Biology subjects

Holmes, M.

Publications and source records attributed to Holmes, M..

14 recordsLinked to original sources

bioRxiv2026

Rapid trait adaptation has contrasting effects on different dimensions of stability under environmental change

Environmental change alters species growth rates and, through species interactions, can produce complex effects on community functioning and composition. Because functional traits mediate both environmental responses and competition, rapid trait adaptation may modify these effects, but its consequences in multispecies communities remain unclear. We used a trait-based model of competitive communities to test how adaptation affects the mean and diversity of species growth responses to environmental change, and how this influences functional and compositional resistance (how unaffected the community is by the environmental change) and resilience (how well it recovers after it has acclimated to the altered environment). Adaptation altered response variance more strongly than the mean by changing the balance between environmental optimisation and competition-driven trait divergence. As a result, adaptation strongly increased compositional stability, but had less clear effects on abundance. Adaptation also generated a trade-off between resistance and resilience: faster adaptation improved resistance but reduced resilience as communities were displaced less by the environmental change and thus recovered less. Overall, adaptation can be stabilising, but higher adaptation rates do not necessarily produce greater stability across all stability components.

ecology↗

bioRxiv2026

Gut Microbiome Composition in Older Adults with PTSD Symptoms and Trauma-Exposed Controls: Findings from the GUMP Study

Background: Emerging evidence suggests that alterations in the gut microbiome may contribute to mental health outcomes through the gut-brain axis. However, older adults with post-traumatic stress disorder (PTSD) remain underrepresented in microbiome research. This pilot study investigated associations between PTSD symptoms, dietary fibre intake, cognitive function, and gut microbiome functional capacity in adults aged 50 years and older. Methods: Participants with PTSD symptoms and trauma-exposed controls (TEC) completed validated assessments of mental health, trauma exposure, and dietary fibre intake. A subset of participants provided stool samples for microbiome analysis and undertook cognitive function assessment. Quantitative PCR was used to assess phylum-level taxonomy and butyrate-producing bacterial pathways (terminal butyrate generating enzyme), with abundances normalised to the 16S rRNA gene. Results: Participants with PTSD demonstrated significantly greater mental health symptom burden and poorer performance on cognitive tasks related to executive function, working memory, and learning. Dietary fibre intake did not differ significantly between PTSD and TEC groups and no significant differences in overall microbial composition were identified at the phylum level. In contrast, differences were more apparent when assessing functional microbiome pathways, with butyrate kinase abundance significantly lower in PTSD participants than TEC participants. When stratified by fibre intake, a greater butyrogenic capacity was observed in the High fibre TEC participants compared to the Low fibre TEC participants, while little difference was observed in the PTSD fibre-stratified groups. Substantial inter-individual variation was also evident across both taxonomic and functional measures. Conclusions: These findings suggest that functional characteristics of the gut microbiome may provide greater insight into PTSD-related biological processes than broad taxonomic measures alone. Dietary fibre intake may be associated with greater butyrate-producing capacity in trauma-exposed older adults without PTSD symptoms, although this relationship appeared less evident among older adults living with PTSD symptoms. These findings support further investigation of microbiome function, diet, and cognition within the gut-brain axis. Larger studies incorporating metagenomic and metabolomic approaches are warranted.

microbiology↗

bioRxiv2026

Functional and biological diversity jointly shape growth and recovery of Synechococcus communities under stressors.

Biodiversity is expected to enhance the stability of ecological communities under environmental stress, but the relative roles of functional, interspecific, and intraspecific diversity remain poorly resolved, particularly under multiple concurrent stressors. We tested how these diversity dimensions shaped the growth and recovery of marine Synechococcus communities in a microcosm experiment manipulating strain composition across four strain-richness levels and two interspecific diversity levels under control, atrazine, warming, and combined atrazine-plus-warming treatments. Functional diversity was quantified from flow-cytometric trait data and analyzed as initial functional diversity during the stress phase and assembled functional diversity during recovery. Contrary to our expectations, higher initial functional diversity was associated with lower community growth during stress, while higher assembled functional diversity was generally associated with weaker recovery. However, these relationships depended on stressor identity and interspecific diversity: in two-species communities, the negative effects of functional diversity were reduced, and under combined stress, higher assembled functional diversity was associated with improved recovery. In contrast, intraspecific diversity consistently enhanced community growth and recovery, while interspecific diversity primarily promoted functional recovery. Together, our results show that functional, interspecific, and intraspecific diversity can influence stress responses through distinct pathways.

ecology↗

bioRxiv2025

T-World: A highly general computational model of a human ventricular myocyte

Cardiovascular disease is the leading cause of death, demanding new tools to improve mechanistic understanding and overcome limitations of stem cell and animal-based research. We introduce T-World, a highly general virtual model of human ventricular cardiomyocyte suitable for multiscale studies. T-World shows comprehensive agreement with human physiology, from electrical activation to contraction, and is the first to replicate all key cellular mechanisms driving life-threatening arrhythmias. Extensively validated on unseen data, it demonstrates strong predictivity across applications and scales. Using T-World we revealed a likely sex-specific arrhythmia risk in females related to restitution properties, identified arrhythmia drivers in type 2 diabetes, and describe unexpected pro-arrhythmic role of NaV1.8 in heart failure. T-World demonstrates strong performance in predicting drug-induced arrhythmia risk and opens new opportunities for predicting and explaining drug efficacy, demonstrated by unpicking effects of mexiletine in Long QT syndrome 2. T-World is available as open-source code and an online app.

cell biology↗

bioRxiv2025

Sex-specific human electromechanical multiscale in-silico models for virtual therapy evaluation

Background and AimWomen are significantly under-represented in cardiovascular research and in the evaluation of treatment safety and efficacy, leading to poorer patient outcomes. Quantification and investigation of sex differences in human electromechanical function and underlying mechanisms is crucial. To address this, we present sex-specific human cellular and biventricular electromechanical models for mechanistic investigations into sex-differences in therapy evaluation through simulations. MethodsProtein genomic expression data from healthy human myocytes were used to calibrate sex-specific models of human cellular electrophysiology, subsequently integrated in biventricular electromechanical models with male and female anatomies. A validation, verification and uncertainty evaluation were implemented at the cellular and biventricular level, including validation using sex-specific datasets from randomised controlled trials for Dofetilide and Verapamil, with known sex-differences. Ionic mechanisms underlying sex-differences in drug response were mechanistically investigated. ResultsSex-specific electromechanical models recapitulate sex-differences from ionic currents to ECG biomarkers including QTc interval (Male: 312ms; Female: 339ms; 9% difference), T-wave amplitude (6-9% difference) and ST-steepness through electrophysiological changes alone. Sex-specific simulations demonstrate both ECG biomarkers and mechanical biomarkers (Female LVEF: 68%, Male LVEF: 50%) within healthy ranges in clinical data for male and female population in the UK Biobank (n= 806, 46% Male). ECGs sex-differences are primarily explained by ionic currents, whereas mechanical sex-differences are driven by anatomical differences, and secondarily through more robust calcium function in females. Under Dofetilide, simulations show exacerbated QT prolongation in women compared to men (54-78% increase in effect), and a T-wave amplitude decrease in males (up to 0.25 mV), consistent with clinical data. This is explained in simulations by lower repolarisation reserve in women (due to low potassium and high calcium currents) than men. Verapamil shows no effect on simulated QT in either sex, and divergent T-wave modulation (increased amplitude in females, decreased in males) consistent with clinical trial data. Simulations identify enhanced contractile reservoir in female compared to male, with lesser decreases to ejection fraction with calcium current block. ConclusionSimulations using novel, sex-specific cellular and biventricular electromechanical models reveal the primary role of ionic currents sex-differences in ECG and drug response, whereas mechanical sex-differences are also underpinned by anatomical differences. Main ContributionsO_LIDevelopment, calibration and validation of sex-specific human ventricular electromechanical, multiscale models. C_LIO_LIAn analysis of clinical randomised trial data in the context of sex-specific effects of multi-channel blockers on the ECG by dosage, where previous analysis focused on pharmacokinetics. C_LIO_LIConsideration of both sex-specific electrophysiology and anatomy explains sex-specific differences on the impact of drugs on ECG and mechanics. C_LIO_LISimulations demonstrate that the reduced repolarisation reserve in females increases the susceptibility to QTc prolongation via potassium channel block compared to males, and more robust calcium dynamics protect against t-wave amplitude reduction and the more severe contractility loss through L-type calcium inhibition observed in males. C_LI

physiology↗

bioRxiv2025

Calibration and validation strategy for electromechanical cardiac digital twins

State-of-the-art cardiac electromechanical modelling and simulation form the basis for recent developments in cardiac Digital Twin technologies. However, a comprehensive evaluation of electromechanical models at cellular, tissue, and organ level has yet to be performed that addresses both ECG and pressure-volume biomarkers. Such an evaluation would build credibility for applications of cardiac Digital Twins in clinical research and therapy development. We aimed to follow ASME V&V40 standards for calibration, validation, and sensitivity analysis of ventricular electromechanical models under healthy conditions, to progress towards the Digital Twin vision. We performed a multi-scaled review of ventricular electromechanics and compiled a dataset for calibration and validation incorporating ECG, pressure-volume, displacement, and strain biomarkers. When applied to a biventricular multiscale model, we achieved healthy calibrated values for the QRS duration (89 ms), QT interval (360 ms), left ventricular ejection fraction (LVEF) (51 %), peak systolic pressure (14 kPa), end diastolic (105 mL) and end systolic volumes (51 mL), peak ejection flow rate (180 mL/ms). Model validation was performed by comparison to displacement and strain biomarkers including systolic atrioventricular plane displacement (1.5 cm), systolic fibre strain (-0.18) and longitudinal strain (-0.15). Sensitivity analysis of model parameters at cellular and ventricular scales was also performed to inform model calibration and as a first step towards uncertainty quantification. We quantified the effects of variability in ionic conductance, mechanical stiffness, cross-bridge cycling dynamics, and systemic circulation on action potential and active tension dynamics at the cellular scale, and on ECG, pressure-volume, displacement, and strain biomarkers at the ventricular scale. Simulations showed that the relationship between healthy LVEF and T wave biomarkers was primarily underpinned by variability in L-type calcium channel conductance and SERCA activity through multi-scale effects. In this study, we provide a systematic framework for credibility assessment of cardiac electromechanical models based on both ECG and mechanical biomarkers, as a foundational step towards the cardiac Digital Twin vision.

bioengineering↗

bioRxiv2024

Distinct alterations in white matter properties and organization related to maternal treatment initiation in neonates exposed to HIV but uninfected

HIV exposed-uninfected (HEU) infants and children are at risk of developmental delays as compared to uninfected unexposed (HUU) populations. The effects of exposure to in utero HIV and ART regimens on the HEU the developing brain are not well understood. In a cohort of 2-week-old newborns, we used diffusion tensor imaging (DTI) tractography and graph theory to examine the influence of HIV and ART exposure in utero on neonate white matter integrity and organisation. The cohort included HEU infants born to mothers who started ART before conception (HEUpre) and after conception (HEUpost), as well as HUU infants from the same community. We investigated HIV exposure and ART duration group differences in DTI metrics (fractional anisotropy (FA) and mean diffusivity (MD)) and graph measures across white matter. We found increased MD in white matter connections involving the thalamus and limbic system in the HEUpre group compared to HUU. We further identified reduced nodal efficiency in the basal ganglia. Within the HEUpost group, we observed reduced FA in cortical-subcortical and cerebellar connections as well as decreased transitivity in the hindbrain area compared to HUU. Overall, our analysis demonstrated distinct alterations in white matter integrity related to the timing of maternal ART initiation that influence regional brain network properties.

neuroscience↗

bioRxiv2021

Increased Brain Volumetric Measurement Precision from Multi-Site 3D T1-weighted 3T Magnetic Resonance Imaging by Correcting Geometric Distortions

Magnetic resonance imaging (MRI) scanner-specific geometric distortions may contribute to scanner induced variability and decrease volumetric measurement precision for multi-site studies. The purpose of this study was to determine whether geometric distortion correction increases the precision of brain volumetric measurements in a multi-site multi-scanner study. Geometric distortion variation was quantified over a one-year period at 10 sites using the distortion fields estimated from monthly 3D T1-weighted MRI geometrical phantom scans. The variability of volume and distance measurements were quantified using synthetic volumes and a standard quantitative MRI (qMRI) phantom. The effects of geometric distortion corrections on MRI derived volumetric measurements of the human brain were assessed in two subjects scanned on each of the 10 MRI scanners and in 150 subjects with cerebrovascaular disease (CVD) acquired across imaging sites. Geometric distortions were found to vary substantially between different MRI scanners but were relatively stable on each scanner over a one-year interval. Geometric distortions varied spatially, increasing in severity with distance from the magnet isocenter. In measurements made with the qMRI phantom, the geometric distortion correction decreased the standard deviation of volumetric assessments by 35% and distance measurements by 42%. The average coefficient of variance decreased by 16% in gray matter and white matter volume estimates in the two subjects scanned on the 10 MRI scanners. Geometric distortion correction using an up-to-date correction field is recommended to increase precision in volumetric measurements made from MRI images.

bioengineering↗

bioRxiv2021

HAM-ART: An optimised culture-free Hi-C metagenomics pipeline for tracking antimicrobial resistance genes in complex microbial communities.

Shotgun metagenomics is a powerful tool to identify antimicrobial resistance (AMR) genes in microbiomes but has the limitation that extrachromosomal DNA, such as plasmids, cannot be linked with the host bacterial chromosome. Here we present a laboratory and bioinformatics pipeline HAM-ART (Hi-C Assisted Metagenomics for Antimicrobial Resistance Tracking) optimised for the generation of metagenome-assembled genomes including both chromosomal and extrachromosomal AMR genes. We demonstrate the performance of the pipeline in a study comparing 100 pig faecal microbiomes from low- and high-antimicrobial use pig farms (organic and conventional farms). We found significant differences in the distribution of AMR genes between low- and high-antimicrobial use farms including a plasmid-borne lincosamide resistance gene exclusive to high-antimicrobial use farms in three species of Lactobacilli. Author SummaryAntimicrobial resistance (AMR) is one of the biggest global health threats humanity is facing. Understanding the emergence and spread of AMR between different bacterial species is crucial for the development of effective countermeasures. In this paper we describe a user-friendly, affordable and comprehensive (laboratory and bioinformatics) workflow that is able to identify, associate and track AMR genes in bacteria. We demonstrate the efficiency and reliability of the method by comparing 50 faecal microbiomes from pig farms with high-antibiotic use (conventional farms), and 50 faecal microbiomes from pig farms with low-antibiotic use (organic farms). Our method provides a novel approach to resistance gene tracking, that also leads to the generation of high quality metagenomic assembled genomes that includes genes on mobile genetic elements, such as plasmids, that would not otherwise be included in these assembled genomes.

bioinformatics↗

bioRxiv2021

Predicting Moisture Content During Maize Nixtamalization Using Machine Learning with NIR Spectroscopy

Lack of high throughput phenotyping systems for determining moisture content during the maize nixtamalization cooking process has led to difficulty in breeding for this trait. This study provides a high throughput, quantitative measure of kernel moisture content during nixtamalization based on NIR scanning of uncooked maize kernels. Machine learning was utilized to develop models based on the combination of NIR spectra and moisture content determined from a scaled-down benchtop cook method. A linear support vector machine (SVM) model with a Spearmans rank correlation coefficient of 0.852 between wet lab and predicted values was developed from 100 diverse temperate genotypes grown in replicate across two environments. This model was applied to NIR data from 501 diverse temperate genotypes grown in replicate in five environments. Analysis of variance revealed environment explained the highest percent of the variation (51.5%), followed by genotype (15.6%) and genotype-by-environment interaction (11.2%). A genome-wide association study identified 26 significant loci across five environments that explained between 5.04% and 16.01% (average = 10.41%). However, genome-wide markers explained 10.54% to 45.99% (average = 31.68%) of the variation, indicating the genetic architecture of this trait is likely complex and controlled by many loci of small effect. This study provides a high-throughput method to evaluate moisture content during nixtamalization that is feasible at the scale of a breeding program and provides important information about the factors contributing to variation of this trait for breeders and food companies to make future strategies to improve this important processing trait. Key MessageMoisture content during nixtamalization can be accurately predicted from NIR spectroscopy when coupled with a support vector machine (SVM) model, is strongly modulated by the environment, and has a complex genetic architecture.

plant biology↗

bioRxiv2018

Resting state functional connectivity patterns associated with pharmacological treatment resistance in temporal lobe epilepsy

There are no functional imaging based biomarkers for pharmacological treatment response in temporal lobe epilepsy (TLE). In this study, we investigated whether there is an association between resting state functional brain connectivity (RsFC) and seizure control in TLE. We screened a large database containing resting state functional magnetic resonance imaging (Rs-fMRI) data from 286 epilepsy patients. Patient medical records were screened for seizure characterization, EEG reports for lateralization and location of seizure foci to establish uniformity of seizure localization within patient groups. Rs-fMRI data from patients with well-controlled left TLE, patients with treatment-resistant left TLE, and healthy controls were analyzed. Healthy controls and cTLE showed similar functional connectivity patterns, whereas trTLE exhibited a significant bilateral decrease in thalamo-hippocampal functional connectivity. This work is the first to demonstrate differences in neural network connectivity between well-controlled and treatment-resistant TLE. These differences are spatially highly focused and suggest sites for the etiology and possibly treatment of TLE. Altered thalamo-hippocampal RsFC thus is a potential new biomarker for TLE treatment resistance.\n\nSummaryResting State functional magnetic resonance imaging (Rs-fMRI), previously utilized to predict lateralization of seizure foci in temporal lobe epilepsy (TLE), is utilized to determine potential mechanisms and biomarkers for treatment-resistant and well-controlled unilateral TLE. We found significant differences in thalamo-hippocampal functional connectivity between treatment-resistant and well-controlled TLE patients. Differences in functional connectivity were focused to thalamo-hippocampal connections and more pronounced in the hemisphere ipsilateral to seizure foci. Aberrant functional connectivity patterns as measured by Rs-fMRI could thus serve as biomarkers for treatment response in TLE.

neuroscience↗

bioRxiv2018

PCSK9 genetic variants, life-long lowering of LDL-cholesterol and cognition: a large-scale Mendelian randomization study

AimsPCSK9 inhibitors lower LDL cholesterol and are efficacious at reducing risk of vascular disease, however questions remain about potential adverse effects on cognitive function. We examined the association of LDL cholesterol-lowering genetic variants in PCSK9 with continuous measures of cognitive ability\n\nMethods and ResultsSix independent SNPs in PCSK9 were used in up to 337,348 individuals from the UK Biobank who underwent measures of cognitive ability (fluid reasoning, reaction time, trial making test and digit symbol coding. Scaled to a 50mg/dL lower LDL cholesterol, the PCSK9 allele score was associated with a lower risk of CHD (odds ratio 0.73; 95% CI: 0.60 to 0.90, P = 0.003). The scaled PCSK9 allele score nominally associated with worse log reaction time (0.04 standard deviations; 95%CI: 0.00, 0.08; P=0.038). Although no strong associations of the PCSK9 allele score were identified with any cognitive trait, the imprecision around the estimates meant that we could not exclude a similar magnitude of effect of genetic inhibition of PCSK9 to that seen with established risk factors, including APOEe4 or smoking status for any of the individual cognition traits. Point estimates for the PCSK9 allele score and cognition traits were all on the harmful side of unity.\n\nConclusionsUsing currently available data in UK Biobank, we are not able to rule out meaningful associations of PCSK9 genetic variants with cognition traits. These data highlight the need for further large-scale genetic analyses and, in parallel, continued pharmacovigilance for patients currently treated with PCSK9 inhibitors.

epidemiology↗

bioRxiv2017

High fidelity detection of crop biomass QTL from low-cost imaging in the field

Above-ground biomass production is a key target for studies of crop abiotic stress tolerance, disease resistance and yield improvement. However, biomass is slow and laborious to evaluate in the field using traditional destructive methods. High-throughput phenotyping (HTP) is widely promoted as a potential solution that can rapidly and non-destructively assess plant traits by exploiting advances in sensor and computing technology. A key potential application of HTP is for quantitative genetics studies that identify loci where allelic variation is associated with variation in crop production. And, the value of performing such studies in the field, where environmental conditions match that of production farming, is recognized. To date, HTP of biomass productivity in field trials has largely focused on expensive and complex methods, which - even if successful - will limit their use to a subset of wealthy research institutions and companies with extensive research infrastructure and highly-trained personnel. Even with investment in ground vehicles, aerial vehicles and gantry systems ranging from thousands to millions of dollars, there are very few examples where Quantitative trait loci (QTLs) detected by HTP of biomass production in a field-grown crop are shown to match QTLs detected by direct measures of biomass traits by destructive harvest techniques. Until such proof of concept for HTP proxies is generated it is unlikely to replace existing technology and be widely adopted. Therefore, there is a need for methods that can be used to assess crop performance by small teams with limited training and at field sites that are remote or have limited infrastructure. Here we use an inexpensive and simple, miniaturized system of hemispherical imaging and light attenuation modeling to identify the same set of key QTLs for biomass production as traditional destructive harvest methods applied to a field-grown Setaria mapping population. This provides a case study of a HTP technology that can deliver results for QTL mapping without high costs or complexity.

plant biology↗

bioRxiv2016

Cannabis use and risk of schizophrenia:a Mendelian randomization study

Cannabis use is observationally associated with an increased risk of schizophrenia, however whether the relationship is causal is not known. To determine the nature of the association between cannabis use on risk of schizophrenia using Mendelian randomization (MR) analysis, we used ten genetic variants previously identified to associate with cannabis use in 32,330 individuals. Genetic variants were used in a MR analyses of the association of genetically determined cannabis on risk of schizophrenia in 34,241 cases and 45,604 controls from predominantly European descent. Estimates from MR were compared to a metaanalysis of observational studies reporting effect estimates for ever use of cannabis and risk of schizophrenia or related disorders. Genetically determined use of cannabis was associated with increased risk of schizophrenia (OR of schizophrenia for users vs. non-users of cannabis: 1.37; 95%CI, 1.09 to 1.67; P-value=0.007). The corresponding estimate from observational analysis was 1.50 (95% CI, 1.10 to 2.00; P-value for heterogeneity = 0.88). The genetic instrument did not show evidence of pleiotropy on MR-Egger (Egger test, P-value=0.292) nor on multivariable MR accounting for tobacco exposure (OR of schizophrenia for users vs. nonusers of cannabis, adjusted for ever vs. never smoker: 1.41; 95% CI, 1.09-1.83). Furthermore, the causal estimate remained robust to sensitivity analyses. These findings strongly support a causal association between genetically determined use of cannabis and risk of schizophrenia. Such robust evidence may inform public health message about the risks of cannabis use, especially regarding its potential mental health consequences.

epidemiology↗