The Contribution of RNA Decay Quantitative Trait Loci to Inter-Individual Variation in Steady-State Gene Expression Levels

Pai, Athma A.; Cain, Carolyn E.; Mizrahi-Man, Orna; Leon, Sherryl De; Lewellen, Noah; Veyrieras, Jean‐Baptiste; Degner, Jacob F.; Gaffney, Daniel J.; Pickrell, Joseph K.; Stephens, Matthew; Pritchard, Jonathan K.; Gilad, Yoav

doi:10.1371/journal.pgen.1003000

Cited by 107 publications

(113 citation statements)

References 94 publications

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“…The possibility that our findings might be extended to a wide range of transcription factors not only affects the current model of eukaryotic gene regulation but also provides a mechanistic model for how gene-specific and condition-specific coupling between mRNA synthesis and degradation might be achieved 51,52 .…”

Section: Discussionmentioning

confidence: 94%

The transcription factor ERG recruits CCR4–NOT to control mRNA decay and mitotic progression

Rambout

Detiffe

Bruyr

et al. 2016

Nat Struct Mol Biol

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nature structural & molecular biology advance online publication a r t i c l e sAlthough undisputable evidence has clearly demonstrated that the nuclear steps of mRNA processing are mechanistically linked to transcription, a conceptual evolution in gene regulation has come with the realization that transcription might also be functionally connected to more remote processes occurring in the cytoplasm, such as mRNA decay 1,2 . Cytoplasmic mRNA decay is initiated by deadenylation, a rate-limiting event during which the poly(A) tail of the transcript is trimmed off by the CCR4-NOT complex, the main deadenylation machinery in eukaryotes 3 . The degradation of specific mRNAs, a key process in the regulation of eukaryotic gene expression, is achieved through the recruitment of the CCR4-NOT complex by sequence-specific RNA-binding proteins (RBPs) or by the microRNA machinery 3,4 . Poly(A)-shortened mRNAs, along with factors involved in the deadenylation, decapping and mRNA-degradation machineries, accumulate in microscopic mRNA-protein complex (mRNP) aggregates called processing bodies (PBs) 5 .The idea of coupling between mRNA synthesis and degradation has recently emerged. Genome-wide expression studies in yeast have shown that mRNA synthesis and decay are mechanistically and functionally coordinated, thus supporting the existence of common molecular effectors [6][7][8][9] . In particular, the CCR4-NOT deadenylation complex was first described as a transcriptional regulator and has been implicated in initiation and elongation by RNA polymerase II 10,11 . More surprisingly, it has also been shown that degradation of yeast mRNAs is determined by cis-acting sequence elements in promoters 12,13 . These findings have led to the concept of mRNA imprinting, in which sequence-specific DNA-binding factors might orchestrate mRNA synthesis and decay. This decay would occur via loading of factors regulating cytoplasmic mRNA degradation onto the transcribing mRNA 14 . However, the identity of these DNA-binding mRNA coordinators is still obscure, and it remains to be tested whether such coupling between transcription and decay also exists in higher eukaryotes. E26 (Ets) proteins, a family of 28 helix-loop-helix transcription factors (TFs) in metazoans, are characterized by a highly conserved DNA-binding ETS domain 15 . Through this domain, Ets factors bind specific gene promoters and act as key regulators in many biological processes including cellular proliferation, apoptosis, differentiation and survival 16 . ERG, FLI1 and the more structurally divergent FEV compose the Erg subfamily of Ets factors and have been identified as driving factors in prostate cancer, Ewing's tumors and leukemias 15,17 . Using ERG as a paradigm, we sought to investigate the possibility that eukaryotic transcription factors might be directly involved in cytoplasmic mRNA decay. We demonstrate that ERG triggers degradation of mRNAs connected to Aurora signaling by recruiting RBPs and the CCR4-NOT deadenylation complex and that this activity is important fo...

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Section: Discussionmentioning

confidence: 94%

The transcription factor ERG recruits CCR4–NOT to control mRNA decay and mitotic progression

Rambout

Detiffe

Bruyr

et al. 2016

Nat Struct Mol Biol

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“…We used this data to compute the CV of expression of the same gene in different cells. The mRNA decay rates of 16,823 genes were estimated in 70 human LCLs (Pai et al 2012). We obtained the mRNA decay rate data to compute the average mRNA decay rate for each gene among these LCL samples.…”

Section: Identification Of Evqtl Using the Dglm Methodsmentioning

confidence: 99%

“…Finally, we hypothesized that variable gene expression may be due to different mRNA decay rates for different genes. To test this, we used the mRNA decay rate data from the study of Pai et al (2012). The correlation between mean mRNA decay rate and CV of expression among genes is not specific as shown by the opposite signs of two correlation coefficients: Spearman's r = 20.027 (P = 0.00498) and Pearson's r = 0.044 (P = 4e-6, n = 11,083; Figure 1E).…”

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confidence: 99%

Additive, Epistatic, and Environmental Effects Through the Lens of Expression Variability QTL in a Twin Cohort

et al. 2014

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The expression of a gene can vary across individuals in the general population, as well as between monozygotic twins. This variable expression is assumed to be due to the influence of both genetic and nongenetic factors. Yet little evidence supporting this assumption has been obtained from empirical data. In this study, we used expression data from a large twin cohort to investigate the influences of genetic and nongenetic factors on variable gene expression. We focused on a set of expression variability QTL (evQTL)-i.e., genetic loci associated with the variance, as opposed to the mean, of gene expression. We identified evQTL for 99, 56, and 79 genes in lymphoblastoid cell lines, skin, and fat, respectively. The differences in gene expression, measured by the relative mean difference (RMD), tended to be larger between pairs of dizygotic (DZ) twins than between pairs of monozygotic (MZ) twins, showing that genetic background influenced the expression variability. Furthermore, a more profound RMD was observed between pairs of MZ twins whose genotypes were associated with greater expression variability than the RMD found between pairs of MZ twins whose genotypes were associated with smaller expression variability. This suggests that nongenetic (e.g., environmental) factors contribute to the variable expression. Lastly, we demonstrated that the formation of evQTL is likely due to partial linkages between eQTL SNPs that are additively associated with the mean of gene expression; in most cases, no epistatic effect is involved. Our findings have implications for understanding divergent sources of gene expression variability.V ARIATION and variability are central concepts in biology (Hallgrímsson and Hall 2005). Although often used interchangeably in the scientific literature, the two are not synonymous. Variation refers to the differences among individuals, whereas variability refers to the potential of a population to vary (Wagner 1995;Wagner and Altenberg 1996). In many cases, greater phenotypic variability (e.g., transcriptional noise) is disadvantageous (Kemkemer et al. 2002;Bahar et al. 2006;Wang and Zhang 2011) unless it gives rise to greater organismal plasticity-first at the level of an individual organism and eventually at the population level. Genetic factors resulting in more variable phenotypes become favored when they enable a population to more effectively respond to environmental changes (Hill and Zhang 2004;Kaern et al. 2005;Acar et al. 2008;Zhang et al. 2009). Thus, understanding to what extent and in what ways genotypes influence phenotypic variability is of fundamental importance.Much effort has been focused on identifying genetic loci such as expression quantitative trait loci, or eQTL (Stranger et al. 2005(Stranger et al. , 2007Choy et al. 2008;Montgomery et al. 2010;Pickrell et al. 2010;Montgomery and Dermitzakis 2011), that affect the average value of a phenotype, while ignoring those that affect the variance of a phenotype. However, there is increasing evidence across species for genet...

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“…Inflammatory disease-associated variants such as those underlying T1D are enriched in signatures of positive selection (43). Some of these variants are eQTL (expression quantitative trait loci) that affect transcriptional rate or mRNA stability (44) and may mediate the effect of inflammatory-disease susceptibility loci (45).…”

Section: Significancementioning

confidence: 99%

Interindividual variation in human T regulatory cells

Ferraro

D’Alise

Raj

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

108

119

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Significance Control of immunologic tolerance and homeostasis rely on regulatory T lymphocytes that express the transcription factor FOXP3. To characterize the interindividual variation of human Treg cells, we performed a genome-wide expression and genotypic analysis of 168 human donors, healthy or affected by type-1 or type-2 diabetes (T1D, T2D). We identify cis -acting genetic variants that condition Treg effector but not specification genes, and gene clusters that suggest Treg-specific regulatory pathways for some key signature genes ( CTLA4 , DUSP4 ). We also identify factors that may control FOXP3 mRNA or protein expression, the specification of the Treg signature, and Treg suppressive efficacy. Although no single transcript correlates with diabetes, overall expression of the Treg signature is perturbed in T1D, but not T2D, patients.

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The Contribution of RNA Decay Quantitative Trait Loci to Inter-Individual Variation in Steady-State Gene Expression Levels

Cited by 107 publications

References 94 publications

The transcription factor ERG recruits CCR4–NOT to control mRNA decay and mitotic progression

The transcription factor ERG recruits CCR4–NOT to control mRNA decay and mitotic progression

Additive, Epistatic, and Environmental Effects Through the Lens of Expression Variability QTL in a Twin Cohort

Interindividual variation in human T regulatory cells

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