microRNAs and genetic diseases

microRNAs (miRNAs) are a class of small RNAs (19-25 nucleotides in length) processed from double-stranded hairpin precursors. They negatively regulate gene expression in animals, by binding, with imperfect base pairing, to target sites in messenger RNAs (usually in 3' untranslated regions) thereby either reducing translational efficiency or determining transcript degradation. Considering that each miRNA can regulate, on average, the expression of approximately several hundred target genes, the miRNA apparatus can participate in the control of the gene expression of a large quota of mammalian transcriptomes and proteomes. As a consequence, miRNAs are expected to regulate various developmental and physiological processes, such as the development and function of many tissue and organs. Due to the strong impact of miRNAs on the biological processes, it is expected that mutations affecting miRNA function have a pathogenic role in human genetic diseases, similar to protein-coding genes. In this review, we provide an overview of the evidence available to date which support the pathogenic role of miRNAs in human genetic diseases. We will first describe the main types of mutation mechanisms affecting miRNA function that can result in human genetic disorders, namely: (1) mutations affecting miRNA sequences; (2) mutations in the recognition sites for miRNAs harboured in target mRNAs; and (3) mutations in genes that participate in the general processes of miRNA processing and function. Finally, we will also describe the results of recent studies, mostly based on animal models, indicating the phenotypic consequences of miRNA alterations on the function of several tissues and organs. These studies suggest that the spectrum of genetic diseases possibly caused by mutations in miRNAs is wide and is only starting to be unravelled

Promiscuity of enhancer, coding and non-coding transcription functions in ultraconserved elements

<p>Abstract</p> <p>Background</p> <p>Ultraconserved elements (UCEs) are highly constrained elements of mammalian genomes, whose functional role has not been completely elucidated yet. Previous studies have shown that some of them act as enhancers in mouse, while some others are expressed in both normal and cancer-derived human tissues. Only one UCE element so far was shown to present these two functions concomitantly, as had been observed in other isolated instances of single, non ultraconserved enhancer elements.</p> <p>Results</p> <p>We used a custom microarray to assess the levels of UCE transcription during mouse development and integrated these data with published microarray and next-generation sequencing datasets as well as with newly produced PCR validation experiments. We show that a large fraction of non-exonic UCEs is transcribed across all developmental stages examined from only one DNA strand. Although the nature of these transcripts remains a mistery, our meta-analysis of RNA-Seq datasets indicates that they are unlikely to be short RNAs and that some of them might encode nuclear transcripts. In the majority of cases this function overlaps with the already established enhancer function of these elements during mouse development. Utilizing several next-generation sequencing datasets, we were further able to show that the level of expression observed in non-exonic UCEs is significantly higher than in random regions of the genome and that this is also seen in other regions which act as enhancers.</p> <p>Conclusion</p> <p>Our data shows that the concurrent presence of enhancer and transcript function in non-exonic UCE elements is more widespread than previously shown. Moreover through our own experiments as well as the use of next-generation sequencing datasets, we were able to show that the RNAs encoded by non-exonic UCEs are likely to be long RNAs transcribed from only one DNA strand.</p

UNCLES: Method for the identification of genes differentially consistently co-expressed in a specific subset of datasets

Background: Collective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently representative of real datasets. Results: Here, we propose an unsupervised method for the unification of clustering results from multiple datasets using external specifications (UNCLES). This method has the ability to identify the subsets of genes consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets, and to identify the subsets of genes consistently co-expressed in all given datasets. We also propose the M-N scatter plots validation technique and adopt it to set the parameters of UNCLES, such as the number of clusters, automatically. Additionally, we propose an approach for the synthesis of gene expression datasets using real data profiles in a way which combines the ground-truth-knowledge of synthetic data and the realistic expression values of real data, and therefore overcomes the problem of faithfulness of synthetic expression data modelling. By application to those datasets, we validate UNCLES while comparing it with other conventional clustering methods, and of particular relevance, biclustering methods. We further validate UNCLES by application to a set of 14 real genome-wide yeast datasets as it produces focused clusters that conform well to known biological facts. Furthermore, in-silico-based hypotheses regarding the function of a few previously unknown genes in those focused clusters are drawn. Conclusions: The UNCLES method, the M-N scatter plots technique, and the expression data synthesis approach will have wide application for the comprehensive analysis of genomic and other sources of multiple complex biological datasets. Moreover, the derived in-silico-based biological hypotheses represent subjects for future functional studies.The National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (Grant Reference Number RP-PG-0310-1004)

UTRdb and UTRsite (RELEASE 2010) : a collection of sequences and regulatory motifs of the untranslated regions of eukaryotic mRNAs

The 5' and 3' untranslated regions of eukaryotic mRNAs (UTRs) play crucial roles in the post-transcriptional regulation of gene expression through the modulation of nucleo-cytoplasmic mRNA transport, translation efficiency, subcellular localization and message stability. UTRdb is a curated database of 5' and 3' untranslated sequences of eukaryotic mRNAs, derived from several sources of primary data. Experimentally validated functional motifs are annotated and also collated as the UTRsite database where more specific information on the functional motifs and cross-links to interacting regulatory protein are provided. In the current update, the UTR entries have been organized in a gene-centric structure to better visualize and retrieve 5' and 3'UTR variants generated by alternative initiation and termination of transcription and alternative splicing. Experimentally validated miRNA targets and conserved sequence elements are also annotated. The integration of UTRdb with genomic data has allowed the implementation of an efficient annotation system and a powerful retrieval resource for the selection and extraction of specific UTR subsets. All internet resources implemented for retrieval and functional analysis of 5' and 3' untranslated regions of eukaryotic mRNAs are accessible at http://utrdb.ba.itb.cnr.it/

g:Profiler—a web server for functional interpretation of gene lists (2011 update)

Functional interpretation of candidate gene lists is an essential task in modern biomedical research. Here, we present the 2011 update of g:Profiler (http://biit.cs.ut.ee/gprofiler/), a popular collection of web tools for functional analysis. g:GOSt and g:Cocoa combine comprehensive methods for interpreting gene lists, ordered lists and list collections in the context of biomedical ontologies, pathways, transcription factor and microRNA regulatory motifs and protein–protein interactions. Additional tools, namely the biomolecule ID mapping service (g:Convert), gene expression similarity searcher (g:Sorter) and gene homology searcher (g:Orth) provide numerous ways for further analysis and interpretation. In this update, we have implemented several features of interest to the community: (i) functional analysis of single nucleotide polymorphisms and other DNA polymorphisms is supported by chromosomal queries; (ii) network analysis identifies enriched protein–protein interaction modules in gene lists; (iii) functional analysis covers human disease genes; and (iv) improved statistics and filtering provide more concise results. g:Profiler is a regularly updated resource that is available for a wide range of species, including mammals, plants, fungi and insects

System wide analyses have underestimated protein abundances and the importance of transcription in mammals

Large scale surveys in mammalian tissue culture cells suggest that the protein expressed at the median abundance is present at 8,000–16,000 molecules per cell and that differences in mRNA expression between genes explain only 10–40% of the differences in protein levels. We find, however, that these surveys have significantly underestimated protein abundances and the relative importance of transcription. Using individual measurements for 61 housekeeping proteins to rescale whole proteome data from Schwanhausser et al. (2011), we find that the median protein detected is expressed at 170,000 molecules per cell and that our corrected protein abundance estimates show a higher correlation with mRNA abundances than do the uncorrected protein data. In addition, we estimated the impact of further errors in mRNA and protein abundances using direct experimental measurements of these errors. The resulting analysis suggests that mRNA levels explain at least 56% of the differences in protein abundance for the 4,212 genes detected by Schwanhausser et al. (2011), though because one major source of error could not be estimated the true percent contribution should be higher. We also employed a second, independent strategy to determine the contribution of mRNA levels to protein expression. We show that the variance in translation rates directly measured by ribosome profiling is only 9% of that inferred by Schwanhausser et al. (2011), and that the measured and inferred translation rates correlate poorly (R2 = 0.14). Based on this, our second strategy suggests that mRNA levels explain ∼84% of the variance in protein levels. We also determined the percent contributions of transcription, RNA degradation, translation and protein degradation to the variance in protein abundances using both of our strategies. While the magnitudes of the two estimates vary, they both suggest that transcription plays a more important role than the earlier studies implied and translation a much smaller role. Finally, the above estimates apply to those genes whose mRNA and protein expression was detected. Based on a detailed analysis by Hebenstreit et al. (2012), we estimate that approximately 40% of genes in a given cell within a population express no mRNA. Since there can be no translation in the absence of mRNA, we argue that differences in translation rates can play no role in determining the expression levels for the ∼40% of genes that are non-expressed

Identification of microRNA activity by Targets' Reverse EXpression

Motivation: Non-coding microRNAs (miRNAs) act as regulators of global protein output. While their major effect is on protein levels of target genes, it has been proven that they also specifically impact on the messenger RNA level of targets. Prominent interest in miRNAs strongly motivates the need for increasing the options available to detect their cellular activity

TFEB regulates lysosomal proteostasis

Loss-of-function diseases are often caused by destabilizing mutations that lead to protein misfolding and degradation. Modulating the innate protein homeostasis (proteostasis) capacity may lead to rescue of native folding of the mutated variants, thereby ameliorating the disease phenotype. In lysosomal storage disorders (LSDs), a number of highly prevalent alleles have missense mutations that do not impair the enzyme's catalytic activity but destabilize its native structure, resulting in the degradation of the misfolded protein. Enhancing the cellular folding capacity enables rescuing the native, biologically functional structure of these unstable mutated enzymes. However, proteostasis modulators specific for the lysosomal system are currently unknown. Here, we investigate the role of the transcription factor EB (TFEB), a master regulator of lysosomal biogenesis and function, in modulating lysosomal proteostasis in LSDs. We show that TFEB activation results in enhanced folding, trafficking and lysosomal activity of a severely destabilized glucocerebrosidase (GC) variant associated with the development of Gaucher disease (GD), the most common LSD. TFEB specifically induces the expression of GC and of key genes involved in folding and lysosomal trafficking, thereby enhancing both the pool of mutated enzyme and its processing through the secretory pathway. TFEB activation also rescues the activity of a β-hexosaminidase mutant associated with the development of another LSD, Tay–Sachs disease, thus suggesting general applicability of TFEB-mediated proteostasis modulation to rescue destabilizing mutations in LSDs. In summary, our findings identify TFEB as a specific regulator of lysosomal proteostasis and suggest that TFEB may be used as a therapeutic target to rescue enzyme homeostasis in LSDs

Patrocles: a database of polymorphic miRNA-mediated gene regulation in vertebrates

The Patrocles database (http://www.patrocles.org/) compiles DNA sequence polymorphisms (DSPs) that are predicted to perturb miRNA-mediated gene regulation. Distinctive features include: (i) the coverage of seven vertebrate species in its present release, aiming for more when information becomes available, (ii) the coverage of the three compartments involved in the silencing process (i.e. targets, miRNA precursors and silencing machinery), (iii) contextual information that enables users to prioritize candidate ‘Patrocles DSPs’, including graphical information on miRNA-target coexpression and eQTL effect of genotype on target expression levels, (iv) the inclusion of Copy Number Variants and eQTL information that affect miRNA precursors as well as genes encoding components of the silencing machinery and (v) a tool (Patrocles finder) that allows the user to determine whether her favorite DSP may perturb miRNA-mediated gene regulation of custom target sequences. To support the biological relevance of Patrocles' content, we searched for signatures of selection acting on ‘Patrocles single nucleotide polymorphisms (pSNPs)’ in human and mice. As expected, we found a strong signature of purifying selection against not only SNPs that destroy conserved target sites but also against SNPs that create novel, illegitimate target sites, which is reminiscent of the Texel mutation in sheep

A Potential Regulatory Role for Intronic microRNA-338-3p for Its Host Gene Encoding Apoptosis-Associated Tyrosine Kinase

MicroRNAs (miRNAs) are important gene regulators that are abundantly expressed in both the developing and adult mammalian brain. These non-coding gene transcripts are involved in post-transcriptional regulatory processes by binding to specific target mRNAs. Approximately one third of known miRNA genes are located within intronic regions of protein coding and non-coding regions, and previous studies have suggested a role for intronic miRNAs as negative feedback regulators of their host genes. In the present study, we monitored the dynamic gene expression changes of the intronic miR-338-3p and miR-338-5p and their host gene Apoptosis-associated Tyrosine Kinase (AATK) during the maturation of rat hippocampal neurons. This revealed an uncorrelated expression pattern of mature miR-338 strands with their host gene. Sequence analysis of the 3′ untranslated region (UTR) of rat AATK mRNA revealed the presence of two putative binding sites for miR-338-3p. Thus, miR-338-3p may have the capacity to modulate AATK mRNA levels in neurons. Transfection of miR-338-3p mimics into rat B35 neuroblastoma cells resulted in a significant decrease of AATK mRNA levels, while the transfection of synthetic miR-338-5p mimics did not alter AATK levels. Our results point to a possible molecular mechanism by which miR-338-3p participates in the regulation of its host gene by modulating the levels of AATK mRNA, a kinase which plays a role during differentiation, apoptosis and possibly in neuronal degeneration