Deoxyribonucleic acid (DNA) doped with transition metal ions shows great versatility for molecular-based biosensors and bioelectronics. Methodologies for developing DNA lattices (formed by synthetic double-crossover tiles) and DNA layers (used by natural salmon) doped with vanadium ions (V), as well as an understanding of the physical characteristics of V-doped DNA nanostructures, are essential in practical applications in interdisciplinary research fields. Here, DNA lattices and layers doped with V are constructed through substrate-assisted growth and drop-casting methods. In addition, enhanced physical characteristics such as the band gap energy, work function, dielectric constant, and susceptibility of V-doped DNA nanostructures with varying V concentration ([V ]) are investigated. The critical concentration ([V ] ) at a given amount of DNA was predicted based on an analysis of the phase transition of DNA lattices from crystalline to amorphous with specific [V ]. Generally, the [V ] provided crucial information on the structural stability and extremum physical characteristics of V-doped DNA nanostructures due to the optimum incorporation of V into DNA. We obtained the optical absorption spectra for energy band gap estimation; Raman spectra for identifying the preferential coordination sites of V in DNA; x-ray photoelectron spectra to examine the chemical state, chemical composition, and functional groups; and ultraviolet photoelectron spectra to estimate the work function. In addition, we addressed the electrical properties (i.e. current, capacitance, dielectric constant, and storage energy) and magnetic properties (magnetic field-dependent and temperature-dependent magnetizations and susceptibility) of DNA layers in the presence of V. The development of biocompatible materials with specific optical, electrical, and magnetic properties is required for future applications because they must have designated functionality, high efficiency, and affordability.
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Proc Natl Acad Sci U S A
January 2025
Institute of Science and Technology Austria, AT-3400 Klosterneuburg, Austria.
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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.
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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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