Comparative analysis of proteome and transcriptome variation in mouse.
Comparative analysis of proteome and transcriptome variation in mouse.
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DOI：
10.1371/journal.pgen.1001393
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发表时间：
2011-06
期刊：
影响因子：
4.5
通讯作者：
Lusis AJ
中科院分区：
文献类型：
--
作者：
Ghazalpour A;Bennett B;Petyuk VA;Orozco L;Hagopian R;Mungrue IN;Farber CR;Sinsheimer J;Kang HM;Furlotte N;Park CC;Wen PZ;Brewer H;Weitz K;Camp DG 2nd;Pan C;Yordanova R;Neuhaus I;Tilford C;Siemers N;Gargalovic P;Eskin E;Kirchgessner T;Smith DJ;Smith RD;Lusis AJ
转录水平和它们编码的蛋白质水平之间的关系还没有在哺乳动物中得到全面的研究，尽管以前在植物和酵母中的工作表明了令人惊讶的适度相关性。我们已经研究了这个问题，使用遗传方法，其中自然变异被用来扰乱转录水平和蛋白质水平之间的近交系小鼠。我们在97个近交系和重组近交系的肝脏中定量了超过5，000种肽和超过22，000种转录物，并集中在7，185种最可遗传的转录物和486种最可靠的蛋白质上。通过微阵列分析一式三份定量转录物水平，并通过液相色谱-质谱法使用基于O（18）-参考的同位素标记方法定量蛋白质。我们发现，只有大约一半的基因的转录本和蛋白质的水平显著相关，平均相关性为0.27，转录本和蛋白质的相关性取决于基因的细胞位置和生物学功能。我们研究了可能导致适度相关性的技术和生物因素。例如，差异剪接明显影响某些基因的分析;但是，基于深度测序，这对相关性的总体估计没有实质性贡献。我们还采用全基因组关联分析来绘制控制转录本和蛋白质水平的基因座。令人惊讶的是，几乎没有重叠的蛋白质和转录映射的基因座之间观察到。我们已经分型了许多临床相关性状的菌株，包括肥胖，脂蛋白水平和组织参数。使用相关性分析，我们发现，少量的临床性状的关系之间的蛋白质和mRNA基因产物的保留，大多数这样的关系是特定的蛋白质水平或转录水平。令人惊讶的是，转录水平与临床性状的相关性比蛋白质水平更强。鉴于高通量技术在临床和基础研究中的广泛应用，本文的研究结果具有实际意义和基础意义。生物学中有一个古老的教条，即在每一个细胞中，生物信息的流动是从DNA到RNA再到蛋白质，后者是决定生物体表型的工作力量。该模型预测，影响临床表型的DNA变化也应该类似地改变RNA和蛋白质水平的细胞水平。在这份报告中，我们通过观察小鼠近交系种群中的DNA变异、这些小鼠肝组织中的RNA和蛋白质变异以及代谢表型变异之间的一致性来测试这一预测。我们发现，各种生物学性状之间的关系并不简单，有相对较小的一致性的RNA水平和相应的蛋白质水平在DNA扰动。此外，我们还发现，令人惊讶的是，代谢性状与RNA水平的相关性比与蛋白质水平的相关性更好。鉴于目前在寻找人类疾病易感性的分子基础方面所做的努力，我们的研究结果突出了临床结果背后信息流的复杂性。
The relationships between the levels of transcripts and the levels of the proteins they encode have not been examined comprehensively in mammals, although previous work in plants and yeast suggest a surprisingly modest correlation. We have examined this issue using a genetic approach in which natural variations were used to perturb both transcript levels and protein levels among inbred strains of mice. We quantified over 5,000 peptides and over 22,000 transcripts in livers of 97 inbred and recombinant inbred strains and focused on the 7,185 most heritable transcripts and 486 most reliable proteins. The transcript levels were quantified by microarray analysis in three replicates and the proteins were quantified by Liquid Chromatography–Mass Spectrometry using O(18)-reference-based isotope labeling approach. We show that the levels of transcripts and proteins correlate significantly for only about half of the genes tested, with an average correlation of 0.27, and the correlations of transcripts and proteins varied depending on the cellular location and biological function of the gene. We examined technical and biological factors that could contribute to the modest correlation. For example, differential splicing clearly affects the analyses for certain genes; but, based on deep sequencing, this does not substantially contribute to the overall estimate of the correlation. We also employed genome-wide association analyses to map loci controlling both transcript and protein levels. Surprisingly, little overlap was observed between the protein- and transcript-mapped loci. We have typed numerous clinically relevant traits among the strains, including adiposity, lipoprotein levels, and tissue parameters. Using correlation analysis, we found that a low number of clinical trait relationships are preserved between the protein and mRNA gene products and that the majority of such relationships are specific to either the protein levels or transcript levels. Surprisingly, transcript levels were more strongly correlated with clinical traits than protein levels. In light of the widespread use of high-throughput technologies in both clinical and basic research, the results presented have practical as well as basic implications. An old dogma in biology states that, in every cell, the flow of biological information is from DNA to RNA to proteins and that the latter act as a working force to determine the organism's phenotype. This model predicts that changes in DNA that affect the clinical phenotype should also similarly change the cellular levels of RNA and protein levels. In this report, we test this prediction by looking at the concordance between DNA variation in population of mouse inbred strains, the RNA and protein variation in the liver tissue of these mice, and variation in metabolic phenotypes. We show that the relationship between various biological traits is not simple and that there is relatively little concordance of RNA levels and the corresponding protein levels in response to DNA perturbations. In addition, we also find that, surprisingly, metabolic traits correlate better to RNA levels than to protein levels. In light of current efforts in searching for the molecular bases of disease susceptibility in humans, our findings highlight the complexity of information flow that underlies clinical outcomes.
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