Biology subjects

Garnett, S.

Publications and source records attributed to Garnett, S..

3 recordsLinked to original sources

bioRxiv2024

Weberviruses are gut-associated phages that infect Klebsiella spp.

Weberviruses are bacteriophages (phages) that can infect and lyse clinically relevant, multidrug-resistant (MDR) strains of Klebsiella. They are an attractive therapeutic option to tackle Klebsiella infections due to their high burst sizes, long shelf life and associated depolymerases. In this study we isolated and characterized seven new lytic phages and compared their genomes with those of their closest relatives. Gene-sharing network, ViPTree proteome and terL gene-sequence-based analyses incorporating all publicly available webervirus genomes [n=258 from isolates, n=65 from metagenome-assembled genome (MAG) datasets] confirmed the seven phages as members of the genus Webervirus and identified a novel genus (Defiantjazzvirus) within the family Drexlerviridae. Using our curated database of 265 isolated phage genomes and 65 MAGs (n=330 total), we found that weberviruses are distributed globally and primarily associated with samples originating from the gut: sewage (154/330, 47 %), wastewater (83/330, 25 %) and human faeces (66/330, 20 %). We identified three distinct clusters of potential depolymerases encoded within the 330 genomes. Due to their global distribution, frequency of isolation and lytic activity against the MDR clinical Klebsiella strains used in this study, we conclude that weberviruses and their depolymerases show promise for development as therapeutic agents against Klebsiella spp.

microbiology↗

bioRxiv2022

The costs of global protected-area expansion (Target 3 of the post-2020 Global Biodiversity Framework) may fall more heavily on lower-income countries

One of the biggest stumbling blocks for global environmental agreements is how higher-income and lower-income countries share the costs of implementing them. This problem has become particularly acute as biodiversity and climate ambitions have increased across recent COPs (Conferences of Parties). Here, we estimate the likely distribution of costs for one of the most ambitious proposals: draft Target 3 of the Global Biodiversity Framework (GBF), which would increase coverage of protected and conserved areas (PCAs) to 30% of global land and sea area - more than triple the current value. Since the GBF does not specify where new PCAs would be placed, we use three scenarios of how Target 3 might be implemented, cost those scenarios, and then compare the mean distribution of costs across World Bank income groups. We find that in relative terms, lower-income countries could face considerably larger financial burdens than high-income countries, even though the benefits of conservation are disproportionately enjoyed by high-income countries. Lower-income countries would also face larger increases in the amount of land or sea under conservation, implying higher opportunity and establishment costs. Resolving this potential cost-sharing inequity may be a key requirement to achieve consensus on draft Target 3, and indeed on ambitious environmental proposals more generally.

ecology↗

bioRxiv2019

MetaNovo: a probabilistic approach to peptide and polymorphism discovery in complex mass spectrometry datasets

BackgroundMicrobiome research is providing important new insights into the metabolic interactions of complex microbial ecosystems involved in fields as diverse as the pathogenesis of human diseases, agriculture and climate change. Poor correlations typically observed between RNA and protein expression datasets make it hard to accurately infer microbial protein synthesis from metagenomic data. Additionally, mass spectrometry-based metaproteomic analyses typically rely on focussed search libraries based on prior knowledge for protein identification that may not represent all the proteins present in a set of samples. Metagenomic 16S rRNA sequencing will only target the bacterial component, while whole genome sequencing is at best an indirect measure of expressed proteomes. We describe a novel approach, MetaNovo, that combines existing open-source software tools to perform scalable de novo sequence tag matching with a novel algorithm for probabilistic optimization of the entire UniProt knowledgebase to create tailored databases for target-decoy searches directly at the proteome level, enabling analyses without prior expectation of sample composition or metagenomic data generation, and compatible with standard downstream analysis pipelines. ResultsWe compared MetaNovo to published results from the MetaPro-IQ pipeline on 8 human mucosal-luminal interface samples, with comparable numbers of peptide and protein identifications, many shared peptide sequences and a similar bacterial taxonomic distribution compared to that found using a matched metagenome database - but simultaneously identified many more non-bacterial peptides than the previous approaches. MetaNovo was also benchmarked on samples of known microbial composition against matched metagenomic and whole genomic database workflows, yielding many more MS/MS identifications for the expected taxa, with improved taxonomic representation, while also highlighting previously described genome sequencing quality concerns for one of the organisms, and identifying a known sample contaminant without prior expectation. ConclusionsBy estimating taxonomic and peptide level information directly on microbiome samples from tandem mass spectrometry data, MetaNovo enables the simultaneous identification of peptides from all domains of life in metaproteome samples, bypassing the need for curated sequence search databases. We show that the MetaNovo approach to mass spectrometry metaproteomics is more accurate than current gold standard approaches of tailored or matched genomic database searches, can identify sample contaminants without prior expectation and yields insights into previously unidentified metaproteomic signals, building on the potential for complex mass spectrometry metaproteomic data to speak for itself. The pipeline source code is available on GitHub1 and documentation is provided to run the software as a singularity-compatible docker image available from the Docker Hub2.

bioinformatics↗