Biology subjects

Field, M. A.

Publications and source records attributed to Field, M. A..

16 recordsLinked to original sources

bioRxiv2026

Advancing Liver Cancer Precision Medicine with TARGET-SL

Background and AimsA major goal of personalised liver oncology is the ability to make targeted predictions about cancer-specific toxicity, however there are limited methods available. To address this, we validated the performance of our bioinformatics framework, TARGET-SL, through ex vivo drug screening. MethodsUsing TARGET-SL we predicted gain of function (GOF), loss of function (LOF) and synthetic lethal (SL) genetic events, and corresponding drug candidates. We validated drug predictions across hepatocellular carcinoma (HCC) cell lines, and a cohort of HCC and cholangiocarcinoma (CCA) patient-derived organoids (PDOs). ResultsFor HCC cells and PDOs we found 37.5% and 25% of the respective selected compounds induced unique target-specific growth inhibition based on genetic biomarkers, suggesting novel biomarker-driven drug sensitivities. ConclusionsOur analyses demonstrate TARGET-SLs potential to enhance personalized drug screening for liver cancer, by focusing on genetically informed targets. This will reduce experimental costs and accelerate the pace of therapeutic discovery. Impact and ImplicationsPrimary liver cancer (PLC) is a cancer with poor prognosis, and current therapies increase survival only for a minority of patients. Through the application of TARGET-SL we can predict, for each patient, the essential genes and corresponding small molecule inhibitors. These data support further investigation in larger patient cohorts and offer the possibility to specify new small molecule inhibitors and to repurpose current drugs for PLC treatment. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/725819v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@10cb252org.highwire.dtl.DTLVardef@8f3049org.highwire.dtl.DTLVardef@ab4467org.highwire.dtl.DTLVardef@17f9d3_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LITARGET-SL can predict gene and drug sensitivities for cell lines and patient-derived organoids C_LIO_LIThis may reduce drug screening costs and accelerate the pace of therapeutic discovery. C_LIO_LITARGET-SL may assist in the repurposing of current drugs and their rapid translation for primary liver cancer C_LIO_LITARGET-SL is tumour-type agnostic, and therefore may have application in other cancers with poor prognosis C_LI

cancer biology↗

bioRxiv2026

Integrative genomics of Plasmodium knowlesi reveals parasite-intrinsic regulators of severe human malaria

To dissect parasite determinants of clinical disease severity in Plasmodium knowlesi, we performed the first integrative genome-wide association (GWAS), transcriptomic, and expression quantitative trait locus (eQTL) analysis from clinical malaria isolates. We identify distinct parasite programs associated with WHO-defined severe disease, characterized by transcriptional activation of stress-response and host-interaction pathways. In contrast, invasion-linked programs were more strongly associated with parasite burden, while immune-evasion pathways showed overlapping but distinct associations with both burden and severity. GWAS and eQTL analyses revealed genetic regulation of transcriptional states, including immune-evasion variant antigen families (SICAvar and kir) and chromatin-associated regulators. Mature gametocyte transcripts were detectable across infections, including those with low parasitaemia, indicating that transmission-stage expression occurs independently of both clinical severity and parasite density. Together, these findings show that severe P. knowlesi malaria is associated with genetically regulated parasite transcriptional programs that are not fully explained by parasite burden.

genomics↗

bioRxiv2025

ConsensusMetaDA: an R Package for consensus-based differential abundance analysis of metagenomic data.

Differential abundance (DA) analysis is critical in metagenomics research for identifying microbial taxa associated with variable biological conditions. Despite numerous mature DA tools being available, they often generate disparate results, making it challenging to obtain consistent and biologically meaningful findings. Tools differ substantially in their input requirements, underlying statistical models, and filtering strategies, yet researchers typically rely on a single tool due to the practical challenges of integrating multiple algorithms with variable workflows and non-standardized output formats. To address this challenge, we developed ConsensusMetaDA, an easy-to-use R package that integrates five widely-used DA tools (ALDEx2, DESeq2, EdgeR, metagenomeSeq, and ADAPT) into a streamlined three-function workflow. The package generates unified output reports and comprehensive visualizations including rarefaction curves, alpha/beta diversity analyses, taxonomic abundance plots, and Venn/UpSet diagrams to visualize algorithmic consensus and disagreement. We benchmarked ConsensusMetaDA on well-characterized public datasets and simulated data, demonstrating that individual tools exhibit complementary strengths and weaknesses. On datasets with known null results, EdgeR and DESeq2 reported false positive differentially abundant OTUs while ALDEx2, metagenomeSeq, and ADAPT correctly identified none. Conversely, simulations revealed that ALDEx2, metagenomeSeq, and ADAPT frequently missed true positives that EdgeR and DESeq2 detected. Our consensus approach improves both sensitivity and specificity compared to any single tool and enables researchers to minimize either false positive or false negative rates based on study-specific priorities. ConsensusMetaDA is freely available with comprehensive documentation at: https://github.com/FieldLabFNQOmics/ConsensusMetaDA

bioinformatics↗

bioRxiv2025

Detecting Somatic Mutations in Rare Clones using Single Cell Multi-Omics

BackgroundSomatic mutations are increasingly recognised as drivers of diseases beyond cancer, including autoimmune disorders. However, identifying rare, cell type-specific causal mutations remains challenging due to their low frequency within heterogeneous cell populations. Traditional bulk sequencing approaches lack the resolution to detect rare variants, underscoring the need for novel methods specifically designed for single-cell data of heterogeneous cell populations. MethodsWe present an integrated single-cell multi-omics computational framework, SCARCE (Single-Cell Analysis of Rare Clonal Events), tailored for single-cell DNA sequencing (scDNA-seq) to statistically prioritise rare somatic mutations within defined cell subpopulations. By comparing variant frequencies across subpopulations, identified through either variant-based clustering or cell type annotation from surface marker expression, we identify variants enriched in specific cell populations. Our method applies multiple user-adjustable filters and statistical enrichment tests to distinguish true somatic variants from technical artifacts. ResultsSCARCE successfully identifies rare somatic mutations across three distinct datasets using technologies including MissionBio Tapestri and clonally-amplified whole-genome sequencing of single cells. We demonstrate that SCARCE correctly isolates and identifies true variants in a cell population comprising just 10 of 16,316 cells (0.06% of the total population). Furthermore, in an extensively characterised sample with known causal variants, SCARCE correctly identifies all known pathogenic variants among its top-ranked candidates. ConclusionsSCARCE offers several advantages over existing tools in the field. By integrating genetic and phenotypic information at single-cell resolution, our approach opens new avenues for understanding the clonal origins of diseases driven by somatic mutations in small cell populations.

bioinformatics↗

bioRxiv2025

CD8+ T cells mediate vaccination-induced lymphatic containment of latent Mycobacterium tuberculosis infection following immunosuppression, while B cells are dispensable

It is estimated that two billion people are latently infected with Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB). Latent Mtb infection (LTBI) can occur in multiple organs, including the lymphatics. The risk of LTBI reactivation increases in immunocompromised conditions, such as coinfection with human immunodeficiency virus (HIV), and during treatment of autoimmune diseases and organ transplantation. The immunological correlates of protection against TB, including against reactivation of LTBI, remain largely elusive. Here, we used a mouse model of latent lymphatic Mtb infection to dissect the immunological mechanisms underlying LTBI containment versus reactivation. We show that immunosuppression-mediated reactivation of lymphatic LTBI and the subsequent spread to non-lymphatic organs can be prevented by vaccination with multiple recombinant BCG (rBCG) strains despite the deficiency of CD4+ T cells. Using spatial transcriptomics, multi-parameter imaging, network analysis and bioinformatic integration of histopathological images, we reveal that immunosuppression is associated with a distinct repositioning of non-CD4 immune cells at the edge of TB lesions within the infection-draining cervical lymph nodes. While B cells increased in numbers, they are dispensable for the containment of LTBI. Lymphatic Mtb infection in different immune cell-deficient mouse strains, antibody-mediated cell depletion and adoptive transfer experiments into highly susceptible mice unequivocally show that vaccination-mediated prevention of LTBI reactivation is critically dependent on CD8+ T cells. These findings have profound implications for our understanding of immunity to TB and the management of LTBI. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=92 SRC="FIGDIR/small/634479v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@151fdd6org.highwire.dtl.DTLVardef@1efed47org.highwire.dtl.DTLVardef@c2e7e0org.highwire.dtl.DTLVardef@555c7_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2024

Chromosomal inversions harbour excess mutational load in the coral, Acropora kenti, on the Great Barrier Reef

The future survival of coral reefs in the Anthropocene depends on the capacity of corals to adapt as oceans warm and extreme weather events become more frequent. Targeted interventions designed to assist evolutionary processes in corals require a comprehensive understanding of the distribution and structure of standing variation, however, efforts to map genomic variation in corals have so far focussed almost exclusively on SNPs, overlooking structural variants that have been shown to drive adaptive processes in other taxa. Here we show that the reef-building coral, Acropora kenti (syn. tenuis) harbors at least five large, highly polymorphic structural variants, all of which exhibit signatures of strongly suppressed recombination in heterokaryotypes, a feature commonly associated with chromosomal inversions. Based on their high minor allele frequency, uniform distribution across habitats, and elevated genetic load, we propose that these inversions in A. kenti are likely to be under balancing selection. An excess of SNPs with high impact on protein coding genes within these loci elevates their importance both as potential targets for adaptive selection and as contributors to genetic decline if coral populations become fragmented or inbred in future.

evolutionary biology↗

bioRxiv2024

Self-reactive B cells traverse a perfect storm of somatic mutagenesis to cause a virus-induced autoimmune disease

The unexplained association between infection and autoimmune disease is strongest for hepatitis C virus-induced cryoglobulinemic vasculitis (HCV-CV). We traced the evolution of the pathogenic rheumatoid factor (RhF) autoantibodies in four HCV-CV patients by deep single cell multi-omic analysis, revealing three sources of B cell somatic mutation converged to drive accumulation of a large disease causing clone. A sensitive method for quantifying low affinity binding revealed three recurring heavy/light chain combinations created by V(D)J recombination bound self IgG but not viral E2 antigen. Whole genome sequencing revealed accumulation of thousands of somatic mutations, at levels comparable to CLL and normal memory B cells, but with 1-2 corresponding to driver mutations found recurrently in B cell leukemia/lymphoma. V(D)J hypermutation created autoantibodies with compromised solubility. In this virus-induced autoimmune disease, infection promotes a perfect storm of somatic mutagenesis in the descendants of a single B cell.

immunology↗

bioRxiv2023

Reversing the decline of threatened koala (Phascolarctos cinereus) populations in New South Wales: Using genomics to define meaningful conservation goals

Genetic management is a critical component of threatened species conservation. Understanding spatial patterns of genetic diversity is essential for evaluating the resilience of fragmented populations to accelerating anthropogenic threats. Nowhere is this more relevant than on the Australian continent, which is experiencing an ongoing loss of biodiversity that exceeds any other developed nation. Using a proprietary genome complexity reduction-based method (DArTSeq), we generated a data set of 3,239 high quality Single Nucleotide Polymorphisms (SNPs) to investigate spatial patterns and indices of genetic diversity in the koala (Phascolarctos cinereus), a highly specialised folivorous marsupial that is experiencing rapid and widespread population declines across much of its former range. Our findings demonstrate that current management divisions across the state of New South Wales (NSW) do not fully represent the distribution of genetic diversity among extant koala populations, and that care must be taken to ensure that translocation paradigms based on these frameworks do not inadvertently restrict gene flow between populations and regions that were historically interconnected. We also recommend that koala populations should be prioritised for conservation action based on the scale and severity of the threatening processes that they are currently faced with, rather than placing too much emphasis on their perceived value (e.g., as reservoirs of potentially adaptive alleles), as our data indicate that existing genetic variation in koalas is primarily partitioned amongst individual animals. As such, the extirpation of koalas from any part of their range represents a potentially critical reduction of genetic diversity for this iconic Australian species.

genomics↗

bioRxiv2023

Small extracellular vesicles but not microvesicles from Opisthorchis viverrini promote cell proliferation in human cholangiocytes.

Chronic infection with O. viverrini has been linked to the development of cholangiocarcinoma (CCA), which is a major public health burden in the Lower Mekong River Basin countries, including Thailand, Lao PDR, Vietnam and Cambodia. Despite its importance, the exact mechanisms by which O. viverrini promotes CCA are largely unknown. In this study, we characterized different extracellular vesicle populations released by O. viverrini (OvEVs) using proteomic and transcriptomic analyses and investigated their potential role in host-parasite interactions. While 120k OvEVs promoted cell proliferation in H69 cells at different concentrations, 15k OvEVs did not produce any effect compared to controls. The proteomic analysis of both populations showed differences in their composition that could contribute to this differential effect. Furthermore, the miRNAs present in 120k EVs were analysed and their potential interactions with human host genes was explored by computational target prediction. Different pathways involved in inflammation, immune response and apoptosis were identified as potentially targeted by the miRNAs present in this population of EVs. This is the first study showing specific roles for different EV populations in the pathogenesis of a parasitic helminth, and more importantly, an important advance towards deciphering the mechanisms used in establishment of opisthorchiasis and liver fluke infection-associated malignancy.

microbiology↗

bioRxiv2023

The Australasian dingo archetype: De novo chromosome-length genome assembly, DNA methylome, and cranial morphology

BackgroundOne difficulty in testing the hypothesis that the Australasian dingo is a functional intermediate between wild wolves and domesticated breed dogs is that there is no reference specimen. Here we link a high-quality de novo long read chromosomal assembly with epigenetic footprints and morphology to describe the Alpine dingo female named Cooinda. It was critical to establish an Alpine dingo reference because this ecotype occurs throughout coastal eastern Australia where the first drawings and descriptions were completed. FindingsWe generated a high-quality chromosome-level reference genome assembly (Canfam_ADS) using a combination of Pacific Bioscience, Oxford Nanopore, 10X Genomics, Bionano, and Hi-C technologies. Compared to the previously published Desert dingo assembly, there are large structural rearrangements on Chromosomes 11, 16, 25 and 26. Phylogenetic analyses of chromosomal data from Cooinda the Alpine dingo and nine previously published de novo canine assemblies show dingoes are monophyletic and basal to domestic dogs. Network analyses show that the mtDNA genome clusters within the southeastern lineage, as expected for an Alpine dingo. Comparison of regulatory regions identified two differentially methylated regions within glucagon receptor GCGR and histone deacetylase HDAC4 genes that are unmethylated in the Alpine dingo genome but hypermethylated in the Desert dingo. Morphological data, comprising geometric morphometric assessment of cranial morphology place dingo Cooinda within population-level variation for Alpine dingoes. Magnetic resonance imaging of brain tissue show she had a larger cranial capacity than a similar-sized domestic dog. ConclusionsThese combined data support the hypothesis that the dingo Cooinda fits the spectrum of genetic and morphological characteristics typical of the Alpine ecotype. We propose that she be considered the archetype specimen for future research investigating the evolutionary history, morphology, physiology, and ecology of dingoes. The female has been taxidermically prepared and is now at the Australian Museum, Sydney.

genomics↗

bioRxiv2021

StabilitySort: assessment of protein stability changes on a genome-wide scale to prioritise potentially pathogenic genetic variation

SummaryMissense mutations that change protein stability are strongly associated with human inherited genetic disease. With the recent availability of predicted structures for all human proteins generated using the AlphaFold2 prediction model, genome-wide assessment of the stability effects of genetic variation can, for the first time, be easily performed. This facilitates the interrogation of personal genetic variation for potentially pathogenic effects through the application of stability metrics. Here, we present a novel algorithm to prioritise variants predicted to strongly destabilise essential proteins, available as both a standalone software package and a web-based tool. We demonstrate the utility of this tool by showing that at values of the Stability Sort Z-score above 1.6, pathogenic, protein-destabilising variants from ClinVar are detected at a 58% enrichment, over and above the destabilising (but presumably non-pathogenic) variation already present in the HapMap NA12878 genome. Availability and ImplementationStabilitySort is available as both a web service (http://130.56.244.113/StabilitySort/) and can be deployed as a standalone system (https://gitlab.com/baaron/StabilitySort). ContactDan.Andrews@anu.edu.au

bioinformatics↗

bioRxiv2020

Spatial population genomics of a recent mosquito invasion

Population genomic approaches can characterise dispersal across a single generation through to many generations in the past, bridging the gap between individual movement and intergenerational gene flow. These approaches are particularly useful when investigating dispersal in recently altered systems, where they provide a way of inferring long-distance dispersal between newly established populations and their interactions with existing populations. Human-mediated biological invasions represent such altered systems which can be investigated with appropriate study designs and analyses. Here we apply temporally-restricted sampling and a range of population genomic approaches to investigate dispersal in a 2004 invasion of Aedes albopictus (the Asian tiger mosquito) in the Torres Strait Islands (TSI) of Australia. We sampled mosquitoes from 13 TSI villages simultaneously and genotyped 373 mosquitoes at genome-wide single nucleotide polymorphisms (SNPs): 331 from the TSI, 36 from Papua New Guinea (PNG), and 4 incursive mosquitoes detected in uninvaded regions. Within villages, spatial genetic structure varied substantially but overall displayed isolation by distance and a neighbourhood size of 232-577. Close kin dyads revealed recent movement between islands 31-203 km apart, and deep learning inferences showed incursive Ae. albopictus had travelled to uninvaded regions from both adjacent and non-adjacent islands. Private alleles and a coancestry matrix indicated direct gene flow from PNG into nearby islands. Outlier analyses also detected four linked alleles introgressed from PNG, with the alleles surrounding 12 resistance-associated cytochrome P450 genes. By treating dispersal as both an intergenerational process and a set of discrete events, we describe a highly interconnected invasive system.

genomics↗

bioRxiv2020

Signatures of selection in the coral holobiont reveal complex adaptations to inshore environments driven by Holocene climate change

Climate change at the Pleistocene/Holocene boundary reshaped many coastal landscapes, and provides an opportunity to study recent adaptive processes in marine species and ecosystems including coral reefs. On the Great Barrier Reef (GBR) sea level rise flooded a vast shelf creating a distinct inshore region which now harbours extensive coral assemblages despite being subject to relatively high turbidity, freshwater input and thermal fluctuations. To investigate how the coral holobiont has adapted to these conditions we first generated a highly contiguous genome assembly for Acropora tenuis based on long-read sequencing, and then used shallow whole-genome resequencing of 148 Acropora tenuis colonies from five inshore locations to model demographic history, identify signatures of selection and profile symbiont communities. We show that corals from Magnetic Island, located in the central inshore region of the GBR, are genetically distinct from those 50-500km further north, reflecting a Pleistocene (250-600Kya) split, whereas photosymbiont genotypes differ between reefs in a pattern more likely to reflect contemporary (Holocene) conditions. We also identified loci in the coral host genome with signatures of positive selection in the northern population and used coalescent simulations to show that these are unlikely to be accounted for by demographic history. Genes at these loci have roles in a diverse range of processes that includes heterotrophic nutrition, osmotic regulation, skeletal development and the establishment and maintenance of symbiosis. Our results show that, in the case of A. tenuis holobionts from the inshore GBR, the genomes of both the coral host and the primary photosymbiont of have been significantly shaped by their environment and illustrate the complexity of adaptations that have occurred in response to past climate change.

genomics↗

bioRxiv2019

Pharmacogenetic variants associated with off-target adverse drug reactions are mostly predicted to be benign

Tools that predict the functional importance of genetic variation almost always rely on sequence conservation across deep evolutionary divergences as a primary discriminator. However, sequence conservation information is misleading when predicting the functional importance of pharmacogenetic variants related to off-target adverse drug reactions. Sequence conservation is largely maintained by evolutionary purifying selection, which has not been relevant for most drugs until very recently, especially for off-target effects. Here, we use a simple classification criteria to identify variants with off-target pharmacogenetic effects from the PharmGKB database. We show that off-target pharmacogenetic variation is predicted mostly to be benign by all state-of-the-art prediction tools we tested. Hence, off-target pharmacogenetic variants are overwhelmingly invisible to all predictive methodologies currently employed. Very different analytical approaches will be needed to address this important problem.\n\nAuthor SummaryWhen a personal genome sequence is obtained for a given person, the sequence is compared to the human reference sequence to identify where it differs from the genome of that person. One application of this information is that it may identify how a specific person may react to particular drugs. However, when computationally predicting the functional importance of a genetic variant, the tools used rely heavily on sequence conservation information to make their prediction. From an evolutionary point of view, the use of drugs to treat diseases is a very recent activity - and one that has not had time to cause certain variants to either be selected for or removed from the population. This produces a blind-spot for tools that predict variant functional effects, especially for drugs with off-target interactions that may produce unanticipated effects.

bioinformatics↗

bioRxiv2019

consensusDE: an R package for assessing consensus of multiple RNA-seq algorithms with RUV correction

Extensive evaluation of RNA-seq methods have demonstrated that no single algorithm consistently outperforms all others. Removal of unwanted variation (RUV) has also been proposed as a method for stabilizing differential expression (DE) results. Despite this, it remains a challenge to run multiple RNA-seq algorithms to identify significant differences common to multiple algorithms, whilst also integrating and assessing the impact of RUV into all algorithms. consensusDE was developed to automate the process of identifying significant DE by combining the results from multiple algorithms with minimal user input and with the option to automatically integrate RUV. consensusDE only requires a table describing the sample groups, a directory containing BAM files or preprocessed count tables and an optional transcript database for annotation. It supports merging of technical replicates, paired analyses and outputs a compendium of plots to guide the user in subsequent analyses. Herein, we also assess the ability of RUV to improve DE stability when combined with multiple algorithms through application to real and simulated data. We find that, although RUV demonstrated improved FDR in a setting of low replication, the effect was algorithm specific and diminished with increased replication, reinforcing increased replication for recovery of true DE genes. We finish by offering some rules and considerations for the application of RUV in a consensus-based setting.\n\nconsensusDE is freely available, implemented in R and available as a Bioconductor package, under the GPL-3 license, along with a comprehensive vignette describing functionality: http://bioconductor.org/packages/consensusDE/

bioinformatics↗

bioRxiv2017

Cleaning clinical genomic data: Simple identification and removal of recurrently miscalled variants in single genomes

Identification of sequence variation from short-read sequence data is subject to common-yet-intermittent miscalling that occurs in a sequence intrinsic manner. We identify that recurrent false positive single nucleotide variants are strongly present in databases of human sequence variation and demonstrate how each individual sample generates a unique set of recurrent false positive variants. These recurrent miscalls result from known difficulties aligning short-read sequence data between redundant genomic regions. We could replicate, catalogue and remove three quarters of these recurrent miscalls for any given exome with as little as ten rounds of read resampling, realignment and recalling. The removal of such misleading variants reduces the search space for identification of disease causing variants.\n\nList of Abbreviations

genomics↗