Outbreaks of both influenza virus and the novel coronavirus SARS-CoV-2 are serious threats to human health and life. It is very important to establish a rapid, accurate test with large-scale detection potential to prevent the further spread of the epidemic. An optimized RPA-Cas12a-based platform combined with digital microfluidics (DMF), the RCD platform, was established to achieve the automated, rapid detection of influenza viruses and SARS-CoV-2. The probe in the RPA-Cas12a system was optimized to produce maximal fluorescence to increase the amplification signal. The reaction droplets in the platform were all at the microliter level and the detection could be accomplished within 30 min due to the effective mixing of droplets by digital microfluidic technology. The whole process from amplification to recognition is completed in the chip, which reduces the risk of aerosol contamination. One chip can contain multiple detection reaction areas, offering the potential for customized detection. The RCD platform demonstrated a high level of sensitivity, specificity (no false positives or negatives), speed (≤30 min), automation and multiplexing. We also used the RCD platform to detect nucleic acids from influenza patients and COVID-19 patients. The results were consistent with the findings of qPCR. The RCD platform is a one-step, rapid, highly sensitive and specific method with the advantages of digital microfluidic technology, which circumvents the shortcomings of manual operation. The development of the RCD platform provides potential for the isothermal automatic detection of nucleic acids during epidemics. Electronic Supplementary Material Supplementary material is available in the online version of this article at 10.1007/s11426-021-1169-1.
Background Tumor cells tend to utilize glycolysis rather than aerobic respiration even under aerobic conditions. OVOL2, an inhibitory C2H2 zinc finger transcription factor, is a potential tumor suppressor in cancers. However, the association between OVOL2 and tumor energy metabolism is unknown. Methods Western blotting was used to determine the expression of OVOL2 in different non-small cell lung cancer (NSCLC) cell lines and mouse models. The metabolic parameters in NSCLC cells following overexpression or knockdown OVOL2 were examined. To define the mechanism by which OVOL2 regulates aerobic glycolysis, interacting protein of OVOl2 and downstream molecular events were identified by luciferase assay and co-immunoprecipitation. We documented the regulatory mechanism in mouse xenograft models. Finally, clinical relevance of OVOL2, NF-κB signaling and GLUT1 was measured by immunostaining. Results OVOL2 is downregulated in NSCLC and overexpression of OVOL2 inhibits the survival of cancer cells. Moreover, OVOL2 directly binds to P65 and inhibits the recruitment of P300 but facilitates the binding of HDAC1 to P65, which in turn negatively regulates NF-κB signaling to suppress GLUT1 translocation and glucose import. In contrast, OVOL2 expression is negatively regulated by NF-κB signaling in NSCLC cells via the ubiquitin–proteasome pathway. Re-expression of OVOL2 significantly compromise NF-κB signaling-induced GLUT1 translocation, aerobic glycolysis in NSCLC cells and mouse models. Immunostaining revealed inverse correlations between the OVOL2 and phosphorylated P65 levels and between the OVOL2 and membrane GLUT1 levels, and a strong correlation between the phosphorylated P65 and membrane GLUT1 levels. Conclusions These results suggest a regulatory circuit linking NF-κB and OVOL2, which highlights the role of NF-κB signaling and OVOL2 in the modulation of glucose metabolism in NSCLC.
Drug-metabolizing enzymes play an important role in the metabolism of drugs in vivo. Their activity is an important factor affecting the rate of drug metabolism, which directly determines the intensity and persistence of drug action. Patients taking medication can be divided into different metabolic types through detection of CYP2C19 drug-metabolizing enzyme gene polymorphisms, which can then be used for medication guidance for clopidogrel. Here, we describe a detection method based on real-time polymerase chain reaction. This method uses multicolor melting curve analysis to accurately identify different mutation sites and genotypes of CYP2C19 * 2, CYP2C19 * 3, and CYP2C19 * 17. The detection limit of plasmid samples was 1 copies/µl; that of genomic samples was 0.1 ng/µl. The system can detect nine types of CYP2C19 * 2/3/17 at three sites in one tube, quickly achieving detection within 1 h. Combined with the sample release agent, sample extraction was completed in 5 s, achieving rapid diagnosis without extraction for timely diagnosis and treatment. Furthermore, the system is not limited to blood samples and can also be applied to oropharyngeal and saliva samples, increasing sampling diversity and convenience. When using clinical blood samples (n=93), the detection system we established was able to quickly and accurately identify different genotypes, and the accuracy and effectiveness of the detection were confirmed by Sanger sequencing.
Drug‐metabolizing enzymes play an important role in the metabolism of drugs in vivo. Their activity is an important factor affecting the rate of drug metabolism, which directly determines the intensity and persistence of drug action. Patients taking medication can be divided into different metabolic types through detection of CYP2C19 drug‐metabolizing enzyme gene polymorphisms, which can then be used for medication guidance for clopidogrel. Here, we describe a detection method based on real‐time polymerase chain reaction. This method uses multicolor melting curve analysis to accurately identify different mutation sites and genotypes of CYP2C19 * 2, CYP2C19 * 3, and CYP2C19 * 17.The detection limit of plasmid samples was 1 copies/μl; that of genomic samples was 0.1 ng/μl. The system can detect nine types of CYP2C19 * 2/3/17 at three sites in one tube, quickly achieving detection within 1 h. Combined with the sample release agent, sample extraction was completed in 5 s, achieving rapid diagnosis without extraction for timely diagnosis and treatment. Furthermore, the system is not limited to blood samples and can also be applied to oropharyngeal and saliva samples, increasing sampling diversity and convenience. When using clinical blood samples (n = 93), the detection system we established was able to quickly and accurately identify different genotypes, and the accuracy and effectiveness of the detection were confirmed by Sanger sequencing.Due to its accuracy, rapidity, simple operation and low cost, detection technology based on real‐time polymerase amplification combined with melting curve analysis is expected to become a powerful tool for detecting and guiding clopidogrel use in countries with limited resources.This article is protected by copyright. All rights reserved
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