Methods for the analysis of triacylglycerols

This article discusses the methods most commonly employed in the analysis of the triacylglycerols (TAGs) in natural fats and considers the main advantages and disadvantages of each and the techniques for optimising analytical conditions. Complete analysis of the composition of a natural fat calls for a method of extracting and purifying the triglyceride fraction, normally by preparatory thin-layer and column chromatography. Determination of the individual components of triglyceride mixtures still entails certain difficulties, namely, the separation and identification of the TAGs in natural fats. High-performance liquid chromatography (HPLC) offers significant advantages over gas and thin-layer chromatography. Many workers have developed non-aqueous, reversed-phase HPLC systems capable of successfully resolving complex mixtures of TAGs, and combining reversed-phase (RP) HPLC and argentation chromatography may improve the results. Identification of the TAGs separated by HPLC becomes an extremely complex task if many different fatty acids are involved and if the sn-stereoscopic positions on the glycerol are to be determined. Enzymatic analysis and chiral-phase chromatography are capable of localising fatty acids on the TAG molecule. In closing, some of the most interesting biomedical applications of TAG analysis are summarised.     

Methods for the analysis of protein-chromatin interactions

The analysis of protein interactions with chromatin is vital for the understanding of DNA sequence recognition in vivo. Chromatin binding requires the interaction of proteins with DNA lying on the macromolecular protein surface of nucleosomes, a situation that can alter factor binding characteristics substantially when compared with naked DNA. It is therefore important to study these protein-DNA interactions in the context of a chromatin substrate, the more physiologically relevant binding situation. In this article we review techniques used in the investigation of protein interactions with defined nucleosomal templates.     

Approaches for the analysis of chlorinated lipids

Leukocytes are key cellular mediators of human diseases through their role in inflammation. Identifying unique molecules produced by leukocytes may provide new biomarkers and mechanistic insights into the role of leukocytes in disease. Chlorinated lipids are generated as a result of myeloperoxidase-containing leukocyte-derived hypochlorous acid targeting the vinyl ether bond of plasmalogens. The initial product of this reaction is α-chlorofatty aldehyde. α-Chlorofatty aldehyde is both oxidized to α-chlorofatty acid and reduced to α-chlorofatty alcohol by cellular metabolism. This review focuses on the separation techniques and quantitative analysis for these chlorinated lipids. For α-chlorofatty acid, the negative charge of carboxylic acids is exploited to detect the chlorinated lipid species of these acids by electrospray ionization mass spectrometry in the negative ion mode. In contrast, α-chlorofatty aldehyde and α-chlorofatty alcohol are converted to pentafluorobenzyl oxime and pentafluorobenzoyl ester derivatives, which are detected by negative ion chemical ionization mass spectrometry. These two detection methods coupled with the use of stable isotope internal standards and either liquid chromatography or gas chromatography provide highly sensitive analytical approaches to measure these novel lipids.     

Biosensor for the enantioselective analysis of S-perindopril

Because S-perindopril enantiomer is the eutomer which is responsible for the angiotensin-converting enzyme inhibition activity, it is necessary to develop a reliable method for its assay from its distomer, the R-enantiomer. For this purpose, an amperometric biosensor was developed based on L-amino acid oxidase. The working range of the described biosensor was 20pmol/L-10 micromol/L on the 7.0-7.4 pH range, with a detection limit of 2pmol/L. The low enantioselectivity for R-perindopril, as compared with S-enantiomer, was demonstrated by both mixed solutions and separate solutions methods (amperometric selectivity coefficient is 1.0 x 10(-4)). The biosensor was also selective towards D-proline and polyvinylpyrrolidone. The amperometric biosensor can be used for enantioselective analysis of S-perindopril in raw material, with an RSD < 1%. The life time (t95%) of the biosensor is three weeks.     

Models for the analysis of radon-exposed populations

Radon-222 is a radioactive decay product of radium-226 and uranium-238, which are found throughout the crust of the earth. Studies of underground miners clearly show that exposure to radon and its decay products increases the risk of developing lung cancer. Data on standardized mortality ratios from eight cohort studies indicate that the radon-lung cancer relationship is statistically homogeneous, even though cohorts are from different types of mines and from different countries. Regression methods for cohort data based on a Poisson probability model permit a thorough consideration of risk patterns. In this report, we review these methods, wherein the disease rate in each cell of a multi-way table is modeled as a function of the cross-classifying variables. The National Academy of Sciences' Committee on the Biological Effects of Ionizing Radiation uses the Poisson regression approach to develop a model for age-specific lung cancer risk which depends on cumulative exposure, age at risk, and time since exposure. This model is reviewed and its implications discussed. The most important determinant of lung cancer is cigarette smoking. This paper discusses relative risk models for analysis of joint exposure to radon and tobacco products. The review of available studies suggests that the joint relationship of radon and smoking with lung cancer is consistent with a multiplicative model, but a submultiplicative relationship is most likely. An additive model is rejected.     

Current methodologies for the analysis of aminoglycosides

The aminoglycosides are a large and diverse class of antibiotics that characteristically contain two or more aminosugars linked by glycosidic bonds to an aminocyclitol component. Structures are presented for over 30 of the most important members of this family of compounds. The use of aminoglycosides in clinical and veterinary medicine and in agriculture is described. Qualitative methods for aminoglycoside analysis include X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). The major part of this article comprises a comprehensive review of quantitative methods for the determination of aminoglycosides. These are microbiological assay, radiochemical assay, radioimmunoassay, enzyme immunoassay, fluoroimmunoassay and other immunoassays, spectrophotometric and other non-separative methods, gas chromatography (GC), thin-layer chromatography (TLC), high-performance liquid chromatography (HPLC), and capillary electrophoresis (CE). Simple spectrophotometric methods may be adequate for the assay of bulk pharmaceuticals and their formulations. Microbiological assays make useful semi-quantitative screening tests for the analysis of veterinary drug residues in food, but rapid enzyme immunoassays are more suitable for accurate measurements of aminoglycosides in complex matrices. Automated immunoassays are the most appropriate methods for serum aminoglycoside determinations during therapeutic drug monitoring. HPLC techniques provide the specificity and sensitivity required for pharmacokinetic and other research studies, while HPLC–MS is employed for the confirmation of veterinary drug residues. The potential for further development of chromatographic and CE methods for the analysis of biological samples is outlined.     

Methods for the analysis of DNA-protein interactions

The interaction of proteins with DNA is a central theme of molecular biology. In this article, we review some of the principal techniques currently used for the identification and characterization of DNA binding proteins, and for investigation of the molecular interactions that are responsible for the recognition of specific DNA sequences.     

Strategy for the sequence analysis of heparin

The versatile biological activities of proteoglycans are mainlymediated by their glycosaminoglycan (GAG) components. Unlikeproteins and nucleic acids, no satisfactory method for sequencingGAGs has been developed. This paper describes a strategy tosequence the GAG chains of heparin. Heparin, prepared from animaltissue, and processed by proteinases and endoglucuronidases,is 90% GAG heparin and 10% peptidoglycan heparin (containingsmall remnants of core protein). Raw porcine mucosal heparinwas labelled on the amino termini of these core protein remnantswith a hydrophobic, fluorescent tag [N-4-(6-dimethylamino-2-benzofuranyl)phenyl (NDBP)-isothiocyanate]. Enrichment of the NDBP-heparinusing phenyl-Sepharose chromatography, followed by treatmentwith a mixture of heparin lyase I and III, resulted in a singleNDBP-linkage region tetrasaccharide, which was characterizedas     

Methods for the analysis of inositol phosphates

Interest in the inositol phospholipids was stimulated by the simultaneous discoveries that the products of hydrolysis of these lipids could serve as messengers to activate to synergistic signaling pathways in hormonally responsive cells, namely, inositol 1,4,5-trisphosphate which causes the release of Ca2+ from intracellular stores and diacylglycerol which promotes the activation of protein kinase C. At the same time, Berridge and co-workers introduced relatively simple approaches to study the inositol phospholipid cycle. These included the use of [3H]inositol to label the inositol metabolites, all of which are confined to this cycle, and of Li+ to decrease the rate of degradation of the inositol phosphates. Water-soluble inositol phosphates and chloroform-soluble inositol phospholipids could then be separated by solvent partition and the inositol phosphates further separated by use of an anion-exchange resin. However, the subsequent application of high-performance liquid chromatography as a separation technique indicated the existence of many isomers of the inositol phosphates formed by different pathways of dephosphorylation and phosphorylation. Mapping of these metabolic pathways may be substantially complete, but novel pathways may still be discovered. We review both old and new methods of analysis of the inositol phosphates for the measurement of mass and radioactivity. Although the complexity of the cycle sometimes demands the use of sophisticated methods of separation and rigorous identification, older and inexpensive methods may still be useful for some purposes.     

A biosensor for the analysis of acetonitrile

A biosensor for monitoring acetonitrile was constructed. A mixed culture was taken from a degradation reactor and mounted on top of a Clark electrode. The amperometric biosensor was placed in a flow-through cell and integrated into a flow injection system. The metabolic response in terms of oxygen consumption was well correlated to the concentration of acetonitrile in standard solutions. However, when the reaction products, acetic acid and ammonia, were also present, the response was erratic, due to the additional metabolic reaction on acetate. By introducing a hydrophobic barrier it was possible to eliminate the negative influence of these charged products and thus to improve the operational selectivity of the sensor.The biosensor showed good stability for analysis during at least 6 days and future work will focus on using it for monitoring and control of degradation processes.     

Computer tools for the analysis of schooling

Synopsis Quantification of fish school structure is difficult because of: (1) the large amount of positional data that must be recorded, (2) the fact that schools are moving, and (3) the fact that schools are threedimensional. Computer tools for addressing these problems include x,y digitizing devices interfaced to computers and interactive computer graphics systems. General computer algorithms now exist for 3-D reconstruction of schools given any two 2-D views such as photographs.Our computerized film analyzer, the Galatea system, is described in detail. This system allows for rapid, accurate input of multiple points which can be followed through each frame in a film sequence. Its software packages perform 3-D reconstructions, analyze undulatory motion, and do extensive statistical tests.     

Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis

Over-representation analysis (ORA) is one of the commonest pathway analysis approaches used for the functional interpretation of metabolomics datasets. Despite the widespread use of ORA in metabolomics, the community lacks guidelines detailing its best-practice use. Many factors have a pronounced impact on the results, but to date their effects have received little systematic attention. Using five publicly available datasets, we demonstrated that changes in parameters such as the background set, differential metabolite selection methods, and pathway database used can result in profoundly different ORA results. The use of a non-assay-specific background set, for example, resulted in large numbers of false-positive pathways. Pathway database choice, evaluated using three of the most popular metabolic pathway databases (KEGG, Reactome, and BioCyc), led to vastly different results in both the number and function of significantly enriched pathways. Factors that are specific to metabolomics data, such as the reliability of compound identification and the chemical bias of different analytical platforms also impacted ORA results. Simulated metabolite misidentification rates as low as 4% resulted in both gain of false-positive pathways and loss of truly significant pathways across all datasets. Our results have several practical implications for ORA users, as well as those using alternative pathway analysis methods. We offer a set of recommendations for the use of ORA in metabolomics, alongside a set of minimal reporting guidelines, as a first step towards the standardisation of pathway analysis in metabolomics.     

Copasetic analysis: a framework for the blind analysis of microarray imagery

From its conception, bioinformatics has been a multidisciplinary field which blends domain expert knowledge with new and existing processing techniques, all of which are focused on a common goal. Typically, these techniques have focused on the direct analysis of raw microarray image data. Unfortunately, this fails to utilise the image's full potential and in practice, this results in the lab technician having to guide the analysis algorithms. This paper presents a dynamic framework that aims to automate the process of microarray image analysis using a variety of techniques. An overview of the entire framework process is presented, the robustness of which is challenged throughout with a selection of real examples containing varying degrees of noise. The results show the potential of the proposed framework in its ability to determine slide layout accurately and perform analysis without prior structural knowledge. The algorithm achieves approximately, a 1 to 3 dB improved peak signal-to-noise ratio compared to conventional processing techniques like those implemented in GenePix when used by a trained operator. As far as the authors are aware, this is the first time such a comprehensive framework concept has been directly applied to the area of microarray image analysis.     

Variable selection for discriminant analysis with Markov random field priors for the analysis of microarray data

MOTIVATION: Discriminant analysis is an effective tool for the classification of experimental units into groups. Here, we consider the typical problem of classifying subjects according to phenotypes via gene expression data and propose a method that incorporates variable selection into the inferential procedure, for the identification of the important biomarkers. To achieve this goal, we build upon a conjugate normal discriminant model, both linear and quadratic, and include a stochastic search variable selection procedure via an MCMC algorithm. Furthermore, we incorporate into the model prior information on the relationships among the genes as described by a gene-gene network. We use a Markov random field (MRF) prior to map the network connections among genes. Our prior model assumes that neighboring genes in the network are more likely to have a joint effect on the relevant biological processes. RESULTS: We use simulated data to assess performances of our method. In particular, we compare the MRF prior to a situation where independent Bernoulli priors are chosen for the individual predictors. We also illustrate the method on benchmark datasets for gene expression. Our simulation studies show that employing the MRF prior improves on selection accuracy. In real data applications, in addition to identifying markers and improving prediction accuracy, we show how the integration of existing biological knowledge into the prior model results in an increased ability to identify genes with strong discriminatory power and also aids the interpretation of the results.