Identification of a molecular basis for the juvenile sleep state
Abstract
Across species, sleep in young animals is critical for normal brain maturation. The molecular determinants of early life sleep remain unknown. Through an RNAi-based screen, we identified a gene, pdm3, required for sleep maturation in Drosophila. Pdm3, a transcription factor, coordinates an early developmental program that prepares the brain to later execute high levels of juvenile adult sleep. PDM3 controls the wiring of wake-promoting dopaminergic (DA) neurites to a sleep-promoting region, and loss of PDM3 prematurely increases DA inhibition of the sleep center, abolishing the juvenile sleep state. RNA-Seq/ChIP-Seq and a subsequent modifier screen reveal that pdm3 represses expression of the synaptogenesis gene Msp300 to establish the appropriate window for DA innervation. These studies define the molecular cues governing sleep behavioral and circuit development, and suggest sleep disorders may be of neurodevelopmental origin.
Data availability
RNA sequencing data deposited to the Gene Expression Omnibus (GSE147337).
-
RNA-Seq of whole Drosophila brains (mid-pupal stage) with panneuronal pdm3 knockdown and controlsNCBI Gene Expression Omnibus, GSE147337.
Article and author information
Author details
Funding
National Institutes of Health (K08 NS090461)
- Matthew S Kayser
National Institutes of Health (DP2 NS111996)
- Matthew S Kayser
National Institutes of Health (T32 HL007953)
- Leela Chakravarti Dilley
National Institutes of Health (F31 NS105447)
- Leela Chakravarti Dilley
Burroughs Wellcome Fund
- Rajan Jain
Burroughs Wellcome Fund
- Matthew S Kayser
March of Dimes Foundation
- Matthew S Kayser
Alfred P. Sloan Foundation
- Matthew S Kayser
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2020, Chakravarti Dilley 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
-
- 3,331
- views
-
- 463
- downloads
-
- 20
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
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)
Further reading
-
- Genetics and Genomics
RNA-binding proteins (RBPs) perform diverse functions including the regulation of chromatin dynamics and the coupling of transcription with RNA processing. However, our understanding of their actions in mammalian neurons remains limited. Using affinity purification, yeast-two-hybrid and proximity ligation assays, we identified interactions of multiple RBPs with neural retina leucine (NRL) zipper, a Maf-family transcription factor critical for retinal rod photoreceptor development and function. In addition to splicing, many NRL-interacting RBPs are associated with R-loops, which form during transcription and increase during photoreceptor maturation. Focusing on DHX9 RNA helicase, we demonstrate that its expression is modulated by NRL and that the NRL–DHX9 interaction is positively influenced by R-loops. ssDRIP-Seq analysis reveals both stranded and unstranded R-loops at distinct genomic elements, characterized by active and inactive epigenetic signatures and enriched at neuronal genes. NRL binds to both types of R-loops, suggesting an epigenetically independent function. Our findings suggest additional functions of NRL during transcription and highlight complex interactions among transcription factors, RBPs, and R-loops in regulating photoreceptor gene expression in the mammalian retina.
-
- Computational and Systems Biology
- Genetics and Genomics
Root causal gene expression levels – or root causal genes for short – correspond to the initial changes to gene expression that generate patient symptoms as a downstream effect. Identifying root causal genes is critical towards developing treatments that modify disease near its onset, but no existing algorithms attempt to identify root causal genes from data. RNA-sequencing (RNA-seq) data introduces challenges such as measurement error, high dimensionality and non-linearity that compromise accurate estimation of root causal effects even with state-of-the-art approaches. We therefore instead leverage Perturb-seq, or high-throughput perturbations with single-cell RNA-seq readout, to learn the causal order between the genes. We then transfer the causal order to bulk RNA-seq and identify root causal genes specific to a given patient for the first time using a novel statistic. Experiments demonstrate large improvements in performance. Applications to macular degeneration and multiple sclerosis also reveal root causal genes that lie on known pathogenic pathways, delineate patient subgroups and implicate a newly defined omnigenic root causal model.