International Mouse Phenotyping Consortium Research Articles

IMPC-based screening revealed that ROBO1 can regulate osteoporosis by inhibiting osteogenic differentiation.

The utilization of denosumab in treating osteoporosis highlights promising prospects for osteoporosis intervention guided by gene targets. While omics-based research into osteoporosis pathogenesis yields a plethora of potential gene targets for clinical transformation, identifying effective gene targets has posed challenges. We first queried the omics data of osteoporosis clinical samples on PubMed, used International Mouse Phenotyping Consortium (IMPC) to screen differentially expressed genes, and conducted preliminary functional verification of candidate genes in human Saos2 cells through osteogenic differentiation and mineralization experiments. We then selected the candidate genes with the most significant effects on osteogenic differentiation and further verified the osteogenic differentiation and mineralization functions in mouse 3T3-E1 and bone marrow mesenchymal stem cells (BMSC). Finally, we used RNA-seq to explore the regulation of osteogenesis by the target gene. We identified PPP2R2A, RRBP1, HSPB6, SLC22A15, ADAMTS4, ATP8B1, CTNNB1, ROBO1, and EFR3B, which may contribute to osteoporosis. ROBO1 was the most significant regulator of osteogenesis in both human and mouse osteoblast. The inhibitory effect of Robo1 knockdown on osteogenic differentiation may be related to the activation of inflammatory signaling pathways. Our study provides several novel molecular mechanisms involved in the pathogenesis of osteoporosis. ROBO1 is a potential target for osteoporosis intervention.

Impact of essential genes on the success of genome editing experiments generating 3313 new genetically engineered mouse lines

The International Mouse Phenotyping Consortium (IMPC) systematically produces and phenotypes mouse lines with presumptive null mutations to provide insight into gene function. The IMPC now uses the programmable RNA-guided nuclease Cas9 for its increased capacity and flexibility to efficiently generate null alleles in the C57BL/6N strain. In addition to being a valuable novel and accessible research resource, the production of 3313 knockout mouse lines using comparable protocols provides a rich dataset to analyze experimental and biological variables affecting in vivo gene engineering with Cas9. Mouse line production has two critical steps – generation of founders with the desired allele and germline transmission (GLT) of that allele from founders to offspring. A systematic evaluation of the variables impacting success rates identified gene essentiality as the primary factor influencing successful production of null alleles. Collectively, our findings provide best practice recommendations for using Cas9 to generate alleles in mouse essential genes, many of which are orthologs of genes linked to human disease.

Identification of novel genes regulating the development of the palate.

The International Mouse Phenotyping Consortium (IMPC) has generated thousands of knockout mouse lines, many of which exhibit embryonic or perinatal lethality. Using micro-computed tomography (micro-CT), the IMPC has created and publicly released 3D image datasets of embryos from these lethal and subviable lines. In this study, we leveraged this dataset to screen homozygous null mutants for anomalies in secondary palate development. We analyzed optical sections from 2,987 embryos at embryonic days E15.5 and E18.5, representing 484 homozygous mutant lines. Our analysis identified 45 novel genes implicated in palatogenesis. Gene set enrichment analysis highlighted biological processes and pathways relevant to palate development and uncovered 18 genes jointly regulating the development of the eye and the palate. These findings present a valuable resource for further research, offer novel insights into the molecular mechanisms underlying palatogenesis, and provide important context for understanding the etiology of rare human congenital disorders involving simultaneous malformations of the palate and other organs, including the eyes, ears, kidneys, and lungs.

A graph theoretical approach to experimental prioritization in genome-scale investigations

The goal of systems biology is to gain a network level understanding of how gene interactions influence biological states, and ultimately inform upon human disease. Given the scale and scope of systems biology studies, resource constraints often limit researchers when validating genome-wide phenomena and potentially lead to an incomplete understanding of the underlying mechanisms. Further, prioritization strategies are often biased towards known entities (e.g. previously studied genes/proteins with commercially available reagents), and other technical issues that limit experimental breadth. Here, heterogeneous biological information is modeled as an association graph to which a high-performance minimum dominating set solver is applied to maximize coverage across the graph, and thus increase the breadth of experimentation. First, we tested our model on retrieval of existing gene functional annotations and demonstrated that minimum dominating set returns more diverse terms when compared to other computational methods. Next, we utilized our heterogenous network and minimum dominating set solver to assist in the process of identifying understudied genes to be interrogated by the International Mouse Phenotyping Consortium. Using an unbiased algorithmic strategy, poorly studied genes are prioritized from the remaining thousands of genes yet to be characterized. This method is tunable and extensible with the potential to incorporate additional user-defined prioritizing information. The minimum dominating set approach can be applied to any biological network in order to identify a tractable subset of features to test experimentally or to assist in prioritizing candidate genes associated with human disease.

GMMID: genetically modified mice information database.

Genetically engineered mouse models (GEMMs) are vital for elucidating gene function and disease mechanisms. An overwhelming number of GEMM lines have been generated, but endeavors to collect and organize the information of these GEMMs are seriously lagging behind. Only a few databases are developed for the information of current GEMMs, and these databases lack biological descriptions of allele compositions, which poses a challenge for nonexperts in mouse genetics to interpret the genetic information of these mice. Moreover, these databases usually do not provide information on human diseases related to the GEMM, which hinders the dissemination of the insights the GEMM provides as a human disease model. To address these issues, we developed an algorithm to annotate all the allele compositions that have been reported with Python programming and have developed the genetically modified mice information database (GMMID; http://www.gmmid.cn), a user-friendly database that integrates information on GEMMs and related diseases from various databases, including National Center for Biotechnology Information, Mouse Genome Informatics, Online Mendelian Inheritance in Man, International Mouse Phenotyping Consortium, and Jax lab. GMMID provides comprehensive genetic information on >70 055 alleles, 65 520 allele compositions, and ∼4000 diseases, along with biologically meaningful descriptions of alleles and allele combinations. Furthermore, it provides spatiotemporal visualization of anatomical tissues mentioned in these descriptions, shown alongside the allele compositions. Compared to existing mouse databases, GMMID considers the needs of researchers across different disciplines and presents obscure genetic information in an intuitive and easy-to-understand format. It facilitates users in obtaining complete genetic information more efficiently, making it an essential resource for cross-disciplinary researchers. Database URL: http://www.gmmid.cn.

Abstract We135: Mechanistic Modeling Identifies Novel Regulators of Cardiac Hypertrophy

Introduction: Cardiac hypertrophy is a leading predictor of heart failure. The mechanisms that drive cardiac hypertrophy are poorly understood from a molecular and cellular perspective, due to the lack of knowledge of the governing signaling. Hypothesis and Aims: I hypothesize that there exist genes that regulate hypertrophy beyond the field’s current understanding that can be identified by combining the publicly available, high-throughput International Mouse Phenotyping Consortium (IMPC) database with network modeling. I aim to use a large-scale network model of cardiac hypertrophy to assess the IMPC genes’ effects on hypertrophic phenotypes and validate these findings in vitro to identify novel regulators. Materials & Methods: The IMPC was established by worldwide mouse clinics using in vivo models to perform single gene knockout experiments with primary phenotyping, forming a database with ~9000 genes, ~1000 of which can be associated with cardiac hypertrophy. 1 st neighbors (genes directly interacting with the hypertrophy network model as evidenced by protein-protein interaction databases) were identified and effects on hypertrophy were assessed through the IMPC and network model knockdown simulations. When both methods suggested hypertrophic regulation, a gene was considered a candidate regulator. siRNA experiments are being performed in neonatal rat cardiomyocytes for validation. High-content imaging and qPCR are used to quantify changes in phenotype and confirm gene knockdown. Results: Combining the IMPC with protein-protein interaction data, 37 1 st neighbor genes were identified. Knockdown simulation identified 2 genes (LRIG1, CBL) as negative regulators. 3 genes (VAV2, LEPR, RASA1) were identified as positive regulators. These genes became candidates for in vitro validation given the network-wide hypertrophic effects enacted by their knockdowns. siRNA experiments are being optimized to fully validate modeling findings. Conclusions: Mechanistic modeling identified 5 genes as novel regulators of cardiac hypertrophy. Sensitivity analysis highlighted known influencers of hypertrophy in the knockdown context. siRNA knockdown experiments are being performed to validate the model predictions.

Expanding the spectrum of phenotypes for MPDZ: Report of four unrelated families and review of the literature.

MPDZ, a gene with diverse functions mediating cell-cell junction interactions, receptor signaling, and binding multivalent scaffold proteins, is associated with a spectrum of clinically heterogeneous phenotypes with biallelic perturbation. Despite its clinical relevance, the mechanistic underpinnings of these variants remain elusive, underscoring the need for extensive case series and functional investigations. In this study, we conducted a systematic review of cases in the literature through two electronic databases following the PRISMA guidelines. We selected nine studies, including 18 patients, with homozygous or compound heterozygous variants in MPDZ and added five patients from four unrelated families with novel MPDZ variants. To evaluate the role of Mpdz on hearing, we analyzed available auditory electrophysiology data from a knockout murine model (Mpdzem1(IMPC)J/em1(IMPC)J) generated by the International Mouse Phenotyping Consortium. Using exome and genome sequencing, we identified three families with compound heterozygous variants, and one family with a homozygous frameshift variant. MPDZ-related disease is clinically heterogenous with hydrocephaly, vision impairment, hearing impairment and cardiovascular disease occurring most frequently. Additionally, we describe two unrelated patients with spasticity, expanding the phenotypic spectrum. Our murine analysis of the Mpdzem1(IMPC)J/em1(IMPC)J allele showed severe hearing impairment. Overall, we expand understanding of MPDZ-related phenotypes and highlight hearing impairment and spasticity among the heterogeneous phenotypes.

Computational identification of disease models through cross-species phenotype comparison.

The use of standardised phenotyping screens to identify abnormal phenotypes in mouse knockouts, together with the use of ontologies to describe such phenotypic features, allows the implementation of an automated and unbiased pipeline to identify new models of disease by performing phenotype comparisons across species. Using data from the International Mouse Phenotyping Consortium (IMPC), approximately half of mouse mutants are able to mimic, at least partially, the human ortholog disease phenotypes as computed by the PhenoDigm algorithm. We found the number of phenotypic abnormalities in the mouse and the corresponding Mendelian disorder, the pleiotropy and severity of the disease, and the viability and zygosity status of the mouse knockout to be associated with the ability of mouse models to recapitulate the human disorder. An analysis of the IMPC impact on disease gene discovery through a publication-tracking system revealed that the resource has been implicated in at least 109 validated rare disease-gene associations over the last decade.

Knockout mice with pituitary malformations help identify human cases of hypopituitarism

BackgroundCongenital hypopituitarism (CH) and its associated syndromes, septo-optic dysplasia (SOD) and holoprosencephaly (HPE), are midline defects that cause significant morbidity for affected people. Variants in 67 genes are associated with CH, but a vast majority of CH cases lack a genetic diagnosis. Whole exome and whole genome sequencing of CH patients identifies sequence variants in genes known to cause CH, and in new candidate genes, but many of these are variants of uncertain significance (VUS).MethodsThe International Mouse Phenotyping Consortium (IMPC) is an effort to establish gene function by knocking-out all genes in the mouse genome and generating corresponding phenotype data. We used mouse embryonic imaging data generated by the Deciphering Mechanisms of Developmental Disorders (DMDD) project to screen 209 embryonic lethal and sub-viable knockout mouse lines for pituitary malformations.ResultsOf the 209 knockout mouse lines, we identified 51 that have embryonic pituitary malformations. These genes not only represent new candidates for CH, but also reveal new molecular pathways not previously associated with pituitary organogenesis. We used this list of candidate genes to mine whole exome sequencing data of a cohort of patients with CH, and we identified variants in two unrelated cases for two genes, MORC2 and SETD5, with CH and other syndromic features.ConclusionsThe screening and analysis of IMPC phenotyping data provide proof-of-principle that recessive lethal mouse mutants generated by the knockout mouse project are an excellent source of candidate genes for congenital hypopituitarism in children.

307 Revealing Candidate Inherited Retinal Disease Candidates via Genome-wide Screening of Knockout Mice

OBJECTIVES/GOALS: The goal of this study is to reveal strong candidate genes for inherited retinal diseases (IRDs) in humans to better understand the mechanisms behind IRD development and reveal potential therapeutic targets. We hope these findings will help improve our understanding of IRDs and, subsequently, diagnostic accuracy and prognosis for IRD patients. METHODS/STUDY POPULATION: The goal of the International Mouse Phenotyping Consortium is to identify the function of all protein-coding genes in the mouse genome via generation and phenotypic characterization of single gene knockout (KO) mice. Using this database, we identified all KO strains associated with abnormal retinal phenotypes, removed all RNA coding genes and pseudogenes, converted to human orthologues, and conducted a literature search for existing research regarding candidate IRD genes and retinal function/abnormalities. A similar process was used for RetNet genes. Subsequently, we performed bioinformatics analysis, including functional annotation (e.g. panther-db), pathway analysis (e.g. KEGG), and string-db to visualize known and predicted protein-protein interactions between the two data sets. RESULTS/ANTICIPATED RESULTS: Analysis of the IMPC database revealed, out of 8481 phenotyped genes, 572 unique protein coding genes were associated with 14 categories of retinal abnormalities such as abnormal retinal vasculature and abnormal retinal thickness, 377 of which have never been associated with retinal pathology in humans or mice. Pathway analysis of the IMPC database highlighted a general metabolism pathway as well as PI3K-Akt and MAPK pathways, not found in RetNet pathway results. Unique clusters from functional annotation clustering of the IMPC include DNA methylation and protein ubiquination. Visualization of protein-protein interactions in string-db between the IMPC (mouse) and RetNet (human) revealed 4 clusters of interest with gold standard RetNet IRD proteins interacting with candidate IMPC IRD proteins. DISCUSSION/SIGNIFICANCE: IMPC analysis revealed 572 candidate IRD genes, 377 of which are novel with no existing independent research related to the retina outside of the IMPC. Bioinformatic analysis reveals 4 strong clusters of interest through string-db where a gold standard RetNet gene interacts with candidate IRD genes as well as many functional pathways of interest.

Automatic Segmentation of Histological Images of Mouse Brains

Using a high-throughput neuroanatomical screen of histological brain sections developed in collaboration with the International Mouse Phenotyping Consortium, we previously reported a list of 198 genes whose inactivation leads to neuroanatomical phenotypes. To achieve this milestone, tens of thousands of hours of manual image segmentation were necessary. The present work involved developing a full pipeline to automate the application of deep learning methods for the automated segmentation of 24 anatomical regions used in the aforementioned screen. The dataset includes 2000 annotated parasagittal slides (24,000 × 14,000 pixels). Our approach consists of three main parts: the conversion of images (.ROI to .PNG), the training of the deep learning approach on the compressed images (512 × 256 and 2048 × 1024 pixels of the deep learning approach) to extract the regions of interest using either the U-Net or Attention U-Net architectures, and finally the transformation of the identified regions (.PNG to .ROI), enabling visualization and editing within the Fiji/ImageJ 1.54 software environment. With an image resolution of 2048 × 1024, the Attention U-Net provided the best results with an overall Dice Similarity Coefficient (DSC) of 0.90 ± 0.01 for all 24 regions. Using one command line, the end-user is now able to pre-analyze images automatically, then runs the existing analytical pipeline made of ImageJ macros to validate the automatically generated regions of interest resulting. Even for regions with low DSC, expert neuroanatomists rarely correct the results. We estimate a time savings of 6 to 10 times.

THU119 Screening Publicly Available Data To Identify Candidate Genes For Congenital Hypopituitarism

Abstract Disclosure: M.L. Brinkmeier: None. J.J. Martínez Mayer: None. B.S. Ellsworth: None. L.T. Raetzman: None. L. Cheung: None. M.I. Perez-Millan: None. S.A. Camper: None. S.W. Davis: None. Congenital hypopituitarism (CH) and its associated syndromes, septo-optic dysplasia and holoprosencephaly, are midline defects that cause significant morbidity for affected people. Variants in 44 genes are associated with CH, but most cases of CH are caused by unknown variants. Whole exome (WES) and whole genome sequencing (WGS) of CH patient DNA identifies variants in genes known to cause CH and variants of uncertain significance (VUS) in genes that are not currently associated with CH. The functional significance of VUS in CH candidate genes can be established using mouse models. Many of the human CH genes were first discovered in mice, and in most cases the mouse phenotype was an excellent predictor of the human phenotype. The International Mouse Phenotyping Consortium (IMPC) is an invaluable resource of phenotyping data generated by the international effort to determine function of all protein coding genes in the mouse genome through targeted disruption. Using the mouse phenotyping data generated by the Deciphering Mechanisms of Developmental Disorders (DMDD) project to characterize targeted alleles that cause embryonic lethality or subviability, we have identified 51 genes that cause embryonic pituitary malformations. The malformations range in severity and include absent adenohypophysis, absent neurohypophysis, hypoplasia, and hyperplasia. Using the GenePaint RNA in situ hybridization data for mouse embryos and single cell RNA sequencing (scRNAseq) data from e14.5 dissected pituitary glands, we confirmed expression for all candidate genes in the tissues that contribute to the developing pituitary gland. Using gene ontology (GO) terms associated with these 51 genes, we identified molecular pathways not previously associated with pituitary gland organogenesis, including cilia formation, amino acid metabolism, and epigenetic regulation. We present here the candidate gene list, scRNAseq expression data, GO term analysis, select images from GenePaint, and select images of the observed malformations. These genes associated with mouse pituitary malformations significantly expand the list of candidate genes for screening in WES and WGS in human cases of CH. Presentation: Thursday, June 15, 2023

Mouse phenome database: curated data repository with interactive multi-population and multi-trait analyses.

The Mouse Phenome Database continues to serve as a curated repository and analysis suite for measured attributes of members of diverse mouse populations. The repository includes annotation to community standard ontologies and guidelines, a database of allelic states for 657 mouse strains, a collection of protocols, and analysis tools for flexible, interactive, user directed analyses that increasingly integrates data across traits and populations. The database has grown from its initial focus on a standard set of inbred strains to include heterogeneous mouse populations such as the Diversity Outbred and mapping crosses and well as Collaborative Cross, Hybrid Mouse Diversity Panel, and recombinant inbred strains. Most recently the system has expanded to include data from the International Mouse Phenotyping Consortium. Collectively these data are accessible by API and provided with an interactive tool suite that enables users' persistent selection, storage, and operation on collections of measures. The tool suite allows basic analyses, advanced functions with dynamic visualization including multi-population meta-analysis, multivariate outlier detection, trait pattern matching, correlation analyses and other functions. The data resources and analysis suite provide users a flexible environment in which to explore the basis of phenotypic variation in health and disease across the lifespan.

The ATP6V1B2 DDOD/DOORS-Associated p.Arg506* Variant Causes Hyperactivity and Seizures in Mice.

The vacuolar H+-ATPase is a multisubunit enzyme which plays an essential role in the acidification and functions of lysosomes, endosomes, and synaptic vesicles. Many genes encoding subunits of V-ATPases, namely ATP6V0C, ATP6V1A, ATP6V0A1, and ATP6V1B2, have been associated with neurodevelopmental disorders and epilepsy. The autosomal dominant ATP6V1B2 p.Arg506* variant can cause both congenital deafness with onychodystrophy, autosomal dominant (DDOD) and deafness, onychodystrophy, osteodystrophy, mental retardation, and seizures syndromes (DOORS). Some but not all individuals with this truncating variant have intellectual disability and/or epilepsy, suggesting incomplete penetrance and/or variable expressivity. To further explore the impact of the p.Arg506* variant in neurodevelopment and epilepsy, we generated Atp6v1b2emR506* mutant mice and performed standardized phenotyping using the International Mouse Phenotyping Consortium (IMPC) pipeline. In addition, we assessed the EEG profile and seizure susceptibility of Atp6v1b2emR506* mice. Behavioral tests revealed that the mice present locomotor hyperactivity and show less anxiety-associated behaviors. Moreover, EEG analyses indicate that Atp6v1b2emR506* mutant mice have interictal epileptic activity and that both heterozygous (like patients) and homozygous mice have reduced seizure thresholds to pentylenetetrazol. Our results confirm that variants in ATP6V1B2 can cause seizures and that the Atp6v1b2emR506* heterozygous mouse model is a valuable tool to further explore the pathophysiology and potential treatments for vacuolar ATPases-associated epilepsy and disorders.

Knockout mouse models as a resource for the study of rare diseases

Rare diseases (RDs) are a challenge for medicine due to their heterogeneous clinical manifestations and low prevalence. There is a lack of specific treatments and only a few hundred of the approximately 7,000 RDs have an approved regime. Rapid technological development in genome sequencing enables the mass identification of potential candidates that in their mutated form could trigger diseases but are often not confirmed to be causal. Knockout (KO) mouse models are essential to understand the causality of genes by allowing highly standardized research into the pathogenesis of diseases. The German Mouse Clinic (GMC) is one of the pioneers in mouse research and successfully uses (preclinical) data obtained from single-gene KO mutants for research into monogenic RDs. As part of the International Mouse Phenotyping Consortium (IMPC) and INFRAFRONTIER, the pan-European consortium for modeling human diseases, the GMC expands these preclinical data toward global collaborative approaches with researchers, clinicians, and patient groups.Here, we highlight proprietary genes that when deleted mimic clinical phenotypes associated with known RD targets (Nacc1, Bach2, Klotho alpha). We focus on recognized RD genes with no pre-existing KO mouse models (Kansl1l, Acsf3, Pcdhgb2, Rabgap1, Cox7a2) which highlight novel phenotypes capable of optimizing clinical diagnosis. In addition, we present genes with intriguing phenotypic data (Zdhhc5, Wsb2) that are not presently associated with known human RDs.This report provides comprehensive evidence for genes that when deleted cause differences in the KO mouse across multiple organs, providing a huge translational potential for further understanding monogenic RDs and their clinical spectrum. Genetic KO studies in mice are valuable to further explore the underlying physiological mechanisms and their overall therapeutic potential.

A consensus score to combine inferences from multiple centres

Experiments in which data are collected by multiple independent resources, including multicentre data, different laboratories within the same centre or with different operators, are challenging in design, data collection and interpretation. Indeed, inconsistent results across the resources are possible. In this paper, we propose a statistical solution for the problem of multi-resource consensus inferences when statistical results from different resources show variation in magnitude, directionality, and significance. Our proposed method allows combining the corrected p-values, effect sizes and the total number of centres into a global consensus score. We apply this method to obtain a consensus score for data collected by the International Mouse Phenotyping Consortium (IMPC) across 11 centres. We show the application of this method to detect sexual dimorphism in haematological data and discuss the suitability of the methodology.

Knockout mice are an important tool for human monogenic heart disease studies.

Mouse models are relevant to studying the functionality of genes involved in human diseases; however, translation of phenotypes can be challenging. Here, we investigated genes related to monogenic forms of cardiovascular disease based on the Genomics England PanelApp and aligned them to International Mouse Phenotyping Consortium (IMPC) data. We found 153 genes associated with cardiomyopathy, cardiac arrhythmias or congenital heart disease in humans, of which 151 have one-to-one mouse orthologues. For 37.7% (57/151), viability and heart data captured by electrocardiography, transthoracic echocardiography, morphology and pathology from embryos and young adult mice are available. In knockout mice, 75.4% (43/57) of these genes showed non-viable phenotypes, whereas records of prenatal, neonatal or infant death in humans were found for 35.1% (20/57). Multisystem phenotypes are common, with 58.8% (20/34) of heterozygous (homozygous lethal) and 78.6% (11/14) of homozygous (viable) mice showing cardiovascular, metabolic/homeostasis, musculoskeletal, hematopoietic, nervous system and/or growth abnormalities mimicking the clinical manifestations observed in patients. These IMPC data are critical beyond cardiac diagnostics given their multisystemic nature, allowing detection of abnormalities across physiological systems and providing a valuable resource to understand pleiotropic effects.

Deep learning enabled multi-organ segmentation of mouse embryos.

The International Mouse Phenotyping Consortium (IMPC) has generated a large repository of three-dimensional (3D) imaging data from mouse embryos, providing a rich resource for investigating phenotype/genotype interactions. While the data is freely available, the computing resources and human effort required to segment these images for analysis of individual structures can create a significant hurdle for research. In this paper, we present an open source, deep learning-enabled tool, Mouse Embryo Multi-Organ Segmentation (MEMOS), that estimates a segmentation of 50 anatomical structures with a support for manually reviewing, editing, and analyzing the estimated segmentation in a single application. MEMOS is implemented as an extension on the 3D Slicer platform and is designed to be accessible to researchers without coding experience. We validate the performance of MEMOS-generated segmentations through comparison to state-of-the-art atlas-based segmentation and quantification of previously reported anatomical abnormalities in a Cbx4 knockout strain. This article has an associated First Person interview with the first author of the paper.

Genome-wide screening reveals the genetic basis of mammalian embryonic eye development

BackgroundMicrophthalmia, anophthalmia, and coloboma (MAC) spectrum disease encompasses a group of eye malformations which play a role in childhood visual impairment. Although the predominant cause of eye malformations is known to be heritable in nature, with 80% of cases displaying loss-of-function mutations in the ocular developmental genes OTX2 or SOX2, the genetic abnormalities underlying the remaining cases of MAC are incompletely understood. This study intended to identify the novel genes and pathways required for early eye development. Additionally, pathways involved in eye formation during embryogenesis are also incompletely understood. This study aims to identify the novel genes and pathways required for early eye development through systematic forward screening of the mammalian genome.ResultsQuery of the International Mouse Phenotyping Consortium (IMPC) database (data release 17.0, August 01, 2022) identified 74 unique knockout lines (genes) with genetically associated eye defects in mouse embryos. The vast majority of eye abnormalities were small or absent eyes, findings most relevant to MAC spectrum disease in humans. A literature search showed that 27 of the 74 lines had previously published knockout mouse models, of which only 15 had ocular defects identified in the original publications. These 12 previously published gene knockouts with no reported ocular abnormalities and the 47 unpublished knockouts with ocular abnormalities identified by the IMPC represent 59 genes not previously associated with early eye development in mice. Of these 59, we identified 19 genes with a reported human eye phenotype. Overall, mining of the IMPC data yielded 40 previously unimplicated genes linked to mammalian eye development. Bioinformatic analysis showed that several of the IMPC genes colocalized to several protein anabolic and pluripotency pathways in early eye development. Of note, our analysis suggests that the serine-glycine pathway producing glycine, a mitochondrial one-carbon donator to folate one-carbon metabolism (FOCM), is essential for eye formation.ConclusionsUsing genome-wide phenotype screening of single-gene knockout mouse lines, STRING analysis, and bioinformatic methods, this study identified genes heretofore unassociated with MAC phenotypes providing models to research novel molecular and cellular mechanisms involved in eye development. These findings have the potential to hasten the diagnosis and treatment of this congenital blinding disease.

Early evolution of enamel matrix proteins is reflected by pleiotropy of physiological functions

Highly specialized enamel matrix proteins (EMPs) are predominantly expressed in odontogenic tissues and diverged from common ancestral gene. They are crucial for the maturation of enamel and its extreme complexity in multiple independent lineages. However, divergence of EMPs occured already before the true enamel evolved and their conservancy in toothless species suggests that non-canonical functions are still under natural selection. To elucidate this hypothesis, we carried out an unbiased, comprehensive phenotyping and employed data from the International Mouse Phenotyping Consortium to show functional pleiotropy of amelogenin, ameloblastin, amelotin, and enamelin, genes, i.e. in sensory function, skeletal morphology, cardiovascular function, metabolism, immune system screen, behavior, reproduction, and respiratory function. Mice in all KO mutant lines, i.e. amelogenin KO, ameloblastin KO, amelotin KO, and enamelin KO, as well as mice from the lineage with monomeric form of ameloblastin were affected in multiple physiological systems. Evolutionary conserved motifs and functional pleiotropy support the hypothesis of role of EMPs as general physiological regulators. These findings illustrate how their non-canonical function can still effect the fitness of modern species by an example of influence of amelogenin and ameloblastin on the bone physiology.