Upon stimulation, immediate-early genes (IEGs) are induced rapidly and transiently without de novo protein synthesis in mammalian neurons (Fowler et al., 2011). This activity-dependent gene regulation is important as it affects the ability of an animal to convert transient stimuli into long-term changes (Huh et al., 2000; Kandel, 2001; Nestler, 2001; Spitzer et al., 2000). Hundreds of IEGs, including those specific to tissues and stimulation paradigms, have been identified in mammals since the discovery of the first neuronal IEG, c-fos, by Greenberg and his colleagues in 1986 (Greenberg et al., 1986; Herschman, 1991; Spiegel et al., 2014). Expression of these IEGs are induced within an hour, usually with big amplitudes (over 10 fold), and many IEGs are shared between different types of neurons (Spiegel et al., 2014). Functionally, these IEGs are highly enriched for transcription factors, which subsequently trigger a secondary transcriptional response (Spiegel et al., 2014; West and Greenberg, 2011). The secondary response genes (SRGs) in contrast take longer to induce (a typical assay is 6 hr post-stimulation), are involved in many different processes, are more neuron-specific and function at least in part to promote neuron survival, dendritic morphogenesis and regulate synapse formation (Bloodgood et al., 2013; Hong et al., 2008; Lin et al., 2008).

Not all IEGs respond to all types of stimulations, even the most robust ones like c-fos, Egr1 and Arc (Bepari et al., 2012; Fields et al., 1997). For example, different stimulation paradigm-dependent Ca²⁺ entry routes initiate different downstream pathways and lead to induction of distinct IEGs (West and Greenberg, 2011). Stimulations other than neural firing like growth factors also induce IEG expression, many of which are the same as those induced with neural firing (Jones et al., 1988; Tullai et al., 2007). Depending on the induction dynamics, Tullai et al. divided the platelet-derived growth factor (PDGF)-induced IEGs in human T98G glioblastoma cells into primary response genes (PRGs) and delayed response genes (DRGs). Dramatic differences were shown between the two categories in terms of their functions, gene lengths, chromatin accessibility and RNA polymerase II occupancy at promoter regions (Fowler et al., 2011; Kim et al., 2010; Tullai et al., 2007). PRGs are usually optimized for rapid induction (such as shorter gene length and more permissive chromatin at promoters), whereas DRGs are not different from other genes in the genome. Notably, PRGs and DRGs are induced independent of protein-synthesis (‘cycloheximide-insensitive’), whereas SRGs are protein-synthesis dependent (‘cycloheximide-sensitive’).

IEGs, or activity-regulated genes (ARGs; they are defined here as induced rapidly with neuronal activity, i.e., mostly within an hour, but without regard to de novo protein synthesis) are poorly defined in organisms other than mammals. Only three genes to date have been identified as responding to increased neural activity in insects: kakusei (in honey bee), stripe (abbreviated as sr, in honey bee) and hr38 (in silkmoth and the fruit fly Drosophila melanogaster) (Fujita et al., 2013; Kiya et al., 2007; Lutz and Robinson, 2013). Moreover, only one genome-wide study using microarrays had been performed more than a decade ago to identify seizure-induced ARGs from fly heads (Guan et al., 2005). More high-throughput studies on Drosophila ARGs are therefore needed, not only for identification and to provide mechanistic insight but also to design new tools to serve as indicators of neuronal activity.

Here, we identified ARGs in a genome-wide manner, in fly brains as well as in sorted neurons; they included dopaminergic neurons (DA) and a subset of circadian-related neurons (PDF+ neurons). Fly ARGs vary with the individual stimulation paradigm and are surprisingly cell type-specific. Fly ARGs are also more functionally diverse and have longer gene lengths compared to mammalian ARGs. Chromatin at transcription start sites (TSS) of fly ARGs is more accessible at baseline than other expressed genes but does not change with stimulation. Lastly, we used bioinformatics to identify key transcription factors that mediate ARG activation. Based on these factors, we generated novel luciferase reporters for in vivo monitoring of neuronal firing.

Using high-throughput sequencing, we identified ARGs in fly brains in response to ChR2-XXL, dTrpA1 and KCl-induced neural firing. Each stimulation paradigm induced expression of about 100 genes, and there is substantial overlap between the 3 sets of ARGs (Figure 3D). The most robust ARGs are the two known genes identified previously, hr38 and sr; they are induced dramatically by ChR2-XXL and dTrpA1 (Figure 3E). They are also induced with KCl albeit with smaller amplitudes (Figure 3E). 10 additional genes are also induced by all three protocols, and even more genes are induced by at least two paradigms (Figure 3D). Although the results dramatically increase the number of common ARGs, there are also a large number of specialized ARG candidate genes (Figure 3D). Not surprisingly perhaps, KCl has the most different ARGs. This is presumably because it is the only in vitro stimulation paradigm and stimulates the whole brain including non-neuronal cells and tissues.

We also used the dTrpA1 paradigm to identify ARGs in sorted neurons. Many of them are specific to neuron type, and even the most robust brain ARGs are not necessarily induced by firing in every region of the brain (Figure 5, Figure 5—figure supplement 1). This is in contrast to heat-induced genes, most of which are shared by the three sources of RNA (Figure 5—figure supplement 2.).

Heat-induced gene expression unfortunately creates a complicated situation: some putative specific ARGs are also induced with the 18°C to 29°C shift in control (no dTrpA1) samples. These genes are thus excluded from the ARG list because they are defined as heat-induced by the cutoffs used in the analysis. Because it is possible that the temperature shift can lead to neuronal firing, e.g., by activating endogenous dTrpA1, there is currently no clear distinction between heat- and firing-induced genes. Future experiments with other tools like ChR2-XXL may be able to provide more information. In any case, the neuron-specific ARGs probably contribute to different physiological responses, which contribute in turn to distinct behavioral responses (Guo et al., 2014; Ueno et al., 2012). The results also provide good candidates as neural activity markers, e.g., Figure 7, and suggest that multiple genes should be tried, to accommodate cell type as well as firing heterogeneity.

Although the in vivo nature of our most important stimulation paradigms precluded using inhibitors to assay the dependence of ARGs on de novo protein synthesis (data not shown), the induction time scales of these fly ARGs, 60–90 min, resemble those of mammalian IEGs. A comparison between fly ARGs with mammalian IEGs therefore seems appropriate.

Whereas mammalian IEGs are highly enriched in transcription factors (Spiegel et al., 2014), they are less enriched in the fly brain and sorted neuron ARGs: ChR2-XXL induced ARGs contain only seven transcription factors among 96 genes; this is a much smaller fraction than in mammals (Figure 1D). KCl-induced ARGs show no enrichment in functions, and dTrpA1-induced ARGs in the cell types have a weak enrichment in different functions, such as steroid hormone receptor activity in brains, imaginal disc growth factor receptor binding in DA neurons and organic anion transmembrane transporter activity in PDF+ neurons (Table 1). The weak enrichment suggests diverse ARG functions, which resemble more closely mammalian DRGs or SRGs rather than PRGs (see below).

Less transcription factor activity and more diverse functions are consistent with the fact that fly homologues of the two most universal mammalian IEGs c-fos and c-jun, kayak and jra respectively, are not induced or poorly induced under most conditions. This is especially true for kayak; jra is induced by both dTrpA1 and KCl in brain but with quite small fold changes (Figure 3—source data 1 and 2).

We also noticed that most fly ARGs show rather small fold-changes upon stimulation (Figure 8). It is unlikely that this is due to brain tissue heterogeneity as similar results were obtained with the sorted neurons. A similar weak induction of seizure-induced genes was previously reported in fly heads (Guan et al., 2005). As only the first 60–90 min after stimulation was assayed, it is possible that the small fold-changes reflects a slower induction time course in flies than in mammals. Another possibility is that the nature of the firing, e.g., firing rate, has an impact on the induction amplitudes, time courses and even the genes induced. The differences between the three stimulation paradigms (Figure 3) indicate that this possibility may have some merit. Nonetheless and taken together with the stimulation-paradigm and cell type-specific induction of ARGs, the modest fold-changes at 60–90 min helps explain the long-standing failure to identify bona fide and well-accepted ARGs in flies.

Figure 8

Download asset Open asset

Instead of having shorter average gene lengths like in mammals, fly ARGs (brain as well as sorted neurons) show even longer gene length distributions than average fly genes (Figure 2C, Figure 9). Several robust ARGs are above 10 kb with large first introns (Figure 2—figure supplement 1). For example, hr38 is ~31 kb and shares high protein sequence identity with the mammalian protein encoded by Nr4a2; it spans only ~16.8 kb. sr, the shorter isoform of which is induced by firing, spans ~11.7 kb, whereas EGR3, the mouse gene that encodes a protein with high identity to SR, is only ~5.2 kb in length. Considering the much smaller genome size and gene size of flies compared to mammals and the potential slower transcription elongation rates in flies, the long gene lengths and large first introns of fly ARGs may counterintuitively serve to significantly slow down induction rates (Ardehali and Lis, 2009). Another possibility is that the large gene size reflects important regulatory elements for firing-mediated transcription embedded in the introns of these genes. Perhaps they are relevant to the diversity and cell type specificity of most fly ARGs.

Figure 9

Download asset Open asset

From this point of view, fly ARGs resemble more closely mammalian DRGs or even SRGs than IEGs (Spiegel et al., 2014; Tullai et al., 2007). Mammalian IEGs are largely shared between different types of neurons such as excitatory and inhibitory neurons (Spiegel et al., 2014), but fly ARGs like SRGs appear quite tissue-type specific, i.e., many genes are only induced in one of the tissue types tested despite similar baseline levels (Figure 5) (Mardinly et al., 2016). This is in line with the fact that fly neurons appear quite heterogeneous, e.g., they have distinct transcript enrichment and electrophysiological properties (Liang et al., 2016; Nagoshi et al., 2010). Although unlikely in our view, it remains possible that PDF+ and DA neurons are outliers and that ARGs in most other neurons are less cell type-specific. Nonetheless, we hypothesize that specific ARGs are an important feature of neuronal identity and play a role in neuron-specific physiological properties. As an example, preliminary analysis indicates that the PDF cell-ARGs are a subset of the mRNAs that undergo circadian oscillations within these neurons ([Kula-Eversole et al., 2010] and data not shown).

The non-identical ARGs with the different paradigms also suggest different mechanisms for responding to different modes of stimulation. A less stereotyped response may be generally characteristic of invertebrates. Even non-mammalian vertebrates may be quantitatively if not qualitatively different from mammals. For example, induction of IEGs like fos and egr1 was also observed in birds and zebrafish but with smaller increases (about 4 to 6-fold) than human and mice (usually well above 10-fold) (Bébien et al., 2003; Burmeister and Fernald, 2005; Lopes et al., 2015; Mokin and Keifer, 2005; Moore and Whitmore, 2014). However, more high-throughput data are required to determine if the non-mammalian vertebrates resemble more closely flies or mammals. In addition, different stimulation paradigms were employed in the non-mammalian vertebrate studies, making the data less comparable. In any case, one can imagine that mammals need a more potent initial transcriptional system than flies to respond to firing. Not only are mammalian neurons bigger with more and longer processes, but they also have much larger genomes (longer genomic DNA and more enhancers to bind); both of these factors may require higher levels of transcripts to generate a required higher abundance of transcription factors. The increase may be required to activate the thousands of enhancers in mammals rather than the hundreds in flies, and/or more structural proteins are needed to change the morphology of ‘giant’ mammalian neurons.

Nonetheless, one feature shared by fly ARGs and mammalian IEGs is the relatively permissive state of the TSS chromatin structure (Fowler et al., 2011). ATAC-seq shows that the chromatin accessibility of the ARG TSS is higher than that of other genes (Figure 6D), and chromatin architecture contributes to gene expression regulation (Wu, 1997). Although no further opening is observed with firing, it is possible that more subtle changes such as histone modifications occur and are not detectable with ATAC-seq (Figure 6A). In the cases of mammals, histones around IEGs promoters are modified to be more accessible to transcription factors at baseline; the modification levels could be even higher with firing (Kim et al., 2010). In any case, the data taken together suggest some blurring of the distinction between IEGs and SRGs in fly ARGs: open chromatin and rapid induction but a diverse set of neuron-specific functions.

Finally, we successfully generated luciferase reporters for in vivo neural activity monitoring based on the ChR2-XXL data (Figure 7). Rather than using the regulatory region of a single gene to construct a reporter, we generalized a previous strategy (Tanenhaus et al., 2012) and used enriched transcription factor binding sites combined with FRT sites for cell type specific expression and a short-lived luciferase gene. This was not only to amplify the signal but also to avoid the situation in which a particular promoter is only responsive in certain neurons or to certain types of stimulation. We thought this was likely considering that ARGs are stimulation paradigm- and cell type-specific. Indeed, we tried many Janelia Research Campus GAL4 drivers, which use different regions of the hr38, sr and cbt promoters for neural activity monitoring, but they were all either unresponsive or generated very low luciferase levels (data not shown).

Two of the three positive reporters (Figure 7) work well for circadian monitoring of neuronal activity in PDF+ neurons (data not shown), suggesting the signal to noise ratio is sufficient for monitoring small subsets of neurons. This result and others indicate that our strategy as well as these specific reporters can provide the temporal and spatial resolution for in vivo monitoring of small numbers of specific neurons. The strategy also builds a foundation for future elaboration and optimization.

1. 1. Xiao Chen

   (2016) Genome-wide Identification of Neuronal Activity-regulated Genes in Drosophila

   Publicly available at NCBI Gene Expression Omnibus (accession no: GSE83976).

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE83976

1. 1. Abruzzi K
   2. Chen X
   3. Nagoshi E
   4. Zadina A
   5. Rosbash M

   (2015) RNA-seq profiling of small numbers of drosophila neurons

   Methods in enzymology 551:369–386.

   https://doi.org/10.1016/bs.mie.2014.10.025

   • PubMed
   • Google Scholar
2. 1. Anders S
   2. Pyl PT
   3. Huber W

   (2015) HTSeq--a Python framework to work with high-throughput sequencing data

   Bioinformatics 31:166–169.

   https://doi.org/10.1093/bioinformatics/btu638

   • PubMed
   • Google Scholar
3. 1. Ardehali MB
   2. Lis JT

   (2009) Tracking rates of transcription and splicing in vivo

   Nature Structural Biol Biology 16:1123–1124.

   https://doi.org/10.1038/nsmb1109-1123

   • PubMed
   • Google Scholar
4. 1. Bébien M
   2. Salinas S
   3. Becamel C
   4. Richard V
   5. Linares L
   6. Hipskind RA

   (2003) Immediate-early gene induction by the stresses anisomycin and arsenite in human osteosarcoma cells involves MAPK cascade signaling to Elk-1, CREB and SRF

   Oncogene 22:1836–1847.

   https://doi.org/10.1038/sj.onc.1206334

   • PubMed
   • Google Scholar
5. 1. Bepari AK
   2. Sano H
   3. Tamamaki N
   4. Nambu A
   5. Tanaka KF
   6. Takebayashi H

   (2012) Identification of optogenetically activated striatal medium spiny neurons by Npas4 expression

   PLoS One 7:e52783.

   https://doi.org/10.1371/journal.pone.0052783

   • PubMed
   • Google Scholar
6. 1. Bloodgood BL
   2. Sharma N
   3. Browne HA
   4. Trepman AZ
   5. Greenberg ME

   (2013) The activity-dependent transcription factor NPAS4 regulates domain-specific inhibition

   Nature 503:121–125.

   https://doi.org/10.1038/nature12743

   • PubMed
   • Google Scholar
7. 1. Brandes C
   2. Plautz JD
   3. Stanewsky R
   4. Jamison CF
   5. Straume M
   6. Wood KV
   7. Kay SA
   8. Hall JC

   (1996) Novel features of drosophila period transcription revealed by real-time luciferase reporting

   Neuron 16:687–692.

   https://doi.org/10.1016/S0896-6273(00)80088-4

   • PubMed
   • Google Scholar
8. 1. Budnik V
   2. White K

   (1987) Genetic dissection of dopamine and serotonin synthesis in the nervous system of drosophila melanogaster

   Journal of Neurogenetics 4:309–314.

   https://doi.org/10.3109/01677068709102351

   • PubMed
   • Google Scholar
9. 1. Burmeister SS
   2. Fernald RD

   (2005) Evolutionary conservation of the egr-1 immediate-early gene response in a teleost

   The Journal of Comparative Neurology 481:220–232.

   https://doi.org/10.1002/cne.20380

   • PubMed
   • Google Scholar
10. 1. Cao G
    2. Platisa J
    3. Pieribone VA
    4. Raccuglia D
    5. Kunst M
    6. Nitabach MN

    (2013) Genetically targeted optical electrophysiology in intact neural circuits

    Cell 154:904–913.

    https://doi.org/10.1016/j.cell.2013.07.027

    • PubMed
    • Google Scholar
11. 1. Dawydow A
    2. Gueta R
    3. Ljaschenko D
    4. Ullrich S
    5. Hermann M
    6. Ehmann N
    7. Gao S
    8. Fiala A
    9. Langenhan T
    10. Nagel G
    11. Kittel RJ

    (2014) Channelrhodopsin-2-XXL, a powerful optogenetic tool for low-light applications

    PNAS 111:13972–13977.

    https://doi.org/10.1073/pnas.1408269111

    • PubMed
    • Google Scholar
12. 1. Edelstein A
    2. Amodaj N
    3. Hoover K
    4. Vale R
    5. Stuurman N

    (2010) Computer control of microscopes using µManager

    Current Protocols in Molecular Biology 14:.

    https://doi.org/10.1002/0471142727.mb1420s92

    • PubMed
    • Google Scholar
13. 1. Eden E
    2. Lipson D
    3. Yogev S
    4. Yakhini Z

    (2007) Discovering motifs in ranked lists of DNA sequences

    PLoS Computational Biology 3:e39.

    https://doi.org/10.1371/journal.pcbi.0030039

    • PubMed
    • Google Scholar
14. 1. Eden E
    2. Navon R
    3. Steinfeld I
    4. Lipson D
    5. Yakhini Z

    (2009) GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists

    BMC Bioinformatics 10:48.

    https://doi.org/10.1186/1471-2105-10-48

    • PubMed
    • Google Scholar
15. 1. Fields RD
    2. Eshete F
    3. Stevens B
    4. Itoh K

    (1997)

    Action potential-dependent regulation of gene expression: temporal specificity in ca2+, cAMP-responsive element binding proteins, and mitogen-activated protein kinase signaling

    Journal of Neuroscience  17:7252–7266.

    • PubMed
    • Google Scholar
16. 1. Fowler T
    2. Sen R
    3. Roy AL

    (2011) Regulation of primary response genes

    Molecular Cell 44:348–360.

    https://doi.org/10.1016/j.molcel.2011.09.014

    • PubMed
    • Google Scholar
17. 1. Fujita N
    2. Nagata Y
    3. Nishiuchi T
    4. Sato M
    5. Iwami M
    6. Kiya T

    (2013) Visualization of neural activity in insect brains using a conserved immediate early gene, Hr38

    Current Biology 23:2063–2070.

    https://doi.org/10.1016/j.cub.2013.08.051

    • PubMed
    • Google Scholar
18. 1. Greenberg ME
    2. Ziff EB
    3. Greene LA

    (1986) Stimulation of neuronal acetylcholine receptors induces rapid gene transcription

    Science 234:80–83.

    https://doi.org/10.1126/science.3749894

    • PubMed
    • Google Scholar
19. 1. Guan Z
    2. Saraswati S
    3. Adolfsen B
    4. Littleton JT

    (2005) Genome-wide transcriptional changes associated with enhanced activity in the drosophila nervous system

    Neuron 48:91–107.

    https://doi.org/10.1016/j.neuron.2005.08.036

    • PubMed
    • Google Scholar
20. 1. Guo F
    2. Cerullo I
    3. Chen X
    4. Rosbash M

    (2014) PDF neuron firing phase-shifts key circadian activity neurons in Drosophila

    eLife 3:e02780.

    https://doi.org/10.7554/eLife.02780

    • Google Scholar
21. 1. Guo F
    2. Yu J
    3. Jung HJ
    4. Abruzzi KC
    5. Luo W
    6. Griffith LC
    7. Rosbash M

    (2016) Circadian neuron feedback controls the drosophila sleep--activity profile

    Nature 536:292–297.

    https://doi.org/10.1038/nature19097

    • PubMed
    • Google Scholar
22. 1. Haynes PR
    2. Christmann BL
    3. Griffith LC

    (2015) A single pair of neurons links sleep to memory consolidation in Drosophila melanogaster

    eLife 4:e03868.

    https://doi.org/10.7554/eLife.03868

    • Google Scholar
23. 1. Helfrich‐Förster C

    (1997) Development of pigment‐dispersing hormone‐immunoreactive neurons in the nervous system of drosophila melanogaster

    The Journal of Comparative Neurology 380:335–354.

    https://doi.org/10.1002/(SICI)1096-9861(19970414)380:3<335::AID-CNE4>3.3.CO;2-M

    • Google Scholar
24. 1. Herschman HR

    (1991) Primary response genes induced by growth factors and tumor promoters

    Annual Review of Biochemistry 60:281–319.

    https://doi.org/10.1146/annurev.bi.60.070191.001433

    • PubMed
    • Google Scholar
25. 1. Hong EJ
    2. McCord AE
    3. Greenberg ME

    (2008) A biological function for the neuronal activity-dependent component of bdnf transcription in the development of cortical inhibition

    Neuron 60:610–624.

    https://doi.org/10.1016/j.neuron.2008.09.024

    • PubMed
    • Google Scholar
26. 1. Huh GS
    2. Boulanger LM
    3. Du H
    4. Riquelme PA
    5. Brotz TM
    6. Shatz CJ

    (2000) Functional requirement for class I MHC in CNS development and plasticity

    Science 290:2155–2159.

    https://doi.org/10.1126/science.290.5499.2155

    • PubMed
    • Google Scholar
27. 1. Jones SD
    2. Hall DJ
    3. Rollins BJ
    4. Stiles CD

    (1988) Platelet-derived growth factor generates at least two distinct intracellular signals that modulate gene expression

    Cold Spring Harbor Symposia on Quantitative Biology 53:531–536.

    https://doi.org/10.1101/SQB.1988.053.01.061

    • PubMed
    • Google Scholar
28. 1. Kandel ER

    (2001) The molecular biology of memory storage: a dialog between genes and synapses

    Bioscience Reports 21:565–611.

    https://doi.org/10.1023/A:1014775008533

    • PubMed
    • Google Scholar
29. 1. Kim TK
    2. Hemberg M
    3. Gray JM
    4. Costa AM
    5. Bear DM
    6. Wu J
    7. Harmin DA
    8. Laptewicz M
    9. Barbara-Haley K
    10. Kuersten S
    11. Markenscoff-Papadimitriou E
    12. Kuhl D
    13. Bito H
    14. Worley PF
    15. Kreiman G
    16. Greenberg ME

    (2010) Widespread transcription at neuronal activity-regulated enhancers

    Nature 465:182–187.

    https://doi.org/10.1038/nature09033

    • PubMed
    • Google Scholar
30. 1. Kiya T
    2. Kunieda T
    3. Kubo T

    (2007) Increased neural activity of a mushroom body neuron subtype in the brains of forager honeybees

    PLoS One 2:e371.

    https://doi.org/10.1371/journal.pone.0000371

    • PubMed
    • Google Scholar
31. 1. Klapoetke NC
    2. Murata Y
    3. Kim SS
    4. Pulver SR
    5. Birdsey-Benson A
    6. Cho YK
    7. Morimoto TK
    8. Chuong AS
    9. Carpenter EJ
    10. Tian Z
    11. Wang J
    12. Xie Y
    13. Yan Z
    14. Zhang Y
    15. Chow BY
    16. Surek B
    17. Melkonian M
    18. Jayaraman V
    19. Constantine-Paton M
    20. Wong GK
    21. Boyden ES

    (2014) Independent optical excitation of distinct neural populations

    Nature Methods 11:338–346.

    https://doi.org/10.1038/nmeth.2836

    • PubMed
    • Google Scholar
32. 1. Kula-Eversole E
    2. Nagoshi E
    3. Shang Y
    4. Rodriguez J
    5. Allada R
    6. Rosbash M

    (2010) Surprising gene expression patterns within and between PDF-containing circadian neurons in drosophila

    PNAS 107:13497–13502.

    https://doi.org/10.1073/pnas.1002081107

    • PubMed
    • Google Scholar
33. 1. Langmead B
    2. Salzberg SL

    (2012) Fast gapped-read alignment with bowtie 2

    Nature Methods 9:357–359.

    https://doi.org/10.1038/nmeth.1923

    • PubMed
    • Google Scholar
34. 1. Liang X
    2. Holy TE
    3. Taghert PH

    (2016) Synchronous drosophila circadian pacemakers display nonsynchronous Ca²⁺ rhythms in vivo

    Science 351:976–981.

    https://doi.org/10.1126/science.aad3997

    • PubMed
    • Google Scholar
35. 1. Lin Y
    2. Bloodgood BL
    3. Hauser JL
    4. Lapan AD
    5. Koon AC
    6. Kim TK
    7. Hu LS
    8. Malik AN
    9. Greenberg ME

    (2008) Activity-dependent regulation of inhibitory synapse development by Npas4

    Nature 455:1198–1204.

    https://doi.org/10.1038/nature07319

    • PubMed
    • Google Scholar
36. 1. Lopes JS
    2. Abril-de-Abreu R
    3. Oliveira RF

    (2015) Brain transcriptomic response to social eavesdropping in zebrafish (Danio rerio)

    PLoS One 10:e0145801.

    https://doi.org/10.1371/journal.pone.0145801

    • PubMed
    • Google Scholar
37. 1. Lutz CC
    2. Robinson GE

    (2013) Activity-dependent gene expression in honey bee mushroom bodies in response to orientation flight

    Journal of Experimental Biology 216:2031–2038.

    https://doi.org/10.1242/jeb.084905

    • PubMed
    • Google Scholar
38. 1. Mardinly AR
    2. Spiegel I
    3. Patrizi A
    4. Centofante E
    5. Bazinet JE
    6. Tzeng CP
    7. Mandel-Brehm C
    8. Harmin DA
    9. Adesnik H
    10. Fagiolini M
    11. Greenberg ME

    (2016) Sensory experience regulates cortical inhibition by inducing IGF1 in VIP neurons

    Nature 531:371–375.

    https://doi.org/10.1038/nature17187

    • PubMed
    • Google Scholar
39. 1. McCarthy DJ
    2. Chen Y
    3. Smyth GK

    (2012) Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation

    Nucleic Acids Research 40:4288–4297.

    https://doi.org/10.1093/nar/gks042

    • PubMed
    • Google Scholar
40. 1. Menet JS
    2. Pescatore S
    3. Rosbash M

    (2014) CLOCK:BMAL1 is a pioneer-like transcription factor

    Genes & Development 28:8–13.

    https://doi.org/10.1101/gad.228536.113

    • PubMed
    • Google Scholar
41. 1. Mokin M
    2. Keifer J

    (2005) Expression of the immediate-early gene-encoded protein Egr-1 (zif268) during in vitro classical conditioning

    Learning & Memory 12:144–149.

    https://doi.org/10.1101/lm.87305

    • PubMed
    • Google Scholar
42. 1. Moore HA
    2. Whitmore D

    (2014) Circadian rhythmicity and light sensitivity of the zebrafish brain

    PLoS One 9:e86176.

    https://doi.org/10.1371/journal.pone.0086176

    • PubMed
    • Google Scholar
43. 1. Nagoshi E
    2. Sugino K
    3. Kula E
    4. Okazaki E
    5. Tachibana T
    6. Nelson S
    7. Rosbash M

    (2010) Dissecting differential gene expression within the circadian neuronal circuit of drosophila

    Nature Neuroscience 13:60–68.

    https://doi.org/10.1038/nn.2451

    • PubMed
    • Google Scholar
44. 1. Nestler EJ

    (2001) Molecular basis of long-term plasticity underlying addiction

    Nature Reviews Neuroscience 2:119–128.

    https://doi.org/10.1038/35053570

    • PubMed
    • Google Scholar
45. 1. Petesch SJ
    2. Lis JT

    (2008) Rapid, transcription-independent loss of nucleosomes over a large chromatin domain at Hsp70 loci

    Cell 134:74–84.

    https://doi.org/10.1016/j.cell.2008.05.029

    • PubMed
    • Google Scholar
46. 1. Robinson MD
    2. McCarthy DJ
    3. Smyth GK

    (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

    Bioinformatics 26:139–140.

    https://doi.org/10.1093/bioinformatics/btp616

    • PubMed
    • Google Scholar
47. 1. Robinson MD
    2. Smyth GK

    (2007) Moderated statistical tests for assessing differences in tag abundance

    Bioinformatics 23:2881–2887.

    https://doi.org/10.1093/bioinformatics/btm453

    • PubMed
    • Google Scholar
48. 1. Robinson MD
    2. Smyth GK

    (2008) Small-sample estimation of negative binomial dispersion, with applications to SAGE data

    Biostatistics 9:321–332.

    https://doi.org/10.1093/biostatistics/kxm030

    • PubMed
    • Google Scholar
49. 1. Rodriguez J
    2. Tang CH
    3. Khodor YL
    4. Vodala S
    5. Menet JS
    6. Rosbash M

    (2013) Nascent-Seq analysis of drosophila cycling gene expression

    PNAS 110:E275–E284.

    https://doi.org/10.1073/pnas.1219969110

    • PubMed
    • Google Scholar
50. 1. Shang Y
    2. Donelson NC
    3. Vecsey CG
    4. Guo F
    5. Rosbash M
    6. Griffith LC

    (2013) Short neuropeptide F is a sleep-promoting inhibitory modulator

    Neuron 80:171–183.

    https://doi.org/10.1016/j.neuron.2013.07.029

    • PubMed
    • Google Scholar
51. 1. Spiegel I
    2. Mardinly AR
    3. Gabel HW
    4. Bazinet JE
    5. Couch CH
    6. Tzeng CP
    7. Harmin DA
    8. Greenberg ME

    (2014) Npas4 regulates excitatory-inhibitory balance within neural circuits through cell-type-specific gene programs

    Cell 157:1216–1229.

    https://doi.org/10.1016/j.cell.2014.03.058

    • PubMed
    • Google Scholar
52. 1. Spitzer NC
    2. Lautermilch NJ
    3. Smith RD
    4. Gomez TM

    (2000) Coding of neuronal differentiation by calcium transients

    BioEssays 22:811–817.

    https://doi.org/10.1002/1521-1878(200009)22:9<811::AID-BIES6>3.0.CO;2-G

    • PubMed
    • Google Scholar
53. 1. Stoleru D
    2. Peng Y
    3. Agosto J
    4. Rosbash M

    (2004) Coupled oscillators control morning and evening locomotor behaviour of drosophila

    Nature 431:862–868.

    https://doi.org/10.1038/nature02926

    • PubMed
    • Google Scholar
54. 1. Tanenhaus AK
    2. Zhang J
    3. Yin JC

    (2012) In vivo circadian oscillation of dCREB2 and NF-κB activity in the drosophila nervous system

    PLoS One 7:e45130.

    https://doi.org/10.1371/journal.pone.0045130

    • PubMed
    • Google Scholar
55. 1. Trapnell C
    2. Pachter L
    3. Salzberg SL

    (2009) TopHat: discovering splice junctions with RNA-Seq

    Bioinformatics 25:1105–1111.

    https://doi.org/10.1093/bioinformatics/btp120

    • PubMed
    • Google Scholar
56. 1. Trapnell C
    2. Roberts A
    3. Goff L
    4. Pertea G
    5. Kim D
    6. Kelley DR
    7. Pimentel H
    8. Salzberg SL
    9. Rinn JL
    10. Pachter L

    (2012) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and cufflinks

    Nature Protocols 7:562–578.

    https://doi.org/10.1038/nprot.2012.016

    • PubMed
    • Google Scholar
57. 1. Tullai JW
    2. Schaffer ME
    3. Mullenbrock S
    4. Sholder G
    5. Kasif S
    6. Cooper GM

    (2007) Immediate-early and delayed primary response genes are distinct in function and genomic architecture

    Journal of Biological Chemistry 282:23981–23995.

    https://doi.org/10.1074/jbc.M702044200

    • PubMed
    • Google Scholar
58. 1. Ueno T
    2. Tomita J
    3. Tanimoto H
    4. Endo K
    5. Ito K
    6. Kume S
    7. Kume K

    (2012) Identification of a dopamine pathway that regulates sleep and arousal in drosophila

    Nature Neuroscience 15:1516–1523.

    https://doi.org/10.1038/nn.3238

    • PubMed
    • Google Scholar
59. 1. Wang JW
    2. Wong AM
    3. Flores J
    4. Vosshall LB
    5. Axel R

    (2003) Two-photon calcium imaging reveals an odor-evoked map of activity in the fly brain

    Cell 112:271–282.

    https://doi.org/10.1016/S0092-8674(03)00004-7

    • PubMed
    • Google Scholar
60. 1. West AE
    2. Greenberg ME

    (2011) Neuronal activity-regulated gene transcription in synapse development and cognitive function

    Cold Spring Harbor Perspectives in Biology 3:a005744.

    https://doi.org/10.1101/cshperspect.a005744

    • PubMed
    • Google Scholar
61. 1. Wu C

    (1997) Chromatin remodeling and the control of gene expression

    Journal of Biological Chemistry 272:28171–28174.

    https://doi.org/10.1074/jbc.272.45.28171

    • PubMed
    • Google Scholar
62. 1. Zhou X
    2. Lindsay H
    3. Robinson MD

    (2014) Robustly detecting differential expression in RNA sequencing data using observation weights

    Nucleic Acids Research 42:e91.

    https://doi.org/10.1093/nar/gku310

    • PubMed
    • Google Scholar

Author details

1. Xiao Chen

   1. Howard Hughes Medical Institute, Brandeis University, Waltham, United States
   2. National Center for Behavioral Genomics, Department of Biology, Brandeis University, Waltham, United States

   Contribution

   XC, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

2. Reazur Rahman

   1. Howard Hughes Medical Institute, Brandeis University, Waltham, United States
   2. National Center for Behavioral Genomics, Department of Biology, Brandeis University, Waltham, United States

   Contribution

   RR, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

3. Fang Guo

   1. Howard Hughes Medical Institute, Brandeis University, Waltham, United States
   2. National Center for Behavioral Genomics, Department of Biology, Brandeis University, Waltham, United States

   Contribution

   FG, Provided advice and help, Conception and design

   Competing interests

   The authors declare that no competing interests exist.

4. Michael Rosbash

   1. Howard Hughes Medical Institute, Brandeis University, Waltham, United States
   2. National Center for Behavioral Genomics, Department of Biology, Brandeis University, Waltham, United States

   Contribution

   MR, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   rosbash@brandeis.edu

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0003-3366-1780

• 6,437

  views

• 894

  downloads

• 77

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.