Natural variation in codon bias and mRNA folding strength interact synergistically to modify protein expression in Saccharomyces cerevisiae.

Codon bias and mRNA folding strength (mF) are hypothesized molecular mechanisms by which polymorphisms in genes modify protein expression. Natural patterns of codon bias and mF across genes as well as effects of altering codon bias and mF suggest that the influence of these 2 mechanisms may vary depending on the specific location of polymorphisms within a transcript. Despite the central role codon bias and mF may play in natural trait variation within populations, systematic studies of how polymorphic codon bias and mF relate to protein expression variation are lacking. To address this need, we analyzed genomic, transcriptomic, and proteomic data for 22 Saccharomyces cerevisiae isolates, estimated protein accumulation for each allele of 1,620 genes as the log of protein molecules per RNA molecule (logPPR), and built linear mixed-effects models associating allelic variation in codon bias and mF with allelic variation in logPPR. We found that codon bias and mF interact synergistically in a positive association with logPPR, and this interaction explains almost all the effects of codon bias and mF. We examined how the locations of polymorphisms within transcripts influence their effects and found that codon bias primarily acts through polymorphisms in domain-encoding and 3' coding sequences, while mF acts most significantly through coding sequences with weaker effects from untranslated regions. Our results present the most comprehensive characterization to date of how polymorphisms in transcripts influence protein expression.

Rotavirus RNA chaperone mediates global transcriptome-wide increase in RNA backbone flexibility.

Due to genome segmentation, rotaviruses must co-package eleven distinct genomic RNAs. The packaging is mediated by virus-encoded RNA chaperones, such as the rotavirus NSP2 protein. While the activities of distinct RNA chaperones are well studied on smaller RNAs, little is known about their global effect on the entire viral transcriptome. Here, we used Selective 2'-hydroxyl Acylation Analyzed by Primer Extension and Mutational Profiling (SHAPE-MaP) to examine the secondary structure of the rotavirus transcriptome in the presence of increasing amounts of NSP2. SHAPE-MaP data reveals that despite the well-documented helix-unwinding activity of NSP2 in vitro, its incubation with cognate rotavirus transcripts does not induce a significant change in the SHAPE reactivities. However, a quantitative analysis of mutation rates measured by mutational profiling reveals a global 5-fold rate increase in the presence of NSP2. We demonstrate that the normalization procedure used in deriving SHAPE reactivities from mutation rates can mask an important global effect of an RNA chaperone. Analysis of the mutation rates reveals a larger effect on stems rather than loops. Together, these data provide the first experimentally derived secondary structure model of the rotavirus transcriptome and reveal that NSP2 acts by globally increasing RNA backbone flexibility in a concentration-dependent manner.

A novel algorithm for ranking RNA structure candidates.

RNA research is advancing at an ever increasing pace. The newest and most state-of-the-art instruments and techniques have made possible the discoveries of new RNAs, and they have carried the field to new frontiers of disease research, vaccine development, therapeutics, and architectonics. Like proteins, RNAs show a marked relationship between structure and function. A deeper grasp of RNAs requires a finer understanding of their elaborate structures. In pursuit of this, cutting-edge experimental and computational structure-probing techniques output several candidate geometries for a given RNA, each of which is perfectly aligned with experimentally determined parameters. Identifying which structure is the most accurate, however, remains a major obstacle. In recent years, several algorithms have been developed for ranking candidate RNA structures in order from most to least probable, though their levels of accuracy and transparency leave room for improvement. Most recently, advances in both areas are demonstrated by rsRNASP, a novel algorithm proposed by Tan et al. rsRNASP is a residue-separation-based statistical potential for three-dimensional structure evaluation, and it outperforms the leading algorithms in the field.