Major gene expression changes and epigenetic remodelling in Nile tilapia muscle after just one generation of domestication

The historically recent domestication of fishes has been essential to meet the protein demands of a growing human population. Selection for traits of interest during domestication is a complex process whose epigenetic basis is poorly understood. Cytosine hydroxymethylation is increasingly recognized as an important DNA modification involved in epigenetic regulation. In the present study, we investigated if hydroxymethylation plays a role in fish domestication and demonstrated for the first time at a genome-wide level and single nucleotide resolution that the muscle hydroxymethylome changes after a single generation of Nile tilapia (Oreochromis niloticus, Linnaeus) domestication. The overall decrease in hydroxymethylcytosine levels was accompanied by the downregulation of 2015 genes in fish reared in captivity compared to their wild progenitors. In contrast, several myogenic and metabolic genes that can affect growth potential were upregulated. There were 126 differentially hydroxymethylated cytosines between groups, which were not due to genetic variation; they were associated with genes involved in immune-, growth- and neuronal-related pathways. Taken together, our data unveil a new role for DNA hydroxymethylation in epigenetic regulation of fish domestication with impact in aquaculture and implications in artificial selection, environmental adaptation and genome evolution

Comparing Artificial Neural Networks, General Linear Models and Support Vector Machines in Building Predictive Models for Small Interfering RNAs

Exogenous short interfering RNAs (siRNAs) induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation approach has on finding and discriminating among predictive models.Three learning techniques were used to develop predictive models for effective siRNA sequences including Artificial Neural Networks (ANNs), General Linear Models (GLMs) and Support Vector Machines (SVMs). Five feature mapping methods were also used to generate models of siRNA activities. The 2 factors of learning technique and feature mapping were evaluated by complete 3x5 factorial ANOVA. Overall, both learning techniques and feature mapping contributed significantly to the observed variance in predictive models, but to differing degrees for precision and accuracy as well as across different kinds and levels of model cross-validation.The methods presented here provide a robust statistical framework to compare among models developed under distinct learning techniques and feature sets for siRNAs. Further comparisons among current or future modeling approaches should apply these or other suitable statistically equivalent methods to critically evaluate the performance of proposed models. ANN and GLM techniques tend to be more sensitive to the inclusion of noisy features, but the SVM technique is more robust under large numbers of features for measures of model precision and accuracy. Features found to result in maximally predictive models are not consistent across learning techniques, suggesting care should be taken in the interpretation of feature relevance. In the models developed here, there are statistically differentiable combinations of learning techniques and feature mapping methods where the SVM technique under a specific combination of features significantly outperforms all the best combinations of features within the ANN and GLM techniques

MicroTar: predicting microRNA targets from RNA duplexes

BACKGROUND: The accurate prediction of a comprehensive set of messenger RNAs (targets) regulated by animal microRNAs (miRNAs) remains an open problem. In particular, the prediction of targets that do not possess evolutionarily conserved complementarity to their miRNA regulators is not adequately addressed by current tools. RESULTS: We have developed MicroTar, an animal miRNA target prediction tool based on miRNA-target complementarity and thermodynamic data. The algorithm uses predicted free energies of unbound mRNA and putative mRNA-miRNA heterodimers, implicitly addressing the accessibility of the mRNA 3' untranslated region. MicroTar does not rely on evolutionary conservation to discern functional targets, and is able to predict both conserved and non-conserved targets. MicroTar source code and predictions are accessible at , where both serial and parallel versions of the program can be downloaded under an open-source licence. CONCLUSION: MicroTar achieves better sensitivity than previously reported predictions when tested on three distinct datasets of experimentally-verified miRNA-target interactions in C. elegans, Drosophila, and mouse

Clustered ChIP-Seq-defined transcription factor binding sites and histone modifications map distinct classes of regulatory elements

<p>Abstract</p> <p>Background</p> <p>Transcription factor binding to DNA requires both an appropriate binding element and suitably open chromatin, which together help to define regulatory elements within the genome. Current methods of identifying regulatory elements, such as promoters or enhancers, typically rely on sequence conservation, existing gene annotations or specific marks, such as histone modifications and p300 binding methods, each of which has its own biases.</p> <p>Results</p> <p>Herein we show that an approach based on clustering of transcription factor peaks from high-throughput sequencing coupled with chromatin immunoprecipitation (Chip-Seq) can be used to evaluate markers for regulatory elements. We used 67 data sets for 54 unique transcription factors distributed over two cell lines to create regulatory element clusters. By integrating the clusters from our approach with histone modifications and data for open chromatin, we identified general methylation of lysine 4 on histone H3 (H3K4me) as the most specific marker for transcription factor clusters. Clusters mapping to annotated genes showed distinct patterns in cluster composition related to gene expression and histone modifications. Clusters mapping to intergenic regions fall into two groups either directly involved in transcription, including miRNAs and long noncoding RNAs, or facilitating transcription by long-range interactions. The latter clusters were specifically enriched with H3K4me1, but less with acetylation of lysine 27 on histone 3 or p300 binding.</p> <p>Conclusion</p> <p>By integrating genomewide data of transcription factor binding and chromatin structure and using our data-driven approach, we pinpointed the chromatin marks that best explain transcription factor association with different regulatory elements. Our results also indicate that a modest selection of transcription factors may be sufficient to map most regulatory elements in the human genome.</p

Transcriptome-Wide Prediction of miRNA Targets in Human and Mouse Using FASTH

Transcriptional regulation by microRNAs (miRNAs) involves complementary base-pairing at target sites on mRNAs, yielding complex secondary structures. Here we introduce an efficient computational approach and software (FASTH) for genome-scale prediction of miRNA target sites based on minimizing the free energy of duplex structure. We apply our approach to identify miRNA target sites in the human and mouse transcriptomes. Our results show that short sequence motifs in the 5′ end of miRNAs frequently match mRNAs perfectly, not only at validated target sites but additionally at many other, energetically favourable sites. High-quality matching regions are abundant and occur at similar frequencies in all mRNA regions, not only the 3′UTR. About one-third of potential miRNA target sites are reassigned to different mRNA regions, or gained or lost altogether, among different transcript isoforms from the same gene. Many potential miRNA target sites predicted in human are not found in mouse, and vice-versa, but among those that do occur in orthologous human and mouse mRNAs most are situated in corresponding mRNA regions, i.e. these sites are themselves orthologous. Using a luciferase assay in HEK293 cells, we validate four of six predicted miRNA-mRNA interactions, with the mRNA level reduced by an average of 73%. We demonstrate that a thermodynamically based computational approach to prediction of miRNA binding sites on mRNAs can be scaled to analyse complete mammalian transcriptome datasets. These results confirm and extend the scope of miRNA-mediated species- and transcript-specific regulation in different cell types, tissues and developmental conditions

An integrated expression atlas of miRNAs and their promoters in human and mouse

MicroRNAs (miRNAs) are short non-coding RNAs with key roles in cellular regulation. As part of the fifth edition of the Functional Annotation of Mammalian Genome (FANTOM5) project, we created an integrated expression atlas of miRNAs and their promoters by deep-sequencing 492 short RNA (sRNA) libraries, with matching Cap Analysis Gene Expression (CAGE) data, from 396 human and 47 mouse RNA samples. Promoters were identified for 1,357 human and 804 mouse miRNAs and showed strong sequence conservation between species. We also found that primary and mature miRNA expression levels were correlated, allowing us to use the primary miRNA measurements as a proxy for mature miRNA levels in a total of 1,829 human and 1,029 mouse CAGE libraries. We thus provide a broad atlas of miRNA expression and promoters in primary mammalian cells, establishing a foundation for detailed analysis of miRNA expression patterns and transcriptional control regions

Comprehensive transcriptomic analyses of tissue, serum, and serum exosomes from hepatocellular carcinoma patients

Background The expression of microRNAs (miRNAs) is a promising prognostic and diagnostic tool in hepatocellular carcinoma (HCC). Here we performed small RNA sequencing (sRNA-seq) of tissue, serum and serum exosomes to investigate changes in miRNA expression between the different sample types and correlated the expression with clinical parameters. We also performed gene expression arrays on tumor and normal tissue. Results Paired tissue, serum and serum exosomes sequencing revealed consistent positive correlation of miR-21 between serum exosomes and tumor tissue, indicating that miR-21 could be exported from tissue to circulation via exosomes. We found that let-7 miRNAs are generally upregulated in serum exosomes compared to whole serum, indicating that these miRNAs could be preferentially loaded into exosomes. Comparing serum from HCC patients with serum from healthy individuals revealed a global increase of miRNAs in serum from HCC patients, including an almost 4-fold increase of several miRNAs, including the liver-specific miR-122. When correlating miRNA expression with clinical parameters we detected significant association between hepatitis B virus (HBV) infection and miR-122 in serum as well as several serum and tissue-miRNAs that correlated with surgery type. We found that miR-141 and miR-146 correlated with cirrhosis in tumor tissue and normal tissue, respectively. Finally, high expression of miR-21 in tumors were associated with poor survival. Focusing on gene expression we found several significant messenger RNAs (mRNAs) between tumor and normal tissue and a Gene Ontology (GO) analysis revealed that these changes were mainly related to cell cycle and metabolism. Further, we detected mRNAs that correlated with cirrhosis and HBV infection in tissue. Finally, GO analysis of predicted targets for miRNAs down-regulated in tumor found that these were enriched for functions related to collagen synthesis. Conclusions Our combined data point to altered miRNA and mRNA expression contributing to both generally impaired lipid metabolism and increased cell proliferation and a miRNA-driven increase in collagen synthesis in HCC. Our results further indicate a correlation in miRNA expression between exosomes, serum, and tissue samples suggesting export from tumors via exosomes. This correlation could provide a basis for a more tumor-specific miRNA profile in serum

DNA hydroxymethylation differences underlie phenotypic divergence of somatic growth in Nile tilapia reared in common garden

Phenotypic plasticity of metabolism and growth are essential for adaptation to new environmental conditions, such as those experienced during domestication. Epigenetic regulation plays a key role in this process but the underlying mechanisms are poorly understood, especially in the case of hydroxymethylation. Using reduced representation 5-hydroxymethylcytosine profiling, we compared the liver hydroxymethylomes in full-sib Nile tilapia with distinct growth rates (3.8-fold difference) and demonstrated that DNA hydroxymethylation is strongly associated with phenotypic divergence of somatic growth during the early stages of domestication. The 2677 differentially hydroxymethylated cytosines between fast- and slow-growing fish were enriched within gene bodies (79%), indicating a pertinent role in transcriptional regulation. Moreover, they were found in genes involved in biological processes related to skeletal system and muscle structure development, and there was a positive association between somatic growth and 5hmC levels in genes coding for growth factors, kinases and receptors linked to myogenesis. Single nucleotide polymorphism analysis revealed no genetic differentiation between fast- and slow-growing fish. In addition to unveiling a new link between DNA hydroxymethylation and epigenetic regulation of growth in fish during the initial stages of domestication, this study suggests that epimarkers may be applied in selective breeding programmes for superior phenotypes.</p