Biology subjects

Ellis, R. J.

Publications and source records attributed to Ellis, R. J..

12 recordsLinked to original sources

bioRxiv2026

Cannabis Use by People with HIV is Associated with an Anti-Inflammatory Immunometabolic Phenotype in Monocyte-Derived Macrophages

Chronic neuroinflammation is associated with comorbidities in people with HIV (PWH) on antiretroviral therapy (ART). While cannabis use is associated with reduced neuroinflammation and neurocognitive impairment (NCI) in PWH, the underlying mechanisms are unknown. To address this gap in knowledge, we analyzed monocyte-derived macrophages (MDMs) from a cohort of 50 PWH and 33 people without HIV (mean age: 61.9 years), categorized by frequency of cannabis use (naive/low, moderate, daily). We performed immunocytochemistry, RNA sequencing, and qPCR on MDMs and quantified related biomarkers in donor plasma. In this cohort study, daily cannabis use in PWH was associated with less global neurocognitive deficits, and with an anti-inflammatory immunometabolic-phenotype in MDMs characterized by (1) a metabolic shift from glycolysis to oxidative phosphorylation, (2) higher mitochondrial numbers, (3) altered cytokine profiles (pro-inflammatory downregulation, anti-inflammatory upregulation), and (4) higher brain-derived neurotrophic factor (BDNF) expression. These cellular changes were corroborated by a plasma biomarker profile in PWH including (1) lower levels of growth differentiation factor 15 and soluble triggering receptor expressed on myeloid cells 2, and (2) higher mature BDNF/precursor BDNF ratios that correlated with better cognition. Thus, cannabis use may mitigate NCI in PWH by immunometabolically reprogramming MDM function towards an anti-inflammatory and neuroprotective state. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/709579v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@15fb1adorg.highwire.dtl.DTLVardef@189d79corg.highwire.dtl.DTLVardef@aa6a89org.highwire.dtl.DTLVardef@3852f8_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2026

Community Curation of Microbial Metabolites Enables Biological Insights of Metabolomics Data

Microbial metabolites play a critical role in regulating ecosystems, including the human body and its microbiota. However, understanding the physiologically relevant role of these molecules, especially through liquid chromatography tandem mass spectrometry (LC-MS/MS)-based untargeted metabolomics, poses significant challenges and often requires manual parsing of a large amount of literature, databases, and webpages. To address this gap, we established the Collaborative Microbial Metabolite Center knowledgebase (CMMC-KB), a platform that fosters collaborative efforts within the scientific community to curate knowledge about microbial metabolites. The CMMC-KB aims to collect comprehensive information about microbial molecules originating from microbial biosynthesis, drug metabolism, exposure-related molecules, food, host-derived molecules, and, whenever available, their known activities. Molecules from other sources, including host-produced, dietary, and pharmaceutical compounds, are also included. By enabling direct integration of this knowledgebase with downstream analytical tools, including molecular networking, we can deepen insights into microbiota and their metabolites, ultimately advancing our understanding of microbial ecosystems.

bioinformatics↗

bioRxiv2025

Protein Compositional Ratio Representation (PCRR)Systematically Improves Human Disease Prediction

Plasma proteomics captures a functional snapshot of human physiology; yet, most machine learning models treat protein abundances as independent variables, ignoring the fact that biological systems and proteomic measurements are inherently compositional. Many molecular processes depend not on absolute concentrations but on relative balances: receptor-ligand stoichiometry, enzyme-substrate ratios, and homeostatic feedbacks that govern signaling and metabolism. We propose that these relationships are best captured through pairwise protein ratios, which more faithfully reflect underlying biochemical constraints than raw expression values. We evaluate a machine learning framework that models pairwise log-ratios of proteins (log(A) -log(B)) as features, thereby encoding compositional structure directly into the learning space. Applied to the ROSMAP plasma proteomics cohort (n = 871), this approach substantially improved the classification of Alzheimers subtypes (NCI, MCI, AD, AD+) with an average AUROC gain of +0.1274 over a strong baseline that incorporated raw proteomics and demographics. The top-ranked ratios (e.g., SEMA3C:TMEM70, IDUA:NPTXR) captured converging pathogenic pillars of Alzheimers disease, including microglial activation, proteostasis dysregulation, and lipid-clearance imbalance, highlighting that ratio-based features recover biologically coherent axes of disease. To assess generality, we scaled the method to the UK Biobank proteomic dataset (n > 53,000; 587 phenotypes). The ratio-based model outperformed raw-level models in 95.1 % of diseases, with statistically significant (FDR < 0.05) gains in 56.7 %. Together, these results suggest that proteomic data should be viewed and modeled as compositional systems, where relative protein abundances carry the accurate functional signal. This insight supports the broader utility of ratio-based representations for disease prediction and biomarker discovery.

bioinformatics↗

bioRxiv2025

Uncovering the Genomic Landscape of Mycobacterium bovis in Wales

Bovine tuberculosis (bTB), caused by the bacterium Mycobacterium bovis, is one of the most pressing animal health issues in Wales today. It negatively impacts cattle health, affects profitability and trade, and can decimate years of genetic improvement towards desirable production traits. It also places a significant burden on the health and well-being of affected farming families. Eradication of bTB requires the understanding of local transmission pathways to target effective disease control interventions. Traditional genotyping methods, such as spoligotyping and VNTR analysis, which have previously been used in Wales to understand the epidemiology of M. bovis, have lacked the discriminatory power to accurately identify local transmission pathways. Recently, whole-genome sequencing has replaced traditional genotyping methods for all M. bovis strains isolated from infected animals in Wales. In this study, we characterised the genomic diversity of M. bovis across Wales by analysing the genome sequence of all 379 M. bovis isolates obtained from culture-positive animals in Wales in 2021. Analyses uncovered three main clusters that are geographically distinct. A further three clusters containing fewer isolates were also geographically separated, two of which had particularly large SNP distances compared to the majority of other Welsh isolates, suggesting independent introductions of M. bovis strains that are not endemic to Wales. Fine-scale and epidemiologically relevant genetic structuring was identified within the six main clusters, indicating region-specific evolution, which can drive local disease dynamics. Finally, A number of SNPs in coding genes were identified that have the potential for important advantageous physiological consequences that may impact host-pathogen interactions and necessitate further investigation.

microbiology↗

bioRxiv2025

MS/MS Mass Spectrometry Filtering Tree for Bile Acid Isomer Annotation.

Bile acids are essential steroids regulating immunity, nutrient absorption, insulin, appetite, and body temperature. Their structural diversity is vast, but due to spectral similarities, MS/MS spectral matching often fails to resolve isomers. This study introduces a proof-of-concept workflow using a mass spectrometry query language filtering tree that distinguishes isomeric bile acids in untargeted LC-MS/MS data. Its application revealed a deoxycholyl-2-aminophenol amidate linked to whole grain consumption.

bioinformatics↗

bioRxiv2024

The microbiome diversifies N-acyl lipid pools - including short-chain fatty acid-derived compounds

N-acyl lipids are important mediators of several biological processes including immune function and stress response. To enhance the detection of N-acyl lipids with untargeted mass spectrometry-based metabolomics, we created a reference spectral library retrieving N-acyl lipid patterns from 2,700 public datasets, identifying 851 N-acyl lipids that were detected 356,542 times. 777 are not documented in lipid structural databases, with 18% of these derived from short-chain fatty acids and found in the digestive tract and other organs. Their levels varied with diet, microbial colonization, and in people living with diabetes. We used the library to link microbial N-acyl lipids, including histamine and polyamine conjugates, to HIV status and cognitive impairment. This resource will enhance the annotation of these compounds in future studies to further the understanding of their roles in health and disease and highlight the value of large-scale untargeted metabolomics data for metabolite discovery.

bioinformatics↗

bioRxiv2024

Empirically establishing drug exposure records directly from untargeted metabolomics data

Despite extensive efforts, extracting information on medication exposure from clinical records remains challenging. To complement this approach, we developed the tandem mass spectrometry (MS/MS) based GNPS Drug Library. This resource integrates MS/MS data for drugs and their metabolites/analogs with controlled vocabularies on exposure sources, pharmacologic classes, therapeutic indications, and mechanisms of action. It enables direct analysis of drug exposure and metabolism from untargeted metabolomics data independent of clinical records. Our library facilitates stratification of individuals in clinical studies based on the empirically detected medications, exemplified by drug-dependent microbiota-derived N-acyl lipid changes in a cohort with human immunodeficiency virus. The GNPS Drug Library holds potential for broader applications in drug discovery and precision medicine.

bioinformatics↗

bioRxiv2023

Unanticipated interacting features of human gut-associated lymphoid tissues link microbiota, intestinal immunity and autoimmunity

Gut-associated lymphoid tissue (GALT) is organised lymphoid tissue that is chronically activated by the intestinal microbiota. It generates the IgA response that is critical for intestinal homeostasis. By iterative application of multiplexed technologies, we identify enrichment of double-negative 2 (DN2:CD27-IgD-CD21loCD11chi) B cells in GALT, where they comprise the majority of intraepithelial and subepithelial B cells. We show that DN2 B cells in GALT interact with DC in the sub-epithelial dome that express DNASE1L3 and microbicides. Unlike in mice, DNASE1L3 in humans does not associate with apoptotic debris, but is located between sampled bacteria and host tissue where it is co-expressed with C1Q, consistent with management of bacterial debris. Thus we demonstrate that DN2 B cells that are otherwise associated with lupus nephritis, and DNASE1L3 and C1q that are lupus autoantigens, are microbiota-associated, interacting components of normal intestinal immunity.

immunology↗

bioRxiv2020

Population structure and transmission of Mycobacterium bovis in Ethiopia

Bovine tuberculosis (bTB) is endemic in cattle in Ethiopia, a country that hosts the largest national cattle herd in Africa. The intensive dairy sector, most of which is peri-urban, has the highest prevalence of disease. Previous studies in Ethiopia have demonstrated that the main cause is Mycobacterium bovis (M. bovis), which has been investigated using conventional molecular tools including deletion typing, spoligotyping and Mycobacterial interspersed repetitive unit-variable number tandem repeat (MIRU-VNTR). Here we use whole genome sequencing (WGS) to examine the population structure of M. bovis in Ethiopia. A total of 134 M. bovis isolates were sequenced including 128 genomes from 85 mainly dairy cattle and six genomes isolated from humans, originating from 12 study sites across Ethiopia. These genomes provided a good representation of the previously described population structure of M. bovis, based on spoligotyping and demonstrated that the population is dominated by the clonal complexes African 2 (Af2) and European 3 (Eu3). A range of within-host diversity was observed amongst the isolates and evidence was found for both short- and long-distance transmission. Detailed analysis of available genomes from the Eu3 clonal complex combined with previously published genomes revealed two distinct introductions of this clonal complex into Ethiopia between 1950 and 1987, likely from Europe. This work is important to help better understand bTB transmission in cattle in Ethiopia and can potentially inform national strategies for bTB control in Ethiopia and beyond.

genomics↗

bioRxiv2020

Phylodynamic analysis of an emergent Mycobacterium bovis outbreak in an area with no previously known wildlife infections

Understanding how an emergent pathogen successfully establishes itself and persists in a previously unaffected population is a crucial problem in disease ecology. In multi-host pathogen systems this problem is particularly difficult, as the importance of each host species to transmission is often poorly characterised, and the epidemiology of the disease is complex. Opportunities to observe and analyse such emergent scenarios are few. Here, we exploit a unique dataset combining densely-collected data on the epidemiological and evolutionary characteristics of an outbreak of Mycobacterium bovis (M. bovis, the causative agent of bovine tuberculosis, bTB) in a population of cattle and badgers in an area considered low-risk for bTB, that has no previous record of either persistent infection in cattle, or of any infection in wildlife. We analyse the outbreak dynamics using a combination of mathematical modelling, machine learning and Bayesian evolutionary analyses. Comparison to M. bovis whole-genome sequences from Northern Ireland confirmed this to be a single introduction of the pathogen from the latter region, with evolutionary analysis supporting an introduction directly into the local cattle population at least six years prior to its first discovery in badgers. Once introduced, the evidence supports M. bovis epidemiological dynamics passing through two phases, the first dominated by cattle-to-cattle transmission before becoming established in the local badger population. These findings emphasise the importance of disease surveillance for early containment of outbreaks, in particular for pathogens not causing immediately evident symptoms in the infected host, and highlight the utility of combining dynamic modelling and phylogenetic analyses for understanding the often complex infection dynamics associated with emergent outbreaks.

ecology↗

bioRxiv2020

Zinc potentiates dopamine neurotransmission and cocaine seeking

Cocaine binds to the dopamine (DA) transporter (DAT) to regulate cocaine reward and seeking behavior. Zinc (Zn2+) also binds to the DAT, but the in vivo relevance of this interaction is unknown. We found that Zn2+ concentrations in postmortem brain (caudate) tissue from humans who died of cocaine overdose were significantly lower than in control subjects. Moreover, the level of striatal Zn2+ content in these subjects negatively correlated with plasma levels of benzoylecgonine, a cocaine metabolite indicative of recent use. In mice, repeated cocaine exposure increased synaptic Zn2+ concentrations in the caudate putamen (CPu) and nucleus accumbens (NAc). Cocaine-induced increases in Zn2+ were dependent on the Zn2+ transporter 3 (ZnT3), a neuronal Zn2+ transporter localized to synaptic vesicle membranes, as ZnT3 knockout (KO) mice were insensitive to cocaine-induced increases in striatal Zn2+. ZnT3 KO mice showed significantly lower electrically-evoked DA release and greater DA clearance when exposed to cocaine compared to controls. ZnT3 KO mice also displayed significant reductions in cocaine locomotor sensitization, conditioned place preference (CPP), self-administration, and reinstatement compared to control mice and were insensitive to cocaine-induced increases in striatal DAT binding. Finally, dietary Zn2+ deficiency in mice resulted in decreased striatal Zn2+ content, cocaine locomotor sensitization, CPP, and striatal DAT binding. These results indicate that cocaine increases synaptic Zn2+ release and turnover/metabolism in the striatum, and that synaptically-released Zn2+ potentiates the effects of cocaine on striatal DA neurotransmission and behavior and is required for cocaine-primed reinstatement. In sum, these findings reveal new insights into cocaines pharmacological mechanism of action and suggest that Zn2+ may serve as an environmentally-derived regulator of DA neurotransmission, cocaine pharmacodynamics, and vulnerability to cocaine use disorders.

neuroscience↗

bioRxiv2018

High-accuracy Decoding of Complex Visual Scenes from Neuronal Calcium Responses

The brain contains billions of neurons defined by diverse cytoarchitectural, anatomical, genetic, and functional properties. Sensory encoding and decoding are popular research areas in the fields of neuroscience, neuroprosthetics and artificial intelligence but the contribution of neuronal diversity to these processes is not well understood. Deciphering this contribution necessitates development of sophisticated neurotechnologies that can monitor brain physiology and behavior via simultaneous assessment of individual genetically-defined neurons during the presentation of discrete sensory cues and behavioral contexts. Neural networks are a powerful technique for formulating hierarchical representations of data using layers of nonlinear transformations. Here we leverage the availability of an unprecedented collection of neuronal activity data, derived from [~]25,000 individual genetically-defined neurons of the parcellated mouse visual cortex during the presentation of 118 unique and complex naturalistic scenes, to demonstrate that neural networks can be used to decode discrete visual scenes from neuronal calcium responses with high ([~]96%) accuracy. Our findings highlight the novel use of neural networks for sensory decoding using neuronal calcium imaging data and reveal a neuroanatomical map of visual decoding strength traversing brain regions, cortical layers, neuron types, and time. Our findings also demonstrate the utility of feature selection in assigning contributions of neuronal diversity to visual decoding accuracy and the low requirement of network architecture complexity for high accuracy decoding in this experimental context.

neuroscience↗