RNA-Seq transcriptome data of the liver of common Pekin, Muscovy, mule and Hinny ducks fed ad libitum or overfed.

Duck species are known to have different ability to fatty liver production in response to overfeeding and gene expression analyses can help to characterize mechanisms involved in these differences. This data article reports the sequencing of RNAs extracted from the liver of Pekin and Muscovy duck species and of their reciprocal hybrids, Mule and Hinny ducks fed ad libitum or overfed. Libraries were prepared by selecting polyadenylated mRNAs and RNA Sequencing (RNASeq) was performed using Illumina HiSeq2000 platform. RNASeq data presented in this article were deposited in the NCBI sequence read archive (SRA) under the accession number SRP144764 and links to these data were also indicated in the Data INRAE repository (https://doi.org/10.15454/JJZ3QQ). Transcriptome analyses of these data were published in Hérault et al. (2019) and Liu et al. (2020).

Development of a relevant strategy using de novo transcriptome assembly method for transcriptome comparisons between Muscovy and common duck species and their reciprocal inter-specific mule and hinny hybrids fed ad libitum and overfed.

BACKGROUND: Common Pekin and Muscovy ducks and their intergeneric hinny and mule hybrids have different abilities for fatty liver production. RNA-Seq analyses from the liver of these different genetic types fed ad libitum or overfed would help to identify genes with different response to overfeeding between them. However RNA-seq analyses from different species and comparison is challenging. The goal of this study was develop a relevant strategy for transcriptome analysis and comparison between different species. RESULTS: Transcriptomes were first assembled with a reference-based approach. Important mapping biases were observed when heterologous mapping were conducted on common duck reference genome, suggesting that this reference-based strategy was not suited to compare the four different genetic types. De novo transcriptome assemblies were then performed using Trinity and Oases. Assemblies of transcriptomes were not relevant when more than a single genetic type was considered. Finally, single genetic type transcriptomes were assembled with DRAP in a mega-transcriptome. No bias was observed when reads from the different genetic types were mapped on this mega-transcriptome and differences in gene expression between the four genetic types could be identified. CONCLUSIONS: Analyses using both reference-based and de novo transcriptome assemblies point out a good performance of the de novo approach for the analysis of gene expression in different species. It also allowed the identification of differences in responses to overfeeding between Pekin and Muscovy ducks and hinny and mule hybrids.

RNA-seq analysis of hepatic gene expression of common Pekin, Muscovy, mule and hinny ducks fed ad libitum or overfed.

BACKGROUND: Duck species are known to have different susceptibility to fatty liver production in response to overfeeding. In order to better describe mechanisms involved in the development of hepatic steatosis and differences between species, transcriptome analyses were conducted on RNAs extracted from the livers of Pekin and Muscovy duck species and of their reciprocal hybrids, Mule and Hinny ducks fed ad libitum or overfed to identify differentially expressed genes and associated functions. RESULTS: After extraction from the liver of ducks from the four genetic types, RNAs were sequenced and sequencing data were analyzed. Hierarchic clustering and principal component analyses of genes expression levels indicated that differences between individuals lie primarily in feeding effect, differences between genetic types being less important. However, Muscovy ducks fed ad libitum and overfed were clustered together. Interestingly, Hinny and Mule hybrid ducks could not be differentiated from each other, according to feeding. Many genes with expression differences between overfed and ad libitum fed ducks were identified in each genetic type. Functional annotation analyses of these differentially expressed genes highlighted some expected functions (carbohydrate and lipid metabolisms) but also some unexpected ones (cell proliferation and immunity). CONCLUSIONS: These analyses evidence differences in response to overfeeding between different genetic types and help to better characterize functions involved in hepatic steatosis in ducks.

Optimal Threshold Determination for Interpreting Semantic Similarity and Particularity: Application to the Comparison of Gene Sets and Metabolic Pathways Using GO and ChEBI.

BACKGROUND: The analysis of gene annotations referencing back to Gene Ontology plays an important role in the interpretation of high-throughput experiments results. This analysis typically involves semantic similarity and particularity measures that quantify the importance of the Gene Ontology annotations. However, there is currently no sound method supporting the interpretation of the similarity and particularity values in order to determine whether two genes are similar or whether one gene has some significant particular function. Interpretation is frequently based either on an implicit threshold, or an arbitrary one (typically 0.5). Here we investigate a method for determining thresholds supporting the interpretation of the results of a semantic comparison. RESULTS: We propose a method for determining the optimal similarity threshold by minimizing the proportions of false-positive and false-negative similarity matches. We compared the distributions of the similarity values of pairs of similar genes and pairs of non-similar genes. These comparisons were performed separately for all three branches of the Gene Ontology. In all situations, we found overlap between the similar and the non-similar distributions, indicating that some similar genes had a similarity value lower than the similarity value of some non-similar genes. We then extend this method to the semantic particularity measure and to a similarity measure applied to the ChEBI ontology. Thresholds were evaluated over the whole HomoloGene database. For each group of homologous genes, we computed all the similarity and particularity values between pairs of genes. Finally, we focused on the PPAR multigene family to show that the similarity and particularity patterns obtained with our thresholds were better at discriminating orthologs and paralogs than those obtained using default thresholds. CONCLUSION: We developed a method for determining optimal semantic similarity and particularity thresholds. We applied this method on the GO and ChEBI ontologies. Qualitative analysis using the thresholds on the PPAR multigene family yielded biologically-relevant patterns.

Semantic particularity measure for functional characterization of gene sets using gene ontology.

BACKGROUND: Genetic and genomic data analyses are outputting large sets of genes. Functional comparison of these gene sets is a key part of the analysis, as it identifies their shared functions, and the functions that distinguish each set. The Gene Ontology (GO) initiative provides a unified reference for analyzing the genes molecular functions, biological processes and cellular components. Numerous semantic similarity measures have been developed to systematically quantify the weight of the GO terms shared by two genes. We studied how gene set comparisons can be improved by considering gene set particularity in addition to gene set similarity. RESULTS: We propose a new approach to compute gene set particularities based on the information conveyed by GO terms. A GO term informativeness can be computed using either its information content based on the term frequency in a corpus, or a function of the term's distance to the root. We defined the semantic particularity of a set of GO terms Sg1 compared to another set of GO terms Sg2. We combined our particularity measure with a similarity measure to compare gene sets. We demonstrated that the combination of semantic similarity and semantic particularity measures was able to identify genes with particular functions from among similar genes. This differentiation was not recognized using only a semantic similarity measure. CONCLUSION: Semantic particularity should be used in conjunction with semantic similarity to perform functional analysis of GO-annotated gene sets. The principle is generalizable to other ontologies.

The duplicated genes database: identification and functional annotation of co-localised duplicated genes across genomes.

BACKGROUND: There has been a surge in studies linking genome structure and gene expression, with special focus on duplicated genes. Although initially duplicated from the same sequence, duplicated genes can diverge strongly over evolution and take on different functions or regulated expression. However, information on the function and expression of duplicated genes remains sparse. Identifying groups of duplicated genes in different genomes and characterizing their expression and function would therefore be of great interest to the research community. The 'Duplicated Genes Database' (DGD) was developed for this purpose. METHODOLOGY: Nine species were included in the DGD. For each species, BLAST analyses were conducted on peptide sequences corresponding to the genes mapped on a same chromosome. Groups of duplicated genes were defined based on these pairwise BLAST comparisons and the genomic location of the genes. For each group, Pearson correlations between gene expression data and semantic similarities between functional GO annotations were also computed when the relevant information was available. CONCLUSIONS: The Duplicated Gene Database provides a list of co-localised and duplicated genes for several species with the available gene co-expression level and semantic similarity value of functional annotation. Adding these data to the groups of duplicated genes provides biological information that can prove useful to gene expression analyses. The Duplicated Gene Database can be freely accessed through the DGD website at http://dgd.genouest.org.

Fine mapping of complex traits in non-model species: using next generation sequencing and advanced intercross lines in Japanese quail.

BACKGROUND: As for other non-model species, genetic analyses in quail will benefit greatly from a higher marker density, now attainable thanks to the evolution of sequencing and genotyping technologies. Our objective was to obtain the first genome wide panel of Japanese quail SNP (Single Nucleotide Polymorphism) and to use it for the fine mapping of a QTL for a fear-related behaviour, namely tonic immobility, previously localized on Coturnix japonica chromosome 1. To this aim, two reduced representations of the genome were analysed through high-throughput 454 sequencing: AFLP (Amplified Fragment Length Polymorphism) fragments as representatives of genomic DNA, and EST (Expressed Sequence Tag) as representatives of the transcriptome. RESULTS: The sequencing runs produced 399,189 and 1,106,762 sequence reads from cDNA and genomic fragments, respectively. They covered over 434 Mb of sequence in total and allowed us to detect 17,433 putative SNP. Among them, 384 were used to genotype two Advanced Intercross Lines (AIL) obtained from three quail lines differing for duration of tonic immobility. Despite the absence of genotyping for founder individuals in the analysis, the previously identified candidate region on chromosome 1 was refined and led to the identification of a candidate gene. CONCLUSIONS: These data confirm the efficiency of transcript and AFLP-sequencing for SNP discovery in a non-model species, and its application to the fine mapping of a complex trait. Our results reveal a significant association of duration of tonic immobility with a genomic region comprising the DMD (dystrophin) gene. Further characterization of this candidate gene is needed to decipher its putative role in tonic immobility in Coturnix.

GO2PUB: Querying PubMed with semantic expansion of gene ontology terms.

BACKGROUND: With the development of high throughput methods of gene analyses, there is a growing need for mining tools to retrieve relevant articles in PubMed. As PubMed grows, literature searches become more complex and time-consuming. Automated search tools with good precision and recall are necessary. We developed GO2PUB to automatically enrich PubMed queries with gene names, symbols and synonyms annotated by a GO term of interest or one of its descendants. RESULTS: GO2PUB enriches PubMed queries based on selected GO terms and keywords. It processes the result and displays the PMID, title, authors, abstract and bibliographic references of the articles. Gene names, symbols and synonyms that have been generated as extra keywords from the GO terms are also highlighted. GO2PUB is based on a semantic expansion of PubMed queries using the semantic inheritance between terms through the GO graph. Two experts manually assessed the relevance of GO2PUB, GoPubMed and PubMed on three queries about lipid metabolism. Experts' agreement was high (kappa = 0.88). GO2PUB returned 69% of the relevant articles, GoPubMed: 40% and PubMed: 29%. GO2PUB and GoPubMed have 17% of their results in common, corresponding to 24% of the total number of relevant results. 70% of the articles returned by more than one tool were relevant. 36% of the relevant articles were returned only by GO2PUB, 17% only by GoPubMed and 14% only by PubMed. For determining whether these results can be generalized, we generated twenty queries based on random GO terms with a granularity similar to those of the first three queries and compared the proportions of GO2PUB and GoPubMed results. These were respectively of 77% and 40% for the first queries, and of 70% and 38% for the random queries. The two experts also assessed the relevance of seven of the twenty queries (the three related to lipid metabolism and four related to other domains). Expert agreement was high (0.93 and 0.8). GO2PUB and GoPubMed performances were similar to those of the first queries. CONCLUSIONS: We demonstrated that the use of genes annotated by either GO terms of interest or a descendant of these GO terms yields some relevant articles ignored by other tools. The comparison of GO2PUB, based on semantic expansion, with GoPubMed, based on text mining techniques, showed that both tools are complementary. The analysis of the randomly-generated queries suggests that the results obtained about lipid metabolism can be generalized to other biological processes. GO2PUB is available at http://go2pub.genouest.org.

Transcriptome profiling of the feeding-to-fasting transition in chicken liver.

BACKGROUND: Starvation triggers a complex array of adaptative metabolic responses including energy-metabolic responses, a process which must imply tissue specific alterations in gene expression and in which the liver plays a central role. The present study aimed to describe the evolution of global gene expression profiles in liver of 4-week-old male chickens during a 48 h fasting period using a chicken 20 K oligoarray. RESULTS: A large number of genes were modulated by fasting (3532 genes with a pvalue corrected by Benjamini-Hochberg < 0.01); 2062 showed an amplitude of variation higher than +/- 40% among those, 1162 presented an human ortholog, allowing to collect functional information. Notably more genes were down-regulated than up-regulated, whatever the duration of fasting (16 h or 48 h). The number of genes differentially expressed after 48 h of fasting was 3.5-fold higher than after 16 h of fasting. Four clusters of co-expressed genes were identified by a hierarchical cluster analysis. Gene Ontology, KEGG and Ingenuity databases were then used to identify the metabolic processes associated to each cluster. After 16 h of fasting, genes involved in ketogenesis, gluconeogenesis and mitochondrial or peroxisomal fatty acid beta-oxidation, were up-regulated (cluster-1) whereas genes involved in fatty acid and cholesterol synthesis were down-regulated (cluster-2). For all genes tested, the microarray data was confirmed by quantitative RT-PCR. Most genes were altered by fasting as already reported in mammals. A notable exception was the HMG-CoA synthase 1 gene, which was up-regulated following 16 and 48 h of fasting while the other genes involved in cholesterol metabolism were down-regulated as reported in mammalian studies. We further focused on genes not represented on the microarray and candidates for the regulation of the target genes belonging to cluster-1 and -2 and involved in lipid metabolism. Data are provided concerning PPARa, SREBP1, SREBP2, NR1H3 transcription factors and two desaturases (FADS1, FADS2). CONCLUSION: This study evidences numerous genes altered by starvation in chickens and suggests a global repression of cellular activity in response to this stressor. The central role of lipid and acetyl-CoA metabolisms and its regulation at transcriptional level are confirmed in chicken liver in response to short-term fasting. Interesting expression modulations were observed for NR1H3, FADS1 and FADS2 genes. Further studies are needed to precise their role in the complex regulatory network controlling lipid metabolism.

Microarray analysis of differential gene expression in the liver of lean and fat chickens.

Excessive adiposity has become a major drawback in meat-type chicken production. However, few studies were conducted to analyze the liver expression of genes involved in pathways and mechanisms leading to adiposity. A previous study performed by differential display on RNAs extracted from chicken livers from lean and fat lines allowed us to isolate cDNA products of genes with putative differential expression. In this study, a cDNA microarray resource was developed from these products together with cDNAs from genes involved in or related to lipid metabolism. This resource was used to analyze gene expression in the liver from lean and fat chickens. Some genes were found with a difference in expression between lean and fat animals and/or correlated to adipose tissue weight. Cytochrome P450 2C45, thought to play a role in biotransformation of steroids and poly-unsaturated fatty acids, was more expressed in lean chickens whereas fatty acid synthase, stearoyl-CoA desaturase, sterol response element binding factor 1 and hepatocyte nuclear factor 4, respectively involved in lipogenesis and its regulation, were more expressed in fat chickens. These results indicate that mechanisms involved in the expression and regulation of lipogenic genes could play a key role in fatness ontogenesis in chickens from lean and fat lines.

Differential expression and genetic variation of hepatic messenger RNAs from genetically lean and fat chickens.

Although excessive adiposity has become a major drawback in meat type chicken production, few of the genes involved in this process have been characterized so far. In order to identify putative genes involved in adiposity, we performed differential display analysis of RNAs extracted from the liver of divergently selected lean and fat chickens. Twenty-six differential products were selected and purified by single strand conformation polymorphism gel electrophoresis before sequencing and Northern blot analyses. An orthologous sequence of a mammalian cytochrome P450 2C subfamily member was proven to be differentially expressed in the liver of lean and fat chickens and could play an important role in the regulation of adiposity. In mammals, these genes are involved in detoxification of xenobiotics and metabolism of some important biological compounds. Four other genes were found differentially expressed to a lower extent. Some unidentified products were shown to be lean or fat specific, with sequence polymorphism and liver specific expression, strongly suggesting that the related gene could be directly involved in adiposity. Our data indicate that differential display can evidence genes with differential expression and with sequence polymorphism, making this strategy more accurate for differential analysis of messenger RNAs.