Jian-Hong Lin

Kevin Primicerio

Tiziano Squartini
email: tiziano.squartini@imtlucca.it

Christian Decker

Claudio J Tessone

Lightning Network: a second path towards centralisation of the Bitcoin economy

The Bitcoin Lightning Network (BLN), a so-called "second layer" payment protocol, was launched in 2018 to scale up the number of transactions between Bitcoin owners. In this paper, we analyse the structure of the BLN over a period of 18 months, ranging from 14 th January 2018 to 13 th July 2019, at the end of which the network has reached 8.216 users, 122.517 active channels and 2.732,5 transacted bitcoins. Here, we consider three representations of the BLN: the daily snapshot one, the weekly snapshot one and the daily-block snapshot one. By studying the topological properties of the binary and weighted versions of the three representations above, we find that the total volume of transacted bitcoins approximately grows as the square of the network size; however, despite the huge activity characterising the BLN, the bitcoins distribution is very unequal: the average Gini coefficient of the node strengths (computed across the entire history of the Bitcoin Lightning Network) is, in fact, 0.88 causing the 10% (50%) of the nodes to hold the 80% (99%) of the bitcoins at stake in the BLN (on average, across the entire period). This concentration brings up the question of which minimalist network model allows us to explain the network topological structure. Like for other economic systems, we hypothesise that local properties of nodes, like the degree, ultimately determine part of its characteristics. Therefore, we have tested the goodness of the Undirected Binary Configuration Model (UBCM) in reproducing the structural features of the BLN: the UBCM recovers the disassortative and the hierarchical character of the BLN but underestimates the centrality of nodes; this suggests that the BLN is becoming an increasingly centralised network, more and more compatible with a core-periphery structure. Further inspection of the resilience of the BLN shows that removing hubs leads to the collapse of the network into many components, an evidence suggesting that this network may be a target for the so-called split attacks.

Introduction

The gain of popularity of Bitcoin 1 has made apparent the problems in terms of scalability of the technology upon which it is based: in fact, only a limited amount of transactions per second -whose number is proportional to the size of a block and its release frequency -can be processed by Bitcoin. This shortcoming may prevent the adoption of this payment network at a global scale, especially when considering that classic payment mechanisms (e.g. traditional credit cards) are able to achieve tens of thousands of transactions per second. A naïve (and short term) solution would be represented by an increase of the block size: larger blocks, however, would require larger validation time, storage capability and bandwidth costs, in turn favouring centralisation, as fewer entities would become able to validate the new blocks that are appended to the Blockchain; moreover, centralisation in the validation process would make the system less resilient, i.e. more prone to faults and attacks.

The Bitcoin Lightning Network (BLN) [START_REF] Poon | The bitcoin lightning network: Scalable off-chain instant payments[END_REF][START_REF] Bartolucci | A percolation model for the emergence of the bitcoin lightning network[END_REF][START_REF] Seres | Topological analysis of bitcoin's lightning network[END_REF] aims at breaking the trade-off between block size and centralisation by processing most of the transactions off-chain: it is a "Layer 2" protocol that can operate on top of Blockchain-based cryptocurrencies such as Bitcoin. The origin of the BLN can be traced back to the birth of Bitcoin itself, as an attempt to create payment channels across which any two users could exchange money without burdening the entire network with their transaction data -thus allowing for cheaper and faster transactions (as both the mining fees and the Blockchain confirmation are no longer required). The BLN can, thus, be seen as a solution that does not sacrifice the key feature of Bitcoin, i.e. decentralisation, that characterises its architecture (i.e. the number of computers constituting the network), its political organisation (i.e. the number of individuals controlling the network) and its wealth distribution (i.e. the number of individuals owning the actual supply), while enhancing the circulation and the exchange of the native assets.

The BLN has recently raised a lot of interest: Seres [START_REF] Seres | Topological analysis of bitcoin's lightning network[END_REF] argued that the BLN structure can be ameliorated to improve its security; Rohrer [START_REF] Rohrer | Discharged payment channels: Quantifying the lightning network's resilience to topology-based attacks[END_REF] showed that the current BLN can be prone to channel exhaustion or attacks aimed at isolating nodes, thus compromising the nodes reachability, the payment success ratio, etc. In this paper, we consider the BLN payment channels across a period of 18 months, i.e. from 14 th January 2018 to 13 th July 2019, and analyze it at both the daily and the weekly timescale. Our results show that the BLN is characterised by an unequal wealth distribution and by a larger-than-expected centrality of nodes, thus suggesting that the BLN indeed suffers from the aforementioned centralisation issue.

Methods

Notation. For each time snapshot t, the BLN can be described as a weighted, undirected network with total number of nodes N (t) and represented by the N (t) × N (t) symmetric matrix W (t)6, 7 whose generic entry w (t) i j indicates the total amount of money exchanged between i and j, across all channels, at time t. The total amount of money exchanged by node i, at time t, is s

(t) i = ∑ N (t) j( =i)=1 w (t)
i j , a quantity that will be also called capacity. For the present analysis, we also consider the BLN binary adjacency matrix A (t) , whose generic entry reads a (t)

i j = 1 if w (t)
i j > 0 and a (t) i j = 0 otherwise. Naturally, the presence of a link between any two nodes i and j, i.e. a (t) i j = 1, indicates that one or more payment channels are open, between the same nodes, at time t and the total number of open channels (i.e. links) is simply provided by

L (t) = ∑ N (t) i=1 ∑ N (t) j=i+1 a (t)
i j . Centrality measures. Indices measuring the centrality of a node aim at quantifying the importance of a node in a network, according to some, specific topological property [START_REF] Bonacich | Power and centrality: A family of measures[END_REF][START_REF] Borgatti | Centrality and network flow[END_REF][START_REF] Newman | An introduction[END_REF][START_REF] Rodrigues | Network centrality: an introduction[END_REF] . Among the measures proposed so far, of particular relevance are the degree centrality, the closeness centrality, the betweenness centrality and the eigenvector centrality. Let us briefly describe them:

• the degree centrality [START_REF] Newman | An introduction[END_REF][START_REF] Rodrigues | Network centrality: an introduction[END_REF] of node i coincides with the degree of node i, i.e. the number of its neighbours, normalized by the maximum attainable value, i.e. N -1:

k c i = k i N -1 (1)
where k i = ∑ N j( =i)=1 a i j . From the definition above, it follows that the most central node, according to the degree variant, is the one connected to all the other nodes;

• the closeness centrality [START_REF] Newman | An introduction[END_REF][START_REF] Rodrigues | Network centrality: an introduction[END_REF] of node i is defined as

c c i = N -1 ∑ N j( =i)=1 d i j (2)
where d i j is the topological distance between nodes i and j, i.e. the length of the shortest path(s) connecting them: in a sense, the closeness centrality answers the question "how reachable is a given node?" by measuring the length of the patterns that connect it to the other vertices. From the definition above, it follows that the most central node, according to the closeness variant, is the one lying at distance 1 by each other node;

• the betweenness centrality [START_REF] Newman | An introduction[END_REF][START_REF] Newman | A measure of betweenness centrality based on random walks[END_REF][START_REF] Pfitzner | Betweenness preference: Quantifying correlations in the topological dynamics of temporal networks[END_REF][START_REF] Bonacich | Some unique properties of eigenvector centrality[END_REF] of node i is given by

b c i = N ∑ s( =i)=1 N ∑ t( =i,s)=1 σ st (i) σ st (3)
where σ st is the total number of shortest paths between node s and t and σ st (i) is the number of shortest paths between nodes s and t that pass through node i. From the definition above, it follows that the most central node, according to the betweenness variant, is the one lying "between" any two other nodes;

• the eigenvector centrality [START_REF] Newman | An introduction[END_REF][START_REF] Bonacich | Some unique properties of eigenvector centrality[END_REF][START_REF] Liu | Locating influential nodes via dynamics-sensitive centrality[END_REF] of node i, e c i , is defined as the i-th element of the eigenvector corresponding to the largest eigenvalue of the binary adjacency matrix (whose existence is ensured by the Perron-Frobenius theorem). According to the definition above, a node with large eigenvector centrality is connected to other "well connected" nodes. In this sense, its behavior is similar to the PageRank centrality index.

Gini coefficient. The Gini coefficient has been introduced to quantify the inequality of a country income distribution [START_REF] Morgan | The anatomy of income distribution[END_REF][START_REF] Crucitti | Centrality measures in spatial networks of urban streets[END_REF] : it ranges between 0 and 1, with a larger Gini coefficient indicating a larger "unevenness" of the income distribution. Here, we apply it to both the distribution of the centrality measures of nodes, i.e.

G c = ∑ N i=1 ∑ N j=1 |c i -c j | 2N ∑ N i=1 c i (4)
where

c i = k c i , c c i , b c
i , e c i and to the distribution of the total amount of money exchanged by the nodes of the BLN, i.e.

G s = ∑ N i=1 ∑ N j=1 |s i -s j | 2N ∑ N i=1 s i . ( 5
)
Centralisation measures. The centrality indices defined above are all normalized between 0 and 1 and provide a rank of the nodes of a network, according to the topological feature chosen for their definition. Sometimes, however, it is useful to compactly describe a certain network structure in its entirety. To this aim, a family of indices has been defined (the so-called centralisation indices), encoding the comparison between the structure of a given network and that of the reference network, according to the chosen index. In mathematical terms, any centralisation index reads

C c = ∑ N i=1 (c * -c i ) max{∑ N i=1 (c * -c i )} , where c * = max{c i } N
i=1 represents the maximum value of the chosen centrality measure computed over the network under consideration and the denominator is calculated over the benchmark, defined as the graph providing the maximum attainable value of the quantity ∑ N i=1 (c *c i ). As it can be proven that the most centralized structure, according to the degree, closeness and betweenness centrality, is the star graph, one can define the corresponding centralisation indices:

• the degree-centralisation index, as

C k c = ∑ N i=1 (k * -k c i ) (N -1)(N -2) ; (6)
• the closeness-centralisation index, as

C c c = ∑ N i=1 (c * -c c i ) (N -1)(N -2)/(2N -3) ; (7)
• the betweenness-centralisation index, as

C b c = ∑ N i=1 (b * -b c i ) (N -1) 2 (N -2)/2 ; ( 8
)
• the eigenvector-centralisation index, as

C e c = ∑ N i=1 (e * -e c i ) ( √ N -1 -1)(N -1)/( √ N -1 + N -1) . ( 9
)
For what concerns the eigenvector index, the star graph does not represent the maximally centralised structure: however, we keep it for the sake of homogeneity with the other quantities.

3/11

Benchmarking the observations. Beside providing an empirical analysis of the BLN, in what follows we will also benchmark our observations against a model discounting available information to some extent. Like for other economic and financial systems, we hypothesise that local properties of nodes ultimately determine the BLN structure: specifically, we focus on the degrees and adopt the the Undirected Binary Configuration Model (UBCM) as a reference model [START_REF] Park | The statistical mechanics of networks[END_REF][START_REF] Squartini | Analytical maximum-likelihood method to detect patterns in real networks[END_REF] . The UBCM captures the idea that the probability for any two nodes to establish a connection depends on their degrees and can be derived within the constrained entropy maximization framework, the score function being represented by Shannon entropy S = -∑ A P(A) ln P(A) (10) and the constraints being represented by the degree sequence {k i } N i=1 . Upon solving the aforementioned optimization problem [START_REF] Park | The statistical mechanics of networks[END_REF][START_REF] Squartini | Analytical maximum-likelihood method to detect patterns in real networks[END_REF] , one derives the probability that any two nodes establish a connection

p i j = x i x j 1 + x i x j , ∀ i < j (11)
the unknowns {x i } N i=1 representing the so-called Lagrange multipliers enforcing the constraints. In order to numerically determine them, one can invoke the likelihood maximization principle, prescribing to search for the maximum of the function

L (x) = ln P(A|x) = ln N ∏ i=1 N ∏ j=i+1 p a i j i j (1 -p i j ) 1-a i j (12)
with respect to the vector {x i } N i=1 , a procedure leading to the resolution of the following system of equations [START_REF] Park | The statistical mechanics of networks[END_REF][START_REF] Squartini | Analytical maximum-likelihood method to detect patterns in real networks[END_REF]

k i = N ∑ j( =i)=1 p i j = N ∑ j( =i)=1 x i x j 1 + x i x j , ∀ i. (13)
Core-periphery detection. Inspecting the evolution of centralisation is useful to understand to what extent the structure of a given network becomes increasingly (dis)similar to that of a star graph; however, although encoding the prototypical centralised structure, carrying out a comparison with such a graph can indeed be too simplistic. Hence, we also check for the presence of the "generalized" star graph structure also known as core-periphery structure, composed by a densely-connected core of nodes surrounded by a periphery of loosely-connected vertices. In order to do so, we implement a recently-proposed approach [START_REF] Van Lidth De Jeude | Detecting core-periphery structures by surprise[END_REF] , prescribing to minimize the score function known as bimodular surprise and reading

S = ∑ i≥l c ∑ j≥l p C i P j V -(C+P) L-(i+ j) V L (14)
where

V = N(N-1)

2

is the total number of node pairs, L = ∑ N i=1 ∑ N j=i+1 a i j is the total number of links, C is the number of node pairs in the core portion of the network, P is the number of node pairs in the periphery portion of the network, l c is the observed number of links in the core and l p is the observed number of links in the periphery. From a technical point of view, S is the p-value of a multivariate hypergeometric distribution [START_REF] Van Lidth De Jeude | Detecting core-periphery structures by surprise[END_REF] .

Data

Since payments in the Bitcoin Lightning Network are source-routed and onion-routed, the sender must have a reasonably up-to-date view of the network topology, in order to pre-compute the entire payment route. Nodes in the BLN regularly broadcast information about the channels they participate in: each time a channel is opened, or any of its details changes, the two endpoints of the channel announce such changes to the rest of the network. This exchange of information, called gossip, allows other nodes to keep their view of the network topology up-to-date, an information that is, then, used to initiate a payment.

The network topology can be visualised by means of the the so-called routing table. For this paper, we took regular snapshots of the routing table (every 15 minutes, between January 14 th 2018, at blockheight 503816, to July 13 th 2019, at blockheight 585844); these snapshots were, then, aggregated into timespans, each timespan representing a constant state of a channel from its start to its end. In addition, this information is enriched with data from the Blockchain: since every channel consists of an unspent transaction output on the Bitcoin Blockchain, we can determine the size of a channel and its open and close dates within minutes. Other heuristics can be used to search for potential channels on the Blockchain, without involving the gossip mechanism: this allows us to put a lower bound on the completeness of our measurements.

In the Bitcoin Blockchain, the time between blocks is Poisson distributed with an expected value of 10 minutes between blocks. On a single day, the expected number of new blocks added to the Blockchain is 144. For the sake of simplicity, and without altering in any way the results, we consider this number of blocks our natural timescale (for example, the blocks of the first day range from the 503816 th one to the 503959 th one while the blocks of the second day range from the 503960 th one to the 504103 rd one). In this paper, three different representations of the BLN are studied, i.e. the daily snapshot one, the weekly snapshot one and the daily-block snapshot one -even if the results of our analysis will be shown for the daily-block snapshot representation only. A daily/weekly snapshot includes all channels that were found to be active during that day/week; a daily-block snapshot consists of all channels that were found to be active at the time the first block of the day was released: hence, the transactions considered for the daily-block representation are a subset of the ones constituting the daily representation.

Results

Empirical analysis of the BLN binary structure. Figure 1 plots the evolution of basic network quantities since launch of the BLN, i.e. the number of nodes, which is a proxy of the number of users, the number of links and the link density. As it can be seen, although the network size increases (for the daily-block snapshot N ranges from 2 to 6476 and L ranges from 1 to 55866; in particular, in the last daily snapshot of our dataset we have 6476 nodes and 54440 links), it becomes sparser. However, two different regimes are visible: a first phase where a steep increase of N and L (descrease of ρ) takes place is followed by a phase during which a much smoother increase (decrease) of the same quantities is observed. Further insight on the BLN evolution can be gained by plotting the link density ρ = 2L N(N-1) versus the total number of nodes N: a trend whose functional form reads ρ ∼ cN -γ , with γ 1, clearly appears. However, such a functional form seems to describe quite satisfactorily the BLN evolution up to the period when N 10 3 : afterwards, a different functional dependence seems to hold. Notice also that the value of the numerical constant c coincides with the value of the average degree, since c

= 2L N-1 = ∑ N i=1 k i N-1
k. By imagining a growth process according to which each new node enters the network by establishing at least one new connection with the existing ones, to ensure that L t ≥ N t -1, a lower-bound on c k can be deduced: c ≥ 2 (fig. 1 shows the trend y = 3N -1 even if the inspection of the evolution of the quantity c = 2L

N-1 reveals that periods where c k assumes different, constant values can be individuated).

In order to comment on the centrality structure of the BLN, let us explicitly draw it: fig. 2 shows the largest connected component of the BLN daily-block snapshot representation on day 16 and on day 34. Several hubs are present (e.g. on day 34, the largest one, having degree k 34 hub = 121, is linked to the 34.3% of nodes): notice that each of them is linked to a plethora of other nodes that, instead, are scarcely linked among themselves. The emergence of structurally-important nodes is further confirmed by plotting the evolution of the Gini index for the distribution of the centrality measures defined in the Methods A visual inspection of the network evolution suggests the presence of a core-periphery structure since its early stages. section (i.e. the degree, the closeness, the betweenness and the eigenvector centrality): fig. 3 shows that G c is increasing for three measures out four, pointing out that the values of centrality are more and more unevenly distributed (irrespectively from the chosen indicator). The flat trend characterizing the closeness centrality could be explained by the presence of nodes with large degree ensuring the vast majority of nodes to be reachable quite easily. On the other hand, the evolution of the centralisation indices indicates that the BLN is not evolving towards a star graph, although the eigenvector centrality reaches quite large values in the middle stages of the BLN history. As anticipated above, imagining that the picture provided by a star-like structure could provide a good description of the BLN topology is indeed too simplistic.

Benchmarking the observations. Let us now benchmark the observations concerning the centrality and the centralisation indices with the predictions for the same quantities output by the UBCM. More specifically, we have computed the expected value of G c and C c (with

c i = k c i , c c i , b c
i , e c i , ∀ i) and the corresponding error, by explicitly sampling the ensembles of networks induced by the UBCM. In fig. 4 we plot and compare the evolution of the observed and expected values of G c and C c , both as functions of N. Such a comparison reveals that the UBCM tends to overestimate the values of the Gini index for the degree, the closeness and the betweenness centrality and to underestimate the values of the Gini index for the eigenvector centrality [START_REF] Nakamoto | Bitcoin: A peer-to-peer electronic cash system[END_REF] . These results point out a behavior that is not reproducible by just enforcing the degree sequence (irrespectively from the chosen index). The evidence that the UBCM predicts a more-heterogeneous-than observed structure, could be explained starting from the result concerning the eigenvector centrality. The latter, in fact, seems to indicate a non-trivial (i.e. not reproducible by lower-order constraints like the degrees) tendency of well-connected nodes to establish connections among themselves -likely, with nodes having a smaller degree attached to them. Such a disassortative structure could explain the less-than-expected level of unevenness characterizing the other centrality measures: in fact, each of the nodes behaving as the "leaves" of the hubs would basically have the same values of degree, closeness and betweenness centrality.

On the other hand, the betweenness-and the eigenvector-centralisation indices suggest that the BLN structure is indeed characterized by some kind of more-than-expected star-likeness: the deviations from the picture provided by such a benchmark, however, could be explained by the co-existence of multiple star-like sub-structures (see also fig. 2 and the Appendix for a more detailed discussion about this point). Core-periphery detection. A clearer picture of the BLN topological structure is provided by the analysis aimed at clarifying the presence of a "core-periphery -like" organization. Inspecting the evolution of the bimodular surprise S across the entire considered period reveals that the statistical significance of the recovered core-periphery structure increases, a result leading to the conclusion that the description of the BLN structure provided by such a model becomes more and more accurate as the network evolves. As an example, fig. 5 shows the detected core-periphery structure on the snapshots depicted in fig. 2: the nodes identified as belonging to the core and to the periphery are, respectively, coloured in blue and yellow.

Empirical analysis of the BLN weighted structure. Let us now move to the empirical analysis of the weighted structure of the BLN, by inspecting the evolution of the total capacity W of (i.e. the total number of bitcoins within) the BLN daily-block snapshot representation: fig. 6 shows the evolution of W as a function of network size N. The trend shown in the same figure reads y = aN b with a = 2 • 10 -5 and b = 2. Although the total number of bitcoin rises, inequality rises as well: in fact, the percentage of nodes holding a given percentage of bitcoins at stake in the BLN steadily decreases (on average, across the entire period, about the 10% (50%) of the nodes holds the 80% (99%) of the bitcoins -see the second panel of fig. 5). This trend is further confirmed by the evolution the Gini coefficient G s , whose value is 0.9 for the last snapshots of our dataset (and whose average value is 0.88 for the daily-block snapshot representation).

Conclusions

The Bitcoin Lightning Network is a sort of "Layer 2" protocol aimed at speeding up the Blockchain, by enabling fast transactions between nodes. Originally designed to allow for cheaper and faster transactions without sacrificing the key feature of Bitcoin, i.e. its decentralisation, it is evolving towards an increasingly centralised architecture, as our analysis reveals. In particular, its structure seems to become increasingly similar to a core-periphery one, with well-connected nodes clustering together (as revealed by the study of the eigenvector centrality). More precisely, our analysis reveals the presence of many star-like sub-structures with the role of centers played by the hubs, seemingly acting as channel-switching nodes. Such a tendency seems to be observable even when considering weighted quantities, as only about 10% (50%) of the nodes hold 80% (99%) of the bitcoins at stake in the BLN (on average, across the entire period); moreover, the average Gini coefficient of the nodes strengths A visual inspection of these networks confirms that star-like sub-structures are present to a much lesser extent with respect to the observed BLN in the same snapshots. Bottom panel: evolution of the comparison between the empirical assortativity coefficient r (blue dots) and its expected value, computed under the UBCM (red diamonds), for the daily-block snapshot representation. The BLN is significantly more disassortative than expected.

Figure 1 .

 1 Figure 1. (colour online) Evolution of the total number of nodes N, total number of links L and link density ρ = 2L N(N-1) of the BLN. By plotting the link density versus the total number of nodes, further insight can be gained on the functional dependence ρ = f (N): in particular, the position ρ ∼ N -1 well describes the link density dependence on N for the snapshots satisfying the condition N ≤ 10 3 .

Figure 2 .

 2 Figure 2. (colour online) Comparison between the largest connected component of the BLN (daily-block snapshot representation) on day 16 (left -95 nodes and 155 links are present) and on day 34 (right -359 nodes and 707 links are present).A visual inspection of the network evolution suggests the presence of a core-periphery structure since its early stages.

Figure 3 .

 3 Figure3. (colour online) Top panels: evolution of the Gini index for the degree, closeness, betweenness and eigenvector centrality for the daily-block snapshot representation: G c is characterised by a rising trend, irrespectively from the chosen indicator, pointing out that the values of centrality are increasingly unevenly distributed. Bottom panels: evolution of the degree-, closeness-, betweenness-and eigenvector-centralisation measures: although the eigenvector-centralization index reaches quite large values in the middle stages of the BLN history, the picture provided by a star graph is too simple to faithfully represent the BLN structure.

7 / 11 Figure 5 .

 7115 Figure 5. (colour online) Core-periphery structure of the BLN daily-block snapshot representation on day 16 (left -95 nodes and 155 links are present) and on day 34 (right -359 nodes and 707 links are present), with core-nodes drawn in blue and periphery-nodes drawn in yellow.

Figure 6 .

 6 Figure 6. (colour online) Evolution of the total capacity of the BLN (left). Percentage of nodes holding the 80%, 90%, 95% and 99% of the total number of bitcoins at stake in the BLN (middle): the former has been computed as the fraction n *N of top nodes whose total capacity amounts at 80%, 90%, 95%, 99% of the total. Evolution of the Gini coefficient G s (right): although the total number of bitcoins rises, inequality rises as well.

Figure 7 .

 7 Figure 7. (colour online) Top panels: comparison between the largest connected component of the BLN (daily-block snapshot representation) generated by the UBCM for the day 16 and the day 34.A visual inspection of these networks confirms that star-like sub-structures are present to a much lesser extent with respect to the observed BLN in the same snapshots. Bottom panel: evolution of the comparison between the empirical assortativity coefficient r (blue dots) and its expected value, computed under the UBCM (red diamonds), for the daily-block snapshot representation. The BLN is significantly more disassortative than expected.

Acknowledgements

Jian-Hong Lin acknowledges support from the China Scholarship Council (no. 2017083010177).

(colour online) Top panels: comparison between the observed Gini index for the degree, closeness, betweenness and eigenvector centrality (blue dots) and their expected value, computed under the UBCM (red diamonds) for the daily-block snapshot representation. Once the information contained into the degree sequence is properly accounted for, a (residual) tendency to centralisation is still visible. Bottom panels: comparison between the observed degree-, closeness-, betweennessand eigenvector-centralisation measures and their expected value computed under the UBCM (red diamonds). Once the information contained into the degree sequence is properly accounted for, the emerging picture is that of a network characterized by some kind of more-than-expected star-likeness: deviations from this benchmark, however, are clearly visible and probably due to the co-existence of many star-like sub-structures (see also fig. 2). is 0.88. These results seems to confirm the tendency for the BLN architecture to become "less distributed", a process having the undesirable consequence of making the BLN increasingly fragile towards attacks and failures.

Authors contributions

Jian-Hong Lin and Kevin Primicerio performed the analysis. Tiziano Squartini, Christian Decker and Claudio J. Tessone designed the research. All authors wrote, reviewed and approved the manuscript.

Appendix

As anticipated in the main text, the UBCM seems to underestimate the extent to which the topological structure of the BLN is disassortative. Figure 7 shows the evolution of the Newman assortativity coefficient [START_REF] Newman | Assortative mixing in networks[END_REF] , defined as

and its expected counterpart under the UBCM: as it is clearly visible, the BLN is more disassortative than expected (i.e. the correlations between degrees are "more negative" than predicted by the UBCM), the reason lying in the presence of the aforementioned star-like sub-structures that, instead, are absent in the model. To further confirm this, we explicitly show two


Maud Cochet

Romain Donneger

Elisabeth Cassier

Florence Gaven

Stefan F Lichtenthaler ||

Philippe Marin

Joël Bockaert

Aline Dumuis

Sylvie Claeysen

5-HT4 Receptors Constitutively Promote the Non-Amyloidogenic Pathway of APP Cleavage and Interact with ADAM10

Keywords: Alpha-secretase, Alzheimer's disease, sAPP alpha, serotonin

In addition to the amyloidogenic pathway, Amyloid Precursor Protein (APP) can be cleaved by asecretases, producing soluble and neuroprotective APP alpha (sAPPa) (non-amyloidogenic pathway) and thus preventing the generation of pathogenic Amyloid-b. However, the mechanisms regulating APP cleavage by a-secretases remain poorly understood. Here, we showed that expression of serotonin type 4 receptors (5-HT4Rs) constitutively (without agonist stimulation) induced APP cleavage by the a-secretase ADAM10 and the release of neuroprotective sAPPa in HEK-293 cells and cortical neurons. This effect was independent of cAMP production. Interestingly, we demonstrated that 5-HT4 receptors physically interacted with the mature form of ADAM10. Stimulation of 5-HT4 receptors by an agonist further increased sAPPa secretion and this effect was mediated by cAMP/Epac signalling. These findings describe a new mechanism whereby a GPCR constitutively stimulates the cleavage of APP by a-secretase and promotes the non-amyloidogenic pathway of APP processing.

INTRODUCTION

According to the amyloid hypothesis, alteration in synaptic transmission and neuronal loss observed in Alzheimer's disease (AD) mainly result from the formation of toxic Amyloid-b (Ab) oligomers followed by the extracellular accumulation of Ab aggregates. Ab is produced by the successive cleavage of a transmembrane Amyloid Precursor Protein (APP) by b-secretase and gsecretase [START_REF] Selkoe | Alzheimer's disease: genes, proteins, and therapy[END_REF]. In addition to this amyloidogenic pathway, APP can be cleaved by a-secretases, a set of membrane-bound proteases of the ADAM (A Disintegrin And Metalloprotease) family, generating the soluble APP ectodomain (sAPPa) and a membrane-bound carboxy-terminal fragment (nonamyloidogenic pathway). As a-secretases cleave APP within the Ab sequence, APP shedding by asecretases prevents the generation of the pathogenic Ab peptide [START_REF] Fahrenholz | Alpha-secretase as a therapeutic target[END_REF][START_REF] Lichtenthaler | Alpha-secretase in Alzheimer's disease: molecular identity, regulation and therapeutic potential[END_REF]. Therefore, enhancing a-secretase expression or activity has been considered as a valuable strategy for inhibiting Ab formation. For instance, it has recently been shown that activation of the transcription of the gene encoding the a-secretase ADAM10, by retinoic acid and sirtuin 1 (SIRT1), reduces Ab production [START_REF] Tippmann | Up-regulation of the alpha-secretase ADAM10 by retinoic acid receptors and acitretin[END_REF][START_REF] Jarvis | Retinoic acid receptor-alpha signalling antagonizes both intracellular and extracellular amyloid-beta production and prevents neuronal cell death caused by amyloid-beta[END_REF][START_REF] Donmez | SIRT1 suppresses beta-amyloid production by activating the alpha-secretase gene[END_REF].

Previous reports have shown that G protein-coupled receptors (GPCRs) can differentially affect Ab peptide production by either modulating the cellular trafficking of APP or by influencing the activity and trafficking of a-, band g-secretases. Moreover, both the expression and the stimulation of GPCRs can affect APP metabolism [START_REF] Thathiah | The role of G protein-coupled receptors in the pathology of Alzheimer's disease[END_REF]. GPCRs that enhance sAPPa production by stimulating a-secretase activities include the muscarinic M1-M3 receptors, mGlu2 metabotropic glutamate receptor, serotonin 2A (5-HT2A) and 5-HT2C receptors, Corticotropin-Releasing Factor (CRF) receptor 1, purinergic receptor P2X7 and Pituitary Adenylate Cyclase-Activating Polypeptide (PACAP) type 1 (PAC1) receptor [START_REF] Thathiah | The role of G protein-coupled receptors in the pathology of Alzheimer's disease[END_REF]. These GPCRs are supposed to have a beneficial effect, because sAPPa exerts neuroprotective and neurotrophic effects and enhances long-term potentiation [START_REF] Furukawa | Increased activity-regulating and neuroprotective efficacy of alphasecretase-derived secreted amyloid precursor protein conferred by a C-terminal heparin-binding domain[END_REF][START_REF] Ishida | Secreted form of beta-amyloid precursor protein shifts the frequency dependency for induction of LTD, and enhances LTP in hippocampal slices[END_REF]. In addition, promoting sAPPa formation generally decreases the production of toxic Ab peptides. Several signalling cascades, such as the PKC, cAMP/PKA, MAP-Kinase and PI3K pathways, have been involved in the stimulation of sAPPa release mediated by these GPCRs [START_REF] Thathiah | G protein-coupled receptors, cholinergic dysfunction, and Abeta toxicity in Alzheimer's disease[END_REF]. However, the mechanisms by which they activate a-secretases are not always well understood.

The 5-HT4 receptor (5-HT4R) is another GPCR raising particular interest in view of its possible influence at different levels in the pathogenesis of AD. Activation of 5-HT4Rs stimulates acetylcholine release in prefrontal cortex and hippocampus and improves learning and memory in various pre-clinical paradigms of memory acquisition and retention [START_REF] Cachard-Chastel | Prucalopride and donepezil act synergistically to reverse scopolamineinduced memory deficit in C57Bl/6j mice[END_REF][START_REF] Bockaert | 5-HT(4) receptors, a place in the sun: act two[END_REF]. These findings suggest that 5-HT4R agonists might be used for improving cholinergic function and cognition, which are both compromised in AD.

Stimulation of 5-HT4Rs also enhances sAPPa release and reduces Ab formation in neuronal cultures [START_REF] Maillet | Crosstalk between Rap1 and Rac regulates secretion of sAPPalpha[END_REF][START_REF] Cho | Activation of 5-HT4 receptors inhibits secretion of beta-amyloid peptides and increases neuronal survival[END_REF]. Whether the effect of 5-HT4Rs on APP metabolism reflects an increase in a-secretase activity or is the result of a modification of cellular trafficking of a-secretases and APP remains to be elucidated. Like many GPCRs, 5-HT4R interacts with a large set of intracellular proteins. Some of them, such as p11 and SNX27, are known to affect 5-HT4R transport and might also influence the trafficking of proteins physically and functionally connected with the receptor [START_REF] Warner-Schmidt | Role of p11 in cellular and behavioral effects of 5-HT4 receptor stimulation[END_REF][START_REF] Joubert | New sorting nexin (SNX27) and NHERF specifically interact with the 5-HT4a receptor splice variant: roles in receptor targeting[END_REF]. Further supporting the implication of 5-HT4R in AD, the number of receptor binding sites is reduced in the hippocampus of patients with AD [START_REF] Reynolds | 5-Hydroxytryptamine (5-HT)4 receptors in post mortem human brain tissue: distribution, pharmacology and effects of neurodegenerative diseases[END_REF]. However, the specific effects of 5-HT4R expression and activation on APP metabolism remain to be elucidated.

In this study, we show that 5-HT4R expression at physiological levels (around 120-550 fmol/mg of protein) [START_REF] Reynolds | 5-Hydroxytryptamine (5-HT)4 receptors in post mortem human brain tissue: distribution, pharmacology and effects of neurodegenerative diseases[END_REF][START_REF] Waeber | Regional distribution and ontogeny of 5-HT4 binding sites in rodent brain[END_REF][START_REF] Domenech | Identification and characterization of serotonin 5-HT4 receptor binding sites in human brain: comparison with other mammalian species[END_REF][START_REF] Bockaert | 5-HT4 receptors[END_REF][START_REF] Manuel-Apolinar | Modifications of 5-HT4 receptor expression in rat brain during memory consolidation[END_REF] enhances sAPPa release in transfected HEK-293 cells and cortical neurons in primary culture via two different processes, a "constitutive" release which has never been reported and a previously described agonist-regulated one [START_REF] Maillet | Crosstalk between Rap1 and Rac regulates secretion of sAPPalpha[END_REF][START_REF] Cho | Activation of 5-HT4 receptors inhibits secretion of beta-amyloid peptides and increases neuronal survival[END_REF]. We also show that 5-HT4 receptors physically associate with ADAM10, a mechanism that may be involved in the constitutive release of sAPPa.

RESULTS AND DISCUSSION

Expression of 5-HT4 receptors enhances sAPP release from HEK-293 cells and cortical neurons. To investigate the impact of 5-HT4R expression on APP metabolism, we first co-transfected HEK-293 cells with the SEAP-tagged APP plasmid (Figure 1A) and increasing amounts of the Myctagged 5-HT4R construct. 5-HT4R density at the cell surface was assessed by ELISA (Figure 1B) and [ 3 H]GR 113808 saturation binding (from 120 to 750 fmol/mg protein corresponding to 25 to 500 ng cDNA/10 7 cells, respectively). Expression of increasing amounts of 5-HT4R did not significantly affect total APP (APP band intensity/actin band intensity = 97 ± 4% of control, 96 ± 7%, 108 ± 13% or 89 ± 18% for 25, 100, 250 or 500 ng of 5-HT4R cDNA respectively, n≥3 per each group) or ADAM10 expression (intensity of ADAM10 bands/actin band intensity = 104 ± 5% of control, 96 ± 11%, 113 ± 18% or 87 ± 21% for 25, 100, 250 or 500 ng of 5-HT4R cDNA respectively, n≥3 per each group) (Figure 1B), while it potently enhanced the release of soluble APP (sAPP) already at low receptor expression (Figure 1B). This effect was independent of the 5-HT4R variant used: the mouse (a, b, e and f) and human (a, g and i) C-terminal variants as well as further truncations of the 5-HT4R C-terminal domain (D358 and D329) could all promote sAPP release (Supplemental Figure 1, A-C). Overexpression of 5-HT4R in primary cultures of cortical neurons produced a comparable increase in sAPP release (Figure 1C) without affecting APP and ADAM10 levels (APP band intensity/actin band intensity = 88 ± 19% of control, intensity of ADAM10 bands/actin band intensity = 111 ± 14% for 2.500 ng of 5-HT4R cDNA n≥3 per each group). Expression of similar amounts of PAC1 or muscarinic M3 receptor, which both increase sAPPa release upon activation by their respective agonists [START_REF] Kojro | The neuropeptide PACAP promotes the alpha-secretase pathway for processing the Alzheimer amyloid precursor protein[END_REF][START_REF] Nitsch | Release of Alzheimer amyloid precursor derivatives stimulated by activation of muscarinic acetylcholine receptors[END_REF], failed to enhance sAPP release in HEK-293 cells in the absence of agonist (Figure 1D). Moreover, comparative levels of over-expressed 5-HT6 receptor, another serotonin receptor positively coupled to the Gs/cAMP pathway, did not induce sAPP production (Figure 1D). Similarly, only over-expression of 5-HT4R, but not of PAC1, M3 or 5-HT6 receptors, in cortical neurons enhanced the release of sAPP from neurons (Figure 1E).

Expression of 5-HT4 receptors specifically enhances sAPPa release.

To identify the nature of the sAPP form (sAPPa or sAPPb) secreted upon 5-HT4R expression, we used a sandwich ELISA kit based on two antibodies, one directed against the N-terminal sequence of APP and the other recognizing the neo-sAPPa epitope. Using this kit, we showed that over-expression of 5-HT4R in both HEK-239 cells and in cortical neurons led to an increase in the release of sAPPa (Figure 2A). Furthermore, SEAPtagged APP in supernatants from transfected HEK-293 cells was immunoprecipitated with the 22C11 antibody and immunodetected using a site-specific antibody (7A6) that specifically recognizes sAPPa, but not shorter sAPP species, such as sAPPb' and sAPPb (Figures. 1A and2B) [START_REF] Kuhn | ADAM10 is the physiologically relevant, constitutive alpha-secretase of the amyloid precursor protein in primary neurons[END_REF]. The immunoreactive signals detected by 7A6 antibody increased concomitantly with 5-HT4R expression, while no significant change of APP and ADAM10 expression was observed (Figure 2B). Mutation of the residues (R 609 D and K 612 E) surrounding the a-secretase cleavage site (a-site APP mutant) can impair about 50% of the a-cleavage of APP [START_REF] De Strooper | Study of the synthesis and secretion of normal and artificial mutants of murine amyloid precursor protein (APP): cleavage of APP occurs in a late compartment of the default secretion pathway[END_REF]. Co-expression of this mutant with 5-HT4R in HEK-293 cells strongly decreased 5-HT4R-induced sAPP release (Figure 2C), indicating that 5-HT4R expression stimulates the non-amyloidogenic a-cleavage of APP.

Several metalloproteinases, including ADAM9, ADAM10 and ADAM17 (also called TACE, Tumour Necrosis Factor a-Converting Enzyme) have been proposed as potential a-secretases that promote the shedding of APP ectodomain. TAPI-1, a metalloproteinase inhibitor, almost completely abolished 5-HT4R-induced sAPPa release from HEK-293 cells (Figure 2D), suggesting the involvement of a member of the ADAM family of metalloproteinases. Moreover, reduction of ADAM10 expression by transfection of ADAM10-specific siRNA (Figure 2E) diminished basal processing of APP in absence of 5-HT4 receptor and also markedly decreased sAPPa release following 5-HT4R expression (Figure 2F). This indicates that ADAM10 contributes significantly to 5-HT4R-induced sAPP release. In contrast, specific inhibitors of b-secretase (KTEEISEVN-statine-VAEF) or g-secretase (L-685,458) did not affect the release of sAPPa induced by 5-HT4R (Supplemental Figure 2, A andB).

5-HT4R stimulation further enhances sAPPa release. Previous studies have shown that 5-HT4R

stimulation by agonists increases sAPPa production [START_REF] Robert | The human serotonin 5-HT4 receptor regulates secretion of non-amyloidogenic precursor protein[END_REF]. Consistent with these findings, treating HEK-293 cells that transiently express 5-HT4R and SEAP-tagged APP with prucalopride, a 5-HT4R agonist, for 2 hrs increased by ~ 50% the release of sAPPa induced by 5-HT4R expression (Figure 3A andB). This effect was blocked by GR 113808 and RO 116-0086, a neutral antagonist and an inverse agonist of 5-HT4R at Gs signalling, respectively. Neither GR 113808 nor RO 116-0086 alone altered the secretion of sAPP in the absence of the agonist (Figure 3B). 5-HT4R stimulation by prucalopride increased sAPPa release by cultured cortical neurons that transiently expressed 5-HT4R and SEAP-tagged APP in a concentration-dependent manner (pEC50 = 0.85 ± 0.22 µM, n = 3), as assessed by monitoring the SEAP activity (Figure 3C). Similarly, prucalopride stimulated sAPPa release from endogenous APP, as assessed using a sAPPa specific ELISA kit (Figure 3D). Like in HEK-293 cells, the prucalopride effect was blocked by GR 113808 and RO 116-0086 (Figure 3E). Treatment of neurons with maximally effective concentrations of various synthetic 5-HT4R agonists (10 µM RS 67333, 3 µM prucalopride, 10 µM BIMU8, 1 µM cisapride) [START_REF] Bockaert | 5-HT(4) receptors: history, molecular pharmacology and brain functions[END_REF] or with 10 µM 5-HT stimulated sAPP secretion to a similar extent as prucalopride. In contrast, 5-HT4R ligands exhibiting either antagonist (GR 113808, ML 10375) or inverse agonist (RO 116-1148, SB 207266) properties on 5-HT4 receptors coupled to Gs [START_REF] Bockaert | 5-HT(4) receptors: history, molecular pharmacology and brain functions[END_REF] did not significantly affect sAPP release from neurons (Figure 3F). However, RO 116-0086 and RO 116-2617, two inverse agonists at Gs signalling decreased basal sAPP release. GPCRs proteins are adapting many conformations. The constitutive 5-HT4R conformation which induces cAMP production (via G protein) is unlikely to be similar to the constitutive conformation of the same 5-HT4 receptor which is producing sAPPa (without a need of G protein). The pharmacology of these two different constitutive conformations is likely to be different. Thus, it is not surprising that some "classical" inverse agonists on the 5-HT4 receptor inducing constitutive cAMP production are not inverse agonists on the constitutive conformation required for sAPPa production.

The constitutive sAPPa release induced by 5-HT4R expression is independent of the cAMP/Epac pathway. As cAMP and Epac (cAMP-responsive Rap1 Guanine Nucleotide Exchange

Factor) regulate sAPPa release elicited by 5-HT4R stimulation (13), we investigated whether receptor coupling to Gs and recruitment of the cAMP/Epac pathway were also necessary for enhancing constitutive sAPPa secretion upon 5-HT4R expression. Expression of a dominant-negative form of Gs strongly reduced agonist-independent cAMP accumulation in HEK-293 cells expressing 5-HT4Rs, but did not affect sAPP release (Figure 4A). Moreover, expression of 5-HT4R mutants with high level of constitutive Gs signalling activity (Y 302 F, N 308 D or A 258 L) [START_REF] Claeysen | Novel brainspecific 5-HT4 receptor splice variants show marked constitutive activity: role of the C-terminal intracellular domain[END_REF][START_REF] Pellissier | Conformational toggle switches implicated in basal constitutive and agonist-induced activated states of 5-hydroxytryptamine-4 receptors[END_REF] increased basal cAMP production but were less active to increase sAPP release in comparison to wild type 5-HT4R (Figure 4B). Similarly, over-expression of a dominant negative form of Epac1 did not modify the constitutive sAPP secretion induced by 5-HT4R expression (Figure 4C), whereas the agonist (prucalopride)-dependent sAPP release was reduced, as previously reported (13) (Figure 4C). As expected, the Epac1 dominant negative form did not affect cAMP production elicited by 5-HT4R activation (Figure 4D). Collectively, these results indicate that only agonist-elicited sAPP secretion is dependent on 5-HT4R coupling to Gs protein and engagement of the cAMP/Epac pathway.

5-HT4 receptors physically interact with the a-secretase ADAM10. To further explore the possible mechanism by which 5-HT4R constitutively activates ADAM10-dependent APP cleavage, we examined whether 5-HT4R physically interacts with this a-secretase. ADAM10 co-immunoprecipited with HA-tagged-5-HT4R in HEK-293 cells (Figure 5A). Although two immunoreactive ADAM10 species with apparent molecular masses of 95 (immature pro-enzyme) and 69 kDa (mature, active form) were detected in whole cell lysates by Western blotting, only the 69 kDa mature form, which originates from the immature form by removal of a 194 amino acid pro-domain and is localized at the plasma membrane [START_REF] Endres | Upregulation of the alpha-secretase ADAM10 -risk or reason for hope?[END_REF], was co-immunoprecipitated with 5-HT4R. Prucalopride stimulation of 5-HT4R did not modify the ability of the receptor to interact with ADAM10, thus demonstrating the existence of a "constitutive" specific complex composed of 5-HT4R and active ADAM10 (Figure 5A). Active ADAM10 did not co-immunoprecipitate with over-expressed 5-HT6 (Figure 5B), PAC1 (Figure 5C) or M3 receptors (Figure 5D). Furthermore, different 5-HT4R mutants (A 258 L, N 308 D and Y 302 F) and truncated forms devoid of the C-terminal domain (D327 and D329) showed the same ability to interact with ADAM10 as the wild type receptor (Supplemental Figure 1, D-G).

Promotion of non-amyloidogenic pathway by 5-HT4 receptors. The nature of APP processing is dependent on its cellular localization and on the cellular proteases involved in its cleavage. The present study shows that 5-HT4R expression, at physiological levels (120-550 fmol/mg proteins) [START_REF] Reynolds | 5-Hydroxytryptamine (5-HT)4 receptors in post mortem human brain tissue: distribution, pharmacology and effects of neurodegenerative diseases[END_REF][START_REF] Waeber | Regional distribution and ontogeny of 5-HT4 binding sites in rodent brain[END_REF][START_REF] Domenech | Identification and characterization of serotonin 5-HT4 receptor binding sites in human brain: comparison with other mammalian species[END_REF][START_REF] Bockaert | 5-HT4 receptors[END_REF][START_REF] Manuel-Apolinar | Modifications of 5-HT4 receptor expression in rat brain during memory consolidation[END_REF], constitutively promotes the non-amyloidogenic processing of APP and that 5-HT4 receptors physically interact with the mature form of the a-secretase ADAM10. Several reports described the overlapping expression of APP and ADAM10 (but not ADAM17) in adult brain neurons [START_REF] Marcinkiewicz | Coordinated expression of beta-amyloid precursor protein and the putative beta-secretase BACE and alpha-secretase ADAM10 in mouse and human brain[END_REF] and suggested that ADAM10 could be the a-secretase involved in the non-amyloidogenic pathway of APP, particularly in neurons [START_REF] Endres | Upregulation of the alpha-secretase ADAM10 -risk or reason for hope?[END_REF]. This assumption was confirmed by RNAi-mediated silencing of ADAM10 and by using conditional ADAM10 knock-out mice [START_REF] Kuhn | ADAM10 is the physiologically relevant, constitutive alpha-secretase of the amyloid precursor protein in primary neurons[END_REF][START_REF] Jorissen | The disintegrin/metalloproteinase ADAM10 is essential for the establishment of the brain cortex[END_REF]. These findings were extended by the present study, which revealed association of the 5-HT4R with ADAM10. The ability of 5-HT4R to physically interact with mature ADAM10 was not shared by other GPCRs known to stimulate sAPPa secretion upon stimulation by their respective agonists. Indeed, expression of the PAC1 and M3 receptors did not trigger the formation of such a protein complex. Moreover, ADAM10 is retained in the ER by an arginine stretch (RRR) [START_REF] Marcello | An arginine stretch limits ADAM10 exit from the endoplasmic reticulum[END_REF]. Future work should test the possibility that association of ADAM10 with 5-HT4R masks this sequence, allowing the translocation of both proteins to the plasma membrane, a process associated with ADAM10 maturation.

It has been previously reported that stimulation of 5-HT4R promotes sAPPα release in a cAMP/Epac-dependent manner (13) and these findings were confirmed in the present study. Although 5-HT4R displays high level of constitutive activity at the Gs-adenylyl cyclase pathway [START_REF] Claeysen | Novel brainspecific 5-HT4 receptor splice variants show marked constitutive activity: role of the C-terminal intracellular domain[END_REF], several lines of evidence argued against a role of the cAMP/Epac pathway in the constitutive effect of 5-HT4R on APP processing: 1) inhibition of cAMP formation by an inverse agonist or by over-expression of the inactive form of Gs had no effect on the level of sAPPα release elicited by 5-HT4R expression; 2) expression of a dominant negative Epac mutant did not affect constitutive induction of sAPPa release by 5-HT4R; and 3) mutants of 5-HT4R that constitutively increase adenylyl cyclase activity did not stimulate sAPPa secretion more than the wild type receptor. Collectively, these findings demonstrate that 5-HT4R promotes the non-amyloidogenic processing of APP via different mechanisms implicating either the canonical Gs protein-dependent signalling (agonist-elicited response) or the formation of a protein complex including the receptor, the ADAM10 a-secretase and, probably, additional protein partners that remain to be identified (agonist-independent response).

One promising strategy to decrease the accumulation of Ab peptides in the brain has been to inhibit band gsecretase activities. However, the development of g-secretase inhibitors has been challenging because this enzyme has many endogenous substrates. Several g-secretase inhibitors have been synthesized and have shown unequivocal efficiency in reducing Ab plaque deposition in preclinical models of AD. However, clinical trials have revealed severe adverse side effects associated with the blockade of Notch processing [START_REF] Palmer | Neuroprotective therapeutics for Alzheimer's disease: progress and prospects[END_REF]. The recent failure of a g-secretase inhibitor to slow down the progression of the disease, accompanied by worsening of cognitive performances has halted its development [START_REF] Panza | REVIEW: gamma-Secretase inhibitors for the treatment of Alzheimer's disease: The current state[END_REF]. However, the recent discovery that down-regulation of GSAP (g-secretase activating protein) selectively prevents Ab peptide production without affecting Notch signalling (36) opens new perspectives.

Strategies for increasing the production of the neuroprotective sAPPa are also actively investigated. 5-HT4R expression clearly increased sAPPa release and it was previously demonstrated that 5-HT4R activation concomitantly decreased Ab formation while increasing sAPPa release [START_REF] Maillet | Crosstalk between Rap1 and Rac regulates secretion of sAPPalpha[END_REF][START_REF] Cho | Activation of 5-HT4 receptors inhibits secretion of beta-amyloid peptides and increases neuronal survival[END_REF].

However, a-cleavage is not always coupled to a reduction in Ab level [START_REF] Lichtenthaler | Alpha-secretase in Alzheimer's disease: molecular identity, regulation and therapeutic potential[END_REF]. The role of ADAM10, the physiological a-secretase in neurons, in the regulation of late-onset AD has been suggested by the discovery of non-synonymous mutations in the ADAM10 pro-domain in seven late-onset AD families [START_REF] Kim | Potential lateonset Alzheimer's disease-associated mutations in the ADAM10 gene attenuate {alpha}secretase activity[END_REF]. In line with these findings, activation of ADAM10 expression by vitamin A derivatives (4) or the transcription factor SIRT1 (6) decrease Ab peptides production and amyloid plaque formation in APP/PSEN1 transgenic mice. In addition, SIRT1-induced ADAM10 expression increases Notch processing and the release of Notch/intracellular domain (NICD), which activates genes involved in neuronal repair in adult brain [START_REF] Costa | Notch to remember[END_REF]. The efficacy of ligands of some GPCRs, such as M1R, 5-HT4R and PAC1R, in reducing Ab production and/or improving cognition in pre-clinical models of AD is now well documented [START_REF] Jones | Novel selective allosteric activator of the M1 muscarinic acetylcholine receptor regulates amyloid processing and produces antipsychotic-like activity in rats[END_REF]. Here, we describe a new mechanism whereby 5-HT4Rs constitutively activate the a-secretase ADAM10 and sAPPa release. Together with the ability of 5-HT4R agonists to increase learning and memory, via acetylcholine release [START_REF] Bockaert | 5-HT(4) receptors, a place in the sun: act two[END_REF], the present findings suggest that this GPCR might represent an interesting target for AD treatment. Several studies reported a significant drop of 5-HT4R binding sites in post-mortem brains of AD patients [START_REF] Reynolds | 5-Hydroxytryptamine (5-HT)4 receptors in post mortem human brain tissue: distribution, pharmacology and effects of neurodegenerative diseases[END_REF][START_REF] Wong | Characterization of [3H]GR 113808 binding to 5-HT4 receptors in brain tissues from patients with neurodegenerative disorders[END_REF][START_REF] Tsang | A serotoninergic basis for hyperphagic eating changes in Alzheimer's disease[END_REF]. In this context, the identification of 5-HT4R-interacting proteins capable of modulating the trafficking of the 5-HT4R/ADAM10 complex will be of high relevance.

METHODS

Plasmids.

The plasmid encoding the inactive form of the Gs protein was kindly given by Dr.

Catherine H. Berlot (Weis Center for Research Geisinger Clinic, Danville, Canada). The plasmids encoding c-Myc-tagged 5-HT4R, HA-tagged 5-HT4R, Flag-tagged 5-HT4R, HA-tagged 5-HT6R and the dominant-negative form of Epac2 were previously described [START_REF] Claeysen | Novel brainspecific 5-HT4 receptor splice variants show marked constitutive activity: role of the C-terminal intracellular domain[END_REF][START_REF] Ozaki | cAMP-GEFII is a direct target of cAMP in regulated exocytosis[END_REF][START_REF] Meffre | 5-HT(6) receptor recruitment of mTOR as a mechanism for perturbed cognition in schizophrenia[END_REF]. The SEAP-tagged APP695 cDNA in pRK5 was generated by adding the secreted embryonic alkaline phosphatase (SEAP) sequence, flanked by glycine linkers and the PspX I (5') or BsrG I (3') restriction sites, to the N-terminus of mouse APP695, after L 398 (Figure 1A). The plasmids encoding the HA-tagged M3 and Myc-tagged PAC1 receptors were kindly provided by Dr. Arnaud Monteil and Dr. Laurent Journot respectively (Institut de Génomique Fonctionnelle, Montpellier, France).

Antibodies. The mouse anti-sAPP antibody (22C11, directed against the N-terminal part of APP; 1:80 dilution) [START_REF] Weidemann | Identification, biogenesis, and localization of precursors of Alzheimer's disease A4 amyloid protein[END_REF] was purchased from Millipore (Billerica, USA). The rat anti-sAPPa antibody (7A6, murine sAPPa-specific, 1:2 dilution) was kindly given by Dr. Stefan F Lichtenthaler (Adolf Butenandt Institute -Ludwig Maximilians University, Munich, Germany) [START_REF] Kuhn | ADAM10 is the physiologically relevant, constitutive alpha-secretase of the amyloid precursor protein in primary neurons[END_REF]. The rabbit anti-ADAM10 antibody (directed against the C-terminal domain, 1:500 dilution), the mouse anti-c-Myc antibody (9E10) and the rabbit anti-Flag antibody were purchased from Sigma-Aldrich (Saint Quentin Fallavier, France). The mouse anti-HA antibody was purchased from Life Technologies (Saint Aubin, France). The rat anti-ADAM10 antibody (directed against the ectodomain domain, 1:500 dilution) was purchased from R &D Systems (Minneapolis,USA). Mouse anti-HA and rabbit anti-GFP antibodies were purchased from Life Technologies-Invitrogen (Carlsbad, USA). The mouse anti-SEAP antibody was purchased from Euromedex (Souffelweyershein, France). Horseradish peroxidase (HRP)-conjugated anti-rabbit and anti-mouse antibodies were from GE Healthcare (Orsay, France).

Materials.

All media, sera and antibiotics used for cell culture were purchased from Lonza (Basel, Switzerland). GR 113808 and GR 125487 were synthesized and generously provided by GlaxoSmithKline (Brentford, UK). 5-HT (serotonin creatine sulfate), Phorbol 12-myristate 13-acetate (PMA), the b-secretase inhibitor KTEEISEVN-statine-VAEF, the g-secretase inhibitor L-685458, (-)isoproterenol hydrochloride and metoclopramide (N-(2-dimethylamino)-ethyl)-4-amino-5-chloro-2methoxybenzamide) were from Sigma. The TNF-a Protease Inhibitor-1 (TAPI-1) was purchased from Merck (Darmstadt, Germany). Prucalopride was synthesized and kindly provided by Dr. Jan Schuurkes (Shire-Movetis n.v., Turnhout, Belgium). RO 116-0086, RO 116-1148 and RO 116-2617 were generously provided by Roche Bioscience (Palo Alto, USA). RS 67333 was purchased from Tocris Bioscience (Bristol, UK). BIMU8 was kindly provided by Boehringer Ingelheim (Rhein, Germany).

Cisapride was synthesized and obtained from Janssen Pharmaceutica (Beerse, Belgium). ML 10375 was a generous gift from Dr. Langlois (CNRS-BIOCIS, Châtenay-Malabry, France). SB 207266 was obtained from Fournier-Debat Laboratories (Daix, France).

Cell culture and transfections. Human Embryonic Kidney 293 (HEK-293) cells were grown in

Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% dialyzed foetal calf serum (FCS). Cells were transfected at 60-70% confluence by electroporation, as previously described [START_REF] Claeysen | Novel brainspecific 5-HT4 receptor splice variants show marked constitutive activity: role of the C-terminal intracellular domain[END_REF]. 24 hrs after transfection, cells were serum starved for 4 hrs before assays.

Primary cultures of cortical neurons were prepared as previously described [START_REF] Dumuis | A nonclassical 5hydroxytryptamine receptor positively coupled with adenylate cyclase in the central nervous system[END_REF]. Briefly, cells dissociated from the cortex of 15-16 day/old Swiss mouse embryos were plated in serum-free medium in 24-well culture dishes (0.5 x 10 6 cells/well). Cultures were maintained at 37°C in a humidified atmosphere in 5% CO2 and 95% H2O, in DMEM/F12 supplemented with 10% of hormone mixture for 4 days. Cortical neurons were transfected by electroporation using AMAXA (Lonza, Basel, Switzerland) according to the manufacturer's instructions. Media were collected for 12 hrs at DIV4.

Small interfering RNA (siRNA) transfection.

A mix of double-stranded siRNA sequences 5'-AAUGAAGAGGGACACUUCCCUdTdT-3' and 5'-AAGUUGCCUCCUCCUAAACCAdTdT-3' (Eurogentec, Liege, Belgium) was used to selectively target ADAM10. A small RNA duplex, which has no silencing effect, was used as control (negative control siRNA duplex from Eurogentec). All the siRNAs used in this study have been described earlier and validated [START_REF] Janes | Adam meets Eph: an ADAM substrate recognition module acts as a molecular switch for ephrin cleavage in trans[END_REF]. Early passage HEK-293 cells at 40% confluence in 24-well plates were transiently transfected with 400 ng of plasmid encoding wild type 5-HT4R and 500 ng of plasmid encoding SEAP-tagged-APP. siRNAs were transfected at 150 nM using Lipofectamine 2000, according to the manufacturer's recommendations (Life Technologies-Invitrogen). Cells were lysed 2 days after siRNA transfection in order to quantify APP expression.

Measurement of sAPP.

To determine the total and intracellular concentration of APP and of secreted APP, cells were washed with PBS at the beginning of the assay and pre-incubated for 2 hrs in serum-free medium supplemented with 5-HT4R ligands and drugs were added for the indicated times.

Cells were then lysed after collection of conditioned supernatants containing secreted proteins according to the two following methods: (i) collected media were centrifuged at 300 x g for 10 min and then at 20,000 x g for 20 min to remove cells and cell debris. Proteins were concentrated by precipitation with 10% ice-cold tricholroacetic acid (TCA) (at 4°C for 2 hrs), centrifuged at 38,000 x g at 4°C for 25 min and pellets were washed with diethyl ether three times. TCA precipitates were reconstituted in solubilization buffer (20 mM HEPES buffer; 250 mM Tris buffer pH6.8; 40% SDS; 40% glycerol; 40% 2-mercaptoethanol and traces of bromophenol blue). The protein amount of each sample was determined with the bicinchoninic acid assay (Sigma). Following precipitation, samples were separated by electrophoresis on 7.5% SDS-PAGE gels and transferred onto nitrocellulose membranes; (ii) in order to measure sAPP release, HEK-293 cells were transiently transfected with APP-SEAP and plated in 6-well culture dishes. Spent media were incubated overnight at 4°C with Dynabeads® protein G (Life Technologies-Invitrogen, Carlsbad, USA) that had been pre-coupled to 4 µg of 22C11 polyclonal antibody. After 5 washes in lysis buffer (20 mM HEPES; 150 mM NaCl; 1% NP40; 10% glycerol; 4 mg/ml dodecylmaltoside) supplemented with protease inhibitors and phosphatase inhibitors (10 mM NaF; 2 mM Na + -vanadate and 1 mM Na + -pyrophosphate), immunoprecipitated proteins were eluted in solubilization buffer and separated by electrophoresis (see above). To detect intracellular APP, cells were lysed in 100 µL lysis buffer (see above), reconstituted in solubilization buffer and separated by electrophoresis (see above). sAPP release was also estimated by measuring the extracellular SEAP activity released by HEK-293 cells transiently transfected with the SEAP-APP plasmid. One day post-transfection, drugs were added for the indicated times (see legends), then cell culture media were collected and SEAP activity measured by adding the chromogenic substrate p-Nitrophenyl phosphate disodium hexahydrate (Sigma) according to the manufacturer's instructions. The reaction was read at 405 nm using an Infinite 2000 luminescence counter (Tecan, Männedorf, Switzerland). Specific quantification of sAPPa release was performed by using the sAPPa ELISA kit from IBL International (Hamburg, Germany) according to the manufacturer's instructions. The reaction was read at 620 nm and 450 nm using an Infinite 2000 luminescence counter (Tecan).

Immunoblotting. Proteins were separated on 7.5% SDS-PAGE gels and transferred by semi-dry blotting onto nitrocellulose membranes (Hybond-C, Amersham Biosciences, Orsay, France).

Membranes were blocked at room temperature in Tris-buffered saline blocking solution containing 5% (w/v) milk powder, 0.25% v/v Tween 20 for 1 hr. Membranes were then incubated with the primary antibodies at 4 °C overnight, washed extensively and incubated with the secondary antibodies at room temperature for 1 hr. Immunoreactivity was detected using the Chemiluminescence Reagent Plus kit (Perkin Elmer, Courtaboeuf, France). Immunoreactive bands were quantified by densitometry using Image J and GraphPad PRISM (GraphPad Software, San Diego, USA).

Co-immunoprecipitation. Cells were seeded in 150 mm-plates (10 6 cells/plate) 24 hrs before the experiment. Cross-linking was carried out in PBS completed with 1.25 mM dithiobissuccinimidyl propionate (DSP) (Pierce, Perbio-Brebières, France) (a membrane-permeable, hydrolysable, covalent cross-linker) for 30 min. The cross-linking reaction was stopped with 50 mM Tris pH 7.4. After one wash in PBS, cells were incubated with lysis buffer (see above) at 4°C for 1 hr. Samples were centrifuged at 20,000 x g for 15 min. Solubilized proteins were incubated at 4°C with 20 µl of Dynabeads® protein G (Life Technologies-Invitrogen) pre-coupled with 4 µg of antibody overnight. Immunoprecipitated proteins were eluted in Laemmli sample buffer, resolved by SDS-PAGE gel electrophoresis and detected by Western blotting.

Determination of cAMP production. HEK-293 cells were transiently transfected with plasmids encoding the indicated receptors and/or SEAP-APP, then seeded in 24-well plates (200,000 cells/well). 24 hrs after transfection, cells were exposed to the indicated concentrations of 5-HT4R ligands in the presence of 0.1 mM L-ascorbic acid and 0.1 mM of the phosphodiesterase inhibitor RO-20-1724, at 37°C in 250 µl of HBS (20 mM HEPES; 150 mM NaCl; 4.2 mM KCl; 0.9 mM CaCl2; 0.5 mM MgCl2; 0.1% glucose; 0.1% BSA). Cells were then lysed by addition of the same volume of Triton-X100 (0.1%). Quantification of cAMP production was performed by HTRF by using the cAMP Dynamic kit (Cisbio Bioassays) according to the manufacturer's instructions.

Cell surface ELISA (Enzyme-Linked Immunosorbent Assay). HEK-293 cells transiently

transfected with the appropriate cDNAs were seeded in 96-well plates (40,000 cells/well). 24 hrs after transfection, cells were treated with drugs, washed, fixed with 4% paraformaldehyde at room temperature for 10 min and blocked with PBS + 1% FCS. Cells were then incubated with primary antibodies in the same buffer for 1 hr followed by HRP conjugated secondary antibodies at 1:8000 for 1hr. After extensive washes, the chromogenic substrate was added (Supersignal® ELISA Femto Maximum Sensitivity, Pierce). Chemiluminescence was detected and quantified with an Infinite 2000 luminescence counter (Tecan).

Statistical Analysis.

All represented experiments were performed in triplicate. Statistical significance was determined with the Student-Newman-Keuls test using GraphPad Prism (GraphPad Software). P values <0.05 were considered as statistically significant. Data corresponded to the mean ± SD of three independent experiments. (B, C) HEK-293 cells (B) or embryonic cortical neurons (C) were transiently transfected with plasmids encoding Myc-tagged 5-HT4R and SEAP-tagged APP (500 ng/10 7 cells or 2,500 ng/6.10 6 cells, respectively). 24 hrs (B) or four days (C) post-transfection, cell surface expression of 5-HT4R was assessed by ELISA in non-permeabilized cells using the anti-Myc antibody (B and C, white bars). In parallel, sAPP release during a 2 hr-period (B) or 12 hr-period (C) was evaluated by measuring the alkaline phosphatase activity (colorimetric assay) of cell supernatants (B and C, grey bars). (B, C) Total APP expression was detected in cell lysates in the corresponding transfection conditions, using the 22C11 antibody. Expression levels of ADAM10, 5-HT4R and actin were provided as controls. (D) HEK-293 cells were transiently transfected with plasmids encoding epitope-tagged 5-HT4R (300 ng/10 7 cells), PAC1R (700 ng/10 7 cells), M3R (300 ng/10 7 cells) or 5-HT6R (75 ng/10 7 cells) to ensure equivalent cellsurface expression of the different receptors, and SEAP-tagged APP (500 ng/10 7 cells). Cell surface expression of the receptors (white bars) and quantification of sAPP release (grey bars) were measured as described in (B) using appropriate primary antibodies. (E) Embryonic cortical neurons were transiently co-transfected with a plasmid encoding epitope-tagged 5-HT4R (800 ng/6.10 6 cells), PAC1R

(1,500 ng/6.10 6 cells), M3R (1,000 ng/6.10 6 cells) or 5-HT6R (800 ng/10 7 cells) and SEAP-tagged APP (2,500 ng/6.10 6 cells). Cell surface expression of the receptors (white bars) and quantification of sAPP release during a 4 day-period was performed as described in (C). CTL, Condition transfected with only SEAP-tagged APP. Data are the means ± SD of at least three independent experiments. * p<0.05, ** p< 0.01 vs. CTL corresponding value.

Figure 1 . 5 -

 15 Figure 1. 5-HT4R expression constitutively enhances sAPP release. (A) Schematic representation of APP. The position of the SEAP-tag is depicted. The epitopes of the anti-sAPP (22C11) and -sAPPa antibodies (7A6) and the b-, b'-, aand g-secretase cleavage sites are also indicated. Mb, membrane.

Figure 2 . 5 -

 25 Figure 2. 5-HT4R specifically enhances sAPPa release. (A) HEK-293 cells were transiently transfected with plasmids encoding Myc-tagged 5-HT4R (250 ng/10 7 cells), SEAP-tagged APP (500 ng/10 7 cells). 2hrs-sAPPa release in the medium was detected using an ELISA kit designed to quantify mouse/rat sAPPa. Cortical neurons were transfected with empty plasmid (CTL) or plasmid encoding Flag-tagged 5-HT4R (250 ng/6.10 6 cells). Four days after transfection, sAPPa released by neurons during 12 hrs was quantified using the same kit. ** p<0.01 vs. corresponding CTL value. (B) Proteins in supernatants from HEK-293 cells transiently transfected with plasmids encoding Myc-tagged 5-HT4R and SEAP-tagged APP (500 ng/10 7 cells) were immunoprecipitated with the 22C11antibody. Immunoprecipitated complexes were analysed by western blotting with the 7A6 (anti-sAPPa) antibody (Upper panel). Expression levels of APP, ADAM10, 5-HT4R and actin in cell lysates were provided as controls (Lower panel). (C) HEK-293 cells were transiently co-transfected with plasmids encoding

Figure 3 . 5 -

 35 Figure 3. 5-HT4R stimulation further enhances sAPPa release induced by 5-HT4R expression. (A, B) HEK-293 cells were transiently transfected with plasmids encoding Myc-tagged 5-HT4R (250 ng/10 7 cells) and SEAP-tagged APP (500 ng/10 7 cells). 24 hrs post-transfection, cells were treated with vehicle alone (CTL) or 1 µM prucalopride in the presence or not of 5 µM GR 113803 or 0.1 µM RO 116-0086 for 2 hrs and then sAPP release was evaluated by measuring the alkaline phosphatase activity in the medium. (C, D) Embryonic cortical neurons were transiently transfected with plasmids encoding Flagtagged 5-HT4R (800 ng /6.10 6 cells) with (C) or without (D) SEAP-tagged APP (2,500 ng/6.10 6 cells).Four days after transfection, neurons were treated with increasing concentrations of prucalopride and sAPP release during 12 hrs was evaluated by measuring the alkaline phosphate activity (C) or by using the sAPPa ELISA kit (D). (E, F) Cortical neurons co-transfected with plasmids encoding Flag-tagged 5-HT4R (250 ng/6.10 6 cells) and SEAP-tagged APP (2,500 ng/6.10 6 cells) were exposed to the indicated 5-HT4R agonists/antagonists for 12 hrs and sAPP release was evaluated by measuring the alkaline phosphatase activity. CTL, Condition transfected with only SEAP-tagged APP. Data are the means ± SD of at least three independent experiments. In (B), (E) and (F), * p<0.05 vs. the corresponding value in cells not treated with agonist.

Figure 4 .

 4 Figure 4. Role of the Gs/cAMP/Epac pathway in 5-HT4R-induced sAPPa release. HEK-293 cells were transiently transfected with plasmids encoding Myc-tagged 5-HT4R (250 ng/10 7 cells), SEAPtagged APP (500 ng/10 7 cells) and a dominant negative Gs protein (A) or a dominant-negative form of Epac1 (500 ng/10 7 cells) (C, D). (B) HEK-293 cells were transiently transfected with plasmids encoding Myc-tagged wild type 5-HT4R or mutants (Y 302 F, N 308 D or A 258 L) with high level of constitutive Gs signalling activity and SEAP-tagged APP (500 ng/10 7 cells each). 24 hrs after transfection, cAMP

Figure 5 . 5 -

 55 Figure 5. 5-HT4R interacts with the a-secretase ADAM10. (A) HEK-293 cells were transiently transfected with plasmids encoding HA-tagged 5-HT4R (250 ng/10 7 cells) and SEAP-tagged APP (500 ng/10 7 cells). 24 hrs after transfection, Cells were treated 2 hours with vehicle alone or with 1 µM prucalopride (Prucalo.), then protein lysates were immunoprecipitated with the anti-HA antibody. Whole cell extracts and immunoprecipitated material were analysed by Western blotting using antibodies against ADAM10 (mature (m) and immature (im) forms) and against HA-tag. (B, C, D) HEK-293 cells were transiently co-transfected with plasmids encoding Myc-(C) or HA-tagged 5-HT4R (B, D), HA-5-HT6 R (B), Myc-PAC1R (C) or HA-M3R (D) and SEAP-tagged APP. 24 hrs after transfection, protein lysates were immunoprecipitated as described in A. (E) Data correspond to the ratio of ADAM10 co-immunoprecipitation to 5-HT4R immunoprecipitation signals. ND, not detectable. CTL, Condition transfected with only SEAP-tagged APP. The data illustrated are representative of three independent experiments performed with different sets of cultured cells.

CTL 5 - 5 -

 55 Figure 1

ACKNOWLEDGMENTS

We are grateful to Gaël Barthet, Federica Bertaso and Vincent Homburger for constructive discussion.

ABBREVIATIONS

APP, Amyloid Precursor Protein; sAPPa, soluble APP alpha; sAPPb, soluble APP beta; SEAP: Secreted Embryonic Alkaline Phosphatase, 5-HT4R, serotonin type 4 receptor; AD, Alzheimer's Disease; ADAM, A Disintegrin And Metalloprotease; Ab, Amyloid-b peptide; GPCR, G Protein-Coupled Receptor.

Funding

Supported by grants from CNRS, INSERM, the French Minister of Research (ANR Blanc 2006, to AD and SC, ANR Blanc 2012 "ADAMGUARD" to JB), the "Fondation pour la Recherche Médicale" (FRM, to PM), the France Alzheimer association (to SC), the Soroptimist International (French Union, to SC), the Languedoc Roussillon region ("Chercheuse d'Avenir" 2012, to SC) and the competence network degenerative dementias (BMBF-KNDD, to SFL). MC was a recipient of fellowships from the CNRS, the Languedoc Roussillon region and FRM. EC was supported by ANR Blanc 2006.

Notes

Binding experiments, cAMP measurements and ELISA were carried out using the ARPEGE Pharmacology Screening Interactome facility at the Institute of Functional Genomics (Montpellier, France).

ASSOCIATED CONTENT Supporting Information

Supplemental Figure 1: 5-HT4R variants (mouse a, b, e and f; human a, g and i), truncated forms (Δ358 and Δ329) and mutants (A 258 L, N 308 D and Y 302 F) promote sAPP release and physically interact with ADAM10; Supplemental Figure 2: sAPP release induced by 5-HT4R expression is not affected by β-or γ-secretase inhibitors; Supplemental figure legends. This material is available free of charge via the Internet at http://pubs.acs.org. Supplemental Figure 2: sAPP release induced by 5-HT 4 R expression is not affected by β-or γsecretase inhibitors.

AUTHOR INFORMATION

5-HT 4 RECEPTORS CONSTITUTIVELY PROMOTE THE NON-AMYLOIDOGENIC PATHWAY OF APP CLEAVAGE AND INTERACT WITH ADAM10

SUPPLEMENTAL FIGURE LEGENDS

SUPPLEMENTAL FIGURE LEGENDS

Supplemental Figure 1