analyse SNP and structural variation in cohorts, and even more to handle variants on different genetic backgrounds. We have demonstrated that by modelling with a directed, acyclic and locally hierarchical genome graph, we can apply new algorithms to accurately genotype dense variation at multiple scales. We also propose a generalisation of VCF for accessing multiscale variation in genome graphs, which we hope will be of wide utility.

bioinformatics↗

bioRxiv2021

Association between land cover, plant genera and pollinator dynamics in mixed-use landscapes

Pollinators are globally threatened by land-use change, but its effect varies depending on the taxa and the intensity of habitat degradation. However, pollinator-landscape studies typically focus on regions of intensive human activities and on a few focal species. Evaluating pollinator responses in landscapes with moderate land-use changes and on multiple pollinator groups would therefore fill an important knowledge gap. This study aims to determine the predictive capacity and effect of habitat characteristics on the relative abundance of multiple pollinator groups in mixed-use landscapes. To do this, we collected field data on the relative abundance of nectivorous birds, bees, beetles, and butterflies across the Tasman Peninsula (Tasmania, Australia). We then applied Random Forests to resolve the effects of land use (protected areas, plantation, and pasture), land cover at different radii (100 m and 2000 m), and plant genera on pollinator abundance. Overall, land cover and plant genera were more important predictors of pollinator abundance than land use. And the effect of land use, land cover, and plant genera varied depending on the pollinating group. Pollinator groups were associated with a range of plant genera, with the native genera Acacia, Leptospermum, Leucopogon, Melaleuca, Pomaderris, and Pultenaea being among the most important predictors. Our results highlight that one size does not fit all--that is pollinator response to different landscape characteristics vary, emphasise the importance of considering multiple habitat factors to manage and support a dynamic pollinator community, and demonstrates how land management can be informed using predictive modelling.

ecology↗

bioRxiv2021

Distinct Patterns of Emergence of SARS-CoV-2 Spike Variants including N501Y in Clinical Samples in Columbus Ohio

Following the worldwide emergence of the p.Asp614Gly shift in the Spike (S) gene of SARS-CoV-2, there have been few recurring pathogenic shifts occurring during 2020, as assessed by genomic sequencing. This situation has evolved in the last several months with the emergence of several distinct variants (first identified in the United Kingdom and South Africa) that manifest multiple changes in the S gene, particularly p.Asn501Tyr (N501Y), that likely have clinical impact. We report here the emergence in Columbus, Ohio in December 2020 of two novel SARS-CoV-2 clade 20G variants. One variant, that has become the predominant virus found in nasopharyngeal swabs in the December 2020-January 2021 period, harbors S p.Gln677His (Q677H), affecting a consensus QTQTN domain near the S1/S2 furin cleavage site, nucleocapsid (N) p.Asp377Tyr (D377Y) and membrane glycoprotein (M) p.Ala85Ser (A85S) mutations, with additional S mutations in subsets. The other variant present in two samples, contains S N501Y, which is a marker of the UK-B.1.1.7 (clade 20I/501Y.V1) strain, but lacks all other mutations from that virus. The Ohio variant is from a different clade and shares multiple mutations with the clade 20G viruses circulating in the area prior to December 2020. These two SARS-CoV-2 viruses, which we show are also present and evolving currently in several other parts of North America, add to the diversity of S gene shifts occurring worldwide. These and other shifts in this period of the pandemic support multiple independent acquisition of functionally significant and potentially complementing mutations affecting the S QTQTN site (Q675H or Q677H) and certain receptor binding domain mutations (e.g., E484K and N501Y).

genomics↗

bioRxiv2020

Nucleotide-resolution bacterial pan-genomics with reference graphs

BackgroundBacterial genomes follow a U-shaped frequency distribution whereby most genomic loci are either rare (accessory) or common (core); the union of these is the pan-genome. The alignable fraction of two genomes from a single species can be low (e.g. 50-70%), such that no single reference genome can access all single nucleotide polymorphisms (SNPs). The pragmatic solution is to choose a close reference, and analyse SNPs only in the core genome. Given much bacterial adaptability hinges on the accessory genome, this is an unsatisfactory limitation. ResultsWe present a novel pan-genome graph structure and algorithms implemented in the software pandora, which approximates a sequenced genome as a recombinant of reference genomes, detects novel variation and then pan-genotypes multiple samples. The method takes fastq as input and outputs a multi-sample VCF with respect to an inferred data-dependent reference genome, and is available at https://github.com/rmcolq/pandora. Constructing a reference graph from 578 E. coli genomes, we analyse a diverse set of 20 E. coli isolates. We show pandora recovers at least 13k more rare SNPs than single-reference based tools, achieves equal or better error rates with Nanopore as with Illumina data, 6-24x lower Nanopore error rates than other tools, and provides a stable framework for analysing diverse samples without reference bias. We also show that our inferred recombinant VCF reference genome is significantly better than simply picking the closest RefSeq reference. ConclusionsThis is a step towards comprehensive cohort analysis of bacterial pan-genomic variation, with potential impacts on genotype/phenotype and epidemiological studies.

bioinformatics↗

bioRxiv2020

Global population structure and genotyping framework for genomic surveillance of the major dysentery pathogen, Shigella sonnei

Shigella sonnei is the most common agent of shigellosis in high-income countries, and causes a significant disease burden in low- and middle-income countries. Antimicrobial resistance is increasingly common in all settings. Whole genome sequencing (WGS) is increasingly utilised for S. sonnei outbreak investigation and surveillance, but comparison of data between studies and labs is challenging. Here, we present a genomic framework and genotyping scheme for S. sonnei to efficiently identify genotype and resistance determinants from WGS data. The scheme is implemented in the software package Mykrobe and tested on thousands of genomes. Applying this approach to analyse >4,000 S. sonnei isolates sequenced in public health labs in three countries identified several common genotypes associated with increased rates of ciprofloxacin resistance and azithromycin resistance, confirming intercontinental spread of highly-resistant S. sonnei clones and demonstrating the genomic framework can facilitate monitoring of the emergence and spread of resistant clones at local and global scales.

microbiology↗

bioRxiv2019

Genetic variation associated with infection and the environment in the accidental pathogen Burkholderia pseudomallei

The environmental bacterium Burkholderia pseudomallei causes melioidosis, an endemic disease in tropical and sub-tropical countries. This bacterium occupies broad ecological niches ranging from soil, contaminated water, single-cell microbes and plants to human infection. Here, we performed genome-wide association studies for genetic determinants of environmental and human adaptation using a combined dataset of 1,011 whole genome sequences of B. pseudomallei from Northeast Thailand (discovery cohort, number of clinical isolates = 325, environmental isolates =428) and Australia (validation cohort, number of clinical isolates = 185, environmental isolates = 73), representing two major global disease hotspots. With these data, we (i) identified 47 genes from 26 distinct loci associated with clinical or environmental isolates from Thailand and replicated 13 loci in an independent Australian cohort; (ii) outlined the selective pressures on the genetic loci (dN/dS) and the frequency at which they had been gained or lost throughout their evolutionary history, reflecting the bacterial adaptability to a wide range of ecological niches; and (iii) highlighted loci implicated in human disease.

microbiology↗

bioRxiv2017

SeroBA: rapid high-throughput serotyping of Streptococcus pneumoniae from whole genome sequence data

Streptococcus pneumoniae is responsible for 240,000 - 460,000 deaths in children under 5 years of age each year. Accurate identification of pneumococcal serotypes is important for tracking the distribution and evolution of serotypes following the introduction of effective vaccines. Recent efforts have been made to infer serotypes directly from genomic data but current software approaches are limited and do not scale well. Here, we introduce a novel method, SeroBA, which uses a hybrid assembly and mapping approach. We compared SeroBA against real and simulated data and present results on the concordance and computational performance against a validation dataset, the robustness and scalability when analysing a large dataset, and the impact of varying the depth of coverage in the cps locus region on sequence-based serotyping. SeroBA can predict serotypes, by identifying the cps locus, directly from raw whole genome sequencing read data with 98% concordance using a k-mer based method, can process 10,000 samples in just over 1 day using a standard server and can call serotypes at a coverage as low as 10x. SeroBA is implemented in Python3 and is freely available under an open source GPLv3 license from: https://github.com/sanger-pathogens/seroba\n\nDATA SUMMARYO_LIThe reference genome Streptococcus pneumoniae ATCC 700669 is available from National Center for Biotechnology Information (NCBI) with the accession number: FM211187\nC_LIO_LISimulated paired end reads for experiment 2 have been deposited in FigShare: https://doi.org/10.6084/m9.figshare.5086054.v1\nC_LIO_LIAccession numbers for all other experiments are listed in Supplementary Table S1 and Supplementary Table S2.\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 STATEMENTThis article describes SeroBA, a k-mer based method for predicting the serotypes of Streptococcus pneumoniae from Whole Genome Sequencing (WGS) data. SeroBA can identify 92 serotypes and 2 subtypes with constant memory usage and low computational costs. We showed that SeroBA is able to reliably predict serotypes at a depth of coverage as low as 10x and is scalable to large datasets.

bioinformatics↗

bioRxiv2017

Inferring synteny between genome assemblies: a systematic evaluation

Identification of synteny between genomes of closely related species is an important aspect of comparative genomics. However, it is unknown to what extent draft assemblies lead to errors in such analysis. To investigate this, we fragmented genome assemblies of model nematodes to various extents and conducted synteny identification and downstream analysis. We first show that synteny between species can be underestimated up to 40% and find disagreements between popular tools that infer synteny blocks. This inconsistency and further demonstration of erroneous gene ontology enrichment tests throws into question the robustness of previous synteny analysis when gold standard genome sequences remain limited. In addition, determining the true evolutionary relationship is compromised by assembly improvement using a reference guided approach with a closely related species. Annotation quality, however, has minimal effect on synteny if the assembled genome is highly contiguous. Our results highlight the need for gold standard genome assemblies for synteny identification and accurate downstream analysis.\n\nAuthor summaryGenome assemblies across all domains of life are currently produced routinely. Initial analysis of any new genome usually includes annotation and comparative genomics. Synteny provides a framework in which conservation of homologous genes and gene order is identified between genomes of different species. The availability of human and mouse genomes paved the way for algorithm development in large-scale synteny mapping, which eventually became an integral part of comparative genomics. Synteny analysis is regularly performed on assembled sequences that are fragmented, neglecting the fact that most methods were developed using complete genomes. Here, we systematically evaluate this interplay by inferring synteny in genome assemblies with different degrees of contiguation. As expected, our investigation reveals that assembly quality can drastically affect synteny analysis, from the initial synteny identification to downstream analysis. Importantly, we found that improving a fragmented assembly using synteny with the genome of a related species can be dangerous, as this a priori assumes a potentially false evolutionary relationship between the species. The results presented here re-emphasize the importance of gold standard genomes to the science community, and should be achieved given the current progress in sequencing technology.

bioinformatics↗

bioRxiv2017

ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads

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 customisable 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. ARIBA is available at https://github.com/sanger-pathogens/ariba.

bioinformatics↗