Logistic tensor decomposition with sparse subspace learning for prediction of multiple disease types of human-virus protein-protein interactions.

Viral infection involves a large number of protein-protein interactions (PPIs) between the virus and the host, and the identification of these PPIs plays an important role in revealing viral infection and pathogenesis. Existing computational models focus on predicting whether human proteins and viral proteins interact, and rarely take into account the types of diseases associated with these interactions. Although there are computational models based on a matrix and tensor decomposition for predicting multi-type biological interaction relationships, these methods cannot effectively model high-order nonlinear relationships of biological entities and are not suitable for integrating multiple features. To this end, we propose a novel computational framework, LTDSSL, to determine human-virus PPIs under different disease types. LTDSSL utilizes logistic functions to model nonlinear associations, sets importance levels to emphasize the importance of observed interactions and utilizes sparse subspace learning of multiple features to improve model performance. Experimental results show that LTDSSL has better predictive performance for both new disease types and new triples than the state-of-the-art methods. In addition, the case study further demonstrates that LTDSSL can effectively predict human-viral PPIs under various disease types.

Kernel Bayesian logistic tensor decomposition with automatic rank determination for predicting multiple types of miRNA-disease associations.

Identifying the association and corresponding types of miRNAs and diseases is crucial for studying the molecular mechanisms of disease-related miRNAs. Compared to traditional biological experiments, computational models can not only save time and reduce costs, but also discover potential associations on a large scale. Although some computational models based on tensor decomposition have been proposed, these models usually require manual specification of numerous hyperparameters, leading to a decrease in computational efficiency and generalization ability. Additionally, these linear models struggle to analyze complex, higher-order nonlinear relationships. Based on this, we propose a novel framework, KBLTDARD, to identify potential multiple types of miRNA-disease associations. Firstly, KBLTDARD extracts information from biological networks and high-order association network, and then fuses them to obtain more precise similarities of miRNAs (diseases). Secondly, we combine logistic tensor decomposition and Bayesian methods to achieve automatic hyperparameter search by introducing sparse-induced priors of multiple latent variables, and incorporate auxiliary information to improve prediction capabilities. Finally, an efficient deterministic Bayesian inference algorithm is developed to ensure computational efficiency. Experimental results on two benchmark datasets show that KBLTDARD has better Top-1 precision, Top-1 recall, and Top-1 F1 for new type predictions, and higher AUPR, AUC, and F1 values for new triplet predictions, compared to other state-of-the-art methods. Furthermore, case studies demonstrate the efficiency of KBLTDARD in predicting multiple types of miRNA-disease associations.

HONMF: integration analysis of multi-omics microbiome data via matrix factorization and hypergraph.

MOTIVATION: The accumulation of multi-omics microbiome data provides an unprecedented opportunity to understand the diversity of bacterial, fungal, and viral components from different conditions. The changes in the composition of viruses, bacteria, and fungi communities have been associated with environments and critical illness. However, identifying and dissecting the heterogeneity of microbial samples and cross-kingdom interactions remains challenging. RESULTS: We propose HONMF for the integrative analysis of multi-modal microbiome data, including bacterial, fungal, and viral composition profiles. HONMF enables identification of microbial samples and data visualization, and also facilitates downstream analysis, including feature selection and cross-kingdom association analysis between species. HONMF is an unsupervised method based on hypergraph induced orthogonal non-negative matrix factorization, where it assumes that latent variables are specific for each composition profile and integrates the distinct sets of latent variables through graph fusion strategy, which better tackles the distinct characteristics in bacterial, fungal, and viral microbiome. We implemented HONMF on several multi-omics microbiome datasets from different environments and tissues. The experimental results demonstrate the superior performance of HONMF in data visualization and clustering. HONMF also provides rich biological insights by implementing discriminative microbial feature selection and bacterium-fungus-virus association analysis, which improves our understanding of ecological interactions and microbial pathogenesis. AVAILABILITY AND IMPLEMENTATION: The software and datasets are available at https://github.com/chonghua-1983/HONMF.

Hierarchical Sea Urchin-like Fe-doped Heazlewoodite for High-Efficient Oxygen Evolution.

Electrochemical water-splitting to produce hydrogen is potential to substitute the traditional industrial coal gasification, but the oxygen evolution kinetics at the anode remains sluggish. In this paper, sea urchin-like Fe doped Ni3S2 catalyst growing on nickel foam (NF) substrate is constructed via a simple two-step strategy, including surface iron activation and post sulfuration process. The NF-Fe-Ni3S2 obtains at temperature of 130 °C (NF-Fe-Ni3S2-130) features nanoneedle-like arrays which are vertically grown on the particles to form sea urchin-like morphology, features high electrochemical surface area. As oxygen evolution catalyst, NF-Fe-Ni3S2-130 exhibits excellent oxygen evolution activities, fast reaction kinetics, and superior reaction stability. The excellent OER performance of sea urchin-like NF-Fe-Ni3S2-130 is mainly ascribed to the high-vertically dispersive of nanoneedles and the existing Fe dopants, which obviously improved the reaction kinetics and the intrinsic catalytic properties. The simple preparation strategy is conducive to establish high-electrochemical-interface catalysts, which shows great potential in renewable energy conversion.

Weighted hypergraph learning and adaptive inductive matrix completion for SARS-CoV-2 drug repositioning.

MOTIVATION: The outbreak of the human coronavirus (SARS-CoV-2) has placed a huge burden on public health and the world economy. Compared with de novo drug discovery, drug repurposing is a promising therapeutic strategy that facilitates rapid clinical treatment decisions, shortens the development process, and reduces costs. RESULTS: In this study, we propose a weighted hypergraph learning and adaptive inductive matrix completion method, WHAIMC, for predicting potential virus-drug associations. Firstly, we integrate multi-source data to describe viruses and drugs from multiple perspectives, including drug chemical structures, drug targets, virus complete genome sequences, and virus-drug associations. Then, WHAIMC establishes an adaptive inductive matrix completion model to improve performance through adaptive learning of similarity relations. Finally, WHAIMC introduces weighted hypergraph learning into adaptive inductive matrix completion to capture higher-order relationships of viruses (or drugs). The results showed that WHAIMC had a strong predictive performance for new virus-drug associations, new viruses, and new drugs. The case study further demonstrates that WHAIMC is highly effective for repositioning antiviral drugs against SARS-CoV-2 and provides a new perspective for virus-drug association prediction. The code and data in this study is freely available at https://github.com/Mayingjun20179/WHAIMC.

Hypergraph-based logistic matrix factorization for metabolite-disease interaction prediction.

MOTIVATION: Function-related metabolites, the terminal products of the cell regulation, show a close association with complex diseases. The identification of disease-related metabolites is critical to the diagnosis, prevention and treatment of diseases. However, most existing computational approaches build networks by calculating pairwise relationships, which is inappropriate for mining higher-order relationships. RESULTS: In this study, we presented a novel approach with hypergraph-based logistic matrix factorization, HGLMF, to predict the potential interactions between metabolites and disease. First, the molecular structures and gene associations of metabolites and the hierarchical structures and GO functional annotations of diseases were extracted to build various similarity measures of metabolites and diseases. Next, the kernel neighborhood similarity of metabolites (or diseases) was calculated according to the completed interactive network. Second, multiple networks of metabolites and diseases were fused, respectively, and the hypergraph structures of metabolites and diseases were built. Finally, a logistic matrix factorization based on hypergraph was proposed to predict potential metabolite-disease interactions. In computational experiments, HGLMF accurately predicted the metabolite-disease interaction, and performed better than other state-of-the-art methods. Moreover, HGLMF could be used to predict new metabolites (or diseases). As suggested from the case studies, the proposed method could discover novel disease-related metabolites, which has been confirmed in existing studies. AVAILABILITY AND IMPLEMENTATION: The codes and dataset are available at: https://github.com/Mayingjun20179/HGLMF. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cobalt Oxide Arrays Anchored to Copper Foam as Efficient Binder-free Anode for Lithium Ion Batteries.

The development of lithium-ion batteries with simplified assembling steps and fast charge capability is crucial for current battery applications. In this study, we propose a simple in-situ strategy for the construction of high-dispersive cobalt oxide (CoO) nanoneedle arrays, which grow vertically on a copper foam substrate. It is demonstrated that this nanoneedle CoO electrodes provide abundant electrochemical surface area. The resulting CoO arrays directly act as binder-free anodes in lithium-ion batteries with the copper foam functioning as the current collector. The highly-dispersed feature of the nanoneedle arrays enhances the effectiveness of active materials, leading to outstanding rate capability and superior long-term cycling stability. These impressive electrochemical properties are attributed to the highly-dispersed self-standing nanoarrays, the advantages of binder-free constituent, and the high exposed surface area of the copper foam substrate compared to copper foil, which enrich active surface area and facilitate charge transfer. The proposed approach to prepare binder-free lithium-ion battery anodes streamlines the electrode fabrication steps and holds significant promise for the future development of the battery industry.

Hypergraph clustering based on modularity feature projection for high-order relationship community detection of microorganisms.

Microbial community is an important part of organisms or ecosystems to maintain health and stability. Analyzing the interaction of microorganisms in the ecosystem and mining the co-occurrence module of the microbial community can deepen the understanding of microbial community function. This could also improve the ability to manipulate the microbial community, thus provide new means for ecological restoration, disease treatment and drug development. Instead of the investigations of pairwise relationships, more and more studies have realized that the higher-order interactions may play important roles in explaining the diversity and complexity of the community. In this study, a hypergraph clustering (HCMFP) based on modularity feature projection is proposed to detect the microbial community in higher-order interaction network among microbes. Specifically, HCMFP uses information entropy to mine the higher-order logical relationships among microbes, and constructs a hypergraph learning model based on modularity feature projection to detect the microbial community. The experimental results show that compared with other methods, HCMFP has better clustering performance and reliable convergence speed. The proposed method is an effective tool for high-order organizations in microbial interaction network. The code and data in this study is freely available at https://github.com/Mayingjun20179/ HCMFP.

Prevalence and pathology of Cephalopina titillator infestation in Camelus bactrianus from Xinjiang, China.

BACKGROUND: In camels, nasopharyngeal myiasis is caused by the larvae of Cephalopina titillator, which parasitize the tissues of nasal and paranasal sinuses, pharynx, and larynx. C. titillator infestation adversely affects the health of camels and decreases milk and meat production and even death. However, the C. titillator infestation in Bactrian camels has not been widely studied. METHODS: The present study was conducted to determine the prevalence and risk factors of C. titillator in Bactrian camels of northwestern Xinjiang. Suspected larvae recovered from infested camels were evaluated for C. titillator by microscopy and polymerase chain reaction. Nucleotide sequences of the partial mitochondrial cytochrome c oxidase subunit I (COX1) and cytochrome b (CYTB) genes from the C. titillator of camels were aligned from the NCBI database. Furthermore, the gross and histopathological alterations associated with C. titillator infestation were evaluated via pathological examination. RESULTS: Of 1263 camels examined 685 (54.2%) camels were infested with suspected C. titillator larvae. Different larval stages were topically detected in the nasal passages and pharynx of the camel heads. Microscopy analysis of the pharyngeal mucosa tissue revealed necrotic tissue debris and some inflammatory cells. Molecular detection of the larval COX1 and CYTB genes indicated that pathogen collected in Bactrian camels was C. titillator. The epidemiological study demonstrated that the prevalence rate of C.titillator infestation was significantly higher in camels of Bestierek Town Pasture (67.2%) and Karamagai Town Pasture (63.6%) compared to Kitagel Town Pasture (38.7%) and Qibal Town Pasture (35.8%) (P < 0.05). No significant difference was observed between the prevalence rates in male (52.6%) and female (54.6%) camels (P > 0.05). The prevalence was higher in warm (64.2%) than that in cold (48.4%) seasons (P < 0.001). The prevalence in camels with non-nomadic method (67.2%) was significantly higher than in animals with nomadic method (47.5%) (P < 0.001). The prevalence of C.titillator infestation was significantly higher in animals of aged 5-10 (60.1%) and aged > 10 (61.1%) years old compared to those of aged < 5 (31.7%) years old camels (P < 0.001). CONCLUSION: Our results confirm that there is a high prevalence of C. titillator in Bactrian camels from Xinjiang, closely related to age, season, pasture environment, and husbandry methods. Developing prevention, diagnosis, and control programs to prevent transmission is necessary.

Deciphering discriminative antibiotic resistance genes and pathogens in agricultural soil following chemical and organic fertilizer.

Fertilizers containing rich nutrients can change the profiles of antibiotic resistant pathogens (ARPs) and antibiotic resistance genes (ARGs) in receiving soils; however, the discriminative ARGs and ARPs in agricultural soil following different fertilizer applications remain unknown. Using metagenomic sequencing combined with binning approach, the present study investigated the discriminative ARGs and ARPs under various fertilizer applications (chemical and organic fertilizer) in a 8-year field experiment. VanR, multidrug ARG transporter, vanS, ermA, and arnA were the discriminative ARGs in the chemical fertilizer group, whereas rosB, multidrug transporter, mexW, and aac(3)-I were enhanced in the organic fertilizer group. The metagenomic binning approach revealed that both fertilizer applications caused pathogen proliferation. Chemical fertilizer caused the increase in the pathogenic genus Luteimonas, and organic fertilizer facilitated the proliferation of the pathogenic genera Dokdonella and Pseudomonas. The pathogenic species Pseudomonas_H sp014836765, carrying mexW and multidrug transporter, was enriched only in the organic fertilizer group, indicating that it was a discriminative ARP in the organic fertilizer group. Our results demonstrated that fertilizer application, particularly organic fertilizer application, can facilitate the proliferation of ARGs and ARPs in the receiving soil, posing the risk of the development and spread of soil-borne ARPs.

Performance improvement for a 2D convolutional neural network by using SSC encoding on protein-protein interaction tasks.

BACKGROUND: The interactions of proteins are determined by their sequences and affect the regulation of the cell cycle, signal transduction and metabolism, which is of extraordinary significance to modern proteomics research. Despite advances in experimental technology, it is still expensive, laborious, and time-consuming to determine protein-protein interactions (PPIs), and there is a strong demand for effective bioinformatics approaches to identify potential PPIs. Considering the large amount of PPI data, a high-performance processor can be utilized to enhance the capability of the deep learning method and directly predict protein sequences. RESULTS: We propose the Sequence-Statistics-Content protein sequence encoding format (SSC) based on information extraction from the original sequence for further performance improvement of the convolutional neural network. The original protein sequences are encoded in the three-channel format by introducing statistical information (the second channel) and bigram encoding information (the third channel), which can increase the unique sequence features to enhance the performance of the deep learning model. On predicting protein-protein interaction tasks, the results using the 2D convolutional neural network (2D CNN) with the SSC encoding method are better than those of the 1D CNN with one hot encoding. The independent validation of new interactions from the HIPPIE database (version 2.1 published on July 18, 2017) and the validation of directly predicted results by applying a molecular docking tool indicate the effectiveness of the proposed protein encoding improvement in the CNN model. CONCLUSION: The proposed protein sequence encoding method is efficient at improving the capability of the CNN model on protein sequence-related tasks and may also be effective at enhancing the capability of other machine learning or deep learning methods. Prediction accuracy and molecular docking validation showed considerable improvement compared to the existing hot encoding method, indicating that the SSC encoding method may be useful for analyzing protein sequence-related tasks. The source code of the proposed methods is freely available for academic research at https://github.com/wangy496/SSC-format/ .

Polymerization retardation isothermal amplification (PRIA): a strategy enables sensitively quantify genome-wide 5-methylcytosine oxides rapidly on handy instruments with nanoscale sample input.

The current methods for quantifying genome-wide 5-methylcytosine (5mC) oxides are still scarce, mostly restricted with two limitations: assay sensitivity is seriously compromised with cost, assay time and sample input; epigenetic information is irreproducible during polymerase chain reaction (PCR) amplification without bisulfite pretreatment. Here, we propose a novel Polymerization Retardation Isothermal Amplification (PRIA) strategy to directly amplify the minute differences between epigenetic bases and others by arranging DNA polymerase to repetitively pass large electron-withdrawing groups tagged 5mC-oxides. We demonstrate that low abundant 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC) and 5-carboxycytosine (5caC) in genomic DNA can be accurately quantified within 10 h with 100 ng sample input on a laboratory real-time quantitative PCR instrument, and even multiple samples can be analyzed simultaneously in microplates. The global levels of 5hmC and 5fC in mouse and human brain tissues, rat hippocampal neuronal tissue, mouse kidney tissue and mouse embryonic stem cells were quantified and the observations not only confirm the widespread presence of 5hmC and 5fC but also indicate their significant variation in different tissues and cells. The strategy is easily performed in almost all research and medical laboratories, and would provide the potential capability to other candidate modifications in nucleotides.

MHSNMF: multi-view hessian regularization based symmetric nonnegative matrix factorization for microbiome data analysis.

BACKGROUND: With the rapid development of high-throughput technique, multiple heterogeneous omics data have been accumulated vastly (e.g., genomics, proteomics and metabolomics data). Integrating information from multiple sources or views is challenging to obtain a profound insight into the complicated relations among micro-organisms, nutrients and host environment. In this paper we propose a multi-view Hessian regularization based symmetric nonnegative matrix factorization algorithm (MHSNMF) for clustering heterogeneous microbiome data. Compared with many existing approaches, the advantages of MHSNMF lie in: (1) MHSNMF combines multiple Hessian regularization to leverage the high-order information from the same cohort of instances with multiple representations; (2) MHSNMF utilities the advantages of SNMF and naturally handles the complex relationship among microbiome samples; (3) uses the consensus matrix obtained by MHSNMF, we also design a novel approach to predict the classification of new microbiome samples. RESULTS: We conduct extensive experiments on two real-word datasets (Three-source dataset and Human Microbiome Plan dataset), the experimental results show that the proposed MHSNMF algorithm outperforms other baseline and state-of-the-art methods. Compared with other methods, MHSNMF achieves the best performance (accuracy: 95.28%, normalized mutual information: 91.79%) on microbiome data. It suggests the potential application of MHSNMF in microbiome data analysis. CONCLUSIONS: Results show that the proposed MHSNMF algorithm can effectively combine the phylogenetic, transporter, and metabolic profiles into a unified paradigm to analyze the relationships among different microbiome samples. Furthermore, the proposed prediction method based on MHSNMF has been shown to be effective in judging the types of new microbiome samples.

Predicting virus-host association by Kernelized logistic matrix factorization and similarity network fusion.

BACKGROUND: Viruses are closely related to bacteria and human diseases. It is of great significance to predict associations between viruses and hosts for understanding the dynamics and complex functional networks in microbial community. With the rapid development of the metagenomics sequencing, some methods based on sequence similarity and genomic homology have been used to predict associations between viruses and hosts. However, the known virus-host association network was ignored in these methods. RESULTS: We proposed a kernelized logistic matrix factorization with integrating different information to predict potential virus-host associations on the heterogeneous network (ILMF-VH) which is constructed by connecting a virus network with a host network based on known virus-host associations. The virus network is constructed based on oligonucleotide frequency measurement, and the host network is constructed by integrating oligonucleotide frequency similarity and Gaussian interaction profile kernel similarity through similarity network fusion. The host prediction accuracy of our method is better than other methods. In addition, case studies show that the host of crAssphage predicted by ILMF-VH is consistent with presumed host in previous studies, and another potential host Escherichia coli is also predicted. CONCLUSIONS: The proposed model is an effective computational tool for predicting interactions between viruses and hosts effectively, and it has great potential for discovering novel hosts of viruses.

Metastable Dumbbell Probe-Based Hybridization Chain Reaction for Sensitive and Accurate Imaging of Intracellular-Specific MicroRNAs In Situ in Living Cells.

Sensitive and accurate imaging of intracellular-specific microRNAs (miRNAs) in situ in living cells is seriously challenged by the susceptibility of nucleic acid probes and the low dynamics of the hybridization reaction in cellular environments. Herein, we engineer a set of new metastable dumbbell probes (M xDPs) to overcome these key limitations by concurrently boosting transfection, antidigestibility, assembly dynamics, and nanostructural uniformity. The M xDPs can maintain their stability up to 16 h in living cells and produce uniform and dense DNA nanostructures rapidly (<2 h) and specifically from a hybridization chain reaction (HCR). A sharp signal from the cascade accumulation of fluorescence resonance energy transfer (FRET) further minimizes the effect of system fluctuations. The M xDPs-based HCR (M xDPHCR) method showed identical performance in the analysis of miR-27a in cell lysate and buffer condition and obtained a limit of detection down to 3.2 pM (corresponding to 160 amol per 50 µL), which is 44-fold lower than on conventional hairpin probes. The M xDPHCR method clearly distinguished normal cells from tumor cells and provided more accurate quantitative information on the intracellular-specific miRNAs. The strategy would offer a powerful tool for visualizing and localizing desired nucleic acids in living cells.

In Situ Enzyme Immobilization with Oxygen-Sensitive Luminescent Metal-Organic Frameworks to Realize "All-in-One" Multifunctions.

Metal-organic frameworks (MOFs) for enzyme immobilization have already shown superior tunable and designable characteristics, however, their diverse responsive properties have rarely been exploited. In this work we integrated a responsive MOF into a MOF-enzyme composite with the purpose of designing an "all-in-one" multifunctional composite with catalytic and luminescence functions incorporated into a single particle. As a proof-of-concept, glucose oxidase (GOx) was encapsulated in situ within an oxygen (O2 )-sensitive, noble-metal-free, luminescent CuI triazolate framework (MAF-2), denoted as GOx@MAF-2. Owing to the rigid scaffold of MAF-2 and confinement effect, the GOx@MAF-2 composite showed significantly improved stability (shelf life of 60 days and heat resistance up to 80 °C) as well as good selectivity and recyclability. More importantly, owing to the O2 sensitivity of MAF-2, the GOx@MAF-2 composite exhibited a rapid and reversible response towards dissolved O2 , thereby allowing direct and ratiometric sensing of glucose without the need for chromogenic substrates, cascade enzymatic reactions, or electrode systems. High sensitivity with a detection limit of 1.4 µm glucose was achieved, and the glucose levels in human sera were accurately determined. This strategy has led to a new application for MOFs that can be facilely extended to other MOF-enzyme composites due to the multifunctionality of MOFs.

A Bioinspired, Highly Transparent Surface with Dry-Style Antifogging, Antifrosting, Antifouling, and Moisture Self-Cleaning Properties.

Transparent coatings with antireflection, antifogging, antifrosting, antifouling, and moisture self-cleaning properties can dramatically improve the efficiency and convenience of optical elements and thus are highly desirable for practical applications. Here, it is demonstrated that a bionic nanocone surface (BNS) fabricated by a facile, low-cost process consisting of template-assisted prepolymer curing followed by surface modification can possess the multiple functions listed above. The polymer coating firmly adheres to a glass substrate due to bonding agents. After SiO2 nanoparticle deposition and low-surface-energy fluorosilane modification, the coating shows low microdroplet adhesion. As a result, the as-prepared BNS exhibits a high transmittance when exposed to fog and good clarity even when the temperature decreases to -20 °C in a humid environment. Dipping the BNS into exemplified graphite powder has almost no influence on the transparency, and the BNS can realize self-cleaning of moisture when the surface is covered with a thick layer of man-made contaminants.

Isothermal Amplification on a Structure-Switchable Symmetric Toehold Dumbbell-Template: A Strategy Enabling MicroRNA Analysis at the Single-Cell Level with Ultrahigh Specificity and Accuracy.

Accurate analysis of microRNAs (miRNAs) at the single-cell level seriously requires analytical methods possessing extremely high sensitivity, specificity and precision. By rational engineering of a structure-switchable symmetric toehold dumbbell-template (STD-template), we propose a novel isothermal symmetric exponential amplification reaction (SEXPAR) method. The sealed and symmetric structure of the STD-template allows exponential amplification reaction (EXPAR) to occur upon every annealing of target miRNA without loss of amplification efficiency. In addition, the rigid and compact structure of the STD-template with an appropriate standard free energy ensures SEXPAR only be activated by target miRNA. As a result, the SEXPAR method isothermally quantified let-7a down to 0.01 zmol (6.02 copies per 10 µL) with an ultrahigh specificity which is efficient enough to discriminate one-base-mismatched miRNAs, and a remarkably high precision even for the determination of 6.02 copies let-7a (the standard deviation was reduced from >60% down to 23%). The dynamic range was also extended to 10 orders of magnitude. The method was successfully applied for the determination of let-7a in human tissues, sera and even single-cell lysate, with obviously better precision than quantitative reverse transcription polymerase chain reaction (RT-qPCR) and other EXPAR-based methods. The SEXPAR method may serve as a powerful technique for the biological research and biomedical studies of miRNAs and other short nucleic acids.

Asymmetric exponential amplification reaction on a toehold/biotin featured template: an ultrasensitive and specific strategy for isothermal microRNAs analysis.

The sensitive and specific analysis of microRNAs (miRNAs) without using a thermal cycler instrument is significant and would greatly facilitate biological research and disease diagnostics. Although exponential amplification reaction (EXPAR) is the most attractive strategy for the isothermal analysis of miRNAs, its intrinsic limitations of detection efficiency and inevitable non-specific amplification critically restrict its use in analytical sensitivity and specificity. Here, we present a novel asymmetric EXPAR based on a new biotin/toehold featured template. A biotin tag was used to reduce the melting temperature of the primer/template duplex at the 5' terminus of the template, and a toehold exchange structure acted as a filter to suppress the non-specific trigger of EXPAR. The asymmetric EXPAR exhibited great improvements in amplification efficiency and specificity as well as a dramatic extension of dynamic range. The limit of detection for the let-7a analysis was decreased to 6.02 copies (0.01 zmol), and the dynamic range was extended to 10 orders of magnitude. The strategy enabled the sensitive and accurate analysis of let-7a miRNA in human cancer tissues with clearly better precision than both standard EXPAR and RT-qPCR. Asymmetric EXPAR is expected to have an important impact on the development of simple and rapid molecular diagnostic applications for short oligonucleotides.