Biology subjects

Devine, J.

Publications and source records attributed to Devine, J..

16 recordsLinked to original sources

bioRxiv2026

Heterochronic Uncoupling of Brain and Face Morphogenesis Drives Premature Fusion in Ciliary Transition Zone Mutants

Normal facial development is highly coordinated with embryonic brain expansion, suggesting the forebrain acts as a physical scaffold dictating the spatial timing of facial prominence apposition and fusion. To test this idea, we compared B9d1 ciliary transition zone mutants, which exhibit altered Hedgehog signaling and premature facial fusion, to a normal murine developmental morphospace. At e11.5, mutant brains are significantly reduced in size, yet intrinsic facial prominence size remains largely normal. Projecting e11.5 and e12.5 mutants into the normal morphospace reveals a severe heterochronic uncoupling: the delayed, narrowed neurocranium mechanically forces normal-sized facial prominences into premature convergence. These results indicate that when brain growth slows, facial prominences initiate contact earlier in restricted spatial bounds. Conversely, this model predicts that an accelerated, expanded brain may act as a physical wedge, increasing the likelihood of cleft lip. By mathematically modeling these structural dependencies, we propose that palatogenesis operates on a continuous phenotypic spectrum, where premature fusion and cleft lip represent alternative physical outcomes conditioned on the underlying brain developmental trajectory.

developmental biology↗

bioRxiv2026

Navigating the window: structural bottlenecks in avian craniofacial integration revealed by the ontogenetic patterning of shape variance

Background: Successful palatal morphogenesis requires the precise spatiotemporal choreography of independent facial prominences. Our previous macroevolutionary analyses identified a convergence in amniote facial growth at primary palatal fusion, associated with reduced shape variability. We hypothesize that this constraint operates within individual species, representing a shared structural bottleneck where brain growth acts as a critical morphogenetic co-factor. To test this idea, we analyzed high-resolution 3D micro-CT images of embryonic chickens (Gallus gallus, N=217) spanning primary palatogenesis. Utilizing 3D geometric morphometrics (3DGM), we quantified a continuous "developmental morphospace" integrating the face and brain. Results: Facial shape follows a highly nonlinear developmental trajectory. Using a continuous sliding-window analysis, we demonstrate that local morphological disparity undergoes a significant system-wide collapse coinciding with primary palatal fusion. Partial Least Squares (PLS) confirms this morphospace pivot is tightly coordinated with neurocranial development. Post-fusion, the modules uncouple: facial allometry switches from convergent to divergent projecting growth, while the brain undergoes spatial encapsulation, subsequently flatlining its morphological disparity. Conclusions: Craniofacial morphogenesis can be described in terms of a tightly regulated biphasic developmental hourglass. The physical tethering of facial prominences creates a brief structurally constrained "shape window" essential for successful fusion. Deviations from this bottleneck, whether driven by altered prominence trajectories, increased local variance, or disruptions to the underlying forebrain landscape, push embryos into unoccupied morphospace, providing a unified mechanical and predictive framework for the spatiotemporal origins of non-syndromic cleft lip.

developmental biology↗

bioRxiv2025

The craniofacial shape of modern humans embodies genomic signatures of evolution, diversity, and clinical conditions

Human craniofacial morphology is a hallmark of our species diversity and evolutionary history, shaped by adaptation, introgression, and global dispersal. Cranial globularization and chin emergence are well-documented morphological transformations whose genetic basis remains poorly understood, whereas Neandertal introgression is primarily documented through genomic evidence. How these evolutionary phenomena relate to craniofacial variation in present-day humans remains largely unresolved. Here, we leverage 3D craniofacial data from over 50,000 UK Biobank participants and employ a multivariate, multiscale genome-wide association approach to define axes of variation aligned with inter-population allele frequency shifts, evolutionary processes, and clinical conditions. We identify continuous craniofacial trends within our cohort that mirror global patterns of genetic diversity, indicating that facial differences between human populations arise at the phenotypic axes already present within a single population. We further demonstrate that modern human-derived alleles underlie the origins of the human chin by reducing midfacial projection relative to other hominins and reveal the persistent effects of Neandertal introgression on craniofacial diversity today. We also model genetically informed endophenotypes for orofacial clefts, obstructive sleep apnoea, and myopia. These findings provide insights into our species evolutionary history and endophenotypes of clinical conditions and establish a framework for contextualizing craniofacial diversity into biologically meaningful axes of variation relevant to diverse scientific disciplines.

genetics↗

bioRxiv2025

Optimizing Polygenic Scores for Complex Morphological Traits: A Case Study in Nasal Shape Prediction

Polygenic scores (PGS) facilitate the prediction of an individuals phenotype from their genotype. Typically, PGS methods apply regularization or use a clumping and thresholding (C+T) approach to handle SNP inclusion. To achieve good prediction accuracy, these approaches rely on effect size estimates from well-powered genome-wide association studies (GWAS). However, this is currently not feasible for morphological shape when phenotyped as univariate traits. Here, we introduce a novel framework to enhance polygenic prediction through three key components: (1)leveraging multivariate GWAS summary statistics for improved SNP selection, (2) defining genetically informative phenotypes, and (3) benchmarking PGS methods to select the optimal model. Our approach integrates multivariate GWAS, which performs an omnibus test against all phenotypic variables jointly with increased power. Specifically, our approach leverages P values from multivariate GWAS to improve SNP selection while maintaining the effect size estimates for the univariate trait under investigation, allowing the use of current PGS tools. We evaluated our proposed method for predicting 3D nasal morphology using a dataset of 52,896 individuals of European ancestry from the UK Biobank. Using the C+T method, SNP selection based on multivariate GWAS resulted in significantly improved phenotypic prediction (P = 9.74e-5) for eigen-shapes, with a mean variance explained of 3.88% (SD = 1.59%) compared to 2.02% (SD = 1.10%) using a traditional univariate approach in the test set (n = 2,896). We also tested whether heritability-optimized phenotypes were more predictable than eigen-shapes derived from principal component analysis (PCA). On average, with SNP selection based on multivariate GWAS using the C+T method, heritability-optimized phenotypes yielded greater predictive performance, with PGS scores explaining 2.72%-10.37% of phenotypic variance, compared to 1.05%-6.84% for eigen-shapes. Furthermore, benchmarking several PGS methods revealed that LDpred2 consistently achieved the best performance for predicting nasal morphology. Our results demonstrate that combining multivariate GWAS P values with optimized phenotypes and advanced PGS models leads to more accurate polygenic prediction for complex morphological traits.

bioinformatics↗

bioRxiv2025

A Quantitative Approach to Mapping Mitochondrial Specialization and Plasticity

Mitochondria are a diverse family of organelles with highly specialized functions. While they share common features, their molecular and functional diversity remains underexplored. Here, we introduce a quantitative pipeline to define the degree of molecular specialization among different mitochondrial phenotypes, or mitotypes. By distilling hundreds of validated mitochondrial genes into 149 biologically interpretable MitoPathway scores, this streamlined mitotyping framework enables investigators to quantify and interpret mitochondrial diversity and plasticity from transcriptomic data across a variety of natural and experimental contexts. Using this approach, we show that mouse and human multi-organ mitotypes segregate along two main axes of mitochondrial specialization, characterize robust longitudinal and perturbation-induced metabolic plasticity in cultured human fibroblasts, and resolve cell-type and single-cell mitochondrial recalibrations across the human brain and sperm developmental trajectory. Together, this framework provides a practical and extensible approach for analyzing mitochondrial specialization in complex biological systems.

cell biology↗

bioRxiv2025

Mapping the GDF15 Arm of the Integrated Stress Response in Human Cells and Tissues

Mitochondrial stress activates the integrated stress response (ISR) and triggers cell-cell communication through the secretion of the metabokine growth differentiation factor 15 (GDF15). However, the gene network underlying the ISR remains poorly defined, particularly across metabolically diverse cellular states and tissues. Using RNAseq data from fibroblasts subjected to eleven metabolic perturbations, including genetic and pharmacological mitochondrial OxPhos defects, we showed that the ISR has multiple arms and developed an ISRGDF15 index quantifying the GDF15 arm of ISR activation in human cells. The ISRGDF15 index was validated using optogenetic activation of the ISR protein kinase R (PKR) in a stable cell line, demonstrating its rapid kinetics preceding to GDF15 gene expression. We then deployed the ISRGDF15 index across 44 postmortem human tissues, reporting that the ISRGDF15 was upregulated in the heart of individuals who died of an acute cause in the emergency room, whereas it was preferentially upregulated in the brain of individuals who died as inpatients after protracted hospital stays. ISRGDF15 was also moderately, positively correlated with age across all tissues. These data highlight multiple distinct ISR pathways and clarify which genes are related to the GDF15 arm of the ISR, yielding an ISRGDF15 index that can be used to investigate tissue-specific and age-related ISR activation in both in vitro cultures and human tissues. Ultimately, we expand our knowledge of the GDF15 arm of the ISR in humans, show its inducibility by the ISR kinase PKR, and demonstrate its applicability to detect ISR activation in specific human tissues.

cell biology↗

bioRxiv2024

The gut microbiome promotes mitochondrial respiration in the brain of a Parkinson's disease mouse model

The pathophysiology of Parkinsons disease (PD) involves gene-environment interactions that impair various cellular processes such as autophagy, lysosomal function, or mitochondrial dysfunction. Specifically, mitochondria-associated gene mutations increase PD risk, mitochondrial respiration is altered in the PD brain, and mitochondrial-damaging toxins cause PD-like motor and gastrointestinal symptoms in animal models. The gut microbiome is altered in PD patients and represents an environmental risk, however a relationship between mitochondrial function and the microbiome in PD has not been previously established. Herein, we report that striatal mitochondria are functionally overactive in -synuclein-overexpressing (ASO) mice, a model of PD, and that microbiome depletion restores respiration and mitochondria-associated gene expression patterns to wild-type levels. ASO mice harboring a complex microbiome produce increased reactive oxygen species in the striatum whereas germ-free counterparts express elevated levels of antioxidant proteins that may buffer against oxidative damage. Indeed, antioxidant treatment improves motor performance in ASO mice and, remarkably, blocking oxidant scavenging in germ-free mice induces -synuclein-dependent motor deficits. Thus, the gut microbiome increases mitochondrial respiration and oxidative stress in the brain, which enhances motor symptoms in a mouse model of PD.

physiology↗

bioRxiv2024

Brain-body mitochondrial distribution patterns lack coherence and point to tissue-specific and individualized regulatory mechanisms

Energy transformation capacity is generally assumed to be a coherent individual trait driven by genetic and environmental factors. This predicts that some individuals should have high and others low mitochondrial oxidative phosphorylation (OxPhos) capacity across organ systems. Here, we test this assumption using multi-tissue molecular and enzymatic activities in mice and humans. Across up to 22 mouse tissues, neither mitochondrial OxPhos capacity nor mtDNA density were correlated between tissues (median r = -0.01-0.16), indicating that animals with high mitochondrial capacity in one tissue can have low capacity in other tissues. Similarly, the multi-tissue correlation structure of RNAseq-based indices of mitochondrial gene expression across 45 tissues from 948 women and men (GTEx) showed small to moderate coherence between only some tissues (regions of the same brain), but not between brain-body tissue pairs in the same person (median r = 0.01). Mitochondrial DNA copy number (mtDNAcn) also lacked coherence across organs and tissues. Mechanistically, tissue-specific differences in mitochondrial gene expression were attributable in part to i) tissue-specific activation of canonical energy sensing pathways including the transcriptional coactivator PGC-111 and the integrated stress response (ISR), and ii) proliferative activity across tissues. Finally, we identify subgroups of individuals with high mitochondrial gene expression in some tissues (e.g., heart) but low expression in others (e.g., skeletal muscle) who display different clinical phenotypic patterns. Taken together, these data raise the possibility that tissue-specific energy sensing pathways may contribute to the idiosyncratic mitochondrial distribution patterns associated with the inter-organ heterogeneity and phenotypic diversity among individuals.

cell biology↗

bioRxiv2024

Psychobiological regulation of plasma and saliva GDF15 dynamics in health and mitochondrial diseases

Growth Differentiation Factor 15 (GDF15) is a protein that reflects mitochondrial energetic stress and is linked to physical and mental health symptoms, aging, and mortality. Here, we tested the hypothesis that GDF15 is a stress-responsive biomarker through a series of observational and experimental studies. We report four main findings. First, in the UK Biobank (n=53,026) and Framingham Heart Study (FHS) Offspring (n=3,460) cohorts, plasma GDF15 levels were elevated in individuals with symptoms of depression and anxiety. In the FHS cohort, GDF15 was also higher in participants exposed to chronic psychosocial stressors, including lower educational attainment, lower family income, and higher job strain. Second, plasma GDF15 levels in the FHS cohort correlated positively with epigenetic clocks measuring biological aging and effect sizes of GDF15 associations with psychosocial stressors were comparable to those observed for the clocks. Third, in a two-participant intensive-sampling study (n=112 days), saliva GDF15 showed a robust awakening response similar to established stress-related hormones. However, it exhibited a distinct negative pattern, peaking at waking and declining by 42-92% within 30-45 minutes. Finally, in two laboratory experiments (n=148), acute social-evaluative stress significant increased GDF15 levels in plasma and saliva within minutes. Together, these findings suggest that psychosocial stress may contribute to mitochondrial energetic stress indexed by GDF15, with implications for aging and health. This work opens new avenues for using GDF15 as a non-invasive biomarker to study the biological embedding of stress and its impact on aging trajectories. Significance statementGrowth Differentiation Factor 15 (GDF15) is a circulating protein elevated with mitochondrial energetic stress, aging, and diseases. Our findings show that GDF15 is elevated with depressive and anxiety symptoms and in those exposed to chronic psychosocial stress. Elevated plasma GDF15 also correlates with accelerated biological aging, as measured by epigenetic clocks. The effect sizes linking GDF15 to psychosocial stressors were comparable to those observed for the epigenetic clocks. Saliva GDF15 shows a robust negative awakening response characterized by elevated levels at awakening before declining within 30-45 minutes. Acute social-evaluative stress induced increase in plasma and saliva GDF15. Together, these findings suggest GDF15 can be used to study the energetic mechanisms for the biological embedding of stress across the lifespan.

physiology↗

bioRxiv2023

Classifying high-dimensional phenotypes with ensemble learning

O_LIClassification is a fundamental task in biology used to assign members to a class. While linear discriminant functions have long been effective, advances in phenotypic data collection are yielding increasingly high-dimensional datasets with more classes, unequal class covariances, and non-linear distributions. Numerous studies have deployed machine learning techniques to classify such distributions, but they are often restricted to a particular organism, a limited set of algorithms, and/or a specific classification task. In addition, the utility of ensemble learning or the strategic combination of models has not been fully explored. C_LIO_LIWe performed a meta-analysis of 33 algorithms across 20 datasets containing over 20,000 high-dimensional shape phenotypes using an ensemble learning framework. Both binary (e.g., sex, environment) and multi-class (e.g., species, genotype, population) classification tasks were considered. The ensemble workflow contains functions for preprocessing, training individual learners and ensembles, and model evaluation. We evaluated algorithm performance within and among datasets. Furthermore, we quantified the extent to which various dataset and phenotypic properties impact performance. C_LIO_LIWe found that discriminant analysis variants and neural networks were the most accurate base learners on average. However, their performance varied substantially between datasets. Ensemble models achieved the highest performance on average, both within and among datasets, increasing average accuracy by up to 3% over the top base learner. Higher class R2 values, mean class shape distances, and between- vs. within-class variances were positively associated with performance, whereas higher class covariance distances were negatively associated. Class balance and total sample size were not predictive. C_LIO_LILearning-based classification is a complex task driven by many hyperparameters. We demonstrate that selecting and optimizing an algorithm based on the results of another study is a flawed strategy. Ensemble models instead offer a flexible approach that is data agnostic and exceptionally accurate. By assessing the impact of various dataset and phenotypic properties on classification performance, we also offer potential explanations for variation in performance. Researchers interested in maximizing performance stand to benefit from the simplicity and effectiveness of our approach made accessible via the R package pheble. C_LI

bioinformatics↗

bioRxiv2023

Quantifying the relationship between cell proliferation and morphology during development of the face

Morphogenesis requires highly coordinated, complex interactions between cellular processes: proliferation, migration, and apoptosis, along with physical tissue interactions. How these cellular and tissue dynamics drive morphogenesis remains elusive. Three dimensional (3D) microscopic imaging poses great promise, and generates elegant images. However, generating even moderate through-put quantified images is challenging for many reasons. As a result, the association between morphogenesis and cellular processes in 3D developing tissues has not been fully explored. To address this critical gap, we have developed an imaging and image analysis pipeline to enable 3D quantification of cellular dynamics along with 3D morphology for the same individual embryo. Specifically, we focus on how 3D distribution of proliferation relates to morphogenesis during mouse facial development. Our method involves imaging with light-sheet microscopy, automated segmentation of cells and tissues using machine learning-based tools, and quantification of external morphology via geometric morphometrics. Applying this framework, we show that changes in proliferation are tightly correlated to changes in morphology over the course of facial morphogenesis. These analyses illustrate the potential of this pipeline to investigate mechanistic relationships between cellular dynamics and morphogenesis during embryonic development.

developmental biology↗

bioRxiv2021

MusMorph, a database of standardized mouse morphology data for morphometric meta-analyses

Complex morphological traits are the product of many genes with transient or lasting developmental effects that interact in anatomical context. Mouse models are a key resource for disentangling such effects, because they offer myriad tools for manipulating the genome in a controlled environment. Unfortunately, phenotypic data are often obtained using laboratory-specific protocols, resulting in self-contained datasets that are difficult to relate to one another for larger scale analyses. To enable meta-analyses of morphological variation, particularly in the craniofacial complex and brain, we created MusMorph, a database of standardized mouse morphology data spanning numerous genotypes and developmental stages, including E10.5, E11.5, E14.5, E15.5, E18.5, and adulthood. To standardize data collection, we implemented an atlas-based phenotyping pipeline that combines techniques from image registration, deep learning, and morphometrics. Alongside stage-specific atlases, we provide aligned micro-computed tomography images, dense anatomical landmarks, and segmentations (if available) for each specimen (N=10,056). Our workflow is open-source to encourage transparency and reproducible data collection. The MusMorph data and scripts are available on FaceBase (www.facebase.org, doi.org/10.25550/3-HXMC) and GitHub (https://github.com/jaydevine/MusMorph).

genetics↗

bioRxiv2020

A precautionary solution to estimation bias in shaping safe harvest boundaries

Biased estimates of population status are a pervasive conservation problem. This problem has plagued assessments of commercial exploitation of marine species and can threaten the sustainability of both populations and fisheries. We develop a computer-intensive approach to minimize adverse effects of persistent estimation bias in assessments by optimizing operational harvest measures (harvest control rules) with closed-loop simulation of resource-management feedback systems: management strategy evaluation. Using saithe (Pollachius virens), a bottom-water, apex predator in the North Sea, as a real-world case study, we illustrate the approach by first diagnosing robustness of the existing harvest control rule and then optimizing it through propagation of biases (overestimated stock abundance and underestimated fishing pressure) along with select process and observation uncertainties. Analyses showed that severe biases lead to overly optimistic catch limits and then progressively magnify the amplitude of catch fluctuation, thereby posing unacceptably high overharvest risks. Consistent performance of management strategies to conserve the resource can be achieved by developing more robust control rules. These rules explicitly account for estimation bias through a computational grid search for a set of control parameters (threshold abundance that triggers management action, Btrigger, and target exploitation rate, Ftarget) that maximize yield while keeping stock abundance above a precautionary level. When the biases become too severe, optimized control parameters- for saithe, raising Btrigger and lowering Ftarget-would safeguard against overharvest risk (<3.5% probability of stock depletion) and provide short-term stability in catch limit (<20% year-to-year variation), thereby minimizing disruption to fishing communities. The precautionary approach to fine-tuning adaptive risk management through management strategy evaluation offers a powerful tool to better shape sustainable harvest boundaries for exploited resource populations when estimation bias persists. By explicitly accounting for emergent sources of uncertainty our proposed approach ensures effective conservation and sustainable exploitation of living marine resources even under profound uncertainty. Open Research StatementData sets and code utilized for this research are available on Figshare. DOI: https://doi.org/10.6084/m9.figshare.13281266

ecology↗

bioRxiv2020

Selection for increased tibia length in mice alters skull shape through parallel changes in developmental mechanisms

Bones in the vertebrate cranial base and limb skeleton grow by endochondral ossification, under the control of growth plates. Mechanisms of endochondral ossification are conserved across growth plates, which increases covariation in size and shape among bones, and in turn may lead to correlated changes in skeletal traits not under direct selection. We used micro-CT and geometric morphometrics to characterize shape changes in the cranium of the Longshanks mouse, which was selectively bred for longer tibiae. We show that Longshanks skulls became longer, flatter, and narrower in a stepwise process. Moreover, we show that these morphological changes likely resulted from developmental changes in the growth plates of the Longshanks cranial base, mirroring changes observed in its tibia. Thus, indirect and non-adaptive morphological changes can occur due to developmental overlap among distant skeletal elements, with important implications for interpreting the evolutionary history of vertebrate skeletal form.

evolutionary biology↗

bioRxiv2019

A Registration and Deep Learning Approach to Automated Landmark Detection for Geometric Morphometrics

O_LIGeometric morphometrics is the statistical analysis of landmark-based shape variation and its covariation with other variables. Over the past two decades, the gold standard of landmark data acquisition has been manual detection by a single observer. This approach has proven accurate and reliable in small-scale investigations. However, big data initiatives are increasingly common in biology and morphometrics. This requires fast, automated, and standardized data collection. Image registration, or the spatial alignment of images, is a fundamental technique in automatic image analysis that is well-poised for such purposes. Yet, in the few studies that have explored the utility of registration-based landmarks for geometric morphometrics, relatively high or catastrophic labelling errors around anatomical extrema are common. Such errors can result in misleading representations of the mean shape, an underestimation of biological signal, and altered variance-covariance patterns. C_LIO_LIWe combine image registration with a deep and domain-specific neural network to automate and optimize anatomical landmark detection for geometric morphometrics. Using micro-computed tomography images of genetically and morphologically variable mouse skulls, we test our landmarking approach under a variety of registration conditions, including different non-linear deformation frameworks (small vs. large) and atlas strategies (single vs. multi). C_LIO_LICompared to landmarks derived from conventional image registration workflows, our optimized landmark data show significant reductions in error at problematic locations (up to 0.63 mm), a 36.4% reduction in average landmark coordinate error, and up to a 45.1% reduction in total landmark distribution error. We achieve significant improvements in estimates of the sample mean shape and variance-covariance structure. C_LIO_LIFor biological imaging datasets and morphometric research questions, our method can eliminate the time and subjectivity of manual landmark detection whilst retaining the biological integrity of these expert annotations. C_LI

evolutionary biology↗

bioRxiv2018

Circadian control of lung inflammation in influenza infection

Influenza is a leading cause of respiratory mortality and morbidity. While inflammation is necessary for fighting infection, a fine balance of anti-viral defense and host tolerance is necessary for recovery. Circadian rhythms have been known to modulate inflammation. However, the importance of diurnal variability in the timing of influenza infection is not well understood. Here we demonstrate that endogenous rhythms influence the cellular response to infection in bronchoalveolar lavage (BAL), the pulmonary transcriptomic profile and lesional histology. This time dependent variability does not reflect alterations in viral replication. Rather, we found that better time-dependent outcomes were associated with a preponderance of NK and NKT cells and lower proportion of monocytes in the lung. Thus, host tolerance, rather than viral burden underlies the diurnal gating of influenza induced lung injury.\n\nSignificance statementOur work demonstrates the importance of circadian rhythms in influenza infection --a condition with significant public health implications. Our findings, which establish the role of the circadian rhythms in maintaining the balance between host tolerance pathways and anti-viral responses confers a new framework for evaluating the relevance of circadian influences on immunity.

immunology↗