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Deep sequencing identification of novel glucocorticoid-responsive miRNAs in apoptotic primary lymphocytes.

Smith LK, Tandon A, Shah RR, Mav D, Scoltock AB, Cidlowski JA - PLoS ONE (2013)

Bottom Line: This analysis identifies the potential presence of over 200 novel glucocorticoid-responsive miRNAs.We have validated the expression of two novel glucocorticoid-responsive miRNAs using small RNA-specific qPCR.Furthermore, through the use of Ingenuity Pathways Analysis (IPA) we determined that the putative targets of these novel validated miRNAs are predicted to regulate cell death processes.

View Article: PubMed Central - PubMed

Affiliation: Molecular Endocrinology Group, Laboratory of Signal Transduction, NIEHS, NIH, Department of Health and Human Services, Research Triangle Park, North Carolina, United States of America.

ABSTRACT
Apoptosis of lymphocytes governs the response of the immune system to environmental stress and toxic insult. Signaling through the ubiquitously expressed glucocorticoid receptor, stress-induced glucocorticoid hormones induce apoptosis via mechanisms requiring altered gene expression. Several reports have detailed the changes in gene expression mediating glucocorticoid-induced apoptosis of lymphocytes. However, few studies have examined the role of non-coding miRNAs in this essential physiological process. Previously, using hybridization-based gene expression analysis and deep sequencing of small RNAs, we described the prevalent post-transcriptional repression of annotated miRNAs during glucocorticoid-induced apoptosis of lymphocytes. Here, we describe the development of a customized bioinformatics pipeline that facilitates the deep sequencing-mediated discovery of novel glucocorticoid-responsive miRNAs in apoptotic primary lymphocytes. This analysis identifies the potential presence of over 200 novel glucocorticoid-responsive miRNAs. We have validated the expression of two novel glucocorticoid-responsive miRNAs using small RNA-specific qPCR. Furthermore, through the use of Ingenuity Pathways Analysis (IPA) we determined that the putative targets of these novel validated miRNAs are predicted to regulate cell death processes. These findings identify two and predict the presence of additional novel glucocorticoid-responsive miRNAs in the rat transcriptome, suggesting a potential role for both annotated and novel miRNAs in glucocorticoid-induced apoptosis of lymphocytes.

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Development of a customized bioinformatics pipeline for the discovery of novel miRNAs from deep sequencing data.(A) This bioinformatics analysis workflow describes the novel miRNA discovery process adapted from miRanalyzer. The analysis pipeline uses next generation sequencing (miRNA-seq) data from untreated (control) or dexamethasone-treated rat primary thymocytes as input. This pipeline divides reads into three files: reads that align to an annotated mature miRNA (“Positive” training set), reads that align to other RNA subtypes (“Negative” training set), or reads that align at unannotated regions (“Test” set). Reads from each of these files are then aligned and alignment results are methodically processed to generate clusters, precursors and predicted secondary structures. Random forest machine learning is then employed to train the models for the prediction of novel miRNAs in the “Test” dataset. The output provides the genomic coordinates of predicted putative novel miRNAs.(B) Table describes total number of reads generated by miRNA-seq of control and dexamethasone treated primary thymocytes analyzed using the novel bioinformatics workflow described above. As expected, the majority of these reads align to known miRNAs when compared to other RNA subtypes. (C) Table summarizes the total number of known and predicted novel miRNAs identified by the bioinformatics workflow as induced or repressed in control and dexamethasone treated rat primary thymocytes. Both known and predicted novel miRNAs exhibit a trend of repressed expression during glucocorticoid-induced apoptosis.
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pone-0078316-g001: Development of a customized bioinformatics pipeline for the discovery of novel miRNAs from deep sequencing data.(A) This bioinformatics analysis workflow describes the novel miRNA discovery process adapted from miRanalyzer. The analysis pipeline uses next generation sequencing (miRNA-seq) data from untreated (control) or dexamethasone-treated rat primary thymocytes as input. This pipeline divides reads into three files: reads that align to an annotated mature miRNA (“Positive” training set), reads that align to other RNA subtypes (“Negative” training set), or reads that align at unannotated regions (“Test” set). Reads from each of these files are then aligned and alignment results are methodically processed to generate clusters, precursors and predicted secondary structures. Random forest machine learning is then employed to train the models for the prediction of novel miRNAs in the “Test” dataset. The output provides the genomic coordinates of predicted putative novel miRNAs.(B) Table describes total number of reads generated by miRNA-seq of control and dexamethasone treated primary thymocytes analyzed using the novel bioinformatics workflow described above. As expected, the majority of these reads align to known miRNAs when compared to other RNA subtypes. (C) Table summarizes the total number of known and predicted novel miRNAs identified by the bioinformatics workflow as induced or repressed in control and dexamethasone treated rat primary thymocytes. Both known and predicted novel miRNAs exhibit a trend of repressed expression during glucocorticoid-induced apoptosis.

Mentions: To identify glucocorticoid-responsive novel miRNAs from deep sequencing data we employed a customized bioinformatics pipeline. This pipeline is based on miRanalyzer, a previously published methodology (also available via web-server) [24]; however, we implemented several significant modifications to the original miRanalyzer approach (see methods). The basis of this computational analysis was to first align miR-analyzer-generated reads to the genome and use ‘machine learning’ to learn from the signal profile of known miRNAs and known non-miRNAs (training). Once the models are trained and able to accurately classify known miRNAs from non-miRNAs, we then use the models to predict novel miRNAs from signals at unannotated regions of the genome (testing) (Figure 1A).


Deep sequencing identification of novel glucocorticoid-responsive miRNAs in apoptotic primary lymphocytes.

Smith LK, Tandon A, Shah RR, Mav D, Scoltock AB, Cidlowski JA - PLoS ONE (2013)

Development of a customized bioinformatics pipeline for the discovery of novel miRNAs from deep sequencing data.(A) This bioinformatics analysis workflow describes the novel miRNA discovery process adapted from miRanalyzer. The analysis pipeline uses next generation sequencing (miRNA-seq) data from untreated (control) or dexamethasone-treated rat primary thymocytes as input. This pipeline divides reads into three files: reads that align to an annotated mature miRNA (“Positive” training set), reads that align to other RNA subtypes (“Negative” training set), or reads that align at unannotated regions (“Test” set). Reads from each of these files are then aligned and alignment results are methodically processed to generate clusters, precursors and predicted secondary structures. Random forest machine learning is then employed to train the models for the prediction of novel miRNAs in the “Test” dataset. The output provides the genomic coordinates of predicted putative novel miRNAs.(B) Table describes total number of reads generated by miRNA-seq of control and dexamethasone treated primary thymocytes analyzed using the novel bioinformatics workflow described above. As expected, the majority of these reads align to known miRNAs when compared to other RNA subtypes. (C) Table summarizes the total number of known and predicted novel miRNAs identified by the bioinformatics workflow as induced or repressed in control and dexamethasone treated rat primary thymocytes. Both known and predicted novel miRNAs exhibit a trend of repressed expression during glucocorticoid-induced apoptosis.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3824063&req=5

pone-0078316-g001: Development of a customized bioinformatics pipeline for the discovery of novel miRNAs from deep sequencing data.(A) This bioinformatics analysis workflow describes the novel miRNA discovery process adapted from miRanalyzer. The analysis pipeline uses next generation sequencing (miRNA-seq) data from untreated (control) or dexamethasone-treated rat primary thymocytes as input. This pipeline divides reads into three files: reads that align to an annotated mature miRNA (“Positive” training set), reads that align to other RNA subtypes (“Negative” training set), or reads that align at unannotated regions (“Test” set). Reads from each of these files are then aligned and alignment results are methodically processed to generate clusters, precursors and predicted secondary structures. Random forest machine learning is then employed to train the models for the prediction of novel miRNAs in the “Test” dataset. The output provides the genomic coordinates of predicted putative novel miRNAs.(B) Table describes total number of reads generated by miRNA-seq of control and dexamethasone treated primary thymocytes analyzed using the novel bioinformatics workflow described above. As expected, the majority of these reads align to known miRNAs when compared to other RNA subtypes. (C) Table summarizes the total number of known and predicted novel miRNAs identified by the bioinformatics workflow as induced or repressed in control and dexamethasone treated rat primary thymocytes. Both known and predicted novel miRNAs exhibit a trend of repressed expression during glucocorticoid-induced apoptosis.
Mentions: To identify glucocorticoid-responsive novel miRNAs from deep sequencing data we employed a customized bioinformatics pipeline. This pipeline is based on miRanalyzer, a previously published methodology (also available via web-server) [24]; however, we implemented several significant modifications to the original miRanalyzer approach (see methods). The basis of this computational analysis was to first align miR-analyzer-generated reads to the genome and use ‘machine learning’ to learn from the signal profile of known miRNAs and known non-miRNAs (training). Once the models are trained and able to accurately classify known miRNAs from non-miRNAs, we then use the models to predict novel miRNAs from signals at unannotated regions of the genome (testing) (Figure 1A).

Bottom Line: This analysis identifies the potential presence of over 200 novel glucocorticoid-responsive miRNAs.We have validated the expression of two novel glucocorticoid-responsive miRNAs using small RNA-specific qPCR.Furthermore, through the use of Ingenuity Pathways Analysis (IPA) we determined that the putative targets of these novel validated miRNAs are predicted to regulate cell death processes.

View Article: PubMed Central - PubMed

Affiliation: Molecular Endocrinology Group, Laboratory of Signal Transduction, NIEHS, NIH, Department of Health and Human Services, Research Triangle Park, North Carolina, United States of America.

ABSTRACT
Apoptosis of lymphocytes governs the response of the immune system to environmental stress and toxic insult. Signaling through the ubiquitously expressed glucocorticoid receptor, stress-induced glucocorticoid hormones induce apoptosis via mechanisms requiring altered gene expression. Several reports have detailed the changes in gene expression mediating glucocorticoid-induced apoptosis of lymphocytes. However, few studies have examined the role of non-coding miRNAs in this essential physiological process. Previously, using hybridization-based gene expression analysis and deep sequencing of small RNAs, we described the prevalent post-transcriptional repression of annotated miRNAs during glucocorticoid-induced apoptosis of lymphocytes. Here, we describe the development of a customized bioinformatics pipeline that facilitates the deep sequencing-mediated discovery of novel glucocorticoid-responsive miRNAs in apoptotic primary lymphocytes. This analysis identifies the potential presence of over 200 novel glucocorticoid-responsive miRNAs. We have validated the expression of two novel glucocorticoid-responsive miRNAs using small RNA-specific qPCR. Furthermore, through the use of Ingenuity Pathways Analysis (IPA) we determined that the putative targets of these novel validated miRNAs are predicted to regulate cell death processes. These findings identify two and predict the presence of additional novel glucocorticoid-responsive miRNAs in the rat transcriptome, suggesting a potential role for both annotated and novel miRNAs in glucocorticoid-induced apoptosis of lymphocytes.

Show MeSH
Related in: MedlinePlus