Dissecting the protein–RNA interface: the role of protein surface shapes and RNA secondary structures in protein–RNA recognition

Protein–RNA interactions are essential for many biological processes. However, the structural mechanisms underlying these interactions are not fully understood. Here, we analyzed the protein surface shape (dented, intermediate or protruded) and the RNA base pairing properties (paired or unpaired nucleotides) at the interfaces of 91 protein–RNA complexes derived from the Protein Data Bank. Dented protein surfaces prefer unpaired nucleotides to paired ones at the interface, and hydrogen bonds frequently occur between the protein backbone and RNA bases. In contrast, protruded protein surfaces do not show such a preference, rather, electrostatic interactions initiate the formation of hydrogen bonds between positively charged amino acids and RNA phosphate groups. Interestingly, in many protein–RNA complexes that interact via an RNA loop, an aspartic acid is favored at the interface. Moreover, in most of these complexes, nucleotide bases in the RNA loop are flipped out and form hydrogen bonds with the protein, which suggests that aspartic acid is important for RNA loop recognition through a base-flipping process. This study provides fundamental insights into the role of the shape of the protein surface and RNA secondary structures in mediating protein–RNA interactions

Identification of Transposable Elements Contributing to Tissue-Specific Expression of Long Non-Coding RNAs

It has been recently suggested that transposable elements (TEs) are re-used as functional elements of long non-coding RNAs (lncRNAs). This is supported by some examples such as the human endogenous retrovirus subfamily H (HERVH) elements contained within lncRNAs and expressed specifically in human embryonic stem cells (hESCs), as required to maintain hESC identity. There are at least two unanswered questions about all lncRNAs. How many TEs are re-used within lncRNAs? Are there any other TEs that affect tissue specificity of lncRNA expression? To answer these questions, we comprehensively identify TEs that are significantly related to tissue-specific expression levels of lncRNAs. We downloaded lncRNA expression data corresponding to normal human tissue from the Expression Atlas and transformed the data into tissue specificity estimates. Then, Fisher’s exact tests were performed to verify whether the presence or absence of TE-derived sequences influences the tissue specificity of lncRNA expression. Many TE–tissue pairs associated with tissue-specific expression of lncRNAs were detected, indicating that multiple TE families can be re-used as functional domains or regulatory sequences of lncRNAs. In particular, we found that the antisense promoter region of L1PA2, a LINE-1 subfamily, appears to act as a promoter for lncRNAs with placenta-specific expression

reactIDR: evaluation of the statistical reproducibility of high-throughput structural analyses towards a robust RNA structure prediction

Abstract Background Recently, next-generation sequencing techniques have been applied for the detection of RNA secondary structures, which is referred to as high-throughput RNA structural (HTS) analyses, and many different protocols have been used to detect comprehensive RNA structures at single-nucleotide resolution. However, the existing computational analyses heavily depend on the experimental methodology to generate data, which results in difficulties associated with statistically sound comparisons or combining the results obtained using different HTS methods. Results Here, we introduced a statistical framework, reactIDR, which can be applied to the experimental data obtained using multiple HTS methodologies. Using this approach, nucleotides are classified into three structural categories, loop, stem/background, and unmapped. reactIDR uses the irreproducible discovery rate (IDR) with a hidden Markov model to discriminate between the true and spurious signals obtained in the replicated HTS experiments accurately, and it is able to incorporate an expectation-maximization algorithm and supervised learning for efficient parameter optimization. The results of our analyses of the real-life HTS data showed that reactIDR had the highest accuracy in the classification of ribosomal RNA stem/loop structures when using both individual and integrated HTS datasets, and its results corresponded the best to the three-dimensional structures. Conclusions We have developed a novel software, reactIDR, for the prediction of stem/loop regions from the HTS analysis datasets. For the rRNA structure analyses, reactIDR was shown to have robust accuracy across different datasets by using the reproducibility criterion, suggesting its potential for increasing the value of existing HTS datasets. reactIDR is publicly available at https://github.com/carushi/reactIDR

LncRNA-dependent nuclear stress bodies promote intron retention through SR protein phosphorylation

A number of long noncoding RNAs (lncRNAs) are induced in response to specific stresses to construct membrane-less nuclear bodies; however, their function remains poorly understood. Here, we report the role of nuclear stress bodies (nSBs) formed on highly repetitive satellite III (HSATIII) lncRNAs derived from primate-specific satellite III repeats upon thermal stress exposure. A transcriptomic analysis revealed that depletion of HSATIII lncRNAs, resulting in elimination of nSBs, promoted splicing of 533 retained introns during thermal stress recovery. A HSATIII-Comprehensive identification of RNA-binding proteins by mass spectrometry (ChIRP-MS) analysis identified multiple splicing factors in nSBs, including serine and arginine-rich pre-mRNA splicing factors (SRSFs), the phosphorylation states of which affect splicing patterns. SRSFs are rapidly de-phosphorylated upon thermal stress exposure. During stress recovery, CDC like kinase 1 (CLK1) was recruited to nSBs and accelerated the re-phosphorylation of SRSF9, thereby promoting target intron retention. Our findings suggest that HSATIII-dependent nSBs serve as a conditional platform for phosphorylation of SRSFs by CLK1 to promote the rapid adaptation of gene expression through intron retention following thermal stress exposure

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Initial and tissue-specific candidate mRNAs with expression levels ≥1 FPKM for the prediction of TINCR-mRNA interactions. Expression levels were derived from RNA-seq data of GTEx consortium (Expression Atlas ID: E-MTAB-2919). One-tailed Fisher’s exact test was applied for comparing initial dataset and tissue-specific dataset. P-values were adjusted for multiple testing with Bonferroni correction. Tissue-specific expression of TINCR was also detected by ROKU [12]. (PDF 19 kb

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Initial and tissue-specific candidate mRNAs with expression levels ≥1 FPKM for the prediction of TINCR-mRNA interactions. Expression levels were derived from RNA-seq data of GTEx consortium (Expression Atlas ID: E-MTAB-2919). One-tailed Fisher’s exact test was applied for comparing initial dataset and tissue-specific dataset. P-values were adjusted for multiple testing with Bonferroni correction. Tissue-specific expression of TINCR was also detected by ROKU [12]. (PDF 19 kb

Extended ensemble simulations of a SARS-CoV-2 nsp1–5’-UTR complex

Non-structural protein 1 (nsp1) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a 180-residue protein which blocks the translation of SARS-CoV-2 infected cells. Although it has been known that SARS-CoV-2\u27s own RNA evades nsp1\u27s host translation shutoff, its molecular mechanism has been poorly understood. We performed an extended ensemble molecular dynamics simulation to investigate the mechanism of the viral RNA evasion. Simulation results showed that the stem loop structure of SARS-CoV-2 RNA 5\u27-untranslated region is recognized by both nsp1\u27s globular region and intrinsically disordered region