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      RNA-seq Reveals Novel Transcriptome of Genes and Their Isoforms in Human Pulmonary Microvascular Endothelial Cells Treated with Thrombin

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          Abstract

          The dysregulation of vascular endothelial cells by thrombin has been implicated in the development of a number of pathologic disorders such as inflammatory conditions, cancer, diabetes, coronary heart disease. However, transcriptional regulation of vascular endothelial cells by thrombin is not completely understood. In the present study, Illumina RNA-seq was used to profile the transcriptome in human pulmonary microvascular endothelial cells (HMVEC-L) treated with thrombin for 6 h to gain insight into thrombin's direct effects on the endothelial function. Out of 100 million total reads from a paired end sequencing assay, 91–94% of the reads were aligned to over 16,000 genes in the reference human genome. Thrombin upregulated 150 known genes and 480 known isoforms, and downregulated 2,190 known genes and 3,574 known isoforms by at least 2 fold. Of note, thrombin upregulated 1,775 previously unknown isoforms and downregulated 12,202 previously unknown isoforms by at least 2 fold. Many genes displayed isoform specific differential expression levels and different usage of transcriptional start sites after the thrombin treatment. The cross comparisons between our RNA-seq data and those of DNA microarray analysis of either 6 h thrombin treated HUVEC or 5 h TNFα treated HMVEC have provided a significant overlapping list of differentially expressed genes, supporting the robust utility of our dataset. Further in-depth follow-up analysis of the transcriptional regulation reported in this study may shed light on molecular pathogenic mechanisms underlying thrombin mediated endothelial dysfunction in various diseases and provide new leads of potential therapeutic targets.

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          Interleukin-17 Contributes to the Pathogenesis of Autoimmune Hepatitis through Inducing Hepatic Interleukin-6 Expression

          T helper cells that produce IL-17 (Th17 cells) have recently been identified as the third distinct subset of effector T cells. Emerging data suggests that Th17 cells play an important role in the pathogenesis of many liver diseases by regulating innate immunity, adaptive immunity, and autoimmunity. In this study, we examine the role and mechanism of Th17 cells in the pathogenesis of autoimmune hepatitis (AIH). The serum levels of IL-17 and IL-23, as well as the frequency of IL-17+ cells in the liver, were significantly elevated in patients with AIH, compared to other chronic hepatitis and healthy controls. The hepatic expressions of IL-17, IL-23, ROR-γt, IL-6 and IL-1β in patients with AIH were also significantly increased and were associated with increased inflammation and fibrosis. IL-17 induces IL-6 expression via the MAPK signaling pathway in hepatocytes, which, in turn, may further stimulate Th17 cells and forms a positive feedback loop. In conclusion, Th17 cells are key effector T cells that regulate the pathogenesis of AIH, via induction of MAPK dependent hepatic IL-6 expression. Blocking the signaling pathway and interrupting the positive feedback loop are potential therapeutic targets for autoimmune hepatitis.
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            From expression to signaling: roles of TREM-1 and TREM-2 in innate immunity and bacterial infection.

            Triggering receptor expressed on myeloid cells (TREM) proteins play important roles in innate and adaptive immunity. Since the discovery of TREM-1 and TREM-2 in 2000, the field has exploded over the past 8 years and currently TREM-1 is thought of as an amplifier of the immune response, while TREM-2 is believed to be a negative regulator. Here, we discuss the current state of the literature on TREMs, with a focus on TREM-1 and TREM-2 and their expression, signaling in mononuclear phagocytes and roles in innate immunity and bacterial infection.
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              A comparison of massively parallel nucleotide sequencing with oligonucleotide microarrays for global transcription profiling

              Background RNA-Seq exploits the rapid generation of gigabases of sequence data by Massively Parallel Nucleotide Sequencing, allowing for the mapping and digital quantification of whole transcriptomes. Whilst previous comparisons between RNA-Seq and microarrays have been performed at the level of gene expression, in this study we adopt a more fine-grained approach. Using RNA samples from a normal human breast epithelial cell line (MCF-10a) and a breast cancer cell line (MCF-7), we present a comprehensive comparison between RNA-Seq data generated on the Applied Biosystems SOLiD platform and data from Affymetrix Exon 1.0ST arrays. The use of Exon arrays makes it possible to assess the performance of RNA-Seq in two key areas: detection of expression at the granularity of individual exons, and discovery of transcription outside annotated loci. Results We found a high degree of correspondence between the two platforms in terms of exon-level fold changes and detection. For example, over 80% of exons detected as expressed in RNA-Seq were also detected on the Exon array, and 91% of exons flagged as changing from Absent to Present on at least one platform had fold-changes in the same direction. The greatest detection correspondence was seen when the read count threshold at which to flag exons Absent in the SOLiD data was set to t<1 suggesting that the background error rate is extremely low in RNA-Seq. We also found RNA-Seq more sensitive to detecting differentially expressed exons than the Exon array, reflecting the wider dynamic range achievable on the SOLiD platform. In addition, we find significant evidence of novel protein coding regions outside known exons, 93% of which map to Exon array probesets, and are able to infer the presence of thousands of novel transcripts through the detection of previously unreported exon-exon junctions. Conclusions By focusing on exon-level expression, we present the most fine-grained comparison between RNA-Seq and microarrays to date. Overall, our study demonstrates that data from a SOLiD RNA-Seq experiment are sufficient to generate results comparable to those produced from Affymetrix Exon arrays, even using only a single replicate from each platform, and when presented with a large genome.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2012
                16 February 2012
                : 7
                : 2
                : e31229
                Affiliations
                [1 ]Department of Pediatrics, Children's Mercy Hospitals and Clinics, University of Missouri School of Medicine, Kansas City, Missouri, United States of America
                [2 ]Department of Biomedical and Health Informatics, Children's Mercy Hospitals and Clinics, University of Missouri School of Medicine, Kansas City, Missouri, United States of America
                [3 ]Dalton Cardiovascular Research Center and Department of Biological Engineering, University of Missouri, Columbia, Missouri, United States of America
                [4 ]Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
                Wayne State University, United States of America
                Author notes

                Conceived and designed the experiments: LQZ SD DF SQY. Performed the experiments: DC MG. Analyzed the data: DPH MG SQY. Contributed reagents/materials/analysis tools: MG DPH. Wrote the paper: LQ MG DC SQY.

                Article
                PONE-D-11-19124
                10.1371/journal.pone.0031229
                3281071
                22359579
                ea3a8d57-3f5b-4ec6-ab96-0a6c32cf3b1b
                Zhang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 28 September 2011
                : 4 January 2012
                Page count
                Pages: 15
                Categories
                Research Article
                Biology
                Biochemistry
                Biotechnology
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