How does an animal sense if it is well nourished or not, and then regulate its metabolism appropriately? This process largely relies on the animal's body deciphering signals that that are transmitted between different organs in the form of molecules and hormones. Many animals—ranging from insects to mammals (including humans)—also use their brains to sense and decipher these nutritional signals.

A signaling pathway involving the hormone insulin controls how various different animals grow and develop—and how long they will live—based on these animals' food intake. Insulin is produced in mammals by an organ called the pancreas. But in the fruit fly Drosophila, this hormone is produced by cells within different tissues, including the insect’s brain.

The fruit fly is used to study many biological processes because it is easy to work with in a laboratory. Insulin-producing cells make and release insulin-like molecules into the insect's hemolymph (a blood-like fluid) in response to sugar and to other nutrients (which are detected via molecules generated in a fruit fly organ called the fat body). The fat body produces lipophorin, a protein which carries fat molecules in the hemolymph, and which is known to be able to move from the hemolymph to the brain and accumulate within the brain. The fat body also produces lipid transfer protein (or LTP), which transfers fats absorbed or made within the insect's gut onto lipophorin, and can also unload fat molecules to other insect cells. If LTP can also enter the brain, and what it might do there, was unclear.

Brankatschk et al.now discover that LTP can cross the ‘blood brain barrier’ in fruit fly larvae and can accumulate over time on their insulin-producing cells and the neurons in direct contact with these cells. This accumulation depends on the flies’ diet: flies fed a diet made from yeast cells accumulated LTP on these neurons, while those fed only on sugar and proteins did not.

Furthermore Brankatschk et al. found that when they switched flies from a yeast-based to a plant-based diet, the larvae grew more slowly and the flies lived longer. Both of the diets contained abundant calories and nutrients, but contained slightly different kinds of fat molecules. The fly larvae on the plant-based diet also accumulated less LTP on their insulin-pathway neurons, and insulin signaling was reduced.

Branskatschk et al. also found that fat molecules from the yeast-based diet activated the cells of the blood brain barrier, and that this encouraged LTP to be transported the brain. Blocking LTP from crossing the blood brain barrier reduced insulin signaling, slowed the growth of the fly larvae, and extended the lifespan of the flies.

These findings of Brankatschk et al. thus reveal that fat-containing molecules carry information about specific nutrients to the brain. The extent to which these mechanisms operate in other animals—such as mammals—remains to be explored.

Nutrient sensing by the central nervous system is emerging as an important regulator of systemic metabolism in both vertebrates and invertebrates (Lam et al., 2005; Levin et al., 2011; Rajan and Perrimon, 2012; Bjordal et al., 2014; Linneweber et al., 2014). Little is known about how nutrition-dependent signals pass the blood brain barrier (BBB) to convey this information. Like the vertebrate BBB, the BBB of Drosophila forms a tight barrier to passive transport, and is formed by highly conserved molecular components (Bundgaard and Abbott, 2008; Stork et al., 2008; Abbott et al., 2010). Its simple structure and genetic accessibility make it an ideal model to study how nutritional signals are communicated to the CNS. Insulin and Insulin-like growth factors are conserved systemic signals that regulate growth and metabolism in response to nutrition. Although Drosophila do not have a single pancreas-like organ, they do produce eight distinct Drosophila Insulin/IGF-like peptides (Dilps) that are expressed in different tissues (Riedel et al., 2011; Colombani et al., 2012; Garelli et al., 2012). A set of three Dilps (Dilp2,3,5), released into circulation by Dilp-producing cells (IPCs) in the brain, have particularly important functions in regulating nutrition-dependent growth and sugar metabolism; ablation of IPCs in the CNS causes Diabetes-like phenotypes, slows growth and development, and produces small, long-lived adult flies (Rulifson et al., 2002; Broughton et al., 2005; Partridge et al., 2011). Systemic Insulin/IGF signaling (IIS) increases in response to dietary sugars, proteins and lipids. Sugars act on IPCs directly to promote Dilp release (Haselton and Fridell, 2010), but other nutrients are sensed indirectly through signals from the fat body—an organ analogous to vertebrate liver/adipose tissue (Colombani et al., 2003; Geminard et al., 2009; Rajan and Perrimon, 2012).

The Drosophila fat body produces two major types of lipoprotein particles: Lipophorin (LPP), the major hemolymph lipid carrier, and Lipid Transfer Particle (LTP). LTP transfers lipids from the intestine to LPP. These lipids include fatty acids from food, as well as from endogenous synthesis in the intestine (Palm et al., 2012). LTP also unloads LPP lipids to other cells (Van Heusden and Law, 1989; Canavoso et al., 2004; Parra-Peralbo and Culi, 2011). LPP crosses the BBB and accumulates throughout the brain. It is required for nutrition-dependent exit of neural stem cells from quiescence (Brankatschk and Eaton, 2010). Here, we investigate possible functions of LTP in the brain.

Immunostaining reveals LTP on specific neurons and glia in larval brains. (Figure 1A,C–E, Figure 1—figure supplement 1–3, and Videos 1–4). First instar brains have on average three LTP-positive neurons per brain lobe, increasing to 13 in early third instar larvae (Figure 1B, Figure 1—figure supplement 1). We used cell type-specific RNAi to distinguish whether LTP in the brain came from circulation, or whether it was produced in the CNS. Knock-down of ltp in the fat body reduces but does not eliminate LTP from circulation (Figure 1F). Staining larval brains from these animals for LTP reveals reduced staining on both neurons and glia (Videos 5 and 6). To investigate this issue in more detail, we quantified LTP-positive neurons after knock-down of ltp in neurons, glia, or fat body. To ensure that we compared larvae of similar developmental stages we quantified glial cell numbers, which increase during larval development (Figure 1B′,G′). Only fat body-specific ltp knock-down reduces neuronal LTP staining in the brain (Figure 1G, Figure 1—figure supplement 3). Thus, LTP particles secreted by the fat body cross the Blood Brain Barrier and become enriched on specific neurons.

Figure 1 with 3 supplements see all

Download asset Open asset

Video 1

Download asset

Video 2

Download asset

Video 3

Download asset

Video 4

Download asset

Video 5

Download asset

Video 6

Download asset

To understand the CNS functions of LTP, we sought to identify the neurons where it accumulated. We first investigated whether LTP co-localized with Dilp2-positive neurons in early third instar larval brains. Dilp2 is present not only in IPCs, which lie dorso-lateral to the big commissure, but also on three neurons per hemisphere that do not produce Dilps. These neurons recruit Dilp2 from IPCs using IMPL2, a Dilp2-binding protein (Bader et al., 2013). Although each hemisphere contains 7-8 neurons that express IMPL2 at this stage, only 3 of these detectably recruit Dilp2, and 2 of these 3 also accumulate LTP. These neurons can be unambiguously identified by the presence of both LTP and Dilp2, by their position, by lack of expression of dilp2-GAL4, and by expression of impL2-GAL4 (Figure 2, Figure 2—figure supplement 1). Henceforth, we refer to these neurons as Dilp2-recruiting neurons (DRNs). Starting in the third larval instar, LTP accumulates on two specific DRNs located dorsal to the big commissure (Figure 2A,C,D, Figure 2—figure supplement 2). Although LTP is found on other neurons at earlier stages, its accumulation on DRNs is developmentally regulated. We also sometimes (6/20 sampled CNS) detect LTP on a subset of IPC neurons (Figure 2B). Thus, upon entering the brain, LTP accumulates on IPCs and specific neurons that recruit Dilp2.

Figure 2 with 2 supplements see all

Download asset Open asset

Thus far, we have described LTP localization in larvae reared on a rich medium: ‘yeast food’ (YF) (Carvalho et al., 2012). Systemic IIS is high in these animals, as reflected by predominantly cytoplasmic localization of FOXO in fat body cells (Figure 3E,E′) (van der Horst and Burgering, 2007). To ask whether neuronal LTP accumulation was regulated by nutrition, we asked how it responded to starvation. Late second instar larvae transferred from YF to food containing only glucose (GF) arrest growth and development. Systemic IIS is strongly reduced, as indicated by nuclear accumulation of FOXO in fat body cells (Figure 3A,A′). Although LTP still circulates (Figure 3G), it accumulates on many fewer neurons in glucose-fed larvae, and is never detected on DRNs (Figure 3F–F″). Thus, the presence of glucose is insufficient to allow high systemic IIS or accumulation of LTP on most neurons.

Figure 3 with 2 supplements see all

Download asset Open asset

We next examined the role of lipids and proteins in promoting neuronal LTP accumulation and systemic IIS. We compared larvae fed on YF, with those transferred at late second instar to a lipid-depleted food (LDF), which contains a yeast autolysate that has been chloroform-extracted to remove lipid. LDF contains proteins and sugars, and has a similar caloric content to YF. However, larvae arrest on LDF because of the absence of sterols (Carvalho et al., 2010), and systemic IIS is low (Figure 3B,B′). Brains of LDF-fed larvae contain more LTP-positive neurons than those of glucose-fed larvae, but fewer than YF-reared larvae. In particular, LTP is never detected on DRNs (Figure 3F–F″).

To address the sterol-dependence of neuronal LTP accumulation, we supplemented LDF with 10 µM cholesterol (LDSF). Larvae transferred from YF to LDSF grow slowly and give rise to viable adults of reduced size (Carvalho et al., 2010). Consistent with this, nuclear FOXO staining in the fat body indicates that systemic IIS is low (Figure 3C,C′; Carvalho et al., 2010). Sterol addition further increases the number of LTP-positive neurons, but still does not allow LTP accumulation on DRNs (Figure 3F–F″, and Figure 3—figure supplement 1). Thus, a component present in yeast food, but not the chloroform-extracted yeast autolysate, is required for accumulation of LTP on DRNs, and for high-level systemic IIS.

We wondered whether lipids in general were required for LTP accumulation on DRNs. Interestingly however, experiments with plant food (PF) suggest that bulk lipid is not sufficient. PF contains no yeast and is based entirely on plant materials. It has the same caloric content as YF, and slightly more lipid with a different fatty acid composition (Carvalho et al., 2012, see ‘Materials and methods’). Surprisingly, LTP is found only occasionally on DRNs in larvae transferred from YF to PF. Depletion of LTP on DRNs occurs within 16 hr of transfer and is reversible in the same time frame (Figure 3F–F″ and Figure 4A,A′,C and Figure 3—figure supplement 2). Strikingly, despite the abundant calories derived from carbohydrates, proteins and lipids, feeding with PF specifically slows the larval growth phase without lengthening embryonic or pupal development. PF also dramatically extends adult lifespan compared to YF (Figure 4E–G). This suggests that systemic IIS is reduced when larvae are fed with PF, compared to YF. Indeed, FOXO is predominantly nuclear in PF-reared larvae (Figure 3D,D′).

Figure 4

Download asset Open asset

To investigate mechanisms underlying the differences in systemic IIS in larvae fed with YF and PF, we examined the pathway at different levels. We first focused on the neuronal activity of the IPCs. Neuronal activity correlates with higher levels of intracellular free calcium, which can be detected using the GCaMP reporter (Reiff et al., 2005). This GFP reporter increases its fluorescence in response to free calcium. We expressed the GCaMP construct either in IPC neurons (under the control of dilp2-GAL4) or in all neurons (under the control of rab3-GAL4) and compared GFP fluorescence in brains from YF and PF-reared larvae. While the activity of most neurons is not affected by feeding with these different foods (Figure 5A,B), the activity of the IPCs was dramatically higher on YF than on PF (Figure 5C,D; Videos 7 and 8). We quantified the number of IPCs exhibiting detectable fluorescence of the GCaMP reporter in 5 brains each of larvae fed with PF or YF. On YF, we detected activity of the GCaMP reporter in 4.8 ± 0.6 neurons per brain lobe, while on PF only 1 ± 1.3 neurons per brain lobe exhibited detectable reporter fluorescence. To ask whether higher neuronal activity of IPCs resulted in elevated release of Dilp2 into the hemolymph, we probed Western blots of hemolymph from YF and PF-reared larvae with antibodies to Dilp2. YF-reared animals have higher levels of circulating Dilp2 than PF-reared animals (Figure 5E). Thus, IPCs release more Dilp2 when animals are fed with YF. We next examined different molecular readouts of IIS in different tissues of PF and YF-reared larvae. YF increases PI3K activity in salivary glands (Figure 5F,G), as revealed by membrane localization of the PH^GFP reporter construct. Furthermore, phosphoAKT levels are higher in fat bodies of YF-reared larvae (Figure 5H). Thus, feeding with YF activates IPCs, causing them to secrete more Dilp2 into circulation, thereby increasing systemic IIS.

Figure 5

Download asset Open asset

Video 7

Download asset

Video 8

Download asset

To examine whether the differences between plant and yeast lipids were responsible for changes in IPC activity, we prepared chloroform extracts of PF and YF and used them to supplement LDF. Yeast lipids supported faster larval growth compared to plant lipids (Figure 5I). Furthermore, the GCaMP reporter reveals that IPC neurons are only active when larvae are fed with yeast lipids (Figure 5J,K; Videos 7 and 8). Thus, IPC neurons respond differently to the types of lipids present in YF and PF.

How does YF promote IPC activity and Dilp release? We wondered whether the YF-dependent localization of LTP to the DRNs might be responsible. To investigate this, we first asked whether IMPL2-expressing neurons actually contact the IPCs. We labeled the projections of IMPL2 expressing neurons by driving a transmembrane Cherry (Cherry) under the control of impL2-Gal4, and stained larval brains for both Dilp2 and IMPL2. A subset of Cherry-labeled projections, also containing IMPL2, extends from the DRNs towards the IPCs (Figure 6B′,B″,B′″). Dilp2 staining reveals a subset of projections from the IPCs extending towards the DRNs (Figure 6A—class 2 projections and Figure 6B,B′″). They meet in a region where IMPL2 begins to colocalize with Dilp2 (Figure 6B′″). Thus, it is likely that DRNs and IPCs communicate with each other directly.

Figure 6 with 1 supplement see all

Download asset Open asset

How does the activity of IMPL2-positive neurons influence that of IPCs? To address this question, we inhibited synaptic release by knocking down Rabs 3 and 27—two Rabs that both contribute to synaptic vesicle release (Mahoney et al., 2006; Pavlos et al., 2010). To do this, we used homologous recombination to generate N-terminal YFP fusions of Rab3 and Rab27 at their endogenous chromosomal loci. This renders them sensitive to knock-down by anti-GFP RNAi (Figure 6—figure supplement 1). Rabs 3 and 27 are expressed almost exclusively in neurons (manuscript in preparation). Thus, even for GAL4 drivers that are active in many tissues, this approach will affect only the neurons that express the GAL4 driver. When anti-GFP RNAi is driven in the IPCs in a background homozygous for both YFPRab3 and YFPRab27, larvae exhibit the slow growth phenotype characteristic of reduced IIS (Figure 6C). Reducing levels of Rab3/Rab27 in the IMPL2-expressing neurons slows larval growth (Figure 6C), and reduces phosphorylation of AKT in the fat body (Figure 5H). This suggests that signals sent by IMPL2-expressing neurons promote systemic IIS.

Since LTP localizes to DRNs only on yeast food, we wondered whether it was required to promote systemic Insulin signaling. We therefore asked whether IIS in YF-reared larvae depended on LTP. RNAi-mediated ltp knock-down in the fat body causes larval arrest in the second or third instar (Palm et al., 2012). Fat body cells contain lower levels of phosphoAKT (Figure 5H) and predominantly nuclear FOXO (Figure 7K,K′). Thus LTP is required for high-level IIS in the fat body on YF.

Figure 7 with 3 supplements see all

Download asset Open asset

Reduced IIS in ltp mutants might reflect a requirement for LTP on DRNs. Alternatively, loss of LTP in other tissues might affect Insulin signaling indirectly. Blocking transport of LTP across the BBB in YF-reared larvae could distinguish these possibilities. We therefore examined requirements for different LDL receptor family proteins in BBB transport of LTP. LTP localizes normally to DRNs and other neurons in larvae double mutant for LDL receptor homologues LpR1 and LpR2 (Figure 7, and Figure 7—figure supplement 1–3; Khaliullina et al., 2009). Thus, LpR1 and LpR2 are not dominant transporters of LTP across the BBB. However, we discovered redundant functions for LRP1 and LRP2 in BBB transport of LTP. Both proteins are predominantly expressed in glial cells (Figure 7A–F). Double mutants die as third instar larvae (not shown). Although LTP circulates at normal levels (Figure 7—figure supplement 1B), the number of LTP-positive neurons is reduced (Figure 7—figure supplement 1A,A′). A similar phenotype is produced by glial-specific double knockdown of LRP1/2, which halves the number of LTP-positive neurons, and completely blocks localization to DRNs. Knock-down of either LRP alone produces intermediate phenotypes, suggesting they function redundantly (Figure 7G–G′″, Figure 7—figure supplement 3). Thus, both LRPs transport LTP across the BBB to DRNs. Transport of LTP to other neurons has a less strict requirement for LRP1/2.

If the absence of LTP on DRNs reduces systemic IIS, then losing LRP1/2 in BBB glia should reproduce this effect. Indeed, glial-specific LRP1/2 knock-down slows larval growth and delays pupariation, and emerging adults live longer—all phenotypes suggesting reduced IIS (Figure 7H–I,M). Consistent with this, levels of circulating Dilp2 are lower in these animals (Figure 5E), AKT is less phosphorylated (Figure 5H), and FOXO is predominantly nuclear in larval fat body cells (Figure 7L,L′). These data support the idea that blocking transport of LTP to DRNs reduces the release of Dilp2 by IPCs, thereby lowering systemic IIS.

What mechanisms promote the BBB transport of LTP to DRNs on yeast food? We first wondered whether levels of LRP1,2 might change. However, we observed no obvious differences in LRP1,2 staining in the brain on YF vs PF (data not shown). However we were surprised to observe dramatically higher levels of free intracellular calcium in BBB glia when larvae are fed YF compared to PF. Driving the free intracellular calcium sensor GCaMP using repo-GAL4 (which is active in all glia) reveals a marked reduction in GFP fluorescence in BBB glia relative to other glia. We confirmed this result by specifically driving the reporter in BBB glia using moody-GAL4 (Figure 8A–D).

Figure 8

Download asset Open asset

To investigate whether Ca++ signaling in BBB glia was sufficient to promote transport of LTP to DRNs, we asked whether ectopically inducing Ca++ influx would allow LTP to accumulate on DRNs even when larvae were fed with PF. To do this, we drove the expression of TRPA1 with moody-GAL4 in PF-reared larvae and stained their brains for LTP and Dilp2. Strikingly, while LTP is undetectable on DRNs of wild type larvae reared on PF, it accumulates to high levels on these neurons when Ca++ influx is induced by TRPA1 expression in BBB cells (Figure 8E,E′). Accumulation is specific to DRNs and neuronal LTP localization in these larvae precisely mimics its localization on YF. Furthermore, Western blotting shows that phosphorylation of AKT in the fat body increases compared to wild type larvae fed with PF (Figure 8G). Taken together, these data show that YF increases free cytoplasmic Ca++ in BBB glia. This is sufficient for the specific transport of LTP to DRNs and elevated systemic IIS. Interestingly, the accumulation of LTP on other neurons does not appear to require elevated Ca++ in BBB cells.

In summary, this work demonstrates a key requirement for lipoproteins in conveying nutritional information across the BBB to specific neurons in the brain. As particles that carry both endogenously synthesized and diet-derived lipids, lipoproteins are well-positioned to perform this function. Our data suggest that transport of LTP across the BBB to DRNs influences communication between DRNs and the Dilp-producing IPCs, increasing the release of Dilp2 into circulation. Since the IPCs also deliver Dilp2 to the DRNs, this indicates that these two neuronal populations may communicate bidirectionally. How might LTP affect the function of DRNs? One possibility is that it acts to deliver a signaling lipid to the DRNs. It could do so either directly, or indirectly by promoting lipid transfer from LPP, which is present throughout the brain (Brankatschk and Eaton, 2010). LTP enrichment on specific neurons may increase lipid transfer to these cells.

This work highlights a key function for BBB cells in transmitting nutritional information to neurons within the brain. Feeding with yeast food increases free calcium in BBB glia, which then increases transport of LTP to DRNs. How might BBB cells detect the difference between yeast and plant food? Our data suggest differences in the lipid composition of yeast and plant-derived foods are responsible. Our previous work has shown that the lipids in these foods differ in their fatty acid composition. Yeast food has shorter and more saturated fatty acids than plant food (Carvalho et al., 2012). How could these nutritional lipids affect the activity of BBB glia? Interestingly, differences in food fatty acid composition are directly reflected in the fatty acids present in membrane lipids of all larval tissues including the brain (Carvalho et al., 2012). Thus, it is possible that the bulk membrane properties of BBB glia are different on these two diets. Membrane lipid composition is known to affect a variety of signaling events (Simons and Toomre, 2000; Lingwood and Simons, 2010). Alternatively, yeast food may influence the specific fatty acids present in signaling lipids that activate BBB glia.

We demonstrate an unexpected functional specialization of the BBB glial network, which permits specific and regulated LTP transport to particular neurons. How this specificity arises is an important question for the future. We note that a subset of glial cells within the brain also accumulates LTP derived from the fat body. Could these represent specific transport routes from the BBB? An alternative possibility is that transport depends on neuronal activity. Mammalian LRP1 promotes localized transfer of IGF in response to neuronal activity (Nishijima et al., 2010). Could LTP delivery by LRP1 and LRP2 in the Drosophila brain depend on similar mechanisms? The remarkable specificity of LTP trafficking in the Drosophila CNS provides a novel framework for understanding information flow between the circulation and the brain.

To what extent might this be relevant to vertebrate systems? While it is clear that the vertebrate brain (unlike that of Drosophila) does not depend on lipoproteins to supply it with bulk sterols (Orth and Bellosta, 2012), this does not rule out possible functions for these particles in nutrient sensing. The vertebrate cerebrospinal fluid is rich in many types of HDL particles, including those containing ApoA-1, which is not expressed in the brain—this suggests that at least some lipoprotein particles in the brain may derive from the circulation (Wang and Eckel, 2014). Consistent with this idea, ApoA-I can target albumin-containing nanoparticles across the BBB in rodents (Zensi et al., 2010). Recent work suggests that lipoproteins may be the source of specific Long Chain Fatty Acids that signal to the hypothalamus to regulate glucose homeostasis, since neuronal lipoprotein lipase is required for this process (Wang et al., 2011; Wang and Eckel, 2014). Thus, it would be interesting to investigate whether circulating mammalian lipoproteins might reach a subset of neurons in the hypothalamus.

It has been known for some time that increasing the amount of yeast in the diet of lab grown Drosophila melanogaster increases the rate of development and adult fertility, but reduces lifespan (Sang, 1949; Leroi et al., 1994; Mair et al., 2005). Here, we show that flies have evolved specific mechanisms to increase systemic IIS in response to yeast, independently of the number of calories in the diet or its proportions of sugars proteins and fats. What pressures could have driven the evolution of such mechanisms? In the wild, Drosophila melanogaster feed on rotting plant material and their diets comprise both fungal and plant components. Drosophila disperse yeasts and transfer them to breeding sites during oviposition improving the nutritional resources available to developing larvae (Markow and O'grady, 2008). Yeast that are able to induce more rapid development of the agents that disperse them may propagate more efficiently. On the other hand, it has been noted that Drosophila species that feed on ephemeral nutrient sources like yeasts or flowers have more rapid rates of development (Markow and O'grady, 2008) than other species. It may be that, even within a single species, the ability to adjust developmental rate to the presence of a short-lived resource is advantageous. Humans subsist on diets of both plant and animal materials that during most of evolution have differed in their availability. It would be interesting to investigate whether Insulin/IGF signaling in humans might respond to qualitative differences in the lipid composition of these nutritional components.

1. 1. Abbott NJ
   2. Patabendige AA
   3. Dolman DE
   4. Yusof SR
   5. Begley DJ

   (2010) Structure and function of the blood-brain barrier

   Neurobiology of Disease 37:13.

   https://doi.org/10.1016/j.nbd.2009.07.030

   • Google Scholar
2. 1. Bader R
   2. Sarraf-Zadeh L
   3. Peters M
   4. Moderau N
   5. Stocker H
   6. Köhler K
   7. Pankratz MJ
   8. Hafen E

   (2013) The IGFBP7 homolog Imp-L2 promotes insulin signaling in distinct neurons of the Drosophila brain

   Journal of Cell Science 126:2571–2576.

   https://doi.org/10.1242/jcs.120261

   • Google Scholar
3. 1. Bjordal M
   2. Arquier N
   3. Kniazeff J
   4. Pin JP
   5. Leopold P

   (2014) Sensing of amino acids in a dopaminergic circuitry promotes rejection of an incomplete diet in Drosophila

   Cell 156:510.

   https://doi.org/10.1016/j.cell.2013.12.024

   • Google Scholar
4. 1. Brankatschk M
   2. Eaton S

   (2010) Lipoprotein particles cross the blood-brain barrier in Drosophila

   The Journal of Neuroscience 30:10441–10447.

   https://doi.org/10.1523/JNEUROSCI.5943-09.2010

   • Google Scholar
5. 1. Broughton SJ
   2. Piper MD
   3. Ikeya T
   4. Bass TM
   5. Jacobson J
   6. Driege Y
   7. Martinez P
   8. Hafen E
   9. Withers DJ
   10. Leevers SJ
   11. Partridge L

   (2005) Longer lifespan, altered metabolism, and stress resistance in Drosophila from ablation of cells making insulin-like ligands

   Proceedings of the National Academy of Sciences of USA 102:3105–3110.

   https://doi.org/10.1073/pnas.0405775102

   • Google Scholar
6. 1. Bundgaard M
   2. Abbott NJ

   (2008) All vertebrates started out with a glial blood-brain barrier 4-500 million years ago

   Glia 56:699–708.

   https://doi.org/10.1002/glia.20642

   • Google Scholar
7. 1. Canavoso LE
   2. Yun HK
   3. Jouni ZE
   4. Wells MA

   (2004) Lipid transfer particle mediates the delivery of diacylglycerol from lipophorin to fat body in larval Manduca sexta

   Journal of Lipid Research 45:456–465.

   https://doi.org/10.1194/jlr.M300242-JLR200

   • Google Scholar
8. 1. Carvalho M
   2. Schwudke D
   3. Sampaio JL
   4. Palm W
   5. Riezman I
   6. Dey G
   7. Gupta GD
   8. Mayor S
   9. Riezman H
   10. Shevchenko A
   11. Kurzchalia TV
   12. Eaton S

   (2010)

   Survival strategies of a sterol auxotroph

   3675–3685, Development, 137, 10.1242/dev.044560.

   • Google Scholar
9. 1. Carvalho M
   2. Sampaio JL
   3. Palm W
   4. Brankatschk M
   5. Eaton S
   6. Shevchenko A

   (2012) Effects of diet and development on the Drosophila lipidome

   Molecular Systems Biology 8:600.

   https://doi.org/10.1038/msb.2012.29

   • Google Scholar
10. 1. Colombani J
    2. Raisin S
    3. Pantalacci S
    4. Radimerski T
    5. Montagne J
    6. Léopold P

    (2003) A nutrient sensor mechanism controls Drosophila growth

    Cell 114:739–749.

    https://doi.org/10.1016/S0092-8674(03)00713-X

    • Google Scholar
11. 1. Colombani J
    2. Andersen DS
    3. Leopold P

    (2012) Secreted peptide Dilp8 coordinates Drosophila tissue growth with developmental timing

    Science 336:582–585.

    https://doi.org/10.1126/science.1216689

    • Google Scholar
12. 1. Garelli A
    2. Gontijo AM
    3. Miguela V
    4. Caparros E
    5. Dominguez M

    (2012) Imaginal discs secrete insulin-like peptide 8 to mediate plasticity of growth and maturation

    Science 336:579–582.

    https://doi.org/10.1126/science.1216735

    • Google Scholar
13. 1. Geminard C
    2. Rulifson EJ
    3. Leopold P

    (2009) Remote control of insulin secretion by fat cells in Drosophila

    Cell Metabolism 10:199–207.

    https://doi.org/10.1016/j.cmet.2009.08.002

    • Google Scholar
14. 1. Haselton AT
    2. Fridell YW

    (2010)

    Adult Drosophila melanogaster as a model for the study of glucose homeostasis

    Aging 2:523–526.

    • Google Scholar
15. 1. Khaliullina H
    2. Panáková D
    3. Eugster C
    4. Riedel F
    5. Carvalho M
    6. Eaton S

    (2009) Patched regulates Smoothened trafficking using lipoprotein-derived lipids

    Development 136:4111–4121.

    https://doi.org/10.1242/dev.041392

    • Google Scholar
16. 1. Lam TK
    2. Pocai A
    3. Gutierrez-Juarez R
    4. Obici S
    5. Bryan J
    6. Aguilar-Bryan L
    7. Schwartz GJ
    8. Rossetti L

    (2005) Hypothalamic sensing of circulating fatty acids is required for glucose homeostasis

    Nature Medicine 11:320–327.

    https://doi.org/10.1038/nm1201

    • Google Scholar
17. 1. Leroi AM
    2. Kim SB
    3. Rose MR

    (1994) The evolution of Phenotypic life-History Trade-Offs - an experimental-study using Drosophila-Melanogaster

    The American Naturalist 144:661.

    https://doi.org/10.1086/285699

    • Google Scholar
18. 1. Levin BE
    2. Magnan C
    3. Dunn-Meynell A
    4. Le Foll C

    (2011) Metabolic sensing and the brain: who, what, where, and how?

    Endocrinology 152:2552–2557.

    https://doi.org/10.1210/en.2011-0194

    • Google Scholar
19. 1. Lingwood D
    2. Simons K

    (2010) Lipid rafts as a membrane-organizing principle

    Science 327:46–50.

    https://doi.org/10.1126/science.1174621

    • Google Scholar
20. 1. Linneweber GA
    2. Jacobson J
    3. Busch KE
    4. Hudry B
    5. Christov CP
    6. Dormann D
    7. Yuan M
    8. Otani T
    9. Knust E
    10. de Bono M
    11. Miguel-Aliaga I

    (2014) Neuronal control of metabolism through nutrient-dependent modulation of tracheal branching

    Cell 156:69–83.

    https://doi.org/10.1016/j.cell.2013.12.008

    • Google Scholar
21. 1. Mahoney TR
    2. Liu Q
    3. Itoh T
    4. Luo S
    5. Hadwiger G
    6. Vincent R
    7. Wang ZW
    8. Fukuda M
    9. Nonet ML

    (2006) Regulation of synaptic transmission by RAB-3 and RAB-27 in Caenorhabditis elegans

    Molecular Biology of the Cell 17:2617–2625.

    https://doi.org/10.1091/mbc.E05-12-1170

    • Google Scholar
22. 1. Mair W
    2. Piper MD
    3. Partridge L

    (2005) Calories do not explain extension of life span by dietary restriction in Drosophila

    PLOS Biology 3:e223.

    https://doi.org/10.1371/journal.pbio.0030223

    • Google Scholar
23. 1. Markow TA
    2. O'grady P

    (2008) Reproductive ecology of Drosophila

    Functional Ecology 22:747.

    https://doi.org/10.1111/j.1365-2435.2008.01457.x

    • Google Scholar
24. 1. Nishijima T
    2. Piriz J
    3. Duflot S
    4. Fernandez AM
    5. Gaitan G
    6. Gomez-Pinedo U
    7. Verdugo JM
    8. Leroy F
    9. Soya H
    10. Nuñez A
    11. Torres-Aleman I

    (2010) Neuronal activity drives localized blood-brain-barrier transport of serum insulin-like growth factor-I into the CNS

    Neuron 67:834–846.

    https://doi.org/10.1016/j.neuron.2010.08.007

    • Google Scholar
25. 1. Orth M
    2. Bellosta S

    (2012) Cholesterol: its regulation and role in central nervous system disorders

    Cholesterol 2012:292598.

    https://doi.org/10.1155/2012/292598

    • Google Scholar
26. 1. Palm W
    2. Sampaio JL
    3. Brankatschk M
    4. Carvalho M
    5. Mahmoud A
    6. Shevchenko A
    7. Eaton S

    (2012) Lipoproteins in Drosophila melanogaster–assembly, function, and influence on tissue lipid composition

    PLOS Genetics 8:e1002828.

    https://doi.org/10.1371/journal.pgen.1002828

    • Google Scholar
27. 1. Panakova D
    2. Sprong H
    3. Marois E
    4. Thiele C
    5. Eaton S

    (2005) Lipoprotein particles are required for Hedgehog and Wingless signalling

    Nature 435:58–65.

    https://doi.org/10.1038/nature03504

    • Google Scholar
28. 1. Parra-Peralbo E
    2. Culi J

    (2011) Drosophila lipophorin receptors mediate the uptake of neutral lipids in oocytes and imaginal disc cells by an endocytosis-independent mechanism

    PLOS Genetics 7:e1001297.

    https://doi.org/10.1371/journal.pgen.1001297

    • Google Scholar
29. 1. Partridge L
    2. Alic N
    3. Bjedov I
    4. Piper MD

    (2011) Ageing in Drosophila: the role of the insulin/Igf and TOR signalling network

    Experimental Gerontology 46:376–381.

    https://doi.org/10.1016/j.exger.2010.09.003

    • Google Scholar
30. 1. Pavlos NJ
    2. Grønborg M
    3. Riedel D
    4. Chua JJ
    5. Boyken J
    6. Kloepper TH
    7. Urlaub H
    8. Rizzoli SO
    9. Jahn R

    (2010) Quantitative analysis of synaptic vesicle Rabs uncovers distinct yet overlapping roles for Rab3a and Rab27b in Ca2+-triggered exocytosis

    The Journal of Neuroscience 30:13441–13453.

    https://doi.org/10.1523/JNEUROSCI.0907-10.2010

    • Google Scholar
31. 1. Rajan A
    2. Perrimon N

    (2012) Drosophila cytokine unpaired 2 regulates physiological homeostasis by remotely controlling insulin secretion

    Cell 151:123–137.

    https://doi.org/10.1016/j.cell.2012.08.019

    • Google Scholar
32. 1. Reiff DF
    2. Ihring A
    3. Guerrero G
    4. Isacoff EY
    5. Joesch M
    6. Nakai J
    7. Borst A

    (2005) In vivo performance of genetically encoded indicators of neural activity in flies

    The Journal of Neuroscience 25:4766–4778.

    https://doi.org/10.1523/JNEUROSCI.4900-04.2005

    • Google Scholar
33. 1. Riedel F
    2. Vorkel D
    3. Eaton S

    (2011) Megalin-dependent yellow endocytosis restricts melanization in the Drosophila cuticle

    Development 138:149–158.

    https://doi.org/10.1242/dev.056309

    • Google Scholar
34. 1. Rulifson EJ
    2. Kim SK
    3. Nusse R

    (2002) Ablation of insulin-producing neurons in flies: growth and diabetic phenotypes

    Science 296:1118–1120.

    https://doi.org/10.1126/science.1070058

    • Google Scholar
35. 1. Sang JH

    (1949)

    The ecological determinants of population growth in a Drosophila culture; the significance of successive batches of larvae

    Physiological Zoology 22:202–210.

    • Google Scholar
36. 1. Schindelin J
    2. Arganda-Carreras I
    3. Frise E
    4. Kaynig V
    5. Longair M
    6. Pietzsch T
    7. Preibisch S
    8. Rueden C
    9. Saalfeld S
    10. Schmid B
    11. Tinevez JY
    12. White DJ
    13. Hartenstein V
    14. Eliceiri K
    15. Tomancak P
    16. Cardona A

    (2012) Fiji: an open-source platform for biological-image analysis

    Nature Methods 9:676–682.

    https://doi.org/10.1038/nmeth.2019

    • Google Scholar
37. 1. Simons K
    2. Toomre D

    (2000) Lipid rafts and signal transduction

    Nature Reviews Molecular Cell Biology 1:31–39.

    https://doi.org/10.1038/35036052

    • Google Scholar
38. 1. Stork T
    2. Engelen D
    3. Krudewig A
    4. Silies M
    5. Bainton RJ
    6. Klämbt C

    (2008) Organization and function of the blood-brain barrier in Drosophila

    The Journal of Neuroscience 28:587–597.

    https://doi.org/10.1523/JNEUROSCI.4367-07.2008

    • Google Scholar
39. 1. van der Horst A
    2. Burgering BM

    (2007) Stressing the role of FoxO proteins in lifespan and disease

    Nature Reviews Molecular Cell Biology 8:440–450.

    https://doi.org/10.1038/nrm2190

    • Google Scholar
40. 1. Van Heusden MC
    2. Law JH

    (1989)

    An insect lipid transfer particle promotes lipid loading from fat body to lipoprotein

    The Journal of Biological Chemistry 264:17287–17292.

    • Google Scholar
41. 1. Venken KJ
    2. Bellen HJ

    (2005) Emerging technologies for gene manipulation in Drosophila melanogaster

    Nature Reviews Genetics 6:167–178.

    https://doi.org/10.1038/nrg1553

    • Google Scholar
42. 1. Wang H
    2. Eckel RH

    (2014) What are lipoproteins doing in the brain?

    Trends in Endocrinology and Metabolism 25:8–14.

    https://doi.org/10.1016/j.tem.2013.10.003

    • Google Scholar
43. 1. Wang H
    2. Astarita G
    3. Taussig MD
    4. Bharadwaj KG
    5. DiPatrizio NV
    6. Nave KA
    7. Piomelli D
    8. Goldberg IJ
    9. Eckel RH

    (2011) Deficiency of lipoprotein lipase in neurons modifies the regulation of energy balance and leads to obesity

    Cell Metabolism 13:105–113.

    https://doi.org/10.1016/j.cmet.2010.12.006

    • Google Scholar
44. 1. Zensi A
    2. Begley D
    3. Pontikis C
    4. Legros C
    5. Mihoreanu L
    6. Büchel C
    7. Kreuter J

    (2010) Human serum albumin nanoparticles modified with apolipoprotein A-I cross the blood-brain barrier and enter the rodent brain

    Journal of Drug Targeting 18:842–848.

    https://doi.org/10.3109/1061186X.2010.513712

    • Google Scholar

Author details

1. Marko Brankatschk

   Department of Molecular, Cell and Developmental Biology, Max Planck Institute for Molecular Cell Biology and Genetics, Dresden, Germany

   Contribution

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

   For correspondence

   brankats@mpi-cbg.de

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0001-5274-4552

2. Sebastian Dunst

   Department of Molecular, Cell and Developmental Biology, Max Planck Institute for Molecular Cell Biology and Genetics, Dresden, Germany

   Contribution

   SD, Photographed some prepared CNS samples shown in (Figure 7A–F) and some prepared samples used to acquire quantification data, Commented on the manuscript

   Competing interests

   The authors declare that no competing interests exist.

3. Linda Nemetschke

   Department of Molecular, Cell and Developmental Biology, Max Planck Institute for Molecular Cell Biology and Genetics, Dresden, Germany

   Contribution

   LN, Staged and photographed larvae shown in Figure 4F–F′; and Figure 7H–H′; Commented on the manuscript

   Competing interests

   The authors declare that no competing interests exist.

4. Suzanne Eaton

   Department of Molecular, Cell and Developmental Biology, Max Planck Institute for Molecular Cell Biology and Genetics, Dresden, Germany

   Contribution

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

   For correspondence

   eaton@mpi-cbg.de

   Competing interests

   The authors declare that no competing interests exist.

• 4,996

  views

• 686

  downloads

• 71

  citations

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