The predictive nature of transcript expression levels on protein expression in adult human brain.
The predictive nature of transcript expression levels on protein expression in adult human brain.
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DOI：
10.1186/s12864-017-3674-x
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发表时间：
2017-04-24
期刊：
影响因子：
4.4
通讯作者：
Babbitt CC
中科院分区：
文献类型：
--
作者：
Bauernfeind AL;Babbitt CC
下一代测序方法是评估转录组表达的金标准。在确定这类研究的生物学意义时，通常假设转录表达水平与蛋白质水平以一种有意义的方式对应。然而，转录和蛋白质表达之间总体相关性的强度是不一致的，特别是在大脑样本中。在对成人大脑样本进行高通量转录(RNA-Seq)和蛋白质组学(液相色谱-串联质谱仪)分析后，我们比较了位于细胞不同区域的支持各种生物学过程、分子功能的转录本和蛋白质表达的相关性。虽然大多数类型的转录本对其相关蛋白的表达具有极弱的预测价值( <的R2值； 为10%)，但编码蛋白激酶和膜相关蛋白的转录本，包括那些属于受体或离子转运体的转录本，是最能预测下游蛋白表达水平的转录本之一。相应蛋白质转录表达的预测价值在人脑样本中是可变的，反映了蛋白质表达的复杂调控。然而，我们发现转录分析适合于评估某些类别蛋白质的表达水平，包括那些修饰蛋白质的蛋白质，如激酶和磷酸酶，调节代谢和突触活动，或与细胞膜相关的蛋白质。这些发现可以用来指导解释灵长类大脑样本的基因表达结果。本文的在线版本(doi：10.1186/s12864-0173674-x)包含补充材料，授权用户可以使用。
Next generation sequencing methods are the gold standard for evaluating expression of the transcriptome. When determining the biological implications of such studies, the assumption is often made that transcript expression levels correspond to protein levels in a meaningful way. However, the strength of the overall correlation between transcript and protein expression is inconsistent, particularly in brain samples. Following high-throughput transcriptomic (RNA-Seq) and proteomic (liquid chromatography coupled with tandem mass spectrometry) analyses of adult human brain samples, we compared the correlation in the expression of transcripts and proteins that support various biological processes, molecular functions, and that are located in different areas of the cell. Although most categories of transcripts have extremely weak predictive value for the expression of their associated proteins (R2 values of < 10%), transcripts coding for protein kinases and membrane-associated proteins, including those that are part of receptors or ion transporters, are among those that are most predictive of downstream protein expression levels. The predictive value of transcript expression for corresponding proteins is variable in human brain samples, reflecting the complex regulation of protein expression. However, we found that transcriptomic analyses are appropriate for assessing the expression levels of certain classes of proteins, including those that modify proteins, such as kinases and phosphatases, regulate metabolic and synaptic activity, or are associated with a cellular membrane. These findings can be used to guide the interpretation of gene expression results from primate brain samples. The online version of this article (doi:10.1186/s12864-017-3674-x) contains supplementary material, which is available to authorized users.