Biology subjects

Horan, K.

Publications and source records attributed to Horan, K..

6 recordsLinked to original sources

bioRxiv2026

Microglia drive demyelination via multiple sclerosis antibodies and BTK signaling

Microglia are the predominant immune cells in multiple sclerosis (MS) demyelinating lesions, where they phagocytose myelin, but whether they destroy myelin or merely scavenge its debris is unknown. Here, we explore whether pathogenic autoantibodies found in MS may induce the phagocytic destruction of myelin by microglia. Applying patient-derived, myelin-targeting antibodies to the mouse cortex, we developed an in vivo model of MS with focal demyelination that depended on epitope specificity and Fc gamma receptor and complement binding. Longitudinal monitoring of microglia-myelin interactions using in vivo two-photon microscopy revealed rapid microglial envelopment of intact myelin driving myelin loss, while single-cell RNA sequencing identified a demyelination-associated microglial signature. Parallel changes were observed in human MS lesions, where microglia enveloped intact myelin and similar genes were upregulated. Inhibition of Brutons tyrosine kinase (BTK) limited microglial transcriptional changes and prevented myelin loss following microglial envelopment. These findings directly implicate microglia in pathological myelin loss and support BTK inhibition as a therapeutic strategy to prevent demyelination by modulating microglia behavior.

neuroscience↗

bioRxiv2025

Chromosome-level genome assembly of the Brazilian merganser (Mergus octosetaceus), a rare and elusive waterfowl species

BackgroundThe Brazilian merganser (Mergus octosetaceus) is a critically endangered bird, with fewer than 250 mature individuals remaining in fragmented populations of the Cerrado biome in Brazil. It is an ecologically demanding species that requires clear water of fast-flowing rivers for diving and foraging, and riverside cavities for nesting. Technical and logistical challenges have restricted sampling and research to few genetic studies so far. The genetic results consistently revealed a low genetic diversity and high inbreeding in the species. Besides, the lack of a reference genome has precluded deeper evolutionary and conservation genetic analyses for the species. ResultsWe generated the first high-quality, chromosome-level genome assembly for Mergus octosetaceus using PacBio HiFi long reads and Arima Hi-C data from a female specimen. The assembly is 1.25 Gb in length, is highly complete, scoring 98.9% of BUSCO completeness, and includes both sex chromosomes (Z and W). Annotated repetitive elements comprise 18.94% of the genome, with evidence of recent activity, particularly among LINEs and LTRs retrotransposons. We also produced and annotated the complete mitochondrial genome, identifying 37 genes and the control region. Comparative synteny analysis with other Anatidae species revealed strong chromosomal conservation, with several inversion events in macrochromosomes. Demographic history reconstruction indicated fluctuations in effective population size, with significant reductions overlapped by major temperature changes, highlighting potential climate sensitivity of this lineage. ConclusionThis reference genome provides a fundamental resource for M. octosetaceus, enabling insights into genome evolution, chromosomal dynamics within Anatidae, and past population history. It is a remarkable foundation for future research and conservation strategies for managing and restoring this critically endangered species.

evolutionary biology↗

bioRxiv2025

Extensive endemic transmission of multidrug resistant Mycobacterium tuberculosis in Bhutan: A retrospective genomic-epidemiological study

Despite decreasing overall tuberculosis notifications, the proportion of multidrug-resistant tuberculosis (MDR-TB) cases are increasing in Bhutan. While most MDR-TB cases are diagnosed among patients in the bordering districts and the capital, current diagnostic tests are limited in their ability to differentiate between the recurrent introductions and local transmission. For the first time, we conducted a retrospective genomic-epidemiological study to provide insights into the population structure, genotypic resistance patterns, and explore recent transmission of drug-resistant TB in Bhutan. Whole genome sequencing was performed on randomly selected drug-resistant and drug-sensitive TB isolates from Bhutan, collected between 2018-2022 at Microbiological Diagnostic Unit Public Health Laboratory in Melbourne, Australia. We investigated drug resistance mutations, and genomic clustering of cases using different single nucleotide polymorphisms (SNPs) thresholds. Of the 203 sequences that passed the quality control, 126 (62.1%) were MDR-TB and 15 (7.4%) were isoniazid-resistant TB. There were four different circulating lineages, with most sequences belonging to lineage 2 (86.2%). Using a SNP-threshold of [≤]12 SNPs, 71% of sequences formed 12 genomic clusters. Surprisingly, the largest cluster included of 88% of all MDR-TB sequences and spanned the entire study period. These cases were highly clonal (mean pairwise SNP-distance of 10, range 0-25). Phylogenetic analysis with publicly available international sequence data showed that this MDR-TB cluster formed a distinct clade. The major burden of MDR-TB in Bhutan appears to be due to recent local transmission of cases resulting in a large, single endemic cluster. This information will be critical for TB control program in Bhutan to tackle the major burden of MDR-TB through enhanced contact tracing of this MDR-TB clade. Additionally, this genomic data will be valuable to regional neighbours to monitor for dissemination of the strain. This study highlights the significant value of investing in TB genomics in resource limited settings globally to gain actionable insights into transmission dynamics.

genomics↗

bioRxiv2023

Bringing TB genomics to the clinic: A comprehensive pipeline to predict antimicrobial susceptibility from genomic data, validated and accredited to ISO standards.

BackgroundWhole genome sequencing (WGS) is increasingly contributing to the clinical management of tuberculosis. Whilst the availability of bioinformatic tools for analysis and clinical reporting of Mycobacterium tuberculosis sequence data is improving, However, there remains a need for accessible, flexible bioinformatic tools that can be easily tailored for clinical reporting needs in different settings and are suitable for accreditation to international standards. MethodsWe developed tbtAMR, a flexible yet comprehensive tool for analysis of Mycobacterium tuberculosis genomic data, including inference of phenotypic susceptibility and lineage calling. Validation was undertaken using local and publicly-available real-world data (phenotype and genotype) and synthetic genomic data to determine the appropriate quality control metrics and extensively validate the pipeline for clinical use. FindingstbtAMR accurately predicted lineages and phenotypic susceptibility for first- and second-line drugs, with equivalent computational and predictive performance compared to other bioinformatics tools currently available. tbtAMR is flexible with modifiable criteria to tailor results to users needs. InterpretationThe tbtAMR tool is suitable for use in clinical and public health microbiology laboratory settings, and can be tailored to specific local needs by non-programmers. We have accredited this tool to ISO standards in our laboratory, and it has been implemented for routine reporting of AMR from genomic sequence data in a clinically relevant timeframe. Reporting templates, validation methods and datasets are provided to offer a pathway for laboratories to adopt and seek their own accreditation for this critical test, to improve the management of tuberculosis globally. FundingVictorian Government Department of Health; Australian National Health and Medical Research Council and Medical Research Futures Fund. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed for studies using the search terms: "Mycobacterium tuberculosis", "clinical", "bioinformatics", "genomics", "drug resistance (OR antimicrobial)", published prior to 31st July 2024 without language restrictions (n=258). We considered all studies from this search that used genomics to infer likely drug-resistance in M. tuberculosis. Many of these studies highlight the challenges in making meaningful interpretations from genomic data for the purpose of inferring AMR for clinical applications. Despite the development of bioinformatics tools and compilation of catalogues of resistance conferring mutations, few of these studies directly address the challenges specific to implementation of a whole genome sequencing (WGS) and bioinformatics pipelines to deliver validated and accredited results for use in clinical applications and none provide sustainable solutions. Added value of this studyTo the best of our knowledge, this study is the first to detail practical solutions to challenges to validating and accrediting the routine inference of AMR from WGS data for clinical applications for treatment of M. tuberculosis. We developed, validated and accredited a bioinformatics tool, tbtAMR, which is robust and flexible, using a data-driven approach to inferring resistance, allowing for simple customisation of behaviour on a large collection of in-house data supplemented with publicly available data. Furthermore, we have made this pipeline and the accompanying data available for use by the wider public health community to aid others in implementing similar programs. Consultation with infectious disease clinicians, microbiologists and epidemiologists has allowed us to develop a robust workflow, that encompasses sequencing, analysis, interpretation, reporting, discrepancy resolution and ongoing verification. Implications of all the available evidenceThis study addressed the specific needs of reporting genomic AMR for M. tuberculosis in a clinical and public health laboratory. Furthermore, this work makes available a pipeline and data for laboratories to utilise to implement genomic AMR for M. tuberculosis. Through the use and further development of publicly available and open-source bioinformatics software, we have demonstrated the feasibility of clinical reporting of genomic AMR results for M. tuberculosis in a low-incidence high-income setting. We believe that this work lays the foundation for making reporting of genomic AMR for M. tuberculosis more accessible to public health and reference laboratories in general.

genomics↗

bioRxiv2022

Bridging the gap between bioinformatics and the clinical and public health microbiology laboratory: An ISO-accredited genomics workflow for antimicrobial resistance

Realising the promise of genomics to revolutionise routine AMR diagnosis and surveillance has been a long-standing challenge in clinical and public health microbiology. We have directly addressed this issue by creating and validating abritAMR, an ISO-accredited bioinformatics platform for genomics-based bacterial AMR gene detection. abritAMR utilises the NCBI AMRFinderPlus for detection of AMR genes and mutations, with additional features to classify AMR determinants into an antibiotic class. We validated abritAMR by comparing with multiplex PCR or gold-standard reference genomes, together representing 1500 different bacteria across 29 genera and covering 415 antibiotic resistance alleles. We also assessed inference of phenotypic resistance by comparing genomic predictions with agar dilution results for 864 Salmonella spp. Performance of abritAMR was excellent, detecting AMR genes with 99.9% accuracy (95% CI 99.9-99.9%), 97.9% sensitivity (95% CI 97.5-98.4%) and 100% specificity (100-100%). Phenotypic inference of resistance for Salmonella spp. was equally impressive, with 98.9% accuracy (98.7-99.1%). Validation data were submitted to the governing authority and ISO15189 accreditation was achieved. Implementation of abritAMR resulted in streamlined bioinformatics and reporting pathways, and it was readily updated and re-verified with database revisions or changes in reporting requirements. abritAMR is publicly and freely available to assist clinical and public health microbiology laboratories everywhere harness the power of AMR genomics in their professional practice.

microbiology↗

bioRxiv2017

Defining a landscape of molecular phenotypes using a simple single sample scoring method

BackgroundGene set scoring provides a useful approach for quantifying concordance between sample transcriptomes and selected molecular signatures. Most methods use information from all samples to score an individual sample, leading to unstable scores in small data sets and introducing biases from sample composition across a data set (e.g. varying numbers of samples for different cancer subtypes). To address these issues we have developed a truly single sample scoring method, and associated R/Bioconductor package singscore.\n\nResultsWe have developed a rank-based single sample scoring method, implemented as a Bioconductor package. We use multiple cancer data sets to compare it against widely-used scoring methods, including GSVA, z-scores, PLAGE, and ssGSEA. Our approach does not depend upon background samples and thus the scores are stable regardless of the composition and number of samples in the gene expression data set. In contrast, scores obtained by GSVA, z-score, PLAGE and ssGSEA can be unstable when less data are available (nsamples < 25). We show that the computational time for singscore is faster than current implementations of GSVA and ssGSEA, and is comparable with that of z-score and PLAGE. The singscore package also produces visualisations and interactive plots that enable exploration of molecular phenotypes.\n\nConclusionsThe single sample scoring method described here is independent of sample composition in gene expression data and thus it provides stable scores that are less likely to be influenced by unwanted variation across samples. These scores can be used for dimensional reduction of transcriptomic data and the phenotypic landscapes obtained by scoring samples against multiple molecular signatures may provide insights for sample stratification.

bioinformatics↗