【目的】探究丝裂原活化蛋白激酶激酶6(mitogen-activated protein kinase kinase6,MAP2K6)基因在湖羊不同发育阶段背最长肌组织中的表达水平,分析该基因的多态性与湖羊生长性状之间的相关性,以期为湖羊的生长性状分子育种提供新的标记...【目的】探究丝裂原活化蛋白激酶激酶6(mitogen-activated protein kinase kinase6,MAP2K6)基因在湖羊不同发育阶段背最长肌组织中的表达水平,分析该基因的多态性与湖羊生长性状之间的相关性,以期为湖羊的生长性状分子育种提供新的标记资源。【方法】利用实时荧光定量PCR检测MAP2K6基因在湖羊(n=15)不同发育阶段背最长肌组织中的表达情况;通过Illumina OvineSNP 50K BeadChip检测湖羊(n=3024)MAP2K6基因的单核苷酸多态性(SNP),利用一般线性模型分析MAP2K6基因SNP位点与湖羊(n=1974)生长性状间的关联性,并使用R语言corrplot包计算湖羊体重与各体尺指标的相关系数。【结果】实时荧光定量PCR检测结果显示,湖羊背最长肌组织中MAP2K6基因表达量在初生到4月龄阶段逐渐升高,且3、4月龄的表达量均极显著高于初生、45日龄和6月龄(P<0.01)。湖羊MAP2K6基因中共检测到2个位点:rs414959578G>A和rs426057803A>G。关联分析结果显示,MAP2K6基因rs414959578G>A位点对湖羊5月龄体重、体高、体斜长、胸围、胸深、胸宽、十字部高、腰角宽,以及6月龄胸围、背膘厚有显著或极显著影响(P<0.05;P<0.01);rs426057803A>G位点对湖羊3月龄管围,5月龄胸围、管围和十字部高以及6月龄背膘厚有显著或极显著影响(P<0.05;P<0.01)。相关性分析结果显示,湖羊体重与体尺指标间存在显著正相关(P<0.05),但6月龄湖羊体斜长与6月龄胸宽、腰角宽,5月龄管围与6月龄腰角宽均不存在显著相关(P>0.05)。【结论】MAP2K6基因与湖羊背最长肌的发育相关,rs414959578G>A和rs426057803A>G位点对湖羊生长性状有显著影响。研究结果可为湖羊生长性状分子标记的挖掘和利用提供一定的理论依据。展开更多
目的基于T2^(*)mapping定量分析业余马拉松运动员足踝部关节软骨的T2^(*)值,并分析其与性别、年龄、身体质量指数(body mass index,BMI)、跑龄、跑量之间的相关性。材料与方法于2023年7月份至2023年9月份招募重庆市长跑运动爱好者48名,...目的基于T2^(*)mapping定量分析业余马拉松运动员足踝部关节软骨的T2^(*)值,并分析其与性别、年龄、身体质量指数(body mass index,BMI)、跑龄、跑量之间的相关性。材料与方法于2023年7月份至2023年9月份招募重庆市长跑运动爱好者48名,其中跑量<300 km/月的36例(中低跑量组),跑量≥300 km/月的12例(高跑量组)。所有受试者均进行单侧无症状踝关节的MRI扫描,扫描序列包括T2^(*)mapping多回波自旋回波(spin echo,SE)序列矢状位、质子密度加权成像脂肪抑制(proton density-weighted imaging fat-saturated,PDWI-FS)序列矢状位、冠状位、横轴位以及T1加权脂肪抑制成像(T1-weighted imaging fat-saturated,T1WI-FS)序列横轴位。沿关节软骨轮廓边缘勾画距骨穹窿、跟骰关节跟骨面、骰骨面及后距下关节跟骨面、距骨面软骨作为感兴趣区(region of interest,ROI),获得相应的T2^(*)值。采用线性回归分析软骨T2^(*)值与年龄、BMI、跑龄的相关性,采用独立样本t检验分析不同跑量及不同性别间的软骨T2^(*)值差异。结果(1)距骨穹窿、跟骰关节跟骨面及骰骨面、后距下关节跟骨面及距骨面软骨T2^(*)值在性别上的差异均具有统计学意义(P=0.001、P<0.001、P=0.002、P=0.008、P=0.004);(2)高跑量组的距骨穹窿、后距下关节跟骨面软骨T2^(*)值高于中低跑量组(P=0.014、0.023),不同跑量的跟骰关节跟骨面及骰骨面、后距下关节距骨面软骨T2^(*)值的差异均无统计学意义(P=0.987、0.072、0.724);(3)距骨穹窿、跟骰关节跟骨面及骰骨面、后距下关节跟骨面、距骨面软骨T2^(*)值均与BMI呈正相关(r=0.376、0.384、0.300、0.422、0.455,P=0.005、0.004、0.019、0.001、0.001)。结论在业余马拉松运动员这一跑步群体中,与中低跑量相比,高跑量更有可能导致距骨穹窿、后距下关节跟骨面软骨损伤;而与较低的BMI相比,高BMI增加了距骨穹窿、跟骰关节跟骨面、骰骨面及后距下关节跟骨面、距骨面软骨损伤的风险。展开更多
Momordica antiviral protein 30 kD(MAP30)is a type I ribosome-inactivating protein(RIP)with antibacterial,anti-HIV and antitumor activities but lacks the ability to target tumor cells.To increase its tumor-targeting ab...Momordica antiviral protein 30 kD(MAP30)is a type I ribosome-inactivating protein(RIP)with antibacterial,anti-HIV and antitumor activities but lacks the ability to target tumor cells.To increase its tumor-targeting ability,the arginine-glycine-aspartic(RGD)peptide and the epidermal growth factor receptor interference(EGFRi)peptide were fused with MAP30,which was named ELRL-MAP30.The efficiency of targeted therapy for triple-negative breast cancer(TNBC)MDA-MB-231 cells,which lack the expression of estrogen receptor(ER),Progesterone receptor(PgR)and human epidermal growth factor receptor-2(HER2),is limited.In this study,we focus on exploring the effect and mechanism of ELRL-MAP30 on TNBC MDA-MB-231 cells.First,we discovered that ELRL-MAP30 significantly inhibited the migration and invasion of MDA-MB-231 cells and induced MDA-MB-231 cell apoptosis.Moreover,ELRL-MAP30 treatment resulted in a significant increase in Bax expression and a decrease in Bcl-2 expression.Furthermore,ELRL-MAP30 triggered apoptosis via the Fak/EGFR/Erk and Ilk/Akt signaling pathways.In addition,recombinant ELRL-MAP30 can inhibit chicken embryonic angiogenesis,and also inhibit the tube formation ability of human umbilical vein endothelial cells(HUVECs),indicating its potential therapeutic effects on tumor angiogenesis.Collectively,these results indicate that ELRL-MAP30 has significant tumor-targeting properties in MDA-MB-231 cancer cells and reveals potential therapeutic effects on angiogenesis.These findings indicate the potential role of ELRL-MAP30 in the targeted treatment of the TNBC cell line MDA-MB-231.展开更多
Background Zonal application maps are designed to represent field variability using key variables that can be translated into tailored management practices.For cotton,zonal maps for crop growth regulator(CGR)applicati...Background Zonal application maps are designed to represent field variability using key variables that can be translated into tailored management practices.For cotton,zonal maps for crop growth regulator(CGR)applications under variable-rate(VR)strategies are commonly based exclusively on vegetation indices(VIs)variability.However,VIs often saturate in dense crop vegetation areas,limiting their effectiveness in distinguishing variability in crop growth.This study aimed to compare unsupervised framework(UF)and supervised framework(SUF)approaches for generat-ing zonal application maps for CGR under VR conditions.During 2022-2023 agricultural seasons,an UF was employed to generate zonal maps based on locally collected field data on plant height of cotton,satellite imagery,soil texture,and phenology data.Subsequently,a SUF(based on historical data between 2020-2021 to 2022-2023 agricultural seasons)was developed to predict plant height using remote sensing and phenology data,aiming to replicate same zonal maps but without relying on direct field measurements of plant height.Both approaches were tested in three fields and on two different dates per field.Results The predictive model for plant height of SUF performed well,as indicated by the model metrics.However,when comparing zonal application maps for specific field-date combinations,the predicted plant height exhibited lower variability compared with field measurements.This led to variable compatibility between SUF maps,which utilized the model predictions,and the UF maps,which were based on the real field data.Fields characterized by much pronounced soil texture variability yielded the highest compatibility between the zonal application maps produced by both SUF and UF approaches.This was predominantly due to the greater consistency in estimating plant development patterns within these heterogeneous field environments.While VR application approach can facilitate product savings during the application operation,other key factors must be considered.These include the availability of specialized machinery required for this type of applications,as well as the inherent operational costs associated with applying a single CGR product which differs from the typical uniform rate applications that often integrate multi-ple inputs.Conclusion Predictive modeling shows promise for assisting in the creation of zonal application maps for VR of CGR applications.However,the degree of agreement with the actual variability in crop growth found in the field should be evaluated on a field-by-field basis.The SUF approach,which is based on plant heigh prediction,demonstrated potential for supporting the development of zonal application maps for VR of CGR applications.However,the degree to which this approach aligns itself with the actual variability in crop growth observed in the field may vary,necessi-tating field-by-field evaluation.展开更多
文摘Momordica antiviral protein 30 kD(MAP30)is a type I ribosome-inactivating protein(RIP)with antibacterial,anti-HIV and antitumor activities but lacks the ability to target tumor cells.To increase its tumor-targeting ability,the arginine-glycine-aspartic(RGD)peptide and the epidermal growth factor receptor interference(EGFRi)peptide were fused with MAP30,which was named ELRL-MAP30.The efficiency of targeted therapy for triple-negative breast cancer(TNBC)MDA-MB-231 cells,which lack the expression of estrogen receptor(ER),Progesterone receptor(PgR)and human epidermal growth factor receptor-2(HER2),is limited.In this study,we focus on exploring the effect and mechanism of ELRL-MAP30 on TNBC MDA-MB-231 cells.First,we discovered that ELRL-MAP30 significantly inhibited the migration and invasion of MDA-MB-231 cells and induced MDA-MB-231 cell apoptosis.Moreover,ELRL-MAP30 treatment resulted in a significant increase in Bax expression and a decrease in Bcl-2 expression.Furthermore,ELRL-MAP30 triggered apoptosis via the Fak/EGFR/Erk and Ilk/Akt signaling pathways.In addition,recombinant ELRL-MAP30 can inhibit chicken embryonic angiogenesis,and also inhibit the tube formation ability of human umbilical vein endothelial cells(HUVECs),indicating its potential therapeutic effects on tumor angiogenesis.Collectively,these results indicate that ELRL-MAP30 has significant tumor-targeting properties in MDA-MB-231 cancer cells and reveals potential therapeutic effects on angiogenesis.These findings indicate the potential role of ELRL-MAP30 in the targeted treatment of the TNBC cell line MDA-MB-231.
文摘Background Zonal application maps are designed to represent field variability using key variables that can be translated into tailored management practices.For cotton,zonal maps for crop growth regulator(CGR)applications under variable-rate(VR)strategies are commonly based exclusively on vegetation indices(VIs)variability.However,VIs often saturate in dense crop vegetation areas,limiting their effectiveness in distinguishing variability in crop growth.This study aimed to compare unsupervised framework(UF)and supervised framework(SUF)approaches for generat-ing zonal application maps for CGR under VR conditions.During 2022-2023 agricultural seasons,an UF was employed to generate zonal maps based on locally collected field data on plant height of cotton,satellite imagery,soil texture,and phenology data.Subsequently,a SUF(based on historical data between 2020-2021 to 2022-2023 agricultural seasons)was developed to predict plant height using remote sensing and phenology data,aiming to replicate same zonal maps but without relying on direct field measurements of plant height.Both approaches were tested in three fields and on two different dates per field.Results The predictive model for plant height of SUF performed well,as indicated by the model metrics.However,when comparing zonal application maps for specific field-date combinations,the predicted plant height exhibited lower variability compared with field measurements.This led to variable compatibility between SUF maps,which utilized the model predictions,and the UF maps,which were based on the real field data.Fields characterized by much pronounced soil texture variability yielded the highest compatibility between the zonal application maps produced by both SUF and UF approaches.This was predominantly due to the greater consistency in estimating plant development patterns within these heterogeneous field environments.While VR application approach can facilitate product savings during the application operation,other key factors must be considered.These include the availability of specialized machinery required for this type of applications,as well as the inherent operational costs associated with applying a single CGR product which differs from the typical uniform rate applications that often integrate multi-ple inputs.Conclusion Predictive modeling shows promise for assisting in the creation of zonal application maps for VR of CGR applications.However,the degree of agreement with the actual variability in crop growth found in the field should be evaluated on a field-by-field basis.The SUF approach,which is based on plant heigh prediction,demonstrated potential for supporting the development of zonal application maps for VR of CGR applications.However,the degree to which this approach aligns itself with the actual variability in crop growth observed in the field may vary,necessi-tating field-by-field evaluation.
