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Harnessing natural sequence variation to dissect posttranscriptional regulatory networks in yeast.

ABSTRACT: Understanding how genomic variation influences phenotypic variation through the molecular networks of the cell is one of the central challenges of biology. Transcriptional regulation has received much attention, but equally important is the posttranscriptional regulation of mRNA stability. Here we applied a systems genetics approach to dissect posttranscriptional regulatory networks in the budding yeast Saccharomyces cerevisiae. Quantitative sequence-to-affinity models were built from high-throughput in vivo RNA binding protein (RBP) binding data for 15 yeast RBPs. Integration of these models with genome-wide mRNA expression data allowed us to estimate protein-level RBP regulatory activity for individual segregants from a genetic cross between two yeast strains. Treating these activities as a quantitative trait, we mapped trans-acting loci (activity quantitative trait loci, or aQTLs) that act via posttranscriptional regulation of transcript stability. We predicted and experimentally confirmed that a coding polymorphism at the IRA2 locus modulates Puf4p activity. Our results also indicate that Puf3p activity is modulated by distinct loci, depending on whether it acts via the 5' or the 3' untranslated region of its target mRNAs. Together, our results validate a general strategy for dissecting the connectivity between posttranscriptional [corrected] regulators and their upstream signaling pathways.

SUBMITTER: Fazlollahi M

PROVIDER: S-EPMC4132183 | biostudies-other | 2014 Jun

REPOSITORIES: biostudies-other

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Harnessing natural sequence variation to dissect posttranscriptional regulatory networks in yeast.

Fazlollahi Mina M Lee Eunjee E Muroff Ivor I Lu Xiang-Jun XJ Gomez-Alcala Pilar P Causton Helen C HC Bussemaker Harmen J HJ

G3 (Bethesda, Md.) 20140617 8

Understanding how genomic variation influences phenotypic variation through the molecular networks of the cell is one of the central challenges of biology. Transcriptional regulation has received much attention, but equally important is the posttranscriptional regulation of mRNA stability. Here we applied a systems genetics approach to dissect posttranscriptional regulatory networks in the budding yeast Saccharomyces cerevisiae. Quantitative sequence-to-affinity models were built from high-throu ...[more]

PMID: 24938291

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Project description:Body mass index (BMI) is a complex disease risk factor known to be influenced by genes acting via both metabolic pathways and appetite regulation. In this study, we aimed to gain insight into the phenotypic consequences of BMI-associated genetic variants, which may be mediated by their expression in different tissues. First, we harnessed meta-analyzed gene expression datasets derived from subcutaneous adipose (n = 1257) and brain (n = 1194) tissue to identify 86 and 140 loci, respectively, which provided evidence of genetic colocalization with BMI. These two sets of tissue-partitioned loci had differential effects with respect to waist-to-hip ratio, suggesting that the way they influence fat distribution might vary despite their having very similar average magnitudes of effect on BMI itself (adipose = 0.0148 and brain = 0.0149 standard deviation change in BMI per effect allele). For instance, BMI-associated variants colocalized with TBX15 expression in adipose tissue (posterior probability [PPA] = 0.97), but not when we used TBX15 expression data derived from brain tissue (PPA = 0.04) This gene putatively influences BMI via its role in skeletal development. Conversely, there were loci where BMI-associated variants provided evidence of colocalization with gene expression in brain tissue (e.g., NEGR1, PPA = 0.93), but not when we used data derived from adipose tissue, suggesting that these genes might be more likely to influence BMI via energy balance. Leveraging these tissue-partitioned variant sets through a multivariable Mendelian randomization framework provided strong evidence that the brain-tissue-derived variants are predominantly responsible for driving the genetically predicted effects of BMI on cardiovascular-disease endpoints (e.g., coronary artery disease: odds ratio = 1.05, 95% confidence interval = 1.04-1.07, p = 4.67 × 10-14). In contrast, our analyses suggested that the adipose tissue variants might predominantly be responsible for the underlying relationship between BMI and measures of cardiac function, such as left ventricular stroke volume (beta = 0.21, 95% confidence interval = 0.09-0.32, p = 6.43 × 10-4).

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Harnessing natural sequence variation to dissect posttranscriptional regulatory networks in yeast.

Publications

Harnessing natural sequence variation to dissect posttranscriptional regulatory networks in yeast.

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