4
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A specific prelimbic-nucleus accumbens pathway controls resilience versus vulnerability to food addiction

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Food addiction is linked to obesity and eating disorders and is characterized by a loss of behavioral control and compulsive food intake. Here, using a food addiction mouse model, we report that the lack of cannabinoid type-1 receptor in dorsal telencephalic glutamatergic neurons prevents the development of food addiction-like behavior, which is associated with enhanced synaptic excitatory transmission in the medial prefrontal cortex (mPFC) and in the nucleus accumbens (NAc). In contrast, chemogenetic inhibition of neuronal activity in the mPFC-NAc pathway induces compulsive food seeking. Transcriptomic analysis and genetic manipulation identified that increased dopamine D2 receptor expression in the mPFC-NAc pathway promotes the addiction-like phenotype. Our study unravels a new neurobiological mechanism underlying resilience and vulnerability to the development of food addiction, which could pave the way towards novel and efficient interventions for this disorder.

          Abstract

          Food addiction is linked to obesity and eating disorders. In a mouse model of food addiction, the authors show that a medial prefrontal cortex-nucleus accumbens pathway is involved in vulnerability and resilience against the development of food addiction-like behavior.

          Related collections

          Most cited references54

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          HTSeq—a Python framework to work with high-throughput sequencing data

          Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found

            Executive Functions

            Executive functions (EFs) make possible mentally playing with ideas; taking the time to think before acting; meeting novel, unanticipated challenges; resisting temptations; and staying focused. Core EFs are inhibition [response inhibition (self-control—resisting temptations and resisting acting impulsively) and interference control (selective attention and cognitive inhibition)], working memory, and cognitive flexibility (including creatively thinking “outside the box,” seeing anything from different perspectives, and quickly and flexibly adapting to changed circumstances). The developmental progression and representative measures of each are discussed. Controversies are addressed (e.g., the relation between EFs and fluid intelligence, self-regulation, executive attention, and effortful control, and the relation between working memory and inhibition and attention). The importance of social, emotional, and physical health for cognitive health is discussed because stress, lack of sleep, loneliness, or lack of exercise each impair EFs. That EFs are trainable and can be improved with practice is addressed, including diverse methods tried thus far.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Differential expression analysis for sequence count data

              High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.
                Bookmark

                Author and article information

                Contributors
                rafael.maldonado@upf.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                7 February 2020
                7 February 2020
                2020
                : 11
                : 782
                Affiliations
                [1 ]ISNI 0000 0001 2172 2676, GRID grid.5612.0, Laboratory of Neuropharmacology-Neurophar, Department of Experimental and Health Sciences, , Universitat Pompeu Fabra (UPF), ; Barcelona, Spain
                [2 ]GRID grid.410607.4, Institute of Physiological Chemistry, , University Medical Center of the Johannes Gutenberg University Mainz, ; Mainz, Germany
                [3 ]Leibniz Institute for Resilience Research, Mainz, Germany
                [4 ]GRID grid.473715.3, Centre for genomic regulation (CRG), , The Barcelona Institute of Science and Technology, ; Barcelona, Spain
                [5 ]ISNI 0000 0001 2177 5516, GRID grid.419043.b, Instituto Cajal, CSIC, ; Madrid, Spain
                [6 ]ISNI 0000 0001 1941 7111, GRID grid.5802.f, Faculty of Biology and Center of Computational Sciences, , Johannes Gutenberg University, ; Mainz, Germany
                [7 ]ISNI 0000 0001 1941 7111, GRID grid.5802.f, Focus Program Translational Neuroscience, , Johannes Gutenberg University Mainz, ; Mainz, Germany
                [8 ]GRID grid.7080.f, Department of Psychobiology and Methodology in Health Sciences, , Universitat Autònoma de Barcelona (UAB), ; Bellaterra, Spain
                [9 ]Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Bellaterra, Spain
                [10 ]GRID grid.7080.f, Unitat de Neurociència Traslacional, ParcTaulí Hospital Universitari, Institut d’Investigació i Innovació ParcTaulí (I3PT), Institut de Neurociències, UAB, ; Bellaterra, Spain
                [11 ]ISNI 0000 0004 1767 9005, GRID grid.20522.37, Hospital del Mar Medical Research Institute (IMIM), ; Barcelona, Spain
                Author information
                http://orcid.org/0000-0003-3286-6696
                http://orcid.org/0000-0001-8507-8154
                http://orcid.org/0000-0001-9847-6960
                http://orcid.org/0000-0003-2097-4788
                http://orcid.org/0000-0003-0853-6865
                http://orcid.org/0000-0002-4487-3028
                http://orcid.org/0000-0002-4359-8773
                Article
                14458
                10.1038/s41467-020-14458-y
                7005839
                32034128
                5da7b69b-7188-4b38-97dd-e04c8f299549
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 12 June 2019
                : 19 December 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100003329, Ministerio de Economía y Competitividad (Ministry of Economy and Competitiveness);
                Award ID: SAF2017-84060-R-AEI/FEDER-UE
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                behavioural genetics,addiction
                Uncategorized
                behavioural genetics, addiction

                Comments

                Comment on this article