Summary Circadian clocks regulate membrane excitability in master pacemaker neurons to control daily rhythms of sleep and wake. Here we find that two distinctly timed electrical drives collaborate to impose rhythmicity on Drosophila clock neurons. In the morning, a voltage-independent sodium conductance via the NA/NALCN ion channel depolarizes these neurons. This current is driven by the rhythmic expression of NCA localization factor-1, linking the molecular clock to ion channel function. In the evening, basal potassium currents peak to silence clock neurons. Remarkably, daily antiphase cycles of sodium and potassium currents also drive mouse clock neuron rhythms. Thus, we reveal an evolutionarily ancient strategy for the neural mechanisms that govern daily sleep and wake.

To compare circadian gene expression within highly discrete neuronal populations, we separately purified and characterized two adjacent but distinct groups of Drosophila adult circadian neurons: the 8 small and 10 large PDF-expressing ventral lateral neurons (s-LNvs and l-LNvs, respectively). The s-LNvs are the principal circadian pacemaker cells, whereas recent evidence indicates that the l-LNvs are involved in sleep and light-mediated arousal. Although half of the l-LNv-enriched mRNA population, including core clock mRNAs, is shared between the l-LNvs and s-LNvs, the other half is l-LNv-and s-LNv-specific. The distribution of four specific mRNAs is consistent with prior characterization of the four encoded proteins, and therefore indicates successful purification of the two neuronal types. Moreover, an octopamine receptor mRNA is selectively enriched in l-LNvs, and only these neurons respond to in vitro application of octopamine. Dissection and purification of l-LNvs from flies collected at different times indicate that these neurons contain cycling clock mRNAs with higher circadian amplitudes as well as at least a 10-fold higher fraction of oscillating mRNAs than all previous analyses of head RNA. Many of these cycling l-LNv mRNAs are well expressed but do not cycle or cycle much less well elsewhere in heads. The results suggest that RNA cycling is much more prominent in circadian neurons than elsewhere in heads and may be particularly important for the functioning of these neurons.PDF neurons | microarrays | cycling mRNAs M any biochemical, physiological, and behavioral processes are governed by a circadian clock, which results in daily oscillations with a period of approximately 24 h. Circadian phenomena have been studied in multiple eukaryotic and even prokaryotic systems, and a large body of evidence now indicates that the biochemical underpinnings of eukaryotic molecular clocks include negative feedback loops of transcription (1-3). These cell-autonomous feedback loops also regulate the expression of so-called "output genes," many of which regulate circadian functions other than core timekeeping (1). In addition to this transcriptional regulation, posttranscriptional modifications such as phosphorylation regulate the stability and activity of clock proteins, and therefore also contribute to accurate timing as well as to robust mRNA and protein oscillations (4-8).In Drosophila, there are about 75 clock neurons on each side of the adult brain. They control adult Drosophila locomotor activity, which peaks twice a day in anticipation of the dawn and dusk transitions. The clock neurons are divided into seven classes based on their anatomical locations and characteristics (9, 10). There are three groups of dorsal neurons (DN1, DN2, and DN3), a lateral posterior neuron, and three groups of lateral neurons. These are the dorsal lateral neurons and the two groups of lateral neurons: the small ventral lateral neurons (s-LNvs) and the large ventral lateral neurons (l-LNvs). Although many genes are expressed similar...

Studies in Drosophila circadian neurons reveal a bifurcation in the Pigment Dispersing Factor (PDF) neuropeptide signaling pathway, independently synchronizing circadian clocks via PKA or acutely controlling neuronal excitability via cAMP.

SignificanceDetermining the state of an individual’s internal physiological clock has important implications for precision medicine, from diagnosing neurological disorders to optimizing drug delivery. To be useful, such a test must be accurate, minimally burdensome to the patient, and robust to differences in patient protocols, sample collection, and assay technologies. TimeSignature is a machine-learning approach to predict physiological time based on gene expression in human blood. A powerful feature is TimeSignature’s generalizability, enabling it to be applied to samples from disparate studies and yield highly accurate results despite systematic differences between the studies. This quality is unique among expression-based predictors and addresses a major challenge in the development of reliable and clinically useful biomarker tests.