Simultaneous photoelectrochemical detection of dual microRNAs by capturing CdS quantum dots and methylene blue based on target-initiated strand displaced amplification

Herein,we propose a novel photoelectrochemical(PEC) biosensor for dual microRNAs(miRNAs) highly sensitive and simultaneous biosensing based on strand displaced amplification(SDA) reaction.The recognition of H_(miR-21) and H_(let-7 a) by microRNA-21 and let-7 a leads to their change in hairpin structures,subsequently initiating the immobilization of abundant CdS quantum dots(CdS QD s) and methylene blue(MB) based on SDA reaction.The immobilized CdS QDs and MB produce both high PEC currents under430 nm light and 627 nm light illumination,respectively,and the generated PEC currents are closely relied on target miRNAs amounts.Thus,highly sensitive and simultaneous detection of microRNA-21 and let-7 a was readily achieved with detection limit at 6.6 fmol/L and 15.4 fmol/L based on 3σ,respectively.Further,this PEC biosensor was applied in simultaneous analysis of miRNA-21 and let-7 a in breast cancer patient's serum with acceptable results.We expect this biosensor will find more useful application in diagnosis of miRNA-related diseases.     

Toehold‐initiated Rolling Circle Amplification for Visualizing Individual MicroRNAs In Situ in Single Cells

The ability to quantitate and visualize microRNAs (miRNAs) in situ in single cells would greatly facilitate the elucidation of miRNA‐mediated regulatory circuits and their disease associations. A toehold‐initiated strand‐displacement process was used to initiate rolling circle amplification of specific miRNAs, an approach that achieves both stringent recognition and in situ amplification of the target miRNA. This assay, termed toehold‐initiated rolling circle amplification (TIRCA), can be utilized to identify miRNAs at physiological temperature with high specificity and to visualize individual miRNAs in situ in single cells within 3 h. TIRCA is a competitive candidate technique for in situ miRNA imaging and may help us to understand the role of miRNAs in cellular processes and human diseases in more detail.     

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.     

Selective In Situ Analysis of Mature microRNAs in Extracellular Vesicles Using a DNA Cage-Based Thermophoretic Assay

Mature microRNAs (miRNAs) in extracellular vesicles (EVs) are involved in different stages of cancer progression, yet it remains challenging to precisely detect mature miRNAs in EVs due to the presence of interfering RNAs (such as longer precursor miRNAs, pre-miRNAs) and the low abundance of tumor-associated miRNAs. By leveraging the size-selective ability of DNA cages and polyethylene glycol (PEG)-enhanced thermophoretic accumulation of EVs, we devised a DNA cage-based thermophoretic assay for highly sensitive, selective, and in situ detection of mature miRNAs in EVs with a low limit of detection (LoD) of 2.05 fM. Our assay can profile EV mature miRNAs directly in serum samples without the interference of pre-miRNAs and the need for ultracentrifugation. A clinical study showed that EV miR-21 or miR-155 had an overall accuracy of 90 % for discrimination between breast cancer patients and healthy donors, which outperformed conventional molecular probes detecting both mature miRNAs and pre-miRNAs. We envision that our assay can advance EV miRNA-based diagnosis of cancer.     

Catalytic Molecular Imaging of MicroRNA in Living Cells by DNA‐Programmed Nanoparticle Disassembly

Molecular imaging is an essential tool for disease diagnostics and treatment. Direct imaging of low‐abundance nucleic acids in living cells remains challenging because of the relatively low sensitivity and insufficient signal‐to‐background ratio of conventional molecular imaging probes. Herein, we report a class of DNA‐templated gold nanoparticle (GNP)–quantum dot (QD) assembly‐based probes for catalytic imaging of cancer‐related microRNAs (miRNA) in living cells with signal amplification capacity. We show that a single miRNA molecule could catalyze the disassembly of multiple QDs with the GNP through a DNA‐programmed thermodynamically driven entropy gain process, yielding significantly amplified QD photoluminescence (PL) for miRNA imaging. By combining the robust PL of QDs with the catalytic amplification strategy, three orders of magnitude improvement in detection sensitivity is achieved in comparison with non‐catalytic imaging probe, which enables facile and accurate differentiation between cancer cells and normal cells by miRNA imaging in living cells.     

Polyphenol-Enriched Blueberry Preparation Controls Breast Cancer Stem Cells by Targeting FOXO1 and miR-145

Scientific evidence supports the early deregulation of epigenetic profiles during breast carcinogenesis. Research shows that cellular transformation, carcinogenesis, and stemness maintenance are regulated by epigenetic-specific changes that involve microRNAs (miRNAs). Dietary bioactive compounds such as blueberry polyphenols may modulate susceptibility to breast cancer by the modulation of CSC survival and self-renewal pathways through the epigenetic mechanism, including the regulation of miRNA expression. Therefore, the current study aimed to assay the effect of polyphenol enriched blueberry preparation (PEBP) or non-fermented blueberry juice (NBJ) on the modulation of miRNA signature and the target proteins associated with different clinical-pathological characteristics of breast cancer such as stemness, invasion, and chemoresistance using breast cancer cell lines. To this end, 4T1 and MB-MDM-231 cell lines were exposed to NBJ or PEBP for 24 h. miRNA profiling was performed in breast cancer cell cultures, and RT-qPCR was undertaken to assay the expression of target miRNA. The expression of target proteins was examined by Western blotting. Profiling of miRNA revealed that several miRNAs associated with different clinical-pathological characteristics were differentially expressed in cells treated with PEBP. The validation study showed significant downregulation of oncogenic miR-210 expression in both 4T1 and MDA-MB-231 cells exposed to PEBP. In addition, expression of tumor suppressor miR-145 was significantly increased in both cell lines treated with PEBP. Western blot analysis showed a significant increase in the relative expression of FOXO1 in 4T1 and MDA-MB-231 cells exposed to PEBP and in MDA-MB-231 cells exposed to NBJ. Furthermore, a significant decrease was observed in the relative expression of N-RAS in 4T1 and MDA-MB-231 cells exposed to PEBP and in MDA-MB-231 cells exposed to NBJ. Our data indicate a potential chemoprevention role of PEBP through the modulation of miRNA expression, particularly miR-210 and miR-145, and protection against breast cancer development and progression. Thus, PEBP may represent a source for novel chemopreventative agents against breast cancer.     

双链特异性核酸酶的生物学和医学应用

双链特异性核酸酶DSN是一种能高选择性地识别并酶切完全匹配的DNA双链或者DNA-RNA杂交双链中的DNA,而对单链DNA和RNA几乎没有作用的核酸酶.DSN酶的上述特点使其在生物和医学等领域广泛应用,主要包括全长cDNA文库的均一化、单核苷酸多态性(SNP)检测和高通量测序等.近年来,DSN酶在microRNAs(miRNAs)的检测领域得以长足发展和应用.miRNAs是一组内源性、非编码短序列RNAs,在生理和病理过程中具有重要作用,但由于其序列短、丰度低等特点, miRNAs的检测一直是临床难题.新近研究主要基于DSN酶信号放大的特点,相继建立了一系列生物传感技术,通过采用不同检测原理如比色法、荧光法、电化学法等实现痕量miRNAs检测.本文就DSN酶在miRNAs检测、SNP检测、全长cDNA文库均一化及高通量测序等方面的生物学应用进行了综述.