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      Maternal emulsifier consumption programs offspring metabolic and neuropsychological health in mice

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          Abstract

          Modern lifestyle is associated with a major consumption of ultra–processed foods (UPF) due to their practicality and palatability. The ingestion of emulsifiers, a main additive in UPFs, has been related to gut inflammation, microbiota dysbiosis, adiposity, and obesity. Maternal unbalanced nutritional habits during embryonic and perinatal stages perturb offspring’s long–term metabolic health, thus increasing obesity and associated comorbidity risk. However, whether maternal emulsifier consumption influences developmental programming in the offspring remains unknown. Here, we show that, in mice, maternal consumption of dietary emulsifiers (1% carboxymethyl cellulose (CMC) and 1% P80 in drinking water), during gestation and lactation, perturbs the development of hypothalamic energy balance regulation centers of the progeny, leads to metabolic impairments, cognition deficits, and induces anxiety–like traits in a sex–specific manner. Our findings support the notion that maternal consumption of emulsifiers, common additives of UPFs, causes mild metabolic and neuropsychological malprogramming in the progeny. Our data call for nutritional advice during gestation.

          Abstract

          Maternal consumption of emulsifiers present in ultra-processed food items has been linked to altered gut function and metabolism in their progeny. This study reveals that maternal emulsifier consumption during gestation and lactation lead to metabolic, cognitive and psychological malprogramming in the offspring.

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          Most cited references60

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          Cytoscape: a software environment for integrated models of biomolecular interaction networks.

          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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                Author and article information

                Contributors
                Role: Data curationRole: Formal analysisRole: InvestigationRole: Methodology
                Role: InvestigationRole: MethodologyRole: Visualization
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: Visualization
                Role: InvestigationRole: Methodology
                Role: InvestigationRole: Methodology
                Role: InvestigationRole: Methodology
                Role: Data curationRole: Formal analysis
                Role: InvestigationRole: Methodology
                Role: Data curationRole: Formal analysisRole: InvestigationRole: Visualization
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                PLOS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                24 August 2023
                August 2023
                24 August 2023
                : 21
                : 8
                : e3002171
                Affiliations
                [1 ] Neuronal Control of Metabolism (NeuCoMe) Laboratory, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
                [2 ] CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Barcelona, Spain
                [3 ] School of Medicine, Universitat de Barcelona, Barcelona, Spain
                INSERM, FRANCE
                Author notes

                The authors have declared that no competing interests exist.

                ‡ These authors jointly supervised this work.

                Author information
                https://orcid.org/0000-0001-7766-9296
                Article
                PBIOLOGY-D-22-02144
                10.1371/journal.pbio.3002171
                10449393
                37616199
                1d8ea005-d873-4696-a774-38da344133e6
                © 2023 Milà-Guasch et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 28 September 2022
                : 24 May 2023
                Page count
                Figures: 7, Tables: 0, Pages: 22
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100000781, European Research Council;
                Award ID: 725004
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100010434, “la Caixa” Foundation;
                Award ID: ID100010434 under agreement LCF/PR/HR19/52160016
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100010665, H2020 Marie Skłodowska-Curie Actions;
                Award ID: 891247
                Award Recipient :
                Funded by: Spanish Ministry of Science and Innovation
                Award ID: IJC2018-037341-I
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100004587, Instituto de Salud Carlos III;
                Award ID: Miguel Servet contract (CP19/00083)
                Award Recipient :
                This study was funded by the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 725004) and supported by: ‘la Caixa’ Foundation (ID100010434) under agreement LCF/PR/HR19/52160016 and the CERCA Programme/Generalitat de Catalunya (to M.C.); Marie Skłodowska-Curie Action fellowship (H2020-MSCA-IF) NEUROPREG (grant agreement no. 891247; to R.H-T.); the Spanish Ministry of Science and Innovation, Juan de la Cierva fellowship (IJC2018-037341-I to S.R.); Miguel Servet contract (CP19/00083) from Instituto de Salud Carlos III co-financed by ERDF (to A.O.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Biochemistry
                Hormones
                Peptide Hormones
                Leptin
                Biology and Life Sciences
                Nutrition
                Diet
                Food
                Medicine and Health Sciences
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                Diabetic Endocrinology
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                Biology and Life Sciences
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                Custom metadata
                The data that underlie the figures are publicly accessible in DOI: 10.6084/m9.figshare.22742759. RNASeq datasets generated and/or analyzed during the current study are available in the NCBI GEO repository under accession number GSE231769 ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE231769).

                Life sciences
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