Jüschke Christoph, Dohnal Ilse, Pichler Peter, Harzer Heike, Swart Remco, Ammerer Gustav, Mechtler Karl, Knoblich Juergen A
Genome Biol. 2013 Nov 30;14(11):r133. doi: 10.1186/gb-2013-14-11-r133.
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.
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.
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.
全基因组转录组分析已在系统层面深入了解基因调控网络。然而,由于定量蛋白质组学的深度有限,我们对转录后基因调控及其对蛋白质复合物化学计量的影响的理解仍较为滞后。
在此,我们采用深度测序和相对与绝对定量等压标签(iTRAQ)技术,以近乎全基因组分辨率确定果蝇脑肿瘤模型的转录本和蛋白质表达变化。我们总共定量了超过6200种组织特异性蛋白质,约占所有转录的蛋白质编码基因的70%。利用我们的综合数据集,我们证明转录后基因调控随生物学功能有很大差异,对于调控转录的基因而言,其差异程度惊人地高。我们将定量数据与蛋白质-蛋白质相互作用数据相结合,表明转录后机制显著增强了蛋白质复合物亚基的协同调控,超出了转录协同调控。有趣的是,我们的结果表明,在果蝇大脑中,只有约11%的注释蛋白质复合物受到协同调控。最后,我们通过分析潜在亚基的协同调控来优化其中一些核心蛋白质复合物的组成。
我们全面的转录组和蛋白质组数据为定量生物学提供了宝贵资源,并为理解肿瘤模型中的转录后基因调控提供了新的见解。