Mechanism of ribosome translation through mRNA secondary structures

AliNA - a deep learning program for RNA secondary structure prediction

Nowadays there are numerous discovered natural RNA variations participating in different cellular processes and artificial RNA, e. g., aptamers, riboswitches. One of the required tasks in the investigation of their functions and mechanism of influence on cells and interaction with targets is the prediction of RNA secondary structures. The classic thermodynamic-based prediction algorithms do not consider the specificity of biological folding and deep learning methods that were designed to resolve this issue suffer from homology-based methods problems. Herein, we present a method for RNA secondary structure prediction based on deep learning - AliNA (ALIgned Nucleic Acids). Our method successfully predicts secondary structures for non-homologous to train-data RNA families thanks to usage of the data augmentation techniques. Augmentation extends existing datasets with easily-accessible simulated data. The proposed method shows a high quality of prediction across different benchmarks including pseudoknots. The method is available on GitHub for free (https://github.com/Arty40m/AliNA).

Long non-coding RNAs reveal new regulatory mechanisms controlling gene expression

A plethora of non-coding RNAs have been found in eukaryotes, notably with the advent of modern sequencing technologies to analyze the transcriptome. Apart from the well-known housekeeping RNA genes (such as the ribosomal RNA or the transfer RNA), many thousands of transcripts detected are not evidently linked to a protein-coding gene. These, so called non-coding RNAs, may code for crucial regulators of gene expression, the small si/miRNAs, for small peptides (translated under specific conditions) or may act as long RNA molecules (antisense, intronic or intergenic long non-coding RNAs or lncRNAs). The lncRNAs interact with members of multiple machineries involved in gene regulation. In this review, we discussed about how plant lncRNAs permitted to discover new regulatory mechanisms acting in epigenetic control, chromatin 3D structure and alternative splicing. These novel regulations diversified the expression patterns and protein variants of target protein-coding genes and are an important element of the response of plants to environmental stresses and their adaptation to changing conditions.

Bioinformatic Assessment of Factors Affecting the Correlation between Protein Abundance and Elongation Efficiency in Prokaryotes

Protein abundance is crucial for the majority of genetically regulated cell functions to act properly in prokaryotic organisms. Therefore, developing bioinformatic methods for assessing the efficiency of different stages of gene expression is of great importance for predicting the actual protein abundance. One of these steps is the evaluation of translation elongation efficiency based on mRNA sequence features, such as codon usage bias and mRNA secondary structure properties. In this study, we have evaluated correlation coefficients between experimentally measured protein abundance and predicted elongation efficiency characteristics for 26 prokaryotes, including non-model organisms, belonging to diverse taxonomic groups The algorithm for assessing elongation efficiency takes into account not only codon bias, but also number and energy of secondary structures in mRNA if those demonstrate an impact on predicted elongation efficiency of the ribosomal protein genes. The results show that, for a number of organisms, secondary structures are a better predictor of protein abundance than codon usage bias. The bioinformatic analysis has revealed several factors associated with the value of the correlation coefficient. The first factor is the elongation efficiency optimization type-the organisms whose genomes are optimized for codon usage only have significantly higher correlation coefficients. The second factor is taxonomical identity-bacteria that belong to the class Bacilli tend to have higher correlation coefficients among the analyzed set. The third is growth rate, which is shown to be higher for the organisms with higher correlation coefficients between protein abundance and predicted translation elongation efficiency. The obtained results can be useful for further improvement of methods for protein abundance prediction.

A Single Transcript Knockdown-Replacement Strategy Employing 5' UTR Secondary Structures to Precisely Titrate Rescue Protein Translation

One overarching goal of gene therapy is the replacement of faulty genes with functional ones. A significant hurdle is presented by the fact that under- or over-expression of a protein may cause disease as readily as coding mutations. There is a clear and present need for pipelines to translate experimentally validated gene therapy strategies to clinical application. To address this we developed a modular, single-transgene expression system for replacing target genes with physiologically expressed variants. In order to accomplish this, we first designed a range of 5' UTR "attenuator" sequences which predictably diminish translation of the paired gene. These sequences provide wide general utility by allowing control over translation from high expression, ubiquitous promoters. Importantly, we demonstrate that this permits an entirely novel knockdown and rescue application by pairing microRNA-adapted shRNAs alongside their respective replacement gene on a single transcript. A noteworthy candidate for this corrective approach is the degenerative and uniformly fatal motor neuron disease ALS. A strong proportion of non-idiopathic ALS cases are caused by varied mutations to the SOD1 gene, and as clinical trials to treat ALS are being initiated, it is important to consider that loss-of-function mechanisms contribute to its pathology as strongly as any other factor. As a generalized approach to treat monogenic diseases caused by heterogeneous mutations, we demonstrate complete and predictable control over replacement of SOD1 in stable cell lines by varying the strength of attenuators.

RR3DD: an RNA global structure-based RNA three-dimensional structural classification database

The three-dimensional (3D) structure of RNA usually plays an important role in the recognition with RNA-binding protein. Along with the discovering of RNAs, several RNA databases are developed to study the functions of RNA based on sequence, secondary structure, local 3D structural motif and global structure. Based on RNA function and structure, different RNAs are classified and stored in SCOR and DARTS, respectively. The classification of RNA structures is useful in RNA structure prediction and function annotation. However, the SCOR and DARTS are not updated any more. In this study, we present an RNA classification database RR3DD based on RNA fold with the global 3D structural similarity. The RR3DD includes 13,601 RNA chains from PDB and mmCIF format structures which are classified into 780 RNA folds. The RNA chains from PDB and mmCIF format structures are aligned and clustered into 675 and 220 RNA folds, respectively. By analysing the RNA structure in RR3DD, we find that there are 11 clusters with more than 50 members. These clusters include rRNAs, riboswitches, tRNAs and so on. By mapping RR3DD into Rfam, we found that some RNAs without annotation by Rfam can be annotated through structural alignment. For example, we analysed tRNAs and found that tRNA were successfully grouped in RR3DD for which Rfam did not classify them into one family. Finally, we provide a web interface of RR3DD offering functions of browsing RR3DD, annotating RNA 3D structure and finding templates for RNA homology modelling.

Transcriptome-Wide Analysis of Interplay between mRNA Stability, Translation and Small RNAs in Response to Neuronal Membrane Depolarization

Experience-dependent changes to neural circuitry are shaped by spatially-restricted activity-dependent mRNA translation. Although the complexity of mRNA translation in neuronal cells is widely appreciated, translational profiles associated with neuronal excitation remain largely uncharacterized, and the associated regulatory mechanisms are poorly understood. Here, we employed ribosome profiling, mRNA sequencing and small RNA sequencing to profile transcriptome-wide changes in mRNA translation after whole cell depolarization of differentiated neuroblast cultures, and investigate the contribution of sequence-specific regulatory mechanisms. Immediately after depolarization, a functional partition between transcriptional and translational responses was uncovered, in which many mRNAs were subjected to significant changes in abundance or ribosomal occupancy, but not both. After an extended (2 h) post-stimulus rest phase, however, these changes became synchronized, suggesting that there are different layers of post-transcriptional regulation which are temporally separated but become coordinated over time. Globally, changes in mRNA abundance and translation were found to be associated with a number of intrinsic mRNA features, including mRNA length, GC% and secondary structures; however, the effect of these factors differed between both post-depolarization time-points. Furthermore, small RNA sequencing revealed that miRNAs and tRNA-derived small RNA fragments were subjected to peak changes in expression immediately after stimulation, during which these molecules were predominantly associated with fluctuations in mRNA abundance, consistent with known regulatory mechanisms. These data suggest that excitation-associated neuronal translation is subjected to extensive temporal coordination, with substantial contributions from a number of sequence-dependent regulatory mechanisms.

Translation-Associated Mutational U-Pressure in the First ORF of SARS-CoV-2 and Other Coronaviruses

Within 4 months of the ongoing COVID-19 pandemic caused by SARS-CoV-2, more than 250 nucleotide mutations have been detected in ORF1ab of the virus isolated from infected persons from different parts of the globe. These observations open up an obvious question about the rate and direction of mutational pressure for further vaccine and therapeutics designing. In this study, we did a comparative analysis of ORF1a and ORF1b by using the first isolate (Wuhan strain) as the parent sequence. We observed that most of the nucleotide mutations are C to U transitions. The rate of synonymous C to U transitions is significantly higher than the rate of non-synonymous ones, indicating negative selection on amino acid substitutions. Further, trends in nucleotide usage bias have been investigated in 49 coronaviruses species. A strong bias in nucleotide usage in fourfold degenerate sites toward uracil residues is seen in ORF1ab of all the studied coronaviruses: both in the ORF1a and in the ORF1b translated thanks to the programmed ribosomal frameshifting that has an efficiency of 14 – 45% in different species. A more substantial mutational U-pressure is observed in ORF1a than in ORF1b perhaps because ORF1a is translated more frequently than ORF1b. Mutational U-pressure is there even in ORFs that are not translated from genomic RNA plus strands, but the bias is weaker than in ORF1ab. Unlike other nucleotide mutations, mutational U-pressure caused by cytosine deamination, mostly occurring during the RNA plus strand replication and also translation, cannot be corrected by the proof-reading machinery of coronaviruses. The knowledge generated on the mutational U-pressure that becomes stronger during translation of viral RNA plus strands has implications for vaccine and nucleoside analog development for treating COVID-19 and other coronavirus infections.

Codon Selection Affects Recruitment of Ribosome-Associating Factors during Translation

An intriguing aspect of protein synthesis is how cotranslational events are managed inside the cell. In this study, we developed an in vivo bimolecular fluorescence complementation assay coupled to SecM stalling (BiFC-SecM) to study how codon usage influences the interactions of ribosome-associating factors that occur cotranslationally. We profiled ribosomal associations of a number of proteins, and observed differential association of chaperone proteins TF, DnaK, GroEL, and translocation factor Ffh as a result of introducing synonymous codon substitutions that change the affinity of the translating sequence to the ribosomal anti-Shine-Dalgarno (aSD) sequence. The use of pausing sequences within proteins regulates their transit within the translating ribosome. Our results indicate that the dynamics between cellular factors and the new polypeptide chain are affected by how codon composition is designed. Furthermore, associating factors may play a role in processes including protein quality control (folding and degradation) and cellular respiration.

Non-tight and tight chemomechanical couplings of biomolecular motors under hindering loads

Biomolecular motors make use of free energy released from chemical reaction (typically ATP hydrolysis) to perform mechanical motion or work. An important issue is whether a molecular motor exhibits tight or non-tight chemomechanical (CM) coupling. The tight CM coupling refers to that each ATPase activity is coupled with a mechanical step, while the non-tight CM coupling refers to that an ATPase activity is not necessarily coupled with a mechanical step. Here, we take kinesin, monomeric DNA helicase, ring-shaped hexameric DNA helicase and ribosome as examples to study this issue. Our studies indicate that some motors such as kinesin, monomeric helicase and ribosome exhibit non-tight CM coupling under hindering forces, while others such as the ring-shaped hexameric helicase exhibit tight or nearly tight CM coupling under any force. For the former, the reduction of the velocity caused by the hindering force arises mainly from the reduction of the CM coupling efficiency, while the ATPase rate is independent or nearly independent of the force. For the latter, the reduction of the velocity caused by the hindering force arises mainly from the reduction of the ATPase rate, while the CM coupling efficiency is independent or nearly independent of the force.