Biology subjects

Byrne, H.

Publications and source records attributed to Byrne, H..

At least 19 recordsLinked to original sources

bioRxiv2026

Brain Age Gap Associations with Externalising Behaviours in Youth: A Longitudinal Investigation in the Adolescent Brain Cognitive Development (ABCD) Cohort

Background. Early adolescence is a period of heightened risk for the emergence of externalising psychopathology, making it an important window for identifying neurodevelopmental risk markers. Differences between predicted brain age and chronological age - termed the brain age gap - reflect variation from age-typical patterns of brain structure. While brain age gap has been closely linked with internalising behaviours in adolescence, less is known about its relationship with externalising symptoms. This study examined whether brain age gap in late childhood/early adolescence (9-11 years) is associated with current externalising symptoms and whether it predicts externalising trajectories over seven-year follow-up. Methods. Participants were 10,516 adolescents (aged 9-11 at baseline; up to 18 years at follow-up) from the Adolescent Brain Cognitive Development (ABCD) Study who met inclusion/exclusion criteria. Brain age gap for each participant was derived from an external validated prediction model (Drobinin et al., 2022) applied to baseline measurements of cortical volume, area, and subcortical volume. Linear mixed-effects models were used to test cross-sectional and longitudinal associations between baseline brain age gap and externalising symptoms, as assessed using the Child Behaviour Checklist. Primary models were adjusted for age, sex, pubertal status, and prior to inclusion of sociodemographic covariates (ethnoracial identity and caregiver highest education). Results. A higher/more positive brain age gap was associated with greater externalising symptoms at baseline (B[SE]=0.067[0.033], p=.042), but this association was removed following sociodemographic adjustment (B[SE]=0.056[0.033], p=.088). Longitudinally, a significant brain age gap x age interaction was identified that remained evident after sociodemographic adjustment, with this relationship weakening across adolescence (B[SE]=-0.007[0.003], p=.033). Conclusions. These findings suggest that a more positive brain age gap, indicating more mature-appearing brain structure for age, in late childhood/early adolescence may be associated with externalising psychopathology, but this relationship is likely to be age- and context-dependent rather than a stable marker of persistent behavioural risk.

neuroscience↗

bioRxiv2026

A mathematical model of tumor growth driven by oxygen lactate exosome feedback

Solid tumors develop within a heterogeneous microenvironment shaped by coupled biochemical and metabolic processes. As a tumor grows, oxygen gradients develop from the periphery towards the core, giving rise to hypoxic regions. Hypoxia induces a metabolic shift from oxidative phosphorylation to glycolysis, leading to elevated lactate production. Experimental studies further indicate that increased lactate levels enhance the secretion of tumor-derived exosomes carrying proteins, mRNA, and lipids, which in turn promote tumor cell proliferation. Together, these interactions form a feedback loop linking oxygen availability, lactate accumulation, and exosome-mediated signaling. Despite growing experimental evidence for each component, the impact of this coupled oxygen lactate exosome axis on tumor growth dynamics has not yet been systematically explored from a mathematical perspective. It remains unclear how the oxygen lactate exosome axis affects tumor growth - impact of nonlocal effects. In this paper, we develop a mechanistic mathematical model to investigate how this feedback mechanisms affects tumor growth. We first consider an in vitro setting oxygen levels are spatially homogeneous and vary periodically in time between normoxic and hypoxic states, capturing key features of cyclic hypoxia observed in vivo. Numerical simulations reveal that exosome mediated feedback has a pronounced effect on tumor volume under chronic hypoxia. In addition, we observe that the tumor volume decreases as the period of cyclic hypoxia increases. We then extend the model to an in vivo framework by incorporating oxygen diffusion and consumption, allowing spatial heterogeneity to emerge. Simulations reveal that tumor cells in normoxic regions benefit indirectly from enhanced exosome secretion driven by lactate produced in hypoxic regions. Overall, the results show how spatially mediated metabolic signaling can amplify tumor growth and aggressiveness, and illustrate how mathematical modeling can increase understanding of non local feedback mechanisms within the tumor microenvironment.

systems biology↗

bioRxiv2026

A mechanical model of multicellular remodelling in epithelial monolayers

Epithelial monolayers are the foundation of many mammalian organs and have a significant impact on numerous biological processes, including the secretion of cytokines, absorption of waste products and barrier function. During organ development epithelia are subject to many external forces due to the growth of the surrounding tissues. They exhibit complex responses to such forces, with deformation occurring on short timescales (seconds), and relaxation and remodelling occurring on longer timescales (minutes or hours). Existing mathematical and computational models do not typically account for subcellular remodelling, assuming instead that the response to an applied force or strain acts on a single timescale. In this paper we extend an off-lattice, cell-centre modelling framework to account for subcellular and tissue remodelling by introducing a Dynamic Reference Frame (DRF). The DRF provides a discrete, cell-based analogue of morpho-elasticity: it decouples the evolution of each cell's mechanical reference configuration from its instantaneous deformation, providing a phenomenological description of subcellular remodelling processes - cytoskeletal reorganisation, myosin turnover and adhesion bond remodelling - at the cellular scale. Using this extended multicellular model, we reproduce multiscale responses to external mechanical forces, including creep and stress relaxation experiments, and demonstrate that the history of applied deformation influences tissue recovery upon release. Additionally we identify and quantify where and how these multiscale responses occur. The resulting framework allows for more detailed descriptions and analyses of the development and function of biological tissues.

biophysics↗

bioRxiv2026

Spatial remodeling of the thymic stroma with age disrupts niches for T cell selection and tolerance

Age-associated thymic involution is a major driver of immunosenescence, yet the cellular and spatial mechanisms coordinating age-related thymic remodeling remain incompletely understood. Combining single-cell transcriptomics, chromatin accessibility profiling, and spatial transcriptomics, we generated a spatially resolved multi-omic atlas of the aging mouse thymus. We show that thymic aging is not simply a process of epithelial loss, but a spatial reorganization of the stroma into new microenvironments, including age-associated epithelial states, a fibroblast-supported epithelial progenitor niche, and tertiary lymphoid structures. This remodeling displaces niches supporting positive and negative thymocyte selection and coincides with an intrinsic decline in cortical thymic epithelial cell function. Ligand-receptor mapping identifies medullary fibroblasts as a signaling hub sustaining epithelial progenitors and promoting tertiary lymphoid structure neogenesis, linking these hallmarks of thymic aging. Together, our findings reframe thymic involution as spatial stromal reorganization that links stromal remodeling to impaired thymopoiesis, central tolerance, and immune aging.

immunology↗

bioRxiv2026

The Brain Age Gap as a Predictor of Alcohol Initiation in Adolescence

BackgroundGrowing evidence suggests regional and network-level brain imaging features in late childhood are predictive of alcohol use in adolescence. However, the directionality of these effects (i.e. whether they reflect accelerated or delayed neuromaturation) are mixed. We applied a Brain Age Gap Estimation (BrainAGE) model to examine whether overall deviations from typical brain aging trajectories are predictive of (1) alcohol initiation and (2) use behaviour (experimentation versus binge drinking) in adolescence. MethodsData from the Adolescent Brain Cognitive Development study release 6.0 were used. Baseline (ages 9-11) structural imaging features (cortical volume, area, and subcortical volume) were used to estimate BrainAGE. Alcohol use was determined using self-report data from the Substance Use Interview and Timeline Follow-Back across follow-ups (waves 1-6; ages 10-17). Logistic generalized mixed effects models examined whether BrainAGE predicted group status between (1) non-initiators (n=3,639) and initiators (n=1,176), and; (2) experimentation (at least one full drink, no binge episodes; n=461) and binge drinking (at least one episode; n=438). ResultsWhen adjusting for age, sex, and pubertal status, a one-standard-deviation decrease in BrainAGE (equivalent to 1.64 years) at baseline was associated with a 9.5% increase in odds of alcohol initiation in adolescence. However, this effect did not survive adjustment for sociodemographic and prior alcohol exposure covariates. Further, BrainAGE did not discriminate between experimentation and binge drinking. ConclusionsFindings suggest BrainAGE in late childhood may reflect potential risk for alcohol initiation, but not behaviours, in adolescence. However, this association likely reflects complex interactions between brain structure and contextual factors, warranting further investigation.

neuroscience↗

bioRxiv2026

Unified Multi-Cohort Harmonisation and Normative Modelling of Neuroimaging Data via Hierarchical GAMLSS

Large-scale neuroimaging studies increasingly pool data across multiple cohorts, scanners, and acquisition protocols, introducing technical between-cohort variation that must be addressed before meaningful biological inference can be drawn. Existing harmonisation methods, particularly ComBat-based approaches, have been widely adopted for this purpose. However, they remain limited by Gaussian assumptions and by their focus on location or location-scale correction. In this study, we propose a unified hierarchical Generalised Additive Models for Location, Scale and Shape (GAMLSS) framework for multi-cohort harmonisation and normative modelling of structural neuroimaging data. The framework models cohort effects directly within all fitted distributional parameters, accommodates any parametric family for which exact inverse mapping is available, and returns harmonised values on the original measurement scale through centile-based quantile mapping. Normative deviation scores are obtained as a direct by-product of the same fitted model, enabling harmonisation and normative inference to be conducted jointly. The method was evaluated in a pooled longitudinal dataset comprising 88,126 observations across 237 structural neuroimaging features from six cohorts spanning childhood to late life: ABCD, IMAGEN, NCANDA, LIFE, UK Biobank, and MAS. Harmonisation performance was compared with ComBat, ComBat-GAM, and ComBat-LS using complementary criteria assessing data retention, residual batch effects, preservation of age-related and sex-related biological signal, and coherence of post-harmonisation lifespan trajectories. GAMLSS achieved near-complete removal of residual cohort effects, retained almost all valid observations post-harmonisation, and showed the strongest overall preservation of biological signal across validation metrics. In particular, it better preserved biologically plausible age trajectories for distributionally complex features such as white matter hypointensity volume, while simultaneously providing harmonised native-scale values and normative deviation scores within a single framework. These findings suggest that hierarchical GAMLSS offers a flexible and practical alternative to existing ComBat-based methods for large-scale neuroimaging harmonisation, particularly for features with non-Gaussian residual distributions and settings where cohort effects extend beyond differences in mean and variance.

neuroscience↗

bioRxiv2025

Longitudinal Neurocognitive Trajectories in a Large Cohort of Youth Who Use Cannabis: Combining Self-Report and Toxicology

Adolescents experience extensive neurocognitive development, with cannabis use potentially impacting developmental trajectories. Here, we comprehensively assess the influence of adolescent cannabis use onset on neurocognitive trajectories and consider how recent delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD) may influence neurocognition. We use the large, diverse longitudinal Adolescent Brain Cognitive Development (ABCD) Study dataset, combining self-reported substance use with objective toxicological tests (hair, urine, breath, oral fluid). Longitudinal mixed methods of the full cohort (n=11,036, ages 9-17; 47% Female/53% Male) investigate time-varying cannabis onset on neurocognitive performance. Primary model covariates include sociodemographics, family history of substance use disorder, prenatal substance exposure, early psychopathology, other substance use, and nesting for participant ID, study site, and family ID. Secondarily, in participants with repeat toxicological hair testing (n=645; 38% Female/62% Male) at ages 12-16, we consider the influence of THC v. CBD v. Controls. Primary models included false discovery rate corrections (FDR-p<.05) while secondary models were interpreted at p<.01. Cannabis group interacted with age to show altered neurocognitive trajectories across domains (immediate recall and delayed memory, processing speed, inhibitory control, visuospatial processing, language, and working memory; {beta}s=-0.11- - 0.52). Secondary models indicated hair-identified THC exposure*age predicted worse episodic memory than in Controls ({beta}=-0.60, p=.007), with no difference between CBD exposed and Controls. Data suggest those who use cannabis show likely pre-existing better cognitive performance during late childhood, with reduced improvement or flattened trajectories over time. These neurocognitive trajectories in youth (ages 9-17) who initiate cannabis use were demonstrated after accounting for within-person change and numerous known confounds and improving accuracy in identifying cannabis use through incorporating toxicological measures. Continued monitoring of this cohort will clarify cannabinoid-cognition relationships into young adulthood, including the impact of timing of cannabis use initiation.

neuroscience↗

bioRxiv2025

Development of a universal imaging "phenome" using Shape, Appearance and Motion (SAM) features and the SAM Observation Tool (SPOT)

Cells are plastic, highly heterogeneous and change over time. High-content timelapse imaging promises to reveal dynamic cell behaviors, enabling more accurate identification of cell state and cell fate prediction for biological hypothesis generation and perturbation screens. To empower live-cell imaging based screen, we report the development of 1) a Shape, Appearance, Motion (SAM) "phenome"; a universal set of 2185 image-derived features that act as a image-"transcriptome" to comprehensively quantify an objects instantaneous phenotype; 2) the SAM-Phenotype-Observation-Tool (SPOT), for image-"sequencing" analysis of phenomes. We validated the effectiveness of unbiased SAM-SPOT workflow on publicly available computer vision and 2D single cell imaging datasets. Importantly, we demonstrated that SAM-phenome outperformed features generated by deep learning AI models trained on >1 million fixed single cell and >5000 single cell video frames, respectively. SAM-phenome and SPOT delivers high-throughput, object-treatment-agnostic, comprehensive screening readouts of dynamics, promising to advance novel molecular target discovery and new medicine development.

bioinformatics↗

bioRxiv2025

Identifying phenotype-genotype-function coupling in 3D organoid imaging using Shape, Appearance and Motion Phenotype Observation Tool (SPOT)

Live cells in tissue are plastic, phenotypically dynamic, and modify their function in response to genetic and environmental perturbations. To unleash the power of live-cell imaging to identify phenotype-genotype-function coupling over time, we report the development of a standardized Shape-Appearance-Motion (SAM) "phenome" and SAM-Phenotype-Observation-Tool (SPOT), that act as an image-"transcriptome" and image-"transcriptome analyzer" respectively, and provide unbiased and comprehensive description of morpho-dynamic phenotypes without prior knowledge. We developed and applied SAM-SPOT to our simulated organoids database with known ground-truth and >1.6 million mouse and human organoid instances with defined genetic and chemical perturbations. SAM-SPOT can effectively and robustly characterize 3D morpho-dynamics from 2D projection videos. Combined with single-cell RNA sequencing, SAM-SPOT revealed that altered WNT signaling, but not mutant RAS or p53, predisposes intestinal organoids to irregular morphogenesis. SAM-SPOT advances biomedical discovery by empowering live-cell imaging to identify phenotype-genotype-function relationships through large-scale and cost-effective label-free live-cell imaging.

cancer biology↗

bioRxiv2025

Structural Covariance Network Properties Predictive of Early Adolescent Alcohol Initiation

ImportanceEarly alcohol initiation (before age 15) is associated with adverse outcomes. Understanding mechanisms behind early alcohol initiation is essential for informing prevention efforts. ObjectiveTo examine whether structural covariance network properties at ages 9-10 years predict early alcohol initiation. DesignCase-control, population-based study design. SettingData from the Adolescent Brain Cognitive Development study were used. Baseline structural brain imaging data (ages 9-10) were used for generation and comparison of structural covariance networks. Data from baseline to 4-year follow-up ([≤]age 15) assessments were used to determine alcohol initiation. ParticipantsParticipants were excluded if they reported consuming a full drink of alcohol at baseline, or did not meet imaging inclusion criteria. Controls were excluded if they had not yet been assessed or were missing substance use data at 4-year follow-up. In total, 3,878 participants met study criteria, of which 182 participants initiated alcohol. Structural covariance network properties were compared between the full sample and a 1:1 propensity-matched sample based on age, sex, race, ethnicity, religion, parental education, prenatal alcohol exposure, and baseline alcohol sipping. Main Outcomes and MeasuresStructural covariance networks were estimated using regional cortical thickness and volume measurements. Measures of network segregation (modularity, clustering coefficient), integration (characteristic path length, global efficiency), and resilience (degree assortativity) were compared between groups. Early alcohol initiation was defined as consuming a full drink between baseline and 4-year follow-up ResultsAlcohol initiators (n=182, median[IQR] age, 10.3[9.9-10.8]; 101 female[55.5%]) demonstrated lower network segregation (modularity: area-under-the-curve[AUC] difference[95%CI]=-0.017[-0.017,-0.007], p=0.030; clustering coefficient: AUC[95%CI]=-0.026[-0.027,-0.012], p=0.0495) and higher network integration (characteristic path length: AUC[95%CI]=-0.106[-0.099,-0.046], p=0.020; global efficiency: AUC[95%CI]=0.011[0.005,0.011], p=0.010), compared to non-initiators (n=3,696, median[IQR] age, 9.9[9.4-10.4]; 1750 female[47.4%]) when controlling for age, sex, and mean cortical thickness. Within the matched sample, only differences in network integration were preserved (characteristic path length: AUC[95%CI]=-0.044[-0.032,0.035], p=0.010; global efficiency: AUC[95%CI]=0.003[-0.003,0.003], p=0.040). There were no differences between full or matched samples when comparing cortical volume structural covariance networks. Conclusions and RelevanceDifferences in cortical thickness structural covariance network properties at ages 9-10 predicted alcohol initiation before age 15. These findings suggest cortical thickness network topology may reflect a neuroanatomical risk marker for early alcohol initiation. Key pointsO_ST_ABSQuestionC_ST_ABSDo structural covariance network properties at age 9-10 years predict alcohol initiation prior to age 15? FindingsIn this case-control study of 3,878 participants, early adolescent alcohol initiators demonstrated differences in cortical thickness network integration and segregation compared to their non-initiating peers. MeaningAlcohol-naive adolescents who initiate alcohol use early in life demonstrate differences in structural brain network organization compared to their abstinent peers, which may reflect a neuroanatomical risk marker for early alcohol use.

neuroscience↗

bioRxiv2025

Exploring the relationship between vascular remodelling and tumour growth using agent-based modelling

We develop a multiscale agent-based model (ABM) to investigate the effect that mechanical interactions between proliferating tumour cells and the surrounding vasculature have on the oxygen supply to the tumour microenvironment (TME), the tumours growth dynamics, and its response to radiotherapy. Our model extends existing models of tumour spheroid growth by incorporating vessel deformation due to mechanical forces between vessel walls and neighbouring tumour cells. These forces generate an effective pressure which compresses vessels, driving occlusion and pruning. This, in turn, leads to a hypoxic oxygen landscape which stimulates angiogenesis. A key feature of our model is the treatment of mechanical cell interactions with the tumour microenvironment, which we represent with two forces. The first is Stokes drag which is widely used in ABMs to represent resistance to cell movement. The second is a friction force which accounts for resistance due to the continual breaking and reforming of cell-extracellular matrix (ECM) adhesions. The importance of this friction force is demonstrated by numerical simulation. When Stokes drag dominates, pressure gradients dissipate across the tissue and vessel compression is negligible. By contrast, as the strength of the friction force increases, larger pressure gradients form, leading to significant vessel compression. We perform extensive numerical simulations to investigate how model parameters that control vascular remodelling and friction influence tumour vascularisation, which we spatially quantify using the cross-pair correlation function. This, in turn, alters the oxygen landscape and drives changes in tumour morphology. Finally, we highlight the importance of accounting for both mechanisms when simulating tumour responses to treatment with radiotherapy. We observe that vascular remodelling critically alters the tumours susceptibility to treatment and post-radiotherapy regrowth. Tumour regrowth is especially impacted by vessel remodelling, with certain vascular landscapes able to rebound quickly post-radiotherapy, resulting in fast tumour regrowth. Author SummaryWe have created an agent-based model (ABM) that accounts for mechanical interactions between tumour cells and associated vasculature. The model simulates vessel occlusion and pruning due to compression by neighbouring tumour cells; and growth of new vessels in response to hypoxia. Incorporating pressure-mediated vessel occlusion into our ABM required development of a friction force representing cell-extracellular matrix (ECM) adhesion. Through numerical simulations, we show that when friction is neglected, pressure gradients dissipate throughout the tissue and vessel compression is negligible. By contrast, as the strength of the friction force increases, larger pressure gradients form, leading to significant vessel compression. We perform extensive model simulations, to investigate how changes in the oxygen landscape caused by vascular remodelling affect the tumours growth dynamics and composition. We show further how vessel remodelling influences a tumours response to radiotherapy. We find tumour regrowth to be especially sensitive to vessel remodelling, with certain vascular landscapes growing rapidly post-radiotherapy, and accelerating tumour regrowth.

cancer biology↗

bioRxiv2025

Spatio-temporal dynamics of M1 and M2 macrophages in a multiphase model of tumor growth

This study investigates the complex dynamics of vascular tumors and their interplay with macrophages, key agents of the innate immune response. We model the tumor microenvironment as a multiphase fluid, with each cellular population treated as a distinct, non-mixing phase. The framework also incorporates diffusible species that are critical for processes such as nutrient transport, angio-genesis, chemotaxis, and macrophage activation. Numerical simulations of our model show how phenotypic and spatial heterogeneity in the macrophage population arises and how such heterogeneity impacts a tumors growth dynamics. Finally, we propose an immunotherapeutic strategy based on the experimental agent vactosertib which promotes an anti-tumor macrophage phenotype. Our simulations demonstrate an increased density of anti-tumor macrophages over the period of a few months, followed by a relapse period where the tumor regains its original dynamics.

bioengineering↗

bioRxiv2024

Characterising cancer cell responses to cyclic hypoxia using mathematical modelling

In vivo observations show that oxygen levels in tumours can fluctuate on fast and slow timescales. As a result, cancer cells can be periodically exposed to pathologically low oxygen levels; a phenomenon known as cyclic hypoxia. Yet, little is known about the response and adaptation of cancer cells to cyclic, rather than, constant hypoxia. Further, existing in vitro models of cyclic hypoxia fail to capture the complex and heterogeneous oxygen dynamics of tumours growing in vivo. Mathematical models can help to overcome current experimental limitations and, in so doing, offer new insights into the biology of tumour cyclic hypoxia by predicting cell responses to a wide range of cyclic dynamics. We develop an individual-based model to investigate how cell cycle progression and cell fate determination of cancer cells are altered following exposure to cyclic hypoxia. Our model can simulate standard in vitro experiments, such as clonogenic assays and cell cycle experiments, allowing for efficient screening of cell responses under a wide range of cyclic hypoxia conditions. Simulation results show that the same cell line can exhibit markedly different responses to cyclic hypoxia depending on the dynamics of the oxygen fluctuations. We also use our model to investigate the impact of changes to cell cycle checkpoint activation and damage repair on cell responses to cyclic hypoxia. Our simulations suggest that cyclic hypoxia can promote heterogeneity in cellular damage repair activity within vascular tumours.

systems biology↗

bioRxiv2024

Understanding Antibody-Target Antigen Interactions and the Avidity Effect Using Mathematical Modelling

Background and PurposeImmunotherapies are designed to exploit the immune system to target pathologies, for instance, but not exclusively, cancer. Monoclonal antibodies (mAbs) are an important class of immunotherapies that induce anti-tumour effects in numerous ways. Fundamental to the success of mAbs in cancer treatments are their interactions with target antigens. For example, binding multiple antigens, which increases binding affinity, termed the avidity effect, has been shown to impact treatment outcomes. However, there has been limited theoretical analysis addressing the impacts of antibody-antigen interactions on avidity, potency, and efficacy. Hence, our aim is to develop a mathematical model to develop insight on these impacts. Experimental ApproachWe analyse an ordinary differential equation model of bivalent, monospecific IgG antibodies binding to membrane bound antigens. We obtain equilibrium solutions of the model and conduct a global parameter sensitivity analysis to identify which antibody-antigen interactions impact quantities, such as antigen occupancy, that contribute to mAb potency and efficacy. Key ResultsWe show that the ratio of antibody to antigen number impacts antigen occupancy, bound antibody number and whether an antibody can bind both its antigen-binding arms. A sensitivity analysis reveals that antigen occupancy and the ratio of bound antibody to total antigen number are sensitive to the antibody-antigen binding rates only for high antibody concentrations. We identify parameter ranges in which the avidity effect is predicted to be large for antigen occupancy and bound antibody numbers. Conclusion and ImplicationsThese results could be used in the preclinical development of mAb therapies by predicting conditions which enhance mAb potency, efficacy, and the avidity effect.

systems biology↗

bioRxiv2023

The landscape of tolerated genetic variation in humans and primates

Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole genome sequencing data for 809 individuals from 233 primate species, and identified 4.3 million common protein-altering variants with orthologs in human. We show that these variants can be inferred to have non-deleterious effects in human based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases. One Sentence SummaryDeep learning classifier trained on 4.3 million common primate missense variants predicts variant pathogenicity in humans.

genomics↗

bioRxiv2023

A global catalog of whole-genome diversity from 233 primate species

The rich diversity of morphology and behavior displayed across primate species provides an informative context in which to study the impact of genomic diversity on fundamental biological processes. Analysis of that diversity provides insight into long-standing questions in evolutionary and conservation biology, and is urgent given severe threats these species are facing. Here, we present high coverage whole-genome data from 233 primate species representing 86% of genera and all 16 families. This dataset was used, together with fossil calibration, to create a nuclear DNA phylogeny and to reassess evolutionary divergence times among primate clades. We found within-species genetic diversity across families and geographic regions to be associated with climate and sociality, but not with extinction risk. Furthermore, mutation rates differ across species, potentially influenced by effective population sizes. Lastly, we identified extensive recurrence of missense mutations previously thought to be human-specific. This study will open a wide range of research avenues for future primate genomic research. One-Sentence SummaryThe whole genome sequences of 233 primate species provide insight into the determinants of genetic diversity, phylogenomics, and human uniqueness.

genomics↗

bioRxiv2023

An allele-resolved nanopore-guided tour of the human placental methylome

The placenta is a temporary organ present during pregnancy that is responsible for coordinating all aspects of pregnancy between the mother and fetus. It has a distinct epigenetic, transcriptomic, and mutational landscape with low levels of methylation, high numbers of transcribed loci, and a high mutational burden relative to somatic tissues. We present this landscape through the application of nanopore sequencing technology to provide a more comprehensive picture of female placental genomics and methylomics along with integrated haplotype-resolved transcriptomic analyses across eight trios. Whole genome sequencing of trios allows robust phasing, permitting comprehensive genome-wide investigation of parent-of-origin methylation and transcription. This enhanced view facilitates identifications of many new differentially methylated regions (DMRs), both conserved and differing between individuals, as well as novel imprinted genes including ILDR2 and RASA1 which are potentially important for healthy placental and fetal development.

genomics↗

bioRxiv2021

Quantification of vascular networks in photoacoustic mesoscopy

Mesoscopic photoacoustic imaging (PAI) enables non-invasive visualisation of tumour vasculature and has the potential to assess prognosis and therapeutic response. Currently, evaluating vasculature using mesoscopic PAI involves visual or semi-quantitative 2D measurements, which fail to capture 3D vessel network complexity, and lack robust ground truths for assessment of segmentation accuracy. Here, we developed an in silico, phantom, in vivo, and ex vivo-validated end-to-end framework to quantify 3D vascular networks captured using mesoscopic PAI. We applied our framework to evaluate the capacity of rule-based and machine learning-based segmentation methods, with or without vesselness image filtering, to preserve blood volume and network structure by employing topological data analysis. We first assessed segmentation performance against ground truth data of in silico synthetic vasculatures and a photoacoustic string phantom. Our results indicate that learning-based segmentation best preserves vessel diameter and blood volume at depth, while rule-based segmentation with vesselness image filtering accurately preserved network structure in superficial vessels. Next, we applied our framework to breast cancer patient-derived xenografts (PDXs), with corresponding ex vivo immunohistochemistry. We demonstrated that the above segmentation methods can reliably delineate the vasculature of 2 breast PDX models from mesoscopic PA images. Our results underscore the importance of evaluating the choice of segmentation method when applying mesoscopic PAI as a tool to evaluate vascular networks in vivo.

bioengineering↗