Identification of programmed translational -1 frameshifting sites in the genome of Saccharomyces cerevisiae

Abstract

Frameshifting is a recoding event that allows the expression of two polypeptides from the same mRNA molecule. Most recoding events described so far are used by viruses and transposons to express their replicase protein. The very few number of cellular proteins known to be expressed by a −1 ribosomal frameshifting has been identified by chance. The goal of the present work was to set up a systematic strategy, based on complementary bioinformatics, molecular biology, and functional approaches, without a priori knowledge of the mechanism involved. Two independent methods were devised. The first looks for genomic regions in which two ORFs, each carrying a protein pattern, are in a frameshifted arrangement. The second uses Hidden Markov Models and likelihood in a two-step approach. When this strategy was applied to the Saccharomyces cerevisiae genome, 189 candidate regions were found, of which 58 were further functionally investigated. Twenty-eight of them expressed a full-length mRNA covering the two ORFs, and 11 showed a −1 frameshift efficiency varying from 5% to 13% (50-fold higher than background), some of which corresponds to genes with known functions. From other ascomycetes, four frameshifted ORFs are found fully conserved. Strikingly, most of the candidates do not display a classical viral-like frameshift signal and would have escaped a search based on current models of frameshifting. These results strongly suggest that −1 frameshifting might be more widely distributed than previously thought.Sequencing programs, along with various projects in the pharmaceutical, agricultural, aquacultural, and forestry industries, are creating an explosion of DNA sequence data. With this abundance of data, there is a growing need for more effective tools and methods to extract vital information from raw DNA sequences. Algorithms for identifying protein-coding regions and predicting complete genes are of particular importance. Since the early 1990s, a number of computer programs for eukaryotic gene identification have been developed: GENMARK (Borodovsky and

Multiple Viral Infections Detected in Phytophthora condilina by Total and Small RNA Sequencing

“…This is a near-cognate codon that can, under certain circumstances, be also recognized by Metionine-tRNAi and is known to be the most efficient non-AUG initiation codon in many systems [ 60 ]. A heptanucleotide slippery site or shifty heptamer motif was observed at the end of the ORF1 (nt positions 3203–3209) with the general sequence X XXY YYZ (spaced triplets represent preframeshift codons) [ 61 ], where X represents A/G/C/U, Y represents A/U and Z represents A/C/U (GGAUUUC), which may facilitate −1 programmed ribosomal frameshifting in PcoRV1 transcripts ( Figure 2 ). From the slippery site a short spacer region of 10 nt appears to precede a H-type pseudoknot between nt 3219–3246 ( Figure 2 ), upstreams of the AUG, a key motif for translation of the downstream ORF, found at nt positions 3305 of PcoRV1.…”

High Diversity of Novel Viruses in the Tree Pathogen Phytophthora castaneae Revealed by High-Throughput Sequencing of Total and Small RNA

“…Interestingly, two continuous methionine codons occur at the beginning of the ORF2. No typical heptanucleotide slippery site or shifty heptamer motif facilitating the −1 programmed ribosomal frameshifting was observed at the end of the ORF1 with the general sequence X XXY YYZ (spaced triplets represent pre-frameshift codons; Bekaert et al, 2005 ) where X represents A/G/C/U, Y defines A/U, and Z represents A/C/U. Likewise, neither a possible slippery site similar to those found in Sclerotinia sclerotiorum megabirnavirus 1 (SsMBV1; AAAAAAC; Wang et al, 2015 ) or Fusarium pseudograminearum megabirnavirus 1 (FpgMB1; GAAAAAC; Zhang et al, 2018 ) was detected.…”

Control of gene expression by translational recoding

“…In the beginning, these approaches were particularly challenging, primarily because of computer memory limitations which have been solved by increased CPU speeds and cloud computing. In general, four different strategies have been employed to identify À1 PRF signals: searches for overlapping reading frames (Moon et al, 2004a,b;Bekaert et al, 2005), searches for known slippery sites (Shah et al, 2002;Wills et al, 2006), neural networks approaches (Bekaert et al, 2003), and programs designed to identify sequence and structure motifs resembling viral À1 PRF signals (Hammell et al, 1999;Jacobs et al, 2007;Belew et al, 2008;Theis et al, 2008). The first method rests on the assumption that À1 PRF events always result in the production of C-terminally extended fusion products.…”

“…A few studies have translated these approaches to the bench. A search for overlapping ORFs combined with the application of hidden Markov models was used to identify 189 candidate genes in the Saccharomyces cerevisiae genome (Bekaert et al, 2003), and a later investigation showed that 28 of 58 candidates expressed full-length mRNAs encompassing both ORFs, 11 of which promoted highly efficient À1 PRF (Bekaert et al, 2005). An important feature of this latter study was the demonstration that most candidates do not contain typical virus-like À1 PRF signals, thus revealing new classes of À1 PRF promoting cis-acting elements.…”