Quantitative screening of influenza A (H7N9) virus without DNA amplification was performed based on single-particle dual-mode total internal reflection scattering (SD-TIRS) with a transmission grating (TG). A gold nanopad was utilized as a substrate for the hybridization of probe DNA molecules with the TIRS nanotag (silver-nanoparticle). The TG effectively isolated the scattering signals in first-order spectral images (n=+1) of the nanotag from that of the substrate, providing excellent enhancement of signal-to-noise and selectivity. By using single-DNA molecule/TIRS nanotag hybridization, target DNA molecules of H7N9 were detected down to 74 zM, which is at least 100,000 times lower than the current detection limit of 9.4fM. By simply modifying the design of the probe DNA molecules, this technique can be used to directly screen other viral DNAs in various human biological samples at the single-molecule level without target amplification.
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http://dx.doi.org/10.1016/j.bios.2016.09.019 | DOI Listing |
Proc Natl Acad Sci U S A
January 2025
Institute of Science and Technology Austria, AT-3400 Klosterneuburg, Austria.
Biophysical constraints limit the specificity with which transcription factors (TFs) can target regulatory DNA. While individual nontarget binding events may be low affinity, the sheer number of such interactions could present a challenge for gene regulation by degrading its precision or possibly leading to an erroneous induction state. Chromatin can prevent nontarget binding by rendering DNA physically inaccessible to TFs, at the cost of energy-consuming remodeling orchestrated by pioneer factors (PFs).
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Department of Bioengineering, California Institute of Technology, Pasadena, CA 91125.
The diversity and heterogeneity of biomarkers has made the development of general methods for single-step quantification of analytes difficult. For individual biomarkers, electrochemical methods that detect a conformational change in an affinity binder upon analyte binding have shown promise. However, because the conformational change must operate within a nanometer-scale working distance, an entirely new sensor, with a unique conformational change, must be developed for each analyte.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing 100193, China.
Host plants and various fungicides inhibit plant pathogens by inducing the release of excessive reactive oxygen species (ROS) and causing DNA damage, either directly or indirectly leading to cell death. The mechanisms by which the oomycete manages ROS stress resulting from plant immune responses and fungicides remains unclear. This study elucidates the role of histone acetylation in ROS-induced DNA damage responses (DDR) to adapt to stress.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Ministry of Education Key Laboratory of Environment Remediation and Ecological Health, Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China.
While iron (Fe) is essential for life and plays important roles for almost all growth related processes, it can trigger cell death in both animals and plants. However, the underlying mechanisms for Fe-induced cell death in plants remain largely unknown. S-nitrosoglutathione reductase (GSNOR) has previously been reported to regulate nitric oxide homeostasis to prevent Fe-induced cell death within root meristems.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Department of Computer Science, Colorado State University, Fort Collins, Colorado, United States of America.
Complex deep learning models trained on very large datasets have become key enabling tools for current research in natural language processing and computer vision. By providing pre-trained models that can be fine-tuned for specific applications, they enable researchers to create accurate models with minimal effort and computational resources. Large scale genomics deep learning models come in two flavors: the first are large language models of DNA sequences trained in a self-supervised fashion, similar to the corresponding natural language models; the second are supervised learning models that leverage large scale genomics datasets from ENCODE and other sources.
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