Dataset Information

Next Generation Sequencing for Long Non-coding RNAs Profile for CD4+ T Cells in the Mouse Model of Acute Asthma.

ABSTRACT: Background and Aims:Although long non-coding RNAs (lncRNAs) have been linked to many diseases including asthma, little is known about lncRNA transcriptomes of CD4+ T cells in asthma. The present study aimed to explore the lncRNAs profile in the CD4+T cells from the mouse model of acute asthma. Methods:Next generation sequencing for lncRNAs and mRNAs was performed on CD4+ T cells from asthma and control mice. Gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) pathway analyses were performed to predict the functions and signal pathways for the aberrant lncRNAs. The selected lncRNAs were further measured using quantitative real-time PCR (polymerase chain reaction) and observed in the fluorescence in situ hybridization (FISH). The lncRNA-mRNA co-expression network was constructed via Pearson's correlation coefficient and Cytoscape 3.6. Results:Next generation sequencing revealed 36 up-regulated lncRNAs and 98 down-regulated lncRNAs in acute asthma compared with controls. KEGG pathway analysis showed that cytokine-cytokine receptor interaction had the highest enrichment scores. A co-expression network was constructed in which 23 lncRNAs and 301 mRNAs altered formed a total of 12424 lncRNA and mRNA pairs. To validate the RNA sequencing results, we measured the 4 different lncRNAs using qPCR. The lncRNA fantom3_9230106C11 was significantly reduced in CD4+ T cells of asthma. Bioinformatics analysis showed that lncRNA fantom3_9230106C11 had the potential to interact with many miRNAs and transcription factors related to Th2 differentiation. Conclusion:This study provided the first evidence for different expression of lncRNAs of CD4+T cells in asthma and may serve as a template for further, larger functional in-depth analyses regarding asthma molecular lncRNAs.

SUBMITTER: Wang Z

PROVIDER: S-EPMC6565949 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Next Generation Sequencing for Long Non-coding RNAs Profile for CD4<sup>+</sup> T Cells in the Mouse Model of Acute Asthma.

Wang Zhengxia Z Ji Ningfei N Chen Zhongqi Z Wu Chaojie C Sun Zhixiao Z Yu Wenqin W Hu Fan F Huang Mao M Zhang Mingshun M

Frontiers in genetics 20190607

<h4>Background and aims</h4>Although long non-coding RNAs (lncRNAs) have been linked to many diseases including asthma, little is known about lncRNA transcriptomes of CD4<sup>+</sup> T cells in asthma. The present study aimed to explore the lncRNAs profile in the CD4<sup>+</sup>T cells from the mouse model of acute asthma.<h4>Methods</h4>Next generation sequencing for lncRNAs and mRNAs was performed on CD4+ T cells from asthma and control mice. Gene ontology (GO) and kyoto encyclopedia of genes ...[more]

PMID: 31231429

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Project description: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.

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.

Project description:BACKGROUND:Next-Generation Sequencing (NGS) technology has exceptionally increased the ability to sequence DNA in a massively parallel and cost-effective manner. Nevertheless, NGS data analysis requires bioinformatics skills and computational resources well beyond the possibilities of many "wet biology" laboratories. Moreover, most of projects only require few sequencing cycles and standard tools or workflows to carry out suitable analyses for the identification and annotation of genes, transcripts and splice variants found in the biological samples under investigation. These projects can take benefits from the availability of easy to use systems to automatically analyse sequences and to mine data without the preventive need of strong bioinformatics background and hardware infrastructure. RESULTS:To address this issue we developed an automatic system targeted to the analysis of NGS data obtained from large-scale transcriptome studies. This system, we named NGS-Trex (NGS Transcriptome profile explorer) is available through a simple web interface http://www.ngs-trex.org and allows the user to upload raw sequences and easily obtain an accurate characterization of the transcriptome profile after the setting of few parameters required to tune the analysis procedure. The system is also able to assess differential expression at both gene and transcript level (i.e. splicing isoforms) by comparing the expression profile of different samples.By using simple query forms the user can obtain list of genes, transcripts, splice sites ranked and filtered according to several criteria. Data can be viewed as tables, text files or through a simple genome browser which helps the visual inspection of the data. CONCLUSIONS:NGS-Trex is a simple tool for RNA-Seq data analysis mainly targeted to "wet biology" researchers with limited bioinformatics skills. It offers simple data mining tools to explore transcriptome profiles of samples investigated taking advantage of NGS technologies.

Dataset Information

Next Generation Sequencing for Long Non-coding RNAs Profile for CD4+ T Cells in the Mouse Model of Acute Asthma.

Publications

Next Generation Sequencing for Long Non-coding RNAs Profile for CD4<sup>+</sup> T Cells in the Mouse Model of Acute Asthma.

Similar Datasets

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