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MicroRNA identity and abundance in porcine skeletal muscles determined by deep sequencing

ABSTRACT: MicroRNAs (miRNA) are short single-stranded RNA molecules that regulate gene expression post-transcriptionally by binding to complementary sequences in the 3' untranslated region (3' UTR) of target mRNAs. MiRNAs participate in the regulation of myogenesis, and identification of the complete set of miRNAs expressed in muscles is likely to significantly increase our understanding of muscle growth and development. To determine the identity and abundance of miRNA in porcine skeletal muscle, we applied a deep sequencing approach. This allowed us to identify the sequences and relative expression levels of 212 annotated miRNA genes, thereby providing a thorough account of the miRNA transcriptome in porcine muscle tissue. The expression levels displayed a very large range, as reflected by the number of sequence reads, which varied from single counts for rare miRNAs to several million reads for the most abundant miRNAs. Moreover, we identified numerous examples of mature miRNAs that were derived from opposite sides of the same predicted precursor stem-loop structures, and also observed length and sequence heterogeneity at the 5' and 3' ends. Furthermore, KEGG pathway analysis suggested that highly expressed miRNAs are involved in skeletal muscle development and regeneration, signal transduction, cell-cell and cell-extracellular matrix communication and neural development and function. Examination of small RNA profiles in 7 isolates of porcine muscle The raw sequences were trimmed to 30 nucleotides, and it was set as a requirement that any sequence must appear at least three times and be present in at least two of the seven libraries. All identical reads within a library were grouped and converted into unique sequences. Reads containing Ns or long tracks (M-BM-!M-CM-^]8) of As were removed and the sequences were trimmed for adaptor-sequences. To annotate the unique sequences, a Decypher Tera-BLASTN Search was performed against a database of mature miRNAs obtained from miRBase (release 12.0). Hits with a match of 16 or more nucleotides to a miRNA from the database were gathered, and the count of each miRNA was normalized to the total number of sequence reads per lane. The outcome of this procedure can be seen in the Aarhus_University_GBI_FC208D2AAXX_blast_mirbase table below.

ORGANISM(S): Sus scrofa

SUBMITTER: Jakob Hedegaard

PROVIDER: E-GEOD-14584 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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MicroRNA identity and abundance in porcine skeletal muscles determined by deep sequencing.

Nielsen M M Hansen J H JH Hedegaard J J Nielsen R O RO Panitz F F Bendixen C C Thomsen B B

Animal genetics 20091116 2

MicroRNAs (miRNA) are short single-stranded RNA molecules that regulate gene expression post-transcriptionally by binding to complementary sequences in the 3' untranslated region (3' UTR) of target mRNAs. MiRNAs participate in the regulation of myogenesis, and identification of the complete set of miRNAs expressed in muscles is likely to significantly increase our understanding of muscle growth and development. To determine the identity and abundance of miRNA in porcine skeletal muscle, we appli ...[more]

PMID: 19917043

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Project description:Sus scrofa (pig, or swine) is one of the most important economic animals and a close biological model for complex human diseases such as obesity and diabetes. It is therefore utterly important to decode the porcine microRNAome (miRNAome) as in the literature only a small portion of it is known. In this work, a comprehensive search for porcine microRNAs (miRNAs) by Illumina sequencing was performed in ten small RNA libraries prepared from mixtures of assorted tissues, which included those collected from fetuses to adult pigs. The millions of the sequencing reads were analyzed with reference to 77 known porcine miRNA precursors (pre-miRNAs) and 3,443 distinct pre-miRNAs of other mammals listed in miRBase 13.0, and the most updated porcine genome (Sscrofa9, April 2009) and available EST sequences. Additionally, miRNA candidates specific to pig are predicated by genome & EST match and hairpin folding. Our search found 72 out of 78 (~92%) known porcine miRNAs and miRNA*s, and 36 previously unannotated miRNA*s are also indentified. Furthermore, we discovered 397 novel miRNAs by mapping to the sequencing transcripts to other mammalian pre-miRNAs and 493 candidate miRNAs which do not map to other mammalian miRNAomes and could be pig-specific. We constructed sequence- and genome-position clusters for the total of 998 miRNA candidates originating from 862 pre-miRNAs, which represent 777 unique miRNA sequences. Together with the six known porcine miRNAs that not been observed in our study, we report herein the sequence families of 783 unique miRNAs and genomic distribution patterns of 622 pre-miRNAs. We preformed q-PCR experiments for selected 30 miRNAs in 47 tissue-specific samples and found agreement between the sequencing data and the q-PCR data. We envision that our report will serve as a valuable resource for future studies aimed at understanding miRNAome of pig Ten small RNA libraries from samples of porcine fetuses (30 days after insemination) to mixed tissues (180 days after birth) at ten developmental stages were sequenced.

Project description:Purpose: microRNA profiles were generated from NIH-3T3 cells control and thapsigargin treated, in duplicate. The goal of this study was to compare microRNA profiles of untreated and thapsigargin treated NIH-3T3 fibroblast cells. Methods: NIH-3T3 cells were grown to confluency and either untreated or treated with 500 nM thapsigargin in media for 24 hours. Cells were harvested with TriZol and RNA isolated according to manufacturers protocol Analysis Outline: Short reads in fastq format were assembled using BclToFastq.pl script from Illumina CASAVA 1.8.1 software pipeline.Read quality was examined using FastQC program (http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc). Adapters were trimmed at the 3'end using Btrim prgram (PMID:21651976), only sequences equal to and longer than 18nt were retained, leading N base was trimmed at the 5' end. Unique reads were collapsed using Raw_data_parse program from miRExpress suite (PMID:19821977) (the result of this process is a file that contains unique sequences in one column and number of times this sequence was found in the library in another). They can be found in *.merge files in trimmed_reads directory. Collapsed reads were reformatted and uploaded into miRanalyzer web-based pipeline (http://bioinfo2.ugr.es/miRanalyzer/miRanalyzer.php; PMID:21515631) and matched to known mature miRNA (miRBase vesion 16), RFAM database (version 15) of known non-coding RNAs and known gene transcripts. The purpose of miRAnalyzer analysis was to only detect known miRNAs, prediction of novel miRNAs was not performed; search parameters were kept at default. MiRanalyzer output is saved in miRanalyzer folder with detailed information about mapping to known miRNA. Known miRNAs were divided into mature, maturestar (star sequences), maturestarunobs (star sequences not in miRBase) and hairpin. For each of the libraries there are files with unique and ambiguous mappings. Differentional expression analysis was based on unique alignments to known miRNAs (mature_unique.txt file in miRanalyzer folder). Mature_unique.txt has following columns: name: mature miRNA ID from miRBase; #unique reads: number of unique reads mapped; readCount: number of reads mapped; norm_expressed_all: normalized to all reads; norm_expressed_mapped: normalized to mapped reads. miRNA expression profiling was performed using edgeR bioconductor package (PMID:20478825). For differential expression analysis, used TMM normalization and analysis using common disperion (using tagwise dispersion yielded the same results). FDR was calculated according to Hochberg-Benjamini procedure (PMID:2218183). Results of differential expression analysis were saved in diff_exp folder as diff_exp.txt. diff_exp.txt contains miRNA concentrations in log scale, log2 ratio of WT to KO; p-values and FDR corrected p-values. miRNAs were sorted by p-value. NIH-3T3 cells grown to confluency and treated with 500 nM thapsigargin in media for 24 hours

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MicroRNA identity and abundance in porcine skeletal muscles determined by deep sequencing

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MicroRNA identity and abundance in porcine skeletal muscles determined by deep sequencing.

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