Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Transcriptome and proteome quantification of a tumor model provides novel insights into post-transcriptional gene regulation

ABSTRACT: Genome-wide transcriptome analyses have allowed for systems- level insights into gene regulatory networks. Due to the limited depth of quantitative proteomics, however, our understanding of post-transcriptional gene regulation and its effects on protein complex stoichiometry are lagging behind. Here, we employ deep sequencing and iTRAQ technology to determine transcript and protein expression changes of a Drosophila brain tumour model at near genome-wide resolution. In total, we quantify more than 6,200 tissue-specific proteins, corresponding to about 70% of all transcribed protein-coding genes. Using our integrated data set, we demonstrate that post-transcriptional gene regulation varies considerably with biological function and is surprisingly high for genes regulating transcription. We combine our quantitative data with protein-protein interaction data and show that post-transcriptional mechanisms significantly enhance co-regulation of protein complex subunits beyond transcriptional co-regulation. Interestingly, our results suggest that only about 11% of the annotated Drosophila protein complexes are co-regulated in the brain. Finally, we refine the composition of some of these core protein complexes by analysing the co-regulation of potential subunits. Our comprehensive transcriptome and proteome data provide a rich resource for quantitative biology and offer novel insights into understanding post- transcriptional gene regulation in a tumour model. Transcriptomes of 1-3 day old adult female Drosophila melanogaster heads of control and brat mutant were generated by deep sequencing, in triplicate, using Illumina GAIIx.

ORGANISM(S): Drosophila melanogaster

SUBMITTER: Thomas Burkard

PROVIDER: E-GEOD-51412 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Using high throughput sequencing of Drosophila head RNA, a small set of miRNAs that undergo robust circadian oscillations in levels were discovered. We concentrated on a cluster of six miRNAs, mir-959-964, all of which peak at about ZT12 or lights-off. The data indicate that the cluster pri-miRNA is transcribed under bona fide circadian transcriptional control and that all 6 mature miRNAs have short half-lives, a requirement for oscillating. Manipulation of food intake dramatically affects the levels and timing of cluster miRNA transcription with no more than minor effects on the core circadian oscillator. This indicates that the central clock regulates feeding, which in turn regulates proper levels and cycling of the cluster miRNAs. Viable Gal4 knock-in as well as cluster knock-out and over-expression strains were used to localize cluster miRNA expression as well as explore their functions. The adult head fat body is a major site of expression, and feeding behavior, innate immunity, metabolism, and perhaps stress responses are under cluster miRNA regulation. The feeding behavior results indicate that there is a feedback circuit between feeding time and cluster miRNA function as well as a surprising role of post-transcriptional regulation in these behaviors and physiology. To address possible functions of the cluster miRNAs, the knock-out strain (mir959-952-KO) as well as a cluster over-expression strain (UAS-cluster, tim-gal4) was assayed for mRNA changes relative to their WT counterparts on Affymetrix expression arrays. The strategy was based on the observation that miRNAs often cause a decrease in the steady-state levels of their target mRNAs (Guo et al., 2010; Lim et al., 2005). Because of the circadian regulation and the possibility of non-fat body expression, the over-expression strain was generated with the broad circadian driver tim-gal4 rather than a fat body driver. To accommodate the possibility that important mRNA changes only appear at certain circadian times, RNA was assayed from heads collected at two different times, ZT4 and ZT16. Analyzed expression data was assayed from total RNA from Drosophila heads in two sample groups consisting of four samples (with eight samples in total): knock-out strain at ZT4 and ZT16, and its respective wildtype control at ZT4 and ZT16; cluster over-expression strain at ZT4 and ZT16 and its respective wildtype control at ZT4 and ZT16.

Project description:Genome-wide transcriptome analyses have allowed for systems- level insights into gene regulatory networks. Due to the limited depth of quantitative proteomics, however, our understanding of post-transcriptional gene regulation and its effects on protein complex stoichiometry are lagging behind. Here, we employ deep sequencing and iTRAQ technology to determine transcript and protein expression changes of a Drosophila brain tumour model at near genome-wide resolution. In total, we quantify more than 6,200 tissue-specific proteins, corresponding to about 70% of all transcribed protein-coding genes. Using our integrated data set, we demonstrate that post-transcriptional gene regulation varies considerably with biological function and is surprisingly high for genes regulating transcription. We combine our quantitative data with protein-protein interaction data and show that post-transcriptional mechanisms significantly enhance co-regulation of protein complex subunits beyond transcriptional co-regulation. Interestingly, our results suggest that only about 11% of the annotated Drosophila protein complexes are co-regulated in the brain. Finally, we refine the composition of some of these core protein complexes by analysing the co-regulation of potential subunits. Our comprehensive transcriptome and proteome data provide a rich resource for quantitative biology and offer novel insights into understanding post- transcriptional gene regulation in a tumour model. Data generated by LC-MS/MS analysis were searched against a database containing the translation of all open reading frames (ORF) in FlyBase (r5.25) and common contaminants concatenated to a reversed decoy database that allowed for estimating the false discovery rate (FDR) using the target-decoy strategy. Proteome Discoverer (version 1.3.0.211, Thermo Fisher Scientific) was used as a search engine interface for Mascot, Sequest, X!-Tandem, and ZCore. Oxidation of methionine was set as dynamic modification, methylthio (C) and iTRAQ4plex label (K, N-terminus) as static modifications. The minimal peptide length was set to seven amino acids, and a maximum of two missed cleavages was allowed for trypsin and LysC digested samples. To allow for an integrative analysis of transcriptome and proteome data, protein level changes were determined using only peptides that mapped unambiguously to one gene. Peptides that could be derived from proteins encoded by different gene models ("shared peptides") were excluded. Furthermore, only peptides that showed less than two-fold difference between duplicate iTRAQ channels were included in the analysis. Peptide identifications from different search engines were combined using a modified version of the combined FDR score. Reporter ion intensities were corrected for isotope impurities in the iTRAQ labels. To account for the error structure and stabilise the variance of the reporter ion intensities a variance stabilising transformation (vsn) was applied. Protein ratios were calculated as the 20% trimmed mean from the median-centred peptide ratios. Proteins were filtered for a maximum FDR of 5%. Additional transcriptomics data will be accessible under the GEO Submission record GSE51412.

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Transcriptome and proteome quantification of a tumor model provides novel insights into post-transcriptional gene regulation

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