MicroRNA-218 instructs proper assembly of hippocampal networks

  1. Seth R Taylor  Is a corresponding author
  2. Mariko Kobayashi
  3. Antonietta Vilella
  4. Durgesh Tiwari
  5. Norjin Zolboot
  6. Jessica X Du
  7. Kathryn R Spencer
  8. Andrea Hartzell
  9. Carol Girgiss
  10. Yusuf T Abaci
  11. Yufeng Shao
  12. Claudia De Sanctis
  13. Gian Carlo Bellenchi
  14. Robert B Darnell
  15. Christina Gross
  16. Michele Zoli
  17. Darwin K Berg
  18. Giordano Lippi  Is a corresponding author
  1. Brigham Young University, United States
  2. Howard Hughes Medical Institute, Rockefeller University, United States
  3. University of Modena and Reggio Emilia, Italy
  4. Cincinnati Children's Hospital Medical Center, United States
  5. Scripps Research Institute, United States
  6. University of California, San Diego, United States
  7. Institute of Genetics and Biophysics A Buzzati-Traverso, Italy

Abstract

The assembly of the mammalian brain is orchestrated by temporally coordinated waves of gene expression. Post-transcriptional regulation by microRNAs (miRNAs) is a key aspect of this program. Indeed, deletion of neuron-enriched miRNAs induces strong developmental phenotypes, and miRNA levels are altered in patients with neurodevelopmental disorders. However, the mechanisms used by miRNAs to instruct brain development remain largely unexplored. Here, we identified miR-218 as a critical regulator of hippocampal assembly. MiR-218 is highly expressed in the hippocampus and enriched in both excitatory principal neurons (PNs) and GABAergic inhibitory interneurons (INs). Early life inhibition of miR-218 results in an adult brain with a predisposition to seizures. Changes in gene expression in the absence of miR-218 suggest that network assembly is impaired. Indeed, we find that miR-218 inhibition results in the disruption of early depolarizing GABAergic signaling, structural defects in dendritic spines, and altered intrinsic membrane excitability. Conditional knockout of Mir218-2 in INs, but not PNs, is sufficient to recapitulate long-term instability. Finally, de-repressing Kif21b and Syt13, two miR-218 targets, phenocopies the effects on early synchronous network activity induced by miR-218 inhibition. Taken together, the data suggest that miR-218 orchestrates formative events in PNs and INs to produce stable networks.

Data availability

RNA-seq data has been deposited to GEO (accession number GSE241245)

The following data sets were generated

Article and author information

Author details

  1. Seth R Taylor

    Division of Biological Sciences, Brigham Young University, Provo, United States
    For correspondence
    seth_taylor@byu.edu
    Competing interests
    The authors declare that no competing interests exist.
  2. Mariko Kobayashi

    Laboratory of Molecular Neuro-oncology, Howard Hughes Medical Institute, Rockefeller University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Antonietta Vilella

    Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
    Competing interests
    The authors declare that no competing interests exist.
  4. Durgesh Tiwari

    Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Norjin Zolboot

    Department of Neuroscience, Scripps Research Institute, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Jessica X Du

    Department of Neuroscience, Scripps Research Institute, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Kathryn R Spencer

    Department of Neuroscience, Scripps Research Institute, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Andrea Hartzell

    Department of Neuroscience, Scripps Research Institute, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Carol Girgiss

    Division of Biological Sciences, University of California, San Diego, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Yusuf T Abaci

    Division of Biological Sciences, University of California, San Diego, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. Yufeng Shao

    Department of Neuroscience, Scripps Research Institute, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
  12. Claudia De Sanctis

    Institute of Genetics and Biophysics A Buzzati-Traverso, Naples, Italy
    Competing interests
    The authors declare that no competing interests exist.
  13. Gian Carlo Bellenchi

    Institute of Genetics and Biophysics A Buzzati-Traverso, Naples, Italy
    Competing interests
    The authors declare that no competing interests exist.
  14. Robert B Darnell

    Laboratory of Molecular Neuro-oncology, Howard Hughes Medical Institute, Rockefeller University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5134-8088
  15. Christina Gross

    Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6057-2527
  16. Michele Zoli

    Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
    Competing interests
    The authors declare that no competing interests exist.
  17. Darwin K Berg

    Division of Biological Sciences, University of California, San Diego, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
  18. Giordano Lippi

    Department of Neuroscience, Scripps Research Institute, La Jolla, United States
    For correspondence
    glippi@scripps.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3911-0525

Funding

National Institutes of Health (1 S10 OD026817-01)

  • Giordano Lippi

Ministero dell'Istruzione, dell'Università e della Ricerca (1R01NS092705)

  • Michele Zoli

National Institutes of Health (2R01NS012601)

  • Darwin K Berg

National Institutes of Health (1R21NS087342)

  • Darwin K Berg

National Institutes of Health (1R01NS121223)

  • Giordano Lippi

National Institutes of Health (1R01NS092705)

  • Christina Gross

Tobacco-Related Disease Research Program (22XT-0016,21FT-0027)

  • Darwin K Berg

Whitehall Foundation (2018-12-55)

  • Giordano Lippi

Autism Speaks (12923)

  • Norjin Zolboot

American Epilepsy Society (12923)

  • Andrea Hartzell

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 experimental procedures at UCSD, CCHMH and SRI were performed as approved by the Institutional Animal Care and Use Committees and according to the National Institutes of Health Guidelines for the Care and Use of Laboratory Animals. Behavioral and in vivo experiments at UNIMORE were conducted in accordance with the European Community Council Directive (86/609/EEC) of November 24, 1986, and approved by the ethics committee (authorization number: 37/2018PR).

Copyright

© 2023, Taylor 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

  • 1,168
    views
  • 179
    downloads
  • 5
    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. Seth R Taylor
  2. Mariko Kobayashi
  3. Antonietta Vilella
  4. Durgesh Tiwari
  5. Norjin Zolboot
  6. Jessica X Du
  7. Kathryn R Spencer
  8. Andrea Hartzell
  9. Carol Girgiss
  10. Yusuf T Abaci
  11. Yufeng Shao
  12. Claudia De Sanctis
  13. Gian Carlo Bellenchi
  14. Robert B Darnell
  15. Christina Gross
  16. Michele Zoli
  17. Darwin K Berg
  18. Giordano Lippi
(2023)
MicroRNA-218 instructs proper assembly of hippocampal networks
eLife 12:e82729.
https://doi.org/10.7554/eLife.82729

Share this article

https://doi.org/10.7554/eLife.82729

Further reading

    1. Neuroscience
    Christine Ahrends, Mark W Woolrich, Diego Vidaurre
    Tools and Resources

    Predicting an individual’s cognitive traits or clinical condition using brain signals is a central goal in modern neuroscience. This is commonly done using either structural aspects, such as structural connectivity or cortical thickness, or aggregated measures of brain activity that average over time. But these approaches are missing a central aspect of brain function: the unique ways in which an individual’s brain activity unfolds over time. One reason why these dynamic patterns are not usually considered is that they have to be described by complex, high-dimensional models; and it is unclear how best to use these models for prediction. We here propose an approach that describes dynamic functional connectivity and amplitude patterns using a Hidden Markov model (HMM) and combines it with the Fisher kernel, which can be used to predict individual traits. The Fisher kernel is constructed from the HMM in a mathematically principled manner, thereby preserving the structure of the underlying model. We show here, in fMRI data, that the HMM-Fisher kernel approach is accurate and reliable. We compare the Fisher kernel to other prediction methods, both time-varying and time-averaged functional connectivity-based models. Our approach leverages information about an individual’s time-varying amplitude and functional connectivity for prediction and has broad applications in cognitive neuroscience and personalised medicine.

    1. Neuroscience
    Jessica Royer, Valeria Kebets ... Boris C Bernhardt
    Research Article Updated

    Complex structural and functional changes occurring in typical and atypical development necessitate multidimensional approaches to better understand the risk of developing psychopathology. Here, we simultaneously examined structural and functional brain network patterns in relation to dimensions of psychopathology in the Adolescent Brain Cognitive Development (ABCD) dataset. Several components were identified, recapitulating the psychopathology hierarchy, with the general psychopathology (p) factor explaining most covariance with multimodal imaging features, while the internalizing, externalizing, and neurodevelopmental dimensions were each associated with distinct morphological and functional connectivity signatures. Connectivity signatures associated with the p factor and neurodevelopmental dimensions followed the sensory-to-transmodal axis of cortical organization, which is related to the emergence of complex cognition and risk for psychopathology. Results were consistent in two separate data subsamples and robust to variations in analytical parameters. Although model parameters yielded statistically significant brain–behavior associations in unseen data, generalizability of the model was rather limited for all three latent components (r change from within- to out-of-sample statistics: LC1within = 0.36, LC1out = 0.03; LC2within = 0.34, LC2out = 0.05; LC3within = 0.35, LC3out = 0.07). Our findings help in better understanding biological mechanisms underpinning dimensions of psychopathology, and could provide brain-based vulnerability markers.