Summary: RNA silencing is a complex, highly conserved mechanism mediated by small RNAs (sRNAs), such as microRNAs (miRNAs), that is known to be involved in a diverse set of biological functions including development, pathogen control, genome maintenance and response to environmental change. Advances in next generation sequencing technologies are producing increasingly large numbers of sRNA reads per sample at a fraction of the cost of previous methods. However, many bioinformatics tools do not scale accordingly, are cumbersome, or require extensive support from bioinformatics experts. Therefore, researchers need user-friendly, robust tools, capable of not only processing large sRNA datasets in a reasonable time frame but also presenting the results in an intuitive fashion and visualizing sRNA genomic features. Herein, we present the UEA sRNA workbench, a suite of tools that is a successor to the web-based UEA sRNA Toolkit, but in downloadable format and with several enhanced and additional features.Availability: The program and help pages are available at http://srna-workbench.cmp.uea.ac.uk.Contact: vincent.moulton@cmp.uea.ac.uk

Small RNAs (sRNAs) are a class of short (20–25 nt) non-coding RNAs that play important regulatory roles in gene expression. An essential first step in understanding their function is to confidently identify sRNA targets. In plants, several classes of sRNAs such as microRNAs (miRNAs) and trans-acting small interfering RNAs have been shown to bind with near-perfect complementarity to their messenger RNA (mRNA) targets, generally leading to cleavage of the mRNA. Recently, a high-throughput technique known as Parallel Analysis of RNA Ends (PARE) has made it possible to sequence mRNA cleavage products on a large-scale. Computational methods now exist to use these data to find targets of conserved and newly identified miRNAs. Due to speed limitations such methods rely on the user knowing which sRNA sequences are likely to target a transcript. By limiting the search to a tiny subset of sRNAs it is likely that many other sRNA/mRNA interactions will be missed. Here, we describe a new software tool called PAREsnip that allows users to search for potential targets of all sRNAs obtained from high-throughput sequencing experiments. By searching for targets of a complete ‘sRNAome’ we can facilitate large-scale identification of sRNA targets, allowing us to discover regulatory interaction networks.

Small RNAs (sRNAs) are short, non-coding RNAs that play critical roles in many important biological pathways. They suppress the translation of messenger RNAs (mRNAs) by directing the RNA-induced silencing complex to their sequence-specific mRNA target(s). In plants, this typically results in mRNA cleavage and subsequent degradation of the mRNA. The resulting mRNA fragments, or degradome, provide evidence for these interactions, and thus degradome analysis has become an important tool for sRNA target prediction. Even so, with the continuing advances in sequencing technologies, not only are larger and more complex genomes being sequenced, but also degradome and associated datasets are growing both in number and read count. As a result, existing degradome analysis tools are unable to process the volume of data being produced without imposing huge resource and time requirements. Moreover, these tools use stringent, non-configurable targeting rules, which reduces their flexibility. Here, we present a new and user configurable software tool for degradome analysis, which employs a novel search algorithm and sequence encoding technique to reduce the search space during analysis. The tool significantly reduces the time and resources required to perform degradome analysis, in some cases providing more than two orders of magnitude speed-up over current methods.

Syntrophies are metabolic cooperations, whereby two organisms co-metabolize a substrate in an interdependent manner. Many of the observed natural syntrophic interactions are mandatory in the absence of strong electron acceptors, such that one species in the syntrophy has to assume the role of electron sink for the other. While this presents an ecological setting for syntrophy to be beneficial, the potential genetic drivers of syntrophy remain unknown to date. Here, we show that the syntrophic sulfate reducing species Desulfovibrio vulgaris displays a stable genetic polymorphism, where only a specific genotype is able to engage in syntrophy with the hydrogenotrophic methanogen Methanococcus maripaludis. This “syntrophic” genotype is characterized by two genetic alterations, one of which is an in-frame deletion in the gene encoding for the ion translocating subunit cooK of the membrane-bound COO-hydrogenase. We show that this genotype presents a specific physiology, in which reshaping of energy conservation in the lactate oxidation pathway enables it to produce sufficient intermediate hydrogen for sustained M. maripaludis growth and thus, syntrophy. To our knowledge, these findings provide for the first time a genetic basis for syntrophy in nature and bring us closer to the rational engineering of syntrophy in synthetic microbial communities.

Somites arising from paraxial mesoderm are a hallmark of the segmented vertebrate body plan. They form sequentially during axis extension and generate musculoskeletal cell lineages. How paraxial mesoderm becomes regionalised along the axis and how this correlates with dynamic changes of chromatin accessibility and the transcriptome remains unknown. Here, we report a spatiotemporal series of ATAC-seq and RNA-seq along the chick embryonic axis. Footprint analysis shows differential coverage of binding sites for several key transcription factors, including CDX2, LEF1 and members of HOX clusters. Associating accessible chromatin with nearby expressed genes identifies cis-regulatory elements (CRE) for TCF15 and MEOX1. We determine their spatiotemporal activity and evolutionary conservation in Xenopus and human. Epigenome silencing of endogenous CREs disrupts TCF15 and MEOX1 gene expression and recapitulates phenotypic abnormalities of anterior–posterior axis extension. Our integrated approach allows dissection of paraxial mesoderm regulatory circuits in vivo and has implications for investigating gene regulatory networks.

RNA silencing (RNA interference, RNAi) is a complex, highly conserved mechanism mediated by short, typically 20-24 nt in length, noncoding RNAs known as small RNAs (sRNAs). They act as guides for the sequence-specific transcriptional and posttranscriptional regulation of target mRNAs and play a key role in the fine-tuning of biological processes such as growth, response to stresses, or defense mechanism.High-throughput sequencing (HTS) technologies are employed to capture the expression levels of sRNA populations. The processing of the resulting big data sets facilitated the computational analysis of the sRNA patterns of variation within biological samples such as time point experiments, tissue series or various treatments. Rapid technological advances enable larger experiments, often with biological replicates leading to a vast amount of raw data. As a result, in this fast-evolving field, the existing methods for sequence characterization and prediction of interaction (regulatory) networks periodically require adapting or in extreme cases, a complete redesign to cope with the data deluge. In addition, the presence of numerous tools focused only on particular steps of HTS analysis hinders the systematic parsing of the results and their interpretation.The UEA small RNA Workbench (v1-4), described in this chapter, provides a user-friendly, modular, interactive analysis in the form of a suite of computational tools designed to process and mine sRNA datasets for interesting characteristics that can be linked back to the observed phenotypes. First, we show how to preprocess the raw sequencing output and prepare it for downstream analysis. Then we review some quality checks that can be used as a first indication of sources of variability between samples. Next we show how the Workbench can provide a comparison of the effects of different normalization approaches on the distributions of expression, enhanced methods for the identification of differentially expressed transcripts and a summary of their corresponding patterns. Finally we describe individual analysis tools such as PAREsnip, for the analysis of PARE (degradome) data or CoLIde for the identification of sRNA loci based on their expression patterns and the visualization of the results using the software. We illustrate the features of the UEA sRNA Workbench on Arabidopsis thaliana and Homo sapiens datasets.

Differences in lifespan between males and females are found across many taxa and may be determined, at least in part, by differential responses to diet. Here we tested the hypothesis that the higher dietary sensitivity of female lifespan is mediated by higher and more dynamic expression in nutrient-sensing pathways in females. We first reanalysed existing RNA-seq data, focusing on 17 nutrient-sensing genes with reported lifespan effects. This revealed, consistent with the hypothesis, a dominant pattern of female-biased gene expression, and among sex-biased genes there tended to be a loss of female-bias after mating. We then tested directly the expression of these 17 nutrient-sensing genes in wild-type third instar larvae, once-mated 5- and 16-day-old adults. This confirmed sex-biased gene expression and showed that it was generally absent in larvae, but frequent and stable in adults. Overall, the findings suggest a proximate explanation for the sensitivity of female lifespan to dietary manipulations. We suggest that the contrasting selective pressures to which males and females are subject create differing nutritional demands and requirements, resulting in sex differences in lifespan. This underscores the potential importance of the health impacts of sex-specific dietary responses.

Maternal diet during gestation and lactation affects the development of skeletal muscles in offspring and determines muscle health in later life. In this paper, we describe the association between maternal low protein diet-induced changes in offspring skeletal muscle and the differential expression (DE) of small non-coding RNAs (sncRNAs). We used a mouse model of maternal protein restriction, where dams were fed either a normal (N, 20%) or a low protein (L, 8%) diet during gestation and newborns were cross-fostered to N or L lactating dams, resulting in the generation of NN, NL and LN offspring groups. Total body and tibialis anterior (TA) weights were decreased in weanling NL male offspring but were not different in the LN group, as compared to NN. However, histological evaluation of TA muscle revealed reduced muscle fibre size in both groups at weaning. Small RNA-sequencing demonstrated DE of multiple miRs, snoRNAs and snRNAs. Bioinformatic analyses of miRs-15a, -34a, -122 and -199a, in combination with known myomiRs, confirmed their implication in key muscle-specific biological processes. This is the first comprehensive report for the DE of sncRNAs in nutrition-associated programming of skeletal muscle development, highlighting the need for further research to unravel the detailed molecular mechanisms.