Direct quantification of M/G ratio from C CP-MAS NMR spectra of alginate powders by multivariate curve resolution

Multivariate curve resolution (MCR) was applied to ¹³C cross-polarisation (CP) magic angle spinning (MAS) nuclear magnetic resonance (NMR) spectra of non-depolymerised alginate powders obtained from brown seaweed plus a pure mannuronate sample isolated from Pseudomonas fluorescens for estimation of the mannuronic acid/guluronic acid ratio (M/G ratio). An excellent MCR model with a correlation coefficient of r² = 0.99 was established between the estimated M/G ratios and the M/G ratios obtained from the traditional ¹H solution state NMR method. The new method allows for successful determination of the M/G ratio independent of the calcium content (at least up to 2.4%, which was the upper limit in this study) with a root mean square error of prediction of 0.05. It is thus concluded that ¹³C CP-MAS NMR in combination with multivariate curve resolution is a reliable, convenient (no sample preparation is required) and relatively rapid method for M/G ratio determinations of alginates and it may serve as a good alternative to the chemical techniques traditionally used.     

Application of multivariate curve resolution alternating least squares method for determination of caffeic acid in the presence of catechin interference

In the current article, preparation and application of a graphene oxide nanosheets-based sensor for electrochemical determination of caffeic acid (CA) in the presence of catechin is described. This measurement was performed using the differential pulse voltammetry (DPV) technique and chemometric methods such as multivariate curve resolution–alternating least squares (MCR–ALS). The modified sensor was characterized by various techniques such as Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, ultraviolet–visible, X-ray diffraction (XRD), and scanning electron microscopy (SEM). Operating conditions and influencing variables (involving several chemical and instrumental variables) were optimized with central composite rotatable design and response surface methodology. The second-order electrochemical data were generated by changing the pulse height in DPV, and after potential shift correction MCR–ALS was applied. Under the optimized conditions, the dynamic range for CA was from 0.5 to 100.0 μM and the detection limit was found to be 1.1 × 10^–9 M. The results revealed that the modified electrode shows an improvement in anodic oxidation activity of CA due to a marked enhancement in the current response compared with the bare carbon paste electrode. The modified electrode demonstrated good sensitivity, selectivity, and stability. The proposed method was successfully applied in determination of caffeic acid in the presence of unexpected electroactive interferences with a very high degree of overlapping such as catechin in real samples.     

Characterization of Cu2+-binding modes in the prion protein by visible circular dichroism and multivariate curve resolution

Visible circular dichroism (CD) spectra from the copper(II) titration of the metal-binding region of the prion protein, residues 57-98, were analyzed using the self-modeling curve resolution method multivariate curve resolution-alternating least squares (MCR-ALS). MCR-ALS is a set of mathematical tools for estimating pure component spectra and composition profiles from mixture spectra. Model-free solutions (e.g., soft models) are produced under the assumption that pure component profiles should be nonnegative and unimodal. Optionally, equality constraints can be used when the concentration or spectrum of one or more species is known. MCR-ALS is well suited to complex biochemical systems such as the prion protein which binds multiple copper ions and thus gives rise to titration data consisting of several pure component spectra with overlapped or superimposed absorption bands. Our study reveals the number of binding modes used in the uptake of Cu²⁺ by the full metal-binding region of the prion protein and their relative concentration profiles throughout the titration. The presence of a non-CD active binding mode can also be inferred. We show that MCR-ALS analysis can be initialized using empirically generated or mathematically generated pure component spectra. The use of small model peptides allows us to correlate specific Cu²⁺-binding structures to the pure component spectra.     

Electrochemical and spectroscopic study on the interaction between isoprenaline and DNA using multivariate curve resolution-alternating least squares

Interaction of isoprenaline (ISO) with calf-thymus DNA was studied by spectroscopic and electrochemical methods. The behavior of ISO was investigated at a glassy carbon electrode (GCE) by cyclic voltammetry (CV) and differential pulse stripping voltammetry (DPSV); ISO was oxidized and an irreversible oxidation peak was observed. The binding constant K and the stoichiometric coefficient m of ISO with DNA were evaluated. Also, with the addition of DNA, hyperchromicity of the UV-vis absorption spectra of ISO was noted, while the fluorescence intensity decreased significantly. Multivariate curve resolution-alternating least squares (MCR-ALS) chemometrics method was applied to resolve the combined spectroscopic data matrix, which was obtained by the UV-vis and fluorescence methods. Pure spectra of ISO, DNA and ISO-DNA complex, and their concentration profiles were then successfully obtained. The results indicated that the ISO molecule intercalated into the base-pairs of DNA, and the complex of ISO-DNA was formed.     

Kinetic analysis of enzymatic chiral resolution by progress curve evaluation

The present study deals with kinetic modeling of enzyme-catalyzed reactions by integral progress curve analysis, and shows how to apply this technique to kinetic resolution of enantiomers. It is shown that kinetic parameters for both enantiomers and the enantioselectivity of the enzyme may be obtained from the progress curve measurement of a racemate only.A parameter estimation procedure has been established and it is shown that the covariance matrix of the obtained parameters is a useful statistical tool in the selection and verification of the model structure. Standard deviations calculated from this matrix have shown that progress curve analysis yields parameter values with high accuracies.Potential sources of systematic errors in (multiple) progress curve analysis are addressed in this article. Amongst these, the following needed to be dealt with: (1) the true initial substrate concentrations were obtained from the final amount of product experimentally measured (mass balancing); (2) systematic errors in the initial enzyme concentration were corrected by incorporating this variable in the fitting procedure as an extra parameter per curve; and (3) enzyme inactivation is included in the model and a first-order inactivation constant is determined.Experimental verification was carried out by continuous monitoring of the hydrolysis of ethyl 2-chloropropionate by carboxylesterase NP and the alpha-chymotrypsin-catalyzed hydrolysis of benzoylalanine mathyl ester in a pH-stat system. Kinetic parameter values were obtained with high accuracies and model predictions were in good agreement with independent measurements of enantiomeric excess values or literature data. (c) 1994 John Wiley & Sons, Inc.     

Variable selection in high-dimensional multivariate binary data with application to the analysis of microbial community DNA fingerprints

In order to understand the relevance of microbial communities on crop productivity, the identification and characterization of the rhizosphere soil microbial community is necessary. Characteristic profiles of the microbial communities are obtained by denaturing gradient gel electrophoresis (DGGE) of polymerase chain reaction (PCR) amplified 16S rDNA from soil extracted DNA. These characteristic profiles, commonly called community DNA fingerprints, can be represented in the form of high-dimensional binary vectors. We address the problem of modeling and variable selection in high-dimensional multivariate binary data and present an application of our methodology in the context of a controlled agricultural experiment.     

A Bayesian analysis of multivariate doubly-interval-censored dental data

A Bayesian survival analysis is presented to examine the effect of fluoride-intake on the time to caries development of the permanent first molars in children between 7 and 12 years of age using a longitudinal study conducted in Flanders. Three problems needed to be addressed. Firstly, since the emergence time of a tooth and the time it experiences caries were recorded yearly, the time to caries is doubly interval censored. Secondly, due to the setup of the study, many emergence times were left-censored. Thirdly, events on teeth of the same child are dependent. Our Bayesian analysis is a modified version of the intensity model of Harkanen et al. (2000, Scandinavian Journal of Statistics 27, 577-588). To tackle the problem of the large number of left-censored observations a similar Finnish data set was introduced. Our analysis shows no convincing effect of fluoride-intake on caries development.     

Application of multivariate resolution methods to the study of biochemical and biophysical processes

Multivariate resolution methods make up a set of mathematical tools that may be applied to the analysis and interpretation of spectroscopic data recorded when monitoring a physical or chemical process with multichannel detectors. The goal of resolution methods is the recovery of chemical and/or physical information from the experimental data. Such data include, for example, the number of intermediates present in a reaction, the rate or equilibrium constants, and the spectra for each one of those intermediates. Multivariate resolution methods have been shown to be useful for the study of biophysical and biochemical processes such as folding/unfolding of proteins or nucleic acids. The present article reviews the most frequently used resolution methods, the limitations on their use, and their latest applications in protein and nucleic acid research.     

DNA microarray analysis of human gingival fibroblasts from healthy and inflammatory gingival tissues

In the inflammatory gingival tissues of patients with periodontitis, cytokines such as interleukin (IL)-1 alpha, IL-1 beta, IL-6, IL-8, and tumor necrosis factor (TNF)-alpha have been detected. Gingival fibroblasts are the major constituents of gingival tissue. We recently demonstrated that lipopolysaccharide (LPS) from periodontopathic bacteria induces inflammatory reactions in various tissues via CD14 and/or Toll-like receptors (TLRs) in gingival tissues [Biochem. Biophys. Res. Commun. 273 (2000) 1161]. To confirm this, we examined the expression of IL-1 alpha, IL-1 beta, IL-6, IL-8, TNF-alpha, CD14, TLR2, and TLR4 in human gingival fibroblasts (HGFs) obtained from patients with healthy or inflammatory gingiva using DNA microarray analysis. We also studied the expression levels of these proteins by flow cytometric analysis (FACS). The expression levels of all eight genes in the HGFs of the Inflammatory group were significantly higher than those in the Healthy group on DNA microarray analysis. FACS revealed that the expression levels of all eight proteins on the HGFs of the Inflammatory group were higher than those on the Healthy group. Our data indicated that these eight proteins in HGFs are involved in inflammatory conditions in the gingiva, including periodontal disease. Our results suggested that these eight proteins, in turn, act directly or indirectly on the immune response by activating host cells involved in inflammatory processes.     

Ultra-sensitive immunodetection of 5'methyl cytosine for DNA methylation analysis on oligonucleotide microarrays.

For the determination of methylation levels in genomic regulatory DNA sequences a high-sensitive assay for detecting 5'methyl-cytosines (5'mC) in non-bisulfite-treated DNA has been established. The system is designed for the application of immunofluorescence using a monoclonal antibody that specifically recognizes 5'mC in single-stranded DNA hybridized to oligonucleotide microarrays. For assay readout an ultra-sensitive fluorescence scanner with submicrometer resolution was used. To minimize autofluorescence 150-microm thin glass slides with an aldehyde-functionalized surface were developed. These methodological improvements allowed the detection of 5'mC in synthetic oligonucleotides hybridized to microarrays with atto molar analytical sensitivity. Using enzymatic fragmented genomic DNA from myeloid leukemia tumor cell lines differences in the methylation status of gene regulatory sequences for E-cadherin, p15/CDKN2b and p16/CDKN2a were demonstrated. Thus, this novel technique can potentially be used for DNA methylation analysis in various scientific fields.     

Development of a spot reliability evaluation score for DNA microarrays

We developed a reliability index named SRED (Spot Reliability Evaluation Score for DNA microarrays) that represents the probability that the calibrated gene expression level from a DNA microarray would be less than a factor of 2 different from that of quantitative real-time polymerase chain reaction assays whose dynamic quantification range is treated statistically to be similar to that of the DNA microarray. To define the SRED score, two parameters, the reproducibility of measurement value and the relative expression value were selected from nine candidate parameters. The SRED score supplies the probability that the expression level in each spot of a microarray is less than a certain-fold different compared to other expression profiling data, such as QRT-PCR. This score was applied to 1,500,000 points of the expression profile in the RIKEN Expression Array Database.     

Application of DNA microarrays to study human alcoholism

An emerging idea is that long-term alcohol abuse results in changes in gene expression in the brain and that these changes are responsible at least partly for alcohol tolerance, dependence and neurotoxicity. The overall goal of our research is to identify genes which are differentially expressed in the brains of well-characterized human alcoholics as compared with non-alcoholics. This should identify as-yet-unknown alcohol-responsive genes, and may well confirm changes in the expression of genes which have been delineated in animal models of alcohol abuse. Cases were carefully selected and samples pooled on the basis of relevant criteria; differential expression was monitored by microarray hybridization. The inherent diversity of human alcoholics can be exploited to identify genes associated with specific pathological processes, as well as to assess the effects of concomitant disease, severity of brain damage, drinking behavior, and factors such as gender and smoking history. Initial results show selective changes in gene expression in alcoholics; of particular importance is a coordinated reduction in genes coding for myelin components.     

癌症DNA甲基化调控位点的识别

DNA甲基化是一种重要的表观遗传学修饰,在基因的转录调控方面具有重要的作用。异常的DNA甲基化可以导致癌症等复杂疾病发生,癌基因相关的DNA甲基化调控位点的识别对于解析癌症的发生发展机制及识别新的癌症标记具有重要意义。本研究通过整合The Cancer Genome Atlas(TCGA)的泛癌症基因组的高通量甲基化谱和基因表达谱,识别癌基因相关的DNA甲基化调控位点。对于每种癌症分批次计算Cp G位点甲基化与相关基因表达之间的相关性,并筛选调控下游基因的Cp G位点(包括强调控位点、弱调控位点和不调控位点),结果表明仅有一半的Cp G位点对下游基因具有调控作用;对癌症间共享的调控位点的分析发现不同癌症间共享的调控位点不尽相同,表明癌症特异的甲基化调控位点的存在。进一步地,对差异甲基化和差异表达基因的功能富集分析揭示了受甲基化调控的基因确实参与了癌症发生发展相关的功能。本研究的结果是对当前甲基化调控位点集的重要补充,也是识别癌症新型分子标记特征的重要资源。     

Cowpea mosaic virus nanoscaffold as signal enhancement for DNA microarrays

Previous studies have shown that a functionalized viral nanoparticle can be used as a fluorescent signal-generating element and enhance detection sensitivity for immunoassays and low density microarrays. In this study, we further tested this ability in commercial DNA microarrays, including Affymetrix high density resequencing microarray. Optimum conditions for NeutrAvidin and dye coupling to a double-cysteine mutant of cowpea mosaic virus (CPMV) were found to be comparable to the commonly used streptavidin-phycoerythrin (SAPE) for high density resequencing microarray. A 3-fold signal enhancement in comparison to Cy5-dCTP controls was obtained when using nanoparticles on control scorecard expression microarrays. Hybridization results from commercially available 8000 rat expression arrays indicate an increment of 14% on the detected features when the virus complex was used as the staining reagent in comparison to Cy5-dCTP controls. The current work shows the utility of the CPMV-dye nanoparticles as a detection reagent in well-established detection platforms.     

Putting microarrays in a context: integrated analysis of diverse biological data

In recent years, multiple types of high-throughput functional genomic data that facilitate rapid functional annotation of sequenced genomes have become available. Gene expression microarrays are the most commonly available source of such data. However, genomic data often sacrifice specificity for scale, yielding very large quantities of relatively lower-quality data than traditional experimental methods. Thus sophisticated analysis methods are necessary to make accurate functional interpretation of these large-scale data sets. This review presents an overview of recently developed methods that integrate the analysis of microarray data with sequence, interaction, localisation and literature data, and further outlines current challenges in the field. The focus of this review is on the use of such methods for gene function prediction, understanding of protein regulation and modelling of biological networks.