Margriet M. W. B. Hendriks

MULTICRITERIA DECISION-MAKING

Abstract Hendriks, M.M.W.B., De Boer, J.H., Smilde, A.K. and Doornbos, D.A., 1992. Multicriteria decision making. Chemometrics and Intelligent Laboratory System , 16: 175–191. Interest is growing in multicriteria decision making (MCDM) techniques and a large number of these techniques are now available. The purpose of this tutorial is to give a theoretical description of some of the MCDM techniques. Besides this we will give an overview of the differences and similarities of the techniques discussed. We have tried to select those techniques that are most frequently described in recent publications on analytical chemical and pharmaceutical subjects and, more important, that give a good survey of the diversity of techniques. We describe five different MCDM methods: Pareto optimality, desirability functions, overlay plots, utility functions and PROMETHEE. These techniques are compared to each other by applying them to a decision making problem in tablet manufacturing.

Reflections on univariate and multivariate analysis of metabolomics data

AbstractMetabolomics experiments usually result in a large quantity of data. Univariate and multivariate analysis techniques are routinely used to extract relevant information from the data with the aim of providing biological knowledge on the problem studied. Despite the fact that statistical tools like the t test, analysis of variance, principal component analysis, and partial least squares discriminant analysis constitute the backbone of the statistical part of the vast majority of metabolomics papers, it seems that many basic but rather fundamental questions are still often asked, like: Why do the results of univariate and multivariate analyses differ? Why apply univariate methods if you have already applied a multivariate method? Why if I do not see something univariately I see something multivariately? In the present paper we address some aspects of univariate and multivariate analysis, with the scope of clarifying in simple terms the main differences between the two approaches. Applications of the t test, analysis of variance, principal component analysis and partial least squares discriminant analysis will be shown on both real and simulated metabolomics data examples to provide an overview on fundamental aspects of univariate and multivariate methods.

D-serine in the developing human central nervous system.

To elucidate the role of D‐serine in human central nervous system, we analyzed D‐serine, L‐serine, and glycine concentrations in cerebrospinal fluid of healthy children and children with a defective L‐serine biosynthesis (3‐phosphoglycerate dehydrogenase deficiency). Healthy children showed high D‐serine concentrations immediately after birth, both absolutely and relative to glycine and L‐serine, declining to low values at infancy. D‐Serine concentrations were almost undetectable in untreated 3‐phosphoglycerate dehydrogenase–deficient patients. In one patient treated prenatally, D‐serine concentration was nearly normal at birth and the clinical phenotype was normal. These observations suggest a pivotal role for D‐serine in normal and aberrant human brain development. Ann Neurol 2006;60:476–480

Metabolic network discovery through reverse engineering of metabolome data

Reverse engineering of high-throughput omics data to infer underlying biological networks is one of the challenges in systems biology. However, applications in the field of metabolomics are rather limited. We have focused on a systematic analysis of metabolic network inference from in silico metabolome data based on statistical similarity measures. Three different data types based on biological/environmental variability around steady state were analyzed to compare the relative information content of the data types for inferring the network. Comparing the inference power of different similarity scores indicated the clear superiority of conditioning or pruning based scores as they have the ability to eliminate indirect interactions. We also show that a mathematical measure based on the Fisher information matrix gives clues on the information quality of different data types to better represent the underlying metabolic network topology. Results on several datasets of increasing complexity consistently show that metabolic variations observed at steady state, the simplest experimental analysis, are already informative to reveal the connectivity of the underlying metabolic network with a low false-positive rate when proper similarity-score approaches are employed. For experimental situations this implies that a single organism under slightly varying conditions may already generate more than enough information to rightly infer networks. Detailed examination of the strengths of interactions of the underlying metabolic networks demonstrates that the edges that cannot be captured by similarity scores mainly belong to metabolites connected with weak interaction strength.

Two Mass-Spectrometric Techniques for Quantifying Serine Enantiomers and Glycine in Cerebrospinal Fluid: Potential Confounders and Age-Dependent Ranges

BACKGROUND The recent discovery and specific functions of D-amino acids in humans are bound to lead to the revelation of D-amino acid abnormalities in human disorders. Therefore, high-throughput analysis techniques are warranted to determine D-amino acids in biological fluids in a routine laboratory setting. METHODS We developed 2 chromatographic techniques, a nonchiral derivatization with chiral (chirasil-L-val column) separation in a GC-MS system and a chiral derivatization with Marfeys reagent and LC- MS analysis. We validated the techniques for D-serine, L-serine, and glycine determination in cerebrospinal fluid (CSF), evaluated several confounders, and determined age-dependent human concentration ranges. RESULTS Quantification limits for D-serine, L-serine, and glycine in cerebrospinal fluid were 0.14, 0.44, and 0.14 micromol/L, respectively, for GC-MS and 0.20, 0.41, and 0.14 micromol/L for LC-MS. Within-run imprecision was <3% for both methods, and between-run imprecision was <13%. Comparison of both techniques with Deming regression yielded coefficients of 0.90 (D-serine), 0.92 (L-serine), and 0.96 (glycine). Sample collection, handling, and transport is uncomplicated-there is no rostrocaudal CSF gradient, no effect of storage at 4 degrees C for 1 week before storage at -80 degrees C, and no effect of up to 3 freeze/thaw cycles. Conversely, contamination with erythrocytes increased D-serine, L-serine, and glycine concentrations. CSF concentrations for 145 apparently healthy controls demonstrated markedly and specifically increased (5 to 9 times) D-serine concentrations during early central nervous system development. CONCLUSIONS These 2 clinically applicable analysis techniques will help to unravel pathophysiologic, diagnostic, and therapeutic issues for disorders associated with central nervous system abnormalities, NMDA-receptor dysfunction, and other pathology associated with D-amino acids.

Quantification of in vivo oxidative damage in Caenorhabditis elegans during aging by endogenous F3-isoprostane measurement

Oxidative damage is thought to be a major cause in development of pathologies and aging. However, quantification of oxidative damage is methodologically difficult. Here, we present a robust liquid chromatography–tandem mass spectrometry (LC‐MS/MS) approach for accurate, sensitive, and linear in vivo quantification of endogenous oxidative damage in the nematode Caenorhabditis elegans, based on F3‐isoprostanes. F3‐isoprostanes are prostaglandin‐like markers of oxidative damage derived from lipid peroxidation by Reactive Oxygen Species (ROS). Oxidative damage was quantified in whole animals and in multiple cellular compartments, including mitochondria and peroxisomes. Mutants of the mitochondrial electron transport proteins mev‐1 and clk‐1 showed increased oxidative damage levels. Furthermore, analysis of Superoxide Dismutase (sod) and Catalase (ctl) mutants uncovered that oxidative damage levels cannot be inferred from the phenotype of resistance to pro‐oxidants alone and revealed high oxidative damage in a small group of chemosensory neurons. Longitudinal analysis of aging nematodes revealed that oxidative damage increased specifically with postreproductive age. Remarkably, aging of the stress‐resistant and long‐lived daf‐2 insulin/IGF‐1 receptor mutant involved distinct daf‐16‐dependent phases of oxidative damage including a temporal increase at young adulthood. These observations are consistent with a hormetic response to ROS.

MetDFBA: incorporating time-resolved metabolomics measurements into dynamic flux balance analysis.

Understanding cellular adaptation to environmental changes is one of the major challenges in systems biology. To understand how cellular systems react towards perturbations of their steady state, the metabolic dynamics have to be described. Dynamic properties can be studied with kinetic models but development of such models is hampered by limited in vivo information, especially kinetic parameters. Therefore, there is a need for mathematical frameworks that use a minimal amount of kinetic information. One of these frameworks is dynamic flux balance analysis (DFBA), a method based on the assumption that cellular metabolism has evolved towards optimal changes to perturbations. However, DFBA has some limitations. It is less suitable for larger systems because of the high number of parameters to estimate and the computational complexity. In this paper, we propose MetDFBA, a modification of DFBA, that incorporates measured time series of both intracellular and extracellular metabolite concentrations, in order to reduce both the number of parameters to estimate and the computational complexity. MetDFBA can be used to estimate dynamic flux profiles and, in addition, test hypotheses about metabolic regulation. In a first case study, we demonstrate the validity of our method by comparing our results to flux estimations based on dynamic 13C MFA measurements, which we considered as experimental reference. For these estimations time-resolved metabolomics data from a feast-famine experiment with Penicillium chrysogenum was used. In a second case study, we used time-resolved metabolomics data from glucose pulse experiments during aerobic growth of Saccharomyces cerevisiae to test various metabolic objectives.

Instrument and process independent binning and baseline correction methods for liquid chromatography-high resolution-mass spectrometry deconvolution.

Setting appropriate bin sizes to aggregate hyphenated high-resolution mass spectrometry data, belonging to similar mass over charge (m/z) channels, is vital to metabolite quantification and further identification. In a high-resolution mass spectrometer when mass accuracy (ppm) varies as a function of molecular mass, which usually is the case while reading m/z from low to high values, it becomes a challenge to determine suitable bin sizes satisfying all m/z ranges. Similarly, the chromatographic process within a hyphenated system, like any other controlled processes, introduces some process driven systematic behavior that ultimately distorts the mass chromatogram signal. This is especially seen in liquid chromatogram-mass spectrometry (LC-MS) measurements where the gradient of the solvent and the washing step cycle-part of the chromatographic process, produce a mass chromatogram with a non-uniform baseline along the retention time axis. Hence prior to any automatic signal decomposition techniques like deconvolution, it is a equally vital to perform the baseline correction step for absolute metabolite quantification. This paper will discuss an instrument and process independent solution to the binning and the baseline correction problem discussed above, seen together, as an effective pre-processing step toward liquid chromatography-high resolution-mass spectrometry (LC-HR-MS) data deconvolution.

Global test for metabolic pathway differences between conditions.

In many metabolomics applications there is a need to compare metabolite levels between different conditions, e.g., case versus control. There exist many statistical methods to perform such comparisons but only few of these explicitly take into account the fact that metabolites are connected in pathways or modules. Such a priori information on pathway structure can alleviate problems in, e.g., testing on individual metabolite level. In gene-expression analysis, Goemans global test is used to this extent to determine whether a group of genes has a different expression pattern under changed conditions. We examined if this test can be generalized to metabolomics data. The goal is to determine if the behavior of a group of metabolites, belonging to the same pathway, is significantly related to a particular outcome of interest, e.g., case/control or environmental conditions. The results show that the global test can indeed be used in such situations. This is illustrated with extensive intracellular metabolomics data from Escherichia coli and Saccharomyces cerevisiae under different environmental conditions.

Integrating metabolomics profiling measurements across multiple biobanks.

To optimize the quality of large scale mass-spectrometry based metabolomics data obtained from semiquantitative profiling measurements, it is important to use a strategy in which dedicated measurement designs are combined with a strict statistical quality control regime. This assures consistently high-quality results across measurements from individual studies, but semiquantitative data have been so far only comparable for samples measured within the same study. To enable comparability and integration of semiquantitative profiling data from different large scale studies over the time course of years, the measurement and quality control strategy has to be extended. We introduce a strategy to allow the integration of semiquantitative profiling data from different studies. We demonstrate that lipidomics data generated in samples from three different large biobanks acquired in the time course of 3 years can be effectively combined when using an appropriate measurement design and transfer model. This strategy paves the way toward an integrative usage of semiquantitative metabolomics data sets of multiple studies to validate biological findings in another study and/or to increase the statistical power for discovery of biomarkers or pathways by combining studies.