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Inference of RNA decay rate from transcriptional profiling highlights the regulatory programs of Alzheimer’s disease

Author

Listed:
  • Rached Alkallas

    (McGill University
    McGill University and Genome Quebec Innovation Centre)

  • Lisa Fish

    (University of California
    University of California
    University of California)

  • Hani Goodarzi

    (University of California
    University of California
    University of California)

  • Hamed S. Najafabadi

    (McGill University
    McGill University and Genome Quebec Innovation Centre)

Abstract

The abundance of mRNA is mainly determined by the rates of RNA transcription and decay. Here, we present a method for unbiased estimation of differential mRNA decay rate from RNA-sequencing data by modeling the kinetics of mRNA metabolism. We show that in all primary human tissues tested, and particularly in the central nervous system, many pathways are regulated at the mRNA stability level. We present a parsimonious regulatory model consisting of two RNA-binding proteins and four microRNAs that modulate the mRNA stability landscape of the brain, which suggests a new link between RBFOX proteins and Alzheimer’s disease. We show that downregulation of RBFOX1 leads to destabilization of mRNAs encoding for synaptic transmission proteins, which may contribute to the loss of synaptic function in Alzheimer’s disease. RBFOX1 downregulation is more likely to occur in older and female individuals, consistent with the association of Alzheimer’s disease with age and gender.

Suggested Citation

  • Rached Alkallas & Lisa Fish & Hani Goodarzi & Hamed S. Najafabadi, 2017. "Inference of RNA decay rate from transcriptional profiling highlights the regulatory programs of Alzheimer’s disease," Nature Communications, Nature, vol. 8(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-00867-z
    DOI: 10.1038/s41467-017-00867-z
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    Cited by:

    1. Anneke Brümmer & Sven Bergmann, 2024. "Disentangling genetic effects on transcriptional and post-transcriptional gene regulation through integrating exon and intron expression QTLs," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    2. Ariel Madrigal & Tianyuan Lu & Larisa M. Soto & Hamed S. Najafabadi, 2024. "A unified model for interpretable latent embedding of multi-sample, multi-condition single-cell data," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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