Antisense oligodeoxynucleotide(ASODN)can directly interfere a series of biological events of the target RNA derived from tumor cells through Watson-Crick base pairing,in turn,plays antitumor therapeutic roles.In the s...Antisense oligodeoxynucleotide(ASODN)can directly interfere a series of biological events of the target RNA derived from tumor cells through Watson-Crick base pairing,in turn,plays antitumor therapeutic roles.In the study,a novel HIF-1αASODN-loaded nanocomposite was formulated to efficiently deliver gene to the target RNA.The physicochemical properties of nanocomposite were characterized using TEM,FTIR,DLS and zeta potentials.The mean diameter of resulting GEL-DGL-FA-ASODN-DCA nanocomposite was about 170–192 nm,and according to the agarose gel retardation assay,the loading amount of ASODN accounted for 166.7 mg/g.The results of cellular uptake showed that the nanocomposite could specifically target to HepG2 and Hela cells.The cytotoxicity assay demonstrated that the toxicity of vectors was greatly reduced by using DCA to reversibly block the cationic DGL.The subcellular distribution images clearly displayed the lysosomal escape ability of the DCA-modified nanocomposite.In vitro exploration of molecular mechanism indicated that the nanocomposite could inhibit m RNA expression and HIF-1αprotein translation at different levels.In vivo optical images and quantitative assay testified that the formulation accumulated preferentially in the tumor tissue.In vivo antitumor efficacy research confirmed that this nanocomposite had significant antitumor activity and the tumor inhibitory rate was 77.99%.These results manifested that the GEL-DGL-FA-ASODNDCA nanocomposite was promising in gene therapeutics for antitumor by interacting directly with target RNA.展开更多
Graph deep learning models,which incorporate a natural inductive bias for atomic structures,are of immense interest in materials science and chemistry.Here,we introduce the Materials Graph Library(MatGL),an open-sourc...Graph deep learning models,which incorporate a natural inductive bias for atomic structures,are of immense interest in materials science and chemistry.Here,we introduce the Materials Graph Library(MatGL),an open-source graph deep learning library for materials science and chemistry.Built on top of the popular Deep Graph Library(DGL)and Python Materials Genomics(Pymatgen)packages,MatGL is designed to be an extensible“batteries-included”library for developing advanced model architectures for materials property predictions and interatomic potentials.At present,MatGL has efficient implementations for both invariant and equivariant graph deep learning models,including the Materials 3-body Graph Network(M3GNet),MatErials Graph Network(MEGNet),Crystal Hamiltonian Graph Network(CHGNet),TensorNet and SO3Net architectures.MatGL also provides several pretrained foundation potentials(FPs)with coverage of the entire periodic table,and property prediction models for out-of-box usage,benchmarking and fine-tuning.Finally,MatGL integrates with PyTorch Lightning to enable efficient model training.展开更多
基金supported by the National Natural Science Foundation of China Fund(No 81541060)Science and Technology Projects from the Science Technology and Innovation Committee of Shenzhen Municipality(grant no.JCJY20170818110340383 and JCJY20170307163529489)。
文摘Antisense oligodeoxynucleotide(ASODN)can directly interfere a series of biological events of the target RNA derived from tumor cells through Watson-Crick base pairing,in turn,plays antitumor therapeutic roles.In the study,a novel HIF-1αASODN-loaded nanocomposite was formulated to efficiently deliver gene to the target RNA.The physicochemical properties of nanocomposite were characterized using TEM,FTIR,DLS and zeta potentials.The mean diameter of resulting GEL-DGL-FA-ASODN-DCA nanocomposite was about 170–192 nm,and according to the agarose gel retardation assay,the loading amount of ASODN accounted for 166.7 mg/g.The results of cellular uptake showed that the nanocomposite could specifically target to HepG2 and Hela cells.The cytotoxicity assay demonstrated that the toxicity of vectors was greatly reduced by using DCA to reversibly block the cationic DGL.The subcellular distribution images clearly displayed the lysosomal escape ability of the DCA-modified nanocomposite.In vitro exploration of molecular mechanism indicated that the nanocomposite could inhibit m RNA expression and HIF-1αprotein translation at different levels.In vivo optical images and quantitative assay testified that the formulation accumulated preferentially in the tumor tissue.In vivo antitumor efficacy research confirmed that this nanocomposite had significant antitumor activity and the tumor inhibitory rate was 77.99%.These results manifested that the GEL-DGL-FA-ASODNDCA nanocomposite was promising in gene therapeutics for antitumor by interacting directly with target RNA.
基金intellectually led by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division under contract No. DE-AC02-05-CH11231 (Materials Project program KC23MP). This research used resources of the National Energy Research Scientific Computing Center (NERSC), a Department of Energy Office of Science User Facility using NERSC award DOE-ERCAP0026371. T.W.Ko also acknowledges the support of the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship, a Schmidt Futures program. We also acknowledged AdvanceSoft Corporation for implementing the LAMMPS interface.
文摘Graph deep learning models,which incorporate a natural inductive bias for atomic structures,are of immense interest in materials science and chemistry.Here,we introduce the Materials Graph Library(MatGL),an open-source graph deep learning library for materials science and chemistry.Built on top of the popular Deep Graph Library(DGL)and Python Materials Genomics(Pymatgen)packages,MatGL is designed to be an extensible“batteries-included”library for developing advanced model architectures for materials property predictions and interatomic potentials.At present,MatGL has efficient implementations for both invariant and equivariant graph deep learning models,including the Materials 3-body Graph Network(M3GNet),MatErials Graph Network(MEGNet),Crystal Hamiltonian Graph Network(CHGNet),TensorNet and SO3Net architectures.MatGL also provides several pretrained foundation potentials(FPs)with coverage of the entire periodic table,and property prediction models for out-of-box usage,benchmarking and fine-tuning.Finally,MatGL integrates with PyTorch Lightning to enable efficient model training.
