Search

Molecular computing is an emerging paradigm that plays an essential role in data storage, bio-computation, and clinical diagnosis with the future trends of more efficient computing scheme, higher modularity with scaled-up circuity and stronger tolerance of corrupted inputs in a complex environment. Towards these goals, we construct a spatially localized, DNA integrated circuits-based classifier (DNA IC-CLA) that can perform neuromorphic architecture-based computation at a molecular level for medical diagnosis. The DNA-based classifier employs a two-dimensional DNA origami as the framework and localized processing modules as the in-frame computing core to execute arithmetic operations (e.g. multiplication, addition, subtraction) for efficient linear classification of complex patterns of miRNA inputs. We demonstrate that the DNA IC-CLA enables accurate cancer diagnosis in a faster (about 3 h) and more effective manner in synthetic and clinical samples compared to those of the traditional freely diffusible DNA circuits. We believe that this all-in-one DNA-based classifier can exhibit more applications in biocomputing in cells and medical diagnostics., (© 2024. The Author(s).)

Rapid and accurate classification of the etiology for acute respiratory illness not only helps establish timely therapeutic plans but also prevents inappropriate use of antibiotics. Host gene expression patterns in peripheral blood can discriminate bacterial from viral causes of acute respiratory infection (ARI) but suffer from long turnaround time, as well as high cost resulting from the measurement methods of microarrays and next-generation sequencing. Here, we developed an automated DNA computing-based platform that can implement an in silico trained classification model at the molecular level with seven different mRNA expression patterns for accurate diagnosis of ARI etiology in 4 hours. By integrating sample loading, marker amplification, classifier implementation, and results reporting into one platform, we obtained a diagnostic accuracy of 87% in 80 clinical samples without the aid of computer and laboratory technicians. This platform creates opportunities toward an accurate, rapid, low-cost, and automated diagnosis of disease etiology in emergency departments or point-of-care clinics.

Artificial nanorobots that can recognize molecular triggers and respond with programable operations provide an inspiring proof-of-principle for personalized theragnostic applications. We have constructed an intelligent DNA nanorobot for autonomous blood anticoagulation in human plasma. The DNA nanorobot comprises a barrel-shaped DNA nanostructure as the framework and molecular reaction cascades embedded as the computing core. This nanorobot can intelligently sense the concentration of thrombin in the local environment and trigger an autonomous anticoagulation when excess thrombin is present. The triggering concentration of thrombin at which the nanorobot responds can be tuned arbitrarily to avoid possible side effects induced by excess thrombin. This makes the nanorobot useful for autonomous anticoagulation in various medical scenarios and inspires a more efficient and safer strategy for future personalized medicine., (© 2020 Wiley-VCH GmbH.)

Telomerase is one of the most common markers of human malignant tumors, such as uterine, stomach, esophageal, breast, colorectal, laryngeal squamous cell, thyroid, bladder, and so on. It is necessary to develop some sensitive but convenient detection methods for telomerase activity determination. In this study, a label-free and ultrasensitive electrochemiluminescence (ECL) biosensor has been fabricated to detect the activity of telomerase extracted from HeLa cells. Thiolated telomerase substrate (TS) primer was immobilized on the gold electrode surface through gold-sulfur (Au-S) interaction and then elongated by telomerase specifically. Then, it was hybridized with complementary DNA to form double-stranded DNA (dsDNA) fragments on the electrode surface, and Ru(phen)3 (2+) has been intercalated into the dsDNA grooves to act as the ECL probe. The enhanced ECL intensity has a linear relationship with the number of HeLa cells in the range of 5∼5000 and with a detection limit of 2 HeLa cells. The proposed ECL biosensor has high specificity to telomerase in the presence of common interferents. The relative standard deviations (RSDs) were <5 % at 100 HeLa cells. The proposed method provides a convenient approach for telomerase-related cancer screening or diagnosis.

An ultrasensitive and specific electrochemiluminescence (ECL) biosensor has been designed for the p53 DNA sequence, which is based on cascade signal amplification of nicking endonuclease assisted target recycling and hyperbranched rolling circle amplification (HRCA). First of all, biotin modified hairpin capture DNA (HP) probe was immobilized on the surface of streptavidin magnespheres paramagnetic particles (PMPs). Target DNA hybridized with the loop portion of the HP probe, therefore unfolding HP to form a double-stranded DNA (dsDNA) containing the specific nicking site of the nicking endonuclease. Then, the nicking endonuclease recognized the specific nicking site and cleaved the HP into two pieces, liberating target DNA and the complementary sequence piece for the padlock probe. The intact target DNA would initiate the next cycle of hybridization and cleavage, thereby releasing multiple complementary sequences for the padlock probes. The liberated complementary sequences hybridized with the padlock probes, subsequently inducing the HRCA reaction and generating numerous dsDNA segments. Herein, Ru(phen)3(2+) was embedded into dsDNA and worked as ECL signal reporter. The reaction products were eventually pretreated by dialysis tube with the cutoff membrane to remove the residual Ru(phen)3(2+) in the solution for the following ECL measurements. Using this cascade amplification strategy, an ultrasensitive p53 DNA sequence detection method was developed with a wide linear range from 0.05 to 100 fM and a low detection limit of 0.02 fM. Moreover, this cascade amplified ECL biosensor had specific recognition capacity for noncomplementary and single- and double-base mismatched DNA. The proposed ECL biosensor might have a great potential in biomedical research and clinic analysis.

We demonstrate a novel protocol for sensitive in situ label-free electrochemical detection of DNA hybridization based on copper complex ([Cu(phen)2](2+), where phen = 1,10-phenanthroline) and graphene (GR) modified glassy carbon electrode. Here, [Cu(phen)2](2+) acted advantageously as both the electrochemical indicator and the anchor for probe DNA immobilization via intercalative interactions between the partial double helix structure of probe DNA and the vertical aromatic groups of phen. GR provided large density of docking site for probe DNA immobilization and increased the electrical conductivity ability of the electrode. The modification procedure was monitored by electrochemical impedance spectroscopy (EIS). Square-wave voltammetry (SWV) was used to explore the hybridization events. Under the optimal conditions, the designed electrochemical DNA biosensor could effectively distinguish different mismatch degrees of complementary DNA from one-base mismatch to noncomplementary, indicating that the biosensor had high selectivity. It also exhibited a reasonable linear relationship. The oxidation peak currents of [Cu(phen)2](2+) were linear with the logarithm of the concentrations of complementary target DNA ranging from 1 × 10(-12) to 1 × 10(-6) M with a detection limit of 1.99 × 10(-13) M (signal/noise = 3). Moreover, the stability of the electrochemical DNA biosensor was also studied., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Analyzing complex single‐nucleotide‐polymorphism (SNP) combinations in the genome is important for research and clinical applications, given that different SNP combinations can generate different phenotypic consequences. Recent works have shown that DNA‐based molecular computing is powerful for simultaneously sensing and analyzing complex molecular information. Here, we designed a switching circuit‐based DNA computational scheme that can integrate the sensing of multiple SNPs and simultaneously perform logical analysis of the detected SNP information to directly report clinical outcomes. As a demonstration, we successfully achieved automatic and accurate identification of 21 different blood group genotypes from 83 clinical blood samples with 100 % accuracy compared to sequencing data in a more rapid manner (3 hours). Our method enables a new mode of automatic and logical sensing and analyzing subtle molecular information for clinical diagnosis, as well as guiding personalized medication. [ABSTRACT FROM AUTHOR]

Artificial nanorobots that can recognize molecular triggers and respond with programable operations provide an inspiring proof‐of‐principle for personalized theragnostic applications. We have constructed an intelligent DNA nanorobot for autonomous blood anticoagulation in human plasma. The DNA nanorobot comprises a barrel‐shaped DNA nanostructure as the framework and molecular reaction cascades embedded as the computing core. This nanorobot can intelligently sense the concentration of thrombin in the local environment and trigger an autonomous anticoagulation when excess thrombin is present. The triggering concentration of thrombin at which the nanorobot responds can be tuned arbitrarily to avoid possible side effects induced by excess thrombin. This makes the nanorobot useful for autonomous anticoagulation in various medical scenarios and inspires a more efficient and safer strategy for future personalized medicine. [ABSTRACT FROM AUTHOR]

Grass carp, Ctenopharyngodon idellus (Valenciennes, 1844), is an economically important species widely cultured in the world, but its genome research resources are largely lacking. The objectives of this study were to construct normalized cDNA libraries for efficient EST analysis, to generate ESTs from these libraries, and to identify EST-related molecular markers such as microsatellites and single nucleotide polymorphisms (SNPs) for genetic analysis of this species. A total of 6,269 ESTs were generated representing 4,815 unique sequences, from which 105 putative microsatellites and 5,228 SNPs were identified. These genome resources provide the material basis for future genetic and functional analyses in this species. [ABSTRACT FROM AUTHOR]

Abstract: Insulin-like growth factor binding protein 7 (IGFBP7) was considered a tumor suppressor gene in lung cancer. However, the mechanism responsible for the downregulation of this gene has not yet been fully understood. In this study, we analyzed the epigenetic inactivation of IGFBP7 expression in human lung cancer. We found that 14 out of 16 lung cancer cell lines showed decreased expression of IGFBP7 compared to control cells by real-time RT-PCR, and 42 out of 90 patients (46.7%) with primary lung tumor exhibited negative staining of IGFBP7 by immunohistochemistry analysis. The IGFBP7 expression could be restored by demethylation agent 5-aza-2′-deoxycytidine (DAC) in 7 cancer cell lines. Methylation status of IGFBP7 was further evaluated by bisulfite sequencing (BS) and methylation-specific-PCR (MSP). It turned out that low expression of IGFBP7 was associated with DNA methylation in lung cancer cell lines and in primary lung tumors (P =0.019). To explore the regulatory role of p53 on IGFBP7, we transfected a wild type p53 expression vector into lung cancer cell lines H1299, H2228, and H82. Forced expression of p53 increased IGFBP7 expression only in H82 harboring no IGFBP7 methylation, while transfection in combination with DAC induced the expression of IGFBP7 in H1299 and H2228, in which IGFBP7 was methylated. Additionally, treatment with p53 inducer adriamycin (ADR) alone or in combination with DAC increased the expression of IGFBP7 in the 3 cell lines. Our data suggest that IGFBP7 is inactivated in lung cancer by DNA hypermethylation in both lung cancer cell lines and primary lung tumors, and IGFBP7 might be regulated by p53 in lung cancer cells. [Copyright &y& Elsevier]