Transboundary Pathogenic microRNA Analysis Framework for Crop Fungi Driven by Biological Big Data and Artificial Intelligence Model

Plant fungal diseases have been affecting the world's agricultural production and economic levels for a long time, such as rice blast, gray tomato mold, potato late blight etc. Recent studies have shown that fungal pathogens transmit microRNA as an effector to host plants for infection. However, bioassay-based verification analysis is time-consuming and challenging, and it is difficult to analyze from a global perspective. With the accumulation of fungal and plant-related data, data analysis methods can be used to analyze pathogenic fungal microRNA further. Based on the microRNA expression data of fungal pathogens infecting plants before and after, this paper discusses the selection strategy of sample data, the extraction strategy of pathogenic fungal microRNA, the prediction strategy of a fungal pathogenic microRNA target gene, the bicluster-based fungal pathogenic microRNA functional analysis strategy and experimental verification methods. A general analysis pipeline based on machine learning and bicluster-based function module was proposed for plant-fungal pathogenic microRNA.The pipeline proposed in this paper is applied to the infection process of Magnaporthe oryzae and the infection process of potato late blight. It has been verified to prove the feasibility of the pipeline. It can be extended to other relevant crop pathogen research, providing a new idea for fungal research on plant diseases. It can be used as a reference for understanding the interaction between fungi and plants.     

Adsorptive Transfer Stripping for Quick Electrochemical Determination of microRNAs in Total RNA Samples

Electrochemical methods hold promise for fast and inexpensive determination of cancer biomarkers, including microRNAs playing an important role in regulation of gene expression. We developed a simple assay utilizing (i) microRNA labeling with osmium(VI) complexes, (ii) hybridization of microRNA with magnetic bead‐immobilized capture probe, and (iii) adsorptive transfer stripping (AdTS) technique in which working electrode is dipped into a microliter‐sized drop for direct adsorption of microRNA. Increased sensitivity and decreased sample consumption was achieved, and nanogram quantities of microRNA present in 500‐fold excess of total RNA were determined. Our results indicate only negligible interference during hybridization from other RNA molecules.     

Architecture of a validated microRNA::target interaction

MicroRNAs are small approximately 22 nucleotide regulators of numerous biological processes and bind target gene messenger RNAs to control gene expression. The C. elegans microRNA let-7 and its target lin-41 were the first microRNA::target interaction to be validated in vivo. let-7 molecules form imperfect duplexes with two required let-7 complementary sites in the lin-41 3' UTR. Here, we show that base pairing at both the 5' and 3' ends of the let-7 binding site, as well as the presence of unpaired RNA residues in the predicted duplexes, are required for lin-41 downregulation. In this study, our model for microRNA::target interactions also demonstrates that the context of a microRNA binding can be critical for function, revealing an unforeseen complexity in microRNA::target interactions.     

Highly sensitive microRNA quantification with zero background signal from silver nanoparticles

MicroRNAs are a class of noncoding RNAs, which play vital roles in numerous cellular processes. Recent studies have confirmed their significance in the theranostics of various diseases. We herein fabricate an electrochemical approach for microRNA quantification. DNA/microRNA/DNA hybridization and electrochemical signals from silver nanoparticles (AgNPs) are employed in this work. DNA1 immobilized on a gold electrode interacts with target microRNA, along with amino group labeled DNA2, to form the sandwich hybrid. The adjacent DNA1 and DNA2 are then ligated, which can keep DNA2 on the electrode surface during the denaturation. Amino group modified at the 5′ end of DNA2 captures AgNPs on the electrode surface, which provide sharp stripping peaks for microRNA quantification. This electrochemical approach offers a simple and sensitive platform for the detection of microRNA, which shows great utility in biomedical research and clinical diagnosis.     

A review of Cloud computing technologies for comprehensive microRNA analyses

Cloud computing revolutionized many fields that require ample computational power. Cloud platforms may also provide huge support for microRNA analysis mainly through disclosing scalable resources of different types. In Clouds, these resources are available as services, which simplifies their allocation and releasing. This feature is especially useful during the analysis of large volumes of data, like the one produced by next generation sequencing experiments, which require not only extended storage space but also a distributed computing environment. In this paper, we show which of the Cloud properties and service models can be especially beneficial for microRNA analysis. We also explain the most useful services of the Cloud (including storage space, computational power, web application hosting, machine learning models, and Big Data frameworks) that can be used for microRNA analysis. At the same time, we review several solutions for microRNA and show that the utilization of the Cloud in this field is still weak, but can increase in the future when the awareness of their applicability grows.     

Intrinsic Electrocatalysis of RNA as a Label‐free and Reagent‐less Tool for Detection of MicroRNAs

We show for the first time that RNA catalyzes hydrogen evolution reaction at mercury‐containing electrodes. We previously showed that DNA is electrocatalytically active, and so we compared heights and potentials of the RNA chronopotentimetric stripping (CPS) peaks with analogous signals of DNA of the same sequence and found out they were very similar. RNA peaks showed differences depending on the RNA base composition. Catalytic nature of CPS peak enabled detection of 25 pM microRNA in electrochemical cell, or 500 pM microRNA in a 5 μL solution drop (corresponding to 2.5 fmole of microRNA). This finding opens the door for simple, label‐free and reagent‐less analysis of low concentrations of RNA molecules.     

<Superscript>1</Superscript>H-nuclear magnetic resonance spectroscopy-based metabolic assessment in a rat model of obesity induced by a high-fat diet

MicroRNAs (miRNAs) are a class of small noncoding RNAs ∼22 nt in length that regulate gene expression and play fundamental roles in multiple biological processes, including cell differentiation, proliferation and apoptosis as well as disease processes. The study of miRNA has thus become a rapidly emerging field in life science. The detection of miRNA expression is a very important first step in miRNA exploration. Several methodologies, including cloning, northern blotting, real-time RT-PCR, microRNA arrays and ISH (in situ hybridization), have been developed and applied successfully in miRNA profiling. This review discusses the main existing microRNA detection technologies, while emphasizing microRNA arrays.     

Dual approach for the colorimetric determination of unamplified microRNAs by using citrate capped gold nanoparticles

The authors describe a method for the colorimetric determination of unamplified microRNA. It is based on the use of citrate-capped gold nanoparticles (AuNPs) and, alternatively, a microRNA-probe hybrid or a magnetically extracted microRNA that serve as stabilizers against the salt-induced aggregation of AuNPs. The absorbance ratios A₅₂₅/A₆₂₅ of the reacted AuNP solutions were used to quantify the amount of microRNA. The assay works in the range of 5–25 pmol microRNA. The lower limit of detection (LOD) is 10 pmol. The performance of the method was tested by detection of microRNA-210-3p in totally extracted urinary microRNA from normal, benign, and bladder cancer subjects. The sensitivity and specificity for qualitative detection of urinary microRNA-210-3p using the assay are 74% and 88% respectively, which is consistent with real time PCR based assays. The assay was applied to the determination of specific microRNA by using its specific oligo targeter or following magnetic isolation of the desired microRNA. The method is simple, cost-efficient, has a short turn-around time and requires minimal equipment and personnel.

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Graphical abstract Schematic of the two detection schemes: In the first approach, matched microRNA hybridizes with its specific probe to stabilize gold nanoparticles (AuNPs) against salt induced aggregation and to leave the red color of the AuNPs unchanged. In the second one, microRNA extracted via magnetic nanoparticles (MNP) stabilizes AuNPs against aggregation.

     

Specific and Direct Amplified Detection of MicroRNA with MicroRNA:Argonaute‐2 Cleavage (miRACle) Beacons

MicroRNA detection is a valuable method for determining cell identity. Molecular beacons are elegant sensors that can transform intracellular microRNA concentration into a fluorescence intensity. While target binding enhances beacon fluorescence, the degree of enhancement is insufficient for demanding applications. The addition of specialty nucleases can enable target recycling and signal amplification, but this process complicates the assay. We have developed and characterized a class of beacons that are susceptible to the endogenous nuclease Argonaute‐2 (Ago2). After purification of the complex by co‐immunoprecipitation, microRNA:Ago2 cleavage (miRACle) beacons undergo site‐ and sequence‐specific cleavage, and show a 13‐fold fluorescence enhancement over traditional beacons. The system can be adapted to any microRNA sequence, and can cleave nuclease‐resistant, non‐RNA bases, potentially allowing miRACle beacons to be designed for cells without interference from non‐specific nucleases.     

Rapid microRNA detection using power-free microfluidic chip: coaxial stacking effect enhances the sandwich hybridization

We present a new method for rapid microRNA detection with a small volume of sample using the power-free microfluidic device driven by degassed PDMS. Target microRNA was detected by sandwich hybridization taking advantage of the coaxial stacking effect. This method allows us to detect miR-21 in 20 min with a 0.5 μL sample volume at a limit of detection of 0.62 nM. Since microRNAs can act as cancer markers, this method might substantially contribute to future point-of-care cancer diagnosis.     

A homogeneous fluorescence sensing platform with water-soluble carbon nanoparticles for detection of microRNA and nuclease activity

Based on the high efficiency of fluorescence quenching and the different affinities of water-soluble carbon nanoparticles (CNPs) towards single-stranded DNA (ssDNA) and double-stranded DNA/RNA hybrid, a novel, rapid and cost-effective assay for detection of microRNA and nuclease activity was developed. The fluorescein-labeled ssDNA probe (FAM-P) could be adsorbed on the surface of CNPs through π-π stacking interaction giving rise to fluorescence quenching. By introduction of microRNA complementary to the DNA probe, the double-stranded DNA/miRNA hybrid could be formed and released from the surface of CNPs resulting in the fluorescence recovery. Thus, microRNA was successfully detected in homogenous fashion without any amplification or enzyme-involving reactions. Moreover, we demonstrated that the nuclease activities of RNase H and DNase I could also be sensitively monitored by using CNPs based on the fluorescence changing of the DNA probe. So, the CNPs provide an excellent homogeneous sensing platform for studying molecular diagnosis and therapeutics.     

Fluorometric determination of microRNA based on strand displacement amplification and rolling circle amplification

The authors describe a fluorometric assay for microRNA. It is based on two-step amplification involving (a) strand displacement replication and (b) rolling circle amplification. The strand displacement amplification system is making use of template DNA (containing a sequence that is complementary to microRNA-21) and nicking enzyme sites. After hybridization, the microRNA strand becomes extended by DNA polymerase chain reaction and then cleaved by the nicking enzyme. The DNA thus produced acts as a primer in rolling circle amplification. Then, the DNA probe SYBR Green II is added to bind to ssDNA to generate a fluorescent signal which increases with increasing concentration of microRNA. The method has a wide detection range that covers the10 f. to 0.1 nM microRNA concentration range and has a detection limit as low as 1.0 fM. The method was successfully applied to the determination of microRNA-21 in the serum of healthy and breast cancer patients.

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Graphical abstract Schematic of a fluorometric microRNA assay based on two-step amplification involving strand displacement replication and rolling circle amplification. DNA probe SYBR Green II is then bound to ssDNA to generate a fluorescent signal which increases with increasing concentration of microRNA.

     

Sequence-specific detection of MicroRNAs by signal-amplifying ribozymes

The rational and straightforward design of hairpin ribozymes that can be sequence-specifically induced by external oligonucleotides is described. Due to intrinsic signal amplification, their sensitivity is at least an order of magnitude increased compared to standard molecular beacons. We applied this system to the detection of microRNAs, a recently discovered class of small endogenous RNA molecules that are involved in gene regulation. We show that the cognate microRNA can reliably and sensitively be detected at low concentrations in a mix of other microRNA sequences. These probes may be useful in applications that require direct detection of minute amounts of small DNAs or RNAs.     

Identification of miR-23a as a novel microRNA normalizer for relative quantification in human uterine cervical tissues

Quantitative real-time RT-PCR (RT-qPCR) is being widely used in microRNA expression research. However, few reports detailed a robust identification and validation strategy for suitable reference genes for normalisation in microRNA RT-qPCR studies. The aim of this study was to identify the most stable reference gene(s) for quantification of microRNA expression analysis in uterine cervical tissues. A microarray was performed on 6 pairs of uterine cervical tissues to identify the candidate reference genes. The stability of candidate reference genes was assessed by RT-qPCR in 23 pairs of uterine cervical tissues. The identified most stable reference genes were further validated in other cohort of 108 clinical uterine cervical samples: (HR-HPV- normal, n=21; HR-HPV+ normal, n=19; cervical intraepithelial neoplasia [CIN], n=47; cancer, n=21), and the effects of normalizers on the relative quantity of target miR-424 were assessed. In the array experiment, miR-26a, miR-23a, miR-200c, let-7a, and miR-1979 were identified as candidate reference genes for subsequent validation. MiR-23a was identified as the most reliable reference gene followed by miR-191. The use of miR-23a and miR-191 to normalize expression data enabled detection of a significant dereg-ulation of miR-424 between normal, CIN and cancer tissue. Our results suggested that miR-23a and miR-191 are the optimal reference microRNAs that can be used for normalization in profiling studies of cervical tissues; miR-23a is a novel microRNA normalizer.     

MicroRNAs电化学生物传感器在乳腺癌检测中的研究进展

MicroRNAs是一类内源性非编码小RNA分子,可调控靶基因的表达。特异性microRNAs的失调在诸如癌症、心血管疾病、免疫疾病、神经退行性疾病和皮肤疾病等的发展过程中起着关键作用,常作为疾病早期诊断和预后的生物标志物。电化学生物传感器由于其灵敏、快速、成本低等优势,已经成为传统microRNAs检测方法的一种很有前途的替代方法。本文综述了基于microRNA电化学生物传感器检测乳腺癌的研究进展,并对其所构建的电化学生物传感器的检测方法进行了分析探讨。     

Network-based ranking methods for prediction of novel disease associated microRNAs

BackgroundMany studies have shown roles of microRNAs on human disease and a number of computational methods have been proposed to predict such associations by ranking candidate microRNAs according to their relevance to a disease. Among them, machine learning-based methods usually have a limitation in specifying non-disease microRNAs as negative training samples. Meanwhile, network-based methods are becoming dominant since they well exploit a “disease module” principle in microRNA functional similarity networks. Of which, random walk with restart (RWR) algorithm-based method is currently state-of-the-art. The use of this algorithm was inspired from its success in predicting disease gene because the “disease module” principle also exists in protein interaction networks. Besides, many algorithms designed for webpage ranking have been successfully applied in ranking disease candidate genes because web networks share topological properties with protein interaction networks. However, these algorithms have not yet been utilized for disease microRNA prediction.MethodsWe constructed microRNA functional similarity networks based on shared targets of microRNAs, and then we integrated them with a microRNA functional synergistic network, which was recently identified. After analyzing topological properties of these networks, in addition to RWR, we assessed the performance of (i) PRINCE (PRIoritizatioN and Complex Elucidation), which was proposed for disease gene prediction; (ii) PageRank with Priors (PRP) and K-Step Markov (KSM), which were used for studying web networks; and (iii) a neighborhood-based algorithm.ResultsAnalyses on topological properties showed that all microRNA functional similarity networks are small-worldness and scale-free. The performance of each algorithm was assessed based on average AUC values on 35 disease phenotypes and average rankings of newly discovered disease microRNAs. As a result, the performance on the integrated network was better than that on individual ones. In addition, the performance of PRINCE, PRP and KSM was comparable with that of RWR, whereas it was worst for the neighborhood-based algorithm. Moreover, all the algorithms were stable with the change of parameters. Final, using the integrated network, we predicted six novel miRNAs (i.e., hsa-miR-101, hsa-miR-181d, hsa-miR-192, hsa-miR-423-3p, hsa-miR-484 and hsa-miR-98) associated with breast cancer.ConclusionsNetwork-based ranking algorithms, which were successfully applied for either disease gene prediction or for studying social/web networks, can be also used effectively for disease microRNA prediction.     

Electrochemical strategy for ultrasensitive detection of microRNA based on MNAzyme-mediated rolling circle amplification on a gold electrode

Microchimica Acta - The authors describe an electrochemical strategy for ultrasensitive and specific detection of microRNA (miRNA). It is based on both multicomponent nucleic acid enzyme (MNAzyme)...