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 regulators and their upstream signaling pathways.

Identifying genetic modulators of the connectivity between transcription factors and their transcriptional targets

“…modulators of transcription factor activity and the effect on expression of their target genes (11,(13)(14)(15)(16).…”

“…Allele replacement was carried out using the diletto perfetto method (55) using the pGSKU-CORE plasmid. The RM1-11a (Mata, ura3Δ0, ho::KanMX) strain containing the BY version of the DIG2 allele (IMY295) was made using the same method, except that the KanMX cassette at the ho locus was first replaced with NatMX (13). Allelic replacement at DIG2 was confirmed by sequencing.…”

“…We call such loci connectivity quantitative trait loci, or cQTLs. cQTLs are distinct from expression QTLs, for which expression (at the level of mRNA or protein) is linked to a chromosomal locus or loci and considered a heritable trait (3,31), and activity QTLs (aQTLs), for which the (inferred) activity of a TFor RNA-binding protein is mapped as a heritable trait (11,13).…”

“…Considering genome-wide mRNA abundance along with information on genotypic variation represents the combined effect of the latter on the regulatory state of the cell (3-7) and can yield better prediction of phenotype (8,9), the combined effect of allelic variation on transcript abundance (8,10), the effect of genetic variation on transcription factor activity (11,12), or transcript stability (13). However, there are few methods that systematically identify the effect of genetic modifiers on the strength of the connection ("connectivity") between a transcription factor and its target genes (14)(15)(16).…”

Network-based approaches that exploit inferred transcription factor activity to analyze the impact of genetic variation on gene expression

“…An early study used eQTL data to analyze post-transcriptional regulation by exploiting modules of co-expressed genes [33]. More recently, the methodology for mapping aQTLs was adapted to identify genetic polymorphisms that modulate the activity of RNA binding proteins acting as post-transcriptional regulators of gene expression [34]. …”