Sex-dimorphic gene expression and ineffective dosage compensation of Z-linked genes in gastrulating chicken embryos

<p>Abstract</p> <p>Background</p> <p>Considerable progress has been made in our understanding of sex determination and dosage compensation mechanisms in model organisms such as <it>C. elegans</it>, <it>Drosophila </it>and <it>M. musculus</it>. Strikingly, the mechanism involved in sex determination and dosage compensation are very different among these three model organisms. Birds present yet another situation where the heterogametic sex is the female. Sex determination is still poorly understood in birds and few key determinants have so far been identified. In contrast to most other species, dosage compensation of bird sex chromosomal genes appears rather ineffective.</p> <p>Results</p> <p>By comparing microarrays from microdissected primitive streak from single chicken embryos, we identified a large number of genes differentially expressed between male and female embryos at a very early stage (Hamburger and Hamilton stage 4), long before any sexual differentiation occurs. Most of these genes are located on the Z chromosome, which indicates that dosage compensation is ineffective in early chicken embryos. Gene ontology analyses, using an enhanced annotation tool for Affymetrix probesets of the chicken genome developed in our laboratory (called Manteia), show that among these male-biased genes found on the Z chromosome, more than 20 genes play a role in sex differentiation.</p> <p>Conclusions</p> <p>These results corroborate previous studies demonstrating the rather inefficient dosage compensation for Z chromosome in birds and show that this sexual dimorphism in gene regulation is observed long before the onset of sexual differentiation. These data also suggest a potential role of non-compensated Z-linked genes in somatic sex differentiation in birds.</p

Nucleic Acids Res

The function of genes is often evolutionarily conserved, and comparing the annotation of ortholog genes in different model organisms has proved to be a powerful predictive tool to identify the function of human genes. Here, we describe Manteia, a resource available online at http://manteia.igbmc.fr. Manteia allows the comparison of embryological, expression, molecular and etiological data from human, mouse, chicken and zebrafish simultaneously to identify new functional and structural correlations and gene-disease associations. Manteia is particularly useful for the analysis of gene lists produced by high-throughput techniques such as microarrays or proteomics. Data can be easily analyzed statistically to characterize the function of groups of genes and to correlate the different aspects of their annotation. Sophisticated querying tools provide unlimited ways to merge the information contained in Manteia along with the possibility of introducing custom user-designed biological questions into the system. This allows for example to connect all the animal experimental results and annotations to the human genome, and take advantage of data not available for human to look for candidate genes responsible for genetic disorders. Here, we demonstrate the predictive and analytical power of the system to predict candidate genes responsible for human genetic diseases

Curation of NISEED, an integrative framework for the digital representation of embryonic development

NISEED (Network for In situ Expression and Embryological Data) is a generic infrastructure for the creation, maintenance and integration of molecular and anatomical information on model organisms. We applied it to ascidians which are marine invertebrate chordates. These animals constitute model organisms of choice for developmental biology because their embryos develop with a small number of cells and an invariant lineage, allowing their study with a cellular level of resolution. In ANISEED (Ascidian NISEED), embryogenesis of ascidian is represented at the level of the genome via functional gene annotations, cis-regulatory elements or gene expression data, at the level of the cell by representing its morphology, fates, lineage, and relations with its neighbors, or at the level of the whole embryo by representing its anatomy and morphogenesis at successive developmental stages. The system provides also tool and standard to enter, annotate, curate and manage data. All results can be accessed through the ANISEED website at &#x22;http://aniseed-ibdm.univ-mrs.fr&#x22;:http://aniseed-ibdm.univ-mrs.fr&#xd;&#xa

Integrative data mining highlights candidate genes for monogenic myopathies

Inherited myopathies are a heterogeneous group of disabling disorders with still barely understood pathological mechanisms. Around 40% of afflicted patients remain without a molecular diagnosis after exclusion of known genes. The advent of high-throughput sequencing has opened avenues to the discovery of new implicated genes, but a working list of prioritized candidate genes is necessary to deal with the complexity of analyzing large-scale sequencing data. Here we used an integrative data mining strategy to analyze the genetic network linked to myopathies, derive specific signatures for inherited myopathy and related disorders, and identify and rank candidate genes for these groups. Training sets of genes were selected after literature review and used in Manteia, a public web-based data mining system, to extract disease group signatures in the form of enriched descriptor terms, which include functional annotation, human and mouse phenotypes, as well as biological pathways and protein interactions. These specific signatures were then used as an input to mine and rank candidate genes, followed by filtration against skeletal muscle expression and association with known diseases. Signatures and identified candidate genes highlight both potential common pathological mechanisms and allelic disease groups. Recent discoveries of gene associations to diseases, like B3GALNT2, GMPPB and B3GNT1 to congenital muscular dystrophies, were prioritized in the ranked lists, suggesting a posteriori validation of our approach and predictions. We show an example of how the ranked lists can be used to help analyze high-throughput sequencing data to identify candidate genes, and highlight the best candidate genes matching genomic regions linked to myopathies without known causative genes. This strategy can be automatized to generate fresh candidate gene lists, which help cope with database annotation updates as new knowledge is incorporated

New data and features for advanced data mining in Manteia

Manteia is an integrative database available online at http://manteia.igbmc.fr which provides a large array of OMICs data related to the development of the mouse, chicken, zebrafish and human. The system is designed to use different types of data together in order to perform advanced datamining, test hypotheses or provide candidate genes involved in biological processes or responsible for human diseases. In this new version of the database, Manteia has been enhanced with new expression data originating from microarray and next generation sequencing experiments. In addition, the system includes new statistics tools to analyze lists of genes in order to compare their functions and highlight their specific features. One of the main novelties of this release is the integration of a machine learning tool called Lookalike that we have developed to analyze the different datasets present in the system in order to identify new disease genes. This tool identifies the key features of known disease genes to provide and rank new candidates with similar properties from the genome. It is also designed to highlight and take into account the specificities of a disease in order to increase the accuracy of its predictions

New data and features for advanced data mining in Manteia

Manteia is an integrative database available online at http://manteia.igbmc.fr which provides a large array of OMICs data related to the development of the mouse, chicken, zebrafish and human. The system is designed to use different types of data together in order to perform advanced datamining, test hypotheses or provide candidate genes involved in biological processes or responsible for human diseases. In this new version of the database, Manteia has been enhanced with new expression data originating from microarray and next generation sequencing experiments. In addition, the system includes new statistics tools to analyze lists of genes in order to compare their functions and highlight their specific features. One of the main novelties of this release is the integration of a machine learning tool called Lookalike that we have developed to analyze the different datasets present in the system in order to identify new disease genes. This tool identifies the key features of known disease genes to provide and rank new candidates with similar properties from the genome. It is also designed to highlight and take into account the specificities of a disease in order to increase the accuracy of its predictions

Evolution et prise de poids après le diagnostic de cancer du sein (suivi d'une étude prospective française)

LYON1-BU Santé (693882101) / SudocSudocFranceF

Représentation digitale du développement embryonnaire (Le système NISEED et ses applications dans l'étude des ascidies)

AIX-MARSEILLE2-BU Sci.Luminy (130552106) / SudocSudocFranceF