Reprograming of sRNA target specificity by the leader peptide peTrpL in response to antibiotic exposure.

Trans-acting regulatory RNAs have the capacity to base pair with more mRNAs than generally detected under defined conditions, raising the possibility that sRNA target specificities vary depending on the specific metabolic or environmental conditions. In Sinorhizobium meliloti, the sRNA rnTrpL is derived from a tryptophan (Trp) transcription attenuator located upstream of the Trp biosynthesis gene trpE(G). The sRNA rnTrpL contains a small ORF, trpL, encoding the 14-aa leader peptide peTrpL. If Trp is available, efficient trpL translation causes transcription termination and liberation of rnTrpL, which subsequently acts to downregulate the trpDC operon, while peTrpL is known to have a Trp-independent role in posttranscriptional regulation of antibiotic resistance mechanisms. Here, we show that tetracycline (Tc) causes rnTrpL accumulation independently of Trp availability. In the presence of Tc, rnTrpL and peTrpL act collectively to destabilize rplUrpmA mRNA encoding ribosomal proteins L21 and L27. The three molecules, rnTrpL, peTrpL, and rplUrpmA mRNA, form an antibiotic-dependent ribonucleoprotein complex (ARNP). In vitro reconstitution of this ARNP in the presence of competing trpD and rplU transcripts revealed that peTrpL and Tc cause a shift of rnTrpL specificity towards rplU, suggesting that sRNA target prioritization may be readjusted in response to changing environmental conditions.

Identification of differentially expressed gene modules in heterogeneous diseases.

MOTIVATION: Identification of differentially expressed genes is necessary for unraveling disease pathogenesis. This task is complicated by the fact that many diseases are heterogeneous at the molecular level and samples representing distinct disease subtypes may demonstrate different patterns of dysregulation. Biclustering methods are capable of identifying genes that follow a similar expression pattern only in a subset of samples and hence can consider disease heterogeneity. However, identifying biologically significant and reproducible sets of genes and samples remain challenging for the existing tools. Many recent studies have shown that the integration of gene expression and protein interaction data improves the robustness of prediction and classification and advances biomarker discovery. RESULTS: Here, we present DESMOND, a new method for identification of Differentially ExpreSsed gene MOdules iN Diseases. DESMOND performs network-constrained biclustering on gene expression data and identifies gene modules-connected sets of genes up- or down-regulated in subsets of samples. We applied DESMOND on expression profiles of samples from two large breast cancer cohorts and have shown that the capability of DESMOND to incorporate protein interactions allows identifying the biologically meaningful gene and sample subsets and improves the reproducibility of the results. AVAILABILITY AND IMPLEMENTATION: https://github.com/ozolotareva/DESMOND. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Translation at first sight: the influence of leading codons.

First triplets of mRNA coding region affect the yield of translation. We have applied the flowseq method to analyze >30 000 variants of the codons 2-11 of the fluorescent protein reporter to identify factors affecting the protein synthesis. While the negative influence of mRNA secondary structure on translation has been confirmed, a positive role of rare codons at the beginning of a coding sequence for gene expression has not been observed. The identity of triplets proximal to the start codon contributes more to the protein yield then more distant ones. Additional in-frame start codons enhance translation, while Shine-Dalgarno-like motifs downstream the initiation codon are inhibitory. The metabolic cost of amino acids affects the yield of protein in the poor medium. The most efficient translation was observed for variants with features resembling those of native Escherichia coli genes.

Predicting RNA secondary structure by a neural network: what features may be learned?

Deep learning is a class of machine learning techniques capable of creating internal representation of data without explicit preprogramming. Hence, in addition to practical applications, it is of interest to analyze what features of biological data may be learned by such models. Here, we describe PredPair, a deep learning neural network trained to predict base pairs in RNA structure from sequence alone, without any incorporated prior knowledge, such as the stacking energies or possible spatial structures. PredPair learned the Watson-Crick and wobble base-pairing rules and created an internal representation of the stacking energies and helices. Application to independent experimental (DMS-Seq) data on nucleotide accessibility in mRNA showed that the nucleotides predicted as paired indeed tend to be involved in the RNA structure. The performance of the constructed model was comparable with the state-of-the-art method based on the thermodynamic approach, but with a higher false positives rate. On the other hand, it successfully predicted pseudoknots. t-SNE clusters of embeddings of RNA sequences created by PredPair tend to contain embeddings from particular Rfam families, supporting the predictions of PredPair being in line with biological classification.

A hierarchy in clusters of cephalopod mRNA editing sites.

RNA editing in the form of substituting adenine with inosine (A-to-I editing) is the most frequent type of RNA editing in many metazoan species. In most species, A-to-I editing sites tend to form clusters and editing at clustered sites depends on editing of the adjacent sites. Although functionally important in some specific cases, A-to-I editing usually is rare. The exception occurs in soft-bodied coleoid cephalopods, where tens of thousands of potentially important A-to-I editing sites have been identified, making coleoids an ideal model for studying of properties and evolution of A-to-I editing sites. Here, we apply several diverse techniques to demonstrate a strong tendency of coleoid RNA editing sites to cluster along the transcript. We show that clustering of editing sites and correlated editing substantially contribute to the transcriptome diversity that arises due to extensive RNA editing. Moreover, we identify three distinct types of editing site clusters, varying in size, and describe RNA structural features and mechanisms likely underlying formation of these clusters. In particular, these observations may explain sequence conservation at large distances around editing sites and the observed dependency of editing on mutations in the vicinity of editing sites.

Adaptive evolution at mRNA editing sites in soft-bodied cephalopods.

BACKGROUND: The bulk of variability in mRNA sequence arises due to mutation-change in DNA sequence which is heritable if it occurs in the germline. However, variation in mRNA can also be achieved by post-transcriptional modification including mRNA editing, changes in mRNA nucleotide sequence that mimic the effect of mutations. Such modifications are not inherited directly; however, as the processes affecting them are encoded in the genome, they have a heritable component, and therefore can be shaped by selection. In soft-bodied cephalopods, adenine-to-inosine RNA editing is very frequent, and much of it occurs at nonsynonymous sites, affecting the sequence of the encoded protein. METHODS: We study selection regimes at coleoid A-to-I editing sites, estimate the prevalence of positive selection, and analyze interdependencies between the editing level and contextual characteristics of editing site. RESULTS: Here, we show that mRNA editing of individual nonsynonymous sites in cephalopods originates in evolution through substitutions at regions adjacent to these sites. As such substitutions mimic the effect of the substitution at the edited site itself, we hypothesize that they are favored by selection if the inosine is selectively advantageous to adenine at the edited position. Consistent with this hypothesis, we show that edited adenines are more frequently substituted with guanine, an informational analog of inosine, in the course of evolution than their unedited counterparts, and for heavily edited adenines, these transitions are favored by positive selection. Our study shows that coleoid editing sites may enhance adaptation, which, together with recent observations on Drosophila and human editing sites, points at a general role of RNA editing in the molecular evolution of metazoans.