Biology subjects

Holmes, J.

Publications and source records attributed to Holmes, J..

15 recordsLinked to original sources

bioRxiv2026

Mechanotransduction tuned by intermediate filaments promotes DNA repair in stiff environments

High mammographic density increases breast cancer risk. Dense tissues are rich in fibrillar collagen, forming a rigid extracellular matrix around the epithelium. Although cancer initiation and progression are closely tied to DNA damage and to the DNA damage response, if and how tissue stiffness affects genome maintenance is not fully elucidated. We found that repair factor recruitment is more efficient, and that DNA breaks are repaired faster by cells on rigid substrates. Mechanistically, hemidesmosomal integrins, intermediate filaments, the LINC complex, and YAP are key mediators of this mechanogenomic response. Rigid tissue contexts favor survival of cells with DNA damage, which may lead to the accumulation of somatic mutations, as identified here with RNAseq analyses of normal breast tissue. Our findings provide new insights into how tissue rigidity influences cancer risk.

cell biology↗

bioRxiv2026

Drag racers and long haulers: the design space of myosin-V-like motor proteins

The ubiquitous myosin class-V molecular motors transport cellular cargo long distances at roughly 0.5 m/s. Plant-specific myosin class-XI motors share nearly identical structural features with myosin-V, yet display velocities from 0.25 to 70 m/s, the fastest transport motor in nature. Empirical observations suggest an inverse relationship between run length and velocity. These observations raise a central question: how can a single motor design support both highly processive "long-hauler" and extremely rapid "drag-racer" motors? We generalize a polymer-based model of force-dependent myosin-V dynamics to encompass the broader design space. The model quantitatively captures experimental data from fast myosin-XI and yields general analytical expressions for key observables. Notably, we derive a Pareto frontier that defines the trade-off between velocity and run distance, as well as the theoretical maximum velocity achievable by such motors. Our results establish the biological limits of natural and synthetic myosin-V-like motor design.

biophysics↗

bioRxiv2026

Combined negative effects of simultaneous perturbations on grassland functionality

1. Grassland ecosystems are pivotal for global biodiversity, carbon (C) storage and food security. This biome, however, together with the ecosystem services it provides, is threatened by global change perturbations. These perturbations rarely occur in isolation and most often affect environments simultaneously, which is a pressing issue in ecology. Nevertheless, most of the research conducted so far has focussed on single simultaneous perturbations or pairs thereof. The few examples considering the effect of more than two simultaneous perturbations focussed only on plants or soil separately, which ignores the potential buffering or feedback effects on ecosystem functioning induced by plant-soils interactions. 2. To tackle this knowledge gap, we used an experimental setup involving mesocosms (soil and plants intact) collected in an extensively managed permanent grassland. Mesocosms were arranged in a greenhouse experiment where we applied individual and combined global change perturbations (up to ten in total) including application of fertilisers, pesticides and antibiotics, simulated trampling and grazing, heat stress and drought. Measured ecosystem functions and properties included a large set of biological, physical and chemical soil and plant associated variables. 3. Results showed that multiple perturbations in most cases had negative effects irrespective of the direction found for individual perturbations. However, in contrast to previous bare soil experiments, we only found scarce evidence for substantial synergistic effects of simultaneous perturbations in our experiment conducted with mesocosms that contained vegetation. This suggests a mitigating impact of plants on soil responses to global change. Moreover, we found a strong dominative effect of drought on ecosystem functioning, driving most of the effects observed. 4. Synthesis: Our results highlight the importance of evaluating global change impacts under more realistic settings, i.e., applying multiple perturbations simultaneously and including plant-soil interactions. Mitigation of global change in terrestrial ecosystems will only be possible once we holistically understand how ecosystems respond to altered conditions.

ecology↗

bioRxiv2026

FastSpeciesTree: Fast and Scalable Species tree Inference

The generation of species trees from genomic data is an important step in understanding evolutionary relationships across the tree of life. With the increasing availability of genomic data, species tree inference methods which can easily and rapidly produce accurate species trees from large datasets are required to enable downstream analyses of these resources. Here we present FastSpeciesTree, a fully automated species tree inference method designed to leverage advances in rapid pairwise sequence alignment and scalable phylogenetic inference methods. We show that FastSpeciesTree is able to produce species trees with accuracies equivalent to trees produced through manual-curation and best-practice approaches. We further demonstrate that FastSpeciesTree can produce these high accuracy phylogenies substantially faster than any competitor method. Finally, we show that FastSpeciesTree is capable of building a species tree comprising almost the entirety of the RefSeq databases available proteomes (n = 33285) in less than 12 hours using standard computing resources. FastSpeciesTree is executed as a single command and can be found at https://github.com/OrthoFinder/FastSpeciesTree.

evolutionary biology↗

bioRxiv2025

OrthoFinder: scalable phylogenetic orthology inference for comparative genomics

Here, we present a major advance of the OrthoFinder method. This extends OrthoFinders high accuracy comparative genomic framework to provide substantially enhanced scalability and accuracy. Specifically, we show that enhanced phylogenetic delineation of orthogroups provides a 7% relative increase in orthogroup inference accuracy. We further demonstrate that a new gene assignment method substantially reduces overall runtime RAM usage without compromising accuracy. The latest version of OrthoFinder is available at https://github.com/OrthoFinder/OrthoFinder.

bioinformatics↗

bioRxiv2024

A common neural signature between genetic and environmental risk

Not everyone is equally likely to experience mental illness. What is the contribution of an individuals genetic background, or experiences of childhood adversity, to that likelihood? And how do these dimensions of risk interact at the level of the brain? We investigated the relationship between genetic liability for mental illness, childhood adversity, and cortico-limbic connectivity in a large developmental sample drawn from the ABCD cohort. First, we used Canonical Correlation Analysis to uncover two genetic dimensions of mental health using polygenic risk scores for ADHD, Anxiety, Depression, and Psychosis. The first dimension represented liability for broad psychopathology which positively correlated with adversity, while the second represented neurodevelopmental-specific risk which negatively interacted with adversity. Next, we investigated the cortico-limbic signature of adversity and genetic liability using Partial Least Squares. We found that the neural correlates of adversity broadly mirrored those of genetic liability, with adversity capturing most of the shared variance. These novel findings suggest that genetic and environmental risk overlap in the neural connections that underlie behavioural symptomatology.

neuroscience↗

bioRxiv2022

A 2.8A structure of zoliflodacin in a DNA-cleavage complex with Staphylococcus aureus DNA gyrase

Since 2000 some thirteen quinolones/fluoroquinolones have been developed and come to market. The quinolones, one of the most successful classes of antibacterial drugs, stabilize DNA-cleavage complexes with DNA gyrase and topo IV, the two bacterial type IIA topoisomerases. The dual targeting of gyrase and topo IV helps decrease the likelihood of resistance developing. Here we report a 2.8 [A] X-ray crystal structure which shows that zoliflodacin, a spiropyrimidinetrione antibiotic, binds in the same DNA-cleavage site(s) as quinolones sterically blocking DNA religation. The structure shows that zoliflodacin interacts with highly conserved residues on GyrB (and does not use the quinolone water-metal ion bridge to GyrA) suggesting it may be more difficult for bacteria to develop target mediated resistance. We found that zoliflodacin had an MIC of 4 {micro}g/mL against Acinetobacter baumannii, an improvement of 4-fold over its progenitor QPT-1. The current phase III clinical trial of zoliflodacin for gonorrhea is due to be read out in 2023. Zoliflodacin, together with the unrelated novel bacterial topoisomerase inhibitor gepotidacin, are likely to become the first entirely novel chemical entities approved against Gram-negative bacteria in the 21st century. Zoliflodacin may also become the progenitor of a new safer class of antibacterial drugs against other problematic Gram-negative bacteria.

biophysics↗

bioRxiv2022

Early adversity changes the economic conditions of structural brain network organisation

Early adversity can change educational, cognitive, and mental health outcomes. However, the neural processes through which early adversity exerts these effects remain largely unknown. We used generative network modelling of the mouse connectome to test whether unpredictable postnatal stress shifts the constraints that govern the formation of the structural connectome. A model that trades off the wiring cost of long-distance connections with topological homophily (i.e. links between regions with shared neighbours) generated simulations that replicate the organisation of the rodent connectome. The imposition of early life adversity significantly shifted the best-performing parameter combinations toward zero, heightening the stochastic nature of the generative process. Put simply, unpredictable postnatal stress changes the economic constraints that shape network formation, introducing greater randomness into the structural development of the brain. While this change may constrain the development of cognitive abilities, it could also reflect an adaptive mechanism. In other words, neural development could harness heightened stochasticity to make networks more robust to perturbation, thereby facilitating effective responses to future threats and challenges. Significance statementChildren who experience adversity early in life - such as chronic poverty or abuse - show numerous neural differences that are linked to poorer cognition and mental health later in life. To effectively mitigate the burden of adversity, it is critical to identify how these differences arise. In this paper, we use computational modelling to test whether growing up in an impoverished and unpredictable environment changes the development of structural connections in the mouse brain. We found that early adversity appears to introduce more stochasticity in the formation of neural architecture. Our findings point to a potential mechanism for how early adversity could change the course of child development.

neuroscience↗

bioRxiv2021

Residue-specific insights into (2x)72 kDa tryptophan synthase obtained from fast-MAS 1H-detected solid-state NMR

Solid-state NMR has emerged as a potent technique in structural biology, suitable for the study of fibrillar, micro-crystalline, and membrane proteins. Recent developments in fast-magic-angle-spinning and proton-detected methods have enabled detailed insights into structure and dynamics, but molecular-weight limitations for the asymmetric part of target proteins have remained at ~30-40 kDa. Here we employ solid-state NMR for atom-specific characterization of the 72 kDa (asymmetric unit) microcrystalline protein tryptophan synthase, an important target in pharmacology and biotechnology, chemical-shift assignments of which we obtain via higher-dimensionality, 4D and 5D solid-state NMR experiments. The assignments for the first time provide comprehensive data for assessment of side chain chemical properties involved in the catalytic turnover, and, in conjunction with first-principles calculations, precise determination of thermodynamic and kinetic parameters is demonstrated for the essential acid-base catalytic residue {beta}K87. The insights provided by this study expand by nearly a factor of two the size limitations widely accepted for NMR today, demonstrating the applicability of solid-state NMR to systems that have been thought to be out of reach due to their complexity.

biochemistry↗

bioRxiv2020

A Bayesian phase 2 model based adaptive design to optimise antivenom dosing: application to a dose-finding trial for a novel Russell's viper antivenom in Myanmar

For most antivenoms there is little information from clinical studies to infer the relationship between dose and efficacy or dose and toxicity. Antivenom dose-finding studies usually recruit too few patients (e.g. less than 20) relative to clinically significant event rates (e.g. 5%). Model based adaptive dose-finding studies make efficient use of accrued patient data by using information across dosing levels, and converge rapidly to the contextually defined optimal dose. Adequate sample sizes for adaptive dose-finding trials can be determined by simulation studies. We propose a model based, Bayesian phase 2 type, adaptive clinical trial design for the characterisation of optimal initial antivenom doses in contexts where both efficacy and toxicity are measured as binary endpoints. This design is illustrated in the context of dose-finding for Daboia siamensis (Eastern Russells viper) envenoming in Myanmar. The design formalises the optimal initial dose of antivenom as the dose closest to that giving a pre-specified desired efficacy, but resulting in less than a pre-specified maximum toxicity. For Russells viper efficacy is defined as the restoration of blood coagulability within six hours, and toxicity is defined as anaphylaxis. Comprehensive simulation studies compared the expected behaviour of the model based design to a simpler rule based design (a modified 3+3 design). The model based design can identify the optimal dose after fewer patients than the rule based design. Open source code for the simulations can be used to calculate sample sizes under a priori beliefs of efficacy and toxicity. Antivenom dose-finding trials would benefit from using standard model based adaptive designs. Dose-finding trials where rare events (e.g. 5% occurrence) are of clinical importance necessitate larger sample sizes than current practice. We will apply the model based design to determine a safe and efficacious dose for a novel lyophilised antivenom to treat Daboia siamensis envenoming in Myanmar. Author summarySnakebite envenoming is one of the most neglected tropical diseases relative to its mortality and morbidity. Antivenoms are the only known effective treatment for snake-bite envenoming but are frequently responsible for high rates of adverse reactions. Clinical development of antivenoms rarely follows the iterative phases of clinical development applied to other drugs. Dosing is typically based on pre-clinical testing. Here we propose a Bayesian model based adaptive design for clinical trials aiming to determine the optimal dose of antivenom needed. Optimality is defined using safety and efficacy thresholds contextual to the study. This design can be applied to all antivenoms which have binary efficacy and toxicity endpoints. Our design formally specifies a desired efficacy and a maximum tolerated toxicity. We use simulation studies to characterise the sample size necessary to determine the optimal dose in different scenarios. The simulation studies highlight the advantages of a model based design over simpler rule based alternatives. We intend to use this design to determine an effective and safe dose of the new lyophilised viper antivenom currently in use to treat Russells viper envenoming in Myanmar.

pharmacology and toxicology↗

bioRxiv2018

Are working memory training effects paradigm-specific?

A randomized controlled trial compared complex span and n-back training regimes to investigate the generality of training benefits across materials and paradigms. The memory items and training intensities were equated across programs, providing the first like-with-like comparison of transfer in these two widely-used training paradigms. The stimuli in transfer tests of verbal and visuo-spatial n-back and complex span differed from the trained tasks, but were matched across the untrained paradigms. Pre-to-post changes were observed for untrained n-back tasks following n-back training. Following complex span training there was equivocal evidence for improvements on a verbal complex span task, but no evidence for changes on an untrained visuo-spatial complex span activity. Relative to a no intervention group, the evidence supported no change on an untrained verbal complex span task following either n-back or complex span training. Equivocal evidence was found for improvements on visuo-spatial complex span and verbal and visuo-spatial n-back tasks following both training regimes. Evidence for selective transfer (comparing the two active training groups) was only found for an untrained visuo-spatial n-back task following n-back training. There was no evidence for cross-paradigm transfer. Thus transfer is constrained by working memory paradigm and the nature of individual processes executed within complex span tasks. However, within-paradigm transfer can occur when the change is limited to stimulus category, at least for n-back.

neuroscience↗

bioRxiv2018

A transdiagnostic study of children with problems of attention, learning and memory (CALM)

BackgroundA substantial proportion of the school-age population experience cognitive-related learning difficulties. Not all children who struggle at school receive a diagnosis, yet their problems are sufficient to warrant additional support. Understanding the causes of learning difficulties is the key to developing effective prevention and intervention strategies for struggling learners. The aim of this project is to apply a transdiagnostic approach to children with cognitive developmental difficulties related to learning to discover the underpinning mechanisms of learning problems.\n\nMethods / DesignA cohort of 1000 children aged 5 to 18 years is being recruited. The sample consists of 800 children with problems in attention, learning and / memory, as identified by a health or educational professional, and 200 typically-developing children recruited from the same schools as those with difficulties. All children are completing assessments of cognition, including tests of phonological processing, short-term and working memory, attention, executive function and processing speed. Their parents/ carers are completing questionnaires about the childs family history, communication skills, mental health and behaviour. Children are invited for an optional MRI brain scan and are asked to provide an optional DNA sample (saliva).\n\nHypothesis-free data-driven methods will be used to identify the cognitive, behavioural and neural dimensions of learning difficulties. Machine-learning approaches will be used to map the multi-dimensional space of the cognitive, neural and behavioural measures to identify clusters of children with shared profiles. Finally, group comparisons will be used to test theories of development and disorder.\n\nDiscussionOur multi-systems approach to identifying the causes of learning difficulties in a heterogeneous sample of struggling learners provides a novel way to enhance our understanding of the common and complex needs of the majority of children who struggle at school. Our broad recruitment criteria targeting all children with cognitive learning problems, irrespective of diagnoses and comorbidities, are novel and make our sample unique. Our dataset will also provide a valuable resource of genetic, imaging and cognitive developmental data for the scientific community.

neuroscience↗

bioRxiv2018

Remapping the cognitive and neural profiles of children who struggle at school

Our understanding of learning difficulties largely comes from children with specific diagnoses or individuals selected from community/clinical samples according to strict inclusion criteria. Applying strict exclusionary criteria overemphasizes within-group homogeneity and between-group differences, and fails to capture comorbidity. Here we identify cognitive profiles in a large heterogeneous sample of struggling learners, using unsupervised machine learning in the form of an artificial neural network. Children were referred to the Centre for Attention Learning and Memory (CALM) by health and education professionals, irrespective of diagnosis or comorbidity, for problems in attention, memory, language, or poor school progress (n=530). Children completed a battery of cognitive and learning assessments, underwent a structural MRI scan, and their parents completed behaviour questionnaires. Within the network, we could identify four groups of children: i) children with broad cognitive difficulties, and severe reading, spelling and maths problems; ii) children with age-typical cognitive abilities and learning profiles; iii) children with working memory problems; and iv) children with phonological difficulties. Despite their contrasting cognitive profiles, the learning profiles for the latter two groups did not differ: both were around 1 SD below age-expected levels on all learning measures. Importantly a childs cognitive profile was not predicted by diagnosis or referral reason. We also constructed whole-brain structural connectomes for children from these four groupings (n=184), alongside an additional group of typically developing children (n=36), and identified distinct patterns of brain organisation for each group. This study represents a novel move towards identifying data-driven neurocognitive dimensions underlying learning-related difficulties in a representative sample of poor learners.\n\nAuthor NoteThe Centre for Attention Learning and Memory (CALM) research clinic is based at and supported by funding from the MRC Cognition and Brain Sciences Unit, University of Cambridge. The Principal Investigators are Joni Holmes (Head of CALM), Susan Gathercole (Chair of CALM Management Committee), Duncan Astle, Tom Manly and Rogier Kievit. Data collection is assisted by a team of researchers and PhD students at the CBSU. This currently includes: Sarah Bishop, Annie Bryant, Sally Butterfield, Fanchea Daily, Laura Forde, Erin Hawkins, Sinead OBrien, Cliodhna OLeary, Joseph Rennie, and Mengya Zhang. The authors wish to thank the many professionals working in childrens services in the South-East and East of England for their support, and to the children and their families for giving up their time to visit the clinic.\n\nResearch HighlightsO_LIfirst study to apply machine learning to understand heterogeneity in struggling learners\nC_LIO_LIlarge sample of struggling learners that includes children with multiple difficulties\nC_LIO_LIrich phenotyping with detailed behavioural, cognitive, and neuroimaging assessments\nC_LI

neuroscience↗

bioRxiv2018

Cognition and behaviour in learning difficulties and ADHD: A dimensional approach

BackgroundAcademic underachievement often accompanies the symptoms of inattention and hyperactivity/ impulsivity associated with ADHD. The aim of the present study is to establish whether learning difficulties have the same cognitive origins in this comorbid condition as in children who do not have ADHD.\n\nMethodsParticipants were 163 school-aged children with learning difficulties. Over a third also had a diagnosis of ADHD. Cognition, behaviour and learning attainments were assessed.\n\nResultsThe sample was distinguished by three cognitive and three behavioural dimensions. Learning was equivalently related to cognitive dimensions for children with and without ADHD. A diagnosis of ADHD was associated only with elevated levels of ADHD symptoms and problems with emotional control.\n\nConclusionsDistinct dimensions underpin academic learning and the control of impulsive and emotional behaviour impaired in ADHD. Phonological deficits are associated with learning problems in literacy and maths, and impairments in nonverbal and executive abilities with mathematical learning difficulties. The comorbid condition of ADHD combined with learning difficulties reflects independent deficits in the cognitive dimensions critical for learning and in the control of impulsive and emotional behaviour.

animal behavior and cognition↗

bioRxiv2017

Data-driven subtyping of behavioural problems associated with ADHD in children

IntroductionMany developmental disorders are associated with deficits in controlling and regulating behaviour. These difficulties are frequently observed across multiple groups of children including children with diagnoses of attention deficit hyperactivity disorder (ADHD), specific learning difficulties, autistic spectrum disorder, or conduct disorder. The co-occurrence of these behavioural problems across disorders typically leads to comorbid diagnoses and can complicate intervention approaches. An alternative to classifying children on the basis of specific diagnostic criteria is to use a data-driven grouping that identifies dimensions of behaviour that meaningfully distinguish groups of children.\n\nMethodsThe sample consisted of 442 children identified by health and educational professionals as having difficulties in attention, learning and/or memory. The current study applied community clustering, a data-driven clustering algorithm, to group children by similarities across scales on a commonly used rating scale, the Conners-3 questionnaire. Further, the current study investigated if the groups identified by the data-driven algorithm could be identified by white matter connectivity using a structural connectomics approach combined with partial least squares analysis.\n\nResultsThe data-driven clustering yielded three distinct groups of children with symptoms of either: (1) elevated inattention and hyperactivity/impulsivity, and poor executive function, (2) learning problems, and (3) aggressive behaviour and problems with peer relationships. These groups were associated with significant inter-individual variation in white matter connectivity of the prefrontal and anterior cingulate cortex.\n\nConclusionIn sum, data-driven classification of executive function difficulties identifies stable groups of children, provides a good account of inter-individual differences, and aligns closely with underlying neurobiological substrates.

neuroscience↗