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Next Generation Deep Sequencing Facilitates Quantitative Analysis of microRNA affected by thapsigargin treatment

ABSTRACT: 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.

ORGANISM(S): Mus musculus

PROVIDER: GSE57138 | GEO | 2014/06/15

SECONDARY ACCESSION(S): PRJNA246123

REPOSITORIES: GEO

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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

Project description:Purpose: Next-generation sequencing technologies allow miRNA detection at an unprecedented sensitivity.Early- to mid-gestational fetal mammalian skin wounds heal rapidly and without scarring. The goals of this study are to obtain the differently expressed miRNAs between late- and mid-gestational fetal Keratinocytes for further studies. Methods: Keratinocytes were obtained from six fetal skin samples dividied into two groups: a mid-gestation group and a late-gestation group. RNA extracted from Keratinocytes was used to prepare a small RNA library for next-generation sequencing (NGS) using an Illumina Genome Analyzer IIx. To uncover potentially novel microRNA (miRNAs), the mirTools 2.0 web server was used to identify candidate novel human miRNAs from the NGS data.UCSC Genome Browser and the Vienna RNAfold web server were used to further validate the novel miRNA candidates. The repeatability of the expression levels detected by NGS was confirmed by real-time quantitative RT-PCR. Results: Using an optimized data analysis workflow, we detected a total of 99,354,224 raw reads from the six samples, 85,252,341 (85.81%) were high-quality reads (≥ 18nt). After alignment to the human genome (GRCH38), the number of genome-aligned reads was 74,678,115 (87.60% of the high-quality reads). The number of sequence reads that correspond to known miRNAs was 61,587,749 (82.47% of the genome-matching reads), as was determined by perfect sequence matching to the database of known miRNAs (miRBase release 18). After removing the matched non-coding RNAs (Release 10), 8,755,258 reads remained for identification of novel miRNA candidates by mirTools 2.0. The results revealed the existence of 202 novel miRNA candidates and 29 known miRNAs that were not listed in miRBase release 18. Of the 202 potential novel miRNAs, 106 candidates were detected by at least 10 counts, by NGS, indicating that they have a higher probability of being novel human miRNAs. Using Student's t-test, 173 known miRNAs and 23 novel miRNA candidates were found to be statistically significant (P values < 0.05). The expression of 22 novel miRNA candidates and 88 known miRNAs was changed by more than 2.0-fold (known: 15 up-regulated and 73 down-regulated; novel: two up-regulated and 20 down-regulated). Conclusions: Our study represents the first detailed analysis of human miRNAs in fetal Keratinocytes. Using mirTools 2.0 web server, we have revealed the existence of 202 novel miRNA candidates. Of the 202 potential novel miRNAs, 106 candidates were detected by at least 10 counts, by NGS, indicating that they have a higher probability of being novel human miRNAs. Taken together, our results provide compelling evidence that dynamic expression of miRNAs in fetal Keratinocytes at different gestational ages. MiRNAs presenting altered expression at different gestational ages in fetal Keratinocytes may contribute to scarless wound healing in early- to mid-gestational fetal Keratinocytes, and thus may be new targets for potential scar prevention and reduction therapies.

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.

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Next Generation Deep Sequencing Facilitates Quantitative Analysis of microRNA affected by thapsigargin treatment

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