Dataset Information

Evaluation of the mirn23a Cluster through an iTRAQ-based Quantitative Proteomic Approach.

ABSTRACT: MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression that are implicated in a number of disease states. MiRNAs can exist as individual entities or may be clustered and transcribed as a single polycistron. The mirn23a cluster consists of three miRNAs: miR-23a, miR-24-2, and miR-27a. Although these miRNAs are transcribed together, they often exist at varying levels in the cell. Despite the fact that the mirn23a cluster is known to play a role in a number of diseases and developmental processes, few direct targets have been identified. In this study, we examined the effects of miR-23a, miR-24-2, miR-27a, or the mirn23a cluster overexpression on the proteome of 70Z/3 pre-B lymphoblast cells. Quantitative mass spectrometry using isobaric tags for relative and absolute quantification (iTRAQ) allowed for the global profiling of cell lines after miRNA overexpression. We identified a number of targets of each miRNA that contained predicted miRNA seed sequences and are likely direct targets. In addition, we discovered a cohort of shared miRNA targets and cluster targets, demonstrating the importance of studying miRNA clusters in their entirety.

SUBMITTER: Ludwig KR

PROVIDER: S-EPMC5305806 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Evaluation of the mirn23a Cluster through an iTRAQ-based Quantitative Proteomic Approach.

Ludwig Katelyn R KR Dahl Richard R Hummon Amanda B AB

Journal of proteome research 20160406 5

MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression that are implicated in a number of disease states. MiRNAs can exist as individual entities or may be clustered and transcribed as a single polycistron. The mirn23a cluster consists of three miRNAs: miR-23a, miR-24-2, and miR-27a. Although these miRNAs are transcribed together, they often exist at varying levels in the cell. Despite the fact that the mirn23a cluster is known to play a role in a number of diseases and develo ...[more]

PMID: 27028342

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Project description:Background:Neuroticism is a core personality trait and a major risk factor for several mental and physical diseases, particularly in females, who score higher on neuroticism than men, on average. However, a better understanding of the expression profiles of proteins in the circulating blood of different neurotic female populations may help elucidate the intrinsic mechanism of neurotic personality and aid prevention strategies on mental and physical diseases associated with neuroticism. Methods:In our study, female subjects were screened for inclusion by the Eysenck Personality Questionnaire (EPQ), Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI) scales and routine physical examination. Subjects who passed the examination and volunteered to participate were grouped by neuroticism using EPQ scores (0 and 1?=?low neuroticism group; >?5?=?high neuroticism group). Proteins in serum samples of the two neuroticism groups were identified using isobaric tags for relative and absolute quantification (iTRAQ) technology. Results:A total of 410 proteins exhibited significant differences between high and low neuroticism, 236 proteins were significantly upregulated and 174 proteins were significantly downregulated. Combine the results of GO and KEGG enrichment analysis of differences proteins between high and low neuroticism with the PPI network, it could be observed that the Alpha-synuclein (SNCA), ATP7A protein (ATP7A), Guanine nucleotide-binding protein G(I)/G(S)/G(O) subunit gamma-2 (GNG2), cyclin-dependent kinase 6 (CDK6), myeloperoxidase (MPO), azurocidin (AZU1), Histone H2B type 1-H (HIST1H2BH), Integrin alpha-M (ITGAM) and Matrix metalloproteinase-9 (MMP9) might participate in the intrinsic mechanism of neuroticism by regulating response to catecholamine stimulus, catecholamine metabolic process, limbic system development and transcriptional misregulation in cancer pathway. Conclusions:Our study revealed the characteristics of the neurotic personality proteome, which might be intrinsic mechanism of the neurotic population.

Project description:High resolution proteomics approaches have been successfully utilized for the comprehensive characterization of the cell proteome. However, in the case of quantitative proteomics an open question still remains, which quantification strategy is best suited for identification of biologically relevant changes, especially in clinical specimens. In this study, a thorough comparison of a label-free approach (intensity-based) and 8-plex iTRAQ was conducted as applied to the analysis of tumor tissue samples from non-muscle invasive and muscle-invasive bladder cancer. For the latter, two acquisition strategies were tested including analysis of unfractionated and fractioned iTRAQ-labeled peptides. To reduce variability, aliquots of the same protein extract were used as starting material, whereas to obtain representative results per method further sample processing and MS analysis were conducted according to routinely applied protocols. Considering only multiple-peptide identifications, LC-MS/MS analysis resulted in the identification of 910, 1092 and 332 proteins by label-free, fractionated and unfractionated iTRAQ, respectively. The label-free strategy provided higher protein sequence coverage compared to both iTRAQ experiments. Even though pre-fraction of the iTRAQ labeled peptides allowed for a higher number of identifications, this was not accompanied by a respective increase in the number of differentially expressed changes detected. Validity of the proteomics output related to protein identification and differential expression was determined by comparison to existing data in the field (Protein Atlas and published data on the disease). All methods predicted changes which to a large extent agreed with published data, with label-free providing a higher number of significant changes than iTRAQ. Conclusively, both label-free and iTRAQ (when combined to peptide fractionation) provide high proteome coverage and apparently valid predictions in terms of differential expression, nevertheless label-free provides higher sequence coverage and ultimately detects a higher number of differentially expressed proteins. The risk for receiving false associations still exists, particularly when analyzing highly heterogeneous biological samples, raising the need for the analysis of higher sample numbers and/or application of adjustment for multiple testing.

Dataset Information

Evaluation of the mirn23a Cluster through an iTRAQ-based Quantitative Proteomic Approach.

Publications

Evaluation of the mirn23a Cluster through an iTRAQ-based Quantitative Proteomic Approach.

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