Impact of regulatory variation from RNA to protein

In the paper

“We found that most QTLs are associated with transcript expression levels, with consequent effects on ribosome and protein levels.”

• Also stated in: § Abstract · boosted

  “In summary, we have shown that while a substantial fraction of regulatory genetic variants influence gene expression at all levels from mRNA to steady state protein abundance, there are also a number of effects with specific impact on particular expression phenotypes.”

In the paper

“Additionally, we identified a class of cis QTLs that affect protein abundance with little or no effect on mRNA or ribosome levels, suggesting that they may arise from differences in post-translational regulation.”

In the paper

“However, eQTLs tend to have significantly reduced effect sizes on protein levels, suggesting that their potential impact on downstream phenotypes is often attenuated or buffered.”

• Also stated in: § Abstract · hedged

  “Yet, both eQTL mapping and effect-size analyses indicate that many eQTLs have attenuated (or absent) effects on steady state protein levels (Fig. S16).”

• Also stated in: § Abstract · plain

  “QTLs affecting mRNA levels are on average attenuated or buffered at the protein level, as has been observed between species (14).”

In the paper

“In contrast, eQTLs showed the lowest overlap with pQTLs (35%), as expected (Fig. 1B).”

In the paper

“Using this approach, we observed a reduced mean effect size for the GEUVADIS eQTLs in the protein data compared to either the RNA-seq data (t-test P = 6.7×10-3) or ribosome data (P = 5.6×10-3; Fig. 1C).”

In the paper

“Among the 4,322 genes quantified for all three phenotypes, 54% of the genes with pQTLs also have a significant rQTL and/or eQTL.”

In the paper

“We found that transcript and protein expression levels – which are the furthest removed processes studied here - are the least correlated (Fig. S9, S10).”

In the paper

“In particular, most (90%) genetic variants associated with ribosome occupancy are also associated with transcript levels (Fig. S13).”

In the paper

“Yet, for psQTLs, low ribosome effect sizes are observed.”

In the paper

“Considering protein levels, with RNA levels as a covariate, we identified 146 protein-specific QTLs (psQTLs) at FDR = 10% (Fig. 2A).”

In the paper

“Thus, for QTLs with discordant effects between transcript and protein, the ribosome data usually tracked with levels of RNA.”

In the paper

“We found that the effect sizes for ribosomal occupancy are similar to the esQTL effect sizes (Fig. 2B).”

In the paper

“Investigating additional annotations (Table 2, Fig. S21), we found that non-synonymous SNPs near acetylation sites showed nominal enrichment for psQTLs.”

In the paper

“In addition to the observation of generally attenuated effect sizes in pQTLs compared with eQTLs, we identified a subset of variants that appear to affect levels of proteins but not mRNA, and hence are candidates to affect post-transcriptional gene regulation.”

In the paper

“We also used the interaction model to identify 76 expression-specific QTLs (esQTLs, interaction model, LRT; FDR = 10%).”

In the paper

“In addition, psQTLs are further enriched for non-synonymous sites (compared to all exonic SNPs, Table 2).”

In the paper

“SNPs within transcribed regions (exonic and UTR) are enriched for more significant psQTL effects, compared to intergenic or intronic SNPs, even within the narrow 20 kb windows tested (Fig. S18, S19, S20).”

In the paper

“Using an alternative approach, an interaction model, we identified 68 psQTLs with significantly larger effects in protein than mRNA (LRT; FDR = 10%).”

In the paper

“and provided evidence consistent with buffering of protein expression with respect to variation introduced at the transcript level.”

What the sources say

• Not checked yetcites [9] Laurent JM Protein abundances are more conserved than mRNA abundances across diverse taxa Proteomics 10 4209 2010 21089048 PMC3113407 source
• Not checked yetcites [10] Schrimpf SP Comparative Functional Analysis of the Caenorhabditis elegans and Drosophila melanogaster Proteomes PLoS Biol 7 e1000048 2009 PMC2650730 19260763 source
• Not checked yetcites [11] Stadler M Fire A Conserved translatome remodeling in nematode species executing a shared developmental transition PLoS genetics 9 e1003739 2013 24098135 PMC3789828 source
• Not checked yetcites [12] Artieri CG Fraser HB Evolution at two levels of gene expression in yeast Genome research 24 411 3 2014 24318729 PMC3941106 source
• Not checked yetcites [13] McManus CJ May GE Spealman P Shteyman A Ribosome profiling reveals post-transcriptional buffering of divergent gene expression in yeast Genome research 24 422 3 2014 24318730 PMC3941107 source

In the paper

“Additionally, we identified a class of cis QTLs that affect protein abundance with little or no effect on mRNA or ribosome levels, suggesting that they may arise from differences in post-translational regulation.”

In the paper

“However, eQTLs tend to have significantly reduced effect sizes on protein levels, suggesting that their potential impact on downstream phenotypes is often attenuated or buffered.”

In the paper

“Yet protein abundances are more direct determinants of cellular functions (4)”

What the sources say

• Not checked yetcites [4] Vogel C Marcotte EM Insights into the regulation of protein abundance from proteomic and transcriptomic analyses Nature reviews Genetics 13 227 4 2012 PMC3654667 22411467 source

In the paper

“Studies in model organisms have shown that variation in mRNA and protein expression levels are often uncorrelated (5-8).”

What the sources say

• Not checked yetcites [5] Ghazalpour A Comparative analysis of proteome and transcriptome variation in mouse PLoS genetics 7 e1001393 6 2011 21695224 PMC3111477 source
• Not checked yetcites [6] Foss EJ Genetic basis of proteome variation in yeast Nature genetics 39 1369 11 2007 17952072 source
• Not checked yetcites [7] Picotti P A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis Nature 494 266 2 14 2013 23334424 PMC3951219 source
• Not checked yetcites [8] Albert FW Treusch S Shockley AH Bloom JS Kruglyak L Genetics of single-cell protein abundance variation in large yeast populations Nature 506 494 2 27 2014 24402228 PMC4285441 source

In the paper

“The high correlations (Spearman's rho 0.7-0.9; R2 of 0.3-0.7; depending on the sample) confirmed that we are able to precisely quantify inter-individual variation in protein levels (Fig. S5).”

In the paper

“Ribosome profiling is an effective way to measure changes in translational regulation using sequencing (11).”

What the sources say

• Not checked yetcites [11] Stadler M Fire A Conserved translatome remodeling in nematode species executing a shared developmental transition PLoS genetics 9 e1003739 2013 24098135 PMC3789828 source

In the paper

“The reduction in effect size observed in protein data is robust with respect to potential technical confounders, including iBAQ intensity level and transcript model complexity (Fig. S15).”

In the paper

“Mean effect sizes calculated in this way are expected to be unbiased with respect to either technical or biological variance.”

In the paper

“Yet, in contrast to comparative work, there are few reports of QTLs associated with protein levels (pQTLs) in humans (15-17).”

What the sources say

• Not checked yetcites [15] Wu L Variation and genetic control of protein abundance in humans Nature 499 79 2013 23676674 PMC3789121 source
• Not checked yetcites [16] Hause RJ Identification and Validation of Genetic Variants that Influence Transcription Factor and Cell Signaling Protein Levels American journal of human genetics 95 194 8 7 2014 25087611 PMC4129400 source
• Not checked yetcites [17] Garge N Identification of quantitative trait loci underlying proteome variation in human lymphoblastoid cells Molecular & cellular proteomics: MCP 9 1383 7 2010 20179311 PMC2938086 source

In the paper

“We thus conclude that the majority of genetic variants affecting transcript levels also alter ribosomal occupancy, typically with a similar magnitude of effect.”

In the paper

“Our observation that many SNPs identified as eQTLs are not associated with differences in protein levels is consistent with the notion that, across species, protein levels diverge less than transcript levels (12-17).”

What the sources say

• Not checked yetcites [12] Artieri CG Fraser HB Evolution at two levels of gene expression in yeast Genome research 24 411 3 2014 24318729 PMC3941106 source
• Not checked yetcites [13] McManus CJ May GE Spealman P Shteyman A Ribosome profiling reveals post-transcriptional buffering of divergent gene expression in yeast Genome research 24 422 3 2014 24318730 PMC3941107 source
• Not checked yetcites [14] Khan Z Primate transcript and protein expression levels evolve under compensatory selection pressures Science (New York, N Y) 342 1100 11 29 2013 PMC3994702 24136357 source
• Not checked yetcites [15] Wu L Variation and genetic control of protein abundance in humans Nature 499 79 2013 23676674 PMC3789121 source
• Not checked yetcites [16] Hause RJ Identification and Validation of Genetic Variants that Influence Transcription Factor and Cell Signaling Protein Levels American journal of human genetics 95 194 8 7 2014 25087611 PMC4129400 source
• Not checked yetcites [17] Garge N Identification of quantitative trait loci underlying proteome variation in human lymphoblastoid cells Molecular & cellular proteomics: MCP 9 1383 7 2010 20179311 PMC2938086 source

In the paper

“The median ratios measured from these peptides matched the expected values closely, indicating that our protein measurements were likely not subject to ratio compression (Figs. S7, S8).”

In the paper

“The identification of psQTLs is generally robust to the choice of technology used to characterize transcript expression (Fig S17).”

In the paper

“Our analysis indicates that this attenuation is not evident at the stage of translation.”

In the paper

“While the overall phenotypic similarity between ribosome occupancy and protein abundance is high, cis-regulatory genetic effects on ribosome occupancy appear to be more strongly shared with mRNA than with protein.”

In the paper

“In summary, we have shown that while a substantial fraction of regulatory genetic variants influence gene expression at all levels from mRNA to steady state protein abundance, there are also a number of effects with specific impact on particular expression phenotypes.”

In the paper

“In addition to the observation of generally attenuated effect sizes in pQTLs compared with eQTLs, we identified a subset of variants that appear to affect levels of proteins but not mRNA, and hence are candidates to affect post-transcriptional gene regulation.”

In the paper

“While the overall phenotypic similarity between ribosome occupancy and protein abundance is high, cis-regulatory genetic effects on ribosome occupancy appear to be more strongly shared with mRNA than with protein.”