1. Neuroscience
Download icon

Increased expression of heme-binding protein 1 early in Alzheimer's disease is linked to neurotoxicity

  1. Oleksandr Yagensky  Is a corresponding author
  2. Mahdokht Kohansal-Nodehi
  3. Saravanan Gunaseelan
  4. Tamara Rabe
  5. Saima Zafar
  6. Inga Zerr
  7. Wolfgang Haertig
  8. Henning Urlaub
  9. John JE Chua  Is a corresponding author
  1. Max Planck Institute for Biophysical Chemistry, Germany
  2. National University of Singapore, Singapore
  3. University Medical Center Göttingen, Germany
  4. University of Leipzig, Germany
Research Article
  • Cited 5
  • Views 3,063
  • Annotations
Cite this article as: eLife 2019;8:e47498 doi: 10.7554/eLife.47498

Abstract

Alzheimer's disease is the most prevalent neurodegenerative disorder leading to progressive cognitive decline. Despite decades of research, understanding AD progression at the molecular level, especially at its early stages, remains elusive. Here, we identified several presymptomatic AD markers by investigating brain proteome changes over the course of neurodegeneration in a transgenic mouse model of AD (3×Tg-AD). We show that one of these markers, heme-binding protein 1 (Hebp1), is elevated in the brains of both 3×Tg-AD mice and patients affected by rapidly-progressing forms of AD. Hebp1, predominantly expressed in neurons, interacts with the mitochondrial contact site complex (MICOS) and exhibits a perimitochondrial localization. Strikingly, wildtype, but not Hebp1-deficient, neurons showed elevated cytotoxicity in response to heme-induced apoptosis. Increased survivability in Hebp1-deficient neurons is conferred by blocking the activation of the mitochondrial-associated caspase signaling pathway. Taken together, our data highlight a role of Hebp1 in progressive neuronal loss during AD progression.

Data availability

All data generated or analysed during this manuscript are included in the manuscript and supporting files. Source data files have been provided for Figs 2, 6, 7, 8 and 9.

The following previously published data sets were used

Article and author information

Author details

  1. Oleksandr Yagensky

    Research Group Protein Trafficking in Synaptic Development and Function, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
    For correspondence
    oleksandr.yagensky@mpibpc.mpg.de
    Competing interests
    The authors declare that no competing interests exist.

  2. Mahdokht Kohansal-Nodehi

    Department of Neurobiology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3898-5197

  3. Saravanan Gunaseelan

    Interactomics and Intracellular Trafficking Laboratory, Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
    Competing interests
    The authors declare that no competing interests exist.

  4. Tamara Rabe

    Department of Genes and Behavior, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
    Competing interests
    The authors declare that no competing interests exist.

  5. Saima Zafar

    Clinical Dementia Center, Department of Neurology, German Center for Neurodegenerative Diseases, University Medical Center Göttingen, Göttingen, Germany
    Competing interests
    The authors declare that no competing interests exist.

  6. Inga Zerr

    Clinical Dementia Center, Department of Neurology, German Center for Neurodegenerative Diseases, University Medical Center Göttingen, Göttingen, Germany
    Competing interests
    The authors declare that no competing interests exist.

  7. Wolfgang Haertig

    Paul Flechsig Institute for Brain Research, University of Leipzig, Leipzig, Germany
    Competing interests
    The authors declare that no competing interests exist.

  8. Henning Urlaub

    Bioanalytics, Institute of Clinical Chemistry, University Medical Center Göttingen, Göttingen, Germany
    Competing interests
    The authors declare that no competing interests exist.

  9. John JE Chua

    Interactomics and Intracellular Trafficking Laboratory, Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
    For correspondence
    phsjcje@nus.edu.sg
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5615-1014

Funding

Deutsche Forschungsgemeinschaft (CH 1385/1-1)

  • John JE Chua

National University of Singapore

  • John JE Chua

Max-Planck-Gesellschaft (Open-access funding)

  • John JE Chua

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

Ethics

Animal experimentation: All animal procedures used here fully comply with the guidelines as stipulated in the section 4 of the Animal Welfare Law of the Federal Republic of Germany (section 4 of TierSchG, Tierschutzgesetz der Bundesrepublik Deutschland) or in accordance with the Principles of Laboratory Animal Care, and approved by the Institutional Animal Care and Use Committee of the National University of Singapore (protocol number: 2015-01121 (R15-1121)). Procedures performed in the animal facility at the Max-Planck-Institute for Biophysical Chemistry, Göttingen, Germany were registered accordingly to the section 11 Abs. 1 TierSchG as documented by 39 20 00_2a Si/rö, dated 11th Dec 2013 ("Erlaubnis, Wirbeltiere zur Versuchszwecken zu züchten und zu halten"), by the Veterinär- und Verbraucherschutzamt für den Landkreis und die Stadt Göttingen and examined regularly by the supervisory veterinary authority of the Landkreis Göttingen. All procedures were supervised by the animal welfare officer and the animal welfare committee of the Max-Planck-Institute for Biophysical Chemistry, Göttingen, Germany established accordingly to the TierSchG and the regulation about animal used in experiments, dated on 1st Aug 2013 (TierSchVersV, Tierschutz-Versuchstier-Verordung).

Human subjects: All experimental protocols were approved and the study conformed to the Code of Ethics of the World Medical Association. All study participants or their legal next of kin gave informed consent and the study was approved by the local ethics committee in Göttingen (No. 24/8/12). All samples were anonymized with regard to their personal data.

Reviewing Editor

  1. Yukiko Goda, RIKEN, Japan

Publication history

  1. Received: April 8, 2019
  2. Accepted: August 25, 2019
  3. Accepted Manuscript published: August 27, 2019 (version 1)
  4. Version of Record published: September 10, 2019 (version 2)

Copyright

© 2019, Yagensky 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

  • 3,063
    Page views
  • 314
    Downloads
  • 5
    Citations

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

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)

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

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

Categories and tags

Research organisms

Further reading

    1. Neuroscience

    DeepEthogram, a machine learning pipeline for supervised behavior classification from raw pixels

    James P Bohnslav et al.
    Tools and Resources Updated

    Videos of animal behavior are used to quantify researcher-defined behaviors of interest to study neural function, gene mutations, and pharmacological therapies. Behaviors of interest are often scored manually, which is time-consuming, limited to few behaviors, and variable across researchers. We created DeepEthogram: software that uses supervised machine learning to convert raw video pixels into an ethogram, the behaviors of interest present in each video frame. DeepEthogram is designed to be general-purpose and applicable across species, behaviors, and video-recording hardware. It uses convolutional neural networks to compute motion, extract features from motion and images, and classify features into behaviors. Behaviors are classified with above 90% accuracy on single frames in videos of mice and flies, matching expert-level human performance. DeepEthogram accurately predicts rare behaviors, requires little training data, and generalizes across subjects. A graphical interface allows beginning-to-end analysis without end-user programming. DeepEthogram’s rapid, automatic, and reproducible labeling of researcher-defined behaviors of interest may accelerate and enhance supervised behavior analysis. Code is available at: https://github.com/jbohnslav/deepethogram.

    1. Developmental Biology
    2. Neuroscience

    Stretching of the retinal pigment epithelium contributes to zebrafish optic cup morphogenesis

    Tania Moreno-Mármol et al.
    Research Article

    The vertebrate eye-primordium consists of a pseudostratified neuroepithelium, the optic vesicle (OV), in which cells acquire neural retina or retinal pigment epithelium (RPE) fates. As these fates arise, the OV assumes a cup-shape, influenced by mechanical forces generated within the neural retina. Whether the RPE passively adapts to retinal changes or actively contributes to OV morphogenesis remains unexplored. We generated a zebrafish Tg(E1-bhlhe40:GFP) line to track RPE morphogenesis and interrogate its participation in OV folding. We show that, in virtual absence of proliferation, RPE cells stretch and flatten, thereby matching the retinal curvature and promoting OV folding. Localized interference with the RPE cytoskeleton disrupts tissue stretching and OV folding. Thus, extreme RPE flattening and accelerated differentiation are efficient solutions adopted by fast-developing species to enable timely optic cup formation. This mechanism differs in amniotes, in which proliferation drives RPE expansion with a much-reduced need of cell flattening.

    1. Neuroscience

    The cell adhesion molecule Sdk1 shapes assembly of a retinal circuit that detects localized edges

    Pierre-Luc Rochon et al.
    Research Article

    Nearly 50 different mouse retinal ganglion cell (RGC) types sample the visual scene for distinct features. RGC feature selectivity arises from their synapses with a specific subset of amacrine (AC) and bipolar cell (BC) types, but how RGC dendrites arborize and collect input from these specific subsets remains poorly understood. Here we examine the hypothesis that RGCs employ molecular recognition systems to meet this challenge. By combining calcium imaging and type-specific histological stains we define a family of circuits that express the recognition molecule Sidekick 1 (Sdk1) which include a novel RGC type (S1-RGC) that responds to local edges. Genetic and physiological studies revealed that Sdk1 loss selectively disrupts S1-RGC visual responses which result from a loss of excitatory and inhibitory inputs and selective dendritic deficits on this neuron. We conclude that Sdk1 shapes dendrite growth and wiring to help S1-RGCs become feature selective.