Myc controls NK cell development, IL-15-driven expansion, and translational machinery

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Abstract

Myc ablation in vivo impacts immature NK cells’ ribosomagenesis and development. Accordingly, mice with NK cells lacking Myc exhibit impaired anticancer immunity.

Abstract

MYC is a pleiotropic transcription factor involved in cancer, cell proliferation, and metabolism. Its regulation and function in NK cells, which are innate cytotoxic lymphocytes important to control viral infections and cancer, remain poorly defined. Here, we show that mice deficient for Myc in NK cells presented a severe reduction in these lymphocytes. Myc was required for NK cell development and expansion in response to the key cytokine IL-15, which induced Myc through transcriptional and posttranslational mechanisms. Mechanistically, Myc ablation in vivo largely impacted NK cells’ ribosomagenesis, reducing their translation and expansion capacities. Similar results were obtained by inhibiting MYC in human NK cells. Impairing translation by pharmacological intervention phenocopied the consequences of deleting or blocking MYC in vitro. Notably, mice lacking Myc in NK cells exhibited defective anticancer immunity, which reflected their decreased numbers of mature NK cells exerting suboptimal cytotoxic functions. These results indicate that MYC is a central node in NK cells, connecting IL-15 to translational fitness, expansion, and anticancer immunity.

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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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limma powers differential expression analyses for RNA-sequencing and microarray studies

Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)

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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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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Author and article information

Contributors

Hanif J Khameneh:

ORCID: https://orcid.org/0000-0002-1737-0388

Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: Writing—original draftRole: Writing—review and editing

Nicolas Fonta: Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Investigation

Alessandro Zenobi:

ORCID: https://orcid.org/0000-0003-2837-7638

Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: Writing—original draftRole: Writing—review and editing

Charlène Niogret: Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Investigation

Pedro Ventura: Role: Investigation

Concetta Guerra:

ORCID: https://orcid.org/0000-0001-5597-6885

Role: Investigation

Ivo Kwee: Role: Formal analysisRole: Methodology

Andrea Rinaldi: Role: Methodology

Matteo Pecoraro: Role: Methodology

Roger Geiger:

ORCID: https://orcid.org/0000-0002-8016-1669

Role: Methodology

Andrea Cavalli: Role: Methodology

Francesco Bertoni: Role: Resources

Eric Vivier: Role: Resources

Andreas Trumpp: Role: Resources

Greta Guarda:

ORCID: https://orcid.org/0000-0001-9243-6116

Role: ConceptualizationRole: Data curationRole: SupervisionRole: Funding acquisitionRole: Writing—original draftRole: Project administrationRole: Writing—review and editing

Journal

Journal ID (nlm-ta): Life Sci Alliance

Journal ID (iso-abbrev): Life Sci Alliance

Journal ID (hwp): lsa

Journal ID (publisher-id): lsa

Title: Life Science Alliance

Publisher: Life Science Alliance LLC

ISSN (Electronic): 2575-1077

Publication date (Electronic): 27 April 2023

Publication date Collection: July 2023

Publication date PMC-release: 27 April 2023

Volume: 6

Issue: 7

Electronic Location Identifier: e202302069

Affiliations

[1 ] Università della Svizzera italiana ( https://ror.org/03c4atk17) , Faculty of Biomedical Sciences, Institute for Research in Biomedicine, Bellinzona, Switzerland;

[2 ] Department of Biochemistry, University of Lausanne, Epalinges, Switzerland;

[3 ] BigOmics Analytics SA, Lugano, Switzerland;

[4 ] Università della Svizzera italiana ( https://ror.org/03c4atk17) , Faculty of Biomedical Sciences, Institute of Oncology Research, Bellinzona, Switzerland;

[5 ] Swiss Institute of Bioinformatics, Lausanne, Switzerland;

[6 ] Oncology Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland;

[7 ] Aix-Marseille Université, Centre National de la Recherche Scientifique, Institut National de la Santé et de la Recherche Médicale, Centre d’Immunologie de Marseille-Luminy, Marseille, France;

[8 ] Innate Pharma Research Laboratories, Marseille, France;

[9 ] APHM, Hôpital de la Timone, Marseille-Immunopôle, Marseille, France;

[10 ] Division of Stem Cells and Cancer, DKFZ, Heidelberg, Germany;

[11 ] HI-STEM: The Heidelberg Institute for Stem Cell Technology and Experimental Medicine gGmbH, Heidelberg, Germany;

Author notes

Correspondence: greta.guarda@ 123456irb.usi.ch

[*]

Hanif J Khameneh and Nicolas Fonta contributed equally to this work

Author information

Hanif J Khameneh https://orcid.org/0000-0002-1737-0388

Alessandro Zenobi https://orcid.org/0000-0003-2837-7638

Concetta Guerra https://orcid.org/0000-0001-5597-6885

Roger Geiger https://orcid.org/0000-0002-8016-1669

Greta Guarda https://orcid.org/0000-0001-9243-6116

Article

Publisher ID: LSA-2023-02069

DOI: 10.26508/lsa.202302069

PMC ID: 10140547

PubMed ID: 37105715

SO-VID: 10499de4-c7a7-41ab-b9cc-dada48ce4ce8

License:

This article is available under a Creative Commons License (Attribution 4.0 International, as described at https://creativecommons.org/licenses/by/4.0/).

History

Date received : 2 April 2023

Date revision received : 19 April 2023

Date accepted : 19 April 2023

Funding

Funded by: Fondazione Leonardo, DOI ;

Award Recipient : G Guarda

Funded by: Fondazione Novartis, DOI ;

Award Recipient : G Guarda

Funded by: Swiss National Science Foundation, DOI ;

Award ID: 310030_185185, 310030_197771

Award Recipient : G Guarda

Funded by: Swiss Cancer Research Foundation, DOI ;

Award ID: KFS 5141-08-2020

Award Recipient : G Guarda

Funded by: European Research Council, DOI http://dx.doi.org/10.13039/501100000781;

Award ID: ERC-2012-StG310890

Award Recipient : G Guarda

Funded by: SHATTER-AML, DOI ;

Award ID: ERC-2022-AdG101055270

Award Recipient : A Trumpp

Funded by: Dietmar Hopp Foundation, DOI ;

Award Recipient : A Trumpp

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Myc controls NK cell development, IL-15-driven expansion, and translational machinery

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African Journal of Empirical Research

Most cited references 80

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

limma powers differential expression analyses for RNA-sequencing and microarray studies

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

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