Analysis of chiral amino acids in conventional and transgenic maize

In this work, a new chiral micellar electrokinetic chromatography with laser-induced fluorescence detection (chiral-MEKC-LIF) method is proposed to identify and quantify D- and L-amino acids in three lines of transgenic maize and their corresponding nontransgenic parental lines grown under identical conditions. The optimized procedure includes amino acids extraction, derivatization with FITC and chiral-MEKC-LIF separation in a background electrolyte composed of 100 mM sodium tetraborate, 80 mM SDS, and 20 mM beta-CD at pH 10.0. The D- and L-forms of Arg, Ser, Ala, Glu, and Asp, corresponding to the majority amino acids usually found in maize, are separated in less than 25 min with efficiencies up to 890,000 plates/m and high sensitivity (i.e., LODs as low as 160 nM were obtained for D-Arg for a signal-to-noise ratio of three), allowing the detection of 1% D-Arg in the presence of 99% of its opposite enantiomer. Using this method, different D-amino acids are detected in all investigated maize samples providing the reproducible quantification of the D-enantiomeric excess (% d-aa) for each amino acid calculated as % D-aa = 100D-aa/(D-aa + L-aa). Thus, significant differences were observed among the % d-aa values for the different conventional varieties (Aristis, Tietar, and PR33P66 maize) as could be expected from their natural variability. More interestingly, comparing each conventional maize with its corresponding transgenic line, very similar % D-aa values were obtained for one of the studied maize couples (Tietar vs Tietar-Bt) what could be presented as a new proof of their substantial equivalence. However, significant differences in the % d-aa values were observed for the other lines of maize studied. It is concluded that enantioselective procedures can open new perspectives in the study of transgenic organisms in order to corroborate (or not) the equivalence with their conventional counterparts.     

油菜转基因育种研究进展

简要介绍了国外油菜转基因育种情况，包括除草剂抗性基因，病原菌抗性基因，耐重金属基因，影响种子贮藏产物基因，影响繁殖特性基因，医药和工业产品基因，以及启动子调节基因等。较详细介绍了湖南农业大学在Bt毒蛋白基因转化甘蓝型油菜育成抗虫新品系、基因工程雄性不育系、恢复系选育和杂种优势利用，以及反义FAD2基因转化甘蓝型油菜双低品种提高油酸含量的研究结果；浙江省农科院利用反义PEP基因转化甘蓝型油菜提高种子含油量的研究结果。     

转Bt＋CpTI双价基因抗虫棉铃虫性的时空表达

以转单价Bt基因抗虫棉品系中心Bt和常规棉苏棉12号对照,研究了转Bt＋CpTi双抗－1抗虫棉对棉铃虫,抗性的时空表达特征,在棉花生长发育进程中,用4－21叶位主茎叶饲喂棉铃虫初孵幼,虫,5天后观察：双抗－1和中心Bt的4－21叶上存活棉铃虫中均无3龄幼虫,苏棉12号中3龄幼虫存占活幼虫的40％以上,双抗－1和中心B棉铃虫的死亡率相近且随叶位升高而下降,分期播 抗虫性测验与各叶位抗虫性测定相似,即双抗－1和中心Bt各播种期的存活棉铃虫中没有3龄虫,苏棉12号各播种期都有3龄虫出现,双抗-1棉铃虫的死亡率与中心Bt相近,且随播种日期推迟棉铃虫死亡率逐渐升高,试验结果表明：双抗－1对棉铃虫的抗性水平与中心Bt相近,且抗性表现为前期高后期低,也对双价抗虫棉的时空表达特征与棉铃虫产生抗性的风险关系进行了讨论。     

Annotation of the human adult urinary metabolome and metabolite identification using ultra high performance liquid chromatography coupled to a linear quadrupole ion trap-orbitrap mass spectrometer

Metabolic profiles of biofluids obtained by atmospheric pressure ionization mass spectrometry-based technologies contain hundreds to thousands of features, most of them remaining unknown or at least not characterized in analytical systems. We report here on the annotation of the human adult urinary metabolome and metabolite identification from electrospray ionization mass spectrometry (ESI-MS)-based metabolomics data sets. Features of biological interest were first of all annotated using the ESI-MS database of the laboratory. They were also grouped, thanks to software tools, and annotated using public databases. Metabolite identification was achieved using two complementary approaches: (i) formal identification by matching chromatographic retention times, mass spectra, and also product ion spectra (if required) of metabolites to be characterized in biological data sets to those of reference compounds and (ii) putative identification from biological data thanks to MS/MS experiments for metabolites not available in our chemical library. By these means, 384 metabolites corresponding to 1484 annotated features (659 in negative ion mode and 825 in positive ion mode) were characterized in human urine samples. Of these metabolites, 192 and 66 were formally and putatively identified, respectively, and 54 are reported in human urine for the first time. These lists of features could be used by other laboratories to annotate their ESI-MS metabolomics data sets.     

Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models

Metabolomics studies generate increasingly complex data tables, which are hard to summarize and visualize without appropriate tools. The use of chemometrics tools, e.g., principal component analysis (PCA), partial least-squares to latent structures (PLS), and orthogonal PLS (OPLS), is therefore of great importance as these include efficient, validated, and robust methods for modeling information-rich chemical and biological data. Here the S-plot is proposed as a tool for visualization and interpretation of multivariate classification models, e.g., OPLS discriminate analysis, having two or more classes. The S-plot visualizes both the covariance and correlation between the metabolites and the modeled class designation. Thereby the S-plot helps identifying statistically significant and potentially biochemically significant metabolites, based both on contributions to the model and their reliability. An extension of the S-plot, the SUS-plot (shared and unique structure), is applied to compare the outcome of multiple classification models compared to a common reference, e.g., control. The used example is a gas chromatography coupled mass spectroscopy based metabolomics study in plant biology where two different transgenic poplar lines are compared to wild type. By using OPLS, an improved visualization and discrimination of interesting metabolites could be demonstrated.     

Comprehensive extraction method integrated with NMR metabolomics: a new bioactivity screening method for plants, adenosine A1 receptor binding compounds in Orthosiphon stamineus Benth

A large number of plant metabolites has provided as an incomparable chemical source for drug development. However, the wide range of the polarity of metabolites has been a big obstacle for full use of the chemical diversity. The initial step conventional extraction method by a single solvent does not make use of all the metabolites contained in plants. Also, it takes a long time to confirm the target activity of a single compound because of tedious separation steps. To solve the problem, a new extraction method coupled to NMR-based metabolomics is applied to identify bioactive natural products. A comprehensive extraction method consisting of a continuous flow of solvent mixtures through plant material was developed to provide extracts with a wider chemical variety than those yielded with a single solvent extraction. As the model experiment, (1)H NMR spectra of the extracts obtained from the comprehensive extraction of Orthosiphon stamineus were subjected to multivariate data analysis to find its adenosine A1 binding activity. On the basis of the results, two flavonoids from a large number of chemicals were clearly verified to show the adenosine A1 binding activity without any further purification steps. This method could provide a solution to the major drawbacks of natural products in drug development.     

Quantitative detection system for maize sample containing combined-trait genetically modified maize

Various countries have established regulations that stipulate the labeling of agricultural commodities, feed, and food products that contain or are made from genetically modified (GM) material or that contain adventitious GM material in amounts that exceed certain threshold levels. While regulations in some countries refer to GM material on a weight per weight (w/w) percentage, the currently applied detection methods do not directly measure the w/w percentage of the GM material. Depending on the particular method and the sample matrix it is applied to, the conversion of analytical results to a w/w percentage is challenging or not possible. The first rapid PCR system for GM maize detection on a single kernel basis has been developed. The equipment for the grinding of individual kernels and a silica membrane-based 96-well DNA extraction kit were both significantly revised and optimized for this particular purpose, respectively. We developed a multiplex real-time PCR method for the rapid quantification of GM DNA sequences in the obtained DNA solutions. In addition, a multiplex qualitative PCR detection method allows for the simultaneous detection of different GM maize traits in each kernel and thereby for identification of individual kernels that contain a combination of two or more GM traits. Especially for grain samples that potentially contain combined-trait GM maize kernels, the proposed methods can deliver informative results in a rapid, precise, and reliable manner.     

Evaluation of a semipolar solvent system as a step toward heteronuclear multidimensional NMR-based metabolomics for 13C-labeled bacteria, plants, and animals

Nuclear magnetic resonance (NMR) has become a key technology in metabolomics, with the use of stable isotope labeling and advanced heteronuclear multidimensional NMR techniques. In this paper, we focus on the evaluation of extraction solvents to improve NMR-based methodologies for metabolomics. Line broadening is a serious barrier to detecting signals and the annotation of metabolites using multidimensional NMR. We evaluated a series of NMR solvents for easy and versatile single-step extraction using the (13)C-labeled photosynthetic bacterium Rhodobacter sphaeroides, which shows pronounced broadening of NMR signals. The performance of each extraction solvent was judged using 2D (1)H-(13)C heteronuclear single quantum coherence (HSQC) spectra, considering three metrics: (1) distribution of the line width at half height, (2) number of observed signals, and (3) the total observed signal intensity. Considering the total rank values for the three metrics, we chose methanol-d(4) (MeOD) as a semipolar extraction solvent that can sufficiently sharpen the line width and affords better-quality NMR spectra. We also evaluated the series of extraction solvents by means of inductively coupled plasma optical emission spectroscopy (ICP-OES) based ionomics approach. It was also indicated that MeOD is useful for excluding paramagnetic ions as well as macromolecules in an easy single-step extraction. MeOD extraction also appeared to be effective for other bacterial and animal samples. An additional advantage of this semipolar solvent is that it supplements the aqueous (polar) buffer system reported by many groups. The flexible, appropriate application of polar and semipolar extraction should contribute to the large-scale analysis of metabolites.     

Quantitation of transgenic Bt event-176 maize using double quantitative competitive polymerase chain reaction and capillary gel electrophoresis laser-induced fluorescence

In this work, a new procedure useful to quantitatively analyze genetically modified organisms (GMOs) in foods is described and applied to analyze transgenic Bt Event-176 maize. The method developed consists of coamplifications of specific DNA maize sequences with internal standards using quantitative competitive PCR (QC-PCR). The QC-PCR products are quantitatively analyzed using a capillary gel electrophoresis (CGE) with laser-induced fluorescence detection (LIF) method developed at our laboratory that utilizes a physically adsorbed coating. The CGE-LIF procedure allows the use of internal standards differing by only 10 bp from the original target fragments, to our knowledge, the smallest size difference that can be found in the bibliography for QC-PCR of GMOs. A spectrofluorometric procedure using ROX reference dye is proposed to solve calibration problems of input DNA concentration. It is demonstrated that the use of ROX drastically enhances the accuracy of the quantitative analysis by QC-PCR. Reproducibility of analysis times and corrected peak areas (measured as target/competitor PCR products ratio) for the CGE-LIF separations are determined to be better than 0.91 and 1.93% (RSD, n = 15) respectively, for three different days. It is shown that CGE-LIF provides better resolution and a signal/noise ratio improvement of approximately 700-fold compared to slab gel electrophoresis. The good possibilities in terms of quantitative analysis of GMOs provided by this new method are confirmed by determining the Bt Event-176 maize content in certified reference maize powder and food samples of known composition. This procedure opens the possibility for accurate quantitation of multiple GMOs in a single run.     

Sampling for metabolome analysis of microorganisms

In the present work we investigated the most commonly applied methods used for sampling of microorganisms in the field of metabolomics in order to unravel potential sources of error previously ignored but of utmost importance for accurate metabolome analysis. To broaden the significance of our study, we investigated different Gram-negative and Gram-positive bacteria, i.e., Bacillus subtilis, Corynebacterium glutamicum, Escherichia coli, Gluconobacter oxydans, Pseudomonas putida, and Zymononas mobilis, and analyzed metabolites from different catabolic and anabolic intracellular pathways. Quenching of cells with cold methanol prior to cell separation and extraction led to drastic loss (>60%) of all metabolites tested due to unspecific leakage. Using fast filtration, Gram-negative bacteria also revealed a significant loss (>80%) when inappropriate washing solutions with low ionic strength were applied. Adapting the ionic strength of the washing solution to that of the cultivation medium could almost completely avoid this problem. Gram-positive strains did not show significant leakage independent of the washing solution. Fast filtration with sampling times of several seconds prior to extraction appears to be a suitable approach for metabolites with relatively high intracellular level and low turnover such as amino acids or TCA cycle intermediates. Comparison of metabolite levels in the culture supernatant and the cell interior revealed that the common assumption of whole broth quenching protocols attributing the metabolites found exclusively to the intracellular pools may not be valid in many cases. In such cases a differential approach correcting for medium-contained metabolites is required.     

Shotgun metabolomics approach for the analysis of negatively charged water-soluble cellular metabolites from mouse heart tissue

A shotgun metabolomics approach using MALDI-TOF/TOF mass spectrometry was developed for the rapid analysis of negatively charged water-soluble cellular metabolites. Through the use of neutral organic solvents to inactivate endogenous enzyme activities (i.e., methanol/chloroform/H2O extraction), in conjunction with a matrix having minimal background noise (9-amnioacridine), a set of multiplexed conditions was developed that allowed identification of 285 peaks corresponding to negatively charged metabolites from mouse heart extracts. Identification of metabolite peaks was based on mass accuracy and was confirmed by tandem mass spectrometry for 90 of the identified metabolite peaks. Through multiplexing ionization conditions, new suites of metabolites could be ionized and "spectrometric isolation" of closely neighboring peaks for subsequent tandem mass spectrometric interrogation could be achieved. Moreover, assignments of ions from isomeric metabolites and quantitation of their relative abundance was achieved in many cases through tandem mass spectrometry by identification of diagnostic fragmentation ions (e.g., discrimination of ATP from dGTP). The high sensitivity of this approach facilitated the detection of extremely low abundance metabolites including important signaling metabolites such as IP3, cAMP, and cGMP. Collectively, these results identify a multiplexed MALDI-TOF/TOF MS approach for analysis of negatively charged metabolites in mammalian tissues.     

New figures of merit for comprehensive functional genomics data: the metabolomics case

In the field of metabolomics, hundreds of metabolites are measured simultaneously by analytical platforms such as gas chromatography/mass spectrometry (GC/MS), liquid chromatography/mass spectrometry (LC/MS) and NMR to obtain their concentration levels in a reliable way. Analytical repeatability (intrabatch precision) is a common figure of merit for the measurement error of metabolites repeatedly measured in one batch on one platform. This measurement error, however, is not constant as its value may depend on the concentration level of the metabolite. Moreover, measurement errors may be correlated between metabolites. In this work, we introduce new figures of merit for comprehensive measurements that can detect these nonconstant correlated errors. Furthermore, for the metabolomics case we identified that these nonconstant correlated errors can result from sample instability between repeated analyses, instrumental noise generated by the analytical platform, or bias that results from data pretreatment.     

MetSign: a computational platform for high-resolution mass spectrometry-based metabolomics

Data analysis in metabolomics is currently a major challenge, particularly when large sample sets are analyzed. Herein, we present a novel computational platform entitled MetSign for high-resolution mass spectrometry-based metabolomics. By converting the instrument raw data into mzXML format as its input data, MetSign provides a suite of bioinformatics tools to perform raw data deconvolution, metabolite putative assignment, peak list alignment, normalization, statistical significance tests, unsupervised pattern recognition, and time course analysis. MetSign uses a modular design and an interactive visual data mining approach to enable efficient extraction of useful patterns from data sets. Analysis steps, designed as containers, are presented with a wizard for the user to follow analyses. Each analysis step might contain multiple analysis procedures and/or methods and serves as a pausing point where users can interact with the system to review the results, to shape the next steps, and to return to previous steps to repeat them with different methods or parameter settings. Analysis of metabolite extract of mouse liver with spiked-in acid standards shows that MetSign outperforms the existing publically available software packages. MetSign has also been successfully applied to investigate the regulation and time course trajectory of metabolites in hepatic liver.     

转修饰豇豆胰蛋白酶抑制剂基因(sck)玉米(Zea mays L.)植株的获得及其抗虫性分析

利用基因枪法将修饰的豇豆胰蛋白酶基因（sck）导入玉米优良自交系E28及340的胚性愈伤组织中,经筛选剂PPT3次筛选及再生过程,获得可育的再生植株。经PCR及Southern blot分子检测,证实所获得的再生植株为转基因植株。豇豆胰蛋白酶抑制剂抑制活性检测及抗虫结果显示：外源基因在植物已获表达,部分转基因植株具有较强的抗虫活性。     

Algorithms and tools for the preprocessing of LC-MS metabolomics data

Metabolomics encompasses the study of small molecules in a biological sample. Liquid Chromatography coupled with Mass Spectrometry (LC-MS) profiling is an important approach for the identification and quantification of metabolites from complex biological samples. The amount and complexity of data produced in an LC-MS profiling experiment demand automatic tools for the preprocessing, analysis, and extraction of useful biological information. Data preprocessing—a topic that covers noise filtering, peak detection, deisotoping, alignment, identification, and normalization—is thus an active area of metabolomics research. Recent years have witnessed development of many software for data preprocessing, and still there is a need for further improvement of the data preprocessing pipeline. This review presents an overview of selected software tools for preprocessing LC-MS based metabolomics data and tries to provide future directions.     

Evaluation of peak picking quality in LC-MS metabolomics data

The output of LC-MS metabolomics experiments consists of mass-peak intensities identified through a peak-picking/alignment procedure. Besides imperfections in biological samples and instrumentation, data accuracy is highly dependent on the applied algorithms and their parameters. Consequently, quality control (QC) is essential for further data analysis. Here, we present a QC approach that is based on discrepancies between replicate samples. First, the quantile normalization of per-sample log-signal distributions is applied to each group of biologically homogeneous samples. Next, the overall quality of each replicate group is characterized by the Z-transformed correlation coefficients between samples. This general QC allows a tuning of the procedure's parameters which minimizes the inter-replicate discrepancies in the generated output. Subsequently, an in-depth QC measure detects local neighborhoods on a template of aligned chromatograms that are enriched by divergences between intensity profiles of replicate samples. These neighborhoods are determined through a segmentation algorithm. The retention time (RT)-m/z positions of the neighborhoods with local divergences are indicative of either: incorrect alignment of chromatographic features, technical problems in the chromatograms, or to a true biological discrepancy between replicates for particular metabolites. We expect this method to aid in the accurate analysis of metabolomics data and in the development of new peak-picking/alignment procedures.     

转基因玉米NK603基体标准物质协同定值实验

对转基因玉米 NK603基体标准物质荧光定量PCR方法的关键参数进行了方法验证,结果表明该方法适合于标准物质量值协同实验。选择7家实验室采用经过验证的方法对3个水平的转基因玉米NK603基体标准物质量值进行了协同实验,通过分析比较各参与实验室的实验条件、标准曲线的参数,进一步证明该方法室间重复性较好。经统计学分析各参与实验室的数据等精度,3个水平基体标准物质的量值和扩展不确定度分别为0.57%±0.11%、1.21%±0.30%和4.92%±1.80%（k=2）。     

Exploring the analysis of structured metabolomics data

In metabolomics research a large number of metabolites are measured that reflect the cellular state under the experimental conditions studied. In many occasions the experiments are performed according to an experimental design to make sure that sufficient variation is induced in the metabolite concentrations. However, as metabolomics is a holistic approach, also a large number of metabolites are measured in which no variation is induced by the experimental design. The presence of such non-induced metabolites hampers traditional data analysis methods as PCA to estimate the true model of the induced variation. The greediness of PCA leads to a clear overfit of the metabolomics data and can lead to a bad selection of important metabolites. In this paper we explore how, why and how severe PCA overfits data with an underlying experimental design. Recently new data analysis methods have been introduced that can use prior information of the system to reduce the overfit. We show that incorporation of prior knowledge of the system under investigation leads to a better estimation of the true underlying structure and to less overfit. The experimental design information together with ASCA is used to improve the analysis of metabolomics data. To show the improved model estimation property of ASCA a thorough simulation study is used and the results are extended to a microbial metabolomics batch fermentation study. The ASCA model is much less affected by the non-induced variation and measurement error than PCA, leading to a much better model of the induced variation.     

Expanding coverage of the metabolome for global metabolite profiling

Mass spectrometry-based metabolomics is the comprehensive study of naturally occurring small molecules collectively known as the metabolome. Given the vast structural diversity and chemical properties of endogenous metabolites, biological extraction and chromatography methods bias the number, property, and concentration of metabolites detected by mass spectrometry and creates a challenge for global untargeted studies. In this work, we used Escherichia coli bacterial cells to explore the influence of solvent polarity, temperature, and pH in extracting polar and nonpolar metabolites simultaneously. In addition, we explored chromatographic conditions involving different stationary and mobile phases that optimize the separation and ionization of endogenous metabolite extracts as well as a mixture of synthetic standards. Our results reveal that hot polar solvents are the most efficient in extracting both hydrophilic and hydrophobic metabolites simultaneously. In addition, ammonium fluoride in the mobile phase substantially improved ionization efficiency in negative electrospray ionization mode by an average increase in signal intensity of 5.7 and over a 2-fold increase in the total number of features detected. The improvement in sensitivity with ammonium fluoride resulted in 3.5 times as many metabolite hits in databases compared to ammonium acetate or formic acid enriched mobile phases and allowed for the identification of unique metabolites involved in fundamental cellular pathways.     

Group aggregating normalization method for the preprocessing of NMR-based metabolomic data

Data normalization plays a crucial role in metabolomics to take into account the inevitable variation in sample concentration and the efficiency of sample preparation procedure. The conventional methods such as constant sum normalization (CSN) and probabilistic quotient normalization (PQN) are widely used, but both methods have their own shortcomings. In the current study, a new data normalization method called group aggregating normalization (GAN) is proposed, by which the samples were normalized so that they aggregate close to their group centers in a principal component analysis (PCA) subspace. This is in contrast with CSN and PQN which rely on a constant reference for all samples. The evaluation of GAN method using both simulated and experimental metabolomic data demonstrated that GAN produces more robust model in the subsequent multivariate data analysis, more superior than both CSN and PQN methods. The current study also demonstrated that some of the differential metabolites identified using the CSN or PQN method could be false positives due to improper data normalization.