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

Chromatography and mass spectrometry-based non-targeted metabolomics for type2 diabetes studies.

Non-targeted metabolomics is a promising tool for the investigation and evaluation of diseases. In this work the development of a metabolomic platform that integrates the chromatographic resolution of UPLC with the high mass accuracy of FT-ICR-MS for the analysis of human plasma samples was proposed. The work flow leads to the experimental application; the study of the non-targeted metabolome of fatty liver individuals, whose pre-diabetic state was characterized by their insulin sensitivity

Sensitivity improvement in hydrophilic interaction chromatography negative mode electrospray ionization mass spectrometry using 2-(2-methoxyethoxy)ethanol as a post-column modifier for non-targeted metabolomics.

The application of ammonia acetate buffered liquid chromatography (LC) eluents is known to concomitantly lead to ion suppression when electrospray ionization mass spectrometry (ESI-MS) detection is used. In negative ESI mode, post column infusion of 2-(2-methoxyethoxy)ethanol (2-MEE) was shown in the literature to help to compensate this adverse effect occurring in reversed phase liquid chromatography mass spectrometry (RP-LC-MS) analyses. Here a setup of direct infusion and hydrophilic interaction chromatography (HILIC) post-column infusion experiments was established in order to investigate systematically the beneficial effects of 2-MEE. We demonstrate that, 2-MEE can help to improve ESI-MS sensitivity in HILIC too and reveal analyte structure specific behaviors. Our study indicates that 2-MEE especially improves ESI response for small and polar molecules. The ESI response of stable isotope labeled amino acids spiked into biological matrices increases up to 50-fold (i.e. D5-l-glutamic acid) when post column infusion of 2-MEE is applied. A non-targeted analysis of a pooled urine sample via HILIC-ESI-QTOF-MS supports this hypothesis. In direct infusion, the combined application of an ammonia acetate buffered solution together with 2-MEE results in an improved ESI response compared to a non-buffered solution. We observed up to 60-fold increased ESI response of l-lysine. We propose this effect is putatively caused by the formation of smaller ESI droplets and stripping of positive charge from ESI droplets due to evaporation of acetic acid anions. In summary, post-column infusion of 2-MEE especially enhances ESI response of small and polar molecules. Therefore it can be regarded as a valuable add-on in targeted or non-targeted metabolomic HILIC-MS studies since this method sets a focus on this molecule category

Mass difference maps and their application for the recalibration of mass spectrometric data in non-targeted 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

Solutions for low and high accuracy mass spectrometric data matching. A data-driven annotation strategy in non-targeted metabolomics.

Ultra High Pressure Liquid Chromatography coupled to mass spectrometry (UHPLC-MS) has become a widespread analytical technique in metabolomics investigations, however the benefit of high performance chromatographic separation is often blunted due to insufficient mass spectrometric accuracy. A strategy that allows for the matching of UHPLC-MS data to highly accurate Direct Infusion Electrospray Ionization (DI-ESI) Fourier Transform Ion Cyclotron Resonance / Mass Spectrometry (FT-ICR/MS) data is developed in this manuscript. Mass difference network (MDiN) based annotation of FT-ICR/MS data and matching to unique UHPLC-MS peaks enables the consecutive annotation of the chromatographic dataset. A direct comparison of experimental m/z values provided no basis for the matching of both platforms. The matching of annotation-based exact neutral masses finally enabled the integration of platform specific multivariate statistical evaluations, minimizing the danger to compare artifacts generated on either platform. The approach was developed on a Non-Alcoholic Fatty Liver disease (NAFLD) dataset