目的分析甲基转移酶3(METTL3)抑制剂STM2457对人肝癌细胞系HepG2的影响,重点研究其对N6-甲基腺苷(m6A)表达的影响及其抗肿瘤机制。方法将HepG2细胞分为实验组(STM2457处理)和对照组(DMSO处理)。利用纳米孔(Nanopore)测序技术,结合m6Anet...目的分析甲基转移酶3(METTL3)抑制剂STM2457对人肝癌细胞系HepG2的影响,重点研究其对N6-甲基腺苷(m6A)表达的影响及其抗肿瘤机制。方法将HepG2细胞分为实验组(STM2457处理)和对照组(DMSO处理)。利用纳米孔(Nanopore)测序技术,结合m6Anet,NanoCount,xPore和GFOLD方法,分别对m6A修饰水平、转录组表达及差异基因进行分析。通过基因本体(GO)和京都基因与基因组百科(KEGG)对差异基因进行功能富集分析。结果STM2457降低HepG2细胞的m6A修饰位点数量(6446 vs 11549)及修饰水平(0.95±0.03 vs 0.98±0.03),差异具有统计学意义(Z=-19.915,P<0.01)。差异基因分析共筛选出109个上调基因和340个下调基因,其中与肝癌发生发展密切相关的基因PDLIM5、AZGP1和RNASET2,其m6A修饰水平降低,而基因表达水平升高。功能富集分析结果显示,差异基因主要富集在细胞黏附、凋亡、翻译调控及肝细胞癌相关通路。结论STM2457通过抑制METTL3活性,降低HepG2细胞的m6A修饰水平,上调基因PDLIM5,AZGP1和RNASET2的表达,促进HepG2细胞凋亡,为肝癌治疗提供潜在治疗靶点。展开更多
Microscopy imaging is fundamental in analyzing bacterial morphology and dynamics,offering critical insights into bacterial physiology and pathogenicity.Image segmentation techniques enable quantitative analysis of bac...Microscopy imaging is fundamental in analyzing bacterial morphology and dynamics,offering critical insights into bacterial physiology and pathogenicity.Image segmentation techniques enable quantitative analysis of bacterial structures,facilitating precise measurement of morphological variations and population behaviors at single-cell resolution.This paper reviews advancements in bacterial image segmentation,emphasizing the shift from traditional thresholding and watershed methods to deep learning-driven approaches.Convolutional neural networks(CNNs),U-Net architectures,and three-dimensional(3D)frameworks excel at segmenting dense biofilms and resolving antibiotic-induced morphological changes.These methods combine automated feature extraction with physics-informed postprocessing.Despite progress,challenges persist in computational efficiency,cross-species generalizability,and integration with multimodal experimental workflows.Future progress will depend on improving model robustness across species and imaging modalities,integrating multimodal data for phenotype-function mapping,and developing standard pipelines that link computational tools with clinical diagnostics.These innovations will expand microbial phenotyping beyond structural analysis,enabling deeper insights into bacterial physiology and ecological interactions.展开更多
Desulfurization of CaO–Al_(2)O_(3) particles in molten steel was observed in situ using high-temperature confocal scanning laser microscopy.The effects of the aluminum and silicon contents of molten steel on desulfur...Desulfurization of CaO–Al_(2)O_(3) particles in molten steel was observed in situ using high-temperature confocal scanning laser microscopy.The effects of the aluminum and silicon contents of molten steel on desulfurization were analyzed.When the total aluminum content in the steel increased from 6 to 1100 ppm,the CaS content in CaO–Al_(2)O_(3) particles increased from 2.1wt%to 84.84wt%after the reaction for 90 s.Furthermore,when the silicon content in the steel increased from 0.01wt%to 2.20wt%,the CaS content in CaO–Al_(2)O_(3) particles increased from 1.53wt%to 79.01wt%after the reaction for 90 s.This indicates that the increase in the aluminum and silicon contents of the steel promoted the desulfurization of CaO–Al_(2)O_(3) particles.A kinetic model was established to predict the CaO–Al_(2)O_(3) particles composition,and the diffusion coefficient of sulfur in CaO–Al_(2)O_(3) particles was 9.375×10^(−10)m^(2)·s^(−1) at 1600℃,which provided a new method for the calculation of diffusion coefficient.展开更多
文摘目的分析甲基转移酶3(METTL3)抑制剂STM2457对人肝癌细胞系HepG2的影响,重点研究其对N6-甲基腺苷(m6A)表达的影响及其抗肿瘤机制。方法将HepG2细胞分为实验组(STM2457处理)和对照组(DMSO处理)。利用纳米孔(Nanopore)测序技术,结合m6Anet,NanoCount,xPore和GFOLD方法,分别对m6A修饰水平、转录组表达及差异基因进行分析。通过基因本体(GO)和京都基因与基因组百科(KEGG)对差异基因进行功能富集分析。结果STM2457降低HepG2细胞的m6A修饰位点数量(6446 vs 11549)及修饰水平(0.95±0.03 vs 0.98±0.03),差异具有统计学意义(Z=-19.915,P<0.01)。差异基因分析共筛选出109个上调基因和340个下调基因,其中与肝癌发生发展密切相关的基因PDLIM5、AZGP1和RNASET2,其m6A修饰水平降低,而基因表达水平升高。功能富集分析结果显示,差异基因主要富集在细胞黏附、凋亡、翻译调控及肝细胞癌相关通路。结论STM2457通过抑制METTL3活性,降低HepG2细胞的m6A修饰水平,上调基因PDLIM5,AZGP1和RNASET2的表达,促进HepG2细胞凋亡,为肝癌治疗提供潜在治疗靶点。
基金financially supported by the Open Project Program of Wuhan National Laboratory for Optoelectronics(No.2022WNLOKF009)the National Natural Science Foundation of China(No.62475216)+2 种基金the Key Research and Development Program of Shaanxi(No.2024GH-ZDXM-37)the Fujian Provincial Natural Science Foundation of China(No.2024J01060)the Startup Program of XMU,and the Fundamental Research Funds for the Central Universities.
文摘Microscopy imaging is fundamental in analyzing bacterial morphology and dynamics,offering critical insights into bacterial physiology and pathogenicity.Image segmentation techniques enable quantitative analysis of bacterial structures,facilitating precise measurement of morphological variations and population behaviors at single-cell resolution.This paper reviews advancements in bacterial image segmentation,emphasizing the shift from traditional thresholding and watershed methods to deep learning-driven approaches.Convolutional neural networks(CNNs),U-Net architectures,and three-dimensional(3D)frameworks excel at segmenting dense biofilms and resolving antibiotic-induced morphological changes.These methods combine automated feature extraction with physics-informed postprocessing.Despite progress,challenges persist in computational efficiency,cross-species generalizability,and integration with multimodal experimental workflows.Future progress will depend on improving model robustness across species and imaging modalities,integrating multimodal data for phenotype-function mapping,and developing standard pipelines that link computational tools with clinical diagnostics.These innovations will expand microbial phenotyping beyond structural analysis,enabling deeper insights into bacterial physiology and ecological interactions.
基金supported by the National Key R&D Program of China(No.2023YFB3709900)the National Nature Science Foundation of China(No.U22A20171)+1 种基金the China Baowu Low Carbon Metallurgy Innovation Foundation(No.BWLCF202315)the High Steel Center(HSC)at North China University of Technology and University of Science and Technology Beijing,China.
文摘Desulfurization of CaO–Al_(2)O_(3) particles in molten steel was observed in situ using high-temperature confocal scanning laser microscopy.The effects of the aluminum and silicon contents of molten steel on desulfurization were analyzed.When the total aluminum content in the steel increased from 6 to 1100 ppm,the CaS content in CaO–Al_(2)O_(3) particles increased from 2.1wt%to 84.84wt%after the reaction for 90 s.Furthermore,when the silicon content in the steel increased from 0.01wt%to 2.20wt%,the CaS content in CaO–Al_(2)O_(3) particles increased from 1.53wt%to 79.01wt%after the reaction for 90 s.This indicates that the increase in the aluminum and silicon contents of the steel promoted the desulfurization of CaO–Al_(2)O_(3) particles.A kinetic model was established to predict the CaO–Al_(2)O_(3) particles composition,and the diffusion coefficient of sulfur in CaO–Al_(2)O_(3) particles was 9.375×10^(−10)m^(2)·s^(−1) at 1600℃,which provided a new method for the calculation of diffusion coefficient.
