C Nissoux
email: fnissoux@laas.fr

T Sim

J-P Laumond

Visibility Based Probabilistic Roadmaps

This paper presents a variant of probabilistic roadmap algorithms that recently appeared as a promising approach to motion planning. We exploit a free-space structuring of the con guration space into visibility domains in order to produce small roadmaps. The algorithm has been implemented within a software platform allowing to address a large class of mechanical systems. Experiments show the e ciency of the approach in capturing narrow passages of collision-free con guration spaces.

Introduction

Due to the continuous increasing power of the computers, probabilistic approaches to motion planning (e.g., 2, 6, 13, 8, 3]) allow today to solve practical problems which were not addressed few years ago. Apart some attempts aiming to provide formal models of complexity [START_REF] Kavraki | Probabilistic roadmaps for robot path planning[END_REF][START_REF] Kavraki | Randomized preprocessing of con guration space for fast path planning[END_REF][START_REF] Bessiere | The \Ariane's clew" algorithm: Global planning with local methods[END_REF][START_REF] Reeds | Optimal paths for a car that goes both forward and backwards[END_REF], the success of such methods remains better noticed than well understood.

This paper proposes a variant of the Probabilistic RoadMap (PRM) algorithm introduced in 6] (and independently in 13] as the Probabilistic Path Planner). These algorithms generate collision-free con gurations randomly and try to link them with a simple local path planning method. A roadmap is then generated, tending to capture the connectivity of the collision-free con guration space CS free .

Our variant of these approaches takes advantage of the visibility notion. While usually each collision-free con guration generated by the PRM algorithm is integrated to the roadmap, our algorithm keeps only congurations which either connect two connected components of the roadmap, or are not \visible" by some so-called guard con gurations. This approach computes roadmaps with small number of nodes. It integrates a termination condition related to the volume of the free space covered by the roadmap. Experimental comparison shows good performances in terms of computation time, especially when applied to con guration spaces with narrow passages.

Section 2 introduces the notion of visibility roadmap. Section 3 describes a simple probabilistic algorithm that computes such roadmaps. In Section 4, we provide a comparison with the basic algorithm classically used in the PRM scheme. Finally, the last section presents several examples of problems solved by the planner that is integrated in the Move3d software platform dedicated to motion planning.

Visibility Roadmaps

De nitions Consider a robot R moving in a workspace W. Let CS be the con guration space of the robot. Let CS free be the collision-free con gu- ration space with respect to W. Let L be any local method that computes a path L(q; q 0 ) (e.g., the straight line segment) between two given con gurations q and q 0 . We de ne the visibility domain of a con guration q for L by:

V is L (q) = fq 0 2 CS free such that L(q; q 0 ) 2 CS free g q is said to be the guard of V is L (q) A con guration is said to be a connection if it lies within the visibility domains of at least two guards.

Consider a coverage of CS free by s visibility do- mains V is L (q i ), 1 i s, such as L(q i ; q j ) 6 2 CS free for any pair of guards (q i ; q j ). Then we build the fol- lowing graph R. Guards are nodes of the graph. For any two intersecting visibility domains V is L (q i ) and q Figure 1: Visibility domain of a con guration and the roadmap de ned by three guards nodes (black) and three connection nodes (white). Here paths L(q;q 0 ) are the straight line segments q; q 0 ].

V is L (q j ), we add a connection node q and two edges (q; q i ) and (q; q j ) (see Figure 1). The graph R clearly veri es the following property:

Property: Let us consider any two con gurations q init and q goal such as there exists a sequence of collision-free paths of type L between them. Then there is a guard node q 1 and a guard node q 2 in R such as: q init 2 V is L (q 1 ), q goal 2 V is L (q 2 ) with q 1 and q 2 lying in a same connected component of R.

This property means that R captures the connectivity of CS free . R is said to be a visibility roadmap.

Comments The notion of visibility roadmap raises several comments. Since the de nition of the visibility domains is related to a local method, it would have been better to use the term of \reachable domain". Both notions are identical when the local method provides elementary straight line segments. We keep the word \visibility" because it is more intuitive. We consider implicitly that R is an undirected graph; that means that L should be symmetric.

The existence of nite coverage by visibility domains depends both on the shape of CS free and on L. Such coverage may not exist 1 . Nevertheless these cases are di cult for any other alternative planners.

Finally the number of guards is not required to be optimal. Optimality refers to the well known and challenging art gallery problem 12].

A probabilistic algorithm

Description The algorithm iteratively processes two sets of nodes: Guard and Connection. The nodes of Guard belonging to a same connected component (i.e. connected by nodes of Connection) are gathered in subsets G i . At each elementary iteration, the algo- rithm selects a collision-free con guration q.

The main loop of the algorithm processes all the current components G i of Guard. It loops over the nodes g in G i , until it nds a node visible from q. The 1 A nite coverage by visibility domains does not exist for non good free spaces 5].

rst time the algorithm succeeds in nding such a visible node g, it memorizes both g and its component G i and switches to the next component G i+1 . When q \sees" another guard g 0 in another component G j , the algorithm adds this con guration to the Connection set and the component G j is merged with the memo- rized G i .

The main loop fails to create a new node in two cases:

either q is visible from only one component; in that case q is rejected. or q is not visible from any guard: in that case it becomes a guard node that creates a new component fqg inserted into set Guard.

Parameter ntry is the number of failures before the insertion of a new guard node. 1=ntry gives an estimation of the volume not yet covered by visibility domains. It estimates the fraction between the non covered volume and the total volume of CS free . This is a critical parameter which controls the end of the algorithm. Hence, the algorithm stops when ntry becomes greater than a user set value M , which means that the coverage of the free space by visibility domains becomes greater than (1 1 M ). Algorithm ntry 0 While (ntry < M) Select a random free con guration q guard ;;Gvis ; For all components Gi of Guard do found FALSE For all nodes g of Gi do If(q belongs to V is(g)) then found TRUE If (:guard)) then guard g; Gvis Gi

Else /* q is a connection node */ Add q to Connection Create edges (q; g) and (q; guard) Merge components Gvis and Gi; until found = TRUE If (:guard) then/* q is a guard node /* Add fqg to Guard; ntry 0 The algorithm is then guaranteed to terminate for any value of M . When it stops, a probabilistic estimation of the percentage of the free space not covered by the guards is 1 M . Therefore, the probability for the local method to fail in connecting two con gurations q init or q goal to the roadmap is (1 1 M ).

The solution path shown in Figure 3 was found from a visibility roadmap computed by the algorithm with M = 400.

Analysis Figure 4 shows the evolution of the freespace coverage for the 6-dof puzzle example (Figure 3) with respect to the number of guards generated by the algorithm. The solid curve plots (1 1 ntry ).

The dashed curve plots the real percentages of the free space coverage (they were computed by testing the visibility of each set of guards with a huge number of free con gurations). As shown by the gure, the estimation rapidly converges toward the actual coverage: the error becomes less than one percent with 100 guards. Also one can note that despite the rather complex shape of CS free for this example, less than hundred guards are su cient to see 98% of the free space.

Side-e ect and limitation Two limitations of the approach are illustrated in Figures 5. In the left gure, two guard nodes have been generated. They fully cover CS free . The intersection domain of both visibil- ity domains is \unfortunately" small. The only way to complete the roadmap is to pick a connection node in the small triangle. Then the algorithm will fail if the parameter M is not su ciently high. Nevertheless this case is only a side-e ect of the algorithm. Indeed, in this example, the probability to select the rst two guards with a small intersection domain is small. We may be overcome this e ect by running the algorithm twice.

The second gure (on the right) illustrates the difcult case to put guards in narrow passages with a probabilistic approach. In the next section we present experimental results comparing the performance of our approach with respect to other probabilistic ap- proaches.

Visibility roadmap versus PRM

The PRM scheme Probabilistic roadmaps (PRM) have been introduced in 6, 13]. The algorithm consists of two phases. It rst constructs a roadmap R that characterizes the connectivity of the free con guration space CS free by randomly selecting a large number of collision-free milestones, and checking if each (or some) pair of milestones can be connected by a collision-free path generated by a simple (usually not complete) local method. When the preprocessing phase has produced a roadmap that is representative of the complexity of the free-space, the query phase can e ciently process motion planning problems by connecting the initial and nal con gurations to the same connected component of R. Several variants or extensions of this basic scheme have been proposed 5, 9, 1] to improve the e ciency of the preprocessing and the connectedness of the produced roadmap, in particular for di cult cases where the free space contains narrow passages.

Comparison With respect to these methods, the main advantage of the visibility roadmap is illustrated in Figure 6. In this example CS free has two con- nected component. Our algorithm will quickly nd the two guards that cover both connected components, and the algorithm will stop just after M elementary loops. At each of these loops the local method will be called only twice, while the basic PRM algorithm should call the local method a lot of times to check whether each new collision-free node can be connected to the roadmap.

With a same number n of random collision-free congurations, the visibility roadmap algorithm will call the local method O(n) times, while a basic PRM algorithm will call it O(n 2 ) times 2 .

2 Limiting the calls to a given neighborhood (see 9] for instance) may allow to reach a linear complexity with a \well" chosen value of the threshold distance; however, a larger size n Obviously, the analysis of this simple example is not a proof. We performed several experiments comparing the performances of Basic-PRM and Visibility-PRM. Figure 7 summarizes the results of one such experiment performed onto a 6-dof \puzzle" example 9]. The free-space of this example consists of two large regions connected by a narrow passage. As noted in 9], the di culty of the problem depends on the relative width of the passage with respect to the smallest dimension of the moving object. It also depends on the total volume of the con guration space. The results summarized in the table were obtained for the following dimensions: the workspace has dimension 200 150 150 with a rectangular passage of width 30 and the moving object is made up ve blocks of length 50 and cross-section 10. Each test was performed by running both algorithms with the same set of con gurations (i.e. the random node generator was initialized with the same seed) until the roadmap captured the connection between the two large regions. All the values of the table were averaged over ten runs performed may be required to maintain the roadmap's connectedness.

with di erent seeds.

Visibility-PRM captured the free space connectivity with a roadmap much smaller than Basic-PRM while maintaining the same coverage of the free space. Moreover, the performance (number of calls to the local method or CPU time) was 12 times better. Notice that both required roughly the same total number of random con gurations.

Similar experiments performed onto this example tend to show that the gain still increases for larger dimensions of the workspace (it drops to 18 when doubling the z dimension) or a smaller width of the passage. On the other side, comparisons performed onto the less constrained examples of Section 5 showed a gain of performance ranging from one to a few times. For all cases, the produced visibility roadmaps are much smaller.

Other Results

Both algorithms were integrated into the Move3d software platform 3 . Move3d serves as a testbed for motion planners that we develop for polyhedral workspaces. It allows to model many kinds of mechanical systems: free-ying, manipulators, nonholonomic mobile vehicles, mobile manipulators... The library contains a set of functionalities that make easy the development of new motion planners. Collision detection is currently performed with the I-Collide library 11].

Figures 8, 9 and 10 present various problems solved with the same version of visibility-PRM. In this version, the algorithm includes starting and goal con gurations as initial guards and stops as soon as it nds a solution path.

Conclusion

In this paper we have introduced the notion of visibility roadmaps as a way to capture the free-space connectivity with a small number of guards and also, to control the end of the roadmap's computation by estimating the quality of the free-space coverage within the algorithm.

The rst results obtained with a preliminary implementation of the algorithm are encouraging, especially for the di cult cases of free-spaces containing narrow passages which usually require dense and expensive roadmaps with PRM schemes.

Although preliminary, these results raise several interesting issues that we expect to address. It may be interesting to investigate the idea of \movable 3 see http://www.laas.fr/~nic/G3D/RIA-g3d.html guards" in order to limit the risk of generating guards whose visibility domains have a very small intersection. Also, a formal analysis of the proposed method would be desirable to better characterize its performance from geometric properties of the free-space such as goodness or expansiveness introduced in 7].

Else ntry ntry + 1 EndFigure 2

 12 Figure2shows examples of visibility roadmaps computed by the algorithm.Termination criterion Each new guard node inserted to the roadmap increases the coverage of CS free . Therefore the probability of generating con- gurations in non covered regions keeps decreasing.

Figure 2 :

 2 Figure 2: Four runs of the algorithm on a simple example with M = 50.

Figure 3 :Figure 4 :

 34 Figure 3: A solution path for the 6-dof puzzle problem presented in 9].

Figure 5 :

 5 Figure 5: Two di cult situations: a/ a pathologic case for our algorithm; both visibility domains have a very small intersection and b/ existence of narrow passage di cult to capture for any random sampling algorithm.

Figure 6 : 7 :

 67 Figure 6: Visibility roadmap versus classical PRM

Figure 8 :

 8 Figure 8: A path for a 6-ddl manipulor found in 120 seconds.

Figure 9 :

 9 Figure 9: A simple problem with a 4-ddl helicopter amidst buildings (< 1 second).

Figure 10 :

 10 Figure 10: A coordinated motion of a 9-ddl nonholonomic mobile manipulator (20 seconds). Here the local method addresses the nonholonomic constraint of the platform via the computation of Reeds and Shepp curves 14].


Mouna El Mehdi

Saloua Takhlidjt

Mélodie Devère

Arnaud Arabo

Marie-Anne Le Solliec

Julie Maucotel

Alexandre Bénani

Emmanuelle Nedelec

Céline Duparc

Benjamin Lefranc

Jérôme Leprince

Youssef Anouar

Gaëtan Prévost

Nicolas Chartrel
email: nicolas.chartrel@univ-rouen.fr

Marie Picot

The 26RFa (QRFP)/GPR103 neuropeptidergic system in mice relays insulin signalling into the brain to regulate glucose homeostasis

Keywords: Diabetes, Glucose homeostasis, Hypothalamus, Insulin, Neuropeptide

Aims/hypothesis 26RFa (pyroglutamilated RFamide peptide [QRFP]) is a biologically active peptide that regulates glucose homeostasis by acting as an incretin and by increasing insulin sensitivity at the periphery. 26RFa is also produced by a neuronal population localised in the hypothalamus. In this study we investigated whether 26RFa neurons are involved in the hypothalamic regulation of glucose homeostasis. Methods 26Rfa +/+ , 26Rfa -/-and insulin-deficient male C57Bl/6J mice were used in this study. Mice received an acute intracerebroventricular (i.c.v.) injection of 26RFa, insulin or the 26RFa receptor (GPR103) antagonist 25e and were subjected to IPGTTs, insulin tolerance tests, acute glucose-stimulated insulin secretion tests and pyruvate tolerance tests (PTTs). Secretion of 26RFa by hypothalamic explants after incubation with glucose, leptin or insulin was assessed. Expression and quantification of the genes encoding 26RFa, agouti-related protein, the insulin receptor and GPR103 were evaluated by quantitative reverse transcription PCR and RNAscope in situ hybridisation. Results Our data indicate that i.c.v.-injected 26RFa induces a robust antihyperglycaemic effect associated with an increase in insulin production by the pancreatic islets. In addition, we found that insulin strongly stimulates 26Rfa expression and secretion by the hypothalamus. RNAscope experiments revealed that neurons expressing 26Rfa are mainly localised in the lateral hypothalamic area, that they co-express the gene encoding the insulin receptor and that insulin induces the expression of 26Rfa in these neurons. Concurrently, the central antihyperglycaemic effect of insulin is abolished in the presence of a GPR103 antagonist and in 26RFa-deficient mice. Finally, our data indicate that the hypothalamic 26RFa neurons are not involved in the central inhibitory effect of insulin on hepatic glucose production, but mediate the central effects of the hormone on its own peripheral production.

Conclusion/interpretation

We have identified a novel mechanism in the hypothalamic regulation of glucose homeostasis, the 26RFa/GPR103 system, and we provide evidence that this neuronal peptidergic system is a key relay for the central regulation of glucose metabolism by insulin.

Introduction

It has been known since the 19th century that the brain, and more specifically the hypothalamus, is involved in the control of glucose homeostasis and diabetes pathogenesis [START_REF] Bernard | Leçons de physiologie expérimentales appliquées à la médecine[END_REF][START_REF] Schwartz | Cooperation between brain and islet in glucose homeostasis and diabetes[END_REF]. Evidence has been provided that the brain accounts for 50% of overall glucose disposal and can normalise glycaemia in rodent models of diabetes [START_REF] Carey | Evidence for central regulation of glucose metabolism[END_REF][START_REF] Fujikawa | Leptin engages a hypothalamic neurocircuitry to permit survival in the absence of insulin[END_REF]. From these findings a new model has emerged in which the control of glucose homeostasis results from complex and highly coordinated interactions between the brain and the pancreatic islets. The crucial role of the brain in the control of glucose metabolism is supported by observations that the injection of glucose, leptin or insulin into discrete hypothalamic areas, including the arcuate nucleus (Arc), the ventromedial hypothalamic nucleus (VMH) and the paraventricular nucleus, lowers blood glucose levels and increases insulin sensitivity in the liver [START_REF] Schwartz | Cooperation between brain and islet in glucose homeostasis and diabetes[END_REF][START_REF] Obici | Hypothalamic insulin signaling is required for inhibition of glucose production[END_REF][START_REF] Lam | Regulation of blood glucose by hypothalamic pyruvate metabolism[END_REF] and that central insulin decreases hepatic glucose production (HGP) [START_REF] Inoue | Central insulin-mediated regulation of HGP[END_REF]. Similar effects are achieved by restoring functional insulin receptors in specific hypothalamic nuclei that otherwise lack them [START_REF] Morton | Leptin regulates insulin sensitivity via phosphatidylinositol-3-OH kinase signaling in mediobasal hypothalamic neurons[END_REF]. Conversely, deletion of insulin receptors or their downstream signalling intermediates from defined hypothalamic neurons induces glucose intolerance and systemic insulin resistance [START_REF] Jordan | Sensing the fuels: glucose and lipid signaling in the CNS controlling energy homeostasis[END_REF][START_REF] Hill | Direct insulin and leptin action on pro-opiomelanocortin neurons is required for normal glucose homeostasis and fertility[END_REF]. More specifically, it was found that the central effects of insulin on glucose homeostasis are mediated only in part by the agouti-related protein (AgRP) neurons of the Arc and not by the proopiomelanocortin (POMC) neurons [START_REF] Hill | Direct insulin and leptin action on pro-opiomelanocortin neurons is required for normal glucose homeostasis and fertility[END_REF][START_REF] Könner | Insulin action in AgRP-expressing neurons is required for suppression of HGP[END_REF]. These observations suggest that insulin targets other neuropeptidergic systems in the hypothalamus to exert its central action on glucose homeostasis and highlight that our knowledge on the molecular identity of the hypothalamic neuronal populations relaying the central action of insulin remains fragmentary.

In this context, the neuropeptidergic 26RFa/GPR103 system is of particular interest. 26RFa (also referred to as pyroglutamilated RFamide peptide [QRFP]) is a hypothalamic neuropeptide that was discovered concurrently by us and others [START_REF] Chartrel | Identification of 26RFa, a hypothalamic neuropeptide of the RFamide peptide family with orexigenic activity[END_REF][START_REF] Fukusumi | A new peptidic ligand and its receptor regulating adrenal function in rats[END_REF][START_REF] Jiang | Identification and characterization of a novel RF-amide peptide ligand for orphan Gprotein-coupled receptor SP9155[END_REF]. It has been identified as the cognate ligand of the human orphan G protein-coupled receptor GPR103 [START_REF] Fukusumi | A new peptidic ligand and its receptor regulating adrenal function in rats[END_REF][START_REF] Jiang | Identification and characterization of a novel RF-amide peptide ligand for orphan Gprotein-coupled receptor SP9155[END_REF][START_REF] Chartrel | The RFamide neuropeptide 26RFa and its role in the control of neuroendocrine functions[END_REF][START_REF] Takayasu | A neuropeptide ligand of the G protein-coupled receptor GPR103 regulates feeding, behavioral arousal, and blood pressure in mice[END_REF][START_REF] Leprince | The Arg-Phe-amide peptide 26RFa/glutamine RF-amide peptide and its receptor: IUPHAR Review 24[END_REF]. Neuroanatomical observations revealed that 26RFaand GPR103-expressing neurons are primarily localised in two hypothalamic nuclei involved in the control of feeding behaviour, that is, the lateral hypothalamic area (LHA) and the VMH [START_REF] Chartrel | Identification of 26RFa, a hypothalamic neuropeptide of the RFamide peptide family with orexigenic activity[END_REF][START_REF] Takayasu | A neuropeptide ligand of the G protein-coupled receptor GPR103 regulates feeding, behavioral arousal, and blood pressure in mice[END_REF][START_REF] Bruzzone | Anatomical distribution and biochemical characterization of the novel RFamide peptide 26RFa in the human hypothalamus and spinal cord[END_REF][START_REF] Bruzzone | Distribution of 26RFa binding sites and GPR103 mRNA in the central nervous system of the rat[END_REF]. Indeed, intracerebroventricular (i.c.v.) administration of 26RFa stimulates food intake [START_REF] Chartrel | Identification of 26RFa, a hypothalamic neuropeptide of the RFamide peptide family with orexigenic activity[END_REF][START_REF] Takayasu | A neuropeptide ligand of the G protein-coupled receptor GPR103 regulates feeding, behavioral arousal, and blood pressure in mice[END_REF][START_REF] Moriya | RFamide peptide QRFP43 causes obesity with hyperphagia and reduced thermogenesis in mice[END_REF][START_REF] Lectez | The orexigenic activity of the hypothalamic neuropeptide 26RFa is mediated by the neuropeptide Y and proopiomelanocortin neurons of the arcuate nucleus[END_REF], and the neuropeptide exerts its orexigenic activity by modulating the neuropeptide Y/POMC system in the Arc [START_REF] Lectez | The orexigenic activity of the hypothalamic neuropeptide 26RFa is mediated by the neuropeptide Y and proopiomelanocortin neurons of the arcuate nucleus[END_REF]. More recently, the involvement of the 26RFa/GPR103 neuropeptidergic system in the control of glucose homeostasis at the periphery was reported. We and others found that 26Rfa (also known as Qrfp) and Gpr103 (also known as Qrfpr) are strongly expressed by beta cells of the pancreatic islets [START_REF] Granata | RFamide peptides 43RFa and 26RFa both promote survival of pancreatic β-cells and human pancreatic islets but exert opposite effects on insulin secretion[END_REF][START_REF] Prévost | Hypothalamic Neuropeptide 26RFa Acts as an Incretin to Regulate Glucose Homeostasis[END_REF][START_REF] Prévost | The neuropeptide 26RFa in the human gut and pancreas: potential involvement in glucose homeostasis[END_REF] and that the neuropeptide prevents cell death and apoptosis of beta cells [START_REF] Granata | RFamide peptides 43RFa and 26RFa both promote survival of pancreatic β-cells and human pancreatic islets but exert opposite effects on insulin secretion[END_REF]. We also showed that 26Rfa is abundantly expressed all along the gut and that i.p. administration of the neuropeptide attenuates glucose-induced hyperglycaemia by increasing plasma insulin through a direct insulinotropic effect on the pancreatic beta cells and by increasing insulin sensitivity [START_REF] Prévost | Hypothalamic Neuropeptide 26RFa Acts as an Incretin to Regulate Glucose Homeostasis[END_REF][START_REF] Prévost | The neuropeptide 26RFa in the human gut and pancreas: potential involvement in glucose homeostasis[END_REF]. Finally, we reported that an oral glucose challenge induces a massive secretion of 26RFa by the gut into the blood, strongly suggesting that this neuropeptide regulates blood or plasma glucose by acting as an incretin [START_REF] Prévost | Hypothalamic Neuropeptide 26RFa Acts as an Incretin to Regulate Glucose Homeostasis[END_REF]. This incretin effect of 26RFa has been confirmed by the observations that administration of a GPR103 antagonist reduces the global glucose-induced incretin effect and also decreases insulin sensitivity [START_REF] Prévost | Neuropeptide 26RFa (QRFP) is a key regulator of glucose homeostasis and its activity is markedly altered in obese/hyperglycemic mice[END_REF] and that 26RFa-deficient mice show impaired regulation of glucose homeostasis [START_REF] El Mehdi | Glucose homeostasis is impaired in mice deficient for the neuropeptide 26RFa (QRFP)[END_REF].

Altogether, these observations prompted us to investigate whether the hypothalamic 26RFa/GPR103 system is involved in the central regulation of glucose homeostasis and may be a target of insulin.

Methods

Animals 26RFa +/+ (wild-type mice), 26RFa -/-(mutant mice) and insulin-deficient male C57Bl/6J mice (2-4 months old), weighing 22-25 g, were used in this study. 26RFa -/-(iCre knock-in) mice were obtained from T. Sakurai (International Institute for Integrative Sleep Medecine, Tsukuba, Ibaraki, Japan). The mutant mice were generated by homologous recombination in embryonic stem cells of 129SvJ mice, which were then implanted in C57 blastocysts using standard procedures. The targeting vector was constructed by replacing the entire coding region of the prepro-26Rfa sequence in exon 2 of the 26Rfa gene with the iCre sequence and pgk-Neo cassette [START_REF] El Mehdi | Glucose homeostasis is impaired in mice deficient for the neuropeptide 26RFa (QRFP)[END_REF]. Insulin deficiency was induced by a single i.p. injection of streptozotocin (150 mg/kg) 7 days before performing the experiments. Insulin deficiency was assessed by measurement of glycaemia and insulinaemia 7 days post streptozotocin administration. Animals that exhibited high basal glycaemia or did not respond to the glucose challenge with a significant hyperglycaemic peak were excluded. Mice were housed with free access to a standard diet (UAR, Villemoisson-sur-Orge, France) and tap water. They were kept in a ventilated room at a temperature of 22 ± 1°C under a 12 h light/12 h dark cycle (light on between 07:00 and 19:00).

All experimental procedures were approved by the Normandy Regional Ethics Committee (authorisation: APAFIS no. 11752-2017100916177319) and were carried out in accordance with Council Directive 86/609/EEC of 24 November 1986.

Animal surgery 26Rfa +/+ , 26Rfa -/-and insulin-deficient mice were anaesthetised with isoflurane and placed in a stereotaxic frame. A stainless steel 26 gauge guide cannula (Phymep, Paris, France) was inserted into the right lateral ventricle (0.8 mm lateral to the bregma and 2.2 mm ventral to the dura mater). A stainless steel dummy small cap for 26 gauge cannulae (Phymep) was inserted to prevent occlusion of the guide cannula and leakage of cerebrospinal fluid. The cannula placement was secured with dental acrylic cement. Correct cannula positioning was confirmed by histological examination after Trypan Blue injection. The i.c.v. injections were performed using a Hamilton syringe (outer diameter 0.5 mm; Hamilton, Reno, NV, USA) attached to polyethylene tubing. The injections were performed under anaesthesia, which was induced by i.p. administration of diazepam (5 mg/kg) and ketamine (100 mg/kg).

Hypothalamic explants Hypothalami from freshly euthanised 26Rfa +/+ mice were quickly removed and transferred into individual tubes containing 3 ml of artificial cerebrospinal fluid medium composed of 20 mmol/l NaHCO 3 , 126 mmol/l NaCl, 0.09 mmol/l Na 2 HPO 4 , 6 mmol/l KCl, 1.4 mmol/l CaCl 2 , 0.09 mmol/l MgSO 4 , 2 mmol/l glucose, 0.18 mg/ml ascorbic acid and 100 g/ml aprotinin. The tubes were maintained at 37°C in 95% O 2 and 5% CO 2 under constant stirring. After incubation with glucose (0.5 or 5 mmol/l), leptin (10 or 100 nmol/l) or insulin (10 or 100 nmol/l) for 4 h, the culture media were collected for 26RFa assay and the hypothalami were stored at -80°C until gene quantification by qRT-PCR.

Quantitative reverse transcription PCR Total RNA from hypothalamic explants of 26Rfa +/+ mice was isolated as previously described [START_REF] Prévost | Neuropeptide 26RFa (QRFP) is a key regulator of glucose homeostasis and its activity is markedly altered in obese/hyperglycemic mice[END_REF]. Relative expression of Gpr103, 26Rfa and Insr genes was quantified by quantitative reverse transcription PCR (qRT-PCR) using appropriate primers (see electronic supplementary material [ESM] Table 1). Actb, the gene encoding β-actin, was used as an internal control for normalisation. PCR was carried out using a Gene Expression Master Mix 2X assay (Applied Biosystems, Courtaboeuf, France) in an ABI Prism 7900HT Fast Real-time PCR System (Applied Biosystems). The purity of the PCR products was assessed using dissociation curves. The amount of target cDNA was calculated using the C t method and expressed by means of the 2 -ΔΔC t method.

Blood glucose and insulin measurements in mice For measurements of basal blood or plasma glucose and insulinaemia, 26Rfa +/+ , 26Rfa -/-and insulin-deficient mice were fasted for 6 h or 12 h before the test with free access to water. For IPGTTs, mice were fasted for 16 h with free access to water and then injected intraperitoneally with glucose (2 g/kg). For insulin tolerance tests, mice were fasted for 6 h with free access to water and then injected intraperitoneally with human insulin (0.75 units/kg body weight; Eli Lilly, Neuilly-sur-Seine, France). For PTTs, mice were fasted for 16 h with free access to water and then injected intraperitoneally with sodium pyruvate (2 g/kg; Sigma Aldrich, Saint-Quentin Fallavier, France). Test substances, including 26RFa (3 μg), the GPR103 antagonist 25e (10 -4 mol/l) and insulin (10 mU), were dissolved in HEPES buffer and injected intracerebroventricularly in a volume of 2 μl. Plasma glucose concentrations were measured in tail vein samples at various times using an Accu-Chek Performa glucometer (Roche Diagnostic, Saint-Égrève, France). Plasma insulin concentrations (in tail vein samples) were determined using the ultrasensitive mouse insulin AlphaLISA detection kit (Perkin Elmer, Villebon-sur-Yvette, France). 26RFa radioimmunoassay Quantification of 26RFa in culture media was carried out using a specific radioimmunoassay set up in the laboratory (3). Briefly, each culture medium sample was diluted (1:1) in a solution of water/trifluoroacetic acid (TFA) (99.9:0.1; vol./vol.) and pumped at a flow rate of 1.5 ml/min through a Sep-Pak C 18 cartridge (Waters, Guyancourt, France). Bound material was eluted with acetonitrile/water/TFA (50:49.9:0.1; vol./vol./vol.) and acetonitrile was evaporated under reduced pressure. Finally, the dried extracts were resuspended in 0.1 mol/l PBS and assayed for 26RFa.

RNAscope in situ hybridisation experiments 26Rfa +/+ and 26Rfa -/-mice were deeply anaesthetised and perfused transcardially with sterile PBS followed by sterile ice-cold phosphate-buffered 4% paraformaldehyde (pH 7.4) 45 min after i.c.v. administration of HEPES buffer or insulin (10 mU). The brains were removed, post-fixed overnight at 4°C in sterile 4% paraformaldehyde and transferred to sterile 15% sucrose (12 h) and then to sterile 30% sucrose (12 h) in 0.1 mol/l PBS (pH 7.4) at 4°C. Finally, 12-μm-thick sections, collected every 40 μm over the whole length of the hypothalamus (15 sections/hypothalamus), were cut on a cryostat and mounted on SuperFrost Plus slides (Thermo Fisher Scientific, Illkirch, France) and subsequently stored at -80°C. Fluorescent in situ hybridisation for the simultaneous detection of 26Rfa, Insr and Agrp transcripts was performed using an RNAscope assay (Advanced Cell Diagnostics [ACD], Rennes, France). The Insr probe targeted the region 7059-8053 (accession number NM_010568.2; ACD, Cat no. 401011-C2). The 26Rfa probe targeted the region 112-1081 (accession number NM_183424.4; ACD, Cat no. 464341-C3). The Agrp probe targeted the region 11-764 (accession number NM_001271806.1; ACD, Cat no. 400711). Negative and positive control probes recognising dihydrodipicolinate reductase and DapB (a bacterial transcript) and PolR2A (C1 channel), PPIB (C2 channel) and UBC (C3 channel), respectively, were processed in parallel with the target probes to ensure tissue RNA integrity and optimal assay performance. In addition, the specificity of the 26Rfa probe was assessed by performing hybridisation of brain sections of 26Rfa -/-mice using the RNAscope procedure; in these mice there was no 26Rfa mRNA signal (ESM Fig. 1). All incubation steps were performed at 40°C using a humidified chamber and a HybEz oven (ACD). On the day of the assay, all the hybridisation steps (i.e. target retrieval, dehydration, probe hybridisation, amplification steps and detection of the probe) were performed according to the online protocol for the RNAscope multifluorescent assay.

Images were captured using a confocal Leica TCS SP-8-X microscope equipped with a 40× objective and a Leica Thunder Imager 3D (DM6 B) (Leica, Paris, France). Maximum intensity projections were made in Fiji/Image J V1.53q (National Institutes of Health, Washington, DC) and images were adjusted for brightness and contrast. Quantitative analysis (number of 26Rfa-expressing neurons and area of expression of 26Rfa labelling) of the hypothalamic sections was performed also using Fiji/Image J V1.53q. Statistical analysis For all the experiments the samples were randomised using a simple randomisation method and the experimenters were blind to group assignment. Statistical analysis was performed using GraphPad Prism 6.0 (GraphPad Software, San Diego, CA). A Kruskal-Wallis test with Dunn's multiple comparison test or ordinary one-way ANOVA with Sidak's multiple comparison was used for comparisons between groups. Two-way ANOVA was used for repeated measures. A post hoc comparison using a Bonferroni, Tukey or Sidak test was applied according to the ANOVA results. All data are presented as mean ± SEM. Statistical significance was set at p<0.05.

Results

Effect of central administration of 26RFa on glucose homeostasis in 26Rfa +/+ and insulin-deficient mice After 6 h or 12 h of fasting, i.c.v. administration of 26RFa did not affect basal plasma glucose levels in 26Rfa +/+ mice during the 90 min period of the test (Fig. 1a,b). By contrast, central injection of 26RFa significantly attenuated ( p <0.01) the hyperglycaemia induced by an IPGTT across the whole duration of the IPGTT in 26Rfa +/+ mice (Fig. 1c). Administration of the 26RFa receptor antagonist 25e strongly diminished (p<0.05) the antihyperglycaemic effect of 26RFa in 26Rfa +/+ mice, whereas 25e alone had no effect on plasma glucose levels (Fig. 1d). An acute glucose-stimulated insulin secretion (AGSIS) test revealed that i.c.v. injection of 26RFa significantly potentiates (p<0.01) glucose-induced insulin production in 26Rfa +/+ mice (Fig. 1e). Concurrently, in streptozotocin-treated mice that are unable to produce and secrete insulin, the central antihyperglycaemic effect of 26RFa was totally abolished (Fig. 1f). Finally, 26RFa had no effect on insulin-induced hypoglycaemia during the first 30 min of an insulin tolerance test in 26Rfa +/+ mice, but the peptide attenuated (p<0.05) the hypoglycaemic action of insulin during the last phase of the test (Fig. 1g). Effect of glucose, leptin and insulin on 26RFa-producing neurons in 26Rfa +/+ mice The impact of factors that are well known to regulate glucose homeostasis, such as glucose, leptin and insulin, on hypothalamic 26RFa-producing neurons was investigated using hypothalamic explants. We found that glucose did not significantly alter the secretion of 26RFa and expression of 26Rfa and its receptor Gpr103 by the whole hypothalamus (Fig. 2a-c). Leptin did not alter 26RFa secretion by the hypothalamic explants (Fig. 2d) but stimulated significantly (p<0.05) the expression of the peptide at a dose of 10 nmol/l (Fig. 2e). The expression of Gpr103 was not impaired by leptin (Fig. 2f). Finally, we found that insulin strongly stimulated 26RFa release by the hypothalamic explants in a dose-dependent manner (p<0.01) (Fig. 2g) without modifying the expression of the peptide or its receptor (Fig. 2h,i).

Neuroanatomical interactions between insulin and 26Rfaexpressing neurons in 26Rfa +/+ mice The neuroanatomical interactions between insulin and 26Rfa-expressing neurons were investigated using RNAscope technology. As a first step we mapped the population of 26Rfa-expressing neurons along the whole length of the hypothalamus (Fig. 3). We observed that the great majority of the 26Rfa-expressing neurons were localised in the LHA (Fig. 3d-i). In addition, scattered 26Rfaexpressing neurons were found in the VMH (Fig. 3b-g) and the basal part of the tuber cinereum area (Fig. 3b-d).

Concurrently, we mapped the expression of Insr and found that it was widely distributed in all the nuclei of the hypothalamus (Fig. 4a). Co-expression in situ hybridisation experiments revealed that 79% of the 26Rfa-expressing neurons of the hypothalamus expressed Insr (Fig. 4b). Higher magnification observations showed that the Arc exhibited strong Agrp and Insr labelling but was totally devoid of 26Rfa-expressing neurons (Fig. 4c). By contrast, the LHA and to a lesser extent the VMH contained a number of 26Rfa-expressing neurons that mostly co-expressed Insr (Fig. 4c). In a second step, we investigated the impact of i.c.v. administration of insulin during an IPGTT on the expression of 26Rfa and Insr using the qRT-PCR and RNAscope approaches. The qRT-PCR experiment revealed that central injection of insulin increases significantly (p<0.05) the hypothalamic expression of 26Rfa, but does not affect Insr mRNA levels (Fig. 5a). Quantitative analysis performed over the entire length of the hypothalamus using the RNAscope approach revealed that i.c.v. administration of insulin induced an increase in the number of 26Rfaexpressing neurons (p=0.14) (Fig. 5b) and in the area of 26RFa labelling (p=0.12) (Fig. 5c). Representative panoramic views taken during the RNAscope procedure in the anterior part of the hypothalamus illustrate the increase in the number of 26Rfa-expressing neurons in insulin-injected brains vs HEPES injected brains (Fig. 5d,e). Higher magnification photomicrographs (right panels) confirm that, under these two conditions, 26Rfa-expressing neurons co-express Insr (Fig. 5d,e).

Effect of blocking the 26RFa/GPR103 system on insulin hypothalamic signalling using 26Rfa +/+ , 26Rfa -/-and insulin-deficient mice As expected, an IPGTT revealed that i.c.v. administration of insulin induces a robust significant antihyperglycaemic effect in 26Rfa +/+ mice (p<0.05 to p<0.001) (Fig. 6a). Interestingly, 26RFa displayed an antihyperglycaemic profile very similar in terms of amplitude and kinetics to that of insulin (Fig. 6a). Coadministration of the GPR103 antagonist 25e with insulin during an IPGTT resulted in a total blockade of the antihyperglycaemic effect of insulin during the first 60 min of the test in 26Rfa +/+ mice, whereas the administration of 25e alone did not affect the glycaemic profile (Fig. 6b). In addition, an IPGTT performed in mice deficient for 26RFa revealed a loss of the antihyperglycaemic effect of insulin compared with the injection of insulin in wild-type animals (Fig. 6c).

The impact of i.c.v. administration of insulin and 26RFa on HGP during a PTT was also investigated. Central injection of insulin in 26Rfa +/+ mice induced a significant inhibition (from p<0.05 to p<0.01) of HGP that was not blocked by the co-administration of the 26RFa receptor antagonist 25e (Fig. 7a). In contrast to insulin, 26RFa did not alter HGP and the co-administration of the peptide with the GPR103 antagonist did not induce any change in the glycaemic profile in 26Rfa +/+ mice (Fig. 7b). Finally, we found that the inhibitory effect of central insulin on HGP was not altered in 26RFa-mutant mice compared with their wild-type littermates (Fig. 7c).

The effect of central insulin on its own peripheral production by the pancreatic islets during an AGSIS test was also examined in 26Rfa +/+ and 26Rfa -/-mice (Fig. 7d). Our data revealed that the stimulating effect of central insulin on its peripheral production was significantly attenuated by the coadministration of the GPR103 antagonist (from p<0.05 to p<0.001) and in 26RFa-deficient mice (p<0.01). Concurrently, we found that, in streptozotocin-treated mice (in which beta cells are not functional), the robust central antihyperglycaemic effect of insulin observed in Fig. 7a was dramatically impaired during an IPGTT (Fig. 7e).

Discussion

Although it is now recognised that the hypothalamus plays an important role in the global regulation of glucose metabolism in coordination with the pancreatic islets [START_REF] Bernard | Leçons de physiologie expérimentales appliquées à la médecine[END_REF][START_REF] Schwartz | Cooperation between brain and islet in glucose homeostasis and diabetes[END_REF], the molecular characterisation of the neuronal circuits involved in the hypothalamic regulation of glucose homeostasis remains fragmentary. Indeed, accumulating evidence supports the view that the AgRP and POMC neuronal systems of the Arc play a pivotal role in the central control of glucose metabolism [START_REF] Hill | Direct insulin and leptin action on pro-opiomelanocortin neurons is required for normal glucose homeostasis and fertility[END_REF][START_REF] Könner | Insulin action in AgRP-expressing neurons is required for suppression of HGP[END_REF]. However, physiological and genetic approaches indicate that these two peptidergic systems are not sufficient to explain and to understand the hypothalamic regulation of glucose homeostasis [START_REF] Könner | Insulin action in AgRP-expressing neurons is required for suppression of HGP[END_REF]. In the present study, we provide evidence that the 26RFa/GPR103 neuropeptidergic system of the hypothalamus plays a key role in the central control of glucose homeostasis.

We found that i.c.v. administration of 26RFa induces a sustained antihyperglycaemic effect that is associated with an increase in insulin production by the pancreatic islets without affecting insulin sensitivity. In addition, we show that this central antihyperglycaemic effect of 26RFa is abolished when a GPR103 antagonist is co-administrated as well as in streptozotocin-treated mice, in which no insulin is produced by the pancreatic islets. Altogether, these findings indicate that 26RFa is involved in the brain regulation of glucose homeostasis and that this central effect of 26RFa is mediated by the activation of its receptor GPR103, leading to stimulation of peripheral insulin production by the pancreatic islets. We previously showed that, at the periphery, 26RFa produced by the gut and the pancreatic islets displays robust antihyperglycaemic activity by acting as an incretin and by triggering insulin sensitivity [START_REF] Prévost | Hypothalamic Neuropeptide 26RFa Acts as an Incretin to Regulate Glucose Homeostasis[END_REF][START_REF] Prévost | The neuropeptide 26RFa in the human gut and pancreas: potential involvement in glucose homeostasis[END_REF][START_REF] Prévost | Neuropeptide 26RFa (QRFP) is a key regulator of glucose homeostasis and its activity is markedly altered in obese/hyperglycemic mice[END_REF]. The present study suggests that the global antihyperglycaemic effect of 26RFa is the result of the dual action of the peptide on the pancreatic islets and on the hypothalamus. 26RFa is not the only factor that is able to regulate glycaemia by acting synergically at the peripheral and central levels. For instance, it has been reported that injection of glucose, leptin or insulin within the hypothalamus lowers blood glucose levels and increases insulin sensitivity in the liver [START_REF] Schwartz | Cooperation between brain and islet in glucose homeostasis and diabetes[END_REF][START_REF] Obici | Hypothalamic insulin signaling is required for inhibition of glucose production[END_REF][START_REF] Lam | Regulation of blood glucose by hypothalamic pyruvate metabolism[END_REF], and that insulin decreases HGP [START_REF] Inoue | Central insulin-mediated regulation of HGP[END_REF]. It has also been observed that central injection of leptin (at doses too low to have any effect outside the brain) is able to normalise glycaemia in mice models of severe insulin deficiency [START_REF] Fujikawa | Leptin engages a hypothalamic neurocircuitry to permit survival in the absence of insulin[END_REF]. Hormones other than leptin can also act in the brain to promote insulin-independent glucose lowering. For instance, the gastrointestinal hormone fibroblast growth factor 19 (FGF19), or its rodent homologue FGF15, when injected centrally, improves glucose tolerance in obese rats [START_REF] Ryan | Fibroblast growth factor-19 action in the brain reduces food intake and body weight and improves glucose tolerance in male rats[END_REF]. Similarly, perfusion of the incretin glucagon-like peptide 1 (GLP-1) in the Arc causes a decrease in HGP [START_REF] Prigeon | Suppression of glucose production by GLP-1 independent of islet hormones: a novel extrapancreatic effect[END_REF]. Recent studies have also reported involvement of the hypothalamic neuropeptide orexin in the brain regulation of glucose metabolism. Indeed, central administration of orexin A during the night-time awake period first elevates blood glucose levels and subsequently lowers daytime glycaemia in normal and diabetic db/db mice by bidirectional regulation of HGP relayed by the autonomic nervous system [START_REF] Tsuneki | Hypothalamic orexin prevents hepatic insulin resistance via daily bidirectional regulation of autonomic nervous system in mice[END_REF][START_REF] Rani | Role of orexins in the central and peripheral regulation of glucose homeostasis: Evidences & mechanisms[END_REF]. Collectively, these observations support the hypothesis that the control of glucose homeostasis results from complex and highly coordinated interactions between the brain and the pancreatic islets.

26RFa is interesting in that it is produced not only at the periphery by the gut and the pancreatic islets [START_REF] Prévost | Hypothalamic Neuropeptide 26RFa Acts as an Incretin to Regulate Glucose Homeostasis[END_REF][START_REF] Prévost | The neuropeptide 26RFa in the human gut and pancreas: potential involvement in glucose homeostasis[END_REF][START_REF] Prévost | Neuropeptide 26RFa (QRFP) is a key regulator of glucose homeostasis and its activity is markedly altered in obese/hyperglycemic mice[END_REF], but also by a discrete neuronal population located in the hypothalamus [START_REF] Chartrel | Identification of 26RFa, a hypothalamic neuropeptide of the RFamide peptide family with orexigenic activity[END_REF][START_REF] Takayasu | A neuropeptide ligand of the G protein-coupled receptor GPR103 regulates feeding, behavioral arousal, and blood pressure in mice[END_REF][START_REF] Bruzzone | Anatomical distribution and biochemical characterization of the novel RFamide peptide 26RFa in the human hypothalamus and spinal cord[END_REF][START_REF] Bruzzone | Distribution of 26RFa binding sites and GPR103 mRNA in the central nervous system of the rat[END_REF]. This observation raises the possibility that 26RFa-producing neurons belong to the glucoregulatory system of the hypothalamus and are responsible for the central effects of the peptide on glucose homeostasis. To test this hypothesis, we investigated whether peripheral factors that are well known to control the regulation of glucose metabolism by the hypothalamus can modulate the expression and secretion of 26RFa by 26Rfa-expressing neurons in hypothalamic explants. Our experiments revealed that neither glucose nor leptin substantially impairs 26RFa secretion. Conversely, we found that insulin drastically stimulates the secretion of 26RFa by the hypothalamus. This finding strongly suggests that the 26RFa neuronal circuitry belongs to the glucoregulatory system of the hypothalamus and may relay, at least in part, the central effects of insulin on the regulation of glucose metabolism. Consistent with this hypothesis, we show that central 26RFa displays a very similar antihyperglycaemic effect to that of insulin in terms of amplitude and kinetics, and that the central antihyperglycaemic effect of insulin is abolished in the presence of a 26RFa receptor antagonist and in mice deficient for 26RFa. In addition, we show that the main population of 26Rfa-expressing neurons is localised in the LHA, with two subpopulations present in the VMH and the basal part of the tuber cinereum area, whereas 26Rfaexpressing neurons are absent in the Arc. We also observed that a number of these 26Rfa-expressing neurons also express Insr and that central administration of insulin induces the expression of 26RFa in neuronal populations of the hypothalamus. Altogether, these complementary observations strongly suggest that 26Rfa-expressing neurons are a target for insulin and mediate the regulatory effect of the hormone on glucose homeostasis within the hypothalamus. Interestingly, it was previously shown that injection of insulin into the Arc lowers blood glucose levels, increases insulin sensitivity in the liver [START_REF] Obici | Hypothalamic insulin signaling is required for inhibition of glucose production[END_REF][START_REF] Lam | Regulation of blood glucose by hypothalamic pyruvate metabolism[END_REF] and decreases HGP [START_REF] Inoue | Central insulin-mediated regulation of HGP[END_REF]. The AgRP neurons of the Arc were identified as one of the neuronal populations targeted by insulin, as specific deletion of the insulin receptor in AgRP neurons partially blunts the ability of insulin to inhibit HGP [START_REF] Könner | Insulin action in AgRP-expressing neurons is required for suppression of HGP[END_REF]. By contrast, ablation of the insulin receptor in the POMC neurons of the Arc or specific activation of these neurons has no effect on glycaemia nor on HGP [START_REF] Könner | Insulin action in AgRP-expressing neurons is required for suppression of HGP[END_REF][START_REF] Steculorum | AgRP neurons control systemic insulin sensitivity via myostatin expression in brown adipose tissue[END_REF]. Here, we provide evidence that insulin targets a new discrete population of hypothalamic neurons that are located not in the Arc but in the LHA: the 26Rfa-expressing neurons.

It is well documented that the central antihyperglycaemic action of insulin is due, in part, to an inhibitory effect of the hormone on HGP [START_REF] Inoue | Central insulin-mediated regulation of HGP[END_REF]. We therefore examined whether 26RFa-expressing neurons mediate the central insulin effects on HGP. We found that 26RFa injected centrally has no effect on HGP, in contrast to insulin, and that the effect of insulin on HGP is not impaired by a blockade of the 26RFa receptor, nor in 26RFa-deficient mice. However, it has been reported that the central antihyperglycaemic effect of insulin is also due to activation of its own production by the pancreatic islets [START_REF] Scherer | Brain insulin signaling in metabolic homeostasis and disease[END_REF]. Our results support this as we show that insulin injected centrally significantly increases plasma insulin levels and that the central antihyperglycaemic effect of insulin is totally abolished in streptozotocin-treated mice that are unable to produce and secrete insulin. In addition, we show that GPR103 blockade or the absence of 26RFa (in 26Rfa -/-mice) abolishes insulin-induced insulin production. These observations indicate that the hypothalamic 26Rfa-expressing neurons are not involved in the central inhibitory effect of insulin on HGP but mediate the central effects of insulin on its own peripheral production. The present findings, together with previous findings, support the original hypothesis that insulin is able to target distinct neuronal populations in the hypothalamus, that is, the AgRP neurons in the Arc that mediate the inhibitory effect of the hormone on HGP, and the 26RFa neurons in the LHA that mediate the stimulatory action of insulin on its own peripheral production. We propose that these two synergical actions may be responsible for the central antihyperglycaemic effect of insulin.

In conclusion, in the present study we identify a novel hypothalamic neuronal circuit that regulates glucose homeostasis, the 26RFa/GPR103 neuropeptidergic system, and provide evidence that this discrete neuronal population is a key relay for the central regulation of glucose metabolism by insulin.

Fig. 1

 1 Fig. 1 Effect of central administration of 26RFa on glucose homeostasis in 26Rfa +/+ and insulin-deficient mice. All experiments were carried out in 26Rfa +/+ mice other than that described in (f). (a, b) Effect of i.c.v. administration of 26RFa (3 μg) on plasma glucose levels in the basal condition after a 6 h (a) or 12 h (b) fasting period (n = 10-15). (c, d) Effect of i.c.v. administration of 26RFa alone (3 μg) (c) or together with the GPR103 antagonist 25e (10 -4 mol/l) (d) on plasma glucose levels during an IPGTT (n = 15). (e) Effect of i.c.v. administration of 26RFa (3 μg) on insulin production during an AGSIS test (n = 11). (f) Effect of i.c.v. administration of 26RFa (3 μg) on plasma glucose levels during an IPGTT in streptozotocin-treated mice (n = 10). (g) Effect of i.c.v. administration of 26RFa (3 μg) on plasma glucose levels during an insulin tolerance test (n = 16). Data are mean ± SEM of four independent experiments for each test, with the AUC shown on a separate graph. T0, time 0. *p<0.05; **p<0.01; ***p<0.001 (26RFa vs vehicle). † p<0.05 (26RFa+25e vs 26RFa alone). ¶ p<0.05; ¶ ¶ p<0.01 (26RFa vs 25e)

Fig. 2 Fig. 3

 23 Fig.2Effect of glucose, leptin and insulin on 26RFa-producing neurons in 26Rfa +/+ mice. (a-i) Effect of various concentrations of glucose, leptin and insulin on the secretion of 26RFa and expression of 26Rfa and

Fig. 4

 4 Fig. 4 Co-expression of 26Rfa with the Insr in the hypothalami of 26Rfa +/+ mice as determined by RNAscope in situ hybridisation. (a) Panoramic section of the anterior part of the hypothalamus showing the wide distribution of Insr (white spots). (b) Quantitative analysis of the number of 26Rfa-expressing neurons that co-express Insr, showing that 79% of the 26RFa neurons express Insr. Data represent mean ± SEM of three independent experiments (n = 5 per condition). (c) Representative photomicrographs of the Arc reveal robust labelling of Agrp (green spots) and Insr (white spots) and the absence of 26Rfa expression. Representative photomicrographs of double labelling in the LHA and the VMH show that 26Rfa (red spots) and Insr (white spots) are coexpressed in the same neurons (arrowheads). However, some 26Rfaexpressing neurons do not express Insr (arrows). Scale bars: (a) 200 μm; (b) 20 μm

Fig. 5

 5 Fig. 5 Effect of i.c.v. administration of insulin on the expression of 26Rfa in the hypothalami of 26Rfa +/+ mice as determined by RNAscope in situ hybridisation. (a) Effect of i.c.v. administration of insulin (10 mU) on the hypothalamic expression of 26Rfa and Insr, showing a significant increase in 26Rfa mRNA levels and no effect on Insr mRNA levels. (b, c) Quantitative analysis of the number of 26Rfa-expressing neurons and the area of 26Rfa labelling over the entire length of the hypothalamus, showing an increase in these two complementary variables in insulininjected brains vs vehicle-injected brains. Data are mean ± SEM of three

Fig. 6

 6 Fig.6Central interaction between insulin and the hypothalamic 26RFa/ GPR103 system in 26Rfa +/+ and 26Rfa -/-mice during an IPGTT. (a) Effect of i.c.v. administration of insulin (10 mU) or 26RFa (3 μg) on plasma glucose levels during an IPGTT in 26Rfa +/+ mice. (b) Effect of i.c.v. administration of insulin (10 mU) alone or with the GPR103 receptor antagonist 25e (10 -4 mol/l) on plasma glucose levels during an IPGTT in 26Rfa +/+ mice. (c) Effect of i.c.v. administration of insulin (10 mU) on plasma glucose levels during an IPGTT in 26Rfa +/+ and 26Rfa -/-mice. Data are mean ± SEM of three independent experiments (n=12-16), with the AUC shown on a separate graph. *p<0.05; **p<0.01; ***p<0.001 (insulin vs vehicle). § p<0.05; § § p<0.01; § § § p<0.001 (26RFa vs vehicle [a] or insulin vs 25e [b], or insulin in 26Rfa -/-mice vs insulin in 26Rfa +/+ mice [c]). ¶ p<0.05; ¶ ¶ p<0.01 (insulin+25e vs 25e alone). † p<0.05;

Fig. 7

 7 Fig. 7 Central interaction between insulin and the hypothalamic 26RFa/ GPR103 system in 26Rfa +/+ , 26Rfa -/-and insulin-deficient mice during a PTT, an AGSIS test and an IPGTT. (a) Effect of i.c.v. administration of insulin (10 mU) alone or with the GPR103 receptor antagonist 25e (10 -4 mol/l) on plasma glucose levels during a PTT in 26Rfa +/+ mice. (b) Effect of i.c.v. administration of 26RFa (3 μg) alone or with the GPR103 receptor antagonist 25e (10 -4 mol/l) on plasma glucose levels during a PTT in 26Rfa +/+ mice. (c) Effect of i.c.v. administration of insulin (10 mU) on plasma glucose levels during a PTT in 26Rfa +/+ and 26Rfa -/- mice. (d) Effect of i.c.v. administration of insulin (10 mU) alone or with the GPR103 receptor antagonist 25e (10 -4 mol/l) on plasma insulin levels during an AGSIS test in 26Rfa +/+ and 26Rfa -/-mice. (e) Effect of i.c.v. administration of insulin (10 mU) on plasma glucose levels during an IPGTT in streptozotocin-treated mice. Data are mean ± SEM of three independent experiments (n=9-15), with the AUC shown on a separate graph. *p<0.05; **p<0.01 (insulin vs vehicle in 26Rfa +/+ mice [a] or insulin in 26Rfa -/-mice vs insulin in 26Rfa +/+ mice [d]); § p<0.5; § § p<0.01, § § § p<0.001 (insulin+25e vs insulin alone in 26Rfa +/+ mice)

  +/+ and 26Rfa -/-mice.

	Data are mean ± SEM of three independent experiments (n=12-16), with
	the AUC shown on a separate graph. *p<0.05; **p<0.01; ***p<0.001
	(insulin vs vehicle). § p<0.05; § § p<0.01; § § § p<0.001 (26RFa vs vehicle [a]
	or insulin vs 25e [b], or insulin in 26Rfa -/-mice vs insulin in 26Rfa +/+
	mice [c]). ¶ p<0.05; ¶ ¶ p<0.01 (insulin+25e vs 25e alone). † p<0.05;
	† † p<0.01; † † † p<0.001 (insulin in 26Rfa +/+ mice vs vehicle in 26Rfa -/-
	mice)

Funding This work was supported by Inserm (U1239), the University of Rouen, the Institute for Research and Innovation in Biomedicine (IRIB), the Agence Nationale pour la Recherche (DIABNET), the Fondation pour la Recherche Médicale (grant no. DEA 20140629966), the Société Francophone du Diabète (grant no. R16038EE) and the Plateforme de Recherche en Imagerie Cellulaire de Normandie (PRIMACEN). The present study was also co-funded by the European Union and the Regional Council of Normandy. The European Union is involved with the Regional Council of Normandy through the European Regional Development Fund (ERDF).

Data availability All data generated or analysed during this study are included in the published article (and its supplementary information files).

Supplementary Information

The online version of this article https://doi. org/10.1007/s00125-022-05706-5 contains peer-reviewed but unedited supplementary material.

Authors' relationships and activities

The authors declare that there are no relationships or activities that might bias, or be perceived to bias, their work.

Contribution statement MEM, MP and NC contributed substantially to the study conception and design and analysis and interpretation of data and wrote the manuscript. ST, JM, AA, MP, M-ALS and MEM performed and analysed the in vivo experiments in mice. ST, CD, MD and MP performed and analysed the RNAscope experiments. MD and MEM performed and analysed the PCR experiments. AB and EN performed and analysed the insulin assays, and JL and BL produced synthetic 26RFa. GP, JL and YA contributed to the study design and critically revised the manuscript for important intellectual content. All authors contributed substantially to the drafting of the manuscript and approved the final version of the paper. NC is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.


Elisa Gavard
email: elisa.gavard@univ-amu.fr

Valérie C Chanoine

Franziska Geringswald

Yufei Tan

Eddy C Cavalli

Jean-Luc Anton

Johannes C Ziegler

Johannes C Ziegler Fundings

Semantic and syntactic prediction in reading and statistical learning: an fMRI study in dyslexia

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

This research was supported by the French National Research Agency, Grant ANR-18-CE28-0006 DYSuccess (Pi: Cavalli. E), the Institute of Convergence ILCB (ANR-16-CONV-0002) and the Excellence Initiative of Aix-Marseille University A*MIDEX (ANR-11-IDEX-0001-02). The work that led to this poster was also supported by AMPIRIC, a center of excellence on research in education and teacher training.

fMRI (PRELIMINARY) RESULTS

Predictive reading task

Serial Reaction Time task

Whole brain analysis

ROI analysis

1. Different neural networks underlying semantic and syntactic predictive processing in reading. 2. Dyslexic students seems to be as good predictors as control students in predictive reading and in the SL task.

Making semantic and syntactic predictions in reading relies on domain-general SL abilities ?

METHODS