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      Regulation of starvation-induced hyperactivity by insulin and glucagon signaling in adult Drosophila

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          Abstract

          Starvation induces sustained increase in locomotion, which facilitates food localization and acquisition and hence composes an important aspect of food-seeking behavior. We investigated how nutritional states modulated starvation-induced hyperactivity in adult Drosophila. The receptor of the adipokinetic hormone (AKHR), the insect analog of glucagon, was required for starvation-induced hyperactivity. AKHR was expressed in a small group of octopaminergic neurons in the brain. Silencing AKHR + neurons and blocking octopamine signaling in these neurons eliminated starvation-induced hyperactivity, whereas activation of these neurons accelerated the onset of hyperactivity upon starvation. Neither AKHR nor AKHR + neurons were involved in increased food consumption upon starvation, suggesting that starvation-induced hyperactivity and food consumption are independently regulated. Single cell analysis of AKHR + neurons identified the co-expression of Drosophila insulin-like receptor (dInR), which imposed suppressive effect on starvation-induced hyperactivity. Therefore, insulin and glucagon signaling exert opposite effects on starvation-induced hyperactivity via a common neural target in Drosophila.

          DOI: http://dx.doi.org/10.7554/eLife.15693.001

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          Animals can be thought of as tightly controlled eating machines. An animal’s brain senses if it is hungry via signals from the nervous system or hormones, and then alters the animal’s behavior to obtain a supply of food. These behaviors include looking for food and eating it; and regulating both food seeking and food consumption behaviors is crucial for the animal’s chances of survival and reproduction.

          Studies that used fruit flies as a model have previously shown that flies walk more when they are hungry. This activity helped the flies to locate and occupy food sources, but it was not clear how this food seeking behavior was regulated.

          Now, Yu, Huang et al. find that a small group of neurons in the fly brain controls food seeking in starving flies. The neurons achieve this by sensing two groups of hormones with opposing activity. These hormones are the fly’s equivalents of glucagon and insulin, which are found in humans and other mammals. In humans, glucagon is released when blood sugar levels are low and stimulates hunger, while insulin is released when blood sugar is high and acts to suppress feelings of hunger. Therefore, food seeking in the flies is under the precise control of signals of hunger and satiety.

          Further experiments show that these fly neurons use a chemical messenger called octopamine to convey the hormone-based signals to other circuits of neurons. Notably, these downstream neurons are not involved in regulating the consumption of food. Therefore, food seeking and eating appear to be independently regulated in fruit flies.

          Further studies are now needed to dissect the downstream circuits of neurons that actually control the food seeking behavior. It will also be important to explore how this behavior is suppressed when a food source is detected.

          DOI: http://dx.doi.org/10.7554/eLife.15693.002

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          Conditional modification of behavior in Drosophila by targeted expression of a temperature-sensitive shibire allele in defined neurons.

          T Kitamoto (2001)
          Behavior is a manifestation of temporally and spatially defined neuronal activities. To understand how behavior is controlled by the nervous system, it is important to identify the neuronal substrates responsible for these activities, and to elucidate how they are integrated into a functional circuit. I introduce a novel and general method to conditionally perturb anatomically defined neurons in intact Drosophila. In this method, a temperature-sensitive allele of shibire (shi(ts1)) is overexpressed in neuronal subsets using the GAL4/UAS system. Because the shi gene product is essential for synaptic vesicle recycling, and shi(ts1) is semidominant, a simple temperature shift should lead to fast and reversible effects on synaptic transmission of shi(ts1) expressing neurons. When shi(ts1) expression was directed to cholinergic neurons, adult flies showed a dramatic response to the restrictive temperature, becoming motionless within 2 min at 30 degrees C. This temperature-induced paralysis was reversible. After being shifted back to the permissive temperature, they readily regained their activity and started to walk in 1 min. When shi(ts1) was expressed in photoreceptor cells, adults and larvae exhibited temperature-dependent blindness. These observations show that the GAL4/UAS system can be used to express shi(ts1) in a specific subset of neurons to cause temperature-dependent changes in behavior. Because this method allows perturbation of the neuronal activities rapidly and reversibly in a spatially and temporally restricted manner, it will be useful to study the functional significance of particular neuronal subsets in the behavior of intact animals. Copyright 2001 John Wiley & Sons, Inc.
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            Sensory detection of food rapidly modulates arcuate feeding circuits.

            Hunger is controlled by specialized neural circuits that translate homeostatic needs into motivated behaviors. These circuits are under chronic control by circulating signals of nutritional state, but their rapid dynamics on the timescale of behavior remain unknown. Here, we report optical recording of the natural activity of two key cell types that control food intake, AgRP and POMC neurons, in awake behaving mice. We find unexpectedly that the sensory detection of food is sufficient to rapidly reverse the activation state of these neurons induced by energy deficit. This rapid regulation is cell-type specific, modulated by food palatability and nutritional state, and occurs before any food is consumed. These data reveal that AgRP and POMC neurons receive real-time information about the availability of food in the external world, suggesting a primary role for these neurons in controlling appetitive behaviors such as foraging that promote the discovery of food.
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              Tracing the Derivation of Embryonic Stem Cells from the Inner Cell Mass by Single-Cell RNA-Seq Analysis

              Introduction The derivation of embryonic stem cells (ESCs) from the inner cell mass (ICM) of mouse blastocysts consisting of about 20 cells occurs in vitro under a variety of culture conditions, such as in the presence of leukemia inhibitory factor (LIF) and fetal calf serum (FCS) (Evans and Kaufman, 1981; Ying et al., 2008). After about 5 days in culture, the inner cell mass outgrowths of blastocysts are disrupted into small clusters of cells and passaged until the establishment of ESC lines. Thus, the ICM cells that, in vivo, are subject to a strict developmental program undergo a transformation into cells with a capacity for infinite self-renewal while retaining pluripotency. The precise molecular changes accompanying this transition remain to be fully elucidated, which is hampered by the limited number of cells available for analysis (Niwa, 2007). Pluripotent E3.5-E4.5 primitive ectoderm/epiblast (PE) and ESCs can both contribute to all three germ layers and the germ line when injected into host blastocysts to form chimera (Niwa, 2007). However, only the ESCs cultured in vitro have the capacity for unlimited self-renewal while retaining their pluripotency (Niwa, 2007; Smith, 2006). Some differences between the ICM and ESCs have been identified, such as the expression of pramel5, pramel6, and pramel7 in the ICM, which are repressed in ESC (Kaji et al., 2007). Other genes, including Dicer (Bernstein et al., 2003; Kanellopoulou et al., 2005; Murchison et al., 2005), Nanog (Chambers et al., 2007; Chambers et al., 2003; Mitsui et al., 2003), Mbd3 (Kaji et al., 2006; Kaji et al., 2007), and Ezh2 (O'Carroll et al., 2001; Shen et al., 2008), are essential for the establishment of pluripotent PE cells in the ICM but dispensable for the maintenance of ESCs. There have been intensive studies on ESCs in recent years, but these have usually been on bulk cells by RNA-Seq, cDNA microarray, SAGE, and EST sequencing (Niwa, 2007; Ivanova et al., 2006; Cloonan et al., 2008). However, the precise changes accompanying the process of conversion of ICM to ESCs remain to be fully elucidated. To gain insight into this process, we used blastocysts from Oct4-ΔPE-GFP transgenic mice and cultured them in vitro under the classical conditions consisting of LIF and FCS used for the derivation of ESCs (Niwa, 2007). The Oct4-ΔPE-GFP reporter we used is under the control of only the distal enhancer for Oct4 (also known as Pou5f1) and lacks the proximal enhancer (Yeom et al., 1996). This GFP reporter shows expression in the E3.5 ICM, E4.5 epiblast, primordial germ cells (PGCs), and ESCs, but not in the postimplantation epiblast or in the epiblast stem cells (EpiSCs) (Yeom et al., 1996; Bao et al., 2009). Notably, the distal enhancer of Oct4 represents the densest binding locus for the key pluripotency-specific transcription factors in ESCs (Chen et al., 2008), which makes it an ideal reporter for tracing the course of changes during the establishment of ESCs from ICM. By analyzing single Oct4-ΔPE-GFP-positive and Oct4-ΔPE-GFP-negative cells, we set out to monitor changes in ICM cells during their progression toward ESCs. We used our recently developed single-cell RNA-Seq transcriptome analysis to investigate the critical early changes during this process (Tang et al., 2009). Results and Discussion Analysis of Individual ICM Outgrowth Cells First, we analyzed the three key pluripotency genes during the course of blastocyst culture and the formation of outgrowths (Figure 1). At each stage, we chose between 10 and 26 single cells for analysis. We generated cDNAs by whole transcriptome amplification (WTA) of these individual cells (see Experimental Procedures for details). All ICM cells (22/22) tested showed high expression of Oct4, Sox2, and Nanog. However, among cells from day 3 outgrowths that had high Oct4 expression, about 39% (7/18) had already lost expression of Nanog and/or Sox2, indicating that they might be losing pluripotency. By contrast, most of the cells from day 5 outgrowths (11/13) that had high Oct4 expression also showed high expression of both Sox2 and Nanog, suggesting that these may represent the earliest population that had acquired or were likely on course to acquire the ESC-like fate with the potential for self-renewal. We were also able to establish an ESC line from a single cell isolated from a day 5 outgrowth (data not shown). As expected, all the ESCs (23/23) had high expression of these three pluripotency genes. Expression Dynamics of 385 Genes in 74 Single Cells from ICM to ESCs Next, we chose 385 pluripotency and early differentiation related genes to monitor their expression in cells from the ICM, as well as from day 3 and day 5 outgrowths, and from ESCs at single-cell resolution (Table S1). All 14 ESCs analyzed had high expression (Ct = 19–28) of Oct4, Sox2, Nanog, Dppa4, Dppa5, Sall4, Utf1, Rex2, and Rif1, indicating their pluripotent character (Figure 2A and Figure S1). By contrast, we detected little or no expression (Ct = 40) of all 23 early differentiation marker genes (ectoderm markers: Pax6, Otx1, Neurod1, Nes, Lhx5, and Hoxb1; mesoderm markers: Tbx2, T, Nkx2-5, Myod1, Myf5, Mesdc1, Mesdc2, Kdr, Isl1, Hand1, and Eomes; endoderm markers: Onecut1, Gata4, Gata5, and Gata6; extraembryonic markers: Cdx2 and Tpbpa) (see Table S1). Similarly, all 14 cells isolated and analyzed from ICM showed high expression of the nine pluripotency-specific genes. However, expression of some genes, for example, c-Myc, which was shown to be an important reprogramming factor for pluripotency (Takahashi and Yamanaka, 2006), was highly heterogeneous in cells from the ICM (Ct = 24–40); this variability was progressively reduced until, finally, all ESCs consistently expressed c-Myc (Figure 2B). Interestingly, we found that Tet1 and Tet2 (Table S1), which were recently shown to mediate DNA demethylation in ESCs, were highly expressed in both ICM and ESCs, but their expression only decreased in Oct4-negative cells present in the ICM outgrowths. Thus, our observations support their importance for pluripotency (Tahiliani et al., 2009). Since ESCs can also be maintained in an undifferentiated state by LIF and BMP4 (Ying et al., 2003), we investigated the expression of a key receptor, Bmpr1a, and found it to be heterogeneous in the ICM (Ct = 27–40). However during the ICM outgrowth, Bmpr1a expression was detected more consistently until, finally, all ESCs (14/14) showed strong expression. This suggests that all ESCs have the potential to respond to Bmp4 signaling (Figure 2C). Conversely, for Bmp4, all ICM cells (14/14) showed high expression, but this declined during the course of ICM outgrowths so that ultimately only about 50% (7/14) of individual ESCs retained Bmp4 expression (Ct = 25–40). This is compatible with the fact that maintenance of ESCs can be achieved by the addition of exogenous Bmp4 or serum, which contains Bmp4 (Ying et al., 2003). During the course of ICM outgrowth toward ESCs, we found clear upregulation of several genes, including Tcf15, Prdm5, Zic3, Ifitm1, Nodal, and Bex1, indicating that they may potentially be important during the transition to ESCs and/or for their subsequent maintenance (Figure 2D). Indeed, Nodal is a known regulator of self-renewal but is not essential for the pluripotency of ESCs (see below). By contrast, there was clear downregulation of some genes during ICM outgrowth, such as Gata4, Gata6, Pramel7, Tbx3, Bmi1, Bcl2l14, Nr5a2, and Amhr2, which potentially have ICM specific development-related functions (Figure 2E). For example, ICM has the potential to develop into primitive endoderm cells, for which Gata4 and Gata6 are crucial regulators (Fujikura et al., 2002; Koutsourakis et al., 1999; Morrisey et al., 1998). Thus, repression of these genes may allow ICM cells to exit from their inherent developmental program as they acquire the ability for self-renewal while retaining pluripotency as ESCs. Molecular Changes during the Transition from ICM to ESCs To understand the dynamic nature of gene expression in individual cells at the whole-genome scale, we randomly selected 12 individual ESCs and generated their digital transcriptome profile (Figure 3A, Figure S2, and Tables S2 and S3) (Tang et al., 2009). Indeed, all of the 12 ESCs analyzed had high expression of Oct4, Sox2, Nanog, Rex1 (also known as Zfp42), Dppa5, and Utf1, which indicates that all of them are in an undifferentiated state and are pluripotent. To confirm the reliability of our single-cell RNA-Seq approach, we compared our data with that obtained from bulk analysis of ESCs (Cloonan et al., 2008). We found that on average, an individual ESC expresses 10,815 genes (RPM > 0.1), which means that we captured expression of at least 94.6% of the genes in a single cell of those detected by deep sequencing in bulk assays of ESCs (Cloonan et al., 2008). Overall, 65.8% (13,326 out of 20,259) of known genes were expressed in 12 single ESCs, which shows that our RNA-Seq data represent an accurate reflection of the entire transcriptome in ESCs at single-cell resolution. To understand the relationship between ESCs and the ICM/Epiblast cells from which they were derived, we compared the single-cell RNA-Seq transcriptomes of these cells (Figure S2) to determine the extent to which ESCs resemble E3.5 ICM or E4.5 Epiblast cells (Nichols et al., 2009). We found that the molecular signature of all undifferentiated ESCs maintained under our culture conditions are clearly different from both ICM and epiblast cells based on the principal component analysis of their transcriptomes. This means that at the molecular level, ESCs are distinct from E3.5 ICM or E4.5 Epiblast (Figure 3A). We detected a large set of genes, which show clear differential expression between ICM/Epiblast and ESCs. (Table S2 and Figure S3. Note 2,475 genes with fold change, FC[ESC/ICM] > 4, p  4, p 10, Figure 4), which indicates that the former set of genes have a higher propensity for a more dynamic regulation of expression among individual cells of the same type. These genes include Hoxd13, Hoxb3, Hoxb5, and Ddx3y that showed highly variable expression in ESCs, whereas Gm364, Tmem80, Hdx, Trpm3, Enox2, Ilvbl, Has3, Pygm, and Fbxw13 showed a great variation in expression within ICM cells. Some genes, such as Tnk1, Myof, Adamts9, Tspan12, Rhox6, Epha7, Dhrs3, Fam189a1, and Nudt18, showed highly variable expression in both ESCs and ICM (Table S2). These variations are probably not because of technical reasons because genes expressed at low levels (RPM 1) between cells of ESCs and ICM, Gene Ontology (GO) analysis showed that the genes involved in cellular growth, cellular assembly, amino acid metabolism, and lipid metabolism were significantly enriched (p 4, or 1.5, p 1.45, p 1.51, p 1.51, p 1.4, p 0.1 RPM) and found that their correlation coefficient is 0.92, confirming the accuracy of our single-cell RNA-Seq data (Figure S3). Alternative Splicing during the ICM Outgrowth at Whole-Genome Scale Alternative splicing plays an important role in defining tissue identity and specificity. It is estimated that nearly 95% of the mammalian multiexon genes express multiple transcript variants through alternative splicing (Chen and Manley, 2009). We wished to know if alternative splicing was a major feature during the outgrowth process of ICM toward ESCs. We addressed the expression dynamics of all the 6,331 transcript variants from the 2,567 RefSeq genes with multiple known isoforms, which has not been addressed previously. 1,852 transcript variants were expressed (at least 5 counts) in either ICM or ESCs. And from them, 417 transcript variants were upregulated (fold change, FC[ESC/ICM, splicing] > 2, p 1.41, p 1.56, p 2.41, p 1.27, p 1.57, p 1.65, p 1.3, p 0.02) for pluripotency-related genes (FC[Day5 Oct4+/Oct4−] > 4, r > 0.6). The loss of this class of miRNAs may contribute to the phenotype of loss of pluripotent Oct4-positive epiblast cells when Dicer is knocked out in early embryos (Bernstein et al., 2003). The second class of miRNAs preferably target the ESC-specific pluripotency genes (FC[ESC/ICM] > 4) (miR-669b, -298, -692, -204, -28, -149, -34a, -182, ↑-129-5p, -133a, -320; the target enrichment is 1.6-fold, p 0.03) in ICM-specific genes (FC[ESC/ICM] < 0.25). The loss of this class of miRNAs may contribute to the phenotype of resistance to differentiation when Dicer or DGCR8, two key components of the miRNA processing pathway, are knocked out in established ESCs (Kanellopoulou et al., 2005; Wang et al., 2007). Taken together, miRNAs may contribute to ESC's ability to maintain the balance between pluripotency and the potential for rapid differentiation, through one set of miRNAs targeting genes that drive differentiation, while a separate set of miRNAs target ESC-specific pluripotency genes. Conclusion Our study provides insight into the dynamic molecular changes that accompany cell-fate changes. During the conversion of ICM cells to ESCs, there is an evident arrest of a normal developmental program, which is subverted in vitro in favor of a potential for unrestricted self-renewal while retaining the ability to undergo differentiation into all the diverse cell types. We demonstrate how both the retention of expression of key genes allows inheritance of a fundamental property of the ICM, namely pluripotency, while other changes in the transcriptome permit exit from a normal developmental program and confer a key property of self-renewal. Changes in epigenetic regulators apparently allow for the stability of the newly acquired epigenotype, which is crucial for the inherent plasticity of ESCs. The conversion from ICM to ESC is also coupled with a role for distinct sets of miRNAs that allow for both self-renewal while the cells retain the ability to respond rapidly to cues for differentiation. Our investigation may serve as a paradigm for other studies, including regulation and differentiation of small numbers of stem cells in adults. Our approach is applicable to studies on small groups of differentiating cells and for gaining insight into how developmental programs might be undermined, leading to the formation of diseased tissues, including cancers. Experimental Procedures Isolation of Embryos and Single Cells All embryos were recovered from 129 females mated with Oct4-ΔPE-GFP transgenic male mice. The transgenic GFP expression of the reporter is under the control of Oct4 promoter and distal enhancer, but the proximal enhancer region is deleted. This GFP transgene reporter shows expression in the E3.5 ICM and E4.5 Epiblast of blastocysts and PGC in vivo and in ESC (Yeom et al., 1996). E3.5 and E4.5 blastocysts were flushed from the uterus of 129 pregnant females. For ESC outgrowth, E3.5 blastocysts were cultured in KSOM medium for the first day and then transferred to GMEM medium (GIBCO, cat. no. 21710-025) with 15% Fetal Calf Serum (FCS) (GIBCO, cat. no. 16000-044) and 1000 U/ml Lif on mitomycin C-treated MEF feeder cells for all later periods. The time when the E3.5 blastocysts were placed into culture was designated as day 0. For the isolation of single cells of E3.5 ICM or E4.5 epiblast, the blastocysts were first placed in a mouse trophoblast antibody for 30 min. Then they were treated by complement for 30 min. After this, the lysed trophectoderm cells were removed and the isolated ICM or epiblast was placed in EGTA-PBS for 10 min. After that, they were furthered treated by Trypsin at 37°C for 5 min. Then they were transferred into GMEM medium with 15% FCS and dissociated into single-cell suspension. The resulting single cells were washed in BSA-PBS twice and prepared to be picked as single cells. For the isolation of blastocyst/ICM outgrowth, it was treated by trypsin for 5 min to dissociate the core part of outgrowth from surrounding trophectoderm progenies. The inner core of cells in the outgrowth was treated with EGTA-PBS for 10 min at room temperature and trypsin for 5 min at 37°C. The core of cells was dissociated by pipetting into a single-cell suspension in GMEM medium with 15% FCS. Next, the GFP-positive and -negative cells were separated manually under a fluorescence microscope. The single cells were washed in BSA-PBS twice before they were picked individually for subsequent analysis. Preparation of Single-Cell cDNAs The single-cell RNA-seq method has been described in detail previously (Tang et al., 2009, 2010). In brief, an individual cell was manually picked and transferred into lysate buffer by a mouth pipette, followed by reverse transcription directly on the whole-cell lysate. Following this procedure, terminal deoxynucleotidyl transferase was used to add a poly(A) tail to the 3′ end of first-strand cDNAs, which was followed by 20 + 9 cycles of PCR to amplify the single-cell cDNAs. RNA-Seq Library Preparation, Sequencing, and Alignment After generation of the target cDNA from a single cell, 100 ng cDNA (0.5–3 kb) was sheared into 80–130 bp fragments. P1 and P2 adaptors were ligated to each end, and the fragments were subjected to 8–10 cycles of PCR amplification. Emulsion PCR reactions were performed by combining 1.6 billion 1 μm diameter beads that had P1 primers covalently attached to their surfaces with 500 pg of single-cell libraries. Applied Biosystems SOLiD sequencer generated 50-base sequences, and AB's whole transcriptome software tools were used to analyze the sequencing reads (http://solidsoftwaretools.com/gf/project/transcriptome/). The reads obtained from each cell were matched to the Mouse genome (mm 9, NCBI Build 37) and reads that aligned uniquely were used in the downstream analysis. These reads were used to create base coverage files (in a wiggle format), which can be viewed directly in the UCSC genome browser, or to detect known or novel exon-exon junctions. Unambiguously mapped reads were first used to generate exon counts and then transcript or gene counts. Feature counts were normalized using the RPM (read per million aligned reads) method, and no adjustment to gene/transcript size was made because our protocol has a limited coverage of 0.5–3 kb from the 3′ end of the transcripts. An alternative analysis was used for alignments that were not aligned to their full length, where reads were aligned to a reference containing exon-exon junctions, using 42 bases on each side for junctions, allowing up to four mismatches for the full length of the read (50 bases) (Tang et al., 2009). The quality of the single-cell RNA-Seq data was analyzed (Figure S7). These analyses showed that our single-cell RNA-Seq data are highly reproducible, reliable, and accurate for ICM outgrowth and ESCs. Real-Time PCR For TaqMan real-time PCR, 1.0 μl of diluted cDNAs was used for each 10 μl real-time PCR (1× PCR Universal Master Mix, 250 nM TaqMan probe, 900 nM of each primer, that are commercially available as ready to use Assays, custom-plated in 384-plates or TaqMan low Density Array cards by Applied Biosystems). All reactions were duplicated. The PCR was done as following using an AB7900 with 384-well plates: first, 95°C for 10 min to activate the Taq polymerase, then 40 cycles of 95°C for 15 sec and 60°C for 1 min. MicroRNA Profiling of ICM and ESCs The detailed protocol is described previously (Tang et al., 2006). In brief, 10 cells were picked into a PCR tube by glass capillary and were lysed by heat treatment at 95°C for 5 min. Then the microRNAs were reverse transcribed into cDNAs by pool of 330 of stem-looped primers. After this, these microRNA cDNAs were amplified by 18 cycles of PCR by 330 forward primers and a universal reverse primer. Finally the cDNAs were split and each individual microRNA was measured by TaqMan probe-directed real-time PCR. Three biological replicates were done for each type of cell.
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                Contributors
                Role: Reviewing editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                09 September 2016
                2016
                : 5
                : e15693
                Affiliations
                [1 ]deptLife Sciences Institute , Zhejiang University , Hangzhou, China
                [2 ]deptInnovation Center for Cell Signaling Network , Zhejiang University , Hangzhou, China
                [3 ]deptDepartment of Molecular and Cell Biology , University of California, Berkeley , Berkeley, United States
                [4]Trinity College Dublin , Ireland
                [5]Trinity College Dublin , Ireland
                Author notes
                [†]

                These authors contributed equally to this work.

                Author information
                http://orcid.org/0000-0001-7758-1120
                Article
                15693
                10.7554/eLife.15693
                5042652
                27612383
                a0dbb284-f8e2-4f8f-9dfd-fff6b2605f8e
                © 2016, Yu et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 01 March 2016
                : 08 September 2016
                Funding
                Funded by: Zhejiang Natural Science Funds;
                Award ID: LR15C060001
                Award Recipient :
                Funded by: Thousand Young Talents Plan of China;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 31522026
                Award Recipient :
                Funded by: Fundamental Research Funds for the Central Universities of China;
                Award ID: 2016QN81010
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Neuroscience
                Research Article
                Custom metadata
                2.5
                Two functionally antagonizing groups of hormones directly regulate starvation-induced increase in locomotion via a common neural target in fruit flies.

                Life sciences
                food seeking,akhr,dinr,octopamine,drosophila,d. melanogaster
                Life sciences
                food seeking, akhr, dinr, octopamine, drosophila, d. melanogaster

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