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      Sphingosine Kinase 1 Regulates the Pulmonary Vascular Immune Response

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          Abstract

          The aberrant proliferation of pulmonary artery smooth muscle (PASMCs) cells is a defining characteristic of pulmonary arterial hypertension (PAH) and leads to increased vascular resistance, elevated pulmonary pressure, and right heart failure. The sphingosine kinase 1 (SPHK1)/sphingosine-1 phosphate/sphingosine-1 phosphate receptor 2 pathway promotes vascular remodeling and induces PAH. The aim of this study was to identify genes and cellular processes that are modulated by over-expression of SPHK1 in human PASMCs (hPASMCs). RNA was purified and submitted for RNA sequencing to identify differentially expressed genes. Using a corrected p-value threshold of <0.05, there were 294 genes significantly up-regulated while 179 were significantly down-regulated. Predicted effects of these differentially expressed genes were evaluated using the freeware tool Enrichr to assess general gene set over-representation (enrichment) and ingenuity pathway analysis (IPA™) for upstream regulator predictions. We found a strong change in genes that regulated the cellular immune response. IL6, STAT1, and PARP9 were elevated in response to SPHK1 over-expression in hPASMCs. The gene set enrichment mapped to a few immune-modulatory signaling networks, including IFNG. Furthermore, PARP9 and STAT1 protein were elevated in primary hPASMCs isolated from PAH patients. In conclusion, these data suggest a role of Sphk1 regulates pulmonary vascular immune response in PAH.

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          Most cited references38

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Contributors
                robmacha@iu.edu
                Journal
                Cell Biochem Biophys
                Cell Biochem Biophys
                Cell Biochemistry and Biophysics
                Springer US (New York )
                1085-9195
                1559-0283
                16 June 2021
                : 1-13
                Affiliations
                [1 ]GRID grid.257413.6, ISNI 0000 0001 2287 3919, Division of Pulmonary, Critical Care, Sleep and Occupational Medicine, , Indiana University School of Medicine, ; Indianapolis, IN USA
                [2 ]GRID grid.412449.e, ISNI 0000 0000 9678 1884, Department of Clinical Pharmacology, School of Pharmacy, , China Medical University, ; Shenyang, Liaoning China
                Author information
                http://orcid.org/0000-0002-4420-4553
                http://orcid.org/0000-0003-4966-1996
                http://orcid.org/0000-0002-7628-7066
                http://orcid.org/0000-0002-3722-5954
                Article
                1006
                10.1007/s12013-021-01006-8
                8206894
                42c93082-c791-4569-b31f-0983381074c8
                © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 24 May 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 81803530-
                Award Recipient :
                Categories
                Original Paper

                Biochemistry
                Biochemistry

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