Multiplex image-based autophagy RNAi screening identifies SMCR8 as ULK1 kinase activity and gene expression regulator

  1. Jennifer Jung
  2. Arnab Nayak
  3. Véronique Schaeffer
  4. Tatjana Starzetz
  5. Achim Klaus Kirsch
  6. Stefan Müller
  7. Ivan Dikic
  8. Michel Mittelbronn
  9. Christian Behrends  Is a corresponding author
  1. Goethe University School of Medicine, Germany
  2. Goethe University, Germany
  3. PerkinElmer, Inc., Germany

Abstract

Autophagy is an intracellular recycling and degradation pathway that depends on membrane trafficking. Rab GTPases are central for autophagy but their regulation especially through the activity of Rab GEFs remains largely elusive. We employed a RNAi screen simultaneously monitoring different populations of autophagosomes and identified 34 out of 186 Rab GTPase, GAP and GEF family members as potential autophagy regulators, amongst them SMCR8. SMCR8 uses overlapping binding regions to associate with C9ORF72 or with a C9ORF72-ULK1 kinase complex holo-assembly, which function in maturation and formation of autophagosomes, respectively. While focusing on the role of SMCR8 during autophagy initiation, we found that kinase activity and gene expression of ULK1 are increased upon SMCR8 depletion. The latter phenotype involved association of SMCR8 with the ULK1 gene locus. Global mRNA expression analysis revealed that SMCR8 regulates transcription of several other autophagy genes including WIPI2. Collectively, we established SMCR8 as multifaceted negative autophagy regulator.

Article and author information

Author details

  1. Jennifer Jung

    Institute of Biochemistry II, Goethe University School of Medicine, Frankfurt, Germany
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9436-4021
  2. Arnab Nayak

    Institute of Biochemistry II, Goethe University School of Medicine, Frankfurt, Germany
    Competing interests
    No competing interests declared.
  3. Véronique Schaeffer

    Institute of Biochemistry II, Goethe University School of Medicine, Frankfurt, Germany
    Competing interests
    No competing interests declared.
  4. Tatjana Starzetz

    Neurological Institute, Goethe University, Frankfurt, Germany
    Competing interests
    No competing interests declared.
  5. Achim Klaus Kirsch

    PerkinElmer, Inc., Hamburg, Germany
    Competing interests
    No competing interests declared.
  6. Stefan Müller

    Institute of Biochemistry II, Goethe University School of Medicine, Frankfurt, Germany
    Competing interests
    No competing interests declared.
  7. Ivan Dikic

    Institute of Biochemistry II, Goethe University School of Medicine, Frankfurt, Germany
    Competing interests
    Ivan Dikic, Senior Editor eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8156-9511
  8. Michel Mittelbronn

    Neurological Institute, Goethe University, Frankfurt, Germany
    Competing interests
    No competing interests declared.
  9. Christian Behrends

    Institute of Biochemistry II, Goethe University School of Medicine, Frankfurt, Germany
    For correspondence
    behrends@em.uni-frankfurt.de
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9184-7607

Funding

Deutsche Forschungsgemeinschaft (SFB1177)

  • Stefan Müller
  • Ivan Dikic
  • Christian Behrends

Munich Cluster of Systems Neurology (EXC 1010 SyNergy)

  • Christian Behrends

Goethe-Universität Frankfurt am Main (EXC115)

  • Ivan Dikic

LOEWE Zentrum (Ub-net)

  • Stefan Müller
  • Ivan Dikic
  • Christian Behrends

European Research Council (ERC,282333-XABA)

  • Christian Behrends

Deutsche Forschungsgemeinschaft (DI 931/3-1)

  • Ivan Dikic

LOEWE Zentrum (Gene and Cell Therapy Frankfurt)

  • Christian Behrends

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

Copyright

© 2017, Jung et al.

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

Metrics

  • 3,862
    views
  • 915
    downloads
  • 71
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Jennifer Jung
  2. Arnab Nayak
  3. Véronique Schaeffer
  4. Tatjana Starzetz
  5. Achim Klaus Kirsch
  6. Stefan Müller
  7. Ivan Dikic
  8. Michel Mittelbronn
  9. Christian Behrends
(2017)
Multiplex image-based autophagy RNAi screening identifies SMCR8 as ULK1 kinase activity and gene expression regulator
eLife 6:e23063.
https://doi.org/10.7554/eLife.23063

Share this article

https://doi.org/10.7554/eLife.23063

Further reading

    1. Biochemistry and Chemical Biology
    2. Microbiology and Infectious Disease
    Mai Nguyen, Elda Bauda ... Cecile Morlot
    Research Article

    Teichoic acids (TA) are linear phospho-saccharidic polymers and important constituents of the cell envelope of Gram-positive bacteria, either bound to the peptidoglycan as wall teichoic acids (WTA) or to the membrane as lipoteichoic acids (LTA). The composition of TA varies greatly but the presence of both WTA and LTA is highly conserved, hinting at an underlying fundamental function that is distinct from their specific roles in diverse organisms. We report the observation of a periplasmic space in Streptococcus pneumoniae by cryo-electron microscopy of vitreous sections. The thickness and appearance of this region change upon deletion of genes involved in the attachment of TA, supporting their role in the maintenance of a periplasmic space in Gram-positive bacteria as a possible universal function. Consequences of these mutations were further examined by super-resolved microscopy, following metabolic labeling and fluorophore coupling by click chemistry. This novel labeling method also enabled in-gel analysis of cell fractions. With this approach, we were able to titrate the actual amount of TA per cell and to determine the ratio of WTA to LTA. In addition, we followed the change of TA length during growth phases, and discovered that a mutant devoid of LTA accumulates the membrane-bound polymerized TA precursor.

    1. Biochemistry and Chemical Biology
    2. Computational and Systems Biology
    Shinichi Kawaguchi, Xin Xu ... Toshie Kai
    Research Article

    Protein–protein interactions are fundamental to understanding the molecular functions and regulation of proteins. Despite the availability of extensive databases, many interactions remain uncharacterized due to the labor-intensive nature of experimental validation. In this study, we utilized the AlphaFold2 program to predict interactions among proteins localized in the nuage, a germline-specific non-membrane organelle essential for piRNA biogenesis in Drosophila. We screened 20 nuage proteins for 1:1 interactions and predicted dimer structures. Among these, five represented novel interaction candidates. Three pairs, including Spn-E_Squ, were verified by co-immunoprecipitation. Disruption of the salt bridges at the Spn-E_Squ interface confirmed their functional importance, underscoring the predictive model’s accuracy. We extended our analysis to include interactions between three representative nuage components—Vas, Squ, and Tej—and approximately 430 oogenesis-related proteins. Co-immunoprecipitation verified interactions for three pairs: Mei-W68_Squ, CSN3_Squ, and Pka-C1_Tej. Furthermore, we screened the majority of Drosophila proteins (~12,000) for potential interaction with the Piwi protein, a central player in the piRNA pathway, identifying 164 pairs as potential binding partners. This in silico approach not only efficiently identifies potential interaction partners but also significantly bridges the gap by facilitating the integration of bioinformatics and experimental biology.