Functional study and regional mapping of 44 hormono-regulated genes isolated from a porcine granulosa cell library

cDNA clones from a pig granulosa cell cDNA library were isolated by differential hybridisation for follicle stimulating hormone (FSH) regulation in granulosa cells in a previous study. The clones that did not match any known sequence were studied for their expression in granulosa cells (treated or not by FSH) and in fresh isolated ovarian follicles mainly by comparative RT-PCR analysis. These results give functional data on genes that may be implicated in follicular growing. These ESTs have been localised on the porcine genome, using a somatic cell hybrid panel, providing new type I markers on the porcine map and information on the comparative map between humans and pigs

Pathway results from the chicken data set using GOTM, Pathway Studio and Ingenuity softwares

Background: As presented in the introduction paper, three sets of differentially regulated genes were found after the analysis of the chicken infection data set from EADGENE. Different methods were used to interpret these results.[br/] Results: GOTM, Pathway Studio and Ingenuity softwares were used to investigate the three lists of genes. The three softwares allowed the analysis of the data and highlighted different networks. However, only one set of genes, showing a differential expression between primary and secondary response gave significant biological interpretation.[br/] Conclusion: Combining these databases that were developed independently on different annotation sources supplies a useful tool for a global biological interpretation of microarray data, even if they may contain some imperfections (e.g. gene not or not well annotated)

Expressed sequence tags for genes: a review

Expressed sequence tags (ESTs) are partial sequences from the extremities of complementary DNA (CDNA) resulting from a single pass sequencing of clones from cDNA libraries, and different ESTs can be obtained from one gene. Sequence information from ESTs can be used for deciphering the function and the organisation of the genome. From a functional viewpoint, they allow the determination of the expression profiles of genes in any particular tissue, in different conditions or status, and thus the identification of regulated genes. In order to identify genes involved in particular processes one can select a specific group of mRNAs. For such a selection, classical techniques include subtraction or differential screening and new techniques, using polymerase chain reaction (PCR) amplification, are now available. For studies on the organisation of the genome the main use of ESTs is the determination of chromosomal localisation of the corresponding genes using a somatic hybrid cell panel. This chromosomal localisation information is needed to identify genes or quantitative trait loci, according to the ’positional candidate’ approach. ESTs also contribute to comparative genetics and they can help to decipher gene function by comparison between species, even genetically distant ones. Thus, combining sequence, functional and localisation data, ESTs contribute to an integrated approach to the genome.Les « étiquettes » correspondent aux séquences des extrémités des ADN complémentaires, obtenues de manière systématique à partir d’une seule réaction de séquençage. Cependant, à partir d’un seul gène plusieurs étiquettes différentes peuvent être obtenues : celles qui correspondent aux deux extrémités de l’ADN complémentaire, aux ADN complémentaires de tailles différentes synthétisés à partir d’un même ARN messager, et aux différents ARN messagers issus d’une même séquence d’ADN génomique. L’identification des gènes correspondants est faite par comparaison avec les séquences nucléiques ou protéiques contenues dans les bases de données publiques (GenBank ou EMBL, SwissProt), en utilisant des logiciels d’alignement automatique tels que FASTA ou BLAST. Les séquences annotées des étiquettes sont stockées dans une base de données particulière, dbEST, et soumises régulièrement à des tests de comparaison avec les bases de données citées. En raison de la présence d’une longue région non codante à l’extrémité 3’ des ARN messagers, les étiquettes de l’extrémité 3’ sont souvent non informatives. La comparaison des étiquettes entre elles permet d’essayer de regrouper celles qui peuvent appartenir à un même gène et de déterminer ainsi une séquence consensus, plus longue et donc plus informative. Au niveau fonctionnel, les étiquettes permettent d’établir les profils d’expression des gènes d’un tissu donné dans différentes situations physiologiques ou expérimentales et donc d’identifier les gènes qui sont régulés. Ces profils sont établis en utilisant les étiquettes pour mesurer la fréquence des différents ADNc dans une génothèque préparée à partir de ce tissu dans les différentes conditions étudiées. Dans une nouvelle stratégie, la SAGE (Serial Analysis of Gene expression), des étiquettes d’une dizaine de nucléotides sont collectées, mises bout à bout et séquencées en série, ce qui permet d’accélérer l’acquisition de ces profils d’expression. Une autre approche est basée sur l’hybridation d’un grand nombre de clones déposés sur une même membrane en nylon «filtres haute densité », ou, dans un format miniature, sur une lame de verre, « microarrays». Pour identifier les gènes impliqués dans des processus bien définis, différentes stratégies de soustraction ou de comparaison permettent de sélectionner une population particulière d’ARN messagers ; les techniques les plus récentes utilisent l’amplification par PCR. Au niveau de l’organisation du génome, les étiquettes contribuent au développement de la cartographie génique : les gènes correspondants sont localisés en utilisant un panel d’hybrides somatiques, les amorces nécessaires pour amplifier l’ADN des hybrides sont choisies grâce aux informations de séquence fournies par les étiquettes. Cette information de localisation chromosomique est indispensable pour identifier les gènes responsables des caractères étudiés par une stratégie de gène candidat positionnel. L’utilisation d’étiquettes d’une autre espèce peut également permettre d’effectuer ces localisations et donc de développer des cartes comparées entre espèces qui mettent en évidence une certaine conservation de l’organisation des gènes sur les chromosomes. Enfin, la conservation des gènes n’est pas limitée à la séquence et à l’organisation : grâce aux étiquettes, des analogies fonctionnelles de gènes appartenant à des espèces génétiquement éloignées ont été décrites et sont recherchées systématiquement pour identifier la fonction des gènes. Ainsi, en permettant de combiner des données de séquence, d’expression et de localisation chromosomique, les étiquettes participent au développement d’une approche intégrée du génome

A pig multi-tissue normalised cDNA library: large-scale sequencing, cluster analysis and 9K micro-array resource generation

<p>Abstract</p> <p>Background</p> <p>Domestic animal breeding and product quality improvement require the control of reproduction, nutrition, health and welfare in these animals. It is thus necessary to improve our knowledge of the major physiological functions and their interactions. This would be greatly enhanced by the availability of expressed gene sequences in the databases and by cDNA arrays allowing the transcriptome analysis of any function.</p> <p>The objective within the AGENAE French program was to initiate a high-throughput cDNA sequencing program of a 38-tissue normalised library and generate a diverse microarray for transcriptome analysis in pig species.</p> <p>Results</p> <p>We constructed a multi-tissue cDNA library, which was normalised and subtracted to reduce the redundancy of the clones. Expressed Sequence Tags were produced and 24449 high-quality sequences were released in EMBL database. The assembly of all the public ESTs (available through SIGENAE website) resulted in 40786 contigs and 54653 singletons. At least one Agenae sequence is present in 11969 contigs (12.5%) and in 9291 of the deeper-than-one-contigs (22.8%). Sequence analysis showed that both normalisation and subtraction processes were successful and that the initial tissue complexity was maintained in the final libraries. A 9K nylon cDNA microarray was produced and is available through CRB-GADIE. It will allow high sensitivity transcriptome analyses in pigs.</p> <p>Conclusion</p> <p>In the present work, a pig multi-tissue cDNA library was constructed and a 9K cDNA microarray designed. It contributes to the Expressed Sequence Tags pig data, and offers a valuable tool for transcriptome analysis.</p

Gene network reconstruction from microarray data

<p>Abstract</p> <p>Background</p> <p>Often, software available for biological pathways reconstruction rely on literature search to find links between genes. The aim of this study is to reconstruct gene networks from microarray data, using Graphical Gaussian models.</p> <p>Results</p> <p>The <it>GeneNet </it>R package was applied to the Eadgene chicken infection data set. No significant edges were found for the list of differentially expressed genes between conditions MM8 and MA8. On the other hand, a large number of significant edges were found among 85 differentially expressed genes between conditions MM8 and MM24.</p> <p>Conclusion</p> <p>Many edges were inferred from the microarray data. Most of them could, however, not be validated using other pathway reconstruction software. This was partly due to the fact that a quite large proportion of the differentially expressed genes were not annotated. Further biological validation is therefore needed for these networks, using for example in vitro invalidation of genes.</p

A genome scan for milk production traits in dairy goats reveals two new mutations in <i>Dgat1</i> reducing milk fat content

The quantity of milk and milk fat and proteins are particularly important traits in dairy livestock. However, little is known about the regions of the genome that influence these traits in goats. We conducted a genome wide association study in French goats and identified 109 regions associated with dairy traits. For a major region on chromosome 14 closely associated with fat content, the Diacylglycerol O-Acyltransferase 1 (DGAT1) gene turned out to be a functional and positional candidate gene. The caprine reference sequence of this gene was completed and 29 polymorphisms were found in the gene sequence, including two novel exonic mutations: R251L and R396W, leading to substitutions in the protein sequence. The R251L mutation was found in the Saanen breed at a frequency of 3.5% and the R396W mutation both in the Saanen and Alpine breeds at a frequencies of 13% and 7% respectively. The R396W mutation explained 46% of the genetic variance of the trait, and the R251L mutation 6%. Both mutations were associated with a notable decrease in milk fat content. Their causality was then demonstrated by a functional test. These results provide new knowledge on the genetic basis of milk synthesis and will help improve the management of the French dairy goat breeding program

A validation study of loci associated with mastitis resistance in two French dairy sheep breeds

The identification of loci associated with resistance to mastitis or of the causative mutations may be helpful in breeding programs for dairy sheep as it is for cattle worldwide. Seven genomic regions that control milk somatic cell counts, an indirect indicator of udder infection, have already been identified in sheep (Spanish Churra, French Lacaune and Italian Sardinian-Lacaune backcross populations). In this study, we used a 960 custom-designed ovine single nucleotide polymorphism (SNP) chip in Lacaune and Manech Tete Rousse dairy sheep to validate these seven genomic regions associated with mastitis. The most significant SNP (rs868996547) on Ovis aries chromosome (OAR) 3 was a previously described mutation in the suppressor of cytokine signalling 2 (SOCS2) gene. An antagonist effect of this causal candidate between health and growth in Lacaune sheep was confirmed. Effects of the mutation on the infectious status of the udder, i.e. increases in milk somatic cell counts and bacteria shedding, were also identified. This SNP was not present in the data available on Manech Tete Rousse. Three other regions associated with mastitis were also confirmed on OAR16 (Manech Tete Rousse), 19 (Lacaune) and 2 (both breeds). For the OAR2 region, we validated previously detected SNPs in several other breeds (Sarda, Churra, and Chios). For significant SNPs in the four mastitis regions, the effect varied from 0.24 to 0.67 phenotypic standard deviation of the traits. Two of the mastitis quantitative trait loci (QTL) regions (OAR2 and 16) that we validated here were also associated in opposite ways with milk production traits in both populations. These results indicate, at least in part, a genomic basis for the trade-off between milk production and mastitis resistance. Four of the seven mastitis QTL regions that were previously identified in independent populations, were confirmed in this study, which demonstrates partial sharing of mastitis-related genetic mechanisms between different distant dairy sheep populations

Author Correction:A genome scan for milk production traits in dairy goats reveals two new mutations in Dgat1 reducing milk fat content (Scientific Reports DOI: 10.1038/s41598-017-02052-0)

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the pape

The GENETPIG database: a tool for comparative mapping in pig (Sus scrofa)

The GENETPIG database has been established for storing and disseminating the results of the European project: ‘GENETPIG: identification of genes controlling economic traits in pig'. The partners of this project have mapped about 630 porcine and human ESTs onto the pig genome. The database collects the mapping results and links them to other sources of mapping data; this includes pig maps as well as available comparative mapping information. Functional annotation of the mapped ESTs is also given when a significant similarity to cognate genes was established. The database is accessible for consultation via the Internet at http://www.infobiogen.fr/services/Genetpi