Tau polarizes an aging transcriptional signature to excitatory neurons and glia

Abstract

Aging is a major risk factor for Alzheimer’s disease (AD), and cell-type vulnerability underlies its characteristic clinical manifestations. We have performed longitudinal, single-cell RNA-sequencing in Drosophila with pan-neuronal expression of human tau, which forms AD neurofibrillary tangle pathology. Whereas tau- and aging-induced gene expression strongly overlap (93%), they differ in the affected cell types. In contrast to the broad impact of aging, tau-triggered changes are strongly polarized to excitatory neurons and glia. Further, tau can either activate or suppress innate immune gene expression signatures in a cell type-specific manner. Integration of cellular abundance and gene expression pinpoints Nuclear Factor Kappa B signaling in neurons as a marker for cellular vulnerability. We also highlight the conservation of cell type-specific transcriptional patterns between Drosophila and human postmortem brain tissue. Overall, our results create a resource for dissection of dynamic, age-dependent gene expression changes at cellular resolution in a genetically tractable model of tauopathy.

Data availability

All original single cell sequencing data have been uploaded to the Accelerating Medicines Parternship (AMP)-AD Knowledge Portal on Synapse and can be accessed through the DOI: https://doi.org/10.7303/syn35798807.1.

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

Article and author information

Author details

  1. Timothy Wu

    Medical Scientist Training Program, Baylor College of Medicine, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5296-2023
  2. Jennifer M Deger

    Medical Scientist Training Program, Baylor College of Medicine, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Hui Ye

    Department of Neurology, Baylor College of Medicine, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3965-9702
  4. Caiwei Guo

    Department of Neuroscience, Baylor College of Medicine, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Justin Dhindsa

    Medical Scientist Training Program, Baylor College of Medicine, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Brandon T Pekarek

    Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Rami Al-Ouran

    Department of Pediatrics, Baylor College of Medicine, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Zhandong Liu

    Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Ismael Al-Ramahi

    Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Juan Botas

    Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5476-5955
  11. Joshua M Shulman

    Department of Neurology, Baylor College of Medicine, Houston, United States
    For correspondence
    joshua.shulman@bcm.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1835-1971

Funding

National Institute on Aging (R01AG057339)

  • Zhandong Liu
  • Juan Botas
  • Joshua M Shulman

Huffington Foundation

  • Zhandong Liu
  • Juan Botas
  • Joshua M Shulman

McGee Family Foundation

  • Joshua M Shulman

Duncan Neurological Research Institute

  • Zhandong Liu
  • Ismael Al-Ramahi
  • Juan Botas
  • Joshua M Shulman

Effie Marie Caine Endowed Chair for Alzheimer's Research

  • Joshua M Shulman

National Institute on Aging (R01AG053960)

  • Joshua M Shulman

National Institute on Aging (U01AG061357)

  • Joshua M Shulman

National Institute on Aging (U01AG046161)

  • Joshua M Shulman

Eunice Kennedy Shriver National Institute of Child Health and Human Development (P50HD103555)

  • Joshua M Shulman

National Institutes of Health (S10OD023469)

  • Joshua M Shulman

National Institutes of Health (S10OD025240)

  • Joshua M Shulman

Cancer Prevention and Research Institute of Texas (RP200504)

  • Joshua M Shulman

Parkinson's Foundation (PF-PRF-830012)

  • Hui Ye

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

Copyright

© 2023, Wu 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

  • 193
    downloads

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. Timothy Wu
  2. Jennifer M Deger
  3. Hui Ye
  4. Caiwei Guo
  5. Justin Dhindsa
  6. Brandon T Pekarek
  7. Rami Al-Ouran
  8. Zhandong Liu
  9. Ismael Al-Ramahi
  10. Juan Botas
  11. Joshua M Shulman
(2023)
Tau polarizes an aging transcriptional signature to excitatory neurons and glia
eLife 12:e85251.
https://doi.org/10.7554/eLife.85251

Share this article

https://doi.org/10.7554/eLife.85251

Further reading

    1. Genetics and Genomics
    2. Microbiology and Infectious Disease
    Chinmaya Jena, Saillesh Chinnaraj ... Nishad Matange
    Research Advance

    Evolution of gene expression frequently drives antibiotic resistance in bacteria. We had previously (Patel and Matange, eLife, 2021) shown that, in Escherichia coli, mutations at the mgrB locus were beneficial under trimethoprim exposure and led to overexpression of dihydrofolate reductase (DHFR), encoded by the folA gene. Here, we show that DHFR levels are further enhanced by spontaneous duplication of a genomic segment encompassing folA and spanning hundreds of kilobases. This duplication was rare in wild-type E. coli. However, its frequency was elevated in a lon-knockout strain, altering the mutational landscape early during trimethoprim adaptation. We then exploit this system to investigate the relationship between trimethoprim pressure and folA copy number. During long-term evolution, folA duplications were frequently reversed. Reversal was slower under antibiotic pressure, first requiring the acquisition of point mutations in DHFR or its promoter. Unexpectedly, despite resistance-conferring point mutations, some populations under high trimethoprim pressure maintained folA duplication to compensate for low abundance DHFR mutants. We find that evolution of gene dosage depends on expression demand, which is generated by antibiotic and exacerbated by proteolysis of drug-resistant mutants of DHFR. We propose a novel role for proteostasis as a determinant of copy number evolution in antibiotic-resistant bacteria.

    1. Evolutionary Biology
    2. Genetics and Genomics
    James Boocock, Noah Alexander ... Leonid Kruglyak
    Research Article

    Expression quantitative trait loci (eQTLs) provide a key bridge between noncoding DNA sequence variants and organismal traits. The effects of eQTLs can differ among tissues, cell types, and cellular states, but these differences are obscured by gene expression measurements in bulk populations. We developed a one-pot approach to map eQTLs in Saccharomyces cerevisiae by single-cell RNA sequencing (scRNA-seq) and applied it to over 100,000 single cells from three crosses. We used scRNA-seq data to genotype each cell, measure gene expression, and classify the cells by cell-cycle stage. We mapped thousands of local and distant eQTLs and identified interactions between eQTL effects and cell-cycle stages. We took advantage of single-cell expression information to identify hundreds of genes with allele-specific effects on expression noise. We used cell-cycle stage classification to map 20 loci that influence cell-cycle progression. One of these loci influenced the expression of genes involved in the mating response. We showed that the effects of this locus arise from a common variant (W82R) in the gene GPA1, which encodes a signaling protein that negatively regulates the mating pathway. The 82R allele increases mating efficiency at the cost of slower cell-cycle progression and is associated with a higher rate of outcrossing in nature. Our results provide a more granular picture of the effects of genetic variants on gene expression and downstream traits.