Biology subjects

Hsiao, W.

Publications and source records attributed to Hsiao, W..

8 recordsLinked to original sources

bioRxiv2026

Atypical BlaIR Two-Component System in Pseudomonas aeruginosa Regulates Virulence but not β-Lactam Resistance

With the rise of antimicrobial resistance, anti-virulence therapeutics are a viable alternative to circumvent resistance pressures. Hypothetical genes and proteins are an under-studied source of potential virulence factor targets. We performed bioinformatic analyses to identify conserved hypothetical genes enriched in pathogenic Pseudomonas aeruginosa but not in non-pathogenic strains. This analysis identified an atypical BlaIR system, which we named pvmSR, that regulated P. aeruginosa virulence in a Caenorhabditis elegans infection model. This is in contrast with the typical BlaIR system from Staphylococcus aureus, which regulates resistance to {beta}-lac-tam antibiotics. The{Delta} pvmSR mutant showed reduced virulence in a C. elegans slow-killing assay. To understand how PvmSR regulated virulence in vivo, we performed dual RNA-seq to analyze transcriptomic changes in both C. elegans and P. aeruginosa. We found that C. elegans responded to P. aeruginosa {Delta}pvmSR infection by decreasing expression of lysosome and phagocytosis pathways. In P. aeruginosa {Delta}pvmSR, we observed decreased gene expression of several known virulence factors including the hydrogen cyanide synthase, hcnC, and heparinase, hepP. Additionally, we observed dysregulation in genes important for quorum sensing and biofilm formation. Collectively, our findings indicated that PvmSR contributed to virulence regulation and may serve as a potential anti-virulence target.

microbiology↗

bioRxiv2025

GAME: Genomic API for Model Evaluation

The rapid expansion of genomics datasets and the application of machine learning has produced sequence-to-activity genomics models with ever-expanding capabilities. However, benchmarking these models on practical applications has been challenging because individual projects evaluate their models in ad hoc ways, and there is substantial heterogeneity of both model architectures and benchmarking tasks. To address this challenge, we have created GAME, a system for large-scale, community-led standardized model benchmarking on user-defined evaluation tasks. We borrow concepts from the Application Programming Interface (API) paradigm to allow for seamless communication between pre-trained models and benchmarking tasks, ensuring consistent evaluation protocols. Because all models and benchmarks are inherently compatible in this framework, the continual addition of new models and new benchmarks is easy. We also developed a Matcher module powered by a large language model (LLM) to automate ambiguous task alignment between benchmarks and models. Containerization of these modules enhances reproducibility and facilitates the deployment of models and benchmarks across computing platforms. By focusing on predicting underlying biochemical phenomena (e.g. gene expression, open chromatin, DNA binding), we ensure that tasks remain technology-independent. We provide examples of benchmarks and models implementing this framework, and anticipate that the community will contribute their own, leading to an ever-expanding and evolving set of models and evaluation tasks. This resource will accelerate genomics research by illuminating the best models for a given task, motivating novel functional genomic benchmarks, and providing a more nuanced understanding of model abilities.

bioinformatics↗

bioRxiv2024

The diversity of clinical Mycobacterium abscessus isolates in morphology, glycopeptidolipids and infection rates in a macrophage model

Mycobacterium abscessus (Mab) colonies adopt smooth (S) or rough (R) morphotypes, which are linked to the presence or absence of glycopeptidolipids (GPL), respectively. Though clinically relevant, the association between GPL levels, morphotype and pathogenesis are poorly understood. To investigate the degree of correlation between Mab morphology, GPL levels, and infectivity, we generated isolates from Mab-positive sputum samples from cystic fibrosis patients. Isolated strains were categorised based on their morphology, GPL profile, and replication rate in macrophages. Our findings revealed that around 50% of isolates displayed mixed morphologies and GPL analysis confirmed a consistent relationship between GPL content and morphotype was only found in smooth isolates. Across morphotype groups, no differences were observed in vitro, yet using a high-content THP-1 cell ex vivo infection model, clinical R strains were observed to replicate at higher levels. Moreover, the proportion of infected macrophages was notably higher among clinical R strains compared to their S counterparts at 72 hours post-infection. Clinical variants also infected at significantly higher rates compared to laboratory strains, highlighting the limited translatability of lab strain infection data to clinical contexts. Our study confirmed the general correlation between morphotype and GPL levels in smooth strains yet unveiled more variability within morphotype groups than previously recognised, particularly during intracellular infection. As the rough morphotype is of highest clinical concern, these findings contribute to the expanding knowledge base surrounding Mab infections, offering insights that can steer diagnostic methodologies, and treatment approaches.

microbiology↗

bioRxiv2023

NGBO: Introducing -omics metadata to biobanking ontology.

BackgroundWith improvements in high throughput sequencing technologies and the constant generation of large biomedical datasets, biobanks increasingly take on the role of managing and delivering not just specimens but also data. However, re-using data from different biobanks is challenged by incompatible data representations. Contextual data describing biobank digital resources often contain unstructured textual information incompatible with computational processes such as automated data discovery and integration. Therefore, a consistent and comprehensive contextual data framework is needed to increase discovery, reusability, and integrability across data sources. MethodsBased on available genomics standards (e.g., Minimum information about a microarray experiment (MIAME)), the College of American Pathologists (CAP) laboratory accreditation requirements, and the Open Biological and Biomedical Ontologies Foundry principles, we developed the Next Generation Biobanking Ontology (NGBO). In addition, we created new terms and re-used concepts from the Ontology for Biomedical Investigations (OBI) and the Ontology for Biobanking (OBIB) to build NGBO. ResultsThe Next Generation Biobanking Ontology https://www.ebi.ac.uk/ols4/ontologies/ngbo is an open application ontology representing omics contextual data, licensed under the Apache License 2.0. The ontology focuses on capturing information about three main activities: wet bench analysis used to generate omics data, bioinformatics analysis used to process and interpret data, and data management. In this paper, we demonstrated the use of the NGBO to add semantic statements to real-life use cases and query data previously stored in unstructured textual format.

bioinformatics↗

bioRxiv2022

ProbeTools: Designing hybridization probes for targeted genomic sequencing of diverse and hypervariable viral taxa

BackgroundSequencing viruses in many specimens is hindered by excessive background material from hosts, microbiota, and environmental organisms. Consequently, enrichment of target genomic material is necessary for practical high-throughput viral genome sequencing. Hybridization probes are widely used for enrichment in many fields, but their application to viral sequencing faces a major obstacle: it is difficult to design panels of probe oligo sequences that broadly target many viral taxa due to their rapid evolution, extensive diversity, and genetic hypervariability. To address this challenge, we created ProbeTools, a package of bioinformatic tools for generating effective viral capture panels, and for assessing coverage of target sequences by probe panel designs in silico. In this study, we validated ProbeTools by designing a panel of 3,600 probes for subtyping the hypervariable haemagglutinin (HA) and neuraminidase (NA) genome segments of avian-origin influenza A viruses (AIVs). Using in silico assessment of AIV reference sequences and in vitro capture on egg-cultured viral isolates, we demonstrated effective performance by our custom AIV panel and ProbeTools suitability for challenging viral probe design applications. ResultsBased on ProbeTools in silico analysis, our panel provided broadly inclusive coverage of 14,772 HA and 11,967 NA reference sequences. 90% of these HA and NA references sequences had 90.8% and 95.1% of their nucleotide positions covered in silico by the panel respectively. We also observed effective in vitro capture on a representative collection of 23 egg-cultured AIVs that included isolates from wild birds, poultry, and humans and representatives from all HA and NA subtypes. 42 of 46 HA and NA segments had over 98.3% of their nucleotide positions significantly enriched by our custom panel. These in vitro results were further used to validate ProbeTools in silico coverage assessment algorithm; 89.2% of in silico predictions were concordant with in vitro results. ConclusionsProbeTools generated an effective panel for subtyping AIVs that can be deployed for genomic surveillance, outbreak prevention, and pandemic preparedness. Effective probe design against hypervariable AIV targets also validated ProbeTools design and coverage assessment algorithms, demonstrating their suitability for other challenging viral capture applications.

genomics↗

bioRxiv2017

MentaLiST - A fast MLST caller for large wgMLST schemes

MLST (multi-locus sequence typing) is a classic technique for genotyping bacteria, widely applied for pathogen outbreak surveillance. Traditionally, MLST is based on identifying sequence types from a small number of housekeeping genes. With the increasing availability of whole-genome sequencing (WGS) data, MLST methods have evolved toward larger typing schemes, based on a few hundred genes (core genome MLST, cgMLST) to a few thousand genes (whole genome MLST, wgMLST). Such large-scale MLST schemes have been shown to provide a finer resolution and are increasingly used in various contexts such as hospital outbreaks or foodborne pathogen outbreaks. This methodological shift raises new computational challenges, especially given the large size of the schemes involved. Very few available MLST callers are currently capable of dealing with large MLST schemes.\n\nWe introduce MentaLiST, a new MLST caller, based on a k-mer voting algorithm and written in the Julia language, specifically designed and implemented to handle large typing schemes. We test it on real and simulated data to show that MentaLiST is faster than any other available MLST caller while providing the same or better accuracy, and is capable of dealing with MLST scheme with up to thousands of genes while requiring limited computational resources. MentaLiST source code and easy installation instructions using a Conda package are available at https://github.com/WGS-TB/MentaLiST.

bioinformatics↗

bioRxiv2017

Antibiotic resistance genes in agriculture and urban influenced watersheds in southwestern British Columbia

BackgroundThe dissemination of antibiotic resistance genes (ARGs) from anthropogenic activities into the environment poses an emerging public health threat. Water constitutes a major vehicle for transport of both biological material and chemical substances. The present study focused on putative antibiotic resistance and integrase genes present in the microbiome of agricultural, urban influenced and protected watersheds in southwestern British Columbia, Canada. A metagenomics approach and high throughput quantitative PCR (HT qPCR) were used to screen for elements of resistance including ARGs and integron-associated integrase genes (intI). Sequencing of bacterial genomic DNA was used to characterize the resistome of microbial communities present in watersheds over a one-year period.\n\nResultsData mining using CARD and Integrall databases enabled the identification of putative antibiotic resistance genes present in watershed samples. Antibiotic resistance genes presence in samples from various watershed locations was low relative to the microbial population (<1 %). Analysis of the metagenomic sequences detected a total of 78 ARGs and intI1 across all watershed locations. The relative abundance and richness of antibiotic resistance genes was found to be highest in agriculture impacted watersheds compared to protected and urban watersheds. Gene copy numbers (GCNs) from a subset of 21 different elements of antibiotic resistance were further estimated using HT qPCR. Most GCNs of ARGs were found to be variable over time. A downstream transport pattern was observed in the impacted watersheds (urban and agricultural) during dry months. Urban and agriculture impacted sites had a higher GCNs of ARGs compared to protected sites. Similar to other reports, this study found a strong association between intI1 and ARGs (e.g., sul1), an association which may be used as a proxy for anthropogenic activities. Chemical analysis of water samples for three major groups of antibiotics was negative. However, the high richness and GCNs of ARGs in impacted sites suggest effects of effluents on microbial communities are occurring even at low concentrations of antimicrobials in the water column.\n\nConclusionAntibiotic resistance and integrase genes in a year-long metagenomic study showed that ARGs were driven mainly by environmental factors from anthropogenized sites in agriculture and urban watersheds. Environmental factors accounted for almost 40% of the variability observed in watershed locations.

microbiology↗

bioRxiv2016

SNVPhyl: A Single Nucleotide Variant Phylogenomics pipeline for microbial genomic epidemiology

MotivationThe recent widespread application of whole-genome sequencing (WGS) for microbial disease investigations has spurred the development of new bioinformatics tools, including a notable proliferation of phylogenomics pipelines designed for infectious disease surveillance and outbreak investigation. Transitioning the use of WGS data out of the research lab and into the front lines of surveillance and outbreak response requires user-friendly, reproducible, and scalable pipelines that have been well validated.\n\nResultsSNVPhyl (Single Nucleotide Variant Phylogenomics) is a bioinformatics pipeline for identifying high-quality SNVs and constructing a whole genome phylogeny from a collection of WGS reads and a reference genome. Individual pipeline components are integrated into the Galaxy bioinformatics framework, enabling data analysis in a user-friendly, reproducible, and scalable environment. We show that SNVPhyl can detect SNVs with high sensitivity and specificity and identify and remove regions of high SNV density (indicative of recombination). SNVPhyl is able to correctly distinguish outbreak from non-outbreak isolates across a range of variant-calling settings, sequencing-coverage thresholds, or in the presence of contamination.\n\nAvailabilitySNVPhyl is available as a Galaxy workflow, Docker and virtual machine images, and a Unix-based command-line application. SNVPhyl is released under the Apache 2.0 license and available at http://snvphyl.readthedocs.io/ or at https://github.com/phac-nml/snvphyl-galaxy.

bioinformatics↗