Improved microRNA quantification in total RNA from clinical samples

microRNAs are small regulatory RNAs that are currently emerging as new biomarkers for cancer and other diseases. In order for biomarkers to be useful in clinical settings, they should be accurately and reliably detected in clinical samples such as formalin fixed paraffin embedded (FFPE) sections and blood serum or plasma. These types of samples represent a challenge in terms of microRNA quantification. A newly developed method for microRNA qPCR using Locked Nucleic Acid (LNA?)-enhanced primers enables accurate and reproducible quantification of microRNAs in scarce clinical samples. Here we show that LNA?-based microRNA qPCR enables biomarker screening using very low amounts of total RNA from FFPE samples and the results are compared to microarray analysis data. We also present evidence that the addition of a small carrier RNA prior to total RNA extraction, improves microRNA quantification in blood plasma and laser capture microdissected (LCM) sections of FFPE samples.     

Intrinsic Noise of microRNA-Regulated Genes and the ceRNA Hypothesis

MicroRNAs are small noncoding RNAs that regulate genes post-transciptionally by binding and degrading target eukaryotic mRNAs. We use a quantitative model to study gene regulation by inhibitory microRNAs and compare it to gene regulation by prokaryotic small non-coding RNAs (sRNAs). Our model uses a combination of analytic techniques as well as computational simulations to calculate the mean-expression and noise profiles of genes regulated by both microRNAs and sRNAs. We find that despite very different molecular machinery and modes of action (catalytic vs stoichiometric), the mean expression levels and noise profiles of microRNA-regulated genes are almost identical to genes regulated by prokaryotic sRNAs. This behavior is extremely robust and persists across a wide range of biologically relevant parameters. We extend our model to study crosstalk between multiple mRNAs that are regulated by a single microRNA and show that noise is a sensitive measure of microRNA-mediated interaction between mRNAs. We conclude by discussing possible experimental strategies for uncovering the microRNA-mRNA interactions and testing the competing endogenous RNA (ceRNA) hypothesis.     

Small RNAs in Human Brain Development and Disorders

Small RNA is a variable and abundant type of non-coding RNAs in brain. The function of these RNAs is mainly unknown. A specific class of small RNA, microRNA, is dynamically regulated in neurogenesis and in embryo brain development. The genes for synaptic formation and some mental retardation disorders are putative targets for microRNA predicted by computational algorithms. The molecular pathways for mental development, common forms of autisms, schizophrenia, and affective disorders have yet to be elucidated. The hypothesis proposed here is that small regulatory RNAs, specifically microRNAs, play a role in human brain development and pathogenesis of brain disorders, especially of neurodevelopmental conditions. Pilot tests using comprehensive arrays of microRNAs demonstrate that microRNAs derived from postmortem human brains are applicable for microRNA expression profiling. The abundant expression of many regulatory small RNAs in human brain implies their biological role that must be tested by functional assays in neurons and by genetic and comparative expression profiling.     

Deep sequencing reveals predominant expression of miR-21 amongst the small non-coding RNAs in retinal microvascular endothelial cells

The retinal vascular endothelium is essential for angiogenesis and is involved in maintaining barrier selectivity and vascular tone. The aim of this study was to identify and quantify microRNAs and other small regulatory non-coding RNAs (ncRNAs) which may regulate these crucial functions. Primary bovine retinal microvascular endothelial cells (RMECs) provide a well-characterized in vitro system for studying angiogenesis. RNA extracted from RMECs was used to prepare a small RNA library for deep sequencing (Illumina Genome Analyzer). A total of 6.8 million reads were mapped to 250 known microRNAs in miRBase (release 16). In many cases, the most frequent isomiR differed from the sequence reported in miRBase. In addition, five novel microRNAs, 13 novel bovine orthologs of known human microRNAs and multiple new members of the miR-2284/2285 family were detected. Several ～30 nucleotide sno-miRNAs were identified, with the most highly expressed being derived from snoRNA U78. Highly expressed microRNAs previously associated with endothelial cells included miR-126 and miR-378, but the most highly expressed was miR-21, comprising more than one-third of all mapped reads. Inhibition of miR-21 with an LNA inhibitor significantly reduced proliferation, migration, and tube-forming capacity of RMECs. The independence from prior sequence knowledge provided by deep sequencing facilitates analysis of novel microRNAs and other small RNAs. This approach also enables quantitative evaluation of microRNA expression, which has highlighted the predominance of a small number of microRNAs in RMECs. Knockdown of miR-21 suggests a role for this microRNA in regulation of angiogenesis in the retinal microvasculature.     

Expression and processing of polycistronic artificial microRNAs and trans-acting siRNAs from transiently introduced transgenes in Solanum lycopersicum and Nicotiana benthamiana

Targeted gene silencing using small regulatory RNAs is a widely used technique for genetic studies in plants. Artificial microRNAs are one common approach, as they have the advantage of producing just a single functional small RNA, which can be designed for high target specificity and low off-target effects. Simultaneous silencing of multiple targets with artificial microRNAs can be achieved by producing polycistronic microRNA precursors. Alternatively, specialized trans-acting short interfering RNA (tasiRNA) precursors can be designed to produce several specific tasiRNAs at once. Here we tested several artificial microRNA- and tasiRNA-based methods for multiplexed gene silencing in Solanum lycopersicum (tomato) and Nicotiana benthamiana. All analyses used transiently expressed transgenes delivered by infiltration of leaves with Agrobacterium tumefacians. Small RNA sequencing analyses revealed that many previously described approaches resulted in poor small RNA processing. The 5′-most microRNA precursor hairpins on polycistronic artificial microRNA precursors were generally processed more accurately than precursors at the 3′-end. Polycistronic artificial microRNAs where the hairpin precursors were separated by transfer RNAs had the best processing precision. Strikingly, artificial tasiRNA precursors failed to be processed in the expected phased manner in our system. These results highlight the need for further development of multiplexed artificial microRNA and tasiRNA strategies. The importance of small RNA sequencing, as opposed to single-target assays such as RNA blots or real-time polymerase chain reaction, is also discussed.     

Computational identification of microRNA targets

Recent experiments have shown that the genomes of organisms such as worm, fly, human, and mouse encode hundreds of microRNA genes. Many of these microRNAs are thought to regulate the translational expression of other genes by binding to partially complementary sites in messenger RNAs. Phenotypic and expression analysis suggests an important role of microRNAs during development. Therefore, it is of fundamental importance to identify microRNA targets. However, no experimental or computational high-throughput method for target site identification in animals has been published yet. Our main result is a new computational method that is designed to identify microRNA target sites. This method recovers with high specificity known microRNA target sites that have previously been defined experimentally. Based on these results, we present a simple model for the mechanism of microRNA target site recognition. Our model incorporates both kinetic and thermodynamic components of target recognition. When we applied our method to a set of 74 Drosophila melanogaster microRNAs, searching 3'UTR sequences of a predefined set of fly mRNAs for target sites which were evolutionary conserved between D. melanogaster and Drosophila pseudoobscura, we found that many key developmental body patterning genes such as hairy and fushi-tarazu are likely to be translationally regulated by microRNAs.     

Refining microRNA target predictions: Sorting the wheat from the chaff

microRNAs are short RNAs that reduce gene expression by binding to their targets. The accurate prediction of microRNA targets is essential to understanding the function of microRNAs. Computational predictions indicate that all human genes may be regulated by microRNAs, with each microRNA possibly targeting thousands of genes. Here we discuss computational methods for identifying mammalian microRNA targets and refining them for further experimental validation. We describe microRNA target prediction resources and procedures and how they integrate with various types of experimental techniques that aim to validate them or further explore their function. We also provide a list of target prediction databases and explain how these are curated.     

Target Repression Induced by Endogenous microRNAs: Large Differences,Small Effects

MicroRNAs are small RNAs that regulate protein levels. It is commonly assumed that the expression level of a microRNA is directly correlated with its repressive activity – that is, highly expressed microRNAs will repress their target mRNAs more. Here we investigate the quantitative relationship between endogenous microRNA expression and repression for 32 mature microRNAs in Drosophila melanogaster S2 cells. In general, we find that more abundant microRNAs repress their targets to a greater degree. However, the relationship between expression and repression is nonlinear, such that a 10-fold greater microRNA concentration produces only a 10% increase in target repression. The expression/repression relationship is the same for both dominant guide microRNAs and minor mature products (so-called passenger strands/microRNA* sequences). However, we find examples of microRNAs whose cellular concentrations differ by several orders of magnitude, yet induce similar repression of target mRNAs. Likewise, microRNAs with similar expression can have very different repressive abilities. We show that the association of microRNAs with Argonaute proteins does not explain this variation in repression. The observed relationship is consistent with the limiting step in target repression being the association of the microRNA/RISC complex with the target site. These findings argue that modest changes in cellular microRNA concentration will have minor effects on repression of targets.     

RNA-binding proteins in RNA interference

Short RNAs (21–27 nt) silence genes that contain homologous nucleotide sequences; this is known as RNA silencing. This review considers the generation of short RNAs from their precursors: double-stranded RNAs, capable of inducing RNA interference, and hairpin RNAs, whose processing yields microRNAs, as well as the properties of RNA-binding domains that were initially identified in proteins operating in RNA interference. The interactions between these domains and known RNA-binding modules within proteins involved in RNA interference and microRNA generation are described.