Ral GTPases promote breast cancer metastasis by controlling biogenesis and organ targeting of exosomes

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Abstract

Cancer extracellular vesicles (EVs) shuttle at distance and fertilize pre-metastatic niches facilitating subsequent seeding by tumor cells. However, the link between EV secretion mechanisms and their capacity to form pre-metastatic niches remains obscure. Using mouse models, we show that GTPases of the Ral family control, through the phospholipase D1, multi-vesicular bodies homeostasis and tune the biogenesis and secretion of pro-metastatic EVs. Importantly, EVs from RalA or RalB depleted cells have limited organotropic capacities in vivoand are less efficient in promoting metastasis. RalA and RalB reduce the EV levels of the adhesion molecule MCAM/CD146, which favors EV-mediated metastasis by allowing EVs targeting to the lungs. Finally, RalA, RalB, and MCAM/CD146, are factors of poor prognosis in breast cancer patients. Altogether, our study identifies RalGTPases as central molecules linking the mechanisms of EVs secretion and cargo loading to their capacity to disseminate and induce pre-metastatic niches in a CD146-dependent manner.

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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Michael Love, Wolfgang Huber, Simon Anders (2014)

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.

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STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

Damian Szklarczyk, Annika L Gable, David Lyon … (2018)

Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.

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HISAT: a fast spliced aligner with low memory requirements.

Daehwan Kim, Ben Langmead, Steven L Salzberg (2019)

HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.

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Author and article information

Contributors

Jacky G Goetz:

ORCID: https://orcid.org/0000-0003-2842-8116

Vincent Hyenne:

ORCID: https://orcid.org/0000-0002-1254-2814

Suzanne R Pfeffer: Role: Senior Editor

Erica A Golemis: Role: Reviewing Editor

Journal

Journal ID (nlm-ta): eLife

Journal ID (iso-abbrev): Elife

Journal ID (publisher-id): eLife

Title: eLife

Publisher: eLife Sciences Publications, Ltd

ISSN (Electronic): 2050-084X

Publication date (Electronic, pub): 06 January 2021

Publication date Collection: 2021

Volume: 10

Electronic Location Identifier: e61539

Affiliations

[1 ]INSERM UMR_S1109, Tumor Biomechanics StrasbourgFrance

[2 ]Université de Strasbourg StrasbourgFrance

[3 ]Fédération de Médecine Translationnelle de Strasbourg (FMTS) StrasbourgFrance

[4 ]Laboratoire de Spectrométrie de Masse BioOrganique (LSMBO), IPHC UMR 7178, CNRS, Université de Strasbourg StrasbourgFrance

[5 ]INSERM UMR_S1109, Genomax StrasbourgFrance

[6 ]CNRS, UMR 7245 MCAM, Muséum National d’Histoire Naturelle de Paris ParisFrance

[7 ]Université de Bordeaux, CNRS, Laboratoire de Biogenèse Membranaire, UMR 5200 Villenave d'OrnonFrance

[8 ]Centre National de la Recherche Scientifique, Université de Strasbourg, Institut des Neurosciences Cellulaires et Intégratives StrasbourgFrance

[9 ]Plateforme Imagerie In Vitro, CNRS UPS 3156 StrasbourgFrance

[10 ]IGBMC Imaging Center CNRS (UMR7104)/ INSERM (U1258)/ Université de Strasbourg IllkirchFrance

[11 ]Team SOAP, CRCINA, INSERM, CNRS, Université de Nantes, Université d’Angers NantesFrance

[12 ]Integrated Center for Oncology, ICO St-HerblainFrance

[13 ]Nanotranslational laboratory, Institut de Cancérologie Strasbourg Europe StrasbourgFrance

[14 ]Équipe de synthèse pour l’analyse (SynPA), Institut Pluridisciplinaire Hubert Curien (IPHC), UMR7178, CNRS/Université de Strasbourg StrasbourgFrance

[15 ]Vincent's Clinical School, Faculty of Medicine, University of New South Wales SydneyAustralia

[16 ]The Kinghorn Cancer Centre, Garvan Institute of Medical Research SydneyAustralia

[17 ]C2VN, INSERM 1263, Inrae 1260, Aix-Marseille Université MarseilleFrance

[18 ]CNRS SNC5055 StrasbourgFrance

Stanford University School of Medicine United States

Fox Chase Cancer Center United States

United States

Author notes

[†]

These authors contributed equally to this work.

Author information

Shima Ghoroghi https://orcid.org/0000-0002-5715-4559

Benjamin Mary https://orcid.org/0000-0003-0815-842X

Nandini Asokan https://orcid.org/0000-0003-0505-6487

Naël Osmani https://orcid.org/0000-0003-1195-753X

François Delalande https://orcid.org/0000-0003-3590-9874

Nicodème Paul https://orcid.org/0000-0003-4680-3012

Frédéric Gros https://orcid.org/0000-0002-6252-4323

Laetitia Fouillen http://orcid.org/0000-0002-1204-9296

Coralie Spiegelhalter https://orcid.org/0000-0001-6372-1647

Alexandre Detappe https://orcid.org/0000-0001-9364-1621

Julie Gavard https://orcid.org/0000-0002-7985-9007

Christine Carapito https://orcid.org/0000-0002-0079-319X

Olivier Lefebvre https://orcid.org/0000-0001-9130-9174

Jacky G Goetz https://orcid.org/0000-0003-2842-8116

Vincent Hyenne https://orcid.org/0000-0002-1254-2814

Article

Publisher ID: 61539

DOI: 10.7554/eLife.61539

PMC ID: 7822591

PubMed ID: 33404012

SO-VID: edc0fbc8-9d8a-4002-996f-623b0e33096b

License:

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

History

Date received : 28 July 2020

Date accepted : 05 January 2021

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100006364, Institut National Du Cancer;

Award ID: PLBIO19-291

Award Recipient : Jacky G Goetz

Funded by: FundRef http://dx.doi.org/10.13039/501100004099, Ligue Contre le Cancer;

Award Recipient : Jacky G Goetz

Funded by: Canceropôle Grand Est;

Award ID: Exosomics

Award Recipient : Jacky G Goetz

Funded by: Plan Cancer;

Award ID: Vesmatic

Award Recipient : Jacky G Goetz

Funded by: FundRef http://dx.doi.org/10.13039/100004337, Roche;

Award Recipient : Jacky G Goetz

Funded by: FundRef http://dx.doi.org/10.13039/501100001665, Agence Nationale de la Recherche;

Award ID: ANR-10-INBS-08-03

Award Recipient : Christine Carapito

Funded by: FundRef http://dx.doi.org/10.13039/501100001665, Agence Nationale de la Recherche;

Award ID: ANR-19-CE44-0019

Award Recipient : Nicolas Vitale

Funded by: FundRef http://dx.doi.org/10.13039/501100001665, Agence Nationale de la Recherche;

Award ID: ANR-11-INBS- 0010

Award Recipient : Laetitia Fouillen

Funded by: FundRef http://dx.doi.org/10.13039/100007391, Association pour la Recherche sur le Cancer;

Award Recipient : Shima Ghoroghi

Funded by: French ministry of Science;

Award Recipient : Benjamin Mary Vincent Mittelheisser

Funded by: Suttons;

Award Recipient : Kendelle Murphy Paul Timpson

Funded by: Sydney catalyst;

Award Recipient : Kendelle Murphy Paul Timpson

Funded by: FundRef http://dx.doi.org/10.13039/501100000925, NHMRC;

Award Recipient : Kendelle Murphy Paul Timpson

Funded by: FundRef http://dx.doi.org/10.13039/501100001102, Cancer Council NSW;

Award Recipient : Kendelle Murphy Paul Timpson

Funded by: FundRef http://dx.doi.org/10.13039/100011711, Pancreatic Cancer Action;

Award Recipient : Kendelle Murphy Paul Timpson

Funded by: Pancreatic cancer resource;

Award Recipient : Kendelle Murphy Paul Timpson

Funded by: FundRef http://dx.doi.org/10.13039/501100003768, University of Strasbourg;

Award Recipient : Jacky G Goetz

Funded by: Region Est;

Award ID: Exosomics

Award Recipient : Jacky G Goetz

Funded by: FundRef http://dx.doi.org/10.13039/501100001677, Inserm;

Award Recipient : Jacky G Goetz

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

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Author impact statement A combination of animal models reveal how the molecular mechanisms of exosome secretion (RalA/B-dependent) are linked to their cargo content and their function in breast cancer pre-metastatic niche formation.

ScienceOpen disciplines: Life sciences

Keywords: exosome,ral gtpase,pre-metastatic niche,human,mouse,zebrafish

Data availability:

ScienceOpen disciplines: Life sciences

Keywords: exosome, ral gtpase, pre-metastatic niche, human, mouse, zebrafish

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Ral GTPases promote breast cancer metastasis by controlling biogenesis and organ targeting of exosomes

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Cancer Immunotherapy

Most cited references 74

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

HISAT: a fast spliced aligner with low memory requirements.

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Cited by 45

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