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      The Munich MIDY Pig Biobank – A unique resource for studying organ crosstalk in diabetes

      1 , 2 , 3 , 4 , 4 , 1 , 2 , 4 , 2 , 1 , 2 , 2 , 3 , 5 , 6 , 3 , 7 ,   3 , 7 , 4 , 8 , 2 , 9 , 10 , 15 , 2 , 2 , 1 , 1 , 11 , 1 , 12 , 1 , 2 , 2 , 2 , 1 , 1 , 13 , 1 , 9 , 2 , 14 , 1 , 1 , 1 , 2 , 2 , 2 , 2 , 1 , 12 , 15 , 16 , 6 , 17 , 3 , 7 , 3 , 5 , 17 , 18 , 12 , 9 , 4 , 4 , 4 , 1 , 19 , 2 , 3 , 4 , , 19

      Molecular Metabolism

      Elsevier

      MIDY, Hyperglycemia, Insulin insufficiency, Pig model, Biobank, Random systematic sampling, Transcriptomics, Proteomics, Metabolomics, Stereology, CE, cholesterol ester, CPT1, carnitine O-palmitoyltransferase 1, ER, endoplasmic reticulum, FFA, free fatty acids, MIDY, mutant INS gene-induced diabetes of youth, PC, phosphatidylcholine, PCA, principal component analysis, SM, sphingomyelin, TAG, triacylglycerol, WT, wild-type

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          Abstract

          Objective

          The prevalence of diabetes mellitus and associated complications is steadily increasing. As a resource for studying systemic consequences of chronic insulin insufficiency and hyperglycemia, we established a comprehensive biobank of long-term diabetic INS C94Y transgenic pigs, a model of mutant INS gene-induced diabetes of youth (MIDY), and of wild-type (WT) littermates.

          Methods

          Female MIDY pigs (n = 4) were maintained with suboptimal insulin treatment for 2 years, together with female WT littermates (n = 5). Plasma insulin, C-peptide and glucagon levels were regularly determined using specific immunoassays. In addition, clinical chemical, targeted metabolomics, and lipidomics analyses were performed. At age 2 years, all pigs were euthanized, necropsied, and a broad spectrum of tissues was taken by systematic uniform random sampling procedures. Total beta cell volume was determined by stereological methods. A pilot proteome analysis of pancreas, liver, and kidney cortex was performed by label free proteomics.

          Results

          MIDY pigs had elevated fasting plasma glucose and fructosamine concentrations, C-peptide levels that decreased with age and were undetectable at 2 years, and an 82% reduced total beta cell volume compared to WT. Plasma glucagon and beta hydroxybutyrate levels of MIDY pigs were chronically elevated, reflecting hallmarks of poorly controlled diabetes in humans. In total, ∼1900 samples of different body fluids (blood, serum, plasma, urine, cerebrospinal fluid, and synovial fluid) as well as ∼17,000 samples from ∼50 different tissues and organs were preserved to facilitate a plethora of morphological and molecular analyses. Principal component analyses of plasma targeted metabolomics and lipidomics data and of proteome profiles from pancreas, liver, and kidney cortex clearly separated MIDY and WT samples.

          Conclusions

          The broad spectrum of well-defined biosamples in the Munich MIDY Pig Biobank that will be available to the scientific community provides a unique resource for systematic studies of organ crosstalk in diabetes in a multi-organ, multi-omics dimension.

          Highlights

          • MIDY pigs represent a model of poorly controlled diabetes mellitus (DM) in humans.

          • A complex biobank was built from 2-year-old MIDY and wild-type pigs using the principles of random systematic sampling.

          • Targeted metabolomics and lipidomics analyses of plasma samples revealed clear separation of MIDY and wild-type pigs.

          • The Munich MIDY Pig Biobank facilitates systematic studies of organ crosstalk in DM in a multi-organ, multi-omics dimension.

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          Most cited references 24

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          Genetics of gene expression and its effect on disease.

          Common human diseases result from the interplay of many genes and environmental factors. Therefore, a more integrative biology approach is needed to unravel the complexity and causes of such diseases. To elucidate the complexity of common human diseases such as obesity, we have analysed the expression of 23,720 transcripts in large population-based blood and adipose tissue cohorts comprehensively assessed for various phenotypes, including traits related to clinical obesity. In contrast to the blood expression profiles, we observed a marked correlation between gene expression in adipose tissue and obesity-related traits. Genome-wide linkage and association mapping revealed a highly significant genetic component to gene expression traits, including a strong genetic effect of proximal (cis) signals, with 50% of the cis signals overlapping between the two tissues profiled. Here we demonstrate an extensive transcriptional network constructed from the human adipose data that exhibits significant overlap with similar network modules constructed from mouse adipose data. A core network module in humans and mice was identified that is enriched for genes involved in the inflammatory and immune response and has been found to be causally associated to obesity-related traits.
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            Molecular mechanisms of opioid receptor-dependent signaling and behavior.

            Opioid receptors have been targeted for the treatment of pain and related disorders for thousands of years and remain the most widely used analgesics in the clinic. Mu (μ), kappa (κ), and delta (δ) opioid receptors represent the originally classified receptor subtypes, with opioid receptor like-1 (ORL1) being the least characterized. All four receptors are G-protein coupled and activate inhibitory G proteins. These receptors form homo- and heterodimeric complexes and signal to kinase cascades and scaffold a variety of proteins.The authors discuss classic mechanisms and developments in understanding opioid tolerance and opioid receptor signaling and highlight advances in opioid molecular pharmacology, behavioral pharmacology, and human genetics. The authors put into context how opioid receptor signaling leads to the modulation of behavior with the potential for therapeutic intervention. Finally, the authors conclude there is a continued need for more translational work on opioid receptors in vivo.
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              An automated shotgun lipidomics platform for high throughput, comprehensive, and quantitative analysis of blood plasma intact lipids

              Blood plasma has gained protagonism in lipidomics studies due to its availability, uncomplicated collection and preparation, and informative readout of physiological status. At the same time, it is also technically challenging to analyze due to its complex lipid composition affected by many factors, which can hamper the throughput and/or lipidomics coverage. To tackle these issues, we developed a comprehensive, high throughput, and quantitative mass spectrometry-based shotgun lipidomics platform for blood plasma lipid analyses. The main hallmarks of this technology are (i) it is comprehensive, covering 22 quantifiable different lipid classes encompassing more than 200 lipid species; (ii) it is amenable to high-throughput, with less than 5 min acquisition time allowing the complete analysis of 200 plasma samples per day; (iii) it achieves absolute quantification, by inclusion of internal standards for every lipid class measured; (iv) it is highly reproducible, achieving an average coefficient of variation of <10% (intra-day), approx. 10% (inter-day), and approx. 15% (inter-site) for most lipid species; (v) it is easily transferable allowing the direct comparison of data acquired in different sites. Moreover, we thoroughly assessed the influence of blood stabilization with different anticoagulants and freeze-thaw cycles to exclude artifacts generated by sample preparation. Practical applications: This shotgun lipidomics platform can be implemented in different laboratories without compromising reproducibility, allowing multi-site studies and inter-laboratory comparisons. This possibility combined with the high-throughput, broad lipidomic coverage and absolute quantification are important aspects for clinical applications and biomarker research.
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                Author and article information

                Contributors
                Journal
                Mol Metab
                Mol Metab
                Molecular Metabolism
                Elsevier
                2212-8778
                13 June 2017
                August 2017
                13 June 2017
                : 6
                : 8
                : 931-940
                Affiliations
                [1 ]Institute of Veterinary Pathology at the Centre for Clinical Veterinary Medicine, LMU Munich, Veterinärstr. 13, D-80539 Munich, Germany
                [2 ]Chair for Molecular Animal Breeding and Biotechnology, Gene Center and Department of Veterinary Sciences, and Center for Innovative Medical Models (CiMM), LMU Munich, Feodor-Lynen-Str. 25, D-81377 Munich, Germany
                [3 ]German Center for Diabetes Research (DZD), Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany
                [4 ]Laboratory for Functional Genome Analysis (LAFUGA), Gene Center, LMU Munich, Feodor-Lynen-Str. 25, D-81377 Munich, Germany
                [5 ]German Mouse Clinic (GMC), Institute of Experimental Genetics, Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany
                [6 ]Genome Analysis Center (GAC), Institute of Experimental Genetics, Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany
                [7 ]Paul Langerhans Institute Dresden of the Helmholtz Zentrum München at the University Hospital and Faculty of Medicine Carl Gustav Carus of TU Dresden, Fetscherstr. 74, D-01307 Dresden, Germany
                [8 ]Animal Physiology, Institute of Agricultural Sciences, ETH Zurich, Universitätsstr. 2, CH-8092 Zurich, Switzerland
                [9 ]Clinic for Small Animal Surgery and Reproduction, Center for Clinical Veterinary Medicine, LMU Munich, Veterinärstr. 13, D-80539 Munich, Germany
                [10 ]Experimental Ophthalmology, Philipps University of Marburg, Baldingerstr., D-35033 Marburg, Germany
                [11 ]Institute for Infectious Diseases and Zoonosis, LMU Munich, Veterinärstr. 13, D-80539 Munich, Germany
                [12 ]Clinic for Swine at the Centre of Clinical Veterinary Medicine, LMU Munich, Sonnenstr. 16, D-85764 Oberschleißheim, Germany
                [13 ]Munich Center of NeuroSciences – Brain & Mind, Großhaderner Str. 2, D-82152 Planegg-Martinsried, Germany
                [14 ]Bavarian State Research Center for Agriculture – Institute for Animal Breeding, Prof.-Dürrwaechter-Platz 1, D-85586 Grub-Poing, Germany
                [15 ]Chair for Animal Physiology, Department of Veterinary Sciences, LMU Munich, Veterinärstr. 13, D-80539 Munich, Germany
                [16 ]Institute of Pathology, LMU Munich, Thalkirchner Str. 36, D-80337 Munich, Germany
                [17 ]Chair of Experimental Genetics, School of Life Science Weihenstephan, Technische Universität München, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany
                [18 ]MWM Biomodels GmbH, Hauptstr. 41, D-84184 Tiefenbach, Germany
                Author notes
                []Corresponding author. Gene Center, Feodor-Lynen-Str. 25, D-81377 Munich, Germany.Gene CenterFeodor-Lynen-Str. 25MunichD-81377Germany ewolf@ 123456lmu.de
                [19]

                Rüdiger Wanke and Eckhard Wolf contributed equally to this work.

                Article
                S2212-8778(17)30315-0
                10.1016/j.molmet.2017.06.004
                5518720
                28752056
                © 2017 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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