Prevalence of Opisthorchis viverrini-Like Fluke Infection in Ducks in Binh Dinh Province, Central Vietnam.

Following the first report of Opisthorchis viverrini infection in a domestic duck in Phu My District of Binh Dinh Province, Central Vietnam, many other cases were observed in the province. We determined the infection rate and intensity of O. viverrini infection in ducks in 4 districts of the province. A total of 178 ducks were randomly selected from 34 farms for examination of flukes in the liver and gall bladder. An infection rate of 34.3% (range 20.7-40.4% among districts) was found; the intensity of infection was 13.8 worms per infected duck (range 1-100). These findings show the role of ducks as a host for O. viverrini, duck genotype, which is sympatric with the human O. viverrini genotype in this province. It also stresses the need for investigations on the zoonotic potential and the life cycle of this parasite.

Nutrikinetic modeling reveals order of genistein phase II metabolites appearance in human plasma.

SCOPE: Genistein from foods or supplements is metabolized by the gut microbiota and the human body, thereby releasing many different metabolites into systemic circulation. The order of their appearance in plasma and the possible influence of food format are still unknown. This study compared the nutrikinetic profiles of genistein metabolites. METHODS AND RESULTS: In a randomized cross-over trial, 12 healthy young volunteers were administered a single dose of 30 mg genistein provided as a genistein tablet, a genistein tablet in low fat milk, and soy milk containing genistein glycosides. A high mass resolution LC-LTQ-Orbitrap FTMS platform detected and quantified in human plasma: free genistein, seven of its phase-II metabolites and 15 gut-derived metabolites. Interestingly, a novel metabolite, genistein-4'-glucuronide-7-sulfate (G-4'G-7S) was identified. Nutrikinetic analysis using population-based modeling revealed the order of appearance of five genistein phase II metabolites in plasma: (1) genistein-4',7-diglucuronide, (2) genistein-7-sulfate, (3) genistein-4'-sulfate-7-glucuronide, (4) genistein-4'-glucuronide, and (5) genistein-7-glucuronide, independent of the food matrix. CONCLUSION: The conjugated genistein metabolites appear in a distinct order in human plasma. The specific early appearance of G-4',7-diG suggests a multistep formation process for the mono and hetero genistein conjugates, involving one or two deglucuronidation steps.

The photographer and the greenhouse: how to analyse plant metabolomics data.

INTRODUCTION: Plant metabolomics experiments yield large amounts of data, too much to be interpretable by eye. Multivariate data analyses are therefore essential to extract and visualise the information of interest. OBJECTIVE: Because multivariate statistical methods may be remote from the expertise of many scientists working in the metabolomics field, this overview provides a step-by-step description of a multivariate data analysis, starting from the experiment and ending with the figures appearing in scientific journals. METHODOLOGY: We developed a thought experiment that explores the relationship between the differences in nutrient levels and three plant developmental descriptors through photography of the greenhouse they grow in. Through this, multivariate data analysis, data preprocessing and model validation are illustrated. Finally some of the presented methods are illustrated by the analysis of a plant metabolomics dataset. CONCLUSION: This paper will familiarize non-specialised researchers with the main concepts in multivariate data analysis and allow them to develop and evaluate metabolomic data analyses more critically.

Multilevel data analysis of a crossover designed human nutritional intervention study.

A new method is introduced for the analysis of 'omics' data derived from crossover designed drug or nutritional intervention studies. The method aims at finding systematic variations in metabolic profiles after a drug or nutritional challenge and takes advantage of the crossover design in the data. The method, which can be considered as a multivariate extension of a paired t test, generates different multivariate submodels for the between- and the within-subject variation in the data. A major advantage of this variation splitting is that each submodel can be analyzed separately without being confounded with the other variation sources. The power of the multilevel approach is demonstrated in a human nutritional intervention study which used NMR-based metabolomics to assess the metabolic impact of grape/wine extract consumption. The variations in the urine metabolic profiles are studied between and within the human subjects using the multilevel analysis. After variation splitting, multilevel PCA is used to investigate the experimental and biological differences between the subjects, whereas a multilevel PLS-DA model is used to reveal the net treatment effect within the subjects. The observed treatment effect is validated with cross model validation and permutations. It is shown that the statistical significance of the multilevel classification model ( p << 0.0002) is a major improvement compared to a ordinary PLS-DA model ( p = 0.058) without variation splitting. Finally, rank products are used to determine which NMR signals are most important in the multilevel classification model.

Biomarkers for lysosomal storage disorders: identification and application as exemplified by chitotriosidase in Gaucher disease.

UNLABELLED: A biomarker is an analyte that indicates the presence of a biological process linked to the clinical manifestations and outcome of a particular disease. An ideal biomarker provides indirect but ongoing determinations of disease activity. In the case of lysosomal storage disorders (LSDs), metabolites or proteins specifically secreted by storage cells are good candidates for biomarkers. Potential clinical applications of biomarkers are found in improved diagnosis, monitoring of disease progression and assessment of therapeutic correction. These applications are illustrated by reviewing the use of plasma chitotriosidase in the clinical management of patients with Gaucher disease, the most common LSD. The ongoing debate on the value of biomarkers in patient management is addressed. Novel analytical methods have revolutionized the identification and measurement of biomarkers at the protein and metabolite level. Recent developments in biomarker discovery by proteomics are described and the future for biomarkers of LSDs is discussed. CONCLUSION: Besides direct applications for biomarkers in patient management, biomarker searches are likely to render new insights into pathophysiological mechanisms and metabolic adaptations, and may provide new targets for therapeutic intervention.

A classification model for the Leiden proteomics competition.

A strategy is presented to build a discrimination model in proteomics studies. The model is built using cross-validation. This cross-validation step can simply be combined with a variable selection method, called rank products. The strategy is especially suitable for the low-samples-to-variables-ratio (undersampling) case, as is often encountered in proteomics and metabolomics studies. As a classification method, Principal Component Discriminant Analysis is used; however, the methodology can be used with any classifier. A data set containing serum samples from breast cancer patients and healthy controls is analysed. Double cross-validation shows that the sensitivity of the model is 82% and the specificity 86%. Potential putative biomarkers are identified using the variable selection method. In each cross-validation loop a classification model is built. The final classification uses a majority voting scheme from the ensemble classifier.

Statistical data processing in clinical proteomics.

This review discusses data analysis strategies for the discovery of biomarkers in clinical proteomics. Proteomics studies produce large amounts of data, characterized by few samples of which many variables are measured. A wealth of classification methods exists for extracting information from the data. Feature selection plays an important role in reducing the dimensionality of the data prior to classification and in discovering biomarker leads. The question which classification strategy works best is yet unanswered. Validation is a crucial step for biomarker leads towards clinical use. Here we only discuss statistical validation, recognizing that biological and clinical validation is of utmost importance. First, there is the need for validated model selection to develop a generalized classifier that predicts new samples correctly. A cross-validation loop that is wrapped around the model development procedure assesses the performance using unseen data. The significance of the model should be tested; we use permutations of the data for comparison with uninformative data. This procedure also tests the correctness of the performance validation. Preferably, a new set of samples is measured to test the classifier and rule out results specific for a machine, analyst, laboratory or the first set of samples. This is not yet standard practice. We present a modular framework that combines feature selection, classification, biomarker discovery and statistical validation; these data analysis aspects are all discussed in this review. The feature selection, classification and biomarker discovery modules can be incorporated or omitted to the preference of the researcher. The validation modules, however, should not be optional. In each module, the researcher can select from a wide range of methods, since there is not one unique way that leads to the correct model and proper validation. We discuss many possibilities for feature selection, classification and biomarker discovery. For validation we advice a combination of cross-validation and permutation testing, a validation strategy supported in the literature.

How to distinguish healthy from diseased? Classification strategy for mass spectrometry-based clinical proteomics.

SELDI-TOF-MS is rapidly gaining popularity as a screening tool for clinical applications of proteomics. Application of adequate statistical techniques in all the stages from measurement to information is obligatory. One of the statistical methods often used in proteomics is classification: the assignment of subjects to discrete categories, for example healthy or diseased. Lately, many new classification methods have been developed, often specifically for the analysis of X-omics data. For proteomics studies a good strategy for evaluating classification results is of prime importance, because usually the number of objects will be small and it would be wasteful to set aside part of these as a 'mere' test set. The present paper offers such a strategy in the form of a protocol which can be used for choosing among different statistical classification methods and obtaining figures of merit of their performance. This paper also illustrates the usefulness of proteomics in a clinical setting, serum samples from Gaucher disease patients, when used in combination with an appropriate classification method.

Assessing the statistical validity of proteomics based biomarkers.

A strategy is presented for the statistical validation of discrimination models in proteomics studies. Several existing tools are combined to form a solid statistical basis for biomarker discovery that should precede a biochemical validation of any biomarker. These tools consist of permutation tests, single and double cross-validation. The cross-validation steps can simply be combined with a new variable selection method, called rank products. The strategy is especially suited for the low-samples-to-variables-ratio (undersampling) case, as is often encountered in proteomics and metabolomics studies. As a classification method, principal component discriminant analysis is used; however, the methodology can be used with any classifier. A dataset containing serum samples from Gaucher patients and healthy controls serves as a test case. Double cross-validation shows that the sensitivity of the model is 89% and the specificity 90%. Potential putative biomarkers are identified using the novel variable selection method. Results from permutation tests support the choice of double cross-validation as the tool for determining error rates when the modelling procedure involves a tuneable parameter. This shows that even cross-validation does not guarantee unbiased results. The validation of discrimination models with a combination of permutation tests and double cross-validation helps to avoid erroneous results which may result from the undersampling.