A human ESC-based screen identifies a role for the translated lncRNA LINC00261 in pancreatic endocrine differentiation

Abstract

Long noncoding RNAs (lncRNAs) are a heterogenous group of RNAs, which can encode small proteins. The extent to which developmentally regulated lncRNAs are translated and whether the produced microproteins are relevant for human development is unknown. Using a human embryonic stem cell (hESC)-based pancreatic differentiation system, we show that many lncRNAs in direct vicinity of lineage-determining transcription factors (TFs) are dynamically regulated, predominantly cytosolic, and highly translated. We genetically ablated ten such lncRNAs, most of them translated, and found that nine are dispensable for pancreatic endocrine cell development. However, deletion of LINC00261 diminishes insulin+ cells, in a manner independent of the nearby TF FOXA2. One-by-one deletion of each of LINC00261's open reading frames suggests that the RNA, rather than the produced microproteins, is required for endocrine development. Our work highlights extensive translation of lncRNAs during hESC pancreatic differentiation and provides a blueprint for dissection of their coding and noncoding roles.

Data availability

All mRNA-seq and Ribo-seq datasets generated for this study have been deposited at GEO under the accession number GSE144682.

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

Article and author information

Author details

  1. Bjoern Gaertner

    Department of Pediatrics, University of California, San Diego, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Sebastiaan van Heesch

    Cardiovascular Research, Max-Delbruck-Center for Molecular Medicine, Berlin, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9593-1980
  3. Valentin Schneider-Lunitz

    Cardiovascular Research, Max-Delbruck-Center for Molecular Medicine, Berlin, Germany
    Competing interests
    The authors declare that no competing interests exist.
  4. Jana Felicitas Schulz

    Cardiovascular Research, Max-Delbruck-Center for Molecular Medicine, Berlin, Germany
    Competing interests
    The authors declare that no competing interests exist.
  5. Franziska Witte

    Cardiovascular Research, Max-Delbruck-Center for Molecular Medicine, Berlin, Germany
    Competing interests
    The authors declare that no competing interests exist.
  6. Susanne Blachut

    Cardiovascular Research, Max-Delbruck-Center for Molecular Medicine, Berlin, Germany
    Competing interests
    The authors declare that no competing interests exist.
  7. Steven Nguyen

    Department of Pediatrics, University of California, San Diego, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Regina Wong

    Department of Pediatrics, University of California, San Diego, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Ileana Matta

    Department of Pediatrics, University of California, San Diego, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Norbert Hübner

    Cardiovascular Research, Max-Delbruck-Center for Molecular Medicine, Berlin, Germany
    Competing interests
    The authors declare that no competing interests exist.
  11. Maike Sander

    Department of Pediatrics and Cellular and Molecular Medicine, University of California, San Diego, La Jolla, United States
    For correspondence
    masander@ucsd.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5308-7785

Funding

National Institutes of Health (DK068471 and DK078803)

  • Maike Sander

Alexander von Humboldt-Stiftung

  • Maike Sander

Larry L. Hillblom Foundation (2015-D-021-FEL)

  • Bjoern Gaertner

European Molecular Biology Organization (ALTF 186-2015)

  • Sebastiaan van Heesch

Horizon 2020 Framework Programme (AdG788970)

  • Norbert Hübner

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

Copyright

© 2020, Gaertner 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

  • 2,750
    views
  • 377
    downloads
  • 35
    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. Bjoern Gaertner
  2. Sebastiaan van Heesch
  3. Valentin Schneider-Lunitz
  4. Jana Felicitas Schulz
  5. Franziska Witte
  6. Susanne Blachut
  7. Steven Nguyen
  8. Regina Wong
  9. Ileana Matta
  10. Norbert Hübner
  11. Maike Sander
(2020)
A human ESC-based screen identifies a role for the translated lncRNA LINC00261 in pancreatic endocrine differentiation
eLife 9:e58659.
https://doi.org/10.7554/eLife.58659

Share this article

https://doi.org/10.7554/eLife.58659

Further reading

    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.

    1. Computational and Systems Biology
    2. Neuroscience
    Brian DePasquale, Carlos D Brody, Jonathan W Pillow
    Research Article Updated

    Accumulating evidence to make decisions is a core cognitive function. Previous studies have tended to estimate accumulation using either neural or behavioral data alone. Here, we develop a unified framework for modeling stimulus-driven behavior and multi-neuron activity simultaneously. We applied our method to choices and neural recordings from three rat brain regions—the posterior parietal cortex (PPC), the frontal orienting fields (FOF), and the anterior-dorsal striatum (ADS)—while subjects performed a pulse-based accumulation task. Each region was best described by a distinct accumulation model, which all differed from the model that best described the animal’s choices. FOF activity was consistent with an accumulator where early evidence was favored while the ADS reflected near perfect accumulation. Neural responses within an accumulation framework unveiled a distinct association between each brain region and choice. Choices were better predicted from all regions using a comprehensive, accumulation-based framework and different brain regions were found to differentially reflect choice-related accumulation signals: FOF and ADS both reflected choice but ADS showed more instances of decision vacillation. Previous studies relating neural data to behaviorally inferred accumulation dynamics have implicitly assumed that individual brain regions reflect the whole-animal level accumulator. Our results suggest that different brain regions represent accumulated evidence in dramatically different ways and that accumulation at the whole-animal level may be constructed from a variety of neural-level accumulators.