Dataset Information

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

ABSTRACT: BACKGROUND: 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. RESULTS: 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 Drosophila brain tumor 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 analyzing the co-regulation of potential subunits. CONCLUSIONS: Our comprehensive transcriptome and proteome data provide a valuable resource for quantitative biology and offer novel insights into understanding post-transcriptional gene regulation in a tumor model.

SUBMITTER: Juschke C

PROVIDER: S-EPMC4053992 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Similar Datasets

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.

Project description:The human pathogen Corynebacterium diphtheriae is the causative agent of diphtheria. In the 1990s a large diphtheria outbreak in Eastern Europe was caused by the strain C. diphtheriae NCTC 13129. Although the genome was sequenced more than a decade ago, not much is known about its transcriptome. Our aim was to use transcriptome sequencing (RNA-Seq) to close this knowledge gap and gain insights into the transcriptional landscape of a C. diphtheriae tox+ strain.We applied two different RNA-Seq techniques, one to retrieve 5'-ends of primary transcripts and the other to characterize the whole transcriptional landscape in order to gain insights into various features of the C. diphtheriae NCTC 13129 transcriptome. By examining the data we identified 1656 transcription start sites (TSS), of which 1202 were assigned to genes and 454 to putative novel transcripts. By using the TSS data promoter regions recognized by the housekeeping sigma factor ?A and its motifs were analyzed in detail, revealing a well conserved -10 but an only weakly conserved -35 motif, respectively. Furthermore, with the TSS data 5'-UTR lengths were explored. The observed 5'-UTRs range from zero length (leaderless transcripts), which make up 20% of all genes, up to over 450 nt long leaders, which may harbor regulatory functions. The C. diphtheriae transcriptome consists of 471 operons which are further divided into 167 sub-operon structures. In a differential expression analysis approach, we discovered that genetic disruption of the iron-sensing transcription regulator DtxR, which controls expression of diphtheria toxin (DT), causes a strong influence on general gene expression. Nearly 15% of the genome is differentially transcribed, indicating that DtxR might have other regulatory functions in addition to regulation of iron metabolism and DT. Furthermore, our findings shed light on the transcriptional landscape of the DT encoding gene tox and present evidence for two tox antisense RNAs, which point to a new way of transcriptional regulation of toxin production.This study presents extensive insights into the transcriptome of C. diphtheriae and provides a basis for future studies regarding gene characterization, transcriptional regulatory networks, and regulation of the tox gene in particular.

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.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure