《Impact of regulatory variation from RNA to protein》由会员分享，可在线阅读，更多相关《Impact of regulatory variation from RNA to protein（25页珍藏版）》请在金锄头文库上搜索。
1、Impact of regulatory variation from RNA to protein Impact of regulatory variation from RNA to protein Reports (LCLs). We collected ribosome profiling data for 72 Yoruba LCLs and quantified protein abundance in 62 of these lines. Genome-wide genotypes and RNA-sequencing data were 1,2333 Alexis Battle
2、,* Zia Khan, Sidney H. Wang, Amy Mitrano, available for all lines (19). 41,2,53 Ribosome profiling is an ef-Michael J. Ford, Jonathan K. Pritchard, Yoav Gilad fective way to measure changes 1 Department of Genetics, Stanford University, Stanford, CA 94305, USA. 2Howard Hughes Medical Institute, Stan
3、ford in translational regulation using University, Stanford, CA 94305, USA. 3Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA. 4sequencing (11). We obtained a MS Bioworks, LLC, 3950 Varsity Drive, Ann Arbor, MI 48108, USA. 5Department of Biology, Stanford University, Stanf
4、ord, CA 94305, USA. median coverage of 12 million mapped reads per sample and, *Present address: Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA. as expected, the ribosome profil- Present address: Department of Computer Science, University of Maryland, College Park
5、, MD 20742, USA. ing reads are highly concentrat- These authors contributed equally to this work. ed within coding regions and Corresponding author. E-mail: pritchstanford.edu (J.K.P); giladuchicago.edu (Y.G.) show an enrichment of a 3-bp periodicity, reflecting the pro-The phenotypic consequences o
6、f expression quantitative trait loci (eQTLs) gression of a translating ribo-are presumably due to their effects on protein expression levels. Yet, the some (figs. S1-S3, table S1). impact of genetic variation, including eQTLs, on protein levels remains We collected relative protein poorly understood
7、. To address this, we mapped genetic variants that are associated with eQTLs, ribosome occupancy (rQTLs), or protein abundance expression measurements using a SILAC internal standard sam-(pQTLs). We found that most QTLs are associated with transcript ple (20) and quantitative protein expression leve
8、ls, with consequent effects on ribosome and protein levels. mass spectrometry (fig. S4). To However, eQTLs tend to have significantly reduced effect sizes on protein levels, suggesting that their potential impact on downstream phenotypes is confirm the quality of the prote-omics data (table S2) we e
9、valu-often attenuated or buffered. Additionally, we identified a class of cis QTLs ated the agreement between that affect protein abundance with little or no effect on mRNA or ribosome measurements of distinct groups levels, suggesting that they may arise from differences in post-of peptides from th
10、e same pro-translational regulation. tein. Differences between these measurements can reflect true To understand the links between genetic and phenotypic biological variation (e.g., splicing), or experimental noise. variation it may be essential to first understand how genetic The high correlations
11、(Spearmans rho 0.7-0.9; R2 of 0.3-0.7; variation impacts the regulation of gene expression. depending on the sample) confirmed that we are able to Previous studies have evaluated the association between precisely quantify inter-individual variation in protein levels variation and transcript expressi
12、on in humans (13). Yet (fig. S5). We also analyzed quantifications of peptides that protein abundances are more direct determinants of cellular overlapped non-synonymous SNPs that were heterozygous functions (4), and the impact of genetic differences on the in either the analyzed or the internal sta
13、ndard sample (fig. multi-stage process of gene expression through S6). The median ratios measured from these peptides transcription and translation to steady state protein levels, matched the expected values closely, indicating that our has not been fully characterized. Studies in model protein meas
14、urements were likely not subject to ratio com-organisms have shown that variation in mRNA and protein pression (figs. S7 and S8). As a final quality check, we considered variation in ex-expression levels are often uncorrelated (58). Comparative studies (913) have suggested that protein expression pr
15、ession levels within and between genes. We found that evolves under greater evolutionary constraint than transcript and protein expression levels which are the fur-transcript levels (14), and provided evidence consistent with thest removed processes studied here - are the least corre-buffering of pr
16、otein expression with respect to variation lated (figs. S9 and S10). Our observations are in agreement introduced at the transcript level. Yet, in contrast to with most high-throughput studies that considered large comparative work, there are few reports of QTLs associated number of samples, although smaller studies have often observed higher correlations (18
