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Biology subjects

Edwards, R. A.

Publications and source records attributed to Edwards, R. A..

At least 19 recordsLinked to original sources

Host species background, defence systems, and phage tail gene architecture shape phage infectivity in cystic fibrosis-associated Achromobacter

Achromobacter species are emerging multidrug-resistant (MDR) pathogens in people with cystic fibrosis. Their increasing resistance has grown an interest in phage therapy as an alternative treatment strategy. However, the factors governing phage susceptibility remain poorly understood, thereby limiting the rational selection of phage candidates. Using 15 strictly lytic Achromobacter phages and 7 clinical cystic fibrosis isolates representing Achromobacter insolitus and Achromobacter xylosoxidans, we demonstrate substantial variation in infection efficiency across all 105 phage-host combinations, variation that could not be discerned from qualitative plaque assays alone. We integrated complete bacterial and phage genomes with quantitative efficiency-of-plating (EOP) assays and lineage-aware Bayesian mixed-effects modelling to show that phage infectivity in Achromobacter is governed predominantly by bacterial lineage and strain identity, accounting for 90% of total variance in log-normalised EOP, with individual strains varying substantially in permissiveness irrespective of species membership. After accounting for this lineage structure, no individual defence system, antimicrobial resistance gene class, or phage tail cluster retained a statistically significant independent or interaction association with infectivity. Together, these findings demonstrate that bacterial strain identity is the primary driver of infection outcome. Host defence systems and phage tail-associated genes remain biologically plausible contributors; their independent effect could not be resolved after accounting for lineage structure, indicating that infection outcomes are largely strain-dependent. This work shifts the question from which individual traits predict infection to how strain lineage and specific host-phage combinations jointly determine infectivity, and argues that quantitative phenotyping of individual phage-host pairs is essential for guiding phage candidate selection and supporting rational cocktail design against multidrug-resistant Achromobacter infections in cystic fibrosis. Impact statementChronic Achromobacter infections in cystic fibrosis are increasingly difficult to treat due to multidrug resistance and biofilm formation. Although phage therapy is a promising alternative, its development is limited by poorly understood and highly variable infectivity. Here, we show that infectivity within a phage host range spans a broad quantitative continuum spanning several orders of magnitude that cannot be captured by qualitative plaque assays. These infection efficiencies are primarily structured by bacterial lineage and strain identity, while the contributions of individual genomic features remain unresolved, given the current sample size. This work provides a framework for predicting phage-host compatibility and supports a shift from empirical screening toward rational, evidence-based phage selection for MDR Achromobacter infections.

molecular biology↗

The Q226H Mutation in Avian H5N1 Hemagglutinin Mediates a Path towards Structural Adaptation in Humans

The global outbreak of highly pathogenic avian influenza (HPAI) A(H5N1) among birds and the spillover to mammals increases the risk for humans. A recent case in British Columbia with a clade 2.3.4.4b H5 virus infection revealed a mixture of 226Q/H in the receptor-binding site of hemagglutinin. While significant changes in pre-existing immunity by H1 or H3 polyclonal sera are not evident, we show that the Q226H mutation enables binding to human-type a2-6 sialic acid receptors. High-resolution cryo-EM structures provide a basis for the alteration in receptor preference and show that a possible path towards human adaptation also requires a conformational change of the bound a2-6-sialylated glycan. Continued surveillance for additional mutations that could enhance this phenotype is warranted.

immunology↗

Computational prediction resolves thousands of homooligomeric phage protein structures

Bacteriophages (phages) play essential roles in microbial systems, yet most phage proteins remain poorly characterised. Protein tertiary and quaternary structure information contributes valuable information about protein function. As many phage proteins function as homooligomers, complexes that consist of multiple identical subunits, there is great interest in computationally predicting their configurations. Here we present a computational framework, the Phage Homomer Level Estimate and Generation Method (PHLEGM) for inferring homooligomeric states directly from the protein sequence by combining AlphaFold-Multimer modelling with inter-subunit interface quality assessment. We proceeded to experimentally validate two out of nine predicted homooligomers using size exclusion chromatography and complementary hydrodynamic techniques. These efforts confirmed our predictions for a dimer and a trimer, highlighting the value of experimentally benchmarked computational predictions and showing the challenges of heterologous phage protein production. Applied to >22,000 phage protein sequences in the PHROGs database, our approach revealed extensive diversity in phage homooligomeric protein complexes. Benchmarking against protein language model-based predictors on a curated reference set of known phage homooligomers demonstrated superior accuracy of our structure-based method, achieving robust performance in classifying protein homooligomeric states, with the highest accuracy observed for trimers and higher-order complexes. These results highlight the value of computational predictions to decipher the complexities of the vast viral sequence space. All predicted complex structures and functional inferences are made publicly available to support structural and functional studies of phage proteins.

microbiology↗

Baktfold: Sensitive protein functional annotation across the microbial tree of life using structural information

The functional annotation of protein sequences has undergone tremendous progress over recent years, but still too-many protein sequences remain as so-called hypothetical proteins after applying state-of-the-art genome annotation software pipelines. Here, we introduce Baktfold, a new command line software tool for the ultra-sensitive but taxon-independent fast annotation of protein sequences across the microbial tree of life. Baktfold conducts sequential protein structure-based searches against four complementary structure databases. Protein sequences are transformed into Foldseek 3Di tokens via the ProstT5 protein language model and subsequently searched against structure databases via Foldseek. All results are exported in GFF3 and INSDC-compliant flat files as well as comprehensive JSON files facilitating automated downstream analysis 100% interoperable with the popular bacterial annotation tool Bakta. We compared Baktfolds performance in terms of wallclock runtime and functional annotation of hypothetical proteins from various sources including bacterial and archaeal isolates, plasmids, metagenomic-assembled genomes and micro-eukaryotes. When benchmarked on over three hundred thousand species representatives across the prokaryotic tree of life, Baktfolds median overall bacterial genome annotation rate is 87.8% compared to 72.9% with Bakta, while Baktfolds median bacterial annotation rate of remaining hypothetical proteins is 50.1% (n=290258). For archaea, Baktfolds overall median annotation rate is 71.5% compared to Prokkas 35.8%, with a median archaeal annotation rate of hypothetical proteins of 68.0% (n=14058), making Baktfold the most sensitive automated archaeal annotation method by far. Baktfold is implemented in Python 3 and runs on MacOS and Linux systems. It is freely available under a MIT license at https://github.com/gbouras13/baktfold. Data SummaryO_LIBaktfold was developed in Python as a command line application for Linux and MacOS C_LIO_LIThe complete source code and documentation are available on GitHub under an MIT license: https://github.com/gbouras13/baktfold C_LIO_LIThe Baktfold database is hosted at Zenodo (https://zenodo.org/records/17347516) mirrored on HuggingFace (https://huggingface.co/datasets/gbouras13/baktfold-db) C_LIO_LIBaktfold is available via bioconda (https://anaconda.org/bioconda/baktfold) and PyPI (https://pypi.org/project/baktfold/) C_LIO_LIBaktfold can also be run without local installation using Google Colab at https://colab.research.google.com/github/gbouras13/baktfold/blob/main/run_baktfold. ipynb C_LIO_LIAll supplementary code, data and files required to reproduce the results of this manuscript are available at https://github.com/gbouras13/baktfold-analysis (code and small data) and https://zenodo.org/records/19333697 (large data) C_LI

bioinformatics↗

Single-cell profiling of synchronous multi-organ metastasis reveals a systemic CD74+ lipid-associated macrophage niche driving polymetastatic breast cancer

Systemic, multi-organ metastasis is the primary cause of breast cancer mortality, yet the biological mechanisms that allow disseminated tumor cells to simultaneously colonize physiologically diverse tissues remain poorly understood. Current paradigms focus on organ-specific tropism, largely overlooking the potential for systemic, conserved and synchronized programs that facilitate widespread colonization. Here, we present a high-resolution, multi-organ atlas of metastatic ecosystems and their niches using a synchronous model of brain, lung, liver, and bone metastasis combined with in vivo proximal niche labeling and single-cell RNA sequencing. We identify a remarkably conserved proximal niche program defined by the accumulation of CD74+ lipid-associated, metastasis-associated macrophages (LA-MAMs) across all metastatic sites. CD74+ LA-MAMs are characterized by a unique metabolic-immune signature and drive T cell suppression. We show that the cytokine Macrophage Migration Inhibitory Factor (MIF), secreted by metastatic cells, acts as the universal paracrine mediator that instructs the LA-MAM phenotype via the CD74 receptor. Interference of the MIF-CD74 axis effectively disrupts the LA-MAM niche, mitigates T cell exhaustion, and reduces metastatic burden across all organs. Analysis of a 100-patient cohort of metastasis samples from different sites confirms that the MIF-CD74 axis is a hallmark of human multi-organ colonization and independently predicts poor post-metastasis survival. Our findings define a synchronized and systemic metastatic niche that can be targeted, providing a mechanistic rationale for neutralizing the MIF-CD74 axis to treat polymetastatic breast cancer.

cancer biology↗

The mood stabilizer lithium alters behaviour and physiology via the gut brain axis.

Lithium, introduced 75 years ago by John Cade1, remains the most effective mood stabilizer for bipolar disorder2. Lithium is proposed to modulate an array of cellular pathways, many ubiquitous to all cells, with pleiotropic roles unlinked to bipolar disorder or lithium responsiveness in genome-wide association studies3,4. These mechanisms cannot explain lithiums specific effects on mood and behaviour. We demonstrate that lithiums primary action is in the periphery, not in the brain itself. Lithium acts in the gut to trigger behavioural and physiological changes, akin to those associated with a torpor-like state, that protect individuals from ingested toxins. Lithium activates gastrointestinal enterochromaffin (EC) cells via their Trpm2 cation channels to modulate afferent vagal and area postrema inputs to the brain. Eliminating these inputs by focal brain lesions eliminates lithiums effects, as does ablation of EC cells or their Trpm2 expression. Lithiums Trpm2-dependent activation of EC cells also occurs in human gut tissue, providing translational relevance for our discovery. These findings challenge the prevailing perception that lithium acts directly on the brain. Via a previously unsuspected gut-brain pathway, lithium engages brain circuitry that reduces arousal and interaction with the external world, therapeutic goals in the manic phase of bipolar disorder.

neuroscience↗

Infection of 5xFAD mice with a mouse-adapted SARS-CoV-2 does not alter Alzheimer's disease neuropathology yet induces wide-spread changes in gene expression across diverse cell types

Alzheimers disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia. It is characterized by cognitive decline and accumulation of amyloid beta (A{beta}) plaques and neurofibrillary tangles. Accumulating evidence indicates that viral infection may worsen and/or increase development of established AD pathology. The COVID-19 pandemic has brought attention to the link between SARS-CoV-2 infection and neurologic conditions that vary in severity and duration, as well as the worsening of clinical symptoms in elderly people with dementia. To better understand potential mechanisms by which SARS-CoV-2 infection impacts AD neuropathology, aged 5xFAD and wildtype (WT) mice were intranasally infected with mouse-adapted SARS-CoV-2 (MA10). Intranasal infection of aged-matched (10-14 month) 5xFAD or wild type (WT) C57BL/6 mice with MA10 resulted in viral infection of the lungs that correlated with acute viral pneumonia characterized by lymphocyte inflammation and antiviral immune responses. Viral RNA was not detected within the central nervous system (CNS) of either WT or 5xFAD mice at days 7 or 21 post-infection (p.i.), nor were there signs of overt glial activation or neuroinflammation. There were no differences in either A{beta} plaque volume or number within the brains of MA10-infected 5xFAD mice compared to uninfected 5xFAD mice. However, bulk RNA sequencing and spatial transcriptomics revealed evidence of altered expression of genes associated with neuronal and glial dysfunction, as well as reduced expression of genes encoding adhesion molecules in vascular endothelial cells. Collectively, these findings demonstrate that MA10 infection did not affect A{beta} plaque size or numbers in 5xFAD mice, yet in both WT and 5xFAD mice, there were numerous down-stream effects on gene expression associated with resident CNS cell function.

pathology↗

Central carbon metabolism switching in lytic versus temperate coral reef viral communities

Coral reefs are declining globally due in part to bacterial overgrowth, a process known as microbialization. However, the role of bacteriophages that may inhibit microbialization by infecting and killing these bacteria remains poorly understood, especially their metabolic impacts on bacterial proliferation. To address this, we analyzed central carbon metabolism gene frequencies in viral communities from healthy (lytic-dominated) and degraded (temperate-dominated) Central Pacific coral reefs. We found that viral metabolism shifted broadly from being dominated by metabolism that builds up pools of central intermediates on degraded reefs dominated by temperate viral infection ("anaplerotic" reactions) to metabolism that consumes these pools to prioritize production of metabolic precursors for virion construction on healthy reefs dominated by lytic infection ("cataplerotic" reactions). This switch was shown by the over-representation of Entner-Doudoroff (ED) glycolysis genes on degraded, temperate-dominated reefs and of pentose phosphate pathway (PPP) and reductive tricarboxylic acid cycle (TCA) genes on healthy, lytic-dominated reefs. As a result of this metabolic dichotomy, our qualitative compartment modeling revealed two distinct ecosystem states: (i) healthy reefs, where lytic viral metabolism enhances viral production and suppresses bacterial overgrowth, and (ii) degraded reefs, where temperate viral metabolism accelerates bacterial proliferation. Because viral switching between lytic and temperate lifestyles is a known function of host physiological state, these findings position viral metabolism as both a driver of reef decline and a conservation lever, with metabolically mediated "re-viralization" offering a novel strategy to restore reef resilience.

ecology↗

agtools: a software framework to manipulate assembly graphs

SummaryAssembly graphs are a fundamental data structure used by genome and metagenome assemblers to represent sequences and their overlap information, facilitating the assembler to construct longer genomic fragments. Apart from their core use in assemblers, assembly graphs have become increasingly important in a range of downstream applications such as metagenomic binning, plasmid detection, viral genome resolution and haplotype phasing. However, there is a need for a comprehensive tool that allows programmatic access to manipulate assembly graphs across different assembly graph formats. Here we present agtools, an open-source Python framework that can analyse and manipulate assembly graphs. agtools provides a command-line interface for tasks such as graph format conversion, segment filtering, and component extraction. It also exposes a Python package interface to load, query and analyse assembly graphs from popular genome and metagenome assemblers. This enables streamlined assembly graph-based analysis that can be integrated into other bioinformatics software and workflows. Availability and implementationThe source code of agtools is hosted on GitHub at https://github.com/Vini2/agtools and the documentation is available at https://agtools.readthedocs.io/. agtools is also installable from Bioconda (https://anaconda.org/bioconda/agtools) and PyPI (https://pypi.org/project/agtools/).

bioinformatics↗

Protein Structure Informed Bacteriophage Genome Annotation with Phold

Bacteriophage (phage) genome annotation is essential for understanding their functional potential and suitability for use as therapeutic agents. Here we introduce Phold, an annotation framework utilising protein structural information that combines the ProstT5 protein language model and structural alignment tool Foldseek. Phold assigns annotations using a database of over 1.36 million predicted phage protein structures with high quality functional labels. Benchmarking reveals that Phold outperforms existing sequence-based homology approaches in functional annotation sensitivity whilst maintaining speed, consistency and scalability. Applying Phold to diverse cultured and metagenomic phage genomes shows it consistently annotates over 50% of genes on an average phage and 40% on an average archaeal virus. Comparisons of phage protein structures to other protein structures across the tree of life reveals that phage proteins commonly have structural homology to proteins shared across the tree of life, particularly those that have nucleic acid metabolism and enzymatic functions. Phold is available as free and open-source software at https://github.com/gbouras13/phold.

bioinformatics↗

Synteny-aware functional annotation of bacteriophage genomes with Phynteny

Accurate genome annotation is fundamental to decoding viral diversity and understanding bacteriophage biology; yet, the majority of bacteriophage genes remain functionally uncharacterised. Bacteriophage genomes often exhibit conserved gene order, or synteny, that reflects underlying constraints in genome architecture and expression. Here, we present Phynteny, a genome-scale, deep learning framework that leverages gene synteny to predict the function of unknown bacteriophage genes. Phynteny integrates protein language model embeddings with positional encoding, bidirectional long short-term memory, and transformer encoders featuring circular attention to learn genome-wide organisational patterns. Trained on a dereplicated dataset of over 280,000 bacteriophage genomes, Phynteny achieves high predictive performance (AUC > 0.84) across the nine PHROG functional categories and confidently assigns putative functions to improve the number of annotated genes in phage isolate genomes by 14%. To assess the validity of these predictions, we compared them with annotations derived independently using protein structural information, revealing broad functional concordance and additional confidence in Phynteny predictions. By incorporating genomic context into functional annotation, Phynteny offers a novel approach to illuminate the functional landscape of viral dark matter and is available at https://github.com/susiegriggo/Phynteny_transformer.

microbiology↗

Fold first, ask later: structure-informed function annotation of Pseudomonas phage proteins

Phages, the viruses of bacteria, harbor an incredibly diverse repertoire of proteins capable of manipulating their bacterial hosts, inspiring many medical and biotechnological applications. However, to date, only a limited subset of that repertoire can be exploited, due to the difficulties in functionally elucidating these proteins. In this study, we investigated several structure-informed approaches to annotate hypothetical proteins from Pseudomonas infecting phages. We curated a representative dataset of over 10,000 proteins derived from NCBI, for which we predicted protein structures with ColabFold and assessed structural similarity via FoldSeek against the PDB, AlphaFold, and Phold databases. We evaluated multiple annotation strategies, including sequence-based (Pharokka), and structure-based (FoldSeek, Phold) methods. Our results show that up to 43 % of truly unannotated proteins can be functionally annotated when combining structure-informed approaches with UniProt-derived annotations. We highlight the complementarity of different databases and the importance of annotation quality filtering. This work provides a valuable resource of predicted structures and annotations, and offers insights into optimizing structure-based annotation pipelines for viral proteins, paving the way for deeper exploration of phage biology and its applications.

bioinformatics↗

Sphae: An automated toolkit for predicting phage therapy candidates from sequencing data

MotivationPhage therapy is a viable alternative for treating bacterial infections amidst the escalating threat of antimicrobial resistance. However, the therapeutic success of phage therapy depends on selecting safe and effective phage candidates. While experimental methods focus on isolating phages and determining their lifecycle and host range, comprehensive genomic screening is critical to identify markers that indicate potential risks, such as toxins, antimicrobial resistance, or temperate lifecycle traits. These analyses are often labor-intensive and time-consuming, limiting the rapid deployment of phage in clinical settings. ResultsWe developed Sphae, an automated bioinformatics pipeline designed to streamline therapeutic potential of a phage in under ten minutes. Using Snakemake workflow manager, Sphae integrates tools for quality control, assembly, genome assessment, and annotation tailored specifically for phage biology. Sphae automates the detection of key genomic markers, including virulence factors, antimicrobial resistance genes, and lysogeny indicators like integrase, recombinase, and transposase, which could preclude therapeutic use. Benchmarked on 65 phage sequences, 28 phage samples showed therapeutic potential, 8 failed during assembly due to low sequencing depth, 22 samples included prophage or virulent markers, and the remaining 23 samples included multiple phage genomes per sample. This workflow outputs a comprehensive report, enabling rapid assessment of phage safety and suitability for phage therapy under these criteria. Sphae is scalable, portable, facilitating efficient deployment across most high-performance computing (HPC) and cloud platforms, expediting the genomic evaluation process. AvailabilitySphae is source code and freely available at https://github.com/linsalrob/sphae, with installation supported on Conda, PyPi, Docker containers.

bioinformatics↗

Colorectal Cancer Stem Cell Subtypes Orchestrate Distinct Tumor Microenvironments

SummarySeveral classification systems have been developed to define tumor subtypes in colorectal cancer (CRC). One system proposes that tumor heterogeneity derives in part from distinct cancer stem cell populations that co-exist as admixtures of varying proportions. However, the lack of single cell resolution has prohibited a definitive identification of these types of stem cells and therefore any understanding of how each influence tumor phenotypes. Here were report the isolation and characterization of two cancer stem cell subtypes from the SW480 CRC cell line. We find these cancer stem cells are oncogenic versions of the normal Crypt Base Columnar (CBC) and Regenerative Stem Cell (RSC) populations from intestinal crypts and that their gene signatures are consistent with the "Admixture" and other CRC classification systems. Using publicly available single cell RNA sequencing (scRNAseq) data from CRC patients, we determine that RSC and CBC cancer stem cells are commonly co-present in human CRC. To characterize influences on the tumor microenvironment, we develop subtype-specific xenograft models and we define their tumor microenvironments at high resolution via scRNAseq. RSCs create differentiated, inflammatory, slow growing tumors. CBCs create proliferative, undifferentiated, invasive tumors. With this enhanced resolution, we unify current CRC patient classification schema with TME phenotypes and organization. O_LITwo cancer stem cell subtypes are isolated and determined to be commonly found as mixed populations in human CRC C_LIO_LIThe heterogeneous SW480 cell line models and unifies two binary cancer stem cell classifications of colorectal cancer C_LIO_LIHigh resolution analysis of xenograft tumors reveals that CRC stem cells are dominant, context-independent organizers of their TME C_LIO_LIRSC/YAP/STAT3-positive CRC stem cells create differentiated tumors that attempt tissue generation via an inflammatory, angiogenic, oncofetal program C_LIO_LICBC/MYC-positive CRC stem cells create tumors that are proliferative, undifferentiated, and invasive C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=137 SRC="FIGDIR/small/591144v1_ufig1.gif" ALT="Figure 1"> View larger version (66K): org.highwire.dtl.DTLVardef@166735aorg.highwire.dtl.DTLVardef@11f3e55org.highwire.dtl.DTLVardef@354a4forg.highwire.dtl.DTLVardef@a18ab1_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

How low can you go? Short-read polishing of Oxford Nanopore bacterial genome assemblies

It is now possible to assemble near-perfect bacterial genomes using Oxford Nanopore Technologies (ONT) long reads, but short-read polishing is still required for perfection. However, the effect of short-read depth on polishing performance is not well understood. Here, we introduce Pypolca (with default and careful parameters) and Polypolish v0.6.0 (with a new careful parameter). We then show that: (1) all polishers other than Pypolca-careful, Polypolish-default and Polypolish-careful commonly introduce false-positive errors at low depth; (2) most of the benefit of short-read polishing occurs by 25x depth; (3) Polypolish-careful never introduces false-positive errors at any depth; and (4) Pypolca-careful is the single most effective polisher. Overall, we recommend the following polishing strategies: Polypolish-careful alone when depth is very low (<5x), Polypolish-careful and Pypolca-careful when depth is low (5-25x), and Polypolish-default and Pypolca-careful when depth is sufficient (>25x). Data SummaryPypolca is open-source and freely available on Bioconda, PyPI, and GitHub (github.com/gbouras13/pypolca). Polypolish is open-source and freely available on Bioconda and GitHub (github.com/rrwick/Polypolish). All code and data required to reproduce analyses and figures are available at github.com/gbouras13/depth_vs_polishing_analysis. All FASTQ sequencing reads are available at BioProject PRJNA1042815. A detailed list of accessions can be found in Table S1.

bioinformatics↗

Predicting stop codon reassignment improves functional annotation of bacteriophages

The majority of bacteriophage diversity remains uncharacterised, and new intriguing mechanisms of their biology are being continually described. Members of some phage lineages, such as the Crassvirales, repurpose stop codons to encode an amino acid by using alternate genetic codes. Here, we investigated the prevalence of stop codon reassignment in phage genomes and subsequent impacts on functional annotation. We predicted 76 genomes within INPHARED and 712 vOTUs from the Unified Human Gut Virome catalogue (UHGV) that repurpose a stop codon to encode an amino acid. We re-annotated these sequences with modified versions of Pharokka and Prokka, called Pharokka-gv and Prokkagv, to automatically predict stop codon reassignment prior to annotation. Both tools significantly improved the quality of annotations, with Pharokka-gv performing best. For sequences predicted to repurpose TAG to glutamine (translation table 15), Pharokka-gv increased the median gene length (median of per genome medians) from 287 to 481 bp for UHGV sequences (67.8% increase) and from 318 to 550 bp for INPHARED sequences (72.9% increase). The re-annotation increased mean coding density from 66.8% to 90.0%, and from 69.0% to 89.8% for UHGV and INPHARED sequences. Furthermore, the proportion of genes that could be assigned functional annotation increased, including an increase in the number of major capsid proteins that could be identified. We propose that automatic prediction of stop codon reassignment before annotation is beneficial to downstream viral genomic and metagenomic analyses.

bioinformatics↗

Hybracter: Enabling Scalable, Automated, Complete and Accurate Bacterial Genome Assemblies

Improvements in the accuracy and availability of long-read sequencing mean that complete bacterial genomes are now routinely reconstructed using hybrid (i.e. short- and long-reads) assembly approaches. Complete genomes allow a deeper understanding of bacterial evolution and genomic variation beyond single nucleotide variants (SNVs). They are also crucial for identifying plasmids, which often carry medically significant antimicrobial resistance (AMR) genes. However, small plasmids are often missed or misassembled by long-read assembly algorithms. Here, we present Hybracter which allows for the fast, automatic, and scalable recovery of near-perfect complete bacterial genomes using a long-read first assembly approach. Hybracter can be run either as a hybrid assembler or as a long-read only assembler. We compared Hybracter to existing automated hybrid and long-read only assembly tools using a diverse panel of samples of varying levels of long-read accuracy with manually curated ground truth reference genomes. We demonstrate that Hybracter as a hybrid assembler is more accurate and faster than the existing gold standard automated hybrid assembler Unicycler. We also show that Hybracter with long-reads only is the most accurate long-read only assembler and is comparable to hybrid methods in accurately recovering small plasmids. Data SummaryO_LIHybracter is developed using Python and Snakemake as a command-line software tool for Linux and MacOS systems. C_LIO_LIHybracter is freely available under an MIT License on GitHub (https://github.com/gbouras13/hybracter) and the documentation is available at Read the Docs (https://hybracter.readthedocs.io/en/latest/). C_LIO_LIHybracter is available to install via PyPI (https://pypi.org/project/hybracter/) and Bioconda (https://anaconda.org/bioconda/hybracter). A Docker/Singularity container is also available at https://quay.io/repository/gbouras13/hybracter. C_LIO_LIAll code used to benchmark Hybracter, including the reference genomes, is publicly available on GitHub (https://github.com/gbouras13/hybracter_benchmarking) with released DOI (https://zenodo.org/doi/10.5281/zenodo.10910108) available at Zenodo. C_LIO_LIThe subsampled FASTQ files used for benchmarking are publicly available at Zenodo with DOI (https://doi.org/10.5281/zenodo.10906937). C_LIO_LIAll super accuracy simplex ATCC FASTQ reads sequenced as a part of this study can be found under BioProject PRJNA1042815. C_LIO_LIAll Hall et al. fast accuracy simplex and super accuracy duplex ATCC FASTQ read files (prior to subsampling) can be found in the SRA under BioProject PRJNA1087001. C_LIO_LIAll raw Lermaniaux et al. FASTQ read files and genomes (prior to subsampling) can be found in the SRA under BioProject PRJNA1020811. C_LIO_LIAll Staphylococcus aureus JKD6159 FASTQ read files and genomes can be found under BioProject PRJNA50759. C_LIO_LIAll Mycobacterium tuberculosis H37R2 FASTQ read files and genomes can be found under BioProject PRJNA836783. C_LIO_LIThe complete list of BioSample accession numbers for each benchmarked sample can be found in Supplementary Table 1. C_LIO_LIThe benchmarking assembly output files are publicly available on Zenodo with DOI (https://doi.org/10.5281/zenodo.10906937). C_LIO_LIAll Pypolca benchmarking outputs and code are publicly available on Zenodo with DOI (https://zenodo.org/doi/10.5281/zenodo.10072192). C_LI Impact StatementComplete bacterial genome assembly using hybrid sequencing is a routine and vital part of bacterial genomics, especially for identification of mobile genetic elements and plasmids. As sequencing becomes cheaper, easier to access and more accurate, automated assembly methods are crucial. With Hybracter, we present a new long-read first automated assembly tool that is faster and more accurate than the widely-used Unicycler. Hybracter can be used both as a hybrid assembler and with long-reads only. Additionally, it solves the problems of long-read assemblers struggling with small plasmids, with plasmid recovery from long-reads only performing on par with hybrid methods. Hybracter can natively exploit the parallelisation of high-performance computing (HPC) clusters and cloud-based environments, enabling users to assemble hundreds or thousands of genomes with one line of code. Hybracter is available freely as source code on GitHub, via Bioconda or PyPi.

bioinformatics↗

Bioenergetic mapping of 'healthy microbiomes' via compound processing potential imprinted in gut and soil metagenomes

Despite mounting evidence of their importance in human health and ecosystem functioning, the definition and measurement of healthy microbiomes remain unclear. More advanced knowledge exists on health associations for compounds used or produced by microbes. Environmental microbiome exposures (especially via soils) also help shape, and may supplement, the functional capacity of human microbiomes. Given the synchronous interaction between microbes, their feedstocks, and micro-environments, with functional genes facilitating chemical transformations, there exists an intriguing opportunity to examine microbiomes in terms of their capacity to process compounds relevant to human health. Here we integrate functional genomics and biochemistry frameworks to derive new quantitative measures of the potential for human gut and environmental soil metagenomes to process major compound classes (e.g., lipids, carbohydrates) and selected biomolecules (e.g., vitamins, short-chain fatty acids) linked to human health. Metagenome functional potential profile data were translated into a universal compound mapping landscape based on bioenergetic van Krevelen mapping of function-level meta-compounds and corresponding functional relative abundances, reflecting imprinted genetic capacity of microbiomes to metabolize an array of different compounds. We show that measures of compound processing potential associated with human health and disease (examining atherosclerotic cardiovascular disease, colorectal cancer, type 2 diabetes and anxious-depressive behavior case studies), and displayed seemingly predictable shifts along gradients of ecological disturbance in plant-soil ecosystems (three case studies). Ecosystem quality explained 60-92% of variation in soil metagenome compound processing potential measures in a post-mining restoration case study dataset. With growing knowledge of the varying proficiency of environmental microbiota to process human health associated compounds, we might design environmental interventions or nature prescriptions to modulate our exposures, thereby advancing microbiota-oriented approaches to human health. Compound processing potential offers a simplified, integrative approach for applying metagenomics in ongoing efforts to understand and quantify the role and linkages of microbiota in environmental- and human-health.

bioinformatics↗