自组装固定化多聚磷酸激酶用于合成ATP和GTP

多聚磷酸激酶(polyphosphate kinase)能够利用广泛分布、成本低廉且稳定的多聚磷酸盐作为磷酸基供体,用于合成ATP和GTP,后者为细胞提供必要的能量并参与合成众多含磷酸重要化合物。该文旨在借助合成生物学新技术,通过多聚磷酸激酶催化、高效廉价地合成ATP和GTP。首先根据不同多聚磷酸激酶家族对不同底物催化能力的分析,筛选了属于多聚磷酸激酶第二家族、来源于耐辐射奇球菌(Deinococcus radiodurans)的聚磷酸激酶Dr PPK2,验证了其同时合成ATP和GTP的活性。接着优化了反应体系中的pH值、Mg^(2+)浓度和催化温度,大幅提高了其合成GTP的效率,最高转化率>92%。为了简化催化流程、节约合成成本,通过添加自组装标签CipA,实现了Dr PPK2的自组装固定化,通过离心可获得高纯度蛋白并实现酶的重复利用,首轮转化率>90%,有效重复效率超过10次。该研究提供了一种方便、高效且易于扩大规模的ATP和GTP生物合成自组装固定化系统,为其他含磷酸的化合物合成提供了借鉴。

Prediction and optimization of flue pressure in sintering process based on SHAP

Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.

Drosophila models used to simulate human ATP1A1 gene mutations that cause Charcot-Marie-Tooth type 2 disease and refractory seizures

Certain amino acids changes in the human Na^(+)/K^(+)-ATPase pump,ATPase Na^(+)/K^(+)transporting subunit alpha 1(ATP1A1),cause Charcot-Marie-Tooth disease type 2(CMT2)disease and refractory seizures.To develop in vivo models to study the role of Na^(+)/K^(+)-ATPase in these diseases,we modified the Drosophila gene homolog,Atpα,to mimic the human ATP1A1 gene mutations that cause CMT2.Mutations located within the helical linker region of human ATP1A1(I592T,A597T,P600T,and D601F)were simultaneously introduced into endogenous Drosophila Atpαby CRISPR/Cas9-mediated genome editing,generating the Atpα^(TTTF)model.In addition,the same strategy was used to generate the corresponding single point mutations in flies(Atpα^(I571T),Atpα^(A576T),Atpα^(P579T),and Atpα^(D580F)).Moreover,a deletion mutation(Atpα^(mut))that causes premature termination of translation was generated as a positive control.Of these alleles,we found two that could be maintained as homozygotes(Atpα^(I571T)and Atpα^(P579T)).Three alleles(Atpα^(A576T),Atpα^(P579)and Atpα^(D580F))can form heterozygotes with the Atpαmut allele.We found that the Atpαallele carrying these CMT2-associated mutations showed differential phenotypes in Drosophila.Flies heterozygous for Atpα^(TTTF)mutations have motor performance defects,a reduced lifespan,seizures,and an abnormal neuronal morphology.These Drosophila models will provide a new platform for studying the function and regulation of the sodium-potassium pump.

Early prediction cardiac arrest in intensive care units:the value of laboratory indicator trends

The incidence of in-hospital cardiac arrest (IHCA) has increased over the past decade,with more than half occurring in intensive care units (ICUs).^([1])ICU cardiac arrest (ICU-CA)presents unique challenges,with worse outcomes than those in monitored wards,highlighting the need for early detection and intervention.^([2])Up to 80%of patients exhibit signs of deterioration hours before IHCA.^([3])Although early warning scores based on vital signs are useful,their eff ectiveness in ICUs is limited due to abnormal physiological parameters.^([4])Laboratory markers,such as sodium,potassium,and lactate,are predictive of poor outcomes,^([5])but static measurements may not capture the patient’s trajectory.Trends in laboratory indicators,such as variability and extremes,may offer better predictive value.^([6])This study aimed to evaluate ICU-CA predictive factors,with a focus on vital signs and trends of laboratory indicators.

Short-Term Rolling Prediction of Tropical Cyclone Intensity Based on Multi-Task Learning with Fusion of Deviation-Angle Variance and Satellite Imagery

Tropical cyclones(TCs)are one of the most serious types of natural disasters,and accurate TC activity predictions are key to disaster prevention and mitigation.Recently,TC track predictions have made significant progress,but the ability to predict their intensity is obviously lagging behind.At present,research on TC intensity prediction takes atmospheric reanalysis data as the research object and mines the relationship between TC-related environmental factors and intensity through deep learning.However,reanalysis data are non-real-time in nature,which does not meet the requirements for operational forecasting applications.Therefore,a TC intensity prediction model named TC-Rolling is proposed,which can simultaneously extract the degree of symmetry for strong TC convective cloud and convection intensity,and fuse the deviation-angle variance with satellite images to construct the correlation between TC convection structure and intensity.For TCs'complex dynamic processes,a convolutional neural network(CNN)is used to learn their temporal and spatial features.For real-time intensity estimation,multi-task learning acts as an implicit time-series enhancement.The model is designed with a rolling strategy that aims to moderate the long-term dependent decay problem and improve accuracy for short-term intensity predictions.Since multiple tasks are correlated,the loss function of 12 h and 24 h are corrected.After testing on a sample of TCs in the Northwest Pacific,with a 4.48 kt root-mean-square error(RMSE)of 6 h intensity prediction,5.78 kt for 12 h,and 13.94 kt for 24 h,TC records from official agencies are used to assess the validity of TC-Rolling.

Dynamic intelligent prediction approach for landslide displacement based on biological growth models and CNN-LSTM

Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Regression models and Neural network models,to perform multi-characteristic coupled displacement prediction because they fail to consider landslide creep characteristics.This paper integrates the creep characteristics of landslides with non-linear intelligent algorithms and proposes a dynamic intelligent landslide displacement prediction method based on a combination of the Biological Growth model(BG),Convolutional Neural Network(CNN),and Long ShortTerm Memory Network(LSTM).This prediction approach improves three different biological growth models,thereby effectively extracting landslide creep characteristic parameters.Simultaneously,it integrates external factors(rainfall and reservoir water level)to construct an internal and external comprehensive dataset for data augmentation,which is input into the improved CNN-LSTM model.Thereafter,harnessing the robust feature extraction capabilities and spatial translation invariance of CNN,the model autonomously captures short-term local fluctuation characteristics of landslide displacement,and combines LSTM's efficient handling of long-term nonlinear temporal data to improve prediction performance.An evaluation of the Liangshuijing landslide in the Three Gorges Reservoir Area indicates that BG-CNN-LSTM exhibits high prediction accuracy,excellent generalization capabilities when dealing with various types of landslides.The research provides an innovative approach to achieving the whole-process,realtime,high-precision displacement predictions for multicharacteristic coupled landslides.

A Machine Learning-Based Observational Constraint Correction Method for Seasonal Precipitation Prediction

Seasonal precipitation has always been a key focus of climate prediction.As a dynamic-statistical combined method,the existing observational constraint correction establishes a regression relationship between the numerical model outputs and historical observations,which can partly predict seasonal precipitation.However,solving a nonlinear problem through linear regression is significantly biased.This study implements a nonlinear optimization of an existing observational constrained correction model using a Light Gradient Boosting Machine(LightGBM)machine learning algorithm based on output from the Beijing National Climate Center Climate System Model(BCC-CSM)and station observations to improve the prediction of summer precipitation in China.The model was trained using a rolling approach,and LightGBM outperformed Linear Regression(LR),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost).Using parameter tuning to optimize the machine learning model and predict future summer precipitation using eight different predictors in BCC-CSM,the mean Anomaly Correlation Coefficient(ACC)score in the 2019–22 summer precipitation predictions was 0.17,and the mean Prediction Score(PS)reached 74.The PS score was improved by 7.87%and 6.63%compared with the BCC-CSM and the linear observational constraint approach,respectively.The observational constraint correction prediction strategy with LightGBM significantly and stably improved the prediction of summer precipitation in China compared to the previous linear observational constraint solution,providing a reference for flood control and drought relief during the flood season(summer)in China.

Short-Term Photovoltaic Power Prediction Based onMulti-Stage Temporal Feature Learning

Harnessing solar power is essential for addressing the dual challenges of global warming and the depletion of traditional energy sources.However,the fluctuations and intermittency of photovoltaic(PV)power pose challenges for its extensive incorporation into power grids.Thus,enhancing the precision of PV power prediction is particularly important.Although existing studies have made progress in short-term prediction,issues persist,particularly in the underutilization of temporal features and the neglect of correlations between satellite cloud images and PV power data.These factors hinder improvements in PV power prediction performance.To overcome these challenges,this paper proposes a novel PV power prediction method based on multi-stage temporal feature learning.First,the improved LSTMand SA-ConvLSTMare employed to extract the temporal feature of PV power and the spatial-temporal feature of satellite cloud images,respectively.Subsequently,a novel hybrid attention mechanism is proposed to identify the interplay between the two modalities,enhancing the capacity to focus on the most relevant features.Finally,theTransformermodel is applied to further capture the short-termtemporal patterns and long-term dependencies within multi-modal feature information.The paper also compares the proposed method with various competitive methods.The experimental results demonstrate that the proposed method outperforms the competitive methods in terms of accuracy and reliability in short-term PV power prediction.

Rockburst prediction based on multi-featured drilling parameters and extreme tree algorithm for full-section excavated tunnel faces

The suddenness, uncertainty, and randomness of rockbursts directly affect the safety of tunnel construction. The prediction of rockbursts is a fundamental aspect of mitigating or even eliminating rockburst hazards. To address the shortcomings of the current rockburst prediction models, which have a limited number of samples and rely on manual test results as the majority of their input features, this paper proposes rockburst prediction models based on multi-featured drilling parameters of rock drilling jumbo. Firstly, four original drilling parameters, namely hammer pressure (Ph), feed pressure (Pf), rotation pressure (Pr), and feed speed (VP), together with the rockburst grades, were collected from 1093 rockburst cases. Then, a feature expansion investigation was performed based on the four original drilling parameters to establish a drilling parameter feature system and a rockburst prediction database containing 42 features. Furthermore, rockburst prediction models based on multi-featured drilling parameters were developed using the extreme tree (ET) algorithm and Bayesian optimization. The models take drilling parameters as input parameters and rockburst grades as output parameters. The effects of Bayesian optimization and the number of drilling parameter features on the model performance were analyzed using the accuracy, precision, recall and F1 value of the prediction set as the model performance evaluation indices. The results show that the Bayesian optimized model with 42 drilling parameter features as inputs performs best, with an accuracy of 91.89%. Finally, the reliability of the models was validated through field tests.

Risk factors for biometry prediction error by Barrett Universal II intraocular lens formula in Chinese patients

AIM:To investigate the influence of postoperative intraocular lens(IOL)positions on the accuracy of cataract surgery and examine the predictive factors of postoperative biometry prediction errors using the Barrett Universal II(BUII)IOL formula for calculation.METHODS:The prospective study included patients who had undergone cataract surgery performed by a single surgeon from June 2020 to April 2022.The collected data included the best-corrected visual acuity(BCVA),corneal curvature,preoperative and postoperative central anterior chamber depths(ACD),axial length(AXL),IOL power,and refractive error.BUII formula was used to calculate the IOL power.The mean absolute error(MAE)was calculated,and all the participants were divided into two groups accordingly.Independent t-tests were applied to compare the variables between groups.Logistic regression analysis was used to analyze the influence of age,AXL,corneal curvature,and preoperative and postoperative ACD on MAE.RESULTS:A total of 261 patients were enrolled.The 243(93.1%)and 18(6.9%)had postoperative MAE<1 and>1 D,respectively.The number of females was higher in patients with MAE>1 D(χ^(2)=3.833,P=0.039).The postoperative BCVA(logMAR)of patients with MAE>1 D was significantly worse(t=-2.448;P=0.025).After adjusting for gender in the logistic model,the risk of postoperative refractive errors was higher in patients with a shallow postoperative anterior chamber[odds ratio=0.346;95% confidence interval(CI):0.164,0.730,P=0.005].CONCLUSION:Risk factors for biometry prediction error after cataract surgery include the patient’s sex and postoperative ACD.Patients with a shallow postoperative anterior chamber are prone to have refractive errors.

Data driven prediction of fragment velocity distribution under explosive loading conditions

This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition.The fragment resultant velocities are correlated with key design parameters including casing dimensions and detonation positions.The paper details the finite element analysis for fragmentation,the characterizations of the dynamic hardening and fracture models,the generation of comprehensive datasets,and the training of the ANN model.The results show the influence of casing dimensions on fragment velocity distributions,with the tendencies indicating increased resultant velocity with reduced thickness,increased length and diameter.The model's predictive capability is demonstrated through the accurate predictions for both training and testing datasets,showing its potential for the real-time prediction of fragmentation performance.

Enhancing rectal cancer liver metastasis prediction:Magnetic resonance imaging-based radiomics,bias mitigation,and regulatory considerations

In this article,we comment on the article by Long et al published in the recent issue of the World Journal of Gastrointestinal Oncology.Rectal cancer patients are at risk for developing metachronous liver metastasis(MLM),yet early prediction remains challenging due to variations in tumor heterogeneity and the limitations of traditional diagnostic methods.Therefore,there is an urgent need for noninvasive techniques to improve patient outcomes.Long et al’s study introduces an innovative magnetic resonance imaging(MRI)-based radiomics model that integrates high-throughput imaging data with clinical variables to predict MLM.The study employed a 7:3 split to generate training and validation datasets.The MLM prediction model was constructed using the training set and subsequently validated on the validation set using area under the curve(AUC)and dollar-cost averaging metrics to assess performance,robustness,and generalizability.By employing advanced algorithms,the model provides a non-invasive solution to assess tumor heterogeneity for better metastasis prediction,enabling early intervention and personalized treatment planning.However,variations in MRI parameters,such as differences in scanning resolutions and protocols across facilities,patient heterogeneity(e.g.,age,comorbidities),and external factors like carcinoembryonic antigen levels introduce biases.Additionally,confounding factors such as diagnostic staging methods and patient comorbidities require further validation and adjustment to ensure accuracy and generalizability.With evolving Food and Drug Administration regulations on machine learning models in healthcare,compliance and careful consideration of these regulatory requirements are essential to ensuring safe and effective implementation of this approach in clinical practice.In the future,clinicians may be able to utilize datadriven,patient-centric artificial intelligence(AI)-enhanced imaging tools integrated with clinical data,which would help improve early detection of MLM and optimize personalized treatment strategies.Combining radiomics,genomics,histological data,and demographic information can significantly enhance the accuracy and precision of predictive models.

IgA血管炎儿童外周血ATP5B mRNA,UBA52 mRNA表达与肾损伤及疾病复发的关系研究

目的 探究IgA血管炎(IgAV)儿童外周血ATP合成酶β5亚基(ATP5B)mRNA,泛素蛋白A52残基核糖体蛋白融合产物(UBA52)mRNA表达与肾损伤及疾病复发的关系。方法 选取2022年1月~2023年10月上海市奉贤区中心医院收治的143例IgAV患儿作为研究对象,根据是否并发累及肾脏进一步分为IgAV肾损伤组(n=82)和IgAV肾非损伤组(n=61);根据术后随访情况将病例分为复发组(n=44)和未复发组(n=99);另选取同期60例健康体检者为对照组。实时荧光定量PCR(qRT-PCR)法检测ATP5B mRNA,UBA52 mRNA表达水平,对比两者在各组中表达情况。多因素Logistic回归分析IgAV患儿发生肾损伤的影响因素;通过受试者工作特征(ROC)曲线分析ATP5B mRNA,UBA52 mRNA表达对IgAV患儿发生肾损伤及疾病复发的预测价值。结果 对照组、IgAV肾非损伤组和IgAV肾损伤组外周血中ATP5B mRNA(1.01±0.21,1.24±0.26,1.46±0.35),UBA52mRNA(1.03±0.19,1.19±0.22,1.42±0.28),Scr(55.69±12.78μmol/L,62.85±13.92μmol/L,76.49±15.34μmol/L),BUN(11.85±2.91mmol/L,13.46±2.78mmol/L,17.54±3.45mmol/L)水平依次升高,组间比较差异具有统计学意义(F=39.666~64.417,均P<0.05)。与未复发组比较,复发组外周血中ATP5B mRNA(1.52±0.34 vs 1.19±0.20),UBA52 mRNA(1.49±0.31 vs1.08±0.21)表达升高,差异具有统计学意义(t=7.253,9.241,均P<0.001)。Logistic回归分析显示,Scr,BUN,ATP5B mRNA,UBA52 mRNA是IgAV发生肾损伤的独立危险因素(均P<0.05)。外周血中ATP5B mRNA联合UBA52 mRNA预测IgAV患儿发生肾损伤的AUC(95%CI)为0.823(0.607~0.914),明显高于两者单独预测(Z=1.952,2.021,均P<0.05);外周血中ATP5B mRNA联合UBA52 mRNA预测IgAV疾病复发的AUC(95%CI)为0.851(0.617~0.958),亦明显优于两者单独预测(Z=2.035,2.098,均P<0.05)。结论 IgAV患儿外周血中ATP5B mRNA,UBA52 mRNA表达水平与IgAV肾损伤和复发相关,两者可作为疾病诊断和预后评估的有效生物标志物。

3例K_(ATP)通道基因突变致新生儿糖尿病的基因及治疗分析

目的提高临床医生对新生儿糖尿病(neonatal diabetes mellitus,NDM)的认识和诊治能力。方法分析3例NDM患儿,对其临床特点、基因及治疗结果进行分析总结并随访预后。结果3例患儿均考虑K_(ATP)通道基因突变,从胰岛素治疗成功过渡到格列本脲口服治疗。2例患儿目前仍在口服格列本脲,考虑为永久性新生儿糖尿病(PNDM),1例患儿治疗半年后停药,考虑为暂时性新生儿糖尿病(TNDM)。3例患儿现均无明显不良反应。结论K_(ATP)通道突变NDM患儿可从胰岛素过渡到格列本脲治疗,尽早完善基因检测,予磺脲类药物治疗可降低治疗成本,增加依从性,改善神经系统预后。

猪链球菌重组磷酸ABC转运体ATP酶的免疫保护效果评价

猪链球菌(S.suis)是一种重要的人畜共患病原菌,给养猪业造成重大的经济损失。为了评估猪链球菌磷酸ABC转运体ATP酶蛋白的免疫保护效果,本研究构建了猪链球菌磷酸ABC转运体ATP酶原核表达载体进行蛋白的重组表达,纯化并制备该重组蛋白的亚单位疫苗,加强免疫接种后两周,检测其血清抗体效价、免疫保护率、各脏器载菌量以及炎性因子表达情况。结果显示,该蛋白经大肠杆菌表达后主要以可溶形式存在。ELISA检测表明该蛋白可以诱发小鼠产生高水平IgG抗体,免疫组小鼠抗体水平显著高于对照组,该重组蛋白对小鼠感染2型猪链球菌(S.suis serotype 2,S.suis 2)的保护率达到60%,显著降低了小鼠大脑、心脏及肺脏的载菌量,免疫组小鼠细胞因子IL-1β、IFN-γ、IL-6、IL-8和TNF-α的mRNA表达显著上调。研究表明猪链球菌重组磷酸ABC转运体ATP酶蛋白具有较强的免疫原性,是猪链球菌病潜在的亚单位疫苗候选蛋白。

线粒体ATP合酶在肿瘤中的研究进展

代谢重编程是肿瘤细胞的重要特征之一。既往研究认为,肿瘤细胞在有氧的情况下,仍然以糖酵解途径来获取ATP,即“瓦伯格效应”。但越来越多的研究表明,线粒体ATP合酶通过OXPHOS途径也参与了肿瘤能量代谢的调控,同时线粒体ATP合酶还能影响活性氧物质及细胞内Ca2+水平,在肿瘤的各阶段中均扮演了重要角色,ATP合酶亚基的表达异常也与肿瘤细胞的耐药机制密不可分。此外ATP合酶与三羧酸循环中间产物通过协调作用调节肿瘤细胞的能量代谢。本文将对线粒体ATP合酶的结构功能及在肿瘤中相关作用进行阐述。

血清lncRNA ATP1A1-AS1对胰腺癌的诊断价值及其对细胞增殖、凋亡和端粒酶活性的影响

目的探讨血清长链非编码RNA Na+/K+ATP酶A1亚基的反义RNA1(ATP1A1-AS1)对胰腺癌的诊断价值及其对胰腺癌细胞增殖、凋亡和端粒酶活性的影响。方法选取2020年4月至2023年10月在安徽省宣城市人民医院就诊的65例胰腺癌患者为研究对象,并选取同时期60例健康体检者作为对照,qRT-PCR检测两组患者血清中ATP1A1-AS1表达水平,受试者工作特征(ROC)曲线分析血清ATP1A1-AS1对胰腺癌的诊断价值。体外培养胰腺癌细胞PANC-1,通过转染ATP1A1-AS1过表达载体上调PANC-1细胞中ATP1A1-AS1表达,MTT检测上调ATP1A1-AS1表达对PANC-1细胞增殖的影响,流式细胞术检测上调ATP1A1-AS1表达对PANC-1细胞凋亡的影响,Western blotting检测上调ATP1A1-AS1表达对PANC-1细胞中细胞周期蛋白D1(CyclinD1)、细胞增殖抗原Ki-67、B淋巴细胞瘤2(Bcl-2)和B淋巴细胞瘤2相关蛋白(Bax)蛋白表达的影响,端粒酶重复序列扩增法检测上调ATP1A1-AS1表达对PANC-1细胞端粒酶活性的影响。结果与对照组比较,胰腺癌患者血清中ATP1A1-AS1的表达水平显著降低(P<0.05)。ROC曲线分析结果显示,血清ATP1A1-AS1诊断胰腺癌的灵敏度为83.08%,特异度为86.67%,ROC曲线下面积为0.900。ATP1A1-AS1表达上调,PANC-1细胞增殖能力、端粒酶活性及细胞CyclinD1、Ki-67、Bcl-2蛋白表达水平降低(P<0.05),凋亡率和Bax蛋白表达水平升高(P<0.05)。结论ATP1A1-AS1在胰腺癌患者血清中呈低表达,可能是胰腺癌诊断的潜在生物学标志物。上调ATP1A1-AS1表达可抑制胰腺癌细胞增殖并诱导细胞凋亡,这可能与调控CyclinD1、Ki-67、Bcl-2和Bax蛋白表达及抑制细胞端粒酶活性有关。

ATP5J通过TOMM20调节线粒体功能并促进人肝细胞癌细胞转移

目的:探讨ATP合成酶H+转运线粒体F0复合体亚基F6(ATP5J)通过调节肝癌细胞线粒体功能介导细胞骨架重塑影响肝癌细胞转移的作用及其机制。方法:培养人肝细胞癌Li-7细胞株,基因修饰ATP5J表达(过表达和敲减)。使用JC-1染色检测每组细胞线粒体膜电位情况;利用DCFH-DA荧光探针检测Li-7细胞的活性氧(ROS)含量;线粒体ATP荧光探针检测线粒体功能;微丝绿色荧光探针(Actin-Tracker Green-488)检测细胞骨架重塑情况;Transwell检测细胞侵袭能力;Western blot检测ATP5J和线粒体外膜转位酶20(TOMM20)的表达水平。结果:过表达ATP5J可上调线粒体膜电位水平和线粒体ATP荧光强度、诱导细胞骨架重塑、促进细胞侵袭和TOMM20蛋白表达,抑制ROS生成(P<0.01)。相反,敲减ATP5J显著降低线粒体膜电位和线粒体ATP荧光强度,显著降低细胞侵袭能力和TOMM20表达,促进ROS产生,阻断细胞骨架重塑(P<0.01)。结论:ATP5J可调节肝癌细胞线粒体能量转化,其通过TOMM20调节线粒体膜电位水平和线粒体ATP产生介导细胞骨架重塑影响肝癌细胞转移。

质膜ATP酶作为新型杀真菌剂靶标的发现与应用

质膜H+-三磷酸腺苷酶(plasma membrane H+-ATPase,PMA),简称质膜ATP酶,属于质子泵家族蛋白,广泛存在于植物和真菌的质膜上;它们的主要作用是维持细胞营养摄取所需的跨膜电化学质子梯度和调控pH值。质膜ATP酶是真菌生命活动所必须的,该酶缺失后,突变体的生长出现明显缺陷甚至无法生长,这使其具有作为杀真菌剂靶标的潜力;另外,由于真菌和植物的质膜ATP酶同源性较低,故以质膜ATP酶为靶标开发的杀真菌剂具有生物安全性。最近明确的酿酒酵母Saccharomyces cerevisiae和粗糙脉孢霉Neurospora crassa质膜ATP酶的冷冻电镜结构揭示了其六聚体的状态,阐明了质膜ATP酶的作用机制,为基于结构的药物设计提供了理论基础。本文主要对真菌中质膜ATP酶的结构和功能进行系统阐述,并对质膜ATP酶作为新型杀真菌剂靶标的研究现状进行综述,旨在为新型杀真菌剂的发现和应用提供理论依据。

ATP含量与外源酶添加对罗非鱼肌原纤维蛋白磷酸化水平的影响

蛋白质磷酸化修饰可影响肌肉品质,为探究外源酶添加对罗非鱼肌原纤维蛋白蛋白质磷酸化水平的影响,在向罗非鱼肌原纤维蛋白溶液中分别加入蛋白激酶A(protein kinase A,PKA)和碱性磷酸酶(alkaline phosphatase,AP)后进行体外孵育,通过十二烷基硫酸钠-聚丙烯酰胺凝胶电泳和荧光染色测定不同时间段内的磷酸化水平变化。并通过将罗非鱼肌肉浸泡在不同浓度ATP溶液中测定肌原纤维蛋白的蛋白磷酸化水平以探究ATP对其的影响。结果显示,在0~72 h,PKA组磷酸化水平均显著高于对照组和AP组,PKA组整体磷酸化水平从0 h的0.35±0.01上升至12 h的0.37±0.01,而后下降至72 h的0.29±0.01,呈先上升后下降的趋势,其中,肌球蛋白重链磷酸化水平和肌动蛋白磷酸化水平分别从0 h的0.73±0.01、0.86±0.01下降至72 h的0.58±0.02和0.68±0.01。当孵育时间为0、4、24和48 h时,3组磷酸化水平均具有显著差异。在外源添加0.3 mol/L ATP后,结果显示肌原纤维蛋白整体磷酸化水平(0.46±0.00)显著高于对照组(0.42±0.01)。PKA可促进罗非鱼肌原纤维蛋白磷酸化修饰,AP则使其去磷酸化。研究表明,宰杀后罗非鱼肌肉中的ATP含量、PKA及AP活性水平是影响蛋白质磷酸化水平的关键因素。本研究可为探明罗非鱼品质变化机制与调控策略提供理论依据。