Biology subjects

Percival, C. J.

Publications and source records attributed to Percival, C. J..

5 recordsLinked to original sources

bioRxiv2025

Reduced Dietary Protein Induces Changes in the Dental Proteome

Experimental studies have demonstrated that nutritional changes during development can result in phenotypic changes to mammalian cheek teeth. This developmental plasticity of tooth morphology is an example of phenotypic plasticity. Because tooth development occurs through complex interactions between manifold processes, there are many potential mechanisms which can contribute to a tooths norm of reaction. Determining the identity of those mechanisms and the relative importance of each of them is one of the main challenges to understanding phenotypic plasticity. Quantitative proteomics combined with experimental studies allow for the identification of potential molecular contributors to a plastic response through quantification of expressed gene products. Here, we present the results of a quantitative proteomics analysis of mature upper first molars in Mus musculus from a controlled feeding experiment. Pregnant and nursing mothers were fed either a low-dietary protein (10%) treatment diet or control (20%) diet. Low-dietary protein was not associated with reduced molar size or skull length. However, expression of tooth-related proteins, immune system proteins, and actin-based myosin proteins were significantly altered in our low-dietary protein proteomics sample. The differential expression of immune proteins along with systematic reduction in actin-based myosin protein expression are novel discoveries for tooth proteomics studies. We propose that studies that aim to elucidate specific mechanisms of molar phenotypic plasticity should prioritize investigations into the relationships between IGF regulation and tooth development and actin-based myosin expression and tooth development. Research HighlightsA low-protein diet during development results in significantly altered protein expression for odontogenetic and osteogenic proteins, immune system proteins, and actin-based myosin proteins within Mus musculus, but does not alter skull length or molar size. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=53 SRC="FIGDIR/small/632248v2_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@2df942org.highwire.dtl.DTLVardef@477dfforg.highwire.dtl.DTLVardef@1a11da0org.highwire.dtl.DTLVardef@a012fc_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

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↗

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↗

bioRxiv2019

Facial shape and allometry quantitative trait locus intervals in the Diversity Outbred mouse are enriched for known skeletal and facial development genes

The biology of how faces are built and come to differ from one another is complex. Discovering the genes that contribute to differences in facial morphology is one key to untangling this complexity, with important implications for medicine and evolutionary biology. This study maps quantitative trait loci (QTL) for skeletal facial shape using Diversity Outbred (DO) mice. The DO is a randomly outcrossed population with high heterozygosity that captures the allelic diversity of eight inbred mouse lines from three subspecies. The study uses a sample of 1147 DO animals (the largest sample yet employed for a shape QTL study in mouse), each characterized by 22 three-dimensional landmarks, 56,885 autosomal and X-chromosome markers, and sex and age classifiers. We identified 37 facial shape QTL across 20 shape principal components (PCs) using a mixed effects regression that accounts for kinship among observations. The QTL include some previously identified intervals as well as new regions that expand the list of potential targets for future experimental study. Three QTL characterized shape associations with size (allometry). Median support interval size was 3.5 Mb. Narrowing additional analysis to QTL for the five largest magnitude shape PCs, we found significant overrepresentation of genes with known roles in growth, skeletal development, and sensory organ development. For most intervals, one or more of these genes lies within 0.25 Mb of the QTLs peak. QTL effect sizes were small, with none explaining more than 0.5% of facial shape variation. Thus, our results are consistent with a model of facial diversity that is influenced by key genes in skeletal and facial development and, simultaneously, is highly polygenic.\n\nAuthor SummaryThe mammalian face is a complex structure serving many functions. We studied the genetic basis for facial skeletal diversity in a large sample of mice from an experimental population designed for the study of complex traits. We quantified the contribution of genetic variation to variation in three-dimensional facial shape across more than 55,000 genetic markers spread throughout the mouse genome. We found 37 genetic regions which are very likely to contribute to differences in facial shape. We then conducted a more detailed analysis of the genetic regions associated with the most variable aspects of facial shape. For these regions, a disproportionately large number of genes are known to be important to growth and to skeletal and facial development. The magnitude of these genetic contributions to differences in facial shape are consistently small. Our results therefore support the notion that facial skeletal diversity is influenced by many genes of small effect, but that some of these small effects may be related to genes that are fundamental to skeletal and facial development.

genetics↗

bioRxiv2019

Aberrant cell segregation in craniofacial primordia and the emergence of facial dysmorphology in craniofrontonasal syndrome

Craniofrontonasal syndrome (CFNS) is a rare X-linked disorder characterized by craniofacial, skeletal, and neurological anomalies and caused by mutations in EFNB1. Heterozygous females are more severely affected by CFNS than hemizygous male patients, a phenomenon called cellular interference that is correlated with cell segregation resulting from EPHRIN-B1 mosaicism. Efnb1 heterozygous mutant mice also exhibit more severe phenotypes than Efnb1 hemizygous males as well as cell segregation, but how craniofacial dysmorphology arises from cell segregation is unknown and CFNS etiology therefore remains poorly understood. Here, we couple geometric morphometric techniques with temporal and spatial interrogation of embryonic cell segregation in mouse models to elucidate mechanisms underlying CFNS pathogenesis. By generating ephrin-B1 mosaicism at different developmental timepoints and in specific cell populations, we find that ephrin-B1 regulates cell segregation independently in early neural development and later in craniofacial development, correlating with the emergence of quantitative differences in face shape. Whereas specific craniofacial shape changes are qualitatively similar in Efnb1 heterozygous and hemizygous mutant embryos, heterozygous embryos are quantitatively more severely affected, indicating that Efnb1 mosaicism exacerbates loss of function phenotypes rather than having a neomorphic effect. Notably, tissue-specific disruption of Efnb1 throughout neural development does not appear to contribute to CFNS dysmorphology, but its disruption within neural crest cell-derived mesenchyme results in phenotypes very similar to widespread loss. Ephrin-B1 can bind and signal with EphB1, EphB2, and EphB3 receptor tyrosine kinases, but the signaling partner(s) relevant to CFNS are unknown. Geometric morphometric analysis of an allelic series of Ephb1; Ephb2; Ephb3 mutant embryos indicates that EphB2 and EphB3 are key receptors mediating Efnb1 hemizygous-like phenotypes, but the complete loss of EphB1-3 does not recapitulate CFNS-like Efnb1 heterozygous severity. Finally, by generating Efnb1+/-; Ephb1; Ephb2; Ephb3 quadruple knockout mice, we determine how modulating cumulative receptor activity influences cell segregation in craniofacial development and find that while EphB2 and EphB3 play an important role in craniofacial cell segregation, EphB1 is more important for cell segregation in the brain; surprisingly, complete loss of EphB1-EphB3 does not completely abrogate cell segregation. Together, these data advance our understanding of the morphogenetic etiology and signaling interactions underlying CFNS dysmorphology.\n\nAuthor SummaryCraniofacial anomalies are extremely common, accounting for one third of all birth defects, but even when the responsible genes are known, it often remains to be determined exactly how development has gone wrong. Craniofrontonasal syndrome (CFNS), which affects multiple aspects of craniofacial development, is a particularly mysterious disorder because it is X-linked, but affects females more severely than males, the opposite situation of most X-linked diseases. The responsible gene has been identified as EFNB1, which encodes the EPHRIN-B1 signaling molecule that regulates cellular position. Why EFNB1+/- heterozygous females exhibit severe stereotypical CFNS phenotypes is not well understood, but it is related to the fact that X chromosome inactivation generates mosaicism for EPHRIN-B1. Using mice harboring mutations in the Efnb1 gene in different embryonic tissues, and in receptor genes Ephb1-3, together with quantitative methods to measure craniofacial structures in developing embryos, we establish the tissue-specific contributions of ephrin-B1 mosaicism to craniofacial dysmorphology. We also examine when ephrin-B1 regulates cellular position during different stages of craniofacial development and which EphB receptors are involved. Our results reveal the specific cellular context and signaling interactions that are likely to underlie CFNS, and provide new understanding of how EPHRIN-B1 may regulate normal craniofacial development.

developmental biology↗