Pre-processing liquid chromatography/high-resolution mass spectrometry data: extracting pure mass spectra by deconvolution from the invariance of isotopic distribution

RATIONALE

Mass spectra obtained by deconvolution of liquid chromatography/high-resolution mass spectrometry (LC/HRMS) data can be impaired by non-informative mass-over-charge (m/z) channels. This impairment of mass spectra can have significant negative influence on further post-processing, like quantification and identification.

METHODS

A metric derived from the knowledge of errors in isotopic distribution patterns, and quality of the signal within a pre-defined mass chromatogram block, has been developed to pre-select all informative m/z channels.

RESULTS

This procedure results in the clean-up of deconvoluted mass spectra by maintaining the intensity counts from m/z channels that originate from a specific compound/molecular ion, for example, molecular ion, adducts, (13) C-isotopes, multiply charged ions and removing all m/z channels that are not related to the specific peak. The methodology has been successfully demonstrated for two sets of high-resolution LC/MS data.

CONCLUSIONS

The approach described is therefore thought to be a useful tool in the automatic processing of LC/HRMS data. It clearly shows the advantages compared to other approaches like peak picking and de-isotoping in the sense that all information is retained while non-informative data is removed automatically.

Evaluation of multiple variate selection methods from a biological perspective: a nutrigenomics case study

Genomics-based technologies produce large amounts of data. To interpret the results and identify the most important variates related to phenotypes of interest, various multivariate regression and variate selection methods are used. Although inspected for statistical performance, the relevance of multivariate models in interpreting biological data sets often remains elusive. We compare various multivariate regression and variate selection methods applied to a nutrigenomics data set in terms of performance, utility and biological interpretability. The studied data set comprised hepatic transcriptome (10,072 predictor variates) and plasma protein concentrations [2 dependent variates: Leptin (LEP) and Tissue inhibitor of metalloproteinase 1 (TIMP-1)] collected during a high-fat diet study in ApoE3Leiden mice. The multivariate regression methods used were: partial least squares "PLS"; a genetic algorithm-based multiple linear regression, "GA-MLR"; two least-angle shrinkage methods, "LASSO" and "ELASTIC NET"; and a variant of PLS that uses covariance-based variate selection, "CovProc." Two methods of ranking the genes for Gene Set Enrichment Analysis (GSEA) were also investigated: either by their correlation with the protein data or by the stability of the PLS regression coefficients. The regression methods performed similarly, with CovProc and GA performing the best and worst, respectively (R-squared values based on "double cross-validation" predictions of 0.762 and 0.451 for LEP; and 0.701 and 0.482 for TIMP-1). CovProc, LASSO and ELASTIC NET all produced parsimonious regression models and consistently identified small subsets of variates, with high commonality between the methods. Comparison of the gene ranking approaches found a high degree of agreement, with PLS-based ranking finding fewer significant gene sets. We recommend the use of CovProc for variate selection, in tandem with univariate methods, and the use of correlation-based ranking for GSEA-like pathway analysis methods.

Comprehensive analysis of umami compounds by ion-pair liquid chromatography coupled to mass spectrometry

An ion-pair LC-ESI-MS method was developed capable of analyzing various reported umami or umami-enhancing compounds, including glutamic acid and 5'-ribonucleotides. The method was validated using tomato and potato samples and showed overall good analytical performance with respect to selectivity, detection limit, linearity, and repeatability. The method was applied to various tomato samples resulting in concentrations of glutamic acid and 5'-ribonucleotides that were in good comparison with literature. The methodology might also be used for the discovery of new umami (enhancing) compounds in an untargeted mode. This was to a certain extent demonstrated for tomato samples by correlating all peaks observed with the ion-pair liquid chromatography-mass spectrometry (LC-MS) method to sensory properties using multivariate statistics.

PRACTICAL APPLICATION

This study describes the development and application of a LC-MS method, which can be used to quantify several known umami (enhancing) compounds in various foods. Furthermore, the method might be useful for the discovery of new umami (enhancing) compounds.

Adipose tissue dysfunction signals progression of hepatic steatosis towards nonalcoholic steatohepatitis in C57BL/6 mice

OBJECTIVE

Nonalcoholic fatty liver disease (NAFLD) is linked to obesity and diabetes, suggesting an important role of adipose tissue in the pathogenesis of NAFLD. Here, we aimed to investigate the interaction between adipose tissue and liver in NAFLD and identify potential early plasma markers that predict nonalcoholic steatohepatitis (NASH).

RESEARCH DESIGN AND METHODS

C57Bl/6 mice were chronically fed a high-fat diet to induce NAFLD and compared with mice fed a low-fat diet. Extensive histological and phenotypical analyses coupled with a time course study of plasma proteins using multiplex assay were performed.

RESULTS

Mice exhibited pronounced heterogeneity in liver histological scoring, leading to classification into four subgroups: low-fat low (LFL) responders displaying normal liver morphology, low-fat high (LFH) responders showing benign hepatic steatosis, high-fat low (HFL) responders displaying pre-NASH with macrovesicular lipid droplets, and high fat high (HFH) responders exhibiting overt NASH characterized by ballooning of hepatocytes, presence of Mallory bodies, and activated inflammatory cells. Compared with HFL responders, HFH mice gained weight more rapidly and exhibited adipose tissue dysfunction characterized by decreased final fat mass, enhanced macrophage infiltration and inflammation, and adipose tissue remodeling. Plasma haptoglobin, IL-1β, TIMP-1, adiponectin, and leptin were significantly changed in HFH mice. Multivariate analysis indicated that in addition to leptin, plasma CRP, haptoglobin, eotaxin, and MIP-1α early in the intervention were positively associated with liver triglycerides. Intermediate prognostic markers of liver triglycerides included IL-18, IL-1β, MIP-1γ, and MIP-2, whereas insulin, TIMP-1, granulocyte chemotactic protein 2, and myeloperoxidase emerged as late markers.

CONCLUSIONS

Our data support the existence of a tight relationship between adipose tissue dysfunction and NASH pathogenesis and point to several novel potential predictive biomarkers for NASH.

The association of 83 plasma proteins with CHD mortality, BMI, HDL-, and total-cholesterol in men: applying multivariate statistics to identify proteins with prognostic value and biological relevance

In this study, we applied the multivariate statistical tool Partial Least Squares (PLS) to analyze the relative importance of 83 plasma proteins in relation to coronary heart disease (CHD) mortality and the intermediate end points body mass index, HDL-cholesterol and total cholesterol. From a Dutch monitoring project for cardiovascular disease risk factors, men who died of CHD between initial participation (1987-1991) and end of follow-up (January 1, 2000) (N = 44) and matched controls (N = 44) were selected. Baseline plasma concentrations of proteins were measured by a multiplex immunoassay. With the use of PLS, we identified 15 proteins with prognostic value for CHD mortality and sets of proteins associated with the intermediate end points. Subsequently, sets of proteins and intermediate end points were analyzed together by Principal Components Analysis, indicating that proteins involved in inflammation explained most of the variance, followed by proteins involved in metabolism and proteins associated with total-C. This study is one of the first in which the association of a large number of plasma proteins with CHD mortality and intermediate end points is investigated by applying multivariate statistics, providing insight in the relationships among proteins, intermediate end points and CHD mortality, and a set of proteins with prognostic value.

Improving the analysis of designed studies by combining statistical modelling with study design information

Background

In the fields of life sciences, so-called designed studies are used for studying complex biological systems. The data derived from these studies comply with a study design aimed at generating relevant information while diminishing unwanted variation (noise). Knowledge about the study design can be used to decompose the total data into data blocks that are associated with specific effects. Subsequent statistical analysis can be improved by this decomposition if these are applied on selected combinations of effects.

Results

The benefit of this approach was demonstrated with an analysis that combines multivariate PLS (Partial Least Squares) regression with data decomposition from ANOVA (Analysis of Variance): ANOVA-PLS. As a case, a nutritional intervention study is used on Apoliprotein E3-Leiden (APOE3Leiden) transgenic mice to study the relation between liver lipidomics and a plasma inflammation marker, Serum Amyloid A. The ANOVA-PLS performance was compared to PLS regression on the non-decomposed data with respect to the quality of the modelled relation, model reliability, and interpretability.

Conclusion

It was shown that ANOVA-PLS leads to a better statistical model that is more reliable and better interpretable compared to standard PLS analysis. From a following biological interpretation, more relevant metabolites were derived from the model. The concept of combining data composition with a subsequent statistical analysis, as in ANOVA-PLS, is however not limited to PLS regression in metabolomics but can be applied for many statistical methods and many different types of data.

Transcriptomics analysis of interactive effects of benzene, trichloroethylene and methyl mercury within binary and ternary mixtures on the liver and kidney following subchronic exposure in the rat

The present research aimed to study the interaction of three chemicals, methyl mercury, benzene and trichloroethylene, on mRNA expression alterations in rat liver and kidney measured by microarray analysis. These compounds were selected based on presumed different modes of action. The chemicals were administered daily for 14 days at the Lowest-Observed-Adverse-Effect-Level (LOAEL) or at a two- or threefold lower concentration individually or in binary or ternary mixtures. The compounds had strong antagonistic effects on each other's gene expression changes, which included several genes encoding Phase I and II metabolizing enzymes. On the other hand, the mixtures affected the expression of "novel" genes that were not or little affected by the individual compounds. The three compounds exhibited a synergistic interaction on gene expression changes at the LOAEL in the liver and both at the sub-LOAEL and LOAEL in the kidney. Many of the genes induced by mixtures but not by single compounds, such as Id2, Nr2f6, Tnfrsf1a, Ccng1, Mdm2 and Nfkb1 in the liver, are known to affect cellular proliferation, apoptosis and tissue-specific function. This indicates a shift from compound specific response on exposure to individual compounds to a more generic stress response to mixtures. Most of the effects on cell viability as concluded from transcriptomics were not detected by classical toxicological endpoints illustrating the benefit of increased sensitivity of assessing gene expression profiling. These results emphasize the benefit of applying toxicogenomics in mixture interaction studies, which yields biomarkers for joint toxicity and eventually can result in an interaction model for most known toxicants.

Effects of growth conditions and processing on Rehmannia glutinosa using fingerprint strategy

Metabolite profiling in combination with multivariate statistics is a sophisticated method for quality assessment of natural products. For the development of a quality control strategy in Traditional Chinese Medicine (TCM), we have measured the metabolite fingerprints of Rehmannia glutinosa by GC-MS. Plants were grown under different climate and soil conditions in a phytotron and were processed by a variable number of repetitive steps to investigate the effects on both growth conditions and processing for material medica of R. glutinosa. The GC-MS data have been analyzed by principal component analysis (PCA) and the new approach of the ANOVA-simultaneous component analysis (ASCA) which can combine the information from a structured data design with multivariate analysis. The results clearly show the effect of the different factors and indicate directions for process improvement. When plants were grown under various temperatures, humidity and light intensities for a short period (3 weeks), no significant changes on studied metabolites were observed. However, significant changes were found between different processing cycles. The present data clearly indicate the importance of strictly controlling processing in R. glutinosa and illustrate the impact of growth conditions. This is the first report on the metabolite profile of R. glutinosa that provides a base for the establishment of a quality control strategy.

Esomeprazole for the prevention of postoperative nausea and vomiting. A randomized, placebo-controlled trial*

BACKGROUND

Postoperative nausea and vomiting still represents a major problem after surgery. Although risk factors for postoperative nausea and vomiting and procedures to reduce postoperative nausea and vomiting have been described, the incidence of postoperative nausea and vomiting remains high. The aim of the present study was to investigate the potential role of the proton pump inhibitor esomeprazole to reduce postoperative nausea and vomiting after elective surgery.

METHODS

In a randomized, double-blind trial, ASA I-III patients at high risk for postoperative nausea and vomiting received esomeprazole tablets 3 x 40 mg or matching placebo the evening before surgery, 2 h preoperatively and 24 h postoperatively. Total intravenous anaesthesia with propofol and remifentanil without nitrous oxide (FiO2 0.5) was used. Patients were interviewed using a standardized postoperative nausea and vomiting questionnaire at discharge from the post-anaesthesia care unit, 6 h and 24 h later. The severity of nausea was estimated on a 0-100 point numerical scale (0 = no nausea, 100 = maximum nausea).

RESULTS

The incidence of vomiting was similar in the esomeprazole (n = 45) and the placebo (n = 48) groups (64.4% vs. 60.5%, P > 0.05). The average nausea score was 17.8 with esomeprazole and was 18.7 with placebo (P > 0.05). Only 24.7% of all patients (esomeprazole 24.4%, placebo 25.0%) did not experience any nausea or vomiting.

CONCLUSION

There is no evidence that prophylactic esomeprazole reduces the incidence of postoperative nausea and vomiting or the degree of postoperative nausea.

[Anaesthesia in cataract surgery for elderly people]

OBJECTIVE

In the cataract surgery the problems of anaesthesia in ophthalmo-surgery and anaesthesia in elderly people are combined. For this patients we compared intravenous anaesthesia with balanced anaesthesia in cardiac and circulatory side effects (security), patients satisfaction and costs.

METHODS

In a prospective, randomised study we compared in 52 (26 vs. 26) elderly patients (ASA II and III, mean age 78.7 +/- 8.6 years) undergoing a cataract operation cardiac and circulatory side effects, recovery time and patients satisfaction with the either type of anaesthesia by a score from 1 = best to 6 = worse and the occurrence of nausea/vomiting (controlled 24 h). The difference in costs were measured by the hospital management.

RESULTS

Cardiac and circulatory side effects with hypotonic reactions were similar in both groups (TIVA 18 = 69.2 %/BA 20 = 76.9 %). Hypertonic reactions were mainly seen in BA (Tiva 1.9 %, BA 69.2 %/p < 0,001). There was no problem in the treatment of these side-effects. The frequency of postoperative nausea and vomiting was lower (but not significantly) in the TIVA group (TIVA 8 %/ BA 27 %) than in the BA group (7 and 4). The recovery times were shorter (p < 0.001) in TIVA, those patients were staying 24 (+/- 6.70) minutes in the post operative unit vs. 59 (+/- 28.83) minutes in BA. Patients satisfaction was better in the TIVA-group (1.65) than in the patients treated with BA. In the calculation of costs there was a disadvantage in BA.

CONCLUSIONS

This study showed, that in respect of cardiac and respiratory side effects TIVA and BA are safe methods in anaesthesia for elderly people in ophthalmic surgery. Nevertheless, because of shorter recovery-times, lower incidence of hypertonic reactiones, more patients satisfaction and lower costs TIVA had better results in this study. We have to ask for the evaluation of economy in anaesthesia that not only prices of medicaments but the complete costs of the cases have to be calculated.

Multivariate Calibration with Least-Squares Support Vector Machines

This paper proposes the use of least-squares support vector machines (LS-SVMs) as a relatively new nonlinear multivariate calibration method, capable of dealing with ill-posed problems. LS-SVMs are an extension of "traditional" SVMs that have been introduced recently in the field of chemistry and chemometrics. The advantages of SVM-based methods over many other methods are that these lead to global models that are often unique, and nonlinear regression can be performed easily as an extension to linear regression. An additional advantage of LS-SVM (compared to SVM) is that model calculation and optimization can be performed relatively fast. As a test case to study the use of LS-SVM, the well-known and important chemical problem is considered in which spectra are affected by nonlinear interferences. As one specific example, a commonly used case is studied in which near-infrared spectra are affected by temperature-induced spectral variation. Using this test case, model optimization, pruning, and model interpretation of the LS-SVM have been demonstrated. Furthermore, excellent performance of the LS-SVM, compared to other approaches, has been presented on the specific example. Therefore, it can be concluded that LS-SVMs can be seen as very promising techniques to solve ill-posed problems. Furthermore, these have been shown to lead to robust models in cases of spectral variations due to nonlinear interferences.

Priority assessment of toxic substances in life cycle assessment. Part II: assessing parameter uncertainty and human variability in the calculation of toxicity potentials

Toxicity potentials are standard values used in life cycle assessment (LCA) to enable a comparison of toxic impacts between substances. This paper presents the results of an uncertainty assessment of toxicity potentials that were calculated with the global nested multi-media fate, exposure and effects model USES-LCA. The variance in toxicity potentials resulting from input parameter uncertainties and human variability was quantified by means of Monte Carlo analysis with Latin Hypercube sampling (LHS). For Atrazine, 2,3,7,8-TCDD and Lead, variation, expressed by the ratio of the 97.5%-ile and the 2.5%-ile, ranges from about 1.5 to 6 orders of magnitude. The major part of this variation originates from a limited set of substance-specific input parameters, i.e. parameters that describe transport mechanisms, substance degradation, indirect exposure routes and no-effect concentrations. Considerable correlations were found between the toxicity potentials of one substance, in particular within one impact category. The uncertainties and correlations reported in the present study may have a significant impact on the outcome of LCA case studies.

Priority assessment of toxic substances in life cycle assessment. Part I: calculation of toxicity potentials for 181 substances with the nested multi-media fate, exposure and effects model USES-LCA

Toxicity potentials are standard values used in life cycle assessment (LCA) to enable a comparison of toxic impacts between substances. In most cases, toxicity potentials are calculated with multi-media fate models. Until now, unrealistic system settings were used for these calculations. The present paper outlines an improved model to calculate toxicity potentials: the global nested multi-media fate, exposure and effects model USES-LCA. It is based on the Uniform System for the Evaluation of Substances 2.0 (USES 2.0). USES-LCA was used to calculate for 181 substances toxicity potentials for the six impact categories freshwater aquatic ecotoxicity, marine aquatic ecotoxicity, freshwater sediment ecotoxicity, marine sediment ecotoxicity, terrestrial ecotoxicity and human toxicity, after initial emission to the compartments air, freshwater, seawater, industrial soil and agricultural soil, respectively. Differences of several orders of magnitude were found between the new toxicity potentials and those calculated previously.