latest microRNA papers (9)
microRNA (miRNA) & quantitative real-time RT-PCR (1)
microRNA (miRNA) & quantitative real-time RT-PCR (2)
microRNA (miRNA) & quantitative real-time RT-PCR (3)
microRNA (miRNA) & quantitative real-time RT-PCR (4)
microRNA (miRNA) & quantitative real-time RT-PCR (5)
microRNA normalisation (7)
RNA interference (RNAi) small inhibiting RNA (siRNA) small activating RNA (saRNA)
latest microRNA papers
microRNA quality and effects on quantification ... UPDATED !
MicroRNA turnover: when, how, and why
Stefan Ruegger and Helge Großhans
Trends in Biochemical Sciences, October 2012, Vol. 37, No. 10
MicroRNAs (miRNAs) are short (22 nucleotide) RNAs that are important for the regulation of numerous biological processes. Accordingly, the expression of miRNAs is itself tightly controlled by mechanisms acting at the level of transcription as well as processing of miRNA precursors. Recently, active degradation of mature miRNAs has been identified as another mechanism that is important for miRNA homeostasis. Here we review the molecular factors and cellular conditions that promote miRNA turnover. We also discuss what is known about the physiological relevance of miRNA decay.
Gene expression. MicroRNA control of protein expression noise
Schmiedel JM, Klemm SL, Zheng Y, Sahay A, Blüthgen N, Marks DS, van Oudenaarden A
Science. 2015 Apr 3;348(6230): 128-132
MicroRNAs (miRNAs) repress the expression of many genes in metazoans by accelerating messenger RNA degradation and inhibiting translation, thereby reducing the level of protein. However, miRNAs only slightly reduce the mean expression of most targeted proteins, leading to speculation about their role in the variability, or noise, of protein expression. We used mathematical modeling and single-cell reporter assays to show that miRNAs, in conjunction with increased transcription, decrease protein expression noise for lowly expressed genes but increase noise for highly expressed genes. Genes that are regulated by multiple miRNAs show more-pronounced noise reduction. We estimate that hundreds of (lowly expressed) genes in mouse embryonic stem cells have reduced noise due to substantial miRNA regulation. Our findings suggest that miRNAs confer precision to protein expression and thus offer plausible explanations for the commonly observed combinatorial targeting of endogenous genes by multiple miRNAs, as well as the preferential targeting of lowly expressed genes.
Data Normalization Strategies for MicroRNA Quantification
Schwarzenbach H, da Silva AM, Calin G, Pantel K
Clin Chem. 2015 61(11): 1333-13342
BACKGROUND: Different technologies, such as quantitative real-time PCR or microarrays, have been developed to measure microRNA (miRNA) expression levels. Quantification of miRNA transcripts implicates data normalization using endogenous and exogenous reference genes for data correction. However, there is no consensus about an optimal normalization strategy. The choice of a reference gene remains problematic and can have a serious impact on the actual available transcript levels and, consequently, on the biological interpretation of data.
CONTENT: In this review article we discuss the reliability of the use of small RNAs, commonly reported in the literature as miRNA expression normalizers, and compare different strategies used for data normalization.
SUMMARY: A workflow strategy is proposed for normalization of miRNA expression data in an attempt to provide a basis for the establishment of a global standard procedure that will allow comparison across studies.
The multilayered complexity of ceRNA crosstalk and competition
Yvonne Tay, John Rinn & Pier Paolo Pandolfi
Nature (2014) 505, 344–352
Recent reports have described an intricate interplay among diverse RNA species, including protein-coding messenger RNAs and non-coding RNAs such as long non-coding RNAs, pseudogenes and circular RNAs. These RNA transcripts act as competing endogenous RNAs (ceRNAs) or natural microRNA sponges — they communicate with and co-regulate each other by competing for binding to shared microRNAs, a family of small non-coding RNAs that are important post-transcriptional regulators of gene expression. Understanding this novel RNA crosstalk will lead to significant insight into gene regulatory networks and have implications in human development and disease.
Precision multidimensional assay for high-throughput microRNA drug discovery
Haefliger B, Prochazka L, Angelici B, Benenson Y
Nat Commun. 2016 7: 10709
Development of drug discovery assays that combine high content with throughput is challenging. Information-processing gene networks can address this challenge by integrating multiple potential targets of drug candidates' activities into a small number of informative readouts, reporting simultaneously on specific and non-specific effects. Here we show a family of networks implementing this concept in a cell-based drug discovery assay for miRNA drug targets. The networks comprise multiple modules reporting on specific effects towards an intended miRNA target, together with non-specific effects on gene expression, off-target miRNAs and RNA interference pathway. We validate the assays using known perturbations of on- and off-target miRNAs, and evaluate an ∼700 compound library in an automated screen with a follow-up on specific and non-specific hits. We further customize and validate assays for additional drug targets and non-specific inputs. Our study offers a novel framework for precision drug discovery assays applicable to diverse target families.
MicroRNAs in heart failure: from biomarker to target for therapy
Vegter EL, van der Meer P, de Windt LJ, Pinto YM, Voors AA
Eur J Heart Fail. 2016 Feb -- Epub ahead of print
MicroRNAs (miRNAs) are increasingly recognized to play important roles in cardiovascular diseases, including heart failure. These small, non-coding RNAs have been identified in tissue and are involved in several pathophysiological processes related to heart failure, such as cardiac fibrosis and hypertrophy. As a result, miRNAs have become interesting novel drug targets, leading to the development of miRNA mimics and antimirs. MicroRNAs are also detected in the circulation, and are proposed as potential diagnostic and prognostic biomarkers in heart failure. However, their role and function in the circulation remains to be resolved. Here, we review the potential roles of miRNAs as circulating biomarkers and as targets for therapy.
miRNA regulates noncoding RNA: a noncanonical function model
Chen X, Liang H, Zhang CY, Zen K.
Trends Biochem Sci. 2012 37(11): 457-459
It has long been assumed that miRNAs can only target protein-coding mRNAs in the cytoplasm. Recent studies, however, reveal miRNAs are also transported from the cytoplasm to the nucleus, where they function in a noncanonical manner to regulate noncoding RNAs. Here, we highlight the working model of these noncanonical miRNAs.
The potential of circulating extracellular small RNAs (smexRNA) in veterinary diagnostics - Identifying biomarker signatures by multivariate data analysis
Spornraft Melanie, Kirchner Benedikt, Michael W. Pfaffl, Riedmaier Irmgard
BDQ Special Issue: Advanced Molecular Diagnostics for Biomarker Discovery (5): 15-22
Worldwide growth and performance-enhancing substances are used in cattle husbandry to increase productivity. In certain countries however e.g., in the EU, these practices are forbidden to prevent the consumers from potential health risks of substance residues in food. To maximize economic profit, ‘black sheep‘ among farmers might circumvent the detection methods used in routine controls, which highlights the need for an innovative and reliable detection method. Transcriptomics is a promising new approach in the discovery of veterinary medicine biomarkers and also a missing puzzle piece, as up to date, metabolomics and proteomics are paramount. Due to increased stability and easy sampling, circulating extracellular small RNAs (smexRNAs) in bovine plasma were small RNA-sequenced and their potential to serve as biomarker candidates was evaluated using multivariate data analysis tools.
After running the data evaluation pipeline, the proportion of miRNAs (microRNAs) and piRNAs (PIWI-interacting small non-coding RNAs) on the total sequenced reads was calculated. Additionally, top 10 signatures were compared which revealed that the readcount data sets were highly affected by the most abundant miRNA and piRNA profiles. To evaluate the discriminative power of multivariate data analyses to identify animals after veterinary drug application on the basis of smexRNAs, OPLS-DA was performed. In summary, the quality of miRNA models using all mapped reads for both treatment groups (animals treated with steroid hormones or the β-agonist clenbuterol) is predominant to those generated with combined data sets or piRNAs alone. Using multivariate projection methodologies like OPLS-DA have proven the best potential to generate discriminative miRNA models, supported by small RNA-Seq data. Based on the presented comparative OPLS-DA, miRNAs are the favorable smexRNA biomarker candidates in the research field of veterinary drug abuse.
Analysis of 13 cell types reveals evidence for the expression of numerous novel primate- and tissue-specific microRNAs
Londin E, Loher P, Telonis AG, Quann K, Clark P, Jing Y, Hatzimichael E, Kirino Y, Honda S, Lally M, Ramratnam B, Comstock CE, Knudsen KE, Gomella L, Spaeth GL, Hark L, Katz LJ, Witkiewicz A, Rostami A, Jimenez SA, Hollingsworth MA, Yeh JJ, Shaw CA, McKenzie SE, Bray P, Nelson PT, Zupo S, Van Roosbroeck K, Keating MJ, Calin GA, Yeo C, Jimbo M, Cozzitorto J, Brody JR, Delgrosso K, Mattick JS, Fortina P, Rigoutsos I
Proc Natl Acad Sci U S A. 2015 112(10): E1106-1115
Two decades after the discovery of the first animal microRNA (miRNA), the number of miRNAs in animal genomes remains a vexing question. Here, we report findings from analyzing 1,323 short RNA sequencing samples (RNA-seq) from 13 different human tissue types. Using stringent thresholding criteria, we identified 3,707 statistically significant novel mature miRNAs at a false discovery rate of ≤ 0.05 arising from 3,494 novel precursors; 91.5% of these novel miRNAs were identified independently in 10 or more of the processed samples. Analysis of these novel miRNAs revealed tissue-specific dependencies and a commensurate low Jaccard similarity index in intertissue comparisons. Of these novel miRNAs, 1,657 (45%) were identified in 43 datasets that were generated by cross-linking followed by Argonaute immunoprecipitation and sequencing (Ago CLIP-seq) and represented 3 of the 13 tissues, indicating that these miRNAs are active in the RNA interference pathway. Moreover, experimental investigation through stem-loop PCR of a random collection of newly discovered miRNAs in 12 cell lines representing 5 tissues confirmed their presence and tissue dependence. Among the newly identified miRNAs are many novel miRNA clusters, new members of known miRNA clusters, previously unreported products from uncharacterized arms of miRNA precursors, and previously unrecognized paralogues of functionally important miRNA families (e.g., miR-15/107). Examination of the sequence conservation across vertebrate and invertebrate organisms showed 56.7% of the newly discovered miRNAs to be human-specific whereas the majority (94.4%) are primate lineage-specific. Our findings suggest that the repertoire of human miRNAs is far more extensive than currently represented by public repositories and that there is a significant number of lineage- and/or tissue-specific miRNAs that are uncharacterized.
Dumbbell-PCR: a method to quantify specific small RNA variants with a single nucleotide resolution at terminal sequences
Honda S and Kirino Y
Nucleic Acids Res. 2015 43(12): e77
Recent advances in next-generation sequencing technologies have revealed that cellular functional RNAs are not always expressed as single entities with fixed terminal sequences but as multiple isoforms bearing complex heterogeneity in both length and terminal sequences, such as isomiRs, the isoforms of microRNAs. Unraveling the biogenesis and biological significance of heterogenetic RNA expression requires distinctive analysis of each RNA variant. Here, we report the development of dumbbell PCR (Db-PCR), an efficient and convenient method to distinctively quantify a specific individual small RNA variant. In Db-PCR, 5'- and 3'-stem-loop adapters are specifically hybridized and ligated to the 5'- and 3'-ends of target RNAs, respectively, by T4 RNA ligase 2 (Rnl2). The resultant ligation products with 'dumbbell-like' structures are subsequently quantified by TaqMan RT-PCR. We confirmed that high specificity of Rnl2 ligation and TaqMan RT-PCR toward target RNAs assured both 5'- and 3'-terminal sequences of target RNAs with single nucleotide resolution so that Db-PCR specifically detected target RNAs but not their corresponding terminal variants. Db-PCR had broad applicability for the quantification of various small RNAs in different cell types, and the results were consistent with those from other quantification method. Therefore, Db-PCR provides a much-needed simple method for analyzing RNA terminal heterogeneity.
Integrative Analysis of MicroRNA and mRNA Data Reveals an Orchestrated Function of MicroRNAs in Skeletal Myocyte Differentiation in Response to TNF-α or IGF1.
Meyer SU, Sass S, Mueller NS, Krebs S, Bauersachs S, Kaiser S, Blum H, Thirion C, Krause S, Theis FJ, Pfaffl MW
PLoS One. 2015 10(8):e0135284 -- eCollection 2015
INTRODUCTION: Skeletal muscle cell differentiation is impaired by elevated levels of the inflammatory cytokine tumor necrosis factor-α (TNF-α) with pathological significance in chronic diseases or inherited muscle disorders. Insulin like growth factor-1 (IGF1) positively regulates muscle cell differentiation. Both, TNF-α and IGF1 affect gene and microRNA (miRNA) expression in this process. However, computational prediction of miRNA-mRNA relations is challenged by false positives and targets which might be irrelevant in the respective cellular transcriptome context. Thus, this study is focused on functional information about miRNA affected target transcripts by integrating miRNA and mRNA expression profiling data.
METHODOLOGY & PRINCIPAL FINDINGS: Murine skeletal myocytes PMI28 were differentiated for 24 hours with concomitant TNF-α or IGF1 treatment. Both, mRNA and miRNA expression profiling was performed. The data-driven integration of target prediction and paired mRNA/miRNA expression profiling data revealed that i) the quantity of predicted miRNA-mRNA relations was reduced, ii) miRNA targets with a function in cell cycle and axon guidance were enriched, iii) differential regulation of anti-differentiation miR-155-5p and miR-29b-3p as well as pro-differentiation miR-335-3p, miR-335-5p, miR-322-3p, and miR-322-5p seemed to be of primary importance during skeletal myoblast differentiation compared to the other miRNAs, iv) the abundance of targets and affected biological processes was miRNA specific, and v) subsets of miRNAs may collectively regulate gene expression.
CONCLUSIONS: Joint analysis of mRNA and miRNA profiling data increased the process-specificity and quality of predicted relations by statistically selecting miRNA-target interactions. Moreover, this study revealed miRNA-specific predominant biological implications in skeletal muscle cell differentiation and in response to TNF-α or IGF1 treatment. Furthermore, myoblast differentiation-associated miRNAs are suggested to collectively regulate gene clusters and targets associated with enriched specific gene ontology terms or pathways. Predicted miRNA functions of this study provide novel insights into defective regulation at the transcriptomic level during myocyte proliferation and differentiation due to inflammatory stimuli.
Whole-body scanning PCR; a highly sensitive method to study the biodistribution of mRNAs, noncoding RNAs and therapeutic oligonucleotides
Boos JA1, Kirk DW, Piccolotto ML, Zuercher W, Gfeller S, Neuner P, Dattler A, Wishart WL, Von Arx F, Beverly M, Christensen J, Litherland K, van de Kerkhof E, Swart PJ, Faller T, Beyerbach A, Morrissey D, Hunziker J, Beuvink I.
Nucleic Acids Res. 2013 41(15): e145
Efficient tissue-specific delivery is a crucial factor in the successful development of therapeutic oligonucleotides. Screening for novel delivery methods with unique tissue-homing properties requires a rapid, sensitive, flexible and unbiased technique able to visualize the in vivo biodistribution of these oligonucleotides. Here, we present whole body scanning PCR, a platform that relies on the local extraction of tissues from a mouse whole body section followed by the conversion of target-specific qPCR signals into an image. This platform was designed to be compatible with a novel RT-qPCR assay for the detection of siRNAs and with an assay suitable for the detection of heavily chemically modified oligonucleotides, which we termed Chemical-Ligation qPCR (CL-qPCR). In addition to this, the platform can also be used to investigate the global expression of endogenous mRNAs and non-coding RNAs. Incorporation of other detection systems, such as aptamers, could even further expand the use of this technology.
ToppMiR: ranking microRNAs and their mRNA targets based on biological functions and context
Chao Wu, Eric E. Bardes, Anil G. Jegga and Bruce J. Aronow
Nucleic Acids Research, 2014, Vol. 12, Web Server issue W107–W113
Identifying functionally significant microRNAs (miRs) and their correspondingly most important messenger RNA targets (mRNAs) in specific biological contexts is a critical task to improve our understanding of molecular mechanisms underlying organismal development, physiology and disease. However, current miR–mRNA target prediction platforms rank miR targets based on estimated strength of physical interactions and lack the ability to rank interactants as a function of their potential to impact a given biological system. To address this, we have developed ToppMiR -- http://toppmir.cchmc.org -- a web-based analytical workbench that allows miRs and mRNAs to be co-analyzed via biologically centered approaches in which gene function associated annotations are used to train a machine learning-based analysis engine. ToppMiR learns about biological contexts based on gene associated information from expression data or from a userspecified set of genes that relate to context-relevant knowledge or hypotheses. Within the biological framework established by the genes in the training set, its associated information content is then used to calculate a features association matrix composed of biological functions, protein interactions and other features. This scoring matrix is then used to jointly rank both the test/candidate miRs and mRNAs. Results of these analyses are provided as downloadable tables or network file formats usable in Cytoscape.
Posttranscriptional Regulatory Networks: From Expression Profi ling to Integrative Analysis of mRNA and MicroRNA Data
Swanhild U. Meyer, Katharina Stoecker, Steffen Sass, Fabian J. Theis and Michael W. Pfaffl
Chapter 15 in Quantitative Real-Time PCR: Methods and Protocols 2014 (Methods in Molecular Biology)
by Roberto Biassoni, Alessandro Raso
Protein coding RNAs are posttranscriptionally regulated by microRNAs, a class of small noncoding RNAs. Insights in messenger RNA (mRNA) and microRNA (miRNA) regulatory interactions facilitate the understanding of fi ne-tuning of gene expression and might allow better estimation of protein synthesis. However, in silico predictions of mRNA–microRNA interactions do not take into account the specifi c transcriptomic status of the biological system and are biased by false positives. One possible solution to predict rather reliable mRNA-miRNA relations in the specifi c biological context is to integrate real mRNA and miRNA transcriptomic data as well as in silico target predictions. This chapter addresses the workfl ow and methods one can apply for expression profi ling and the integrative analysis of mRNA and miRNA data, as well as how to analyze and interpret results, and how to build up models of posttranscriptional regulatory networks.
Biogenesis of small RNAs in animals.
V. Narry Kim, Jinju Han and Mikiko C. Siomi
Nat Rev Mol Cell Biol. 2009 10(2): 126-139
Small RNAs of 20-30 nucleotides can target both chromatin and transcripts, and thereby keep both the genome and the transcriptome under extensive surveillance. Recent progress in high-throughput sequencing has uncovered an astounding landscape of small RNAs in eukaryotic cells. Various small RNAs of distinctive characteristics have been found and can be classified into three classes based on their biogenesis mechanism and the type of Argonaute protein that they are associated with: microRNAs (miRNAs), endogenous small interfering RNAs (endo-siRNAs or esiRNAs) and Piwi-interacting RNAs (piRNAs). This Review summarizes our current knowledge of how these intriguing molecules are generated in animal cells.
TNF-α and IGF1 modify the microRNA signature in skeletal muscle cell differentiation.
Meyer SU, Thirion C, Polesskaya A, Bauersachs S, Kaiser S, Krause S, Pfaffl MW
Cell Commun Signal. 2015 13: 4
BACKGROUND: Elevated levels of the inflammatory cytokine TNF-α are common in chronic diseases or inherited or degenerative muscle disorders and can lead to muscle wasting. By contrast, IGF1 has a growth promoting effect on skeletal muscle. The molecular mechanisms mediating the effect of TNF-α and IGF1 on muscle cell differentiation are not completely understood. Muscle cell proliferation and differentiation are regulated by microRNAs (miRNAs) which play a dominant role in this process. This study aims at elucidating how TNF-α or IGF1 regulate microRNA expression to affect myoblast differentiation and myotube formation.
RESULTS: In this study, we analyzed the impact of TNF-α or IGF1 treatment on miRNA expression in myogenic cells. Results reveal that i) TNF-α and IGF1 regulate miRNA expression during skeletal muscle cell differentiation in vitro, ii) microRNA targets can mediate the negative effect of TNF-α on fusion capacity of skeletal myoblasts by targeting genes associated with axon guidance, MAPK signalling, focal adhesion, and neurotrophin signalling pathway, iii) inhibition of miR-155 in combination with overexpression of miR-503 partially abrogates the inhibitory effect of TNF-α on myotube formation, and iv) MAPK/ERK inhibition might participate in modulating the effect of TNF-α and IGF1 on miRNA abundance.
CONCLUSIONS: The inhibitory effects of TNF-α or the growth promoting effects of IGF1 on skeletal muscle differentiation include the deregulation of known muscle-regulatory miRNAs as well as miRNAs which have not yet been associated with skeletal muscle differentiation or response to TNF-α or IGF1. This study indicates that miRNAs are mediators of the inhibitory effect of TNF-α on myoblast differentiation. We show that intervention at the miRNA level can ameliorate the negative effect of TNF-α by promoting myoblast differentiation. Moreover, we cautiously suggest that TNF-α or IGF1 modulate the miRNA biogenesis of some miRNAs via MAPK/ERK signalling. Finally, this study identifies indicative biomarkers of myoblast differentiation and cytokine influence and points to novel RNA targets.