Global metabolic changes induced by plant-derived pyrrolizidine alkaloids following a human poisoning outbreak and in a mouse model

Several hundred cases of Hirmi Valley Liver Disease (HVLD), an often fatal liver injury, occurred from 2001 to 2011 in a cluster of rural villages in Tigray, Ethiopia. HVLD is principally caused by contamination of the food supply with plant derived pyrrolizidine alkaloids (PAs), with high exposure to the pesticide DDT among villagers increasing their susceptibility. In an untargeted global approach we aimed to identify metabolic changes induced by PA exposure through 1H NMR spectroscopic based metabolic profiling. We analysed spectra acquired from urine collected from HVLD cases and controls and a murine model of PA exposure and PA/DDT co-exposure, using multivariate partial least squares discriminant analysis. In the human models we identified changes in urinary concentrations of tyrosine, pyruvate, bile acids, N-acetylglycoproteins, N-methylnicotinamide and formate, hippurate, p-cresol sulphate, p-hydroxybenzoate and 3-(3-hydroxyphenyl) propionic acid. Tyrosine and p-cresol sulphate were associated with both exposure and disease. Similar changes to tyrosine, one-carbon intermediates and microbial associated metabolites were observed in the mouse model, with tyrosine correlated with the extent of liver damage. These results provide mechanistic insight and implicate the gut microflora in the human response to challenge with toxins. Pathways identified here may be useful in translational research and as “exposome” signals

Metabonomics and Intensive Care

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency medicine 2016. Other selected articles can be found online at http://www.biomedcentral.com/collections/annualupdate2016. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901

Synchronization in periodically driven and coupled stochastic systems-A discrete state approach

Wir untersuchen das Verhalten von stochastischen bistabilen und erregbaren Systemen auf der Basis einer Modellierung mit diskreten Zuständen. In Ergänzung zum bekannten Markovschen Zwei-Zustandsmodell bistabiler stochastischer Dynamik stellen wir ein nicht Markovsches Drei-Zustandsmodell für erregbare Systeme vor. Seine relative Einfachheit, verglichen mit stochastischen Modellen erregbarer Dynamik mit kontinuierlichem Phasenraum, ermöglicht eine teilweise analytische Auswertung in verschiedenen Zusammenhängen. Zunächst untersuchen wir den gemeinsamen Einfluß eines periodischen Treibens und Rauschens. Dieser wird entweder mit Hilfe spektraler Größen oder durch Synchronisation des Systems mit dem treibenden Signal charakterisiert. Wir leiten analytische Ausdrücke für die spektrale Leistungsverstärkung und das Signal-zu-Rauschen Verhältnis für periodisch getriebene Renewal-Prozesse her und wenden diese auf das diskrete Modell für erregbare Dynamik an. Stochastische Synchronization des Systems mit dem treibenden Signal wird auf der Basis der Diffusionseigenschaften der Übergangsereignisse zwischen den diskreten Zuständen untersucht. Wir leiten allgemeine Formeln her, um die mittlere Häufigkeit dieser Ereignisse sowie deren effektiven Diffusionskoeffizienten zu berechnen. Über die konkrete Anwendung auf die untersuchten diskreten Modelle hinaus stellen diese Ergebnisse ein neues Werkzeug für die Untersuchung periodischer Renewal-Prozesse dar. Schließlich betrachten wir noch das Verhalten global gekoppelter bistabiler und erregbarer Systeme. Im Gegensatz zu bistabilen System können erregbare Systeme synchronisiert werden und zeigen kohärente Oszillationen. Alle Untersuchungen des nicht Markovschen Drei-Zustandsmodells werden mit dem prototypischen Modell für erregbare Dynamik, dem FitzHugh-Nagumo System, verglichen und zeigen eine gute Übereinstimmung.We investigate the behavior of stochastic bistable and excitable dynamics based on a discrete state modeling. In addition to the well known Markovian two state model for bistable dynamics we introduce a non Markovian three state model for excitable systems. Its relative simplicity compared to stochastic models of excitable dynamics with continuous phase space allows to obtain analytical results in different contexts. First, we study the joint influence of periodic signals and noise, both based on a characterization in terms of spectral quantities and in terms of synchronization with the periodic driving. We present expressions for the spectral power amplification and signal to noise ratio for renewal processes driven by periodic signals and apply these results to the discrete model for excitable systems. Stochastic synchronization of the system to the driving signal is investigated based on diffusion properties of the transition events between the discrete states. We derive general results for the mean frequency and effective diffusion coefficient which, beyond the application to the discrete models considered in this work, provide a new tool in the study of periodically driven renewal processes. Finally the behavior of globally coupled excitable and bistable units is investigated based on the discrete state description. In contrast to the bistable systems, the excitable system exhibits synchronization and thus coherent oscillations. All investigations of the non Markovian three state model are compared with the prototypical continuous model for excitable dynamics, the FitzHugh-Nagumo system, revealing a good agreement between both models

An HR-MAS MR Metabolomics Study on Breast Tissues Obtained with Core Needle Biopsy

BACKGROUND: Much research has been devoted to the development of new breast cancer diagnostic measures, including those involving high-resolution magic angle spinning (HR-MAS) magnetic resonance (MR) spectroscopic techniques. Previous HR-MAS MR results have been obtained from post-surgery samples, which limits their direct clinical applicability. METHODOLOGY/PRINCIPAL FINDINGS: In the present study, we performed HR-MAS MR spectroscopic studies on 31 breast tissue samples (13 cancer and 18 non-cancer) obtained by percutaneous core needle biopsy. We showed that cancer and non-cancer samples can be discriminated very well with Orthogonal Projections to Latent Structure-Discriminant Analysis (OPLS-DA) multivariate model on the MR spectra. A subsequent blind test showed 69% sensitivity and 94% specificity in the prediction of the cancer status. A spectral analysis showed that in cancer cells, taurine- and choline-containing compounds are elevated. Our approach, additionally, could predict the progesterone receptor statuses of the cancer patients. CONCLUSIONS/SIGNIFICANCE: HR-MAS MR metabolomics on intact breast tissues obtained by core needle biopsy may have a potential to be used as a complement to the current diagnostic and prognostic measures for breast cancers

Identification of serum biomarkers of hepatocarcinoma through liquid chromatography/mass spectrometry-based metabonomic method

Late diagnosis of hepatocarcinoma (HCC) is one of the most primary factors for the poor survival of patients. Thereby, identification of sensitive and specific biomarkers for HCC early diagnosis is of great importance in biological medicine to date. In the present study, serum metabolites of the HCC patients and healthy controls were investigated using the improved liquid chromatography–mass spectrometry (LC/MS). A wavelet-based method was utilized to find and align peaks of LC–MS. The characteristic peaks were selected by performing a two-sample t test statistics (p value <0.05). Clustering analysis based on principal component analysis showed a clear separation between HCC patients and healthy individuals. The serum metabolite, namely 1-methyladenosine, was identified as the characteristic metabolite for HCC. Moreover, receiver–operator curves were calculated with 1-methyladenosine and/or alpha fetal protein (AFP). The higher area under curve value was achieved in 1-methyladenosine group than AFP group (0.802 vs. 0.592), and the diagnostic model combining 1-methyladenosine with AFP exhibited significant improved sensitivity, which could identify those patients who missed the diagnosis of HCC by determining serum AFP alone. Overall, these results suggested that LC/MS-based metabonomic study is a potent and promising strategy for identifying novel biomarkers of HCC

Combined transcriptomic-(1)H NMR metabonomic study reveals yhat monoethylhexyl phthalate stimulates adipogenesis and glyceroneogenesis in human adipocytes

Adipose tissue is a major storage site for lipophilic environmental contaminants. The environmental metabolic disruptor hypothesis postulates that some pollutants can promote obesity or metabolic disorders by activating nuclear receptors involved in the control of energetic homeostasis. In this context, monoethylhexyl phthalate (MEHP) is of particular concern since it was shown to activate the peroxisome proliferator-activated receptor γ (PPARγ) in 3T3-L1 murine preadipocytes. In the present work, we used an untargeted, combined transcriptomic-(1)H NMR-based metabonomic approach to describe the overall effect of MEHP on primary cultures of human subcutaneous adipocytes differentiated in vitro. MEHP stimulated rapidly and selectively the expression of genes involved in glyceroneogenesis, enhanced the expression of the cytosolic phosphoenolpyruvate carboxykinase, and reduced fatty acid release. These results demonstrate that MEHP increased glyceroneogenesis and fatty acid reesterification in human adipocytes. A longer treatment with MEHP induced the expression of genes involved in triglycerides uptake, synthesis, and storage; decreased intracellular lactate, glutamine, and other amino acids; increased aspartate and NAD, and resulted in a global increase in triglycerides. Altogether, these results indicate that MEHP promoted the differentiation of human preadipocytes to adipocytes. These mechanisms might contribute to the suspected obesogenic effect of MEHP

Feature selection in the reconstruction of complex network representations of spectral data

Complex networks have been extensively used in the last decade to characterize and analyze complex systems, and they have been recently proposed as a novel instrument for the analysis of spectra extracted from biological samples. Yet, the high number of measurements composing spectra, and the consequent high computational cost, make a direct network analysis unfeasible. We here present a comparative analysis of three customary feature selection algorithms, including the binning of spectral data and the use of information theory metrics. Such algorithms are compared by assessing the score obtained in a classification task, where healthy subjects and people suffering from different types of cancers should be discriminated. Results indicate that a feature selection strategy based on Mutual Information outperforms the more classical data binning, while allowing a reduction of the dimensionality of the data set in two orders of magnitud

Forecasting individual breast cancer risk using plasma metabolomics and biocontours

Breast cancer is a major cause of death for women. To improve treatment, current oncology research focuses on discovering and validating new biomarkers for early detection of cancer; so far with limited success. Metabolic profiling of plasma samples and auxiliary lifestyle information was combined by chemometric data fusion. It was possible to create a biocontour, which we define as a complex pattern of relevant biological and phenotypic information. While single markers or known risk factors have close to no predictive value, the developed biocontour provides a forecast which, several years before diagnosis, is on par with how well most current biomarkers can diagnose current cancer. Hence, while e.g. mammography can diagnose current cancer with a sensitivity and specificity of around 75 %, the currently developed biocontour can predict that there is an increased risk that breast cancer will develop in a subject 2–5 years after the sample is taken with sensitivity and specificity well above 80 %. The model was built on data obtained in 1993–1996 and tested on persons sampled a year later in 1997. Metabolic forecasting of cancer by biocontours opens new possibilities for early prediction of individual cancer risk and thus for efficient screening. This may provide new avenues for research into disease mechanisms. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-015-0793-8) contains supplementary material, which is available to authorized users

Early intervention with Bifidobacterium lactis NCC2818 modulates the host-microbe interface independent of the sustained changes induced by the neonatal environment

Inflammatory and metabolic diseases can originate during early-life and have been correlated with shifts in intestinal microbial ecology. Here we demonstrate that minor environmental fluctuations during the early neonatal period had sustained effects on the developing porcine microbiota and host-microbe interface. These inter-replicate effects appear to originate during the first day of life, and are likely to reflect very early microbiota acquisition from the environment. We statistically link early systemic inflammation with later local increases in inflammatory cytokine (IL-17) production, which could have important enteric health implications. Immunity, intestinal barrier function, host metabolism and host-microbiota co-metabolism were further modified by Bifidobacterium lactis NCC2818 supplementation, although composition of the in situ microbiota remained unchanged. Finally, our robust model identified novel, strong correlations between urinary metabolites (eg malonate, phenylacetylglycine, alanine) and mucosal immunoglobulin (IgM) and cytokine (IL-10, IL-4) production, thus providing the possibility of the development of urinary ‘dipstick’ tests to assess non-accessible mucosal immune development and identify early precursors (biomarkers) of disease. These results have important implications for infants exposed to neonatal factors including caesarean delivery, antibiotic therapy and delayed discharge from hospital environments, which may predispose to the development of inflammatory and metabolic diseases in later life

Characterisation of Human Embryonic Stem Cells Conditioning Media by 1H-Nuclear Magnetic Resonance Spectroscopy

BACKGROUND: Cell culture media conditioned by human foreskin fibroblasts (HFFs) provide a complex supplement of protein and metabolic factors that support in vitro proliferation of human embryonic stem cells (hESCs). However, the conditioning process is variable with different media batches often exhibiting differing capacities to maintain hESCs in culture. While recent studies have examined the protein complement of conditioned culture media, detailed information regarding the metabolic component of this media is lacking. METHODOLOGY/PRINCIPAL FINDINGS: Using a (1)H-Nuclear Magnetic Resonance ((1)H-NMR) metabonomics approach, 32 metabolites and small compounds were identified and quantified in media conditioned by passage 11 HFFs (CMp11). A number of metabolites were secreted by HFFs with significantly higher concentration of lactate, alanine, and formate detected in CMp11 compared to non-conditioned media. In contrast, levels of tryptophan, folate and niacinamide were depleted in CMp11 indicating the utilisation of these metabolites by HFFs. Multivariate statistical analysis of the (1)H-NMR data revealed marked age-related differences in the metabolic profile of CMp11 collected from HFFs every 24 h over 72 h. Additionally, the metabolic profile of CMp11 was altered following freezing at -20°C for 2 weeks. CM derived from passage 18 HFFs (CMp18) was found to be ineffective at supporting hESCs in an undifferentiated state beyond 5 days culture. Multivariate statistical comparison of CMp11 and CMp18 metabolic profiles enabled rapid and clear discrimination between the two media with CMp18 containing lower concentrations of lactate and alanine as well as higher concentrations of glucose and glutamine. CONCLUSIONS/SIGNIFICANCE: (1)H-NMR-based metabonomics offers a rapid and accurate method of characterising hESC conditioning media and is a valuable tool for monitoring, controlling and optimising hESC culture media preparation