Kinetic modelling of competition and depletion of shared miRNAs by competing endogenous RNAs

Non-conding RNAs play a key role in the post-transcriptional regulation of mRNA translation and turnover in eukaryotes. miRNAs, in particular, interact with their target RNAs through protein-mediated, sequence-specific binding, giving rise to extended and highly heterogeneous miRNA-RNA interaction networks. Within such networks, competition to bind miRNAs can generate an effective positive coupling between their targets. Competing endogenous RNAs (ceRNAs) can in turn regulate each other through miRNA-mediated crosstalk. Albeit potentially weak, ceRNA interactions can occur both dynamically, affecting e.g. the regulatory clock, and at stationarity, in which case ceRNA networks as a whole can be implicated in the composition of the cell's proteome. Many features of ceRNA interactions, including the conditions under which they become significant, can be unraveled by mathematical and in silico models. We review the understanding of the ceRNA effect obtained within such frameworks, focusing on the methods employed to quantify it, its role in the processing of gene expression noise, and how network topology can determine its reach.Comment: review article, 29 pages, 7 figure

FastJet user manual

FastJet is a C++ package that provides a broad range of jet finding and analysis tools. It includes efficient native implementations of all widely used 2-to-1 sequential recombination jet algorithms for pp and e+e- collisions, as well as access to 3rd party jet algorithms through a plugin mechanism, including all currently used cone algorithms. FastJet also provides means to facilitate the manipulation of jet substructure, including some common boosted heavy-object taggers, as well as tools for estimation of pileup and underlying-event noise levels, determination of jet areas and subtraction or suppression of noise in jets.Comment: 69 pages. FastJet 3 is available from http://fastjet.fr

Chromosome 10q26-driven age-related macular degeneration is associated with reduced levels of HTRA1 in human retinal pigment epithelium

Genome-wide association studies have identified the chromosome 10q26 (Chr10) locus, which contains the age-related maculopathy susceptibility 2 (ARMS2) and high temperature requirement A serine peptidase 1 (HTRA1) genes, as the strongest genetic risk factor for age-related macular degeneration (AMD) [L.G. Fritsche et al., Annu. Rev. Genomics Hum. Genet. 15, 151–171, (2014)]. To date, it has been difficult to assign causality to any specific single nucleotide polymorphism (SNP), haplotype, or gene within this region because of high linkage disequilibrium among the disease-associated variants [J. Jakobsdottir et al. Am. J. Hum. Genet. 77, 389–407 (2005); A. Rivera et al. Hum. Mol. Genet. 14, 3227–3236 (2005)]. Here, we show that HTRA1 messenger RNA (mRNA) is reduced in retinal pigment epithelium (RPE) but not in neural retina or choroid tissues derived from human donors with homozygous risk at the 10q26 locus. This tissue-specific decrease is mediated by the presence of a noncoding, cis-regulatory element overlapping the ARMS2 intron, which contains a potential Lhx2 transcription factor binding site that is disrupted by risk variant rs36212733. HtrA1 protein increases with age in the RPE–Bruch’s membrane (BM) interface in Chr10 nonrisk donors but fails to increase in donors with homozygous risk at the 10q26 locus. We propose that HtrA1, an extracellular chaperone and serine protease, functions to maintain the optimal integrity of the RPE–BM interface during the aging process and that reduced expression of HTRA1 mRNA and protein in Chr10 risk donors impairs this protective function, leading to increased risk of AMD pathogenesis. HtrA1 augmentation, not inhibition, in high-risk patients should be considered as a potential therapy for AMD

Performance evaluation of commercial miRNA expression array platforms

<p>Abstract</p> <p>Background</p> <p>microRNAs (miRNA) are short, endogenous transcripts that negatively regulate the expression of specific mRNA targets. The relative abundance of miRNAs is linked to function <it>in vivo </it>and miRNA expression patterns are potentially useful signatures for the development of diagnostic, prognostic and therapeutic biomarkers.</p> <p>Finding</p> <p>We compared the performance characteristics of four commercial miRNA array technologies and found that all platforms performed well in separate measures of performance.</p> <p>Conclusions</p> <p>The Ambion and Agilent platforms were more accurate, whereas the Illumina and Exiqon platforms were more specific. Furthermore, the data analysis approach had a large impact on the performance, predominantly by improving precision.</p

Expression of Regulatory Platelet MicroRNAs in Patients with Sickle Cell Disease

Background: Increased platelet activation in sickle cell disease (SCD) contributes to a state of hypercoagulability and confers a risk of thromboembolic complications. The role for post-transcriptional regulation of the platelet transcriptome by microRNAs (miRNAs) in SCD has not been previously explored. This is the first study to determine whether platelets from SCD exhibit an altered miRNA expression profile. Methods and Findings: We analyzed the expression of miRNAs isolated from platelets from a primary cohort (SCD = 19, controls = 10) and a validation cohort (SCD = 7, controls = 7) by hybridizing to the Agilent miRNA microarrays. A dramatic difference in miRNA expression profiles between patients and controls was noted in both cohorts separately. A total of 40 differentially expressed platelet miRNAs were identified as common in both cohorts (p-value 0.05, fold change>2) with 24 miRNAs downregulated. Interestingly, 14 of the 24 downregulated miRNAs were members of three families - miR-329, miR-376 and miR-154 - which localized to the epigenetically regulated, maternally imprinted chromosome 14q32 region. We validated the downregulated miRNAs, miR-376a and miR-409-3p, and an upregulated miR-1225-3p using qRT-PCR. Over-expression of the miR-1225-3p in the Meg01 cells was followed by mRNA expression profiling to identify mRNA targets. This resulted in significant transcriptional repression of 1605 transcripts. A combinatorial approach using Meg01 mRNA expression profiles following miR-1225-3p overexpression, a computational prediction analysis of miRNA target sequences and a previously published set of differentially expressed platelet transcripts from SCD patients, identified three novel platelet mRNA targets: PBXIP1, PLAGL2 and PHF20L1. Conclusions: We have identified significant differences in functionally active platelet miRNAs in patients with SCD as compared to controls. These data provide an important inventory of differentially expressed miRNAs in SCD patients and an experimental framework for future studies of miRNAs as regulators of biological pathways in platelets. © 2013 Jain et al

Differences in vertebrate microRNA expression

MicroRNAs (miRNAs) attenuate gene expression by means of translational inhibition and mRNA degradation. They are abundant, highly conserved, and predicted to regulate a large number of transcripts. Several hundred miRNA classes are known, and many are associated with cell proliferation and differentiation. Many exhibit tissue-specific expression, which aids in evaluating their functions, and it has been assumed that their high level of sequence conservation implies a high level of expression conservation. A limited amount of data supports this, although discrepancies do exist. By comparing the expression of ≈100 miRNAs in medaka and chicken with existing data for zebrafish and mouse, we conclude that the timing and location of miRNA expression is not strictly conserved. In some instances, differences in expression are associated with changes in miRNA copy number, genomic context, or both between species. Variation in miRNA expression is more pronounced the greater the differences in physiology, and it is enticing to speculate that changes in miRNA expression may play a role in shaping the physiological differences produced during animal development

Cloning and expression of new microRNAs from zebrafish

MicroRNAs (miRNAs) play an important role in development and regulate the expression of many animal genes by post-transcriptional gene silencing. Here we describe the cloning and expression of new miRNAs from zebrafish. By high-throughput sequencing of small-RNA cDNA libraries from 5-day-old zebrafish larvae and adult zebrafish brain we found 139 known miRNAs and 66 new miRNAs. For 65 known miRNAs and for 11 new miRNAs we also cloned the miRNA star sequence. We analyzed the temporal and spatial expression patterns for 35 new miRNAs and for 32 known miRNAs in the zebrafish by whole mount in situ hybridization and northern blotting. Overall, 23 of the 35 new miRNAs and 30 of the 32 known miRNAs could be detected. We found that most miRNAs were expressed during later stages of development. Some were expressed ubiquitously, but many of the miRNAs were expressed in a tissue-specific manner. Most newly discovered miRNAs have low expression levels and are less conserved in other vertebrate species. Our cloning and expression analysis indicates that most abundant and conserved miRNAs in zebrafish are now known

miRNA Regulatory Circuits in ES Cells Differentiation: A Chemical Kinetics Modeling Approach

MicroRNAs (miRNAs) play an important role in gene regulation for Embryonic Stem cells (ES cells), where they either down-regulate target mRNA genes by degradation or repress protein expression of these mRNA genes by inhibiting translation. Well known tables TargetScan and miRanda may predict quite long lists of potential miRNAs inhibitors for each mRNA gene, and one of our goals was to strongly narrow down the list of mRNA targets potentially repressed by a known large list of 400 miRNAs. Our paper focuses on algorithmic analysis of ES cells microarray data to reliably detect repressive interactions between miRNAs and mRNAs. We model, by chemical kinetics equations, the interaction architectures implementing the two basic silencing processes of miRNAs, namely “direct degradation” or “translation inhibition” of targeted mRNAs. For each pair (M,G) of potentially interacting miRMA gene M and mRNA gene G, we parameterize our associated kinetic equations by optimizing their fit with microarray data. When this fit is high enough, we validate the pair (M,G) as a highly probable repressive interaction. This approach leads to the computation of a highly selective and drastically reduced list of repressive pairs (M,G) involved in ES cells differentiation