Ribosome profiling reveals post-transcriptional buffering of divergent gene expression in yeast

Abstract

Understanding the patterns and causes of phenotypic divergence is a central goal in evolutionary biology. Much work has shown that mRNA abundance is highly variable between closely related species. However, the extent and mechanisms of post-transcriptional gene regulatory evolution are largely unknown. Here we used ribosome profiling to compare transcript abundance and translation efficiency in two closely related yeast species (S. cerevisiae and S. paradoxus). By comparing translation regulatory divergence to interspecies differences in mRNA sequence features, we show that differences in transcript leaders and codon bias substantially contribute to divergent translation. Globally, we find that translation regulatory divergence often buffers species differences in mRNA abundance, such that ribosome occupancy is more conserved than transcript abundance. We used allele-specific ribosome profiling in interspecies hybrids to compare the relative contributions of cis-and trans-regulatory divergence to species differences in mRNA abundance and translation efficiency. The mode of gene regulatory divergence differs for these processes, as trans-regulatory changes play a greater role in divergent mRNA abundance than in divergent translation efficiency. Strikingly, most genes with aberrant transcript abundance in F1 hybrids (either over-or underexpressed compared to both parent species) did not exhibit aberrant ribosome occupancy. Our results show that interspecies differences in translation contribute substantially to the evolution of gene expression. Compensatory differences in transcript abundance and translation efficiency may increase the robustness of gene regulation.

Understanding biases in ribosome profiling experiments reveals signatures of translation dynamics in yeast

“…For data from a particular experiment, the peaks corresponding to different codon identities occupy roughly the same range of offsets downstream (Figs 3B and S4), but across experiments carried out by different groups, the locations and shapes of the set of peaks change considerably (Figs 3C and S5). The centers of peaks vary from as close as *7 codons downstream in data from Nedialkova [29] to as far away as *50 codons downstream in data from McManus [27], with other CHX experiments densely populating the range of offsets between these observed extremes. Peaks become broader in width and smaller in maximum magnitude the farther downstream they are located.…”

“…The exact mechanism of this inhibition, however, is not completely understood, with recent studies suggesting that CHX binds to a ribosome's E-site along with a deacylated tRNA to block further translocation [19,20]. The majority of the rapidly growing body of ribosome profiling experiments in yeast have followed this original CHX-pretreatment protocol [13,[21][22][23][24][25][26][27][28][29][30]. Several groups have applied a variety of conceptually similar computational methods to the data produced by these experiments to infer the average speed with which each codon identity is translated.…”

Conserved non-AUG uORFs revealed by a novel regression analysis of ribosome profiling data

“…4). Our previous work showed that differences in upstream AUG frequency between S. cerevisiae and S. paradoxus were negatively correlated with differences in translation efficiency (McManus et al 2014). However, the increased transcript leader length and rate of AUG uORFs in S. cerevisiae may be specific to the laboratory strain, S288C.…”

“…Growth of yeast strains, RNA extraction, and mRNA enrichment Strains (Scannell et al 2011;McManus et al 2014) were grown to mid-log phase in 50 mL liquid YEPD media at 30°C (S. cerevisiae S288C, S. paradoxus CBS432, S. kudriavzevii FM1340) or at 24°C (S. uvarum JRY9191). Cells were pelleted and resuspended in 1 mL RNA buffer (500 mM NaCl, 200 mM Tris-HCl pH 7.5, 10 mM EDTA).…”

“…We applied uORF-seqr to Ribo-seq data from S. cerevisiae and S. paradoxus (McManus et al 2014), as well as new data from S. uvarum. Controlling the FDR at β = 0.05, we observed 982 uORFs in 791 S. cerevisiae genes, of which a substantial number overlap with predictions previously reported (Supplemental Table S3).…”