Signal Peptide Efficiency: From High-Throughput Data to Prediction and Explanation.

The passage of proteins across biological membranes via the general secretory (Sec) pathway is a universally conserved process with critical functions in cell physiology and important industrial applications. Proteins are directed into the Sec pathway by a signal peptide at their N-terminus. Estimating the impact of physicochemical signal peptide features on protein secretion levels has not been achieved so far, partially due to the extreme sequence variability of signal peptides.

Flavor Profiling Using Comprehensive Mass Spectrometry Analysis of Metabolites in Tomato Soups.

Trained sensory panels are regularly used to rate food products but do not allow for data-driven approaches to steer food product development. This study evaluated the potential of a molecular-based strategy by analyzing 27 tomato soups that were enhanced with yeast-derived flavor products using a sensory panel as well as LC-MS and GC-MS profiling. These data sets were used to build prediction models for 26 different sensory attributes using partial least squares analysis.

Oxylipid Profile of Low-Dose Aspirin Exposure: A Pharmacometabolomics Study.

Background: While aspirin is a well-established and generally effective anti-platelet agent, considerable inter-individual variation in drug response exists, for which mechanisms are not completely understood. Metabolomics allows for extensive measurement of small molecules in biological samples, enabling detailed mapping of pathways involved in drug response.

Methods And Results: We used a mass-spectrometry-based metabolomics platform to investigate the changes in the serum oxylipid metabolome induced by an aspirin intervention (14 days, 81 mg/day) in healthy subjects (n=156).

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.

Correlated measurement error hampers association network inference.

Modern chromatography-based metabolomics measurements generate large amounts of data in the form of abundances of metabolites. An increasingly popular way of representing and analyzing such data is by means of association networks. Ideally, such a network can be interpreted in terms of the underlying biology.

Network identification of hormonal regulation.

Relations among hormone serum concentrations are complex and depend on various factors, including gender, age, body mass index, diurnal rhythms and secretion stochastics. Therefore, endocrine deviations from healthy homeostasis are not easily detected or understood. A generic method is presented for detecting regulatory relations between hormones.

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.

A lipidomic analysis approach to evaluate the response to cholesterol-lowering food intake.

Plant sterols (PS) are well known to reduce serum levels of total cholesterol and LDL-cholesterol. Lipidomics potentially provides detailed information on a wide range of individual serum lipid metabolites, which may further add to our understanding of the biological effects of PS. In this study, lipidomics analysis was applied to serum samples from a placebo-controlled, parallel human intervention study (n = 97) of 4-week consumption of two PS-enriched, yoghurt drinks differing in fat content (based on 0.

Inferring differences in the distribution of reaction rates across conditions.

Elucidating changes in the distribution of reaction rates in metabolic pathways under different conditions is a central challenge in systems biology. Here we present a method for inferring regulation mechanisms responsible for changes in the distribution of reaction rates across conditions from correlations in time-resolved data. A reversal of correlations between conditions reveals information about regulation mechanisms.

Detecting regulatory mechanisms in endocrine time series measurements.

The regulatory mechanisms underlying pulsatile secretion are complex, especially as it is partly controlled by other hormones and the combined action of multiple agents. Regulatory relations between hormones are not directly observable but may be deduced from time series measurements of plasma hormone concentrations. Variation in plasma hormone levels are the resultant of secretion and clearance from the circulation.

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.

Generic framework for high-dimensional fixed-effects ANOVA.

In functional genomics it is more rule than exception that experimental designs are used to generate the data. The samples of the resulting data sets are thus organized according to this design and for each sample many biochemical compounds are measured, e.g.

Simplivariate models: uncovering the underlying biology in functional genomics data.

One of the first steps in analyzing high-dimensional functional genomics data is an exploratory analysis of such data. Cluster Analysis and Principal Component Analysis are then usually the method of choice. Despite their versatility they also have a severe drawback: they do not always generate simple and interpretable solutions.

Reverse engineering of metabolic networks, a critical assessment.

Inferring metabolic networks from metabolite concentration data is a central topic in systems biology. Mathematical techniques to extract information about the network from data have been proposed in the literature. This paper presents a critical assessment of the feasibility of reverse engineering of metabolic networks, illustrated with a selection of methods.

Whole body composition analysis by the BodPod air-displacement plethysmography method in children with phenylketonuria shows a higher body fat percentage.

Background: Phenylketonuria (PKU) causes irreversible central nervous system damage unless a phenylalanine (PHE) restricted diet with amino acid supplementation is maintained. To prevent growth retardation, a protein/amino acid intake beyond the recommended dietary protein allowance is mandatory. However, data regarding disease and/or diet related changes in body composition are inconclusive and retarded growth and/or adiposity is still reported.

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.

Simplivariate models: ideas and first examples.

One of the new expanding areas in functional genomics is metabolomics: measuring the metabolome of an organism. Data being generated in metabolomics studies are very diverse in nature depending on the design underlying the experiment. Traditionally, variation in measurements is conceptually broken down in systematic variation and noise where the latter contains, e.

Familial disease with a risk of sudden death: a longitudinal study of the psychological consequences of predictive testing for long QT syndrome.

Background: Since 1996, in the Netherlands, cardiac and molecular screening has been performed in families with the long QT syndrome, a potentially life-threatening but treatable cardiac arrhythmia syndrome. The psychological consequences of predictive cardiac and molecular screening in these families are relatively unknown.

Objective: A psychological study was initiated to investigate the extent and course of distress caused by this new form of predictive genetic testing.

The sucrose regulated transcription factor bZIP11 affects amino acid metabolism by regulating the expression of ASPARAGINE SYNTHETASE1 and PROLINE DEHYDROGENASE2.

Translation of the transcription factor bZIP11 is repressed by sucrose in a process that involves a highly conserved peptide encoded by the 5' leaders of bZIP11 and other plant basic region leucine zipper (bZip) genes. It is likely that a specific signaling pathway operating at physiological sucrose concentrations controls metabolism via a feedback mechanism. In this paper bZIP11 target processes are identified using transiently increased nuclear bZIP11 levels and genome-wide expression analysis.

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.