Biology subjects

Barnes, M.

Publications and source records attributed to Barnes, M..

At least 19 recordsLinked to original sources

bioRxiv2026

A Compendium of 49 Experimental SBS Signatures for Decoding Human Cancer Mutational Processes

Human cancer genomes harbor distinct mutational patterns that reflect past processes of DNA damage and repair. However, the precise attribution of these signatures to specific chemical carcinogens lacks a standardized experimental reference framework. To address this gap, we curated 4,282 genome-wide sequencing datasets from 42 model systems across five species exposed to 146 cancer-risk agents. This platform yielded 49 robust experimental single-base substitution signatures (eSS), with 28 matching 19 established COSMIC signatures and 21 defining novel mutational processes. We reconstructed 24 COSMIC signatures, assigning candidate etiologies to five signatures of unknown origin and revising two contested assignments. Pan-cancer decomposition detected four eSS-like mutational processes enriched in smokers across 4,951 tumors. Lastly, independent single-molecule sequencing of primary human organoids reproduced these profiles with high fidelity, confirming true platform-independent biological reproducibility across complex human models. This eSS repertoire provides a reference that links human mutational processes to mechanistic classes of DNA damage.

genomics↗

bioRxiv2026

The effect of environmental variation on the diversity and composition of the three-spined stickleback microbiome

The fish skin microbiome serves as a protective barrier, influencing host health and facilitating interactions between the host and its environment. While several studies have characterised the composition and roles of the fish skin microbiome, there remains a paucity of data on how environmental variation influences these microbes in natural populations. Here, we used 16S rRNA gene sequencing to characterise the skin microbiome of wild three-spined stickleback populations and examine how environmental factors influence microbial diversity and community composition across 17 freshwater lochs on the island of North Uist, Scotland. Analysis of 239 samples revealed a set of dominant bacterial genera commonly associated with other fish species, including Janthinobacterium, Pseudomonas, Acinetobacter, and Psychrobacter, that constituted a core skin microbiota across lochs. Microbiome composition was primarily shaped by environmental variables, particularly habitat, water pH, conductivity, and metal concentrations, with pH emerging as a key driver of community structure. Host sex also influenced microbiome variation, with several taxa differing in relative abundance between males and females. Alpha-diversity was higher among stickleback fish from lochs with a neutral pH compared with those from alkaline and acidic environments. Differential abundance analyses identified 27 and 24 amplicon sequence variants (ASVs), respectfully, associated with variations in pH and host sex, including members of Psychrobacter, Sphingobacterium, Carnobacterium, Chryseobacterium, and Arthrobacter, highlighting the combined influence of environmental and host factors on microbiome composition in wild fish populations in freshwater environments.

microbiology↗

bioRxiv2026

Sleep renormalizes learning-perturbed cortical population dynamics to stabilize memory

The brain must preserve learning-induced circuit changes without destabilizing the network dynamics required for future learning. We show that sleep addresses this tension through perturbation-dependent renormalization of cortical dynamics. Using high-density electroencephalography in humans following declarative learning and a matched non-learning control, we found that learning displaced cortical population dynamics during wakefulness, followed by an opposing reorganization during non-rapid eye movement (NREM) sleep. The preceding waking perturbation constrained this sleep response across time, cortical space, and individuals: NREM activity evolved along the wake-defined state-space direction, regions with larger waking perturbations showed stronger opposing responses, and larger perturbations predicted stronger restorative trajectories. This cross-state geometry generalized to held-out participants and predicted sleep-specific memory benefit beyond either state alone or their simple difference. Neural stability is therefore achieved by regulating trajectories rather than returning to a fixed baseline, with plasticity-induced perturbations specifying how cortical state space is reorganized to preserve learned structure while enabling further change.

neuroscience↗

bioRxiv2025

The SjD Map: An interactive pathway tour into Sjogren's disease signalling mechanisms

ObjectivesSjogrens disease (SjD) remains a major unmet medical challenge, marked by biological complexity, patient heterogeneity, and a lack of curative treatments. To advance the understanding of its pathogenesis and support therapeutic discovery, we developed a comprehensive knowledgebase in the form of a Molecular Interaction Map (MIM). MethodsDifferential expression analysis was performed on peripheral blood samples from SjD patients and healthy controls across three datasets: GSE51092 (190 SjD vs 32 controls), UKPSSR (151 SjD vs 29 controls) and PRECISESADS (304 SjD vs 341 controls). Pathway enrichment analyses provided guidance for MIM construction, which was further refined through literature mining to integrate data-driven results with curated knowledge. ResultsA total of 1,625 differentially expressed genes (DEGs) were identified: 725 from PRECISESADS, 1,162 from GSE51092, and 239 from UKPSSR, with 25 DEGs shared across all three datasets. Among these, nine common DEGs were associated with interferon signalling, reinforcing experimental evidence pointing to its pivotal role in SjD. Enrichment analyses revealed 146 pathways, 43 of which were successfully incorporated into the MIM. The resulting SjD Map freely accessible at https://sjdmap.elixir-luxembourg.org/, comprises 829 molecular entities connected by 598 interactions, with 45% of the information depicted supported by transcriptomic data and 47% derived from literature. The map also includes overlays of experimental data and clinical trial information. ConclusionThis first comprehensive Sjogrens Disease Map, developed through a hybrid data-driven and literature-based approach, offers an integrative view of SjD pathogenesis. It supports visualisation of mechanistic pathways, omics-based data overlays, enables incorporation of user data and drug queries. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/674876v1_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@1bdc517org.highwire.dtl.DTLVardef@1d68077org.highwire.dtl.DTLVardef@18c079forg.highwire.dtl.DTLVardef@487b64_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO C_FIG Key messagesO_LIThis is the first molecular interaction map specific of Sjogrens Disease C_LIO_LIIt enables comprehensive visualisation of affected pathways through integrated transcriptomic and literature-based evidence, following systems biology graphical notation schemes C_LIO_LIThe map may support therapeutic discovery by linking molecular mechanisms to clinical data and drug targets C_LI

systems biology↗

bioRxiv2025

Immunomodulation by AZD1656 reverses cardiac dysfunction, metabolic remodelling and reduces infarct size in type 2 diabetic cardiomyopathy

Type 2 diabetes (T2D) precipitates diabetic cardiomyopathy (dbCM), a condition characterized by chronic inflammation, metabolic dysregulation and impaired cardiac performance. Here we show that the glucokinase activator AZD1656, originally developed for glycaemic control but later identified to have immunomodulatory effects, reverses cardiac dysfunction and metabolic remodelling in dbCM. In obese, hyperglycaemic db/db mice with diastolic dysfunction, six weeks of AZD1656 treatment improved myocardial performance, reduced infarct size and enhanced post-ischaemic recovery. Integrated metabolic, functional and histological analyses revealed restoration of mitochondrial metabolism and attenuation of fibrosis. Mechanistically, AZD1656 remodelled the cardiac immune landscape by promoting regulatory T-cell infiltration. These findings demonstrate a link between cardiac inflammation and metabolic remodelling in dbCM and highlight that modulation of immune cells and metabolism can protect the diabetic heart. Targeting immunometabolic pathways may therefore offer a therapeutic strategy to alleviate cardiac dysfunction and reduce infarct vulnerability in T2D

physiology↗

bioRxiv2025

Early disruptions in vitamin D receptor signaling induces persistent developmental behavior deficits in zebrafish larvae

A critical function of the nervous system is to rapidly process sensory information and initiate appropriate behavioral responses. Defects in sensory processing and behavior selection are commonly observed in neuro-psychiatric conditions including anxiety, autism (ASD), and schizophrenia. The etiology of sensory processing disorders remains equivocal; however, it is hypothesized that extrinsic environmental factors can play fundamental roles. In this study we examine the importance of vitamin D (1, 25-dihydroxyvitamin D3) receptor signaling during early life stage development on sensory processing and neurobehavioral health outcomes. While vitamin D has traditionally been associated with mineral ion homeostasis, accumulating evidence suggests non-calcemic roles for vitamin D including early neurodevelopment. Here we demonstrate that systemic disruption of vitamin D receptor (VDR) signaling with a conditional dominant negative (dnVDR) transgenic zebrafish line results in specific visual and acoustic sensorimotor behavior defects. Induction of dnVDR between 24-72 hours post fertilization (hpf) results in modulation of visual motor response with demonstrate attenuation in acute activity and hypolocomotion across multiple swimming metrics when assayed at 6- and 28-days post fertilization (dpf). Disruption in VDR signaling additionally resulted in a strong and specific attenuation of the Long-Latency C-bends (LLC) within the acoustic startle response at 6 dpf while Short-Latency C-bends (SLC) were moderately impacted. Pre-pulse inhibition (PPI) was not impacted in young larvae however exhibited a significantly attenuated response at 28 dpf suggesting an inability to properly modulate their startle responses later in development and persistent effects of VDR modulation during early development. Overall, our data demonstrate that modulation of vitamin D signaling during critical windows of development irreversibly disrupts the development of neuronal circuitry associated with sensory processing behaviors which may have significant implications to neurobehavioral health outcomes.

neuroscience↗

bioRxiv2025

Deep learning-driven morphology analysis enables label-free classification of therapeutic agent- naive versus resistant cancer cells

Therapeutic drug treatments of solid tumors are often undermined by various resistance mechanisms. Identification of drug-resistance phenotypes at the single cell level is challenging because conventional molecular methods are cell-destructive, labor-intensive, and cost-prohibitive. To overcome these challenges, we developed an orthogonal approach to drug-resistance phenotyping, through the use of deep-learning-driven morphology analysis of single, high resolution cell images. Specifically, we trained deep learning-based drug resistance classifiers using cell images from 5 different cell lines that were rendered resistant to 5 different therapeutic agents, using a foundation model framework. With high accuracy, the classifier correctly predicted naive or resistance phenotypes across all cancer types and across all the therapeutic agent types (chemotherapeutic, targeted) tested. These results showed that morphology can capture complex phenotype information in the context of drug treatment. To demonstrate the potential clinical utility of the drug resistance classifier, it was applied to a dissociated tumor biopsy and the resulting phenotype predictions were in close concordance with scRNASeq analysis of the biopsy. Our study highlights the potential of deep-learning-driven morphology analysis to provide complex phenotype information, and ultimately shape oncology drug treatment strategies at the patient-level in a clinical context.

cancer biology↗

bioRxiv2024

Ecological synchrony in human-modified landscapes under a changing climate

Different aspects of ecological systems, biotic or abiotic, often fluctuate in coordinated patterns over space and time. Such high concordance between ecological processes is often referred to as ecological synchrony. Anthropogenic activities, including and beyond climate change, have the potential to alter ecological synchrony by disrupting or enhancing existing synchrony. Despite many local studies, we have a limited systematic understanding of how ecological synchrony is shaped by management in human-dominated landscapes at regional to continental scales. From a macrosystems perspective, we review how anthropogenic activities, particularly beyond climate change, alter ecological synchrony across levels of ecological organization, from the ecosystem level to the population level. For each level, we use a large-scale case study to demonstrate ways to quantify the impacts of human modifications on synchrony using big data from remote sensing, surveys, and observatory networks. For example, we detected possible homogenization of population dynamics of bird species in North America. These changes in ecological synchrony, although in different forms, often represent challenges to ecological and social systems. Collaborative research efforts that integrate emerging open data streams moving forward will be able to provide insights into the effects of different anthropogenic drivers and the consequences of changes in synchrony.

ecology↗

bioRxiv2023

Development of resident and migratory three-spined stickleback, Gasterosteus aculeatus.

The three-spined stickleback (Gasterosteus aculeatus) is a teleost fish and a model organism in evolutionary ecology, useful for both laboratory and natural experiments. It is especially valued for the substantial intraspecific variation in morphology, behaviour and genetics. Classic work of Swarup (1958) [1] has described the development in the laboratory of embryos from a single freshwater population, but this was carried out at higher temperature than many stickleback would encounter in the wild and variation between populations was not addressed. Here we describe the development of embryos from two sympatric, saltwater ecotypes of stickleback from North Uist, Scotland raised at 14{degrees}C, the approximate temperature of North Uist lochs in the breeding season. The two ecotypes were (a) a large, migratory form in which the adults are completely plated with bony armour and (b) a smaller, low-plated form that is resident year-round in saltwater lagoons. By monitoring embryos every 24-hours post fertilisation, important characteristics of development were observed and photographed to provide a reference for North Uist ecotypes at this temperature. Hatching success was greater than 85% and did not differ between resident and migratory stickleback, but migratory eggs hatched significantly earlier than the resident ecotype. Our work provides a framework that can now be used to compare stickleback populations that may also grow in distinct environmental conditions, to help understand the breadth of normal developmental features and to characterise abnormal development.

ecology↗

bioRxiv2023

Assigning mutational signatures to individual samples and individual somatic mutations with SigProfilerAssignment

Analysis of mutational signatures is a powerful approach for understanding the mutagenic processes that have shaped the evolution of a cancer genome. Here we present SigProfilerAssignment, a desktop and an online computational framework for assigning all types of mutational signatures to individual samples. SigProfilerAssignment is the first tool that allows both analysis of copy-number signatures and probabilistic assignment of signatures to individual somatic mutations. As its computational engine, the tool uses a custom implementation of the forward stagewise algorithm for sparse regression and nonnegative least squares for numerical optimization. Analysis of 2,700 synthetic cancer genomes with and without noise demonstrates that SigProfilerAssignment outperforms four commonly used approaches for assigning mutational signatures. SigProfilerAssignment is freely available at https://github.com/AlexandrovLab/SigProfilerAssignment with a web implementation at https://cancer.sanger.ac.uk/signatures/assignment/.

bioinformatics↗

bioRxiv2023

Visualizing and exploring patterns of large mutational events with SigProfilerMatrixGenerator

BackgroundAll cancers harbor somatic mutations in their genomes. In principle, mutations affecting between one and fifty base pairs are generally classified as small mutational events. Conversely, large mutational events affect more than fifty base pairs, and, in most cases, they encompass copy-number and structural variants affecting many thousands of base pairs. Prior studies have demonstrated that examining patterns of somatic mutations can be leveraged to provide both biological and clinical insights, thus, resulting in an extensive repertoire of tools for evaluating small mutational events. Recently, classification schemas for examining large-scale mutational events have emerged and shown their utility across the spectrum of human cancers. However, there has been no standard bioinformatics tool that allows visualizing and exploring these large-scale mutational events ResultsHere, we present a new version of SigProfilerMatrixGenerator that now delivers integrated capabilities for examining large mutational events. The tool provides support for examining copy-number variants and structural variants under two previously developed classification schemas and it supports data from numerous algorithms and data modalities. SigProfilerMatrixGenerator is written in Python with an R wrapper package provided for users that prefer working in an R environment. ConclusionsThe new version of SigProfilerMatrixGenerator provides the first standardized bioinformatics tool for optimized exploration and visualization of two previously developed classification schemas for copy number and structural variants. The tool is freely available at https://github.com/AlexandrovLab/SigProfilerMatrixGenerator with an extensive documentation at https://osf.io/s93d5/wiki/home/.

bioinformatics↗

bioRxiv2022

Topography of mutational signatures in human cancer

The somatic mutations found in a cancer genome are imprinted by different mutational processes. Each process exhibits a characteristic mutational signature, which can be affected by the genome architecture. However, the interplay between mutational signatures and topographical genomic features has not been extensively explored. Here, we integrate mutations from 5,120 whole-genome sequenced tumours from 40 cancer types with 516 topographical features from ENCODE to evaluate the effect of nucleosome occupancy, histone modifications, CTCF binding, replication timing, and transcription/replication strand asymmetries on the cancer-specific accumulation of mutations from distinct mutagenic processes. Most mutational signatures are affected by topographical features with signatures of related aetiologies being similarly affected. Certain signatures exhibit periodic behaviours or cancer-type specific enrichments/depletions near topographical features, revealing further information about the processes that imprinted them. Our findings, disseminated via COSMIC, provide a comprehensive online resource for exploring the interactions between mutational signatures and topographical features across human cancer. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=175 SRC="FIGDIR/small/493921v2_figu1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@abf065org.highwire.dtl.DTLVardef@33ad99org.highwire.dtl.DTLVardef@ca37b6org.highwire.dtl.DTLVardef@fa7c6_HPS_FORMAT_FIGEXP M_FIG C_FIG HIGHLIGHTSO_LIComprehensive topography analysis of mutational signatures encompassing 82,890,857 somatic mutations in 5,120 whole-genome sequenced tumours integrated with 516 tissue-matched topographical features from the ENCODE project. C_LIO_LIThe accumulation of somatic mutations from most mutational signatures is affected by nucleosome occupancy, histone modifications, CTCF binding sites, transcribed regions, or replication strand/timing. C_LIO_LIMutational signatures with related aetiologies are consistently characterized by similar genome topographies across tissue types. C_LIO_LITopography analysis allows both separating signatures from different aetiologies and understanding the genomic specificity of clustered somatic mutations. C_LIO_LIA comprehensive online resource, disseminate through the COSMIC signatures database, that allows researchers to explore the interactions between somatic mutational processes and genome architecture within and across cancer types. C_LI

cancer biology↗

bioRxiv2022

TCGADEPMAP -- Mapping Translational Dependencies and Synthetic Lethalities within The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA) has yielded unprecedented genetic and molecular characterization of the cancer genome, yet the functional consequences and patient-relevance of many putative cancer drivers remain undefined. TCGADEPMAP is the first hybrid map of translational tumor dependencies that was built from machine learning of gene essentiality in the Cancer Dependency Map (DEPMAP) and then translated to TCGA patients. TCGADEPMAP captured well-known and novel cancer lineage dependencies, oncogenes, and synthetic lethalities, demonstrating the robustness of TCGADEPMAP as a translational dependency map. Exploratory analyses of TCGADEPMAP also unveiled novel synthetic lethalities, including the dependency of PAPSS1 driven by loss of PAPSS2 which is collaterally deleted with the tumor suppressor gene PTEN. Synthetic lethality of PAPSS1/2 was validated in vitro and in vivo, including the underlying mechanism of synthetic lethality caused by the loss of protein sulfonation that requires PAPSS1 or PAPSS2. Moreover, TCGADEPMAP demonstrated that patients with predicted PAPSS1/2 synthetic lethality have worse overall survival, suggesting that these patients are in greater need of drug discovery efforts to target PAPSS1. Other map "extensions" were built to capture unique aspects of patient-relevant tumor dependencies using the flexible analytical framework of TCGADEPMAP, including translating gene essentiality to drug response in patient-derived xenograft (PDX) models (i.e., PDXEDEPMAP) and predicting gene tolerability within normal tissues (GTEXDEPMAP). Collectively, this study demonstrates how translational dependency maps can be used to leverage the rapidly expanding catalog of patient genomic datasets to identify and prioritize novel therapeutic targets with the best therapeutic indices.

cancer biology↗

bioRxiv2022

Global hypo-methylation in a subgroup of glioblastoma enriched for an astrocytic signature is associated with increased invasion and altered immune landscape

We describe a new molecular subgroup of glioblastoma, the most prevalent malignant adult brain tumour, harbouring a bias towards hypomethylation at defined differentially methylated regions. This epigenetic signature correlates with an enrichment for an astrocytic gene signature, which together with the identification of enriched predicted binding sites of transcription factors known to cause demethylation and to be involved in astrocytic/glial lineage specification, point to a shared ontogeny between this glioblastoma subgroup and astroglial progenitors. At functional level, increased invasiveness, at least in part mediated by SRPX2, and macrophage infiltration characterise this glioblastoma subgroup.

cancer biology↗

bioRxiv2021

INfrastructure for a PHAge REference Database: Identification of large-scale biases in the current collection of phage genomes

BackgroundWith advances in sequencing technology and decreasing costs, the number of bacteriophage genomes that have been sequenced has increased markedly in the last decade. Materials and MethodsWe developed an automated retrieval and analysis system for bacteriophage genomes, INPHARED (https://github.com/RyanCook94/inphared), that provides data in a consistent format. ResultsAs of January 2021, 14,244 complete phage genomes have been sequenced. The data set is dominated by phages that infect a small number of bacterial genera, with 75% of phages isolated only on 30 bacterial genera. There is further bias with significantly more lytic phage genomes than temperate within the database, resulting in ~54% of temperate phage genomes originating from just three host genera. Within phage genomes, putative antibiotic resistance genes were found in higher frequencies in temperate phages than lytic phages. ConclusionWe provide a mechanism to reproducibly extract complete phage genomes and highlight some of the biases within this data, that underpins our current understanding of phage genomes.

microbiology↗

bioRxiv2020

Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor

Mutational signature analysis is commonly performed in genomic studies surveying cancer and normal somatic tissues. Here we present SigProfilerExtractor, an automated tool for accurate de novo extraction of mutational signatures for all types of somatic mutations. Benchmarking with a total of 34 distinct scenarios encompassing 2,500 simulated signatures operative in more than 60,000 unique synthetic genomes and 20,000 synthetic exomes demonstrates that SigProfilerExtractor outperforms thirteen other tools across all datasets with and without noise. For genome simulations with 5% noise, reflecting high-quality genomic datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true positive signatures while yielding more than 5-fold less false positive signatures. Applying SigProfilerExtractor to 4,643 whole-genome sequenced and 19,184 whole-exome sequenced cancers reveals four previously missed mutational signatures. Two of the signatures are confirmed in independent cohorts with one of these signatures associating with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting mutational signatures, and several novel mutational signatures including a signature putatively attributed to direct tobacco smoking mutagenesis in bladder cancer and in normal bladder epithelium.

bioinformatics↗

bioRxiv2020

Generating realistic null hypothesis of cancer mutational landscapes using SigProfilerSimulator

Performing a statistical test requires a null hypothesis. In cancer genomics, a key challenge is the fast generation of accurate somatic mutational landscapes that can be used as a realistic null hypothesis for making biological discoveries. Here we present SigProfilerSimulator, a powerful tool that is capable of simulating the mutational landscapes of thousands of cancer genomes at different resolutions within seconds. Applying SigProfilerSimulator to 2,144 whole-genome sequenced cancers reveals: (i) that most doublet base substitutions are not due to two adjacent single base substitutions but likely occur as single genomic events; (ii) that an extended sequencing context of +/-2bp is required to more completely capture the patterns of substitution mutational signatures in human cancer; (iii) information on false-positive discovery rate of commonly used bioinformatics tools for detecting driver genes. SigProfilerSimulators breadth of features allows one to construct a tailored null hypothesis and use it for evaluating the accuracy of other bioinformatics tools or for downstream statistical analysis for biological discoveries.

genomics↗

bioRxiv2019

Enzymatic degradation of RNA causes widespread protein aggregation in cell and tissue lysates

Most proteins in cell and tissue lysates are soluble. Here, we show that many of these proteins, including several that are implicated in neurodegenerative diseases, are maintained in a soluble and functional state by association with endogenous RNA, as degradation of RNA invariably leads to protein aggregation. We identify the importance of nucleic acid structure, with single-stranded pyrimidine-rich bulges or loops surrounded by double-stranded regions being particularly efficient in this role, revealing an apparent one-to-one protein-nucleic acid stoichiometry. The relationship of these findings to pathological protein aggregation is suggested by our discovery that protein aggregates isolated from brain tissue from Amyotrophic Lateral Sclerosis patients can be rendered soluble after refolding by both RNA and synthetic oligonucleotides. Together, these findings open new avenues for understanding the mechanism behind protein aggregation and shed light on how certain proteins remain soluble.

molecular biology↗