Background: Translation is a central process of life, and its regulation is crucial for cell growth. In this article, focusing on two model organisms, Escherichia coli and Saccharomyces cerevisiae, we study how three major local features of a gene's coding sequence (its adaptation to the tRNA pool, its amino acid charge, and its mRNA folding energy) affect its translation elongation. Results: We find that each of these three different features has a non-negligible distinct correlation with the speed of translation elongation. In addition, each of these features might contribute independently to slowing down ribosomal speed at the beginning of genes, which was suggested in previous studies to improve ribosomal allocation and the cost of translation, and to decrease ribosomal jamming. Remarkably, a model of ribosomal translation based on these three basic features highly correlated with the genomic profile of ribosomal density. The robustness to transcription errors in terms of the values of these features is higher at the beginnings of genes, suggesting that this region is important for translation. Conclusions: The reported results support the conjecture that translation elongation speed is affected by the three coding sequence determinants mentioned above, and not only by adaptation to the tRNA pool; thus, evolution shapes all these determinants along the coding sequences and across genes to improve the organism's translation efficiency.

Significance: Hundreds of microRNAs become dramatically elevated in the plasma or serum of acetaminophen (APAP) overdose patients and then recover back toward normal during successful treatment with the APAP antidote, N -acetyl cysteine (NAC). Importantly, the elevation of these circulating miRNAs can precede the rise in the standard biomarker, alanine aminotransferase (ALT), and the recovery of these miRNAs during NAC treatment is more rapid than ALT. We identify a set of 11 miRNAs whose profiles and dynamics in circulation during NAC treatment can discriminate APAP hepatotoxicity from another common hepatotoxic condition, ischemic hepatitis. These findings suggest that miRNAs are sensitive diagnostic and prognostic biomarkers for liver injury with broad potential for use in monitoring drug-induced liver injury in clinical and research contexts.

Background: Multicellular animals and bacteria frequently engage in predator-prey and host-pathogen interactions, such as the well-studied relationship betweenPseudomonas aeruginosaand the nematodeCaenorhabditis elegans. This study investigates the genomic and genetic basis of bacterial-driven variability inP. aeruginosavirulence towardsC. elegansto provide evolutionary insights into host-pathogen relationships.Results: Natural isolates ofP. aeruginosathat exhibit diverse genomes display a broad range of virulence towardsC. elegans. Using gene association and genetic analysis, we identify accessory genome elements that correlate with virulence, including both known and novel virulence determinants. Among the novel genes, we find a viral-like mobile element, thetegblock, that impairs virulence and whose acquisition is restricted by CRISPR-Cas systems. Further genetic and genomic evidence suggests that spacer-targeted elements preferentially associate with lower virulence while the presence of CRISPR-Cas associates with higher virulence.Conclusions: Our analysis demonstrates substantial strain variation inP. aeruginosavirulence, mediated by specific accessory genome elements that promote increased or decreased virulence. We exemplify that viral-like accessory genome elements that decrease virulence can be restricted by bacterial CRISPR-Cas immune defense systems, and suggest a positive, albeit indirect, role for host CRISPR-Cas systems in virulence maintenance.

MicroRNAs are regulators of gene expression whose functions are critical for normal development and physiology. We have previously characterized mutations in a Caenorhabditis elegans microRNA-specific Argonaute ALG-1 (Argonaute-like gene) that are antimorphic [alg-1(anti)]. alg-1(anti) mutants have dramatically stronger microRNA-related phenotypes than animals with a complete loss of ALG-1. ALG-1(anti) miRISC (microRNA induced silencing complex) fails to undergo a functional transition from microRNA processing to target repression. To better understand this transition, we characterized the small RNA and protein populations associated with ALG-1(anti) complexes in vivo. We extensively characterized proteins associated with wild-type and mutant ALG-1 and found that the mutant ALG-1(anti) protein fails to interact with numerous miRISC cofactors, including proteins known to be necessary for target repression. In addition, alg-1(anti) mutants dramatically overaccumulated microRNA* (passenger) strands, and immunoprecipitated ALG-1(anti) complexes contained nonstoichiometric yields of mature microRNA and microRNA* strands, with some microRNA* strands present in the ALG-1(anti) Argonaute far in excess of the corresponding mature microRNAs. We show complex and microRNA-specific defects in microRNA strand selection and microRNA* strand disposal. For certain microRNAs (for example mir-58), microRNA guide strand selection by ALG-1(anti) appeared normal, but microRNA* strand release was inefficient. For other microRNAs (such as mir-2), both the microRNA and microRNA* strands were selected as guide by ALG-1(anti), indicating a defect in normal specificity of the strand choice. Our results suggest that wild-type ALG-1 complexes recognize structural features of particular microRNAs in the context of conducting the strand selection and microRNA* ejection steps of miRISC maturation.

Background: MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally via base-pairing with complementary sequences on messenger RNAs (mRNAs). Due to the technical challenges involved in the application of high-throughput experimental methods, datasets of direct bona fide miRNA targets exist only for a few model organisms. Machine learning (ML)-based target prediction models were successfully trained and tested on some of these datasets. There is a need to further apply the trained models to organisms in which experimental training data are unavailable. However, it is largely unknown how the features of miRNA–target interactions evolve and whether some features have remained fixed during evolution, raising questions regarding the general, cross-species applicability of currently available ML methods. Results: We examined the evolution of miRNA–target interaction rules and used data science and ML approaches to investigate whether these rules are transferable between species. We analyzed eight datasets of direct miRNA–target interactions in four species (human, mouse, worm, cattle). Using ML classifiers, we achieved high accuracy for intra-dataset classification and found that the most influential features of all datasets overlap significantly. To explore the relationships between datasets, we measured the divergence of their miRNA seed sequences and evaluated the performance of cross-dataset classification. We found that both measures coincide with the evolutionary distance between the compared species. Conclusions: The transferability of miRNA–targeting rules between species depends on several factors, the most associated factors being the composition of seed families and evolutionary distance. Furthermore, our feature-importance results suggest that some miRNA–target features have evolved while others remained fixed during the evolution of the species. Our findings lay the foundation for the future development of target prediction tools that could be applied to “non-model” organisms for which minimal experimental data are available. Availability and implementation: The code is freely available at https://github.com/gbenor/TPVOD.

Background: MicroRNAs (miRNAs) are an abundant class of small noncoding RNAs (20-24 nts) that can affect gene expression by post-transcriptional regulation of mRNAs. They play important roles in several biological processes (e.g., development and cell cycle regulation). Numerous bioinformatics methods have been developed to identify the function of miRNAs by predicting their target mRNAs. Some viral organisms also encode miRNAs, a fact that contributes to the complex interactions between viruses and their hosts. A need arises to understand the functional relationship between viral and host miRNAs and their effect on viral and host genes. Our approach to meet this challenge is to identify modules where viral and host miRNAs cooperatively regulate host gene expression. Results: We present a method to identify groups of viral and host miRNAs that cooperate in post-transcriptional gene regulation, and their target genes that are involved in similar biological processes. We call these groups (genes and miRNAs of human and viral origin) - modules. The modules are found in a new two-stage procedure, which we call bi-targeting, and is presented in this paper. The stages are (i) a new and efficient target prediction, and (ii) a new method for clustering objects of three different data types. In this work we integrate multiple information sources, including miRNA-target binding information, miRNA expression profiles, and GO annotations. Our hypotheses and the methods have been tested on human and Epstein Barr virus (EBV) miRNAs and human genes, for which we found 34 modules. We provide supporting evidence from biological and medical literature for two of our modules. Our code and data are available at http://www.cs.bgu.ac.il/~vaksler/BiTargeting.htm Conclusions: The presented algorithm, which makes use of diverse biological data, is demonstrated to be an efficient approach for finding bi-targeting modules of viral and human miRNAs. These modules can contribute to a better understanding of viral-host interactions and the role that miRNAs play in them.