硫酸氧钒毒性的核磁共振代谢组学方法研究

采用基于核磁共振(NMR)的代谢组学方法,结合生化指标分析及组织病理学检测,研究了具有类胰岛素活性的硫酸氧钒(VOSO4)对Wistar大鼠的毒性作用.通过不同剂量的VOSO4对Wistar大鼠连续灌胃给药16d,收集大鼠的血清和尿液,并采集样品的1H NMR谱进行多变量数据统计分析来辨识其特征代谢物,然后采用TICL(a web Tool for automatic Interpretation of Compound List)方法建立特征代谢物的代谢网络模型,分析受影响的主要代谢途径及其相互关系.研究结果表明:高剂量组(45mg/kg)和低剂量组(15mg/kg)的特征代谢物含量与对照组存在明显的差异;与对照组相比,高剂量和低剂量组血清中乳酸、肌氨酸酐以及牛磺酸等代谢物的含量增加,尿液中氧化三甲胺(TMAO)、肌酐、牛磺酸和甘氨酸等代谢物的含量增加,并呈现显著的剂量依赖关系;给药组中乙酸和琥珀酸的含量都降低.这些结果说明VOSO4可能影响大鼠体内的糖代谢、脂类代谢及肠道菌群代谢等多个代谢系统,高剂量的VOSO4会导致肝脏毒性和肾脏损伤.     

基于超高效液相色谱-离子阱-飞行时间质谱联用的白血病细胞及耐药细胞非靶标代谢组学研究

建立了一种基于超高效液相色谱-离子阱-飞行时间质谱(UPLC-IT-TOF MS)联用的非靶标代谢组学方法,研究了人慢性髓系白血病细胞及其阿霉素耐药细胞(K562和K562-ADM)之间的代谢物差异。考察了细胞破碎方法、提取溶剂体系和溶剂体积对细胞代谢物提取效果的影响,实验发现采用超声破碎法,以800μL的80%甲醇提取时可得到较多的代谢特征峰,方法重复性和稳定性较高。将建立的方法应用于K562及K562-ADM细胞的代谢组差异研究,对所得数据进行主成分分析(PCA)和正交偏最小二乘法判别分析(OPLS-DA)。结果显示2种细胞的代谢特征存在显著差异,并发现40个对分类有显著贡献的代谢物,差异代谢物主要参与氨基酸代谢、鞘脂代谢、嘌呤代谢和嘧啶代谢等通路。该方法可用于K562和K562-ADM细胞的代谢组差异研究,可望为白血病细胞耐药性的代谢组学研究提供帮助。     

肝细胞癌、肝硬化患者血清中代谢物组研究

采用核磁共振方法检测肝硬化、肝细胞癌(简称肝癌)患者和健康人血清中代谢物,研究3组血清代谢物组的差异.利用偏最小二乘法-判别式分析(partial least square-discriminant analysis, PLS-DA)对NMR谱数据进行模式识别分析,探讨利用基于1H NMR代谢组学技术诊断肝癌的可行性.结果表明,与健康人相比,肝硬化、肝癌患者血清中脂质(低密度脂蛋白和极低密度脂蛋白)、胆碱、乙酰乙酸等含量减少,谷氨酰胺、丙酮酸、苯丙氨酸、酪氨酸等含量增加.PLS-DA分析结果显示肝癌患者可与健康人、肝硬化患者鉴别开来,肝癌诊断灵敏度达921%,假阳性率为5.7%,优于血清甲胎蛋白(alpha-fetoprotein, AFP)检测.研究结果表明,基于1H NMR代谢组学技术结合PLS-DA的方法具有灵敏、准确、重复性好等优点,有助于肝癌早期诊断.     

基于超高效液相色谱-高分辨质谱和渗透压校正样本浓度变异性的尿液代谢组学分析

尿液是代谢组学研究中主要关注的体液样本之一。尿液样本中的代谢物浓度受饮食、疾病等因素影响变异较大,这极大阻碍了高质量组学数据的获取和可靠生物标志物的鉴定。研究为克服尿液样本的浓度变异性,在原始数据采集前,根据样本渗透压的大小,针对性地调整进样量或者稀释样本,从而确保代谢组学分析样本的渗透压与进样量的乘积相当,再经超高效液相色谱-高分辨质谱技术(UPLC-HRMS)分析,采用总离子丰度或总有用峰面积(MSTUS)对数据集进行归一化处理。研究利用临床样本及其梯度稀释的溶液,对该方法与现有研究普遍使用的方法进行了比较,随后通过先天性肾积水患者及健康志愿者的尿液样本做了进一步的方法学验证。数据集经校正后,峰面积RSD<30%的提取峰数量增加,主成分分析结果较校正前有更高的组内聚集和组间分群效应,正交偏最小二乘判别分析的统计模型更不易过拟合。与肌酐比较,渗透压值与质谱信号间呈现了更好的线性关系。以上结果表明,数据采集前通过样本渗透压进行校正,能有效消除因样本本身代谢物浓度变化引起的组内差异,提高方法的重复性和统计模型的可靠度。以渗透压为基准的校正策略,比肌酐校正法适用范围更广,结果也更准确。研究可对后续各类来源的尿液代谢组学研究提供数据归一化的指导和参考。     

核磁共振代谢组学数据预处理中的自适应分段积分方法

提出一种用于核磁共振代谢组学数据预处理的自适应分段积分方法. 通过计算各数据点统计特性, 并根据相邻数据点的统计差特性进行自适应积分, 克服了目前普遍采用的等间隔分段积分法可能存在的缺陷(如统计差异性相反的信号相互抵消、微弱特征信号被掩盖及谱图信噪比下降等), 从而避免了对后续统计分析所产生的负面影响. 为比较自适应分段积分和等间隔积分对数据预处理的效果, 分别采用计算机模拟数据和饮食差异人群两种模型进行分析. 研究结果表明, 新方法能够有效地削弱噪声和非特异信号的影响, 提高后续的主成分分析结果的可靠性, 使代谢组学数据分析更具生物学意义.     

MS-IAS:集成的质谱代谢组学数据分析系统

针对代谢组学研究中的数据处理问题,本研究建立了基于质谱的数据分析系统MS-IAS(Mass spectrometry based integrated analysis system).此系统集成了特征选择、聚类、分类等多种方法,用以处理质谱数据,具有多种统计分析方法能对所选的特征变量进行比较,以发现与所研究问题相关的潜在生物标志物.MS-IAS支持数据与多种算法结果可图形化显示,有助于对数据的解释与分析.以肝病患者的质谱代谢组数据为例,展示MS-IAS的功能,两种特征选择算法从数据集中筛选出了40个对肝病具有区分能力的特征变量,展示了MS-IAS成为代谢组学研究中的通用质谱数据分析系统的潜力.     

基于核磁共振技术的代谢组学研究在口腔疾病中的进展

代谢组学是近年来发展起来的组学技术,越来越多地被用到生物技术各个领域。通过定性定量分析复杂生物样品中小分子代谢物的变化,反映生物体的生理病理状态。在口腔疾病研究中,代谢组学研究也获得了密切关注,它可以为临床研究提供重要参考信息。核磁共振技术可以无损伤、非侵入性地获取生物体的信息,使其在代谢组学研究中迅速发展。本文综述了基于核磁共振技术的代谢组学研究在口腔癌、牙周病、龋病等口腔疾病方面的应用,并对其发展趋势做出展望。     

模型药物毒性血清^1H NMR谱代谢组学的Fisher判别分析研究

基于核磁共振技术的代谢组学是近年发展起来的一种新的组学技术,主要利用生物体液的核磁共振谱提供生物体内全部小分子代谢物的丰富信息.然而,噪声的存在影响了模式识别方法分析的准确度.近年来小波变换以其多分辨率分析的特性、方法简单、快速等优点成为一种有效的去除分析信号噪声的方法.本实验通过运用小波变换去除噪声、校正基线后,再进行Fisher判别分析,得到了较传统分析更为清晰的代谢标识物,建立了良好的代谢模型.     

甲亢患者血清和尿液的核磁共振代谢组学研究

应用基于核磁共振(NMR)的代谢组学方法, 研究甲状腺功能亢进(简称甲亢)患者和健康人群的血清和尿液, 分析甲亢疾病的特征代谢物. 实验收集33个甲亢患者和17个健康志愿者的血清样品以及53个甲亢患者和58个健康志愿者的尿液样品, 采用多元统计分析方法研究甲亢组和对照组血清和尿液中的内源性代谢差异. 结果表明, 甲亢组血清中的胆碱、葡萄糖和三甲胺等物质的含量升高, 而VLDL, LDL和胆固醇等脂质以及乳酸、糖蛋白和丙氨酸等代谢物的含量下降; 甲亢组尿液中的葡萄糖、柠檬酸、牛磺酸以及肌氨酸等代谢物的含量升高, 而马尿酸、TMAO、甲酸和琥珀酸等代谢物的含量下降. 结果表明, 甲亢病不仅影响了糖类、脂类和蛋白质三大物质的代谢, 还对能量代谢、肝肠循环和肠道微生物等多个生理系统产生显著影响, 并且可能造成肝脏及肾脏等器官的损伤.     

代谢组学方法研究水飞蓟宾对四氯化碳致小鼠肝损伤的保护作用

运用代谢组学方法研究四氯化碳(CCl4)致小鼠肝损及水飞蓟宾肝保护作用的机理. 通过气相色谱-质谱(GC-MS)技术分析CCl4及水飞蓟宾作用下的小鼠肝组织匀浆及血浆代谢物谱. 使用正交偏最小二乘判别分析法研究正常组与CCl4模型组之间的代谢物谱差异, 并通过变量重要性投影(VIP)选取肝组织与血浆中各12种标志性代谢物. 使用主成分分析法研究水飞蓟宾预防性及治疗性给药对CCl4肝损的干预效果. 通过比较选取的标志性代谢物的含量的差异, 探讨了水飞蓟宾对CCl4致小鼠肝损的保护机理. 结果表明, CCl4作用后, 小鼠机体能量代谢、氨基酸代谢及脂类代谢都受到不同程度的影响. 水飞蓟宾能有效地缓解CCl4所造成的体内线粒体功能及氨基酸代谢紊乱. 采用代谢组学方法能较全面地反应生物体的生理及代谢状态, 并可应用于物质毒性和药效评价研究.     

Effect of scaling on class modeling with the simca method

The first step in multivariate analysis is almost always the scaling of the variables. The pattern recognition technique SIMCA provides the possibility of scaling the variables over all the objects of the training set (classical scaling), or only over the objects belonging to the same group (separate scaling). The former method of scaling is the more used. The effect of separate scaling on the classification of objects with SIMCA is investigated for a data set consisting of the percentage distribution of fatty acids in olive oils originating from two neighbouring regions in Italy. It is shown that separate scaling has a beneficial effect on the classification.     

A Method for Clustering and Screening of Long-dimensional Chemical Data Based on Fingerprints and Similarity Measurements

A method for the treatment of long-dimensional chemical data arrays is presented in this work with the aim of maximising classification models. The method is based on the construction of fingerprints and the subsequent generation of a similarity matrix. The similarity calculation has been modified through a scaling process to take into account different significance shown by the variables. The method was applied to spectral measurements of wines and several aspects were studied, namely: threshold considered in the construction of fingerprints and patterns, weighting factor used for scaling, normalisation method, etc. The application of both Principal Components Analysis and Soft-Independent Modelling of Class Analogies to the similarity matrices gave better classifications of the information than those obtained using original data.     

Variable scaling and nearest neighbor methods

When employing nearest neighbor classifiers scaling of input variables is often useful. In this paper we propose a small modification in usual data preprocessing: scaling of variables should be done by use of pooled variances instead of overall ones. Thus prediction accuracy is distinctly improved in some situations. Copyright © 2008 John Wiley & Sons, Ltd.     

Asphaltene precipitation modeling through ACE reaping of scaling equations

Precipitation and deposition of asphaltene have undesirable effects on the petroleum industry by increasing operational costs due to reduction of well productivity as well as catalyst poisoning.Herein we propose a reliable model for quantitative estimation of asphaltene precipitation.Scaling equation is the most powerful and popular model for accurate prediction of asphaltene precipitated out of solution in crudes without regard to complex nature of asphaltene.We employed a new mathematical-based approach known as alternating conditional expectation(ACE)technique for combining results of different scaling models in order to increase the accuracy of final estimation.Outputs of three well-known scaling equations,including Rassamdana(RE),Hu(HU),and Ashoori(AS),are input to ACE and the final output is produced through a nonlinear combination of scaling equations.The proposed methodology is capable of significantly increasing the precision of final estimation via a divide-andconquer principle in which ACE functions as the combiner.Results indicate the superiority of the proposed method compared with other individual scaling equation models.     

Use of modified smooth exterior scaling method as an absorbing potential and its application

Here, we propose a new complex path to achieve better absorption during the propagation of a wavepacket. In the proposed modified smooth exterior scaling (SES) method, scaling function, θ(x), has been chosen as a real function rather than complex (as used in a conventional smooth exterior scaling method). It greatly reduces the artificial reflections from the boundary edges. This modified SES method is applied to study the multiphoton dissociation of H(2)(+) in intense laser field. The resonance states are calculated accurately.     

Discarding or downweighting high-noise variables in factor analytic models

This work examines the factor analysis of matrices where the proportion of signal and noise is very different in different columns (variables). Such matrices often occur when measuring elemental concentrations in environmental samples. In the strongest variables, the error level may be a few percent. For the weakest variables, the data may consist almost entirely of noise. This paper demonstrates that the proper scaling of weak variables is critical. It is found that if a few weak variables are scaled to too high a weight in the analysis, the errors in computed factors would grow, possibly obscuring the weakest factor(s) by the increased noise level. The mathematical explanation of this phenomenon is explored by means of Givens rotations. It is shown that the customary form of principal component analysis (PCA), based on autoscaling the original data, is generally very ineffective because the scaling of weak variables becomes much too high. Practical advice is given for dealing with noisy data in both PCA and positive matrix factorization (PMF).     

A nonparametric scaling equation of state,developed on the basis of the Migdal’s phenomenological theory and Benedek’s hypothesis

A new nonparametric scaling equation with density and temperature as variables is proposed using the phenomenological theory of critical phenomena and the experimentally confirmed Benedek’s hypothesis, on the basis of which we assume that the behavior of a number of thermodynamic functions for the critical and near-critical isochores in the neighborhood of an asymptotic critical point is similar. In comparison to Scofield’s linear model (LM), the proposed scale equation is not inferior to known nonparametric equations of the same type; in contrast to the latter, however, its physical grounds are just as valid as the LM equation.     

Density scaling and exchange-correlation energy

The exchange-correlation energy is studied using the density scaling proposed by Chan and Handy [G. K.-L. Chan and N. C. Handy, Phys. Rev. A 59, 2670 (1999)]. It is shown that there exists a value of the scaling factor for which the correlation energy disappears. The optimized potential method and the Krieger-Li-Iafrate approach are generalized to incorporate correlation.     

A model for carbon nanotube-DNA hybrid using one-dimensional density of states

The Oil Transfer Technique (OTT) was developed by Taisne et al. [1] to measure coalescence during emulsification and has been applied since in several studies. One of the main drawbacks of this technique is that it only gives a qualitative measure of coalescence. This paper proposes a new evaluation method of OTT experimental results for estimating qualitative coalescence rates, e.g. for investigating the scaling of coalescence with emulsification parameters (such as homogenizing pressure, and emulsifier concentration). The method is based on comparison with simulated OTT experiments using bivariate Population Balance Equation models. Simulations have been performed under a wide variety of conditions in order to investigate the influence of assumptions on coalescence and fragmentation kernels. These investigations show that the scaling of coalescence rates could be determined accurately when the scaling of efficient residence time of drops in the active region of homogenization is known. The proposed evaluation method is also exemplified by analyzing OTT data from two previously published studies.     

Uncertainties in scaling factors for ab initio vibrational frequencies

Vibrational frequencies determined from ab initio calculations are often scaled by empirical factors. An empirical scaling factor partially compensates for the errors arising from vibrational anharmonicity and incomplete treatment of electron correlation. These errors are not random but are systematic biases. We report scaling factors for 40 combinations of theory and basis set, intended for predicting the fundamental frequencies from computed harmonic frequencies. An empirical scaling factor carries uncertainty. We quantify and report, for the first time, the uncertainties associated with the scaling factors. The uncertainties are larger than generally acknowledged; the scaling factors have only two significant digits. For example, the scaling factor for HF/6-31G(d) is 0.8982 +/- 0.0230 (standard uncertainty). The uncertainties in the scaling factors lead to corresponding uncertainties in predicted vibrational frequencies. The proposed method for quantifying the uncertainties associated with scaling factors is based on the Guide to the Expression of Uncertainty in Measurement, published by the International Organization for Standardization (ISO). The data used are from the Computational Chemistry Comparison and Benchmark Database (CCCBDB), maintained by the National Institute of Standards and Technology, which includes more than 3939 independent vibrations for 358 molecules.