Direct epitranscriptomic regulation of mammalian translation initiation through N4-acetylcytidine

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Context sequences of translation initiation codon in plants

In this survey of 5074 plant genes for their AUG context sequences, purines are present at the _3 and +4 positions in about 80% of the sequences. Although this observation is similar to the vertebrate consensus sequence, the number of plant mRNAs with purines at the _3 position is lower and at the +4 position is higher than reported for vertebrate mRNAs. Higher plants have an AC-rich consensus sequence, caA(A/C)aAUGGCg as a context of translation initiator codon. Between the two major groups of angiosperms, the context of the AUG codon in dicot mRNAs is aaA(A/C)aAUGGCu which is similar to the higher-plant consensus but monocot mRNAs have c(a/c)(A/G)(A/C)cAUGGCG as a consensus which exhibits an overall similarity with the vertebrate consensus. The experimental evidence regarding the importance of the AUG context in plants is discussed.     

Feature selection for the prediction of translation initiation sites

Translation initiation sites （TISs） are important signals in cDNA sequences. In many previous attempts to predict TISs in cDNA sequences, three major factors affect the prediction performance： the nature of the cDNA sequence sets, the relevant features selected. and the classification methods used. In this paper, we examine different approaches to select and integrate relevant features for TIS prediction. The top selected significant features include the features from the position weight matrix and the propensity matrix, the number of nucleotide C in the sequence downstream ATG, the number of downstream stop codons. the number of upstream ATGs, and the number of some amino acids, such as amino acids A and D. With the numerical data generated from these features, different classification methods, including decision tree. naive Bayes, and support vector machine, were applied to three independent sequence sets. The identified significant features were found to be biologically meaningful. while the experiments showed promising results.     

Communication between eukaryotic translation initiation factors 5 and 1A within the ribosomal pre-initiation complex plays a role in start site selection

During eukaryotic translation initiation, the 43 S ribosomal pre-initiation complex scans the mRNA in search of an AUG codon at which to begin translation. Start codon recognition halts scanning and triggers a number of events that commit the complex to beginning translation at that point on the mRNA. Previous studies in vitro and in vivo have indicated that eukaryotic initiation factors (eIFs) 1, 2 and 5 play key roles in these events. In addition, it was reported recently that the C-terminal domain of eIF1A is involved in maintaining the fidelity of start codon recognition. The molecular mechanisms by which these factors work together to ensure fidelity of start site selection remain poorly understood. Here, we report the quantitative characterization of energetic interactions between eIF1A, eIF5 and the AUG codon in an in vitro reconstituted yeast translation initiation system. Our results show that recognition of an AUG codon by the 43 S complex triggers an interaction between eIF5 and eIF1A, resulting in a shift in the equilibrium between two states of the pre-initiation complex. This AUG-dependent change may be a reorganization from a scanning-competent state to a scanning-incompetent state. Mutations in both eIF1A and eIF5 that increase initiation at non-AUG codons in vivo weaken the interaction between the two factors upon AUG recognition, while specifically strengthening it in response to a UUG codon. These data suggest strongly that the interaction between eIF1A and eIF5 is involved in maintaining the fidelity of start codon recognition in vivo.     

Conservation of translation initiation sites based on dinucleotide frequency and codon usage in Escherichia coli K-12 (W3110): non-random distribution of A/T-rich sequences immediately upstream of the translation initiation codon.

Dinucleotide frequencies are useful for characterizing consensus elements as a minimum unit of nucleotide sequence because the neighborhood relations of nucleotide sequences are reflected in dinucleotides. Using a consensus score based on dinucleotide frequencies and intra-species codon usage heterogeneity, denoted by the Z1 parameter, we report the relationship between nucleotide conservation at the translation initiation sites of genes in the Escherichia coli K-12 genome (W3110) and codon usage in its downstream genes. Significant positive correlations were obtained in three regions centered at -13, -4, and +7, which correspond to the Shine-Dalgarno element, the A + T element immediately upstream of the translation initiation site, and the downstream box, respectively.     

Mapping the active sites of bacterial translation initiation factor IF3

IF3C is the C-terminal domain of Escherichia coli translation initiation factor 3 (IF3) and is responsible for all functions of this translation initiation factor but for its ribosomal recycling. To map the number and nature of the active sites of IF3 and to identify the essential Arg residue(s) chemically modified with 2,3-butanedione, the eight arginine residues of IF3C were substituted by Lys, His, Ser and Leu, generating 32 variants that were tested in vitro for all known IF3 activities. The IF3-30S subunit interaction was inhibited strongly by substitutions of Arg99, Arg112, Arg116, Arg147 and Arg168, the positive charges being important at positions 116 and 147. The 70S ribosome dissociation was affected by mutations of Arg112, Arg147 and, to a lesser extent, of Arg99 and Arg116. Pseudo-initiation complex dissociation was impaired by substitution of Arg99 and Arg112 (whose positive charges are important) and, to a lesser extent, of Arg116, Arg129, Arg133 and Arg147, while the dissociation of non-canonical 30S initiation complexes was preserved at wild-type levels in all 32 mutants. Stimulation of mRNA translation was reduced by mutations of Arg116, Arg129 and, to a lesser extent, of Arg99, Arg112 and Arg131 whereas inhibition of non-canonical mRNA translation was affected by substitutions of Arg99, Arg112, Arg168 and, to a lesser extent, Arg116, Arg129 and Arg131. Finally, repositioning the mRNA on the 30S subunit was affected weakly by mutations of Arg133, Arg131, Arg168, Arg147 and Arg129. Overall, the results define two active surfaces in IF3C, and indicate that the different functions of IF3 rely on different molecular mechanisms involving separate active sites.     

Prediction of translation initiation sites in human mRNA sequences with AUG start codon in weak Kozak context: A neural network approach

Translation of eukaryotic mRNAs is often regulated by nucleotides around the start codon. A purine at position −3 and a guanine at position +4 contribute significantly to enhance the translation efficiency. Algorithms to predict the translation initiation site often fail to predict the start site if the sequence context is not present. We have developed a neural network method to predict the initiation site of mRNA sequences that lack the preferred nucleotides at the positions −3 and +4 surrounding the translation initiation site. Neural networks of various architectures comprising different number of hidden layers were designed and tested for various sizes of windows of nucleotides surrounding translation initiation sites. We found that the neural network with two hidden layers showed a sensitivity of 83% and specificity of 73% indicating a vastly improved performance in successfully predicting the translation initiation site of mRNA sequences with weak Kozak context. WeakAUG server is freely available at http://bioinfo.iitk.ac.in/AUGPred/.     

Identification of compounds that decrease the fidelity of start codon recognition by the eukaryotic translational machinery

Translation initiation in eukaryotes involves more than a dozen protein factors. Alterations in six factors have been found to reduce the fidelity of start codon recognition by the ribosomal preinitiation complex in yeast, a phenotype referred to as Sui(-). No small molecules are known that affect the fidelity of start codon recognition. Such compounds would be useful tools for probing the molecular mechanics of translation initiation and its regulation. To find compounds with this effect, we set up a high-throughput screen using a dual luciferase assay in S. cerevisiae. Screening of over 55,000 compounds revealed two structurally related molecules that decrease the fidelity of start codon selection by approximately twofold in the dual luciferase assay. This effect was confirmed using additional in vivo assays that monitor translation from non-AUG start codons. Both compounds increase translation of a natural upstream open reading frame previously shown to initiate translation at a UUG. The compounds were also found to exacerbate increased use of UUG as a start codon (Sui(-) phenotype) conferred by haploinsufficiency of wild-type eukaryotic initiation factor (eIF) 1, or by mutation in eIF1. Furthermore, the effects of the compounds are suppressed by overexpressing eIF1, which is known to restore the fidelity of start codon selection in strains harboring Sui(-) mutations in various other initiation factors. Together, these data strongly suggest that the compounds affect the translational machinery itself to reduce the accuracy of selecting AUG as the start codon.     

Computational analysis of miRNA-mediated repression of translation: implications for models of translation initiation inhibition

The mechanism by which miRNAs inhibit translation has been under scrutiny both in vivo and in vitro. Divergent results have led to the suggestion that miRNAs repress translation by a variety of mechanisms including blocking the function of the cap in stimulating translation. However, these analyses largely only examine the final output of the multistep process of translation. This raises the possibility that when different steps in translation are rate limiting, miRNAs might show different effects on protein production. To examine this possibility, we modeled the process of translation initiation and examined how the effects of miRNAs under different conditions might be explained. Our results suggest that different effects of miRNAs on protein production in separate experiments could be due to differences in rate-limiting steps. This analysis does not rule out that miRNAs directly repress the function of the cap structure, but it demonstrates that the observations used to argue for this effect are open to alternative interpretations. Taking all the data together, our analysis is consistent with the model that miRNAs may primarily repress translation initiation at a late step.     

PMERGE: Computational filtering of paralogous sequences from RAD‐seq data

Restriction‐site associated DNA sequencing (RAD‐seq) can identify and score thousands of genetic markers from a group of samples for population‐genetics studies. One challenge of de novo RAD‐seq analysis is to distinguish paralogous sequence variants (PSVs) from true single‐nucleotide polymorphisms (SNPs) associated with orthologous loci. In the absence of a reference genome, it is difficult to differentiate true SNPs from PSVs, and their impact on downstream analysis remains unclear. Here, we introduce a network‐based approach, PMERGE that connects fragments based on their DNA sequence similarity to identify probable PSVs. Applying our method to de novo RAD‐seq data from 150 Atlantic salmon (Salmo salar) samples collected from 15 locations across the Southern Newfoundland coast allowed the identification of 87% of total PSVs identified through alignment to the Atlantic salmon genome. Removal of these paralogs altered the inferred population structure, highlighting the potential impact of filtering in RAD‐seq analysis. PMERGE is also applied to a green crab (Carcinus maenas) data set consisting of 242 samples from 11 different locations and was successfully able to identify and remove the majority of paralogous loci (62%). The PMERGE software can be run as part of the widely used Stacks analysis package.     

Activation of translation initiation factor eIF2B by insulin requires phosphatidyl inositol 3-kinase

Eukaryotic initiation factor eIF2B mediates a key regulatory step in peptide-chain initiation and is acutely activated by insulin, although it is not clear how. Inhibitors of phosphatidylinositide 3-kinase blocked activation of eIF2B, although rapamycin, which inhibits the p70 S6 kinase pathway, did not. Furthermore, a dominant negative mutant of PI 3-kinase also prevented activation of eIF2B, while a Sos-mutant, which blocks MAP kinase activation, did not. The data demonstrate that a pathway distinct from MAP and p70 S6 kinases regulates eIF2B. Glycogen synthase kinase-3 (GSK-3) phosphorylates and inactivates eIF2B. In all cases, eIF2B and GSK-3 were regulated reciprocally. Dominant negative PI 3-kinase abolished the insulin-induced inhibition of GSK-3. These data strongly support the hypothesis that insulin activates eIF2B through a signalling pathway involving PI 3-kinase and inhibition of GSK-3.     

Pan‐cancer RNA‐seq data stratifies tumours by some hallmarks of cancer

Numerous genetic and epigenetic alterations cause functional changes in cell biology underlying cancer. These hallmark functional changes constitute potentially tissue‐independent anticancer therapeutic targets. We hypothesized that RNA‐Seq identifies gene expression changes that underly those hallmarks, and thereby defines relevant therapeutic targets. To test this hypothesis, we analysed the publicly available TCGA‐TARGET‐GTEx gene expression data set from the University of California Santa CruzToil recompute project using WGCNA to delineate co‐correlated ‘modules’ from tumour gene expression profiles and functional enrichment of these modules to hierarchically cluster tumours. This stratified tumours according to T cell activation, NK‐cell activation, complement cascade, ATM, Rb, angiogenic, MAPK, ECM receptor and histone modification signalling. These correspond to the cancer hallmarks of avoiding immune destruction, tumour‐promoting inflammation, evading growth suppressors, inducing angiogenesis, sustained proliferative signalling, activating invasion and metastasis, and genome instability and mutation. This approach did not detect pathways corresponding to the cancer enabling replicative immortality, resisting cell death or deregulating cellular energetics hallmarks. We conclude that RNA‐Seq stratifies tumours along some, but not all, hallmarks of cancer and, therefore, could be used in conjunction with other analyses collectively to inform precision therapy.