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

Anti-ferromagnetic ordering in arrays of superconducting pi-rings

We report experiments in which one dimensional (1D) and two dimensional (2D) arrays of YBa2Cu3O7-x-Nb pi-rings are cooled through the superconducting transition temperature of the Nb in various magnetic fields. These pi-rings have degenerate ground states with either clockwise or counter-clockwise spontaneous circulating supercurrents. The final flux state of each ring in the arrays was determined using scanning SQUID microscopy. In the 1D arrays, fabricated as a single junction with facets alternating between alignment parallel to a [100] axis of the YBCO and rotated 90 degrees to that axis, half-fluxon Josephson vortices order strongly into an arrangement with alternating signs of their magnetic flux. We demonstrate that this ordering is driven by phase coupling and model the cooling process with a numerical solution of the Sine-Gordon equation. The 2D ring arrays couple to each other through the magnetic flux generated by the spontaneous supercurrents. Using pi-rings for the 2D flux coupling experiments eliminates one source of disorder seen in similar experiments using conventional superconducting rings, since pi-rings have doubly degenerate ground states in the absence of an applied field. Although anti-ferromagnetic ordering occurs, with larger negative bond orders than previously reported for arrays of conventional rings, long-range order is never observed, even in geometries without geometric frustration. This may be due to dynamical effects. Monte-Carlo simulations of the 2D array cooling process are presented and compared with experiment.Comment: 10 pages, 15 figure

A re-appraisal of volume status and renal function impairment in chronic heart failure: combined effects of pre-renal failure and venous congestion on renal function

The association between cardiac failure and renal function impairment has gained wide recognition over the last decade. Both structural damage in the form of systemic atherosclerosis and (patho) physiological hemodynamic changes may explain this association. As regards hemodynamic factors, renal impairment in chronic heart failure is traditionally assumed to be mainly due to a decrease in cardiac output and a subsequent decrease in renal perfusion. This will lead to a decrease in glomerular filtration rate and a compensatory increase in tubular sodium retention. The latter is a physiological renal response aimed at retaining fluids in order to increase cardiac filling pressure and thus renal perfusion. In heart failure, however, larger increases in cardiac filling pressure are needed to restore renal perfusion and thus more volume retention. In this concept, in chronic heart failure, an equilibrium exists where a certain degree of congestion is the price to be paid to maintain adequate renal perfusion and function. Recently, this hypothesis was challenged by new studies, wherein it was found that the association between right-sided cardiac filling pressures and renal function is bimodal, with worse renal function at the highest filling pressures, reflecting a severely congested state. Renal hemodynamic studies suggest that congestion negatively affects renal function in particular in patients in whom renal perfusion is also compromised. Thus, an interplay between cardiac forward failure and backward failure is involved in the renal function impairment in the congestive state, presumably along with other factors. Only few data are available on the impact of intervention in volume status on the cardio-renal interaction. Sparse data in cardiac patients as well as evidence from cohorts with primary renal disease suggest that specific targeting of volume overload may be beneficial for long-term outcome, in spite of a certain further decrease in renal function, at least in the context of current treatment where possible reflex neurohumoral activation is ameliorated by the background treatment by blockers of the renin–angiotensin–aldosterone system

Individual differences in metabolomics: individualised responses and between-metabolite relationships

Many metabolomics studies aim to find ‘biomarkers’: sets of molecules that are consistently elevated or decreased upon experimental manipulation. Biological effects, however, often manifest themselves along a continuum of individual differences between the biological replicates in the experiment. Such differences are overlooked or even diminished by methods in standard use for metabolomics, although they may contain a wealth of information on the experiment. Properly understanding individual differences is crucial for generating knowledge in fields like personalised medicine, evolution and ecology. We propose to use simultaneous component analysis with individual differences constraints (SCA-IND), a data analysis method from psychology that focuses on these differences. This method constructs axes along the natural biochemical differences between biological replicates, comparable to principal components. The model may shed light on changes in the individual differences between experimental groups, but also on whether these differences correspond to, e.g., responders and non-responders or to distinct chemotypes. Moreover, SCA-IND reveals the individuals that respond most to a manipulation and are best suited for further experimentation. The method is illustrated by the analysis of individual differences in the metabolic response of cabbage plants to herbivory. The model reveals individual differences in the response to shoot herbivory, where two ‘response chemotypes’ may be identified. In the response to root herbivory the model shows that individual plants differ strongly in response dynamics. Thereby SCA-IND provides a hitherto unavailable view on the chemical diversity of the induced plant response, that greatly increases understanding of the system

Statistical validation of megavariate effects in ASCA

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Induced paramagnetic states by localized π\pi -loops in grain boundaries

Recent experiments on high-temperature superconductors show paramagnetic behavior localized at grain boundaries (GB). This paramagnetism can be attributed to the presence unconventional d-wave induced $\pi$-junctions. By modeling the GB as an array of $\pi$ and conventional Josephson junction we determine the conditions of the occurrence of the paramagnetic behavior.Comment: 4 pages, 4 figures, submitted to Phys. Rev. Let

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

The muscle metabolome differs between healthy and frail older adults

Populations around the world are aging rapidly. Age-related loss of physiological functions negatively affects quality of life. A major contributor to the frailty syndrome of aging is loss of skeletal muscle. In this study we assessed the skeletal muscle biopsy metabolome of healthy young, healthy older and frail older subjects to determine the effect of age and frailty on the metabolic signature of skeletal muscle tissue. In addition, the effects of prolonged whole-body resistance-type exercise training on the muscle metabolome of older subjects were examined. The baseline metabolome was measured in muscle biopsies collected from 30 young, 66 healthy older subjects and 43 frail older subjects. Follow-up samples from frail older (24 samples) and healthy older subjects (38 samples) were collected after 6 months of prolonged resistance-type exercise training. Young subjects were included as a reference If thisgroup. Primary differences in skeletal muscle metabolite levels between young and healthy older subjects were related to mitochondrial function, muscle fiber type, and tissue turnover. Similar differences were observed when comparing frail older subjects with healthy older subjects at baseline. Prolonged resistance-type exercise training resulted in an adaptive response of amino acid metabolism, especially reflected in branched chain amino acids and genes related to tissue remodeling. The effect of exercise training on branched-chain amino acid-derived acylcarnitines in older subjects points to a downward shift in branched-chain amino acid catabolism upon training. We observed only modest correlations between muscle and plasma metabolite levels, which pleads against the use of plasma metabolites as a direct read-out of muscle metabolism and stresses the need for direct assessment of metabolites in muscle tissue biopsies

Prognostic value of galectin-3, a novel marker of fibrosis, in patients with chronic heart failure: data from the DEAL-HF study

Biomarkers are increasingly being used in the management of patients with chronic heart failure (HF). Galectin-3 is a recently developed biomarker associated with fibrosis and inflammation, and it may play a role in cardiac remodeling in HF. We determined its prognostic value in patients with chronic HF. Patients with chronic HF (New York Heart Association functional class III or IV) who participated in the Deventer-Alkmaar heart failure study were studied. Galectin-3 levels were determined at baseline using a novel optimized enzyme-linked immunosorbent assay. Univariate and multivariate analyses were used to determine the prognostic value of this biomarker. We studied 232 patients; their mean age was 71 +/- A 10 years, 72% were male, and 96% were in NYHA class III. During a follow-up period of 6.5 years, 98 patients died. Galectin-3 was a significant predictor of mortality risk after adjustment for age and sex, and severity of HF and renal dysfunction, as assessed by NT-proBNP and estimated glomerular filtration rate, respectively (hazard ratio per standard deviation 1.24, 95% CI 1.03-1.50, P = 0.026). Plasma galectin-3 is a novel prognostic marker in patients with chronic HF. Its prognostic value is independent of severity of HF, as assessed by NT-proBNP levels, and it may potentially be used in the management of such patients

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