Social Behavioral Deficits in Krushinsky-Molodkina Rats, an Animal Model of Audiogenic Epilepsy.

In clinical practice, epilepsy is often comorbid with the autism spectrum disorders (ASDs). This warrants a search of animal models to uncover putative overlapping neuronal mechanisms. The Krushinsky-Molodkina (KM) rat strain is one of the oldest inbred animal models for human convulsive epilepsies. We analyzed the behavioral response of adult seizure-naive KM males in three-chambered tests for social preference. We found that a presence of social stimuli (encaged unfamiliar Wistar rats of the same age and sex) evoked a reduced or reversed exploratory response in freely moving KM individuals. The epilepsy-prone rats demonstrated remarkably shortened bouts of social contacts and displayed less locomotion around the stranger rat-containing boxes, together with a pronounced freezing response. The decrease in social preference was not due to a general decrease in activity, since relative measures of activity, the index of sociability, were decreased, too. The susceptibility to audiogenic seizures was verified in the KM cohort but not seen in the control Wistar group. We propose the KM rat strain as a new animal model for comorbid ASD and epilepsy.

Linking the FTO obesity rs1421085 variant circuitry to cellular, metabolic, and organismal phenotypes in vivo.

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.

Mass Difference Maps and Their Application for the Recalibration of Mass Spectrometric Data in Nontargeted Metabolomics.

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 high-precision 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.

Metabolomic investigations in cerebrospinal fluid of Parkinson's disease.

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.

How representative are dissolved organic matter (DOM) extracts? A comprehensive study of sorbent selectivity for DOM isolation.

Solid phase extraction (SPE) has become a widespread method for isolating dissolved organic matter (DOM) of diverse origin such as fresh and marine waters. This study investigated the DOM extraction selectivity of 24 commercially available SPE sorbents under identical conditions (pH = 2, methanol elution) on the example of Suwannee River (SR) water and North Sea (NS) water by using DOC analysis and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). Proton nuclear magnetic resonance (1H NMR) spectroscopy was employed to assess leaching behavior, and HLB sorbent was found to leach substantially, among others. Variable DOC recoveries observed for SR DOM and NS DOM were primarily caused by the respective molecular composition, with subordinated and heterogeneous contributions of relative salinity. Scatter of average H/C and O/C elemental ratios and gross alignment in mass-edited H/C ratios according to five established coarse SPE characteristics was near identical for SR DOM and NS DOM. FTMS-based principal component analysis (PCA) provided essentially analogous alignment of SR DOM and NS DOM molecular compositions according to the five established groups of SPE classification, and corroborated the sorption-mechanism-based selectivity of DOM extraction in both cases. Evaluation of structural blanks and leaching of SPE cartridges requires NMR spectroscopy because FT-ICR mass spectrometry alone will not reveal inconspicuous displacements of continual bulk signatures caused by leaching of SPE resin constituents.

Challenges of metabolomics in human gut microbiota research.

The review highlights the role of metabolomics in studying human gut microbial metabolism. Microbial communities in our gut exert a multitude of functions with huge impact on human health and disease. Within the meta-omics discipline, gut microbiome is studied by (meta)genomics, (meta)transcriptomics, (meta)proteomics and metabolomics. The goal of metabolomics research applied to fecal samples is to perform their metabolic profiling, to quantify compounds and classes of interest, to characterize small molecules produced by gut microbes. Nuclear magnetic resonance spectroscopy and mass spectrometry are main technologies that are applied in fecal metabolomics. Metabolomics studies have been increasingly used in gut microbiota related research regarding health and disease with main focus on understanding inflammatory bowel diseases. The elucidated metabolites in this field are summarized in this review. We also addressed the main challenges of metabolomics in current and future gut microbiota research. The first challenge reflects the need of adequate analytical tools and pipelines, including sample handling, selection of appropriate equipment, and statistical evaluation to enable meaningful biological interpretation. The second challenge is related to the choice of the right animal model for studies on gut microbiota. We exemplified this using NMR spectroscopy for the investigation of cross-species comparison of fecal metabolite profiles. Finally, we present the problem of variability of human gut microbiota and metabolome that has important consequences on the concepts of personalized nutrition and medicine.