A bulged-type enzyme-free recognition strategy designed for single nucleotide polymorphisms integrating with label-free electrochemical biosensor.

Compared to conventional nucleic acid detection methods, label-free single nucleotide polymorphism (SNP) detection presents challenging due to the necessity of discerning single base mismatches, especially in the field of enzyme-free detection. In this study, we introduce a novel bulged-type DNA duplex probe designed to significantly amplify single-base differences. This probe is integrated with programmable DNA-based nanostructures to develop a sensitive, label-free biosensor for nonenzymatic SNP detection. The duplex probe with one bulge could selectively identify wild-typed DNA (WT) and mutant-type DNA (MT) based on a competitive strand displacement reaction mechanism. The hyperbranched HCR (HHCR) by incorporating of hairpin DNA into the DNA tetrahedron and surface-tethering on the portable screen printing electrode (SPCE) significantly favor the formation of negatively charged DNA nanostructure. We harnessed strong repulsion of DNA nanostructure towards the electroactive [Fe(CN)6]³â»/4⁻ in combination with electrochemical technique to create a label-free biosensor. This simple, enzyme-free and label-free biosensor could detect MT with a detection limit of 56 aM, even in multiple sequence backgrounds. The study served as the proof-of-concept for the integration of enzyme-free competitive mechanism and label-free strategy, which can be extended as a powerful tool to various fields.

Programmable Computational RNA Droplets Assembled via Kissing-Loop Interaction.

DNA droplets, artificial liquid-like condensates of well-engineered DNA sequences, allow the critical aspects of phase-separated biological condensates to be harnessed programmably, such as molecular sensing and phase-state regulation. In contrast, their RNA-based counterparts remain less explored despite more diverse molecular structures and functions ranging from DNA-like to protein-like features. Here, we design and demonstrate computational RNA droplets capable of two-input AND logic operations. We use a multibranched RNA nanostructure as a building block comprising multiple single-stranded RNAs. Its branches engaged in RNA-specific kissing-loop (KL) interaction enables the self-assembly into a network-like microstructure. Upon two inputs of target miRNAs, the nanostructure is programmed to break up into lower-valency structures that are interconnected in a chain-like manner. We optimize KL sequences adapted from viral sequences by numerically and experimentally studying the base-wise adjustability of the interaction strength. Only upon receiving cognate microRNAs, RNA droplets selectively show a drastic phase-state change from liquid to dispersed states due to dismantling of the network-like microstructure. This demonstration strongly suggests that the multistranded motif design offers a flexible means to bottom-up programming of condensate phase behavior. Unlike submicroscopic RNA-based logic operators, the macroscopic phase change provides a naked-eye-distinguishable readout of molecular sensing. Our computational RNA droplets can be applied to in situ programmable assembly of computational biomolecular devices and artificial cells from transcriptionally derived RNA within biological/artificial cells.

In silico bioactivity prediction of proteins interacting with graphene-based nanomaterials guides rational design of biosensor.

Graphene-based nanomaterials have attracted significant attention for their potentials in biomedical and biotechnology applications in recent years, owing to the outstanding physical and chemical properties. However, the interaction mechanism and impact on biological activity of macro/micro biomolecules still require more concerns and further research in order to enhance their applicability in biosensors, etc. Herein, an integrated method has been developed to predict the protein bioactivity performance when interacting with nanomaterials for protein-based biosensor. Molecular dynamics simulation and molecular docking technique were consolidated to investigate several nanomaterials: C60 fullerene, single-walled carbon nanotube, pristine graphene and graphene oxide, and their effect when interacting with protein. The adsorption behavior, secondary structure changes and protein bioactivity changes were simulated, and the results of protein activity simulation were verified in combination with atomic force spectrum, circular dichroism spectrum fluorescence and electrochemical experiments. The best quantification alignment between bioactivity obtained by simulation and experiment measurements was further explored. The two proteins, RNase A and Exonuclease III, were regarded as analysis model for the proof of concept, and the prediction accuracy of protein bioactivity could reach up to 0.98. The study shows an easy-to-operate and systematic approach to predict the effects of graphene-based nanomaterials on protein bioactivity, which holds guiding significance for the design of protein-related biosensors. In addition, the proposed prediction model is not limited to carbon-based nanomaterials and can be extended to other types of nanomaterials. This facilitates the rapid, simple, and low-cost selection of efficient and biosafe nanomaterials candidates for protein-related applications in biosensing and biomedical systems.

A novel biomimetic electrochemical taste-biosensor based on conformational changes of the taste receptor.

Taste sensor, a useful tool which could detect and identify thousands of different chemical substances in liquid environments, has attracted continuous concern from beverage and foodstuff industry and its consumers. Although many taste sensing methods have been extensively developed, the assessment of tastant content remains challenging due to the limitations of sensor selectivity and sensitivity. Here we present a novel biomimetic electrochemical taste-biosensor based on bioactive sensing elements and immune amplification with nanomaterials carrier to address above concerns, while taking sweet taste perception as a model. The proposed biosensor based on ligand binding domain (T1R2 VFT) of human sweet taste receptor protein showed human mimicking character and initiated the application of immune recognition in gustation biosensor, which can precisely and sensitively distinguish sweet substances against other related gustation substances with detection limit of 5.1 pM, far less than that of taste sensors without immune amplification whose detection limit was 0.48 nM. The performance test demonstrated the biosensor has the capacity of monitoring the response of sweet substances in real food environments, which is crucial in practical. This biomimetic electrochemical taste-biosensor can work as a new screening platform for newly developed tastants and disclose sweet perception mechanism.

A biosensing system employing nonlinear dynamic analysis-assisted neural network for drug-induced cardiotoxicity assessment.

Preclinical investigation of drug-induced cardiotoxicity is of importance for drug development. To evaluate such cardiotoxicity, in vitro high-throughput interdigitated electrode-based recording of cardiomyocytes mechanical beating is widely used. To automatically analyze the features from the beating signals for drug-induced cardiotoxicity assessment, artificial neural network analysis is conventionally employed and signals are segmented into cycles and feature points are located in the cycles. However, signal segmentation and location of feature points for different signal shapes require design of specific algorithms. Consequently, this may lower the efficiency of research and the applications of such algorithms in signals with different morphologies are limited. Here, we present a biosensing system that employs nonlinear dynamic analysis-assisted neural network (NDANN) to avoid the signal segmentation process and directly extract features from beating signal time series. By processing beating time series with fixed time duration to avoid the signal segmentation process, this NDANN-based biosensing system can identify drug-induced cardiotoxicity with accuracy over 0.99. The individual drugs were classified with high accuracies over 0.94 and drug-induced cardiotoxicity levels were accurately predicted. We also evaluated the generalization performance of the NDANN-based biosensing system in assessing drug-induced cardiotoxicity through an independent dataset. This system achieved accuracy of 0.85-0.95 for different drug concentrations in identification of drug-induced cardiotoxicity. This result demonstrates that our NDANN-based biosensing system has the capacity of screening newly developed drugs, which is crucial in practical applications. This NDANN-based biosensing system can work as a new screening platform for drug-induced cardiotoxicity and improve the efficiency of bio-signal processing.

A Portable Smartphone-Based System for the Detection of Blood Calcium Using Ratiometric Fluorescent Probes.

Hypocalcemia is a disease that adversely affects the production and reproduction of dairy cows. A portable device for rapid bovine blood calcium sensing has been growing in demand. Herein, we report a smartphone-based ratiometric fluorescence probe (SRFP) platform as a new way to detect and quantify calcium ions (Ca2+) in blood serum. Specifically, we employed a cost-effective and portable smartphone-based platform coupled with customized software that evaluates the response of Ca2+ ions to ratiometric fluorescence probe in bovine serum. The platform consists of a three-dimensional (3D) printed housing and low-cost optical components that excite fluorescent probe and selectively transmit fluorescence emissions to smartphones. The customized software is equipped with a calibration model to quantify the acquired fluorescence images and quantify the concentration of Ca2+ ions. The ratio of the green channel to the red channel bears a highly reproducible relationship with Ca2+ ions concentration from 10 µM to 40 µM in bovine serum. Our detection system has a limit of detection (LOD) of 1.8 µM in bovine serum samples and the recoveries of real samples ranged from 92.8% to 110.1%, with relative standard deviation (RSD) ranging from 1.72% to 4.89%. The low-cost SRFP platform has the potential to enable campesino to rapidly detect Ca2+ ions content in bovine serum on-demand in any environmental setting.

Synthesis and antiviral activity of conformational analogues of leucamide A.

In order to study the effect of heterocyclic core conformational state of leucamide A on its anti-influenza virus A activity, five conformational analogues were prepared by replacing the Pro-Leu dipeptide in the molecule with various amino acids. The amino acids used were of 2 to 6 carbons. The results showed that these replacements not only changed the conformational relationship between the 4,2-bisheterocycle tandem pair and the third heterocycle, but also had dramatic effect on its activity against influenza virus A.

[Biocathode denitrification in a two-columnar microbial fuel cell].

The biocathode of the two-columnar microbial fuel cell was used to denitrify. Factors influencing denitrification performance and power production were studied. When the external resistance decreased from 50 omega to 5 omega, the nitrate removal rate increased from 0.26 mg/(L x h) to 0.76 mg/(L x h). The nitrite accumulated to 55 mg/L with the external resistance decreasing to 5 omega. The nitrate degradation followed the zero order reaction model when the initial nitrate concentration was 20-120 mg/L. The power generation was not affected by the nitrate concentration distinctly. The nitrite concentration increased with the initial nitrate concentration. The nitrite removal could be enhanced by adding organic matter, without significant influence on the power generation.

Synthesis and biological evaluation of novel bisheterocycle-containing compounds as potential anti-influenza virus agents.

A series of novel 4,2-bisheterocycle tandem derivatives consisting of a methyloxazole and thiazole subunit were synthesized. Many compounds were found to inhibit human influenza A virus. Several analogues exhibited moderate biological activity and could serve as leads for further optimizations for antivirus research.