sigReannot: an oligo-set re-annotation pipeline based on similarities with the Ensembl transcripts and Unigene clusters

<p>Abstract</p> <p>Background</p> <p>Microarray is a powerful technology enabling to monitor tens of thousands of genes in a single experiment. Most microarrays are now using oligo-sets. The design of the oligo-nucleotides is time consuming and error prone. Genome wide microarray oligo-sets are designed using as large a set of transcripts as possible in order to monitor as many genes as possible. Depending on the genome sequencing state and on the assembly state the knowledge of the existing transcripts can be very different. This knowledge evolves with the different genome builds and gene builds. Once the design is done the microarrays are often used for several years. The biologists working in EADGENE expressed the need of up-to-dated annotation files for the oligo-sets they share including information about the orthologous genes of model species, the Gene Ontology, the corresponding pathways and the chromosomal location.</p> <p>Results</p> <p>The results of SigReannot on a chicken micro-array used in the EADGENE project compared to the initial annotations show that 23% of the oligo-nucleotide gene annotations were not confirmed, 2% were modified and 1% were added. The interest of this up-to-date annotation procedure is demonstrated through the analysis of real data previously published.</p> <p>Conclusion</p> <p>SigReannot uses the oligo-nucleotide design procedure criteria to validate the probe-gene link and the Ensembl transcripts as reference for annotation. It therefore produces a high quality annotation based on reference gene sets.</p

Hepatic lipogenesis gene expression in two experimental egg-laying lines divergently selected on residual food consumption

Two Rhode Island Red egg-laying lines have been divergently selected on residual food intake (low intake R- line, high intake R+ line) for 19 generations. In addition to direct response, correlated responses have altered several other traits such as carcass adiposity and lipid contents of several tissues, the R+ animals being leaner than the R- ones. In a search for the biological origin of the differences observed in fat deposit, the hepatic mRNA amounts of genes involved in lipid metabolism were investigated. No difference was found between lines for mRNA levels of ATP citrate-lyase, acetyl-CoA carboxylase, fatty acid synthase, malic enzyme and CCAAT/enhancer binding protein α, a transcription factor acting on several lipogenesis genes. The genes coding for stearoyl-CoA desaturase and apolipoprotein A1 displayed significantly lower mRNA levels in the R+ cockerels compared to the R-. All together these mRNA levels explained 40% of the overall variability of abdominal adipose tissue weight, suggesting an important role of both genes in the fatness variability

Inferring gene networks using a sparse factor model approach, Statistical Learning and Data Science

The availability of genome-wide expression data to complement the measurements of a phenotypic trait opens new opportunities for identifying biologic processes and genes that are involved in trait expression. Usually differential analysis is a preliminary step to identify the key biological processes involved in the variability of the trait of interest. However, this variability shall be viewed as resulting from a complex combination of genes individual contributions. In other words, exploring the interactions between genes viewed in a network structure which vertices are genes and edges stand for inhibition or activation connections gives much more insight on the internal structure of expression profiles. Many currently available solutions for network analysis have been developed but an efficient estimation of the network from high-dimensional data is still a questioning issue. Extending the idea introduced for differential analysis by Friguet et al. (2009) [1] and Blum et al. (2010) [2], we propose to take advantage of a factor model structure to infer gene networks. This method shows good inferential properties and also allows an efficient testing strategy for the significance of partial correlations, which provides an interesting tool to explore the community structure of the networks. We illustrate the performance of our method comparing it with competitors through simulation experiments. Moreover, we apply our method in a lipid metabolism study that aims at identifying gene networks underlying the fatness variability in chickens

Messenger RNA levels and transcription rates of hepatic lipogenesis genes in genetically lean and fat chickens

Levels of body fat content in commercial meat chickens have prompted research in order to control the development of this trait. Based on experimentally selected divergent lean and fat lines, many studies have shown that liver metabolism has a major role in the fatness variability. In order to identify which genes are involved in this variability, we investigated the expression of several genes implicated in the hepatic lipid metabolism. The studied genes code for enzymes of fatty acid synthesis [ATP citrate-lyase (ACL), acetyl-CoA carboxylase (ACC), fatty acid synthase (FAS), malic enzyme (ME), stearoyl-CoA desaturase (SCD1)], for an apolipoprotein [apolipoprotein A1 (APOA1)], and for the CCAAT/enhancer binding protein α (C/EBPα), which is a transcription factor implied in the regulation of several genes of lipid metabolism. The results show that the fat-line chickens display significantly higher hepatic transcription rates and mRNA levels than the lean-line chickens for the ACL, ME and APOA1 genes. This suggests that these genes could be responsible for the phenotypic fatness variability

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)

Using transcriptome profiling to characterize QTL regions on chicken chromosome 5

<p>Abstract</p> <p>Background</p> <p>Although many QTL for various traits have been mapped in livestock, location confidence intervals remain wide that makes difficult the identification of causative mutations. The aim of this study was to test the contribution of microarray data to QTL detection in livestock species. Three different but complementary approaches are proposed to improve characterization of a chicken QTL region for abdominal fatness (AF) previously detected on chromosome 5 (GGA5).</p> <p>Results</p> <p>Hepatic transcriptome profiles for 45 offspring of a sire known to be heterozygous for the distal GGA5 AF QTL were obtained using a 20 K chicken oligochip. mRNA levels of 660 genes were correlated with the AF trait. The first approach was to dissect the AF phenotype by identifying animal subgroups according to their 660 transcript profiles. Linkage analysis using some of these subgroups revealed another QTL in the middle of GGA5 and increased the significance of the distal GGA5 AF QTL, thereby refining its localization. The second approach targeted the genes correlated with the AF trait and regulated by the GGA5 AF QTL region. Five of the 660 genes were considered as being controlled either by the AF QTL mutation itself or by a mutation close to it; one having a function related to lipid metabolism (HMGCS1). In addition, a QTL analysis with a multiple trait model combining this 5 gene-set and AF allowed us to refine the QTL region. The third approach was to use these 5 transcriptome profiles to predict the paternal Q versus q AF QTL mutation for each recombinant offspring and then refine the localization of the QTL from 31 cM (100 genes) at a most probable location confidence interval of 7 cM (12 genes) after determining the recombination breakpoints, an interval consistent with the reductions obtained by the two other approaches.</p> <p>Conclusion</p> <p>The results showed the feasibility and efficacy of the three strategies used, the first revealing a QTL undetected using the whole population, the second providing functional information about a QTL region through genes related to the trait and controlled by this region (HMGCS1), the third could drastically refine a QTL region.</p

A gene-based radiation hybrid map of chicken microchromosome 14: Comparison to human and alignment to the assembled chicken sequence

We present a gene-based RH map of the chicken microchromosome GGA14, known to have synteny conservations with human chromosomal regions HSA16p13.3 and HSA17p11.2. Microsatellite markers from the genetic map were used to check the validity of the RH map and additional markers were developed from chicken EST data to yield comparative mapping data. A high rate of intra-chromosomal rearrangements was detected by comparison to the assembled human sequence. Finally, the alignment of the RH map to the assembled chicken sequence showed a small number of discordances, most of which involved the same region of the chromosome spanning between 40.5 and 75.9 cR6000 on the RH map

Expanding Duplication of Free Fatty Acid Receptor-2 (GPR43) Genes in the Chicken Genome

International audienceFree fatty acid receptors (FFAR) belong to a family of five G-protein coupled receptors that are involved in the regulation of lipidmetabolism, so that their loss of function increases the risk of obesity. The aim of this study was to determine the expansion of genesencoding paralogs of FFAR2 in the chicken, considered as amodel organism for developmental biology and biomedical research. Byestimating the gene copy number using quantitative polymerase chain reaction, genomic DNA resequencing, and RNA sequencingdata, we showed the existence of 23 ±1.5 genes encoding FFAR2 paralogs in the chicken genome. The FFAR2 paralogs shared anidentity from 87.2%up to 99%. Extensive gene conversion was responsible for this high degree of sequence similarities betweenthese genes, and this concerned especially the four amino acids known to be critical for ligand binding. Moreover, elevated nonsynonymous/synonymous substitutionratios onsomeamino acids withinor inclose-vicinity of the ligand-bindinggroove suggest thatpositive selectionmay have reduced the effective rate of gene conversion in this region, thus contributing to diversify the function ofsome FFAR2 paralogs. All the FFAR2 paralogs were located on a microchromosome in a same linkage group. FFAR2 genes wereexpressed in different tissues and cells such as spleen, peripheral blood mononuclear cells, abdominal adipose tissue, intestine, andlung, with the highest rate of expression in testis. Further investigations are needed to determine whether these chicken-specificevents along evolution are the consequence of domestication and may play a role in regulating lipid metabolism in this species