A transcriptomics resource reveals a transcriptional transition during ordered sarcomere morphogenesis in flight muscle

  1. Maria L Spletter  Is a corresponding author
  2. Christiane Barz
  3. Assa Yeroslaviz
  4. Xu Zhang
  5. Sandra B Lemke
  6. Adrien Bonnard
  7. Erich Brunner
  8. Giovanni Cardone
  9. Konrad Basler
  10. Bianca H Habermann
  11. Frank Schnorrer  Is a corresponding author
  1. Max Planck Institute of Biochemistry, Germany
  2. Aix Marseille University, France
  3. University of Zurich, Switzerland

Abstract

Muscles organise pseudo-crystalline arrays of actin, myosin and titin filaments to build force-producing sarcomeres. To study sarcomerogenesis, we have generated a transcriptomics resource of developing Drosophila flight muscles and identified 40 distinct expression profile clusters. Strikingly, most sarcomeric components group in two clusters, which are strongly induced after all myofibrils have been assembled, indicating a transcriptional transition during myofibrillogenesis. Following myofibril assembly, many short sarcomeres are added to each myofibril. Subsequently, all sarcomeres mature, reaching 1.5 µm diameter and 3.2 µm length and acquiring stretch-sensitivity. The efficient induction of the transcriptional transition during myofibrillogenesis, including the transcriptional boost of sarcomeric components, requires in part the transcriptional regulator Spalt major. As a consequence of Spalt knock-down, sarcomere maturation is defective and fibers fail to gain stretch-sensitivity. Together, this defines an ordered sarcomere morphogenesis process under precise transcriptional control - a concept that may also apply to vertebrate muscle or heart development.

Data availability

Processed data from DESeq2, Mfuzz and GO-Elite are available in Supplementary Files 1, 2, 4. mRNA-Seq data are publicly available from NCBI's Gene Expression Omnibus (GEO) under accession number GSE107247. Fiji scripts for analysis of sarcomere length, myofibril width and myofibril diameter are available from https://imagej.net/MyofibrilJ. Raw data used to generate all plots presented in figure panels are available in the source data files for Figures 1, 5, 6, 7 and 8. Data on statistical test results are presented in Supplementary File 5.

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Maria L Spletter

    Muscle Dynamics Group, Max Planck Institute of Biochemistry, Martinsried, Germany
    For correspondence
    maria.spletter@bmc.med.lmu.de
    Competing interests
    The authors declare that no competing interests exist.
  2. Christiane Barz

    Muscle Dynamics Group, Max Planck Institute of Biochemistry, Martinsried, Germany
    Competing interests
    The authors declare that no competing interests exist.
  3. Assa Yeroslaviz

    Computational Biology Group, Max Planck Institute of Biochemistry, Martinsried, Germany
    Competing interests
    The authors declare that no competing interests exist.
  4. Xu Zhang

    Muscle Dynamics Group, Max Planck Institute of Biochemistry, Martinsried, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1628-9895
  5. Sandra B Lemke

    Muscle Dynamics Group, Max Planck Institute of Biochemistry, Martinsried, Germany
    Competing interests
    The authors declare that no competing interests exist.
  6. Adrien Bonnard

    IBDM, Aix Marseille University, Marseille, France
    Competing interests
    The authors declare that no competing interests exist.
  7. Erich Brunner

    Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
    Competing interests
    The authors declare that no competing interests exist.
  8. Giovanni Cardone

    Imaging Facility, Max Planck Institute of Biochemistry, Martinsried, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4712-1451
  9. Konrad Basler

    Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
    Competing interests
    The authors declare that no competing interests exist.
  10. Bianca H Habermann

    Computational Biology Group, Max Planck Institute of Biochemistry, Martinsried, Germany
    Competing interests
    The authors declare that no competing interests exist.
  11. Frank Schnorrer

    Muscle Dynamics Group, Max Planck Institute of Biochemistry, Martinsried, Germany
    For correspondence
    frank.schnorrer@univ-amu.fr
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9518-7263

Funding

Max-Planck-Gesellschaft

  • Maria L Spletter
  • Christiane Barz
  • Assa Yeroslaviz
  • Xu Zhang
  • Sandra B Lemke
  • Bianca H Habermann
  • Frank Schnorrer

Agence Nationale de la Recherche (ANR-10-INBS-04- 01)

  • Frank Schnorrer

Agence Nationale de la Recherche (ANR ACHN)

  • Frank Schnorrer

Centre National de la Recherche Scientifique

  • Xu Zhang
  • Adrien Bonnard
  • Bianca H Habermann
  • Frank Schnorrer

European Molecular Biology Organization (EMBO-LTR 688-2011)

  • Maria L Spletter

Alexander von Humboldt-Stiftung

  • Maria L Spletter

National Institute for Health Research (5F32AR062477)

  • Maria L Spletter

H2020 European Research Council (ERC Grant 310939)

  • Frank Schnorrer

Aix-Marseille Université (ANR-11-IDEX-0001-02)

  • Bianca H Habermann
  • Frank Schnorrer

Agence Nationale de la Recherche (ANR-11- LABX-0054)

  • Frank Schnorrer

European Molecular Biology Organization (EMBO-YIP)

  • Frank Schnorrer

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

Reviewing Editor

  1. K VijayRaghavan, National Centre for Biological Sciences, Tata Institute of Fundamental Research, India

Publication history

  1. Received: December 4, 2017
  2. Accepted: May 26, 2018
  3. Accepted Manuscript published: May 30, 2018 (version 1)
  4. Version of Record published: June 18, 2018 (version 2)

Copyright

© 2018, Spletter 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

  • 3,349
    Page views
  • 474
    Downloads
  • 39
    Citations

Article citation count generated by polling the highest count across the following sources: Scopus, Crossref, PubMed Central.

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. Maria L Spletter
  2. Christiane Barz
  3. Assa Yeroslaviz
  4. Xu Zhang
  5. Sandra B Lemke
  6. Adrien Bonnard
  7. Erich Brunner
  8. Giovanni Cardone
  9. Konrad Basler
  10. Bianca H Habermann
  11. Frank Schnorrer
(2018)
A transcriptomics resource reveals a transcriptional transition during ordered sarcomere morphogenesis in flight muscle
eLife 7:e34058.
https://doi.org/10.7554/eLife.34058

Further reading

    1. Cell Biology
    John G Dingus, Jonathan CY Tang ... Constance L Cepko
    Research Article

    Conventional antibodies and their derived fragments are difficult to deploy against intracellular targets in live cells, due to their bulk and structural complexity. Nanobodies provide an alternative modality, with well-documented examples of intracellular expression. Despite their promise as intracellular reagents, there has not been a systematic study of nanobody intracellular expression. Here, we examined intracellular expression of 75 nanobodies from the Protein Data Bank. Surprisingly, a majority of these nanobodies were unstable in cells, illustrated by aggregation and clearance. Using comparative analysis and framework mutagenesis, we developed a general approach that stabilized a great majority of nanobodies that were originally unstable intracellularly, without significantly compromising target binding. This approach led to the identification of distinct sequence features that impacted the intracellular stability of tested nanobodies. Mutationally stabilized nanobody expression was found to extend to in vivo contexts, in the murine retina and in E. coli. These data provide for improvements in nanobody engineering for intracellular applications, potentiating a growing field of intracellular interrogation and intervention.

    1. Cell Biology
    Benjamin Barsi-Rhyne, Aashish Manglik, Mark von Zastrow
    Research Article Updated

    β-Arrestins are master regulators of cellular signaling that operate by desensitizing ligand-activated G-protein-coupled receptors (GPCRs) at the plasma membrane and promoting their subsequent endocytosis. The endocytic activity of β-arrestins is ligand dependent, triggered by GPCR binding, and increasingly recognized to have a multitude of downstream signaling and trafficking consequences that are specifically programmed by the bound GPCR. However, only one biochemical ‘mode’ for GPCR-mediated triggering of the endocytic activity is presently known – displacement of the β-arrestin C-terminus (CT) to expose clathrin-coated pit-binding determinants that are masked in the inactive state. Here, we revise this view by uncovering a second mode of GPCR-triggered endocytic activity that is independent of the β-arrestin CT and, instead, requires the cytosolic base of the β-arrestin C-lobe (CLB). We further show each of the discrete endocytic modes is triggered in a receptor-specific manner, with GPCRs that bind β-arrestin transiently (‘class A’) primarily triggering the CLB-dependent mode and GPCRs that bind more stably (‘class B’) triggering both the CT and CLB-dependent modes in combination. Moreover, we show that different modes have opposing effects on the net signaling output of receptors – with the CLB-dependent mode promoting rapid signal desensitization and the CT-dependent mode enabling prolonged signaling. Together, these results fundamentally revise understanding of how β-arrestins operate as efficient endocytic adaptors while facilitating diversity and flexibility in the control of cell signaling.