Mammalian Phenotype Ontology Research Articles

Mouse Genome Informatics: an integrated knowledgebase system for the laboratory mouse.

Mouse Genome Informatics (MGI) is a federation of expertly curated information resources designed to support experimental and computational investigations into genetic and genomic aspects of human biology and disease using the laboratory mouse as a model system. The Mouse Genome Database (MGD) and the Gene Expression Database (GXD) are core MGI databases that share data and system architecture. MGI serves as the central community resource of integrated information about mouse genome features, variation, expression, gene function, phenotype, and human disease models acquired from peer-reviewed publications, author submissions, and major bioinformatics resources. To facilitate integration and standardization of data, biocuration scientists annotate using terms from controlled metadata vocabularies and biological ontologies (e.g. Mammalian Phenotype Ontology, Mouse Developmental Anatomy, Disease Ontology, Gene Ontology, etc.), and by applying international community standards for gene, allele, and mouse strain nomenclature. MGI serves basic scientists, translational researchers, and data scientists by providing access to FAIR-compliant data in both human-readable and compute-ready formats. The MGI resource is accessible at https://informatics.jax.org. Here, we present an overview of the core data types represented in MGI and highlight recent enhancements to the resource with a focus on new data and functionality for MGD and GXD.

The mechanisms underlying alcohol-induced decreased splenic size: A network meta-analysis study.

Organ weight change is widely accepted as a measure of toxicologic pathology. We and other groups have shown that excessive alcohol exposure leads to decreased spleen weight in rodents. This study explores the mechanisms underlying alcohol-induced splenic injury through a network meta-analysis. QIAGEN Ingenuity Pathway Analysis (IPA) and Mammalian Phenotype (MP) Ontology were used to identify alcohol-related molecules associated with the small spleen phenotype. Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and IPA bioinformatics tools were then used to analyze the biologic processes and enriched signaling pathways engaging these molecules. In addition, the "downstream effects analysis" algorithm was used to quantify alcohol's effects. IPA identified 623 molecules affected by alcohol and a Venn diagram revealed that 26 of these molecules overlapped with those associated with the MP Ontology of small spleen. The 26 molecules are TGFB1, CASP8, MTOR, ESR1, CXCR4, CAMK4, NFKBIA, DRD2, BCL2, FAS, PEBP1, TRAF2, ATM, IGHM, EDNRB, MDM2, GLRA1, PRF1, TLR7, IFNG, ALOX5, FOXO1, IL15, APOE, IKBKG, and RORA. Some of the 26 molecules were also associated with the MP Ontology of abnormal white pulp and red pulp morphology of the spleen, abnormal splenic cell ratio, decreased splenocyte number, abnormal spleen physiology, increased splenocyte apoptosis, and reduced splenocyte proliferation. STRING and IPA "Core Analysis" showed that these molecules were mainly involved in pathways related to cell apoptosis, proliferation, migration, and immune responses. IPA's "Molecular Activity Predictor" tool showed that concurrent effects of activation and inhibition of these molecules led to decreased spleen size by modulating apoptosis, proliferation, and migration of splenocytes. Our network meta-analysis revealed that excessive alcohol exposure can damage the spleen through a variety of molecular mechanisms, thereby affecting immune function and human health. We found that alcohol-mediated splenic atrophy is largely mediated by increased apoptosis signaling, migration of cells, and inhibition of splenocyte proliferation.

Improving the classification of cardinality phenotypes using collections

MotivationPhenotypes are observable characteristics of an organism and they can be highly variable. Information about phenotypes is collected in a clinical context to characterize disease, and is also collected in model organisms and stored in model organism databases where they are used to understand gene functions. Phenotype data is also used in computational data analysis and machine learning methods to provide novel insights into disease mechanisms and support personalized diagnosis of disease. For mammalian organisms and in a clinical context, ontologies such as the Human Phenotype Ontology and the Mammalian Phenotype Ontology are widely used to formally and precisely describe phenotypes. We specifically analyze axioms pertaining to phenotypes of collections of entities within a body, and we find that some of the axioms in phenotype ontologies lead to inferences that may not accurately reflect the underlying biological phenomena.ResultsWe reformulate the phenotypes of collections of entities using an ontological theory of collections. By reformulating phenotypes of collections in phenotypes ontologies, we avoid potentially incorrect inferences pertaining to the cardinality of these collections. We apply our method to two phenotype ontologies and show that the reformulation not only removes some problematic inferences but also quantitatively improves biological data analysis.

Identification of the regulatory mechanism of ACE2 in COVID-19-induced kidney damage with systems genetics approach.

Studies showed that SARS-CoV-2 can directly target the kidney and induce renal damage. As the cell surface receptor for SARS-CoV-2 infection, the angiotensin-converting enzyme 2 (ACE2) plays a pivotal role for renal physiology and function. Thus, it is important to understand ACE2 through which pathway influences the pathogenesis of renal damage induced by COVID-19. In this study, we first performed an eQTL mapping for Ace2 in kidney tissues in 53 BXD mice strains. Results demonstrated that Ace2 is highly expressed and strongly controlled by a genetic locus on chromosome 16 in the kidney, with six genes (Dnase1, Vasn, Usp7, Abat, Mgrn1, and Rbfox1) dominated as the upstream modulator, as they are highly correlated with Ace2 expression. Gene co-expression analysis showed that Ace2 co-variates are significantly involved in the renin-angiotensin system (RAS) pathway which acts as a reno-protector. Importantly, we also found that Ace2 is positively correlated with Pdgf family members, particularly Pdgfc, which showed the most association among the 76 investigated growth factors. Mammalian Phenotype Ontology enrichment indicated that the cognate transcripts for both Ace2 and Pdgfc were mainly involved in regulating renal physiology and morphology. Among which, Cd44, Egfr, Met, Smad3, and Stat3 were identified as hub genes through protein-protein interaction analysis. Finally, in aligning with our systems genetics findings, we found ACE2, pdgf family members, and RAS genes decreased significantly in the CAKI-1 kidney cancer cells treated with S protein and receptor binding domain structural protein. Collectively, our data suggested that ACE2 work with RAS, PDGFC, as well as their cognate hub genes to regulate renal function, which could guide for future clinical prevention and targeted treatment for COVID-19-induced renal damage outcomes. KEY MESSAGES: • Ace2 is highly expressed and strongly controlled by a genetic locus on chromosome 16 in the kidney. • Ace2 co-variates are enriched in the RAS pathway. • Ace2 is strongly correlated with the growth factor Pdgfc. • Ace2 and Pdgfc co-expressed genes involved in the regulation of renal physiology and morphology. • SARS-CoV-2spike glycoprotein induces down-regulation of Ace2, RAS, and Pdgfc.

P-544 CNV is a genetic factor underlying risk of neurodevelopmental disorder in a child of a woman with miscarriage in anamnesis

Abstract Study question What underlies an increased risk of having children with neurodevelopmental disorders in women with miscarriage? Summary answer Women with miscarriage have a statistically significant increase in the frequency of giving birth to children with intellectual disabilities due to CNV. What is known already A link between woman’s history of miscarriage and neurodevelopmental disorders (NDD) in her children was shown recently (PMID:30058166, PMID:33580539). In women with sporadic and recurrent miscarriage, the risk of having a child with NDD is increased by 13% and 30%, respectively (PMID:33580539). About half of miscarriage cases can be due to genetic causes. Genetic abnormalities also underlie NDD. It is likely that CNV may be a common etiological factor for both pathologies. Our aim was to assess the frequency and characteristics of CNV in groups of mothers having a child with NDD and presence or absence of miscarriages in anamnesis. Study design, size, duration Families with children with NDD and maternal obstetric history were investigated (n = 405). They were divided into groups based on the presence (n = 190) or absence (n = 215) of pathogenic CNV in a child. Further each group was divided into subgroups with the presence (n = 37, n = 21) or absence (n = 153, n = 194) of miscarriage to determine the association of CNV with pregnancy loss and NDD. Genes involved in CNVs were obtained to identify pathogenetically significant molecular pathways. Participants/materials, setting, methods The DNA of patients and their parents was isolated from blood lymphocytes. Probands with NDD were studied using aCGH on SurePrint G3 Human CGH Microarray Kit, 8 × 60K (Agilent Technologies). Validation of CNVs and determination of their origin was carried out using qPCR. Enrichment analysis was performed by the Enrichr resource and the Mammalian Phenotype ontology. The results of the analysis were considered significant at p ≤ 0.05. The chi-squared test was used for statistical analysis. Main results and the role of chance For the first time we showed that pathogenic CNVs were statistically significantly more common in families with miscarriage having a child with NDD (p = 0.006). In the groups with or without miscarriage, respectively, 16 and 66 deletions and 21 and 87 duplications were found. Thus, no predominance of a certain type of CNV was found (p = 0.991). The origin was established for 49 CNVs out of 190 (26%). The group with miscarriage included 8 maternally inherited and 2 de novo CNVs, while the non-miscarriage group included 33 inherited (mat-18, pat-15) and 6 de novo variants (p = 0.899). Enrichment analysis revealed 26 and 36 categories, respectively, in miscarriage and non-miscarriage groups. Common categories between the groups were maternal imprinting, abnormal cochlear innervation pattern, abnormal GABA-mediated receptor currents, increased skeletal muscle fiber size. Separately, the miscarriage group included one category associated with abnormal head morphology. The group without miscarriage included categories associated with development, functioning and anomalies of different organs. Although the enrichment analysis identified categories that may be associated with the pathologies included in this study, at this stage of data collection it was not possible to explain why some CNV can lead to either miscarriage or child with NDD in the same family. Limitations, reasons for caution There is no data on the causes of miscarriage, at least on the presence or absence of maternally inherited CNV in abortions. A small sample size does not allow us to identify differences in the structure of CNVs between groups with and without miscarriage. Wider implications of the findings Accumulating data potentially can be used for predicting the risk of having a child with NDD in families with miscarriage. If one of the parents carries pathogenic CNV with pleiotropic effect prenatal diagnosis or PGT-SR should be recommended. This study was supported by the Russian Science Foundation № 21-65-00017, https://rscf.ru/project/21-65-00017/. Trial registration number not applicable

Identification of HOX signatures contributing to oral cancer phenotype

The role of evolutionarily conserved homeobox-containing HOX genes as transcriptional regulators in the developmental specification of organisms is well known. The contribution of HOX genes involvement in oral cancer phenotype has yet to be fully ascertained. TCGA-HNSC HTSeq-counts and clinical data were retrieved from the GDC portal for oral cavity neoplasms. GEO datasets (GSE72627, GSE30784, GSE37991) were accessed and analyzed using GEO2R. Differential HOX gene expression was profiled using the DESeq2 R package with a log2 fold change cut-off (− 1 and + 1) and Benjamini–Hochberg p-adjusted value at ≤ 0.01. Gene set over-representation analysis and semantic analysis associated with the disease ontology was performed using the ClusterProfiler R package, and pathway over-representation analysis was performed using IMPaLa. HOX protein interaction network was constructed using the Pathfind R package. HOX phenotype associations were performed using Mammalian Phenotype Ontology, Human Phenotype Ontology, PhenGenI associations, Jensen tissues, and OMIM entries. Drug connectivity mapping was carried out with Dr. Insight R package. HOXA2 was upregulated in oral dysplasia but silenced during tumor progression. Loss of HOXB2 expression was consistent in the potentially malignant oral lesions as well as in the primary tumor. HOXA7, HOXA10, HOXB7, HOXC6, HOXC10, HOXD10, and HOXD11 were consistently upregulated from premalignancy to malignancy and were notably associated with risk factors. Overrepresentation analysis suggested HOXA10 was involved in the transcriptional misregulation contributing to the oral cancer phenotype. HOX genes subnetwork analysis showed crucial interactions with cell cycle regulators, growth responsive elements, and proto-oncogenes. Phenotype associations specific to the oral region involving HOX genes provide intrinsic cues to tumor development. The 5′ HOX genes were aberrantly upregulated during oral carcinogenesis reflecting their posterior prevalence.

Comparing common and unique disruptions in transposable elements and gene networks in two humanized mouse models of AD.

Alzheimer's Disease (AD) is an widespread neurodegenerative disorder, most common in elderly and Veteran populations, culminating in dementia with additional non-cognitive behavioral and metabolic symptomatology, such as hyperactivity and disrupted sleep/wake patterns, that promote weight loss. Weight loss In demented patients is associated with adverse outcomes like accelerated AD progression and institutionalization. Mouse models of tau deposition, such as Tg4510 mice, and both tau and amyloidosis, such as 3X Tg, develop both cognitive and non-cognitive symptoms. To identify early common and unique factors affecting gene networks in these different molecular models of AD, we analyzed RNA-seq data from young Tg4510 and 3X Tg mice to compare the common and unique transcripts and transposable elements (TE) expression changes that may initiate the behavioral and metabolic derangements in AD. We analyzed RNA-seq from two month old Tg4510 mice (n=5) vs. controls (n=6) and 3X Tg (n=3) and controls (n=3). Using our established bioinformatics TTESA pipeline, reads were aligned to reference genome and measured gene expression or TEs with featureCounts. Pathway enrichment analysis using Visual Annotation Display was used to discover common functional and disease pathways using Gene Ontology, Mammalian Phenotype Ontology, and Protein Ontology. TTESA results detected significantly differentially expressed TEs in both models (p<0.001) albeit 3X Tg expressed four times more TEs. The majority of significantly differentially expressed TEs were retrotransposons (68-85%), mainly of long terminal repeats (LTRs; 50-70%), and some LINE retroelements (10-30%) and a few SINEs (5%) in either transgenic model. VLaD pathway analysis revealed many biological processes upregulated in transgenic mice, including non-cognitive behavioral pathways were downregulated, such as the regulation of circadian and sleep/wake behavior, however with different genes altered within these networks (e.g., Tg3x p<0.032; Ncor1, Rasd1, Rgs16; Tg4510 Ciart, Dbp, Per2, Per3 p<0.0165). There were also unique pathways to each model, e.g., 3X Tg: subcellular organelle structure, cytoskeleton, tubulin and actin binding, whereas Tg4510 dopamine receptor binding and bHLH transcription factor binding. Based on our data, early dysregulation of TEs may be involved altered neural gene expression networks underlying neurogenesis, behavioral changes, and molecular function in two different mouse models of AD.

P–366 MicroRNAs at the embryo-maternal interface have effects on endometrial cell proliferation

Abstract Study question Whether cell-free microRNAs are part of the embryo-maternal interactome with possible effects on processes related to implantation. Summary answer Specific microRNAs cause major transcriptomic changes in uterine cells and alter cellular proliferation which is pivotal for the implantation of the incoming embryo. What is known already A plethora of molecules present at the uterine luminal fluid including cytokines, growth factors, and adhesion proteins are involved in implantation. However little is known about the roles of extracellular microRNAs (miRNAs) at the embryo-maternal interface. MicroRNAs act mainly as gene regulators and a single miRNA can have thousands of gene targets. MiRNAs are released by blastocysts and uterine cells internalize miRNAs that are present in the extracellular environment. To date there is limited evidence on the molecular actions of these cell-free miRNAs and their effects on processes related to implantation. Study design, size, duration Human endometrial stromal cells (hESCs) were cultured in complete growth medium for 8 consecutive passages. A miRNA mimic experiment in 6 replications was carried out in which endometrial cells were transfected with miR–371a. Gene changes in the hESCs were studied with genome-wide microarray technology and the results were validated in vitro with PCR. Participants/materials, setting, methods The miR–371a mimic was transfected in hESCs using a Lipofectamine reagent. RNA was extracted and the samples were processed with microarray Clariom™ Human Assays using Affymetrix®. The transcriptomic profiles between transfected and control cells were compared using Partek®. Differentially expressed genes were considered significant when p-value was &amp;lt;0.05, false discovery rate, FDR ≤ 0.05 with Benjamini-Hochberg correction, and fold-change of &amp;gt; 1.5 or &amp;lt; –1.5. Functional enrichment analysis was carried out using WebGestalt and Enrichr. Main results and the role of chance MiR–371a altered the expression of 4.760 genes in endometrial cells (p &amp;lt; 0.05, fold-change 1.5). A total of 16 biological processes, 23 cellular components, and 24 molecular pathways were disrupted by this miRNA. WebGestalt analysis found 159 enriched categories including increase of negative cell cycle regulation, apoptosis signalling, and cycle arrest and decreased cell proliferation. Cell cycle was one of the most affected pathways in KEGG analysis with at least 54 genes dysregulated. Mammalian phenotype ontology analysis found 4.818 affected phenotypes, including decreased cell proliferation (58 genes), increased apoptosis (48 genes) and abnormal cell cycle (41 genes). Key-genes of endometrial proliferation at the window of implantation were significantly downregulated, including: CD44, PGR; IGFs, FGFs, and HAND2. Moreover, at least 25% decreased hESCs proliferation was verified in vitro after transfection. These negative effects of miR–371a in cell cycle could disturb implantation of the incoming embryo, since intense cellular proliferation is necessary for establishment of the implantation site. Limitations, reasons for caution These results are limited to miR–371a actions on human endometrial stromal cells. It is likely that miRNAs, cytokines, growth factors, and other molecules form complex regulatory networks that control uterine receptivity and embryo implantation. Wider implications of the findings: MiRNAs are important mediators of the embryo-maternal interactome. Their actions are likely involved in implantation-related processes including inter-cellular communication, decidualization, adhesion, invasion, and establishment of the implantation site. Embryo-secreted miRNAs change the transcriptome of the neighboring endometrial cells with effects on implantation-related pathways, serving thus secretory functions. Trial registration number N/A

Ontogenetic Pleiotropy of Genes Involved in CNVs in Human Spontaneous Abortions

Using chromosome microarray analysis, 52 samples of placental tissues from first trimester human spontaneous abortions were examined. One hundred twenty copy number variations (CNVs) were identified, affecting one or more genes (total of 427 genes). Using enrichment analysis with the mammalian phenotype ontology, all genes were divided into 183 categories (p ≤ 0.05). The embryogenesis category included 22 genes: AIP, BMP4, BMP5, CDKN1C, EXT1, GAB1, H19, HOXD13, IGF2, KIT, LDHA, NKX2-5, NRK, PEG3, PHLDA2, SMCHD1, SMN1, TBX3, TGIF1, TH, TLX2, and TRR. In this paper, the functions of each of the above genes and pathological phenotypes associated with mutations in them are discussed. A hypothesis of the pleiotropic effect of genes involved in CNVs in spontaneous abortions is proposed.

Copy number variations in individuals with conotruncal heart defects reveal some shared developmental pathways irrespective of 22q11.2 deletion status.

Over 50% of patients with 22q11.2 deletion syndrome (DS) have a conotruncal or related cardiac defect (CTRD). We hypothesized that similar genetic variants, developmental pathways and biological functions, contribute to disease risk for CTRD in patients without a 22q11.2 deletion (ND-CTRD) and with a 22q11.2 deletion (DS-CTRD). To test this hypothesis, we performed rare CNV (rCNV)-based analyses on 630 ND-CTRD cases and 602 DS-CTRD cases with comparable cardiac lesions separately and jointly. First, we detected a collection of heart development related pathways from Gene Ontology and Mammalian Phenotype Ontology analysis. We then constructed gene regulation networks using unique genes collected from the rCNVs found in the ND-CTRD and DS-CTRD cohorts. These gene networks were clustered and their predicted functions were examined. We further investigated expression patterns of those unique genes using publicly available mouse embryo microarray expression data from single-cell embryos to fully developed hearts. By these bioinformatics approaches, we identified a commonly shared gene expression pattern in both the ND-CTRD and DS-CTRD cohorts. Computational analysis of gene functions characterized with this expression pattern revealed a collection of significantly enriched terms related to cardiovascular development. By our combined analysis of rCNVs in the ND-CTRD and DS-CTRD cohorts, a group of statistically significant shared pathways, biological functions, and gene expression patterns were identified that can be tested in future studies for their biological relevance.

Representing glycophenotypes: semantic unification of glycobiology resources for disease discovery.

While abnormalities related to carbohydrates (glycans) are frequent for patients with rare and undiagnosed diseases as well as in many common diseases, these glycan-related phenotypes (glycophenotypes) are not well represented in knowledge bases (KBs). If glycan-related diseases were more robustly represented and curated with glycophenotypes, these could be used for molecular phenotyping to help to realize the goals of precision medicine. Diagnosis of rare diseases by computational cross-species comparison of genotype–phenotype data has been facilitated by leveraging ontological representations of clinical phenotypes, using Human Phenotype Ontology (HPO), and model organism ontologies such as Mammalian Phenotype Ontology (MP) in the context of the Monarch Initiative. In this article, we discuss the importance and complexity of glycobiology and review the structure of glycan-related content from existing KBs and biological ontologies. We show how semantically structuring knowledge about the annotation of glycophenotypes could enhance disease diagnosis, and propose a solution to integrate glycophenotypes and related diseases into the Unified Phenotype Ontology (uPheno), HPO, Monarch and other KBs. We encourage the community to practice good identifier hygiene for glycans in support of semantic analysis, and clinicians to add glycomics to their diagnostic analyses of rare diseases.

PBRIT: gene prioritization by correlating functional and phenotypic annotations through integrative data fusion.

MotivationComputational gene prioritization can aid in disease gene identification. Here, we propose pBRIT (prioritization using Bayesian Ridge regression and Information Theoretic model), a novel adaptive and scalable prioritization tool, integrating Pubmed abstracts, Gene Ontology, Sequence similarities, Mammalian and Human Phenotype Ontology, Pathway, Interactions, Disease Ontology, Gene Association database and Human Genome Epidemiology database, into the prediction model. We explore and address effects of sparsity and inter-feature dependencies within annotation sources, and the impact of bias towards specific annotations.ResultspBRIT models feature dependencies and sparsity by an Information-Theoretic (data driven) approach and applies intermediate integration based data fusion. Following the hypothesis that genes underlying similar diseases will share functional and phenotype characteristics, it incorporates Bayesian Ridge regression to learn a linear mapping between functional and phenotype annotations. Genes are prioritized on phenotypic concordance to the training genes. We evaluated pBRIT against nine existing methods, and on over 2000 HPO-gene associations retrieved after construction of pBRIT data sources. We achieve maximum AUC scores ranging from 0.92 to 0.96 against benchmark datasets and of 0.80 against the time-stamped HPO entries, indicating good performance with high sensitivity and specificity. Our model shows stable performance with regard to changes in the underlying annotation data, is fast and scalable for implementation in routine pipelines.Availability and implementation http://biomina.be/apps/pbrit/; https://bitbucket.org/medgenua/pbrit.Supplementary information Supplementary data are available at Bioinformatics online.

Comparative gene expression profiles in healthy and autoimmune mice

Comparative gene expression profiles in healthy and autoimmune mice

Comparative gene expression profiles in healthy and autoimmune mice

Comparative gene expression profiles in healthy and autoimmune mice

Mouse Genome Informatics (MGI) Is the International Resource for Information on theLaboratory Mouse.

Mouse Genome Informatics (MGI, http://www.informatics.jax.org/ ) web resources provide free access to meticulously curated information about the laboratory mouse. MGI's primary goal is to help researchers investigate the genetic foundations of human diseases by translating information from mouse phenotypes and disease models studies to human systems. MGI provides comprehensive phenotypes for over 50,000 mutant alleles in mice and provides experimental model descriptions for over 1500 human diseases. Curated data from scientific publications are integrated with those from high-throughput phenotyping and gene expression centers. Data are standardized using defined, hierarchical vocabularies such as the Mammalian Phenotype (MP) Ontology, Mouse Developmental Anatomy and the Gene Ontologies (GO). This chapter introduces you to Gene and Allele Detail pages and provides step-by-step instructions for simple searches and those that take advantage of the breadth of MGI data integration.

RNA-Sequencing Analysis Reveals a Regulatory Role for Transcription Factor Fezf2 in the Mature Motor Cortex.

Forebrain embryonic zinc finger (Fezf2) encodes a transcription factor essential for the specification of layer 5 projection neurons (PNs) in the developing cerebral cortex. As with many developmental transcription factors, Fezf2 continues to be expressed into adulthood, suggesting it remains crucial to the maintenance of neuronal phenotypes. Despite the continued expression, a function has yet to be explored for Fezf2 in the PNs of the developed cortex. Here, we investigated the role of Fezf2 in mature neurons, using lentiviral-mediated delivery of a shRNA to conditionally knockdown the expression of Fezf2 in the mouse primary motor cortex (M1). RNA-sequencing analysis of Fezf2-reduced M1 revealed significant changes to the transcriptome, identifying a regulatory role for Fezf2 in the mature M1. Kyoto Encyclopedia Genes and Genomes (KEGG) pathway analyses of Fezf2-regulated genes indicated a role in neuronal signaling and plasticity, with significant enrichment of neuroactive ligand-receptor interaction, cell adhesion molecules and calcium signaling pathways. Gene Ontology analysis supported a functional role for Fezf2-regulated genes in neuronal transmission and additionally indicated an importance in the regulation of behavior. Using the mammalian phenotype ontology database, we identified a significant overrepresentation of Fezf2-regulated genes associated with specific behavior phenotypes, including associative learning, social interaction, locomotor activation and hyperactivity. These roles were distinct from that of Fezf2-regulated genes identified in development, indicating a dynamic transition in Fezf2 function. Together our findings demonstrate a regulatory role for Fezf2 in the mature brain, with Fezf2-regulated genes having functional roles in sustaining normal neuronal and behavioral phenotypes. These results support the hypothesis that developmental transcription factors are important for maintaining neuron transcriptomes and that disruption of their expression could contribute to the progression of disease phenotypes.

Searching the Mouse Genome Informatics (MGI) Resources for Information on Mouse Biology from Genotype to Phenotype.

The Mouse Genome Informatics (MGI) resource provides the research community with access to information on the genetics, genomics, and biology of the laboratory mouse. Core data in MGI include gene characterization and function, phenotype and disease model descriptions, DNA and protein sequence data, gene expression data, vertebrate homologies, SNPs, mapping data, and links to other bioinformatics databases. Semantic integration is supported through the use of standardized nomenclature, and through the use of controlled vocabularies such as the mouse Anatomical Dictionary, the Mammalian Phenotype Ontology, and the Gene Ontologies. MGI extracts and organizes data from primary literature. MGI data are shared with and widely displayed from other bioinformatics resources. The database is updated weekly with curated annotations, and regularly adds new datasets and features. This unit provides a guide to using the MGI bioinformatics resource. © 2016 by John Wiley & Sons, Inc.

The Human Phenotype Ontology in 2017.

Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human Phenotype Ontology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, disease-phenotype annotations and the algorithms that operate on these. These components are being used for computational deep phenotyping and precision medicine as well as integration of clinical data into translational research. The HPO is being increasingly adopted as a standard for phenotypic abnormalities by diverse groups such as international rare disease organizations, registries, clinical labs, biomedical resources, and clinical software tools and will thereby contribute toward nascent efforts at global data exchange for identifying disease etiologies. This update article reviews the progress of the HPO project since the debut Nucleic Acids Research database article in 2014, including specific areas of expansion such as common (complex) disease, new algorithms for phenotype driven genomic discovery and diagnostics, integration of cross-species mapping efforts with the Mammalian Phenotype Ontology, an improved quality control pipeline, and the addition of patient-friendly terminology.

Visual annotation display (VLAD): a tool for finding functional themes in lists of genes.

Experiments that employ genome scale technology platforms frequently result in lists of tens to thousands of genes with potential significance to a specific biological process or disease. Searching for biologically relevant connections among the genes or gene products in these lists is a common data analysis task. We have implemented a software application for uncovering functional themes in sets of genes based on their annotations to bio-ontologies, such as the gene ontology and the mammalian phenotype ontology. The application, called VisuaL Annotation Display (VLAD), performs a statistical analysis to test for the enrichment of ontology terms in a set of genes submitted by a researcher. The results for each analysis using VLAD includes a table of ontology terms, sorted in decreasing order of significance. Each row contains the term, statistics such as the number of annotated terms, the p value, etc., and the symbols of annotated genes. An accompanying graphical display shows portions of the ontology hierarchy, where node sizes are scaled based on p values. Although numerous ontology term enrichment programs already exist, VLAD is unique in that it allows users to upload their own annotation files and ontologies for customized term enrichment analyses, supports the analysis of multiple gene sets at once, provides interfaces to customize graphical output, and is tightly integrated with functional and biological details about mouse genes in the Mouse Genome Informatics (MGI) database. VLAD is available as a web-based application from the MGI web site (http://proto.informatics.jax.org/prototypes/vlad/). Electronic supplementary materialThe online version of this article (doi:10.1007/s00335-015-9570-2) contains supplementary material, which is available to authorized users.

Expanding the mammalian phenotype ontology to support automated exchange of high throughput mouse phenotyping data generated by large-scale mouse knockout screens.

BackgroundA vast array of data is about to emerge from the large scale high-throughput mouse knockout phenotyping projects worldwide. It is critical that this information is captured in a standardized manner, made accessible, and is fully integrated with other phenotype data sets for comprehensive querying and analysis across all phenotype data types. The volume of data generated by the high-throughput phenotyping screens is expected to grow exponentially, thus, automated methods and standards to exchange phenotype data are required.ResultsThe IMPC (International Mouse Phenotyping Consortium) is using the Mammalian Phenotype (MP) ontology in the automated annotation of phenodeviant data from high throughput phenotyping screens. 287 new term additions with additional hierarchy revisions were made in multiple branches of the MP ontology to accurately describe the results generated by these high throughput screens.ConclusionsBecause these large scale phenotyping data sets will be reported using the MP as the common data standard for annotation and data exchange, automated importation of these data to MGI (Mouse Genome Informatics) and other resources is possible without curatorial effort. Maximum biomedical value of these mutant mice will come from integrating primary high-throughput phenotyping data with secondary, comprehensive phenotypic analyses combined with published phenotype details on these and related mutants at MGI and other resources.