Follicle-stimulating hormone and luteinizing hormone increase Ca2+ in the granulosa cells of mouse ovarian follicles

In mammalian ovarian follicles, follicle stimulating hormone (FSH) and luteinizing hormone (LH) signal primarily through the G-protein G(s) to elevate cAMP, but both of these hormones can also elevate Ca2+ under some conditions. Here, we investigate FSH- and LH-induced Ca2+ signaling in intact follicles of mice expressing genetically encoded Ca2+ sensors, Twitch-2B and GCaMP6s. At a physiological concentration (1 nM), FSH elevates Ca2+ within the granulosa cells of preantral and antral follicles. The Ca2+ rise begins several minutes after FSH application, peaks at similar to 10 min, remains above baseline for another similar to 10 min, and depends on extracellular Ca2+. However, suppression of the FSH-induced Ca2+ increase by reducing extracellular Ca2+ does not inhibit FSH-induced phosphorylation ofMAP kinase, estradiol production, or the acquisition of LH responsiveness. Like FSH, LH also increases Ca2+, when applied to preovulatory follicles. At a physiological concentration (10 nM), LH elicits Ca2+ oscillations in a subset of cells in the outer mural granulosa layer. These oscillations continue for at least 6 h and depend on the activity of G(q) family G-proteins. Suppression of the oscillations by G(q) inhibition does not inhibit meiotic resumption, but does delay the time to 50% ovulation by about 3 h. In summary, both FSH and LH increase Ca2+ in the granulosa cells of intact follicles, but the functions of these Ca2+ rises are only starting to be identified. Summary Sentence Both FSH and LH increase Ca2+ in the granulosa cells of intact ovarian follicles from mice expressing genetically encoded sensors

The Mouse Genome Database (MGD): comprehensive resource for genetics and genomics of the laboratory mouse

The Mouse Genome Database (MGD, http://www.informatics.jax.org) is the international community resource for integrated genetic, genomic and biological data about the laboratory mouse. Data in MGD are obtained through loads from major data providers and experimental consortia, electronic submissions from laboratories and from the biomedical literature. MGD maintains a comprehensive, unified, non-redundant catalog of mouse genome features generated by distilling gene predictions from NCBI, Ensembl and VEGA. MGD serves as the authoritative source for the nomenclature of mouse genes, mutations, alleles and strains. MGD is the primary source for evidence-supported functional annotations for mouse genes and gene products using the Gene Ontology (GO). MGD provides full annotation of phenotypes and human disease associations for mouse models (genotypes) using terms from the Mammalian Phenotype Ontology and disease names from the Online Mendelian Inheritance in Man (OMIM) resource. MGD is freely accessible online through our website, where users can browse and search interactively, access data in bulk using Batch Query or BioMart, download data files or use our web services Application Programming Interface (API). Improvements to MGD include expanded genome feature classifications, inclusion of new mutant allele sets and phenotype associations and extensions of GO to include new relationships and a new stream of annotations via phylogenetic-based approaches

The Protein Ontology: a structured representation of protein forms and complexes

The Protein Ontology (PRO) provides a formal, logically-based classification of specific protein classes including structured representations of protein isoforms, variants and modified forms. Initially focused on proteins found in human, mouse and Escherichia coli, PRO now includes representations of protein complexes. The PRO Consortium works in concert with the developers of other biomedical ontologies and protein knowledge bases to provide the ability to formally organize and integrate representations of precise protein forms so as to enhance accessibility to results of protein research. PRO (http://pir.georgetown.edu/pro) is part of the Open Biomedical Ontology Foundry

The Mouse Genome Database (MGD): premier model organism resource for mammalian genomics and genetics

The Mouse Genome Database (MGD) is the community model organism database for the laboratory mouse and the authoritative source for phenotype and functional annotations of mouse genes. MGD includes a complete catalog of mouse genes and genome features with integrated access to genetic, genomic and phenotypic information, all serving to further the use of the mouse as a model system for studying human biology and disease. MGD is a major component of the Mouse Genome Informatics (MGI, http://www.informatics.jax.org/) resource. MGD contains standardized descriptions of mouse phenotypes, associations between mouse models and human genetic diseases, extensive integration of DNA and protein sequence data, normalized representation of genome and genome variant information. Data are obtained and integrated via manual curation of the biomedical literature, direct contributions from individual investigators and downloads from major informatics resource centers. MGD collaborates with the bioinformatics community on the development and use of biomedical ontologies such as the Gene Ontology (GO) and the Mammalian Phenotype (MP) Ontology. Major improvements to the Mouse Genome Database include comprehensive update of genetic maps, implementation of new classification terms for genome features, development of a recombinase (cre) portal and inclusion of all alleles generated by the International Knockout Mouse Consortium (IKMC)

The Mouse Genome Database: enhancements and updates

The Mouse Genome Database (MGD) is a major component of the Mouse Genome Informatics (MGI, http://www.informatics.jax.org/) database resource and serves as the primary community model organism database for the laboratory mouse. MGD is the authoritative source for mouse gene, allele and strain nomenclature and for phenotype and functional annotations of mouse genes. MGD contains comprehensive data and information related to mouse genes and their functions, standardized descriptions of mouse phenotypes, extensive integration of DNA and protein sequence data, normalized representation of genome and genome variant information including comparative data on mammalian genes. Data for MGD are obtained from diverse sources including manual curation of the biomedical literature and direct contributions from individual investigator’s laboratories and major informatics resource centers, such as Ensembl, UniProt and NCBI. MGD collaborates with the bioinformatics community on the development and use of biomedical ontologies such as the Gene Ontology and the Mammalian Phenotype Ontology. Recent improvements in MGD described here includes integration of mouse gene trap allele and sequence data, integration of gene targeting information from the International Knockout Mouse Consortium, deployment of an MGI Biomart, and enhancements to our batch query capability for customized data access and retrieval

Errors in chromosome segregation during oogenesis and early embryogenesis

Errors in chromosome segregation occurring during human oogenesis and early embryogenesis are very common. Meiotic chromosome development during oogenesis is subdivided into three distinct phases. The crucial events, including meiotic chromosome pairing and recombination, take place from around 11 weeks until birth. Oogenesis is then arrested until ovulation, when the first meiotic division takes place, with the second meiotic division not completed until after fertilization. It is generally accepted that most aneuploid fetal conditions, such as trisomy 21 Down syndrome, are due to maternal chromosome segregation errors. The underlying reasons are not yet fully understood. It is also clear that superimposed on the maternal meiotic chromosome segregation errors, there are a large number of mitotic errors taking place post-zygotically during the first few cell divisions in the embryo. In this chapter, we summarise current knowledge of errors in chromosome segregation during oogenesis and early embryogenesis, with special reference to the clinical implications for successful assisted reproduction

The representation of protein complexes in the Protein Ontology (PRO)

BACKGROUND: Representing species-specific proteins and protein complexes in ontologies that are both human- and machine-readable facilitates the retrieval, analysis, and interpretation of genome-scale data sets. Although existing protin-centric informatics resources provide the biomedical research community with well-curated compendia of protein sequence and structure, these resources lack formal ontological representations of the relationships among the proteins themselves. The Protein Ontology (PRO) Consortium is filling this informatics resource gap by developing ontological representations and relationships among proteins and their variants and modified forms. Because proteins are often functional only as members of stable protein complexes, the PRO Consortium, in collaboration with existing protein and pathway databases, has launched a new initiative to implement logical and consistent representation of protein complexes. DESCRIPTION: We describe here how the PRO Consortium is meeting the challenge of representing species-specific protein complexes, how protein complex representation in PRO supports annotation of protein complexes and comparative biology, and how PRO is being integrated into existing community bioinformatics resources. The PRO resource is accessible at http://pir.georgetown.edu/pro/. CONCLUSION: PRO is a unique database resource for species-specific protein complexes. PRO facilitates robust annotation of variations in composition and function contexts for protein complexes within and between species

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases

The MetaCyc database (http://metacyc.org/) provides a comprehensive and freely accessible resource for metabolic pathways and enzymes from all domains of life. The pathways in MetaCyc are experimentally determined, small-molecule metabolic pathways and are curated from the primary scientific literature. MetaCyc contains more than 1800 pathways derived from more than 30 000 publications, and is the largest curated collection of metabolic pathways currently available. Most reactions in MetaCyc pathways are linked to one or more well-characterized enzymes, and both pathways and enzymes are annotated with reviews, evidence codes and literature citations. BioCyc (http://biocyc.org/) is a collection of more than 1700 organism-specific Pathway/Genome Databases (PGDBs). Each BioCyc PGDB contains the full genome and predicted metabolic network of one organism. The network, which is predicted by the Pathway Tools software using MetaCyc as a reference database, consists of metabolites, enzymes, reactions and metabolic pathways. BioCyc PGDBs contain additional features, including predicted operons, transport systems and pathway-hole fillers. The BioCyc website and Pathway Tools software offer many tools for querying and analysis of PGDBs, including Omics Viewers and comparative analysis. New developments include a zoomable web interface for diagrams; flux-balance analysis model generation from PGDBs; web services; and a new tool called Web Groups

Comprehensive analysis of karyotypic mosaicism between trophectoderm and inner cell mass

Aneuploidy has been well-documented in blastocyst embryos, but prior studies have been limited in scale and/or lack mechanistic data. We previously reported preclinical validation of microarray 24-chromosome preimplantation genetic screening in a 24-h protocol. The method diagnoses chromosome copy number, structural chromosome aberrations, parental source of aneuploidy and distinguishes certain meiotic from mitotic errors. In this study, our objective was to examine aneuploidy in human blastocysts and determine correspondence of karyotypes between trophectoderm (TE) and inner cell mass (ICM). We disaggregated 51 blastocysts from 17 couples into ICM and one or two TE fractions. The average maternal age was 31. Next, we ran 24-chromosome microarray molecular karyotyping on all of the samples, and then performed a retrospective analysis of the data. The average per-chromosome confidence was 99.95%. Approximately 80% of blastocysts were euploid. The majority of aneuploid embryos were simple aneuploid, i.e. one or two whole-chromosome imbalances. Structural chromosome aberrations, which are common in cleavage stage embryos, occurred in only three blastocysts (5.8%). All TE biopsies derived from the same embryos were concordant. Forty-nine of 51 (96.1%) ICM samples were concordant with TE biopsies derived from the same embryos. Discordance between TE and ICM occurred only in the two embryos with structural chromosome aberration. We conclude that TE karyotype is an excellent predictor of ICM karyotype. Discordance between TE and ICM occurred only in embryos with structural chromosome aberrations