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Impact of Regulatory Variation from RNA to Protein

ABSTRACT: Genetic variants that impact gene regulation are important contributors to human phenotypic variation. For this reason, considerable efforts have been made to identify genetic associations with differences in mRNA levels of nearby genes, namely, cis expression quantitative trait loci (eQTLs). The phenotypic consequences of eQTLs are presumably due, in most cases, to their ultimate effects on protein expression levels. Yet, only few studies have quantified the impact of genetic variation on proteins levels directly. It remains unclear how faithfully eQTLs are reflected at the protein level, and whether there is a significant layer of cis regulatory variation acting primarily on translation or steady state protein levels. To address these questions, we measured ribosome occupancy by high-throughput sequencing, and relative protein levels by high-resolution quantitative mass spectrometry, in a panel of lymphoblastoid cell lines (LCLs) in which we had previously measured transcript expression using RNA sequencing. We then mapped genetic variants that are associated with changes in transcript expression (eQTLs), ribosome occupancy (rQTLs), or protein abundance (pQTLs). Most of the QTLs we detected are associated with transcript expression levels, with consequent effects on ribosome and protein levels. However, we found that eQTLs tend to have significantly reduced effect sizes on protein levels, suggesting that their potential impact on downstream phenotypes is often attenuated or buffered. Additionally, we confirmed the presence of a class of cis QTLs that specifically affect protein abundance with little or no effect on mRNA levels; most of these QTLs have little effect on ribosome occupancy, and hence may arise from differences in post-translational regulation.

INSTRUMENT(S): LTQ Orbitrap Velos

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): B Cell

DISEASE(S): Disease Free

SUBMITTER: Zia Khan

LAB HEAD: Yoav Gilad

PROVIDER: PXD001406 | Pride | 2014-12-19

REPOSITORIES: Pride

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Project description:Genetic variants that impact gene regulation are important contributors to human phenotypic variation. For this reason, considerable efforts have been made to identify genetic associations with differences in mRNA levels of nearby genes, namely, cis expression quantitative trait loci (eQTLs). The phenotypic consequences of eQTLs are presumably due, in most cases, to their ultimate effects on protein expression levels. Yet, only few studies have quantified the impact of genetic variation on proteins levels directly. It remains unclear how faithfully eQTLs are reflected at the protein level, and whether there is a significant layer of cis regulatory variation acting primarily on translation or steady state protein levels. To address these questions, we measured ribosome occupancy by high-throughput sequencing, and relative protein levels by high-resolution quantitative mass spectrometry, in a panel of lymphoblastoid cell lines (LCLs) in which we had previously measured transcript expression using RNA sequencing. We then mapped genetic variants that are associated with changes in transcript expression (eQTLs), ribosome occupancy (rQTLs), or protein abundance (pQTLs). Most of the QTLs we detected are associated with transcript expression levels, with consequent effects on ribosome and protein levels. However, we found that eQTLs tend to have significantly reduced effect sizes on protein levels, suggesting that their potential impact on downstream phenotypes is often attenuated or buffered. Additionally, we confirmed the presence of a class of cis QTLs that specifically affect protein abundance with little or no effect on mRNA levels; most of these QTLs have little effect on ribosome occupancy, and hence may arise from differences in post-translational regulation. We measured level of translation transcriptome-wide in lymphoblastoid cell lines derived from 72 HapMap Yoruba individuals using ribosome profiling assay, for which we have transcript level, protein level (62 out of 72) and genotype information collected.

Project description:We used microarrays to identify the variation of basal gene expression level among 287 lymphoblastoid cell lines. Radiation therapy is used to treat half of all cancer patients. Response to radiation therapy varies widely among patients. Therefore, we performed a genome-wide association study (GWAS) to identify biomarkers to help predict radiation response using 277 ethnically defined human lymphoblastoid cell lines (LCLs). Basal gene expression levels and 1.3 million genome-wide SNP markers from both Affymetrix and Illumina platforms were assayed for all 277 human LCLs. MTS (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium) assays for radiation cytotoxicity were also performed to obtain area under the curve (AUC) as a radiation response phenotype for use in the association studies. Functional validation of candidate genes, selected from an integrated analysis that used SNP, expression and AUC data, was performed with multiple cancer cell lines using specific siRNA knockdown, followed by MTS and colony-forming assays. 27 loci, each containing at least 2 SNPs within 50kb with p-values <10-4, were associated with radiation AUC. 270 expression probe sets were associated with radiation AUC with p<10-3. The integrated analysis identified 50 SNPs in 14 of the 27 loci that were associated with both AUC and the expression of 39 genes that were also associated with radiation AUC (p<10-3). Functional validation using siRNA knockdown in multiple tumor cell lines showed that C13orf34, MAD2L1, PLK4, TPD52 and DEPDC1B each significantly altered radiation sensitivity in at least 2 cancer cell lines. Studies performed with LCLs can help to identify novel biomarkers that might contribute to variation in response to radiation therapy and enhance our understanding of mechanisms underlying that variation. EBV-transformed LCLs from 95 African-American (AA), 96 Caucasian-American (CA), and 96 Han Chinese-American (HCA) unrelated healthy subjects (sample sets HD100AA, HD100CAU, HD100CHI) were purchased from the Coriell Cell Repository (Camden, NJ).

			Action	DRS
	GM18486.txt	Txt
	GM18486_MSB15024_01B.raw	Raw
	GM18486_MSB15024_02B.raw	Raw
	GM18486_MSB15024_03B.raw	Raw
	GM18486_MSB15024_04B.raw	Raw

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Impact of Regulatory Variation from RNA to Protein

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