ObjectiveIron deficiency is a common complication in patients with IBD and oral iron therapy is suggested to exacerbate IBD symptoms. We performed an open-labelled clinical trial to compare the effects of per oral (PO) versus intravenous (IV) iron replacement therapy (IRT).DesignThe study population included patients with Crohn's disease (CD; N=31), UC (N=22) and control subjects with iron deficiency (non-inflamed, NI=19). After randomisation, participants received iron sulfate (PO) or iron sucrose (IV) over 3 months. Clinical parameters, faecal bacterial communities and metabolomes were assessed before and after intervention.ResultsBoth PO and IV treatments ameliorated iron deficiency, but higher ferritin levels were observed with IV. Changes in disease activity were independent of iron treatment types. Faecal samples in IBD were characterised by marked interindividual differences, lower phylotype richness and proportions of Clostridiales. Metabolite analysis also showed separation of both UC and CD from control anaemic participants. Major shifts in bacterial diversity occurred in approximately half of all participants after IRT, but patients with CD were most susceptible. Despite individual-specific changes in phylotypes due to IRT, PO treatment was associated with decreased abundances of operational taxonomic units assigned to the species Faecalibacterium prausnitzii, Ruminococcus bromii, Dorea sp. and Collinsella aerofaciens. Clear IV-specific and PO-specific fingerprints were evident at the level of metabolomes, with changes affecting cholesterol-derived host substrates.ConclusionsShifts in gut bacterial diversity and composition associated with iron treatment are pronounced in IBD participants. Despite similar clinical outcome, oral administration differentially affects bacterial phylotypes and faecal metabolites compared with IV therapy.Trial registration numberclinicaltrial.gov (NCT01067547).

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This paper proposes improved guidelines for dissolved organic matter (DOM) isolation by solid phase extraction (SPE) with a styrene−divinylbenzene copolymer (PPL) sorbent, which has become an established method for the isolation of DOM from natural waters, because of its ease of application and appreciable carbon recovery. Suwannee River water was selected to systematically study the effects of critical SPE variables such as loading mass, concentration, flow rate, and up-scaling on the extraction selectivity of the PPL sorbent. High-field Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) and proton nuclear magnetic resonance ( 1 H NMR) spectroscopy were performed to interpret the DOM chemical space of eluates, as well as permeates and wash liquids with molecular resolution. Up to 89% dissolved organic carbon (DOC) recovery was obtained with a DOC/PPL mass ratio of 1:800 at a DOC concentration of 20 mg/L. With the application of larger loading volumes, low proportions of highly oxygenated compounds were retained on the PPL sorbent. The effects of the flow rate on the extraction selectivity of the sorbent were marginal. Up-scaling had a limited effect on the extraction selectivity with the exception of increased self-esterification with a methanol solvent, resulting in methyl ester groups. Furthermore, the SPE/ PPL extract exhibited highly authentic characteristics in comparison with original water and reverse osmosis samples. These findings will be useful for reproducibly isolating DOM with representative molecular compositions from various sources and concentrations and minimizing potential inconsistencies among interlaboratory comparative studies.

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The underlying mechanisms of Parkinson´s disease are not completely revealed. Especially, early diagnostic biomarkers are lacking. To characterize early pathophysiological events, research is focusing on metabolomics. In this case-control study we investigated the metabolic profile of 31 Parkinson´s disease-patients in comparison to 95 neurologically healthy controls. The investigation of metabolites in CSF was performed by a 12 Tesla SolariX Fourier transform-ion cyclotron resonance-mass spectrometer (FT-ICR-MS). Multivariate statistical analysis sorted the most important biomarkers in relation to their ability to differentiate Parkinson versus control. The affected metabolites, their connection and their conversion pathways are described by means of network analysis. The metabolic profiling by FT-ICR-MS in CSF yielded in a good group separation, giving insights into the disease mechanisms. A total number of 243 metabolites showed an affected intensity in Parkinson´s disease, whereas 15 of these metabolites seem to be the main biological contributors. The network analysis showed a connection to the tricarboxylic cycle (TCA cycle) and therefore to mitochondrial dysfunction and increased oxidative stress within mitochondria. The metabolomic analysis of CSF in Parkinson´s disease showed an association to pathways which are involved in lipid/ fatty acid metabolism, energy metabolism, glutathione metabolism and mitochondrial dysfunction.

Modern high-resolution mass spectrometry provides the great potential to analyze exact masses of thousands of molecules in one run. In addition, the high instrumental mass accuracy allows for highprecision formula assignments narrowing down tremendously the chemical space of unknown compounds. The adequate values for a mass accuracy are normally achieved by a proper calibration procedure that usually implies using known internal or external standards. This approach might not always be sufficient in cases when systematic error is highly prevalent. Therefore, additional recalibration steps are required. In this work, the concept of mass difference maps (MDiMs) is introduced with a focus on the visualization and investigation of all the pairwise differences between considered masses. Given an adequate reference list of sufficient size, MDiMs can facilitate the detection of a systematic error component. Such a property can be potentially applied for spectral recalibration. Consequently, a novel approach to describe the process of the correction of experimentally derived masses is presented. The method is based on the estimation of the density of data points on MDiMs using Gaussian kernels followed by a curve fitting with an adapted version of the particle swarm optimization algorithm. The described recalibration procedure is examined on simulated as well as real mass spectrometric data. For the latter case, blood plasma samples were analyzed by Fourier transform ion cyclotron resonance mass spectrometry. Nevertheless, due to its inherent flexibility, the method can be easily extended to other low-and high-resolution platforms and/or sample types.

BackgroundInterpreting non-targeted metabolomics data remains a challenging task. Signals from non-targeted metabolomics studies stem from a combination of biological causes, complex interactions between them and experimental bias/noise. The resulting data matrix usually contain huge number of variables and only few samples, and classical techniques using nonlinear mapping could result in computational complexity and overfitting. Independent Component Analysis (ICA) as a linear method could potentially bring more meaningful results than Principal Component Analysis (PCA). However, a major problem with most ICA algorithms is the output variations between different runs and the result of a single ICA run should be interpreted with reserve.ResultsICA was applied to simulated and experimental mass spectrometry (MS)-based non-targeted metabolomics data, under the hypothesis that underlying sources are mutually independent. Inspired from the Icasso algorithm, a new ICA method, MetICA was developed to handle the instability of ICA on complex datasets. Like the original Icasso algorithm, MetICA evaluated the algorithmic and statistical reliability of ICA runs. In addition, MetICA suggests two ways to select the optimal number of model components and gives an order of interpretation for the components obtained.ConclusionsCorrelating the components obtained with prior biological knowledge allows understanding how non-targeted metabolomics data reflect biological nature and technical phenomena. We could also extract mass signals related to this information. This novel approach provides meaningful components due to their independent nature. Furthermore, it provides an innovative concept on which to base model selection: that of optimizing the number of reliable components instead of trying to fit the data. The current version of MetICA is available at https://github.com/daniellyz/MetICA.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0970-4) contains supplementary material, which is available to authorized users.

Variants in FTO have the strongest association with obesity; however, it is still unclear how those noncoding variants mechanistically affect whole-body physiology. We engineered a deletion of the rs1421085 conserved cis-regulatory module (CRM) in mice and confirmed in vivo that the CRM modulates Irx3 and Irx5 gene expression and mitochondrial function in adipocytes. The CRM affects molecular and cellular phenotypes in an adipose depot–dependent manner and affects organismal phenotypes that are relevant for obesity, including decreased high-fat diet–induced weight gain, decreased whole-body fat mass, and decreased skin fat thickness. Last, we connected the CRM to a genetically determined effect on steroid patterns in males that was dependent on nutritional challenge and conserved across mice and humans. Together, our data establish cross-species conservation of the rs1421085 regulatory circuitry at the molecular, cellular, metabolic, and organismal level, revealing previously unknown contextual dependence of the variant’s action.