Biology subjects

Inglis, L. K.

Publications and source records attributed to Inglis, L. K..

6 recordsLinked to original sources

bioRxiv2025

Prophages as a source of antimicrobial resistance genes in the human microbiome

Prophages--viruses that integrate into bacterial genomes--are ubiquitous in the microbial realm. Prophages contribute significantly to horizontal gene transfer, including the potential spread of antimicrobial resistance (AMR) genes, because they can collect host genes. Understanding their role in the human microbiome is essential for fully understanding AMR dynamics and possible clinical implications. We analysed almost 15,000 bacterial genomes for prophages and AMR genes. The bacteria were isolated from diverse human body sites and geographical regions, and their genomes were retrieved from GenBank. AMR genes were detected in 6.6% of bacterial genomes, with a higher prevalence in people with symptomatic diseases. We found a wide variety of AMR genes combating multiple drug classes. We discovered AMR genes previously associated with plasmids, such as blaOXA-23 in Acinetobacter baumannii prophages or genes found in prophages in species they had not been previously described in, such as mefA-msrD in Gardnerella prophages, suggesting prophage-mediated gene transfer of AMR genes. Prophages encoding AMR genes were found at varying frequencies across body sites and geographical regions, with Asia showing the highest diversity of AMR genes. ImportanceAntimicrobial resistance (AMR) is a growing threat to public health, and understanding how resistance genes spread between bacteria is essential for controlling their dissemination. Bacteriophages, viruses that infect bacteria, have been recognised as potential vehicles for transferring these resistance genes, but their role in the human microbiome remains poorly understood. We examined nearly 15,000 bacterial genomes from various human body sites and regions worldwide to investigate how often prophages carry AMR genes in the human microbiome. Although AMR genes were uncommon in prophages, we identified diverse resistance genes across multiple bacterial species and drug classes, including some typically associated with plasmids. These findings reveal that prophages may contribute to the spread of resistance genes, highlighting an overlooked mechanism in the dynamics of AMR transmission. Ongoing monitoring of prophages is critical to fully understanding the pathways through which resistance genes move within microbial communities and impact human health.

bioinformatics↗

bioRxiv2024

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↗

bioRxiv2023

Prophage rates in the human microbiome vary by body site and host health.

Phages integrated into a bacterial genome-called prophages-continuously monitor the health of the host bacteria to determine when to escape the genome, protect their host from other phage infections, and may provide genes that promote bacterial growth. Prophages are essential to almost all microbiomes, including the human microbiome. However, most human microbiome studies focus on bacteria, ignoring free and integrated phages, so we know little about how these prophages affect the human microbiome. We compared the prophages identified in 11,513 bacterial genomes isolated from human body sites to characterise prophage DNA in the human microbiome. Here, we show that prophage DNA comprised an average of 1-5% of each bacterial genome. The prophage content per genome varies with the isolation site on the human body, the health of the human, and whether the disease was symptomatic. The presence of prophages promotes bacterial growth and sculpts the microbiome. However, the disparities caused by prophages vary throughout the body.

genomics↗

bioRxiv2023

The Promise and Pitfalls of Prophages

Phages dominate every ecosystem on the planet. While virulent phages sculpt the microbiome by killing their bacterial hosts, temperate phages provide unique growth advantages to their hosts through lysogenic conversion. Many prophages benefit their host, and prophages are responsible for genotypic and phenotypic differences that separate individual microbial strains. However, the microbes also endure a cost to maintain those phages: additional DNA to replicate and proteins to transcribe and translate. We have never quantified those benefits and costs. Here, we analysed over two and a half million prophages from over half a million bacterial genome assemblies. Analysis of the whole dataset and a representative subset of taxonomically diverse bacterial genomes demonstrated that the normalised prophage density was uniform across all bacterial genomes above 2 Mbp. We identified a constant carrying capacity of phage DNA per bacterial DNA. We estimated that each prophage provides cellular services equivalent to approximately 2.4 % of the cells energy or 0.9 ATP per bp per hour. We demonstrate analytical, taxonomic, geographic, and temporal disparities in identifying prophages in bacterial genomes that provide novel targets for identifying new phages. We anticipate that the benefits bacteria accrue from the presence of prophages balance the energetics involved in supporting prophages. Furthermore, our data will provide a new framework for identifying phages in environmental datasets, diverse bacterial phyla, and from different locations.

bioinformatics↗

bioRxiv2023

Phables: from fragmented assemblies to high-quality bacteriophage genomes

MotivationMicrobial communities influence both human health and different environments. Viruses infecting bacteria, known as bacteriophages or phages, play a key role in modulating bacterial communities within environments. High-quality phage genome sequences are essential for advancing our understanding of phage biology, enabling comparative genomics studies, and developing phage-based diagnostic tools. Most available viral identification tools consider individual sequences to determine whether they are of viral origin. As a result of the challenges in viral assembly, fragmentation of genomes can occur, leading to the need for new approaches in viral identification. Therefore, the identification and characterisation of novel phages remain a challenge. ResultsWe introduce Phables, a new computational method to resolve phage genomes from fragmented viral metagenome assemblies. Phables identifies phage-like components in the assembly graph, models each component as a flow network, and uses graph algorithms and flow decomposition techniques to identify genomic paths. Experimental results of viral metagenomic samples obtained from different environments show that Phables recovers on average over 49% more high-quality phage genomes compared to existing viral identification tools. Furthermore, Phables can resolve variant phage genomes with over 99% average nucleotide identity, a distinction that existing tools are unable to make. Availability and ImplementationPhables is available on GitHub at https://github.com/Vini2/phables. Contactvijini.mallawaarachchi@flinders.edu.au

bioinformatics↗

bioRxiv2021

Philympics 2021: Prophage Predictions Perplex Programs

Most bacterial genomes contain integrated bacteriophages--prophages--in various states of decay. Many are active and able to excise from the genome and replicate, while others are cryptic prophages, remnants of their former selves. Over the last two decades, many computational tools have been developed to identify the prophage components of bacterial genomes, and it is a particularly active area for the application of machine learning approaches. However, progress is hindered and comparisons thwarted because there are no manually curated bacterial genomes that can be used to test new prophage prediction algorithms. Here, we present a library of gold-standard bacterial genome annotations that include manually curated prophage annotations, and a computational framework to compare the predictions from different algorithms. We use this suite to compare all extant stand-alone prophage prediction algorithms to identify their strengths and weaknesses. We provide a FAIR dataset for prophage identification, and demonstrate the accuracy, precision, recall, and f1 score from the analysis of seven different algorithms for the prediction of prophages. We discuss caveats and concerns in this analysis and how those concerns may be mitigated.

bioinformatics↗