Pro-opiomelanocortin (POMC) neuron translatome signatures underlying obesogenic gestational malprogramming in mice

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Abstract

Objective

Maternal unbalanced nutritional habits during embryonic development and perinatal stages perturb hypothalamic neuronal programming of the offspring, thus increasing obesity-associated diabetes risk. However, the underlying molecular mechanisms remain largely unknown. In this study we sought to determine the translatomic signatures associated with pro-opiomelanocortin (POMC) neuron malprogramming in maternal obesogenic conditions.

Methods

We used the RiboTag mouse model to specifically profile the translatome of POMC neurons during neonatal (P0) and perinatal (P21) life and its neuroanatomical, functional, and physiological consequences.

Results

Maternal high-fat diet (HFD) exposure did not interfere with offspring's hypothalamic POMC neuron specification, but significantly impaired their spatial distribution and axonal extension to target areas. Importantly, we established POMC neuron-specific translatome signatures accounting for aberrant neuronal development and axonal growth. These anatomical and molecular alterations caused metabolic dysfunction in early life and adulthood.

Conclusions

Our study provides fundamental insights on the molecular mechanisms underlying POMC neuron malprogramming in obesogenic contexts.

Graphical abstract

Highlights

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Cell-specific POMC neuron translatome profiling in neonatal and perinatal developmental stages.
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Maternal obesogenic environment alters progeny's POMC neuron translatome signatures.
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Maternal HFD impairs POMC spatial distribution and axonal outgrowth, which is related to offspring's metabolic dysfunction.
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The HFD intervention stage (early life or adulthood) differentially impact POMC neuron transcriptional signatures.

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Most cited references 25

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edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

Mark Robinson, Davis J. McCarthy, Gordon K. Smyth (2009)

Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au

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TopHat: discovering splice junctions with RNA-Seq

Cole Trapnell, Lior Pachter, Steven Salzberg (2011)

Motivation: A new protocol for sequencing the messenger RNA in a cell, known as RNA-Seq, generates millions of short sequence fragments in a single run. These fragments, or ‘reads’, can be used to measure levels of gene expression and to identify novel splice variants of genes. However, current software for aligning RNA-Seq data to a genome relies on known splice junctions and cannot identify novel ones. TopHat is an efficient read-mapping algorithm designed to align reads from an RNA-Seq experiment to a reference genome without relying on known splice sites. Results: We mapped the RNA-Seq reads from a recent mammalian RNA-Seq experiment and recovered more than 72% of the splice junctions reported by the annotation-based software from that study, along with nearly 20 000 previously unreported junctions. The TopHat pipeline is much faster than previous systems, mapping nearly 2.2 million reads per CPU hour, which is sufficient to process an entire RNA-Seq experiment in less than a day on a standard desktop computer. We describe several challenges unique to ab initio splice site discovery from RNA-Seq reads that will require further algorithm development. Availability: TopHat is free, open-source software available from http://tophat.cbcb.umd.edu Contact: cole@cs.umd.edu Supplementary information: Supplementary data are available at Bioinformatics online.

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Cell-type-specific isolation of ribosome-associated mRNA from complex tissues.

Elisenda Sanz, Linghai Yang, Thomas Su … (2009)

Gene profiling techniques allow the assay of transcripts from organs, tissues, and cells with an unprecedented level of coverage. However, most of these approaches are still limited by the fact that organs and tissues are composed of multiple cell types that are each unique in their patterns of gene expression. To identify the transcriptome from a single cell type in a complex tissue, investigators have relied upon physical methods to separate cell types or in situ hybridization and immunohistochemistry. Here, we describe a strategy to rapidly and efficiently isolate ribosome-associated mRNA transcripts from any cell type in vivo. We have created a mouse line, called RiboTag, which carries an Rpl22 allele with a floxed wild-type C-terminal exon followed by an identical C-terminal exon that has three copies of the hemagglutinin (HA) epitope inserted before the stop codon. When the RiboTag mouse is crossed to a cell-type-specific Cre recombinase-expressing mouse, Cre recombinase activates the expression of epitope-tagged ribosomal protein RPL22(HA), which is incorporated into actively translating polyribosomes. Immunoprecipitation of polysomes with a monoclonal antibody against HA yields ribosome-associated mRNA transcripts from specific cell types. We demonstrate the application of this technique in brain using neuron-specific Cre recombinase-expressing mice and in testis using a Sertoli cell Cre recombinase-expressing mouse.

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Author and article information

Contributors

Roberta Haddad-Tóvolli

Jordi Altirriba

Arnaud Obri

Elena Eyre Sánchez

Iñigo Chivite

Maria Milà-Guasch

Sara Ramírez

Alicia G. Gómez-Valadés

Macarena Pozo

Jasmine Burguet

Licio A. Velloso

Marc Claret

Journal

Journal ID (nlm-ta): Mol Metab

Journal ID (iso-abbrev): Mol Metab

Title: Molecular Metabolism

Publisher: Elsevier

ISSN (Electronic): 2212-8778

Publication date PMC-release: 15 February 2020

Publication date Collection: June 2020

Publication date (Electronic): 15 February 2020

Volume: 36

Electronic Location Identifier: 100963

Affiliations

[1 ]Neuronal Control of Metabolism (NeuCoMe) Laboratory, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain

[2 ]Laboratory of Metabolism, Department of Internal Medicine Specialties, Faculty of Medicine, University of Geneva, Geneva, Switzerland

[3 ]Institut Jean-Pierre Bourgin, INRAE, AgroParisTech, Université Paris-Saclay, 78000, Versailles, France

[4 ]Laboratory of Cell Signaling, Obesity and Comorbidities Research Center, State University of Campinas (UNICAMP), Brazil

[5 ]CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Barcelona, Spain

[6 ]School of Medicine, Universitat de Barcelona, Barcelona, Spain

Author notes

[∗ ]Corresponding author. Neuronal Control of Metabolism (NeuCoMe) Laboratory, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain. haddad@ 123456clinic.cat

[∗∗ ]Corresponding author. Neuronal Control of Metabolism (NeuCoMe) Laboratory, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona, Spain. mclaret@ 123456clinic.cat

[7]

Roberta Haddad-Tóvolli and Jordi Altirriba contributed equally to this work.

[8]

Lead contact

Article

Publisher ID: S2212-8778(20)30035-1 Publisher ID: 100963

DOI: 10.1016/j.molmet.2020.02.006

PMC ID: 7152705

PubMed ID: 32283518

SO-VID: fcb3e4d1-5b43-40e1-8aa0-b545f4ffa068

License:

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

History

Date received : 15 January 2020

Date revision received : 7 February 2020

Date accepted : 10 February 2020

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Pro-opiomelanocortin (POMC) neuron translatome signatures underlying obesogenic gestational malprogramming in mice

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Abstract

Objective

Methods

Results

Conclusions

Graphical abstract

Highlights

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Communication Through Coherence

Most cited references 25

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

TopHat: discovering splice junctions with RNA-Seq

Cell-type-specific isolation of ribosome-associated mRNA from complex tissues.

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Cited by 7

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