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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. 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.

OTHER RELATED OMICS DATASETS IN: PRJNA222987

INSTRUMENT(S): LTQ Orbitrap Velos

ORGANISM(S): Drosophila Melanogaster (fruit Fly)

SUBMITTER: Christoph JÃ¼schke

PROVIDER: PXD000478 | Pride | 2013-11-14

REPOSITORIES: Pride

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

Jüschke Christoph C Dohnal Ilse I Pichler Peter P Harzer Heike H Swart Remco R Ammerer Gustav G Mechtler Karl K Knoblich Juergen A JA

Genome biology 20131130 11

<h4>Background</h4>Genome-wide transcriptome analyses have given 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.<h4>Results</h4>Here, we employ deep sequencing and the isobaric tag for relative and absolute quantification (iTRAQ) technology to determine transcript and protein expression changes of a Dros ...[more]

PMID: 24289286

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Project description:Many biological processes are intrinsically dynamic, incurring profound changes at both molecular and physiological levels. Systems analyses of such processes incorporating large-scale transcriptome or proteome profiling can be quite revealing. Although consistency between mRNA and proteins is often implicitly assumed in such studies, instances of divergent trends are frequently observed indicating the existence of post-transcriptional regulation. Here, we present a comparative transcriptome and proteome analysis of growth and stationary phase adaptation in Streptomyces coelicolor, taking the time-dynamics of process into consideration. These processes are of immense interest in streptomycetes as they encompass the physiological transformations eliciting biosynthesis of many naturally occurring therapeutic agents. A shotgun proteomics approach based on mass spectrometric analysis of isobaric stable isotope labeled peptides iTRAQ(TM) enabled identification and rapid quantification of approximately 14% of the theoretical proteome of S. coelicolor. Independent principal component analyses of this and DNA microarray-derived transcriptome data revealed that the prominent patterns in both protein and mRNA domains are surprisingly well correlated. Despite this general consensus, by employing a systematic concordance analysis we estimated that over 30% of the analyzed genes likely exhibited significantly divergent patterns, of which nearly one-third displayed even opposing trends. Integrating this data with biological information, we discovered that certain groups of functionally related genes were co-regulated independently as mRNA and/or proteins despite observations of apparent mRNA-protein discordance. This clearly suggests that post-transcriptional regulatory mechanisms are prominent even amongst prokaryotes while reaffirming the plausibility of such mechanisms acting in a concerted fashion at a protein complex or sub-pathway level. Keywords: Time course Time-course study involved analysis of 8 samples (7h, 11h, 14h, 16h, 22h, 26h, 34h, 38h). Of these, 14h, 22h, 26h and 38h were analyzed in duplicate arrays. All arrays used cDNA labeled with Alexa647 and gDNA labeled with Cy3.

Project description:The recent development within high-throughput technologies for expression profiling has allowed for parallel analysis of transcriptomes and proteomes in biological systems such as comparative analysis of transcript and protein levels of tissue regulated genes. Until now, such studies of have only included microarray or short length sequence tags for transcript profiling. Furthermore, most comparisons of transcript and protein levels have been based on absolute expression values from within the same tissue and not relative expression values based on tissue ratios. Presented here is a novel study of two porcine tissues based on integrative analysis of data from expression profiling of identical samples using cDNA microarray, 454-sequencing and iTRAQ-based proteomics. Sequence homology identified 2.541 unique transcripts that are detectable by both microarray hybridizations and 454-sequencing of 1.2 million cDNA tags. Both transcript-based technologies showed high reproducibility between sample replicates of the same tissue, but the correlation across these two technologies was modest. Thousands of genes being differentially expressed were identified with microarray. Out of the 306 differentially expressed genes, identified by 454-sequencing, 198 (65 %) were also found by microarray. The relationship between the regulation of transcript and protein levels was analyzed by integrating iTRAQ-based proteomics data. Protein expression ratios were determined for 354 genes, of which 148 could be mapped to both microarray and 454-sequencing data. A comparison of the expression ratios from the three technologies revealed that differences in transcript and protein levels across heart and muscle tissues are positively correlated. We show that the reproducibility within cDNA microarray and 454-sequencing is high, but that the agreement across these two technologies is modest. We demonstrate that the regulation of transcript and protein levels across identical tissue samples is positively correlated when the tissue expression ratios are used for comparison. The results presented are of interest in systems biology research in terms of integration and analysis of high-throughput expression data from mammalian tissues. Keywords: tissue comparison, platform comparison Tissue samples of heart (HEA) and Longissimus dorsi (LDO) were prepared by pooling from five healthy Hampshire gilts at age four to six months and division into six sub-samples, three for each tissue named HEA1, HEA2, HEA3, LDO1, LDO2 and LDO3. The exact same six tissue samples were used for subsequent expression profiling with cDNA microarray, 454-sequencing and iTRAQ-based proteomics. A reference sample for the cDNA microarray experiment and iTRAQ-based proteomics was constructed by combining the six samples. In the microarray experiment, three cDNA microarray slides were used per sample.

Dataset Information

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

Publications

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

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