Biology subjects

Giles, S. K.

Publications and source records attributed to Giles, S. K..

6 recordsLinked to original sources

bioRxiv2026

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↗

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

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↗

bioRxiv2023

Novel crAssphage isolates exhibit conserved gene order and purifying selection of the host specificity protein

Bacteroides, the prominent bacteria in the human gut, play a crucial role in degrading complex polysaccharides. Their abundance is influenced by phages belonging to the Crassvirales order. Despite identifying over 600 Crassvirales genomes computationally, only few have been successfully isolated. Continued efforts in isolation of more Crassvirales genomes can provide insights into phage-host-evolution and infection mechanisms. We focused on wastewater samples, as potential sources of phages infecting various Bacteroides hosts. Sequencing, assembly, and characterization of isolated phages revealed 14 complete genomes belonging to three novel Crassvirales species infecting Bacteroides cellulosilyticus WH2. These species, Kehishuvirus sp. tikkala strain Bc01, Kolpuevirus sp. frurule strain Bc03, and Rudgehvirus jaberico strain Bc11, spanned two families, and three genera, displaying a broad range of virion productions. Upon testing all successfully cultured Crassvirales species and their respective bacterial hosts, we discovered that they do not exhibit co-evolutionary patterns with their bacterial hosts. Furthermore, we observed variations in gene similarity, with greater shared similarity observed within genera. However, despite belonging to different genera, the three novel species shared a unique structural gene that encodes the tail spike protein. When investigating the relationship between this gene and host interaction, we discovered evidence of purifying selection, indicating its functional importance. Moreover, our analysis demonstrated that this tail spike protein binds to the TonB-dependent receptors present on the bacterial host surface. Combining these observations, our findings provide insights into phage-host interactions and present three Crassvirales species as an ideal system for controlled infectivity experiments on one of the most dominant members of the human enteric virome. Impact statementBacteriophages play a crucial role in shaping microbial communities within the human gut. Among the most dominant bacteriophages in the human gut microbiome are Crassvirales phages, which infect Bacteroides. Despite being widely distributed, only a few Crassvirales genomes have been isolated, leading to a limited understanding of their biology, ecology, and evolution. This study isolated and characterized three novel Crassvirales genomes belonging to two different families, and three genera, but infecting one bacterial host, Bacteroides cellulosilyticus WH2. Notably, the observation confirmed the phages are not co-evolving with their bacterial hosts, rather have a shared ability to exploit similar features in their bacterial host. Additionally, the identification of a critical viral protein undergoing purifying selection and interacting with the bacterial receptors opens doors to targeted therapies against bacterial infections. Given Bacteroides role in polysaccharide degradation in the human gut, our findings advance our understanding of the phage-host interactions and could have important implications for the development of phage-based therapies. These discoveries may hold implications for improving gut health and metabolism to support overall well-being. Data summaryThe genomes used in this research are available on Sequence Read Archive (SRA) within the project, PRJNA737576. Bacteroides cellulosilyticus WH2, Kehishuvirus sp. tikkala strain Bc01, Kolpuevirus sp. frurule strain Bc03, and Rudgehvirus jaberico strain Bc11 are all available on GenBank with accessions NZ_CP072251.1 (B. cellulosilyticus WH2), QQ198717 (Bc01), QQ198718 (Bc03), and QQ198719 (Bc11), and we are working on making the strains available through ATCC. The 3D protein structures for the three Crassvirales genomes are available to download at doi.org/10.25451/flinders.21946034.

bioinformatics↗

bioRxiv2022

OligoFlow: rapid and sensitive virus quantification using flow cytometry and oligonucleotide hybridization

Flow cytometry is an established method for the detection and enumeration of viruses. However, the technique is unable to target specific viral species. Here, we present OligoFlow, a novel method for the rapid detection and enumeration of viruses by incorporating flow cytometry with species specific oligonucleotide hybridization. Using Ostried herpesvirus and dengue virus as model organisms, we demonstrate high-level detection and specificity. Our results represent a significant advancement in viral flow cytometry, opening the possibilities for the rapid identification of viruses in time critical settings.

microbiology↗

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↗