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

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. A strategy is proposed to extract inhibition, activation, thresholds and circadian synchronicity from concentration data, using particular association methods. Time delayed associations between hormone concentrations and/or extracted secretion pulse profiles reveal the information on regulatory mechanisms. The above mentioned regulatory mechanisms are illustrated with simulated data. Additionally, data from a lean cohort of healthy control subjects is used to illustrate activation (ACTH and cortisol) and circadian synchronicity (ACTH and TSH) in real data. The simulation and the real data both consist of 145 equidistant samples per individual, matching a 24-hr time span with 10 minute intervals. The results of the simulation and the real data are in concordance

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. The BodPod whole body air-displacement plethysmography method is a fast, safe and accurate technique to measure body composition. AIM: To gain more insight into the body composition of children with PKU. METHODS: Patients diagnosed with PKU born between 1991 and 2001 were included. Patients were identified by neonatal screening and treated in our centre. Body composition was measured using the BodPod system (Life Measurement Incorporation©). Blood PHE values determined every 1–3 months in the year preceding BodPod analysis were collected. Patients were matched for gender and age with data of healthy control subjects. Independent samples t tests, Mann–Whitney and linear regression were used for statistical analysis. RESULTS: The mean body fat percentage in patients with PKU (n = 20) was significantly higher compared to healthy controls (n = 20) (25.2% vs 18.4%; p = 0.002), especially in girls above 11 years of age (30.1% vs 21.5%; p = 0.027). Body fat percentage increased with rising body weight in patients with PKU only (R = 0.693, p = 0.001), but did not correlate with mean blood PHE level (R = 0.079, p = 0.740). CONCLUSION: Our data show a higher body fat percentage in patients with PKU, especially in girls above 11 years of age

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.g. technical variation. There is increasing evidence that this distinction does not hold (or is too simple) for metabolomics data. A more useful distinction is in terms of informative and non-informative variation where informative relates to the problem being studied. In most common methods for analyzing metabolomics (or any other high-dimensional x-omics) data this distinction is ignored thereby severely hampering the results of the analysis. This leads to poorly interpretable models and may even obscure the relevant biological information. We developed a framework from first data analysis principles by explicitly formulating the problem of analyzing metabolomics data in terms of informative and non-informative parts. This framework allows for flexible interactions with the biologists involved in formulating prior knowledge of underlying structures. The basic idea is that the informative parts of the complex metabolomics data are approximated by simple components with a biological meaning, e.g. in terms of metabolic pathways or their regulation. Hence, we termed the framework ‘simplivariate models’ which constitutes a new way of looking at metabolomics data. The framework is given in its full generality and exemplified with two methods, IDR analysis and plaid modeling, that fit into the framework. Using this strategy of ‘divide and conquer’, we show that meaningful simplivariate models can be obtained using a real-life microbial metabolomics data set. For instance, one of the simple components contained all the measured intermediates of the Krebs cycle of E. coli. Moreover, these simplivariate models were able to uncover regulatory mechanisms present in the phenylalanine biosynthesis route of E. coli

Neuronal differentiation of hair-follicle-bulge-derived stem cells co-cultured with mouse cochlear modiolus explants

Stem-cell-based repair of auditory neurons may represent an attractive therapeutic option to restore sensorineural hearing loss. Hair-follicle-bulge-derived stem cells (HFBSCs) are promising candidates for this type of therapy, because they (1) have migratory properties, enabling migration after transplantation, (2) can differentiate into sensory neurons and glial cells, and (3) can easily be harvested in relatively high numbers. However, HFBSCs have never been used for this purpose. We hypothesized that HFBSCs can be used for cell-based repair of the auditory nerve and we have examined their migration and incorporation into cochlear modiolus explants and their subsequent differentiation. Modiolus explants obtained from adult wild-type mice were cultured in the presence of EF1α-copGFP-transduced HFBSCs, constitutively expressing copepod green fluorescent protein (copGFP). Also, modiolus explants without hair cells were co-cultured with DCX-copGFP-transduced HFBSCs, which demonstrate copGFP upon doublecortin expression during neuronal differentiation. Velocity of HFBSC migration towards modiolus explants was calculated, and after two weeks, co-cultures were fixed and processed for immunohistochemical staining. EF1α-copGFP HFBSC migration velocity was fast: 80.5 ± 6.1 μm/h. After arrival in the explant, the cells formed a fascicular pattern and changed their phenotype into an ATOH1-positive neuronal cell type. DCX-copGFP HFBSCs became green-fluorescent after integration into the explants, confirming neuronal differentiation of the cells. These results show that HFBSC-derived neuronal progenitors are migratory and can integrate into cochlear modiolus explants, while adapting their phenotype depending on this micro-environment. Thus, HFBSCs show potential to be employed in cell-based therapies for auditory nerve repair

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. On the basis of the observation that functional genomics data often contain both informative and non-informative variation, we propose a method that finds sets of variables containing informative variation. This informative variation is subsequently expressed in easily interpretable simplivariate components

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. typically thousands of gene-expressions or hundreds of metabolites. This results in high-dimensional data sets with an underlying experimental design. Several methods have recently become available for analyzing such data while utilizing the underlying design. We review these methods by putting them in a unifying and general framework to facilitate understanding the (dis-)similarities between the methods. The biological question dictates which method to use and the framework allows for building new methods to accommodate a range of such biological questions. The framework is built on well known fixed-effect ANOVA models and subsequent dimension reduction. We present the framework both in matrix algebra as well as in more insightful geometrical terms. We show the workings of the different special cases of our framework with a real-life metabolomics example from nutritional research and a gene-expression example from the field of virology

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 tong 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. METHODS We carried out a prospective study to assess the extent and course of disease-related anxiety and depression, caused by predictive genetic testing, in applicants and their partners from the time of first consultation until 18 months after the disclosure of the result of genetic testing. RESULTS Seventy-seven applicants and 57 partners were investigated for measures of distress in 3 assessments. Those individuals who received an uncertain electrocardiogram result seemed especially vulnerable for distress, at least in the short term. The distress Levels in the whole group of applicants were largely restored within 18 months. However, the disease-related anxiety scores in carriers remained relatively increased at tong term. As compared with partners of noncarriers, partners of mutation carriers had higher levels of disease-related anxiety at all 3 assessments. CONCLUSION Predictive testing for long QT syndrome consisting of cardiologic testing followed by molecular testing leads to distress, especially in carriers with an uncertain electrocardiogram and their partners at first visit. These distress levels return to normal at tong term. However, for carriers with an uncertain electrocardiogram, the incidence of clinically relevant distress was high, most probably also. caused by the consequences of having the disease