Dataset Information

Identification of large intergenic non-coding RNAs in bovine muscle using next-generation transcriptomic sequencing.

ABSTRACT: BACKGROUND: The advent of large-scale gene expression technologies has helped to reveal in eukaryotic cells, the existence of thousands of non-coding transcripts, whose function and significance remain mostly poorly understood. Among these non-coding transcripts, long non-coding RNAs (lncRNAs) are the least well-studied but are emerging as key regulators of diverse cellular processes. In the present study, we performed a survey in bovine Longissimus thoraci of lincRNAs (long intergenic non-coding RNAs not overlapping protein-coding transcripts). To our knowledge, this represents the first such study in bovine muscle. RESULTS: To identify lincRNAs, we used paired-end RNA sequencing (RNA-Seq) to explore the transcriptomes of Longissimus thoraci from nine Limousin bull calves. Approximately 14-45 million paired-end reads were obtained per library. A total of 30,548 different transcripts were identified. Using a computational pipeline, we defined a stringent set of 584 different lincRNAs with 418 lincRNAs found in all nine muscle samples. Bovine lincRNAs share characteristics seen in their mammalian counterparts: relatively short transcript and gene lengths, low exon number and significantly lower expression, compared to protein-encoding genes. As for the first time, our study identified lincRNAs from nine different samples from the same tissue, it is possible to analyse the inter-individual variability of the gene expression level of the identified lincRNAs. Interestingly, there was a significant difference when we compared the expression variation of the 418 lincRNAs with the 10,775 known selected protein-encoding genes found in all muscle samples. In addition, we found 2,083 pairs of lincRNA/protein-encoding genes showing a highly significant correlated expression. Fourteen lincRNAs were selected and 13 were validated by RT-PCR. Some of the lincRNAs expressed in muscle are located within quantitative trait loci for meat quality traits. CONCLUSIONS: Our study provides a glimpse into the lincRNA content of bovine muscle and will facilitate future experimental studies to unravel the function of these molecules. It may prove useful to elucidate their effect on mechanisms underlying the genetic variability of meat quality traits. This catalog will complement the list of lincRNAs already discovered in cattle and therefore will help to better annotate the bovine genome.

SUBMITTER: Billerey C

PROVIDER: S-EPMC4073507 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Identification of large intergenic non-coding RNAs in bovine muscle using next-generation transcriptomic sequencing.

Billerey Coline C Boussaha Mekki M Esquerré Diane D Rebours Emmanuelle E Djari Anis A Meersseman Cédric C Klopp Christophe C Gautheret Daniel D Rocha Dominique D

BMC genomics 20140619

<h4>Background</h4>The advent of large-scale gene expression technologies has helped to reveal in eukaryotic cells, the existence of thousands of non-coding transcripts, whose function and significance remain mostly poorly understood. Among these non-coding transcripts, long non-coding RNAs (lncRNAs) are the least well-studied but are emerging as key regulators of diverse cellular processes. In the present study, we performed a survey in bovine Longissimus thoraci of lincRNAs (long intergenic no ...[more]

PMID: 24948191

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Project description:BACKGROUND: Genetic information based on molecular markers has increasingly being used in cattle breeding improvement programmes, as a mean to improve conventionally phenotypic selection. Advances in molecular genetics have led to the identification of several genetic markers associated with genes affecting economic traits. Until recently, the identification of the causative genetic variants involved in the phenotypes of interest has remained a difficult task. The advent of novel sequencing technologies now offers a new opportunity for the identification of such variants. Despite sequencing costs plummeting, sequencing whole-genomes or large targeted regions is still too expensive for most laboratories. A transcriptomic-based sequencing approach offers a cheaper alternative to identify a large number of polymorphisms and possibly to discover causative variants. In the present study, we performed a gene-based single nucleotide polymorphism (SNP) discovery analysis in bovine Longissimus thoraci, using RNA-Seq. To our knowledge, this represents the first study done in bovine muscle. RESULTS: Messenger RNAs from Longissimus thoraci from three Limousin bull calves were subjected to high-throughput sequencing. Approximately 36-46 million paired-end reads were obtained per library. A total of 19,752 transcripts were identified and 34,376 different SNPs were detected. Fifty-five percent of the SNPs were found in coding regions and ~22% resulted in an amino acid change. Applying a very stringent SNP quality threshold, we detected 8,407 different high-confidence SNPs, 18% of which are non synonymous coding SNPs. To analyse the accuracy of RNA-Seq technology for SNP detection, 48 SNPs were selected for validation by genotyping. No discrepancies were observed when using the highest SNP probability threshold. To test the usefulness of the identified SNPs, the 48 selected SNPs were assessed by genotyping 93 bovine samples, representing mostly the nine major breeds used in France. Principal component analysis indicates a clear separation between the nine populations. CONCLUSIONS: The RNA-Seq data and the collection of newly discovered coding SNPs improve the genomic resources available for cattle, especially for beef breeds. The large amount of variation present in genes expressed in Limousin Longissimus thoracis, especially the large number of non synonymous coding SNPs, may prove useful to study the mechanisms underlying the genetic variability of meat quality traits.

Project description:BACKGROUND:Long non-coding RNAs (lncRNAs) are key regulators of diverse cellular processes. Although a number of studies have reported the identification of bovine lncRNAs across many tissues, very little is known about the identity and characteristics of lncRNAs in bovine oocytes. METHODS:A bovine oocyte cDNA library was constructed and sequenced using the Illumina HiSeq 2000 sequencing system. The oocyte transcriptome was constructed using the ab initio assembly software Scripture and Cufflinks. The assembled transcripts were categorized to identify the novel intergenic transcripts, and the coding potential of these novel transcripts was assessed using CPAT and PhyloCSF. The resulting candidate long intergenic non-coding RNAs (lincRNAs) transcripts were further evaluated to determine if any of them contain any known protein coding domains in the Pfam database. RT-PCR was used to analyze the expression of oocyte-expressed lincRNAs in various bovine tissues. RESULTS:A total of 85 million raw reads were generated from sequencing of the bovine oocyte library. Transcriptome reconstruction resulted in the assembly of a total of 42,396 transcripts from 37,678 genomic loci. Analysis of the assembled transcripts using the step-wide pipeline resulted in the identification of 1535 oocyte lincRNAs corresponding to 1183 putative non-coding genes. A comparison of the oocyte lincRNAs with the lncRNAs reported in other bovine tissues indicated that 970 of the 1535 oocyte lincRNAs appear to be unique to bovine oocytes. RT-PCR analysis of 5 selected lincRNAs showed either specific or predominant expression of 4 lincRNAs in the fetal ovary. Functional prediction of the oocyte-expressed lincRNAs suggested their involvement in oogenesis through regulating their neighboring protein-coding genes. CONCLUSIONS:This study provides a starting point for future research aimed at understanding the roles of lncRNAs in controlling oocyte development and early embryogenesis in cattle.

Project description:BackgroundDeep RNA sequencing (RNAseq) has opened a new horizon for understanding global gene expression. The functional annotation of non-model mammalian genomes including bovines is still poor compared to that of human and mouse. This particularly applies to tissues without direct significance for milk and meat production, like skin, in spite of its multifunctional relevance for the individual. Thus, applying an RNAseq approach, we performed a whole transcriptome analysis of pigmented and nonpigmented bovine skin to describe the comprehensive transcript catalogue of this tissue.ResultsA total of 39,577 unique primary skin transcripts were mapped to the bovine reference genome assembly. The majority of the transcripts were mapped to known transcriptional units (65%). In addition to the reannotation of known genes, a substantial number (10,884) of unknown transcripts (UTs) were discovered, which had not previously been annotated. The classification of UTs was based on the prediction of their coding potential and comparative sequence analysis, subsequently followed by meticulous manual curation. The classification analysis and experimental validation of selected UTs confirmed that RNAseq data can be used to amend the annotation of known genes by providing evidence for additional exons, untranslated regions or splice variants, by approving genes predicted in silico and by identifying novel bovine loci. A large group of UTs (4,848) was predicted to potentially represent long noncoding RNA (lncRNA). Predominantly, potential lncRNAs mapped in intergenic chromosome regions (4,365) and therefore, were classified as potential intergenic lncRNA. Our analysis revealed that only about 6% of all UTs displayed interspecies conservation and discovered a variety of unknown transcripts without interspecies homology but specific expression in bovine skin.ConclusionsThe results of our study demonstrate a complex transcript pattern for bovine skin and suggest a possible functional relevance of novel transcripts, including lncRNA, in the modulation of pigmentation processes. The results also indicate that the comprehensive identification and annotation of unknown transcripts from whole transcriptome analysis using RNAseq data remains a tremendous future challenge.

Project description:BackgroundSmall ncRNAs (sncRNAs) offer great hope as biomarkers of disease and response to treatment. This has been highlighted in the context of several medical conditions such as cancer, liver disease, cardiovascular disease, and central nervous system disorders, among many others. Here we assessed several steps involved in the development of an ncRNA biomarker discovery pipeline, ranging from sample preparation to bioinformatic processing of small RNA sequencing data.MethodsA total of 45 biological samples were included in the present study. All libraries were prepared using the Illumina TruSeq Small RNA protocol and sequenced using the HiSeq2500 or MiSeq Illumina sequencers. Small RNA sequencing data was validated using qRT-PCR. At each stage, we evaluated the pros and cons of different techniques that may be suitable for different experimental designs. Evaluation methods included quality of data output in relation to hands-on laboratory time, cost, and efficiency of processing.ResultsOur results show that good quality sequencing libraries can be prepared from small amounts of total RNA and that varying degradation levels in the samples do not have a significant effect on the overall quantification of sncRNAs via NGS. In addition, we describe the strengths and limitations of three commercially available library preparation methods: (1) Novex TBE PAGE gel; (2) Pippin Prep automated gel system; and (3) AMPure XP beads. We describe our bioinformatics pipeline, provide recommendations for sequencing coverage, and describe in detail the expression and distribution of all sncRNAs in four human tissues: whole-blood, brain, heart and liver.ConclusionsUltimately this study provides tools and outcome metrics that will aid researchers and clinicians in choosing an appropriate and effective high-throughput sequencing quantification method for various study designs, and overall generating valuable information that can contribute to our understanding of small ncRNAs as potential biomarkers and mediators of biological functions and disease.

Dataset Information

Identification of large intergenic non-coding RNAs in bovine muscle using next-generation transcriptomic sequencing.

Publications

Identification of large intergenic non-coding RNAs in bovine muscle using next-generation transcriptomic sequencing.

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