Reza Sharif-Naeini

Joost H A Folgering

Delphine Bichet

Fabrice Duprat

Inger Lauritzen

Malika Arhatte

Martine Jodar

Franck Alexandra Dedman

Claude Chatelain

Uwe Schulte

Franck C Chatelain

Kevin Retailleau

Laurent Loufrani

Amanda Patel

Frederick Sachs

Patrick Delmas

Dorien J M Peters

Eric Honore
email: honore@ipmc.cnrs.fr

Polycystin-1 and -2 Dosage Regulates Pressure Sensing

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INTRODUCTION

Autosomal-dominant polycystic kidney disease (ADPKD) is a multisystem disorder characterized by renal and extrarenal cysts, as well as cardiovascular abnormalities [START_REF] Arnaout | Molecular genetics and pathogenesis of autosomal dominant polycystic kidney disease[END_REF][START_REF] Delmas | Polycystins: from mechanosensation to gene regulation[END_REF][START_REF] Harris | Polycystic kidney disease[END_REF]. Mutations in either PKD1 or PKD2 gene, encoding polycystin-1 (TRPP1) or -2 (TRPP2), respectively (for recent nomenclature see [START_REF] Nilius | Transient receptor potential cation channels in disease[END_REF], account for this common hereditary disease [START_REF] Harris | Polycystic kidney disease[END_REF]. TRPP1 is a large integral membrane glycoprotein with 11 transmembrane domains and an extensive aminoterminal extracellular domain involved in cell-cell, cell-matrix interaction and signaling pathways (for review see [START_REF] Wilson | Polycystic kidney disease[END_REF]. TRPP1 interacts with TRPP2, a six transmembrane domain protein of the TRP ion channel family (for review see [START_REF] Giamarchi | The versatile nature of the calcium-permeable cation channel TRPP2[END_REF]. Polycystins also interact with multiple partners, including the TRP channel subunits TRPC1 and TRPV4 [START_REF] Kottgen | TRPP2 and TRPV4 form a polymodal sensory channel complex[END_REF][START_REF] Tsiokas | Specific association of the gene product of PKD2 with the TRPC1 channel[END_REF], as well as several elements of the cytoskeleton (for reviews see [START_REF] Chen | Submembraneous microtubule cytoskeleton: interaction of TRPP2 with the cell cytoskeleton[END_REF][START_REF] Delmas | Polycystins: from mechanosensation to gene regulation[END_REF][START_REF] Harris | Polycystic kidney disease[END_REF][START_REF] Wilson | Polycystic kidney disease[END_REF].

By associating through the coiled-coil motifs in their cytosolic carboxy-terminal domains, TRPP1 and TRPP2 have been proposed to form a ''receptor-ion channel complex'' [START_REF] Hanaoka | Co-assembly of polycystin-1 and À2 produces unique cation-permeable currents[END_REF]. TRPP1 and TRPP2 colocalize at the membrane of the primary cilia of renal epithelial cells and endothelial cells where they are proposed to transduce luminal shear stress (i.e., fluid flow parallel to the cell surface) into a calcium signal [START_REF] Nauli | Polycystins 1 and 2 mediate mechanosensation in the primary cilium of kidney cells[END_REF][START_REF] Nauli | Endothelial cilia are fluid shear sensors that regulate calcium signaling and nitric oxide production through polycystin-1[END_REF]. With its large extracellular amino-terminal domain, TRPP1 has been suggested to be a mechanical sensor regulating the opening of the associated calcium-permeable channel TRPP2 [START_REF] Nauli | Polycystins 1 and 2 mediate mechanosensation in the primary cilium of kidney cells[END_REF]. Loss of this mechanosensory function (i.e., flow sensing) is thought to result in altered cellular signaling subsequently leading to cyst formation [START_REF] Nauli | Polycystins and mechanosensation in renal and nodal cilia[END_REF].

In endothelial primary cilia, as reported in kidney epithelial cells [START_REF] Nauli | Polycystins 1 and 2 mediate mechanosensation in the primary cilium of kidney cells[END_REF], TRPP1 and TRPP2 are also involved in fluid shear sensing and regulate calcium signaling and nitric oxide release, thus contributing to vasodilation in response to an increase in blood flow [START_REF] Aboualaiwi | Ciliary polycystin-2 is a mechanosensitive calcium channel involved in nitric oxide signaling cascades[END_REF][START_REF] Nauli | Endothelial cilia are fluid shear sensors that regulate calcium signaling and nitric oxide production through polycystin-1[END_REF]. However, polycystins are also abundantly expressed in arterial myocytes [START_REF] Boulter | Cardiovascular, skeletal, and renal defects in mice with a targeted disruption of the Pkd1 gene[END_REF][START_REF] Qian | Analysis of the polycystins in aortic vascular smooth muscle cells[END_REF], which respond to intraluminal pressure that causes wall stretch, instead of flow. An increase in intraluminal pressure causes a gradual depolarization of the vascular smooth muscle cells (VSMCs) that is linked to the opening of nonselective stretchactivated cation channels (SACs), followed by the opening of voltage-gated calcium channels, resulting in an increase in intracellular Ca 2+ and myocyte constriction (the myogenic or Bayliss response) [START_REF] Brayden | Transient receptor potential (TRP) channels, vascular tone and autoregulation of cerebral blood flow[END_REF][START_REF] Hill | Arteriolar myogenic signalling mechanisms: implications for local vascular function[END_REF][START_REF] Voets | TRPCs, GPCRs and the Bayliss effect[END_REF]. The possible involvement of polycystins in pressure sensing has not yet been investigated.

Here, we demonstrate that the TRPP1/TRPP2 ratio regulates SAC activity via the filamin A (FLNa)/F-actin cytoskeletal network and modulates the arterial myogenic response to intraluminal pressure.

RESULTS

TRPP2 Expression Reduces SAC Activity

To determine the effect of TRPP1 and TRPP2 on SAC activity, we expressed the proteins either individually or together in COS cells, an expression system previously used to study mechanosensitive ion channels [START_REF] Gottlieb | Revisiting TRPC1 and TRPC6 mechanosensitivity[END_REF][START_REF] Honore | Desensitization of mechano-gated K2P channels[END_REF]. In cell-attached recordings of mock-transfected cells, brief negative pressure pulses applied through the recording electrode evoked a gradual and reversible opening of endogenous cation nonselective SACs (Figure 1A), with a single-channel conductance of 29.2 ± 0.3 pS and a reversal potential (E rev ) of 0.1 ± 0.3 mV (n = 13; Figure 1B). Surprisingly, while overexpression of TRPP1 alone had no significant effect (Figure 1C), overexpression of TRPP2 produced a dramatic and dose-dependent inhibition of SAC activity (Figures 1C and 1D,inset,and Figure 1E). Neither single-channel conductance, ionic selectivity (see Figure S1 available online), or open probability in patches with a single channel were changed by TRPP2 overexpression. The percentage of active patches was reduced from 94% ± 1% (n = 264) in mock-transfected cells to 70% ± 5% in cells expressing TRPP2 (p < 0.001, n = 90; data not shown). Interestingly, SAC inhibition was also observed with a disease-causing TRPP2 mutant in which a nonsense mutation generates a truncated form of TRPP2 (hTRPP2-742X or the mouse equivalent 40,30,40,20,and 22,for control,hTRPP1,hTRPP2,hTRPP1+hTRPP2,respectively. mTRPP2-740X) lacking the ER retention signal (Figures 1E,S2A, and S6) [START_REF] Mochizuki | PKD2, a gene for polycystic kidney disease that encodes an integral membrane protein[END_REF]. This inhibitory effect of TRPP2 on SACs was also present in cell lines originating from different tissues including renal epithelium (LLC-PK1), dorsal root ganglion neurons (F11), and vascular smooth muscle (MOVAS, A7R5, and mouse aortic explants), thus probably representing a fundamental cellular property (Figure S2A). The effect of TRPP2 expression was selective for SACs, as the activity of other channel types, including ASIC and Kv, were not modified by TRPP2 overexpression (Figures S2B andS2C). Moreover, other TRP channels shown to directly interact with TRPP2 and/ or claimed to be implicated in mechanotransduction [START_REF] Christensen | TRP channels in mechanosensation: direct or indirect activation?[END_REF][START_REF] Gottlieb | Revisiting TRPC1 and TRPC6 mechanosensitivity[END_REF][START_REF] Kottgen | TRPP2 and TRPV4 form a polymodal sensory channel complex[END_REF][START_REF] Maroto | TRPC1 forms the stretch-activated cation channel in vertebrate cells[END_REF][START_REF] Pedersen | Transient receptor potential channels in mechanosensing and cell volume regulation[END_REF][START_REF] Spassova | A common mechanism underlies stretch activation and receptor activation of TRPC6 channels[END_REF][START_REF] Tsiokas | Specific association of the gene product of PKD2 with the TRPC1 channel[END_REF], including TRPC1, TRPV4 and TRPC6, failed to affect SAC activity (Figure S2D).

TRPP1 Reverses SAC Inhibition by TRPP2

Interestingly, coexpression of TRPP1 with TRPP2 reversed the inhibitory effect of TRPP2 on SACs (Figures 1C and1E). To examine whether the reversal by TRPP1 requires an interaction with TRPP2, we expressed TRPP1 with TRPP2-742X, which lacks the coiled-coil TRPP1 binding domain. TRPP1 could not rescue SAC activity in TRPP2-742X-transfected COS cells (Figure 1E), demonstrating that the reversal of SAC inhibition is dependent on an interaction between TRPP1 and TRPP2. Moreover, this reversal was specific to TRPP1, as coexpression with either TRPC1 or TRPV4, also known to interact with TRPP2 (Kott-gen et al., 2008;[START_REF] Tsiokas | Specific association of the gene product of PKD2 with the TRPC1 channel[END_REF], failed to reverse SAC inhibition by TRPP2 (Figure S2D). These observations indicate that the dosage of TRPP2 relative to TRPP1 modulates the activity of SACs in expression systems. Here, we detected no significant exogenous channel current expression at the plasma membrane after transient transfection of TRPP1 and/or TRPP2 (wild-type [WT] or 742X) in both MOVAS (data not shown) and COS cells using either single channel or whole cell patch clamp recording configurations (Figure S7).

Pkd1 Inactivation Reduces SAC Activity in Arterial Myocytes

If the TRPP1/TRPP2 dosage modulates SAC activity in a native system known to express both polycystins, we would expect that lowering the expression of TRPP1 may cause a TRPP2mediated inhibition of SAC activity. We inactivated the Pkd1 gene selectively in mouse smooth muscle cells using the Cre/ lox strategy (with no alteration in the level of expression of Pkd2 or other relevant TRP subunits; see Experimental Procedures) [START_REF] Holtwick | Smooth muscle-selective deletion of guanylyl cyclase-A prevents the acute but not chronic effects of ANP on blood pressure[END_REF] and examined SAC activity in VSMCs acutely dissociated from resistance mesenteric arteries of Pkd1 +/+ , Pkd1SM del/+ , or Pkd1SM del/del mice. Measurements of arterial pressure revealed no significant difference between the Pkd1 genotypes (Figure S8). In cell-attached patches, brief negative pressure pulses applied through the recording electrode evoked a gradual and reversible opening of SACs (Figure 2A) with biophysical properties similar to those found in COS cells (single-channel conductance: 28.5 ± 0.4 pS; n = 11). (A) Single-channel currents (cell-attached patch configuration) induced by negative pipette pressures in a Pkd1 +/+ VSMC at a holding potential of À80 mV. (B) Mean currents in isolated VSMCs from Pkd1 +/+ , Pkd1SM del/+ , or Pkd1SM del/del mice. Sample numbers for (B), (C), and (E) are: n = 70, 46, and 64 for Pkd1 +/+ , Pkd1SM del/+ , and Pkd1SM del/del cells, respectively. (C) Mean (±SEM) pressure-dependent SAC activity (NPo). Data are fitted with Boltzmann functions (P 0.5 = À41.8 ± 2.9; À43.9 ± 4.9; and À42.7 ± 3.5 mm Hg; k = 13.8 ± 2.3; 13.3 ± 4.0; 8.8 ± 2.9; for Pkd1 +/+ , Pkd1SM del/+ , and Pkd1SM del/del cells, respectively). The variability in kinetics (Figures 1C and2B) can be explained by different viscoelastic properties between VSMCs and COS cells [START_REF] Sachs | Mechanosensitive ion channels in nonspecialized cells[END_REF]. Ionic substitutions (E rev : À3.5 ± 2.1 mV, n = 11, with NaCl; À5.2 ± 3.2 mV, n = 12, with sodium gluconate; À30.5 ± 3.8 mV, n = 4, with choline chloride; Figure 2D and data not shown), as well as pharmacological experiments, indicated that these channels were cationic, nonselective, and inhibited by classical blockers of SACs (À78.0 ± 14.0%, n = 13, with 30 mM Gd 3+ and À89.0 ± 9.0%, n = 7, with 5 mM GsTMx4). Moreover, pressure-induced inward SAC currents at negative membrane potentials could be recorded when Ca 2+ (or Ba 2+ ) was the only possible charge carrier, thus indicating Ca 2+ permeability of the channel (Figure S9). Remarkably, the activity of SACs was significantly lower in VSMCs from Pkd1SM del/+ and Pkd1SM del/del compared to Pkd1 +/+ mice (Fig- ures 2B and2C). The GsTMx4-sensitive SACs component was significantly smaller in arterial myocytes from Pkd1SM del/del mice, as compared to those isolated from Pkd1 +/+ mice (Figure S10). Neither the single-channel conductance (Figure 2D) nor the open channel probability (Po) changed in patches with a single channel, indicating that the number of active SACs is decreased in VSMCs from Pkd1SM del/+ and Pkd1SM del/del mice. Indeed, the percentage of active patches (i.e., with SAC opening) was significantly lower in VSMCs of Pkd1SM del/+ and Pkd1SM del/del mice (Figure 2E). The density of the L-type voltage-dependent calcium current, which plays a key role in arterial excitation-contraction coupling, was not different between mesenteric VSMCs isolated from Pkd1 +/+ and Pkd1SM del/del mice (5.61 ± 0.81 pA/pF in Pkd1 +/+ compared to 6.05 ± 1.17 pA/pF in Pkd1SM del/del ; at +10 mV; Figure 2F). These data show that in VSMCs reducing TRPP1 expression causes a specific decrease in SAC activity.

Pkd1 Inactivation Impairs the Arterial Myogenic Response Stretch-activated channels are known to be involved in the myogenic tone, a key function of resistance arteries in response to an increase in intraluminal pressure [START_REF] Brayden | Transient receptor potential (TRP) channels, vascular tone and autoregulation of cerebral blood flow[END_REF][START_REF] Hill | Arteriolar myogenic signalling mechanisms: implications for local vascular function[END_REF][START_REF] Lee | Mechanosensitive nonselective cation channel facilitation by endothelin-1 is regulated by protein kinase C in arterial myocytes[END_REF][START_REF] Mederos Y Schnitzler | Gq-coupled receptors as mechanosensors mediating myogenic vasoconstriction[END_REF]. Indeed, specific pharmacological inhibition of SACs with the spider toxin GsMTx4 [START_REF] Suchyna | Identification of a peptide toxin from Grammostola spatulata spider venom that blocks cation-selective stretch-activated channels[END_REF] significantly reduced the myogenic tone of resistance mesenteric arteries (Figure S11). Because the activity of SACs was significantly reduced in the absence of TRPP1, we examined whether the myogenic contractility of resistance arteries may also be affected. Mesenteric arteries isolated from Pkd1 +/+ mice produced a robust myogenic contraction when intraluminal pressure was increased (Figures 3A and3B). However, arteries isolated from Pkd1SM del/+ and Pkd1SM del/del mice showed a profound decrease in myogenic tone (Figures 3A and3B). Furthermore, the threshold pressure for myogenic contraction was significantly shifted to higher pressure in arteries with decreased Pkd1 expression (Figure 3B). This indicates that these arteries have to be distended further before triggering mechanosensory feedback (Figures 3A and3B). Additionally, we observed no change in vascular reactivity to KCl or phenylephrine in mesenteric arteries from 12-to 16-week-old Pkd1SM del/del mice (Figure 3C), which confirms findings from a related study using 20-week-old Pkd1 (global) heterozygote mice [START_REF] Morel | PKD1 haploinsufficiency is associated with altered vascular reactivity and abnormal calcium signaling in the mouse aorta[END_REF]. These results support the assumption that L-type calcium channel activity was unchanged and that intracellular calcium stores, as well as the contractile apparatus, were not significantly altered by Pkd1 deletion. Finally, the wall/lumen ratio did not vary between genotypes (Figure S3A). Moreover, the stress-strain curves of mesenteric arteries were identical (Figure S3B), indicating that the contribution of elastin and collagen to arterial wall elasticity was unaltered by Pkd1 inactivation. Thus, the expression of TRPP1 in VSMCs appears essential for pressure-dependent mesenteric artery SAC activity and myogenic tone, but does not affect other arterial properties.

Depletion of TRPP2 in TRPP1-Deficient VSMCs Restores Both SAC Activity and Myogenic Tone We hypothesized that when TRPP2 is released from its interaction with TRPP1, either by lowering TRPP1 expression (Pkd1SM del/+ or Pkd1SM del/del ) or by overexpressing TRPP2, it impairs SAC activity and lowers myogenic tone. Therefore, reducing the expression of TRPP2 in arteries lacking Pkd1 should restore SAC opening and consequently the myogenic tone. Indeed, knocking down Pkd2 by about 80% with an siRNA (Figure S4) increased SAC activity in Pkd1SM del/del VSMCs and the percentage of active patches (Figures 4A-4C). Additionally, Pkd2 knockdown restored myogenic tone in Pkd1SM del/del mesenteric arteries (Figures 4D and4E). These results suggest that free TRPP2 (unbound to TRPP1) is responsible, at least in large part, for the decrease in SAC function and myogenic tone in Pkd1SM del/+ and Pkd1SM del/del arteries.

Role of the Actin Cytoskeleton in SAC Inhibition by TRPP2

The cortical F-actin cytoskeleton exerts a tonic inhibitory control over SAC activity, presumably by reducing the amount of stretch-induced increase in bilayer tension sensed by the channel protein [START_REF] Lauritzen | Cross-talk between the mechano-gated K2P channel TREK-1 and the actin cytoskeleton[END_REF][START_REF] Morris | Mechanoprotection of the plasma membrane in neurons and other non-erythroid cells by the spectrin-based membrane skeleton[END_REF]. Because TRPP2 is known to interact with various cytoskeletal elements (for reviews see [START_REF] Chen | Submembraneous microtubule cytoskeleton: interaction of TRPP2 with the cell cytoskeleton[END_REF][START_REF] Delmas | Polycystins: from mechanosensation to gene regulation[END_REF][START_REF] Harris | Polycystic kidney disease[END_REF][START_REF] Wilson | Polycystic kidney disease[END_REF], we examined whether SAC inhibition by the F-actin cytoskeleton may be enhanced in the presence of TRPP2. Using the actin monomer-sequestering agent latrunculin A, we demonstrate that disruption of the F-actin cytoskeleton increased SAC activity in COS cells (Figures 5A and5B), thus confirming the tonic inhibitory influence of the actin cytoskeleton. A similar effect on SAC activity was observed when we used the barbed-end capping agent cytochalasin D at concentrations known to induce F-actin depolymerization through capping-induced growth arrest and actin dimer formation (Figure 5B). Remarkably, these treatments fully reversed SAC inhibition by TRPP2 in COS cells and restored channel activity (Figures 5A and5B). Latrunculin A similarly increased SAC activity in VSMCs from Pkd1SM +/+ and Pkd1SM del/del arteries and suppressed the difference between both genotypes (Figure 5C). However, microtubule-disrupting agents, including colchicine or nocodazole, did not modify SAC inhibition by TRPP2 (Figure 5B). Conversely, actin stabilization by jasplakinolide inhibited SAC activity and mimicked the effect of TRPP2 in COS cells (Figure 5B). These data demonstrate that the F-actin cytoskeleton is involved in SAC inhibition by TRPP2.

Filamin A Interacts with TRPP2 and Is Required for SAC Inhibition In a search for proteins that bind to TRPP2, we performed a proteomic screen in the mouse VSMC line MOVAS. Besides known TRPP2 partners, including TRPP1 and the InsP 3 receptor, we identified the actin crosslinking protein FLNa [START_REF] Glogauer | The role of actin-binding protein 280 in integrin-dependent mechanoprotection[END_REF][START_REF] Kainulainen | Cell death and mechanoprotection by filamin a in connective tissues after challenge by applied tensile forces[END_REF][START_REF] Stossel | Filamins as integrators of cell mechanics and signalling[END_REF]) (Table S1).

Immunoprecipitation in cotransfected cells confirmed this interaction (Figures 6A andS12). To determine whether the effect of TRPP2 on SAC activity is dependent on FLNa, we expressed TRPP2 in a human melanoma cell line (M2 or FLNa KO) that does not express FLNa and in M2 cells stably transfected with FLNa (A7 or FLNa WT) [START_REF] Cunningham | Actin-binding protein requirement for cortical stability and efficient locomotion[END_REF] (Figure S5). The single-channel conductance of SACs was similar in both cell lines (29.1 ± 0.8 pS for FLNa KO cells, n = 37, compared to 29.2 ± 0.8 pS for FLNa WT cells, n = 27; p = 0.888), but the net (E) Mean (±SEM) myogenic tone (MT) at 75 mm Hg in Pkd1 +/+ and Pkd1SM del/del arteries treated with siRNA-NT (n = 5 for Pkd1 +/+ and 10 for Pkd1SM del/del ) or siRNA-TRPP2-A (n = 4 for Pkd1 +/+ and 9 for Pkd1SM del/del ). **p < 0.01 and ***p < 0.001 represent significant difference from Pkd1SM del/del siRNA-TRPP2. channel activity was significantly higher in FLNa KO cells (Figures 6B-6D). Importantly, while the inhibition of SAC activity by TRPP2 was prominent in FLNa WT cells, it was absent in FLNa KO cells (Figure 6D). This indicates that FLNa is critically required for the inhibitory effect of TRPP2 on SACs.

DISCUSSION

Although polycystins have been associated with the mechanosensory function of flow sensing in both epithelial and endothelial cells [START_REF] Nauli | Polycystins 1 and 2 mediate mechanosensation in the primary cilium of kidney cells[END_REF][START_REF] Nauli | Endothelial cilia are fluid shear sensors that regulate calcium signaling and nitric oxide production through polycystin-1[END_REF], how they contribute to cellular mechanosensitivity still remains unknown. In this study, we demonstrate that SAC activity is strongly reduced in VSMC membranes and arterial myogenic tone is impaired when there is an overabundance of free TRPP2, either by decreasing TRPP1 or increasing TRPP2. The inhibitory effect is specific to TRPP2 based on the fact that expression of other TRP channel subunits, including TRPC1 and TRPC6, has no effect on SAC activity. Moreover, the activity of Kv and ASIC was unaffected by TRPP2 overexpression and there was no change in L-type Ca 2+ channel activity following inactivation of Pkd1, indicating that TRPP2 specifically regulates the activity of SACs.

Our results differ from the proposed role of the TRPP1/TRPP2 complex as a flow sensor in the primary cilium of renal epithelial and endothelial cells where inactivation of either TRPP1 or TRPP2 impairs flow sensing [START_REF] Nauli | Polycystins 1 and 2 mediate mechanosensation in the primary cilium of kidney cells[END_REF][START_REF] Nauli | Endothelial cilia are fluid shear sensors that regulate calcium signaling and nitric oxide production through polycystin-1[END_REF]. In VSMCs, it is the TRPP1/TRPP2 ratio that regulates the activity of native nonselective cation SACs. In the case of pressure sensing by arterial myocytes, TRPP2 inhibits mechanosensitivity and TRPP1 reverses this inhibition, while in the case of flow sensing by the primary cilium, both TRPP1 and TRPP2 promote mechanosensitivity [START_REF] Nauli | Polycystins 1 and 2 mediate mechanosensation in the primary cilium of kidney cells[END_REF][START_REF] Nauli | Endothelial cilia are fluid shear sensors that regulate calcium signaling and nitric oxide production through polycystin-1[END_REF].

We demonstrated that although TRPP1 and TRPP2 are known to function as a complex [START_REF] Hanaoka | Co-assembly of polycystin-1 and À2 produces unique cation-permeable currents[END_REF], TRPP2 can independently influence SAC activity, and that TRPP1 reverses this effect. Other independent functions for TRPP2 have been previously reported. For instance, in nodal cells, where TRPP1 is absent, TRPP2 has been implicated in the specification of the left-right axis [START_REF] Pennekamp | The ion channel polycystin-2 is required for left-right axis determination in mice[END_REF]. Antagonism between TRPP1 and TRPP2 has also been shown in rat sympathetic (A) Mean SAC currents (cell attached patch configuration) at a holding potential of À80 mV in COS-7 cells transfected with empty expression vector (mock) or mTRPP2-740X. Cells were incubated with the vehicle 0.1% DMSO (CTRL) (top panels; n = 89 and 80 for control and mTRPP2-740X, respectively) or with 3 mM latrunculin A (bottom panels; n = 38 and 37 for control and mTRPP2-740X, respectively) (LTRC) for 1 hr prior recordings. (B) Mean (±SEM) SAC currents in COS-7 cells expressing empty expression vector (mock shown in white) or mTRPP2-740X (shown in black). Cells were incubated with 0.1% DMSO (n = 89 and 80 for mock and mTRPP2-740X, respectively), 3 mM latrunculin A (LTRC) for 1 hr (n = 38 and 37 for mock and mTRPP2-740X, respectively), 1 mM cytochalasin D (cyto D) for 1 hr (n = 10 for both mock and mTRPP2-740X), 3 mM jasplakinolide (JASP) for 1 hr (n = 10 for both mock and mTRPP2-740X), 500 mM colchicine (COL) for 2 hr (n = 10 for both mock and mTRPP2-740X), or 10 mM nocodazole (NOC) for 1.5 hr (n = 10 for both mock and mTRPP2-740X). All values are given as relative to the maximum control current in the mock transfected cells (À80 mm Hg) in the cell-attached configuration at a holding potential of À80 mV. The difference between mock and mTRPP2-740X is only significant (p < 0.05) in the CTRL, COL, and NOC-treated conditions. (C) Mean (±SEM) pressure-dependent SAC activity (NPo) of Pkd1 +/+ (left panel) and Pkd1SM del/del (right panel) cells recorded in the cell attached configuration at a holding potential of À80 mV. Cells were incubated with 0.1% DMSO (open and closed circles; n = 83 and 45 for Pkd1 +/+ and Pkd1SM del/del , respectively) or with 3 mM latrunculin A (open and closed triangles; n = 68 and 47 for Pkd1 +/+ and Pkd1SM del/del , respectively) for 24 hr before recordings. Data are fitted with Boltzmann functions (P 0.5 = À32.1 ± 0.6; À31.0 ± 1.9; À36.7 ± 2.2; and À31.0 ± 1.2 mm Hg; k = 5.4 ± 0.5; 4.7 ± 1.8; 5.5 ± 1.9; and 7.2 ± 1.0; for Pkd1 +/+ DMSO treated, Pkd1 +/+ latrunculin treated, Pkd1SM del/del DMSO treated, and Pkd1SM del/del latrunculin treated, respectively). Dotted curve in the right panel shows the Boltzmann function for DMSO-treated Pkd1 +/+ cells. The difference was significant (p < 0.01) between Pkd1 +/+ and Pkd1SM del/del when DMSO treated, but not after latrunculin A treatment. neurons where the constitutive activation of G i/o proteins by TRPP1 can be reversed by TRPP2 [START_REF] Delmas | Constitutive activation of G-proteins by polycystin-1 is antagonized by polycystin-2[END_REF]. Therefore, independent function and antagonism between TRPP1 and TRPP2 are not unique to SAC regulation.

The inhibition of SAC mechanosensitivity by TRPP2 may be the result of several direct or indirect actions. One possible mechanism is that free TRPP2 interacts directly with endogenous SACs, thus acting as a dominant-negative subunit. Indeed, several TRP subunits, some of which were proposed to be involved in mechanotransduction, including TRPC1 and TRPV4, interact with TRPP2 [START_REF] Kottgen | TRPP2 and TRPV4 form a polymodal sensory channel complex[END_REF][START_REF] Tsiokas | Specific association of the gene product of PKD2 with the TRPC1 channel[END_REF]. However, none of those subunits had any effect on SAC activity in the present study.

Alternatively, TRPP2 may affect SAC activity by interacting with regulatory proteins, including elements of the cytoskeleton such as a-actinin, CD2AP, mDia1, tropomyosin-1, and troponin I (for reviews see [START_REF] Chen | Submembraneous microtubule cytoskeleton: interaction of TRPP2 with the cell cytoskeleton[END_REF][START_REF] Delmas | Polycystins: from mechanosensation to gene regulation[END_REF][START_REF] Harris | Polycystic kidney disease[END_REF][START_REF] Wilson | Polycystic kidney disease[END_REF]. Here, we show that the actin cytoskeleton is indeed implicated in SAC inhibition by TRPP2, as this effect was abolished by F-actin disruption. Moreover, the actin stabilizer jasplakinolide mimicked the effect of TRPP2 and inhibited SAC activity, as expected from a stiffening of the cytoskeleton (mechanoprotection). Additionally, we show that FLNa is a novel cytoskeletal element interacting with TRPP2. FLNa stiffens the cell cortex by virtue of its ability to crosslink adjacent actin filaments and increases actin polymerization/ gelation rates in vitro (for review see [START_REF] Stossel | Filamins as integrators of cell mechanics and signalling[END_REF]. Conse- (A) Coimmunoprecipitation experiment demonstrating the interaction between HA-tagged TRPP2 and myc-tagged FLNa in cotransfected COS cells. left lane, transfected with myc-tagged FLNa; right lane, transfected with myc-tagged FLNa and HA-tagged TRPP2. See Figure S12 for whole blots. (B) Mean SAC currents in M2 (FLNa KO) (n = 63) and A7 (n = 66) cells (FLNa WT) at a holding potential of À80 mV. (C) Mean (±SEM) NPo as a function of pressure in FLNa KO (white) and FLNa WT (black) cells. Expression of TRPP2 in FLNa KO (n = 48) and FLNa WT (n = 57) cells did not affect single SAC conductance (data not shown). Data are fitted with Boltzmann functions (P 0.5 = À25.0 ± 4.5 mmHg; À19.2 ± 5.6 mmHg; k = À16.6 ± 4.5; À16.4 ± 6.1, for FLNa KO and FLNA WT cells, respectively). (D) Mean (±SEM) SAC activity (NPo at À70 mmHg) of FLNa KO (white) and FLNa WT (black) cells transfected with mock (GFP) or TRPP2. ** represents significant differences between TRPP2and mock-transfected condition (p < 0.01); NS, not significant. quently, localized increases in FLNa may cause an accumulation of crosslinked F-actin in the cell cortex and reduce SAC activity [START_REF] D'addario | Cytoprotection against mechanical forces delivered through beta 1 integrins requires induction of filamin A[END_REF][START_REF] Kainulainen | Cell death and mechanoprotection by filamin a in connective tissues after challenge by applied tensile forces[END_REF]. Here, we demonstrate that the activity of nonselective SACs is reduced in the presence of FLNa and that the inhibitory effect of TRPP2 is abolished when FLNa is absent. However, we cannot exclude the possibility that TRPP2 could directly interact with the SAC subunits, acting as negative regulator, but in a FLNa/F-actin-dependent manner. According to this scheme, TRPP2 could also be considered as a SACs-FLNa linker.

Because the inhibitory effect of TRPP2 was present in the mutant TRPP2-742X, which lacks an ER retention signal and is thus more efficiently targeted to the plasma membrane, as well as in the WT TRPP2, which is mainly found in the ER [START_REF] Chen | Transport function of the naturally occurring pathogenic polycystin-2 mutant, R742X. Biochem[END_REF][START_REF] Mochizuki | PKD2, a gene for polycystic kidney disease that encodes an integral membrane protein[END_REF], the subcellular localization of TRPP2 (ER versus plasma membrane) does not appear to be essential for SAC inhibition (Figure S6).

Previous work on cloned SACs, including the bacterial channel MscL or the mammalian channel TREK-1, indicate that these channels are directly activated by membrane tension (the bilayer model) [START_REF] Honore | Desensitization of mechano-gated K2P channels[END_REF][START_REF] Kung | A possible unifying principle for mechanosensation[END_REF][START_REF] Sukharev | A large-conductance mechanosensitive channel in E. coli encoded by mscL alone[END_REF]. The cortical cytoskeleton divides the bilayer into submicrometer microdomains [START_REF] Hamill | Induced membrane hypo/hyper-mechanosensitivity: a limitation of patch-clamp recording[END_REF][START_REF] Sachs | Mechanosensitive ion channels in nonspecialized cells[END_REF] whose radius of curvature is much smaller than the radii of the patch or the cell (Figure 7). According to Laplace's law, T = Pr/2 (where T is tension, P pressure, and r radius of patch curvature), tension in the bilayer decreases with the radius of curvature [START_REF] Hamill | Induced membrane hypo/hyper-mechanosensitivity: a limitation of patch-clamp recording[END_REF][START_REF] Sachs | Mechanosensitive ion channels in nonspecialized cells[END_REF]. Disruption of the cytoskeleton either chemically (latrunculin A) or mechanically results in an increase in the radius of curvature of microdomains, leading to an increase in tension and SAC activity for a given transmembrane pressure [START_REF] Hamill | Induced membrane hypo/hyper-mechanosensitivity: a limitation of patch-clamp recording[END_REF][START_REF] Sachs | Mechanosensitive ion channels in nonspecialized cells[END_REF][START_REF] Wan | Activation of mechanosensitive currents in traumatized membrane[END_REF]. This mechanism explains the increase in channel mechanosensitivity after cytoskeleton disruption [START_REF] Hamill | Induced membrane hypo/hyper-mechanosensitivity: a limitation of patch-clamp recording[END_REF][START_REF] Wan | Activation of mechanosensitive currents in traumatized membrane[END_REF]. The data show that TRPP2 has the opposite effect, inhibiting SAC function via FLNa that reinforces F-actin (Figure 7).

This study is the first direct demonstration for the role of polycystins in pressure sensing. The TRPP1/TRPP2 ratio controls SAC mechanosensitivity through FLNa coupled to the actin cytoskeleton and affects the conversion of intraluminal pressure to local bilayer tension. Our findings reveal a novel molecular mechanism explaining how polycystins regulate cellular mechanosensitivity. This study is not only relevant to ADPKD but may also be of importance for a better understanding of other physiological or disease states associated with pressure sensing.

EXPERIMENTAL PROCEDURES

Dissociation of Vascular Smooth Muscle Cells

Male 12-16 weeks old mice of all three genotypes (Pkd1 +/+ , Pkd1SM del/+ , and Pkd1SM del/del ) were used. Mice were anesthetized using isoflurane and their gut was excised. A third-order mesenteric artery (<200 mm internal diameter, 3 to 5 mm length) was dissected and incubated (20 min at 37 C) in a solution With a given pressure (P) drop across the patch, the tension (T) in the microdomain bilayer ($100 nm scale) is a function of their radius (r) of curvature rather than that of the mean curvature of the patch. TRPP2 is represented at the plasma membrane and in the membrane of the ER. The cytoskeleton lattice mechanically reinforces the bilayer-protecting channels from excess force. TRPP2 binding to FLNa increases actin crosslinking and makes the microdomains smaller, so that according to of Laplace's Law (T = Pr/2) there will be less tension for a given pressure. When TRPP2 or FLNa is absent, when TRPP1 is coexpressed with TRPP2, or when the actin cytoskeleton is disrupted with latrunculin A or cytochalasin D, the microdomain radius of curvature increases, resulting in a stronger activation of SACs for a given pressure. of papain (1 mg/ml) and DTT (1 mg/ml), followed by a second incubation (5 min at 37 C) in a solution of collagenase F (0.7 mg/ml) and collagenase H (0.3 mg/ml). The papain/DTT and collagenase mixture was dissolved in DCML solution, composed of 140 mM NaCl , 5.6 mM KCl, 2 mM MgCl 2 , 10 mM HEPES, 10 mM glucose, 0.1 mM CaCl 2 , and 1 mg/ml bovine serum albumin. Arteries were rinsed three times in DCML solution and triturated. Cell suspensions were placed on 35 mm poly-D-lysine-coated dishes and kept at 4 C for at least 45 min before recording. Recordings were only performed on elongated cells.

Electrophysiology

Electrophysiological procedure has been previously described elsewhere [START_REF] Chemin | A phospholipid sensor controls mechanogating of the K + channel TREK-1[END_REF]. Single channel cell-attached recordings were performed on acutely dissociated VSMCs at a holding potential (V hold ) of À80 mV. Unless stated otherwise, pipette solution contained 140 mM NaCl, 5 mM KCl, 1 mM CaCl 2 , 1 mM MgCl 2 , 10 mM TEA-Cl, 5 mM 4-AP, and 10 mM HEPES (pH 7.35). Bath solution contained 140 mM KCl, 1 mM MgCl 2 , 10 mM HEPES, and 10 mM glucose (pH 7.25). The osmolality of all solutions was adjusted to about 320 mOsm. Membrane patches were stimulated with brief negative pressure pulses, from 0 to À100 mm Hg in steps of À10 mm Hg, through the recording electrode using a pressure-clamp device (ALA High Speed Pressure Clamp-1 system; ALA-scientific). In whole-cell recordings, for recording L-type Ca 2+ channel currents, pipette solution contained 110 mM CsCl, 20 mM TEA-Cl, 3 mM Na 2 ATP, 3.5 mM MgCl 2 , 10 mM EGTA, 10 mM HEPES (pH 7.25) (300 mOsm). Bath solution contained 135 mM NaCl, 5 mM KCl, 10 mM BaCl 2 , 1 mM MgCl 2 , 10 mM HEPES, 10 mM glucose, 0.1 mM tetrodotoxin (pH 7.35). Cell capacitance was noted for each recording, and no difference was observed between Pkd1 +/+ (28.0 ± 1.7 pF; n = 22) and Pkd1SM del/del (25.5 ± 1.6 pF; n = 15; p = 0.23) cells. VSMCs were stimulated from a holding voltage of À60 mV with brief (0.4 s) voltage steps from À90 to +60 mV in 10 mV increments. After a protocol with normal external solution, a second one was performed in the presence of 2 mM nifedipine. Subtracted current traces (control -nifedipine) are shown in Figure 2.

For experiments with cultured cells, the pipette medium contained 150 mM NaCl, 5 mM KCl, 1 mM CaCl 2 , and 10 mM HEPES (pH 7.4 with NaOH). The bath medium contained 155 mM KCl, 5 mM EGTA, 3 mM MgCl 2 , and 10 mM HEPES 10 (pH 7.2 with KOH). The osmolality of all solutions was adjusted to about 320 mOsm. For ion-selectivity experiments, the external NaCl was replaced by either sodium gluconate or choline chloride. In whole-cell recordings, F11 cells were superfused with normal bath NaCl solution, with bath solution at pH 5.5 (for the ASIC channels), or with bath solution containing a cocktail of K + channel blockers (10 mM TEA, 5 mM 4-AP, and 10 mM glibenclamide). V hold for all experiments was À80 mV. For K + channel activity the cells were stimulated with a voltage ramp (À100 to +100 mV; 1 s). Current density was determined by dividing the subtracted current [stimulated current (pH 5.5) -baseline current (pH 7.4)] by the cell capacitance, for the ASIC currents. Current density was determined by dividing the subtracted current [current at +80 mV (no blockers) -current at +80 mV (in the presence of K + channel blockers)] by the cell capacitance, for the K + currents.

Cytoskeleton-disrupting agents (Sigma-Aldrich) were used as described in the figure legends. Actin and tubulin polymerization was visualized by immunofluorescence microscopy.

Isobaric Arteriography

Male mice aged between 12 and 16 weeks of all genotypes were used. Mice were anesthetized using isoflurane and their was gut excised. A third-order mesenteric artery (<200 mm internal diameter, 3 to 5 mm long) was dissected, cannulated at both ends, and mounted in a video-monitored perfusion system as previously described [START_REF] Halpern | In vitro methodology for resistance arteries[END_REF]. The arterial segment was bathed in a physiological solution (PSS) composed of 119 mM NaCl 119, 4.7 mM KCl, 2.5 mM CaCl 2 , 1.17 mM MgSO 4 , 25 mM NaHCO 3 , 1.18 mM KH 2 4 , 0.027 mM EDTA, and 5.5 mM glucose, oxygenated with a mixture of CO 2 (5%) and O 2 (20%) in N 2 . The artery was pressurized at 75 mm Hg and its diameter and wall thickness were measured and recorded continuously using a video monitoring system (Living System Instrumentation Inc.). Data were collected using the Spike2 software (CED) and analyzed offline. Vessels were first allowed to stabilize for at least 30 min and were then stimulated with 80 mM KCl to verify vessel viability. Next, endothelium integrity was assessed by testing the vasodilator effect of acetylcholine (1 mM) after preconstriction with phenylephrine (1 mM). Then, a pressure diameter curve (active curve) was established by increasing intraluminal pressure from 10 to 125 mm Hg in a stepwise fashion (10, 25, 50, 75, 100, and 125). Pressure was then brought back to 75 mm Hg and the PSS solution was changed to one without CaCl 2 and with EGTA (5 mM), sodium nitroprusside (50 mM), and papaverine (50 mM). After a 15 min incubation in the Ca-free PSS, a pressure-diameter curve (passive curve) was performed. The KCl and phenylephrine responses were calculated using the following formula: (D À D 75P )/D 75P ; where D is the amplitude of contraction and D 75P is the lumen diameter under passive conditions at 75 mm Hg. Percentage of myogenic tone at each pressure was calculated using the following formula: [(D pass À D act )/D pass ] 3 100, where D pass is the diameter under passive conditions and D act under active conditions. Stress-strain curves, an index of arterial elasticity, were established using the following formulas. Stress: [(0.00133 3 P x 3 D Px )/(W L + W R )]; where P x is the pressure (mm Hg), D Px the passive diameter at pressure x, and W L and W R are the thickness of the left and right walls, respectively (in mm). Strain: [(D Px À D P10 )/D P10 ], where D P10 is the diameter at 10 mm Hg.

Cell culture, transfection, generation, and breeding of smooth musclespecific Pkd1 knockout mice, arterial pressure measurements, molecular biology, siRNA transfection, real-time quantitative PCR, protein analysis, immunohistochemistry, proteomic screen, and immunoprecipitation methods are described in Supplemental Experimental Procedures.

SUPPLEMENTAL DATA

Supplemental Data contain Supplemental Experimental Procedures, 12 figures, and one table and can be found with this article online at http:// www.cell.com/supplemental/S0092-8674(09)01125-8.

Figure 1 .

 1 Figure 1. SAC Inhibition by TRPP2 (A) Single-channel currents recorded in the cell-attached patch configuration and induced by increasing negative pipette pressure (0, À10, and À30 mm Hg) in a mock-transfected COS-7 cell at a holding potential of À80 mV. SAC activity was significantly inhibited (À67%, n = 18) by the cup-former cationic amphipath chlorpromazine (50 mM; not shown). (B) Mean (± standard error of the mean [SEM]) i-V relationship of single SACs from mock-transfected COS-7 cells stimulated with a negative (À30 mm Hg) pressure pulse in the inside out configuration (n = 6). (C) Mean SAC currents in COS-7 cells transfected with mock, hTRPP1, hTRPP2, and hTRPP1 + hTRPP2 plasmids at a holding potential of À80 mV in the cellattached patch configuration. (D) Mean (±SEM) NPo as a function of pressure in cells transfected with the control, hTRPP1, and hTRPP2. Data are fitted with Boltzmann functions (P 0.5 = À21.7 ± 2.4; À16.1 ± 2.0; and À24.6 ± 5.6 mm Hg; k = 6.1 ± 0.8; 4.3 ± 0.6; and 8.2 ± 1.8; for control, hTRPP1 and hTRPP2 transfected cells, respectively). (Inset) Dosedependent SAC inhibition by TRPP2 (n = 31, 10, 20, and 30). (E) Relative mean (±SEM) NPo at À60 mm Hg for control, hTRPP1, hTRPP2 (or hTRPP2-742X), and hTRPP1 and hTRPP2 (or hTRPP2-742X) cotransfected. n = 40,40, 30, 40, 20, and 22, for control, hTRPP1, hTRPP2, hTRPP1+hTRPP2, respectively.

Figure 2 .

 2 Figure 2. Pkd1 Inactivation Reduces SAC Activity in Vascular Smooth Muscle Cells

  (D) Mean (±SEM) i-V relationships of single SACs from Pkd1 +/+ (white circles; n = 11), Pkd1SM del/+ (gray circles; n = 18), and Pkd1SM del/del (black circles; n = 8) VSMCs at À50 mm Hg. (E) Mean percentage (±SEM) of active patches at À100 mm Hg for Pkd1 +/+ (white bars), Pkd1SM del/+ (gray bars), and Pkd1SM del/del (black bars) cells. (F) Mean (±SEM) nifedipine (2 mM)-sensitive barium currents recorded in the whole-cell configuration in Pkd1 +/+ (n = 17) and Pkd1SM del/del (n = 15) VSMCs. (Inset) Nifedipine-sensitive barium currents in a Pkd1 +/+ VSMC stimulated from À60 mV to À50, À10, and 10 mV.

Figure 3 .

 3 Figure 3. Pkd1 Inactivation Impairs the Mesenteric Artery Myogenic Response (A) Changes in lumen diameter in Pkd1 +/+ (top panel) and Pkd1SM del/del (lower panel) arteries as a function of pressure. Black traces represent the active curve in the presence of 2.5 mM CaCl 2 and red traces represent the passive curve (distensibility) in the absence of CaCl 2 and the presence of sodium nitroprusside, papaverine, and EGTA. (B) Mean (±SEM) myogenic tone (percentage of passive diameter) in Pkd1 +/+ (white symbols), Pkd1SM del/+ (gray symbols), and Pkd1SM del/del (black symbols) arteries. Data are fitted with Boltzmann functions (P 0.5 = 78.5 ± 3.7; 94.2 ± 8.0; and 101.4 ± 8.5 mm Hg; k = 14.2 ± 2.5; 11.5 ± 2.7; 7.0 ± 1.7; for Pkd1 +/+ , Pkd1SM del/+ , and Pkd1SM del/del arteries, respectively). (C) Mean (±SEM) KCl-and phenylephrine-induced contractions in the three genotypes at 75 mm Hg.

Figure 4 .

 4 Figure 4. Knocking down TRPP2 Rescues SAC Activity and Myogenic Tone in the Pkd1 Knockout Mesenteric Arteries (A) Mean SAC currents in VSMCs treated with siRNA-NT (n = 83 for Pkd1 +/+ and 86 for Pkd1SM del/del cells) or siRNA-TRPP2-A (n = 47) at a holding potential of À80 mV. (B) Mean (±SEM) SAC activity (NPo at À60 mm Hg) of VSMC from Pkd1 +/+ (white bars) and Pkd1SM del/del (black bars) arteries treated with siRNA-NT (n = 83 for Pkd1 +/+ and 93 for Pkd1SM del/del cells) or siRNA-TRPP2-A (n = 44 for Pkd1 +/+ and 49 for Pkd1SM del/del cells). *p < 0.05, represents significant difference from Pkd1 +/+ siRNA-NT. Similar results were obtained with a second siRNA against TRPP2 (siRNA-TRPP2-B; not shown). (C) Mean (±SEM) percentage of active patches in Pkd1 +/+ and Pkd1SM del/del cells transfected with either NT or TRPP2-A siRNAs. Same n values as in (B). Asterisk represents significant difference from Pkd1 +/+ siRNA-NT. (D) Mean (±SEM) myogenic tone (MT) in Pkd1SM del/del arteries treated with the siRNA-NT (n = 9) or the siRNA-TRPP2-A (n = 7) as a function of intraluminal pressure. (E) Mean (±SEM) myogenic tone (MT) at 75 mm Hg in Pkd1 +/+ and Pkd1SM del/del arteries treated with siRNA-NT (n = 5 for Pkd1 +/+ and 10 for Pkd1SM del/del ) or siRNA-TRPP2-A (n = 4 for Pkd1 +/+ and 9 for Pkd1SM del/del ). **p < 0.01 and ***p < 0.001 represent significant difference from Pkd1SM del/del siRNA-TRPP2.

Figure 5 .

 5 Figure 5. Disruption of the Actin Cytoskeleton Reverses SAC Inhibition by TRPP2

Figure 6 .

 6 Figure 6. TRPP2 Inhibition of SAC Activity Requires FLNa

Figure 7 .

 7 Figure 7. Cartoon Illustrating the Bilayer Convoluted by the Anchoring Cytoskeleton-''The Upholstery Model''

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ACKNOWLEDGMENTS

We are grateful to the ANR 2005 cardiovasculaire-obe ´site ´-diabe `te, to the ANR 2008 du ge `ne a `la physiopathologie, to the Association for information and research on genetic kidney disease France, to the Fondation del Duca, to the Fondation de la recherche me ´dicale, to the Fondation de France, to EEC Marie-Curie fellowship 039328 (J.H.A.F.), to the Fondation de recherche sur l'hypertension arte ´rielle, to the Fondation de la recherche sur le cerveau, to the Human Frontier Science Program long-term fellowship LT-00555 (R.S.N.), to the Socie ´te ´Ge ´ne ´rale AM, to the Universite ´de Nice Sophia Antip-olis, and to the Centre National de la Recherche Scientifique for financial support. We are grateful to T.P. Stossel for providing the M2 and A7 cell lines and FLNa constructs and to L. Tsokias for providing the human and mouse Pkd2 clones. We are grateful to G. Romey and J. Barhanin for valuable discussions.


Sihem Amer-Yahia

Georgia Koutrika

Martin Braschler

Diego Calvanese

Davide Lanti

Hendrik Lücke-Tieke

Alessandro Mosca

Tarcisio Mendes De Farias

Dimitris Papadopoulos

Yogendra Patil

Guillem Rull

Ellery Smith

Dimitrios Skoutas

Srividya Subramanian

Kurt Stockinger

INODE: Building an End-to-End Data Exploration System in Practice Authors' Copy

A full-fledged data exploration system must combine different access modalities with a powerful concept of guiding the user in the exploration process, by being reactive and anticipative both for data discovery and for data linking. Such systems are a real opportunity for our community to cater to users with different domain and data science expertise.

We introduce INODE -an end-to-end data exploration systemthat leverages, on the one hand, Machine Learning and, on the other hand, semantics for the purpose of Data Management (DM). Our vision is to develop a classic unified, comprehensive platform that provides extensive access to open datasets, and we demonstrate it in three significant use cases in the fields of Cancer Biomarker Research, Research and Innovation Policy Making, and Astrophysics. INODE offers sustainable services in (a) data modeling and linking, (b) integrated query processing using natural language, (c) guidance, and (d) data exploration through visualization, thus facilitating the user in discovering new insights. We demonstrate that our system is uniquely accessible to a wide range of users from larger scientific communities to the public. Finally, we briefly illustrate how this work paves the way for new research opportunities in DM.

INTRODUCTION

The Data Management (DM) community has been actively catering to Machine Learning (ML) research by developing systems and algorithms that enable data preparation and flexible model learning. This has resulted in several major contributions in developing ML pipelines, and formalizing algebras and languages to facilitate and debug model learning, as well as designing and implementing algorithms and systems to speed up ML routines [START_REF] Sparks | KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics[END_REF]. Conversely, existing work that leverages ML for DM [START_REF] Stoica | Systems and ML: When the Sum is Greater than Its Parts[END_REF] is nascent and covers the use of ML for query optimization [START_REF] Kristo | The Case for a Learned Sorting Algorithm[END_REF] or for database indexing [START_REF] Kraska | The Case for Learned Index Structures[END_REF]. This paper makes the case for democratizing Intelligent Data Exploration by leveraging ML for DM.

Traditionally, database systems assume the user has a specific query in mind, and can express it in the language the system understands (e.g., SQL). However, today, users with different technical backgrounds, roles, and tasks are accessing and leveraging voluminous and complex data sources. In many scenarios, they are only partially familiar with the data and its structure, and their user information needs are not well-formed. In such settings, expanding traditional query answering to data exploration is a natural consequence and requirement and with it comes the need to redesign systems accordingly. This need translates to several challenges at different levels.

(Interaction). Regarding interaction with the system, the biggest challenge is to enable the user to express her needs through a variety of access modalities, ranging from SQL and SPARQL to natural language (NL) and visual query interfaces, that can be used and intermingled depending on the user needs and expertise as well as the data exploration scenario. The second challenge is that of user guidance, i.e., users should be allowed to provide feedback to the system, and the system should leverage that feedback to improve subsequent exploration steps.

(Linking). Once a user need has been formulated and sent to the system, a search is executed over a (fixed) data set. Users may be aware which additional data sets could be of interest. However, they do not always know how to correctly link, integrate, and query more than one data source to generate rich information. This introduces the challenges of data linking, so that new data sources can be added to the system, as well as knowledge generation, so that queries over unstructured data can be supported. Both of these aim at enabling the continuous expansion of the "pool" of available data sources, thus making more data available to users.

(Guidance). Traditionally, the system will return to the user a set of tuples that concludes the search. There is a lot of work on how to improve performance for query workloads (predict future queries, build indices adaptively, etc.), but still the system has a rather passive role: anticipating or at best trying to predict the next query and then optimize its performance accordingly. Hence, the challenge of system proactiveness arises. The output is not only the set of results but also recommendations for subsequent queries or exploration choices. In our vision, the system guides the user to find interesting, relevant or unexpected data and actively participates in shaping the query workload.

In a nutshell, a full-fledged data exploration system must combine different access modalities with a powerful concept of guiding the user in the exploration process. It must be reactive and anticipative; co-shaping with the user the data exploration process. Finally, while data integration has been around for a while, the ability to Step 2: System parses query and matches keywords against the available ontology to enable term disambiguation; the user iterates the process.

Step 3: System visualizes various cancer types that are similar to lung cancer. The distance metric between the diseases can be chosen by the user, e.g. by semantic distance.

tie together data discovery and linking is a central question in an intelligent data exploration system.

(Evaluation). An essential part of our proposal is the development of an evaluation framework to enable the end-to-end assessment of an intelligent data exploration system. This requires to formalize system metrics and human metrics that are necessary for data linking and integration, multi-modal data access, guidance, and visualization.

Related Work. Several systems address components of our vision. A number of them address interaction by enabling NL-to-SQL [START_REF] Blunschi | Soda: Generating Sql for business users[END_REF], SQL-to-NL [START_REF] Kokkalis | Logos: a system for translating queries into narratives[END_REF] or both [START_REF] Jeffrey | Ava: From Data to Insights Through Conversations[END_REF] (see a summary in [START_REF] Affolter | A comparative survey of recent natural language interfaces for databases[END_REF]). Recommendation strategies can be leveraged to guide users [START_REF] Liu | Improvement of SQL Recommendation on Scientific Database[END_REF]. Work on interactive data exploration aims at helping the user discover interesting data patterns based on an integration of classification algorithms and data management optimization techniques [START_REF] Dimitriadou | AIDE: An Active Learning-Based Approach for Interactive Data Exploration[END_REF]. Each of the above-mentioned systems tackles specific data management challenges as so-called insular solutions. However, these insular solutions have not been integrated to tackle the end-to-end aspect of intelligent data exploration targeted at a wide range of different end users.

Combining all the challenges above requires an elaborated system whose multi-aspect behavior and functionality is the result of a synergy between disjoint technologies, and integrates them into a new ensemble. This gives rise to multiple approaches that vary in computational complexity, and raises new challenges that can benefit from recent advances in ML.

In summary, this paper makes the following contributions: One-size-does-not-fit-all when building a full-fledged data exploration system. For instance, the exploration operators are not all the same across different domains since exploring health data requires different semantics than exploring galaxies. Our aim is to encapsulate that semantics in higher level constructs, e.g. exploration by example, by natural language and by recommendation. Similarly, our aim is to build the components necessary for a full fledged system. We illustrate the need for intelligent data exploration with relevant use cases (Section 2). We describe INODE1 , a system that we are currently building as part of a project funded by the European commission (Section 3). To fully complete our vision, we provide open research challenges to be addressed at the intersection of DM and ML (Section 4).

USE CASES

In this section, we describe two of our three use cases -cancer research and astrophysics -and show how INODE can tackle them. The system is targeted for domain scientists as well as the general public.

Use Case 1: Cancer Research (Natural Language and Visual Data Exploration). Fred is a biologist who studies cancer. His goal is to find which specific biomarkers are indicators for a certain type of lung cancer. He needs natural language exploration. INODE offers support for NL queries, query recommendations, and interactive visualizations triggered by NL queries (see Figure 1). For instance, Fred starts with a request in NL for the topics related to lung cancer but is not sure how to continue after inspecting the results. INODE steps up and recommends different options: to expand the search using experimental drugs for treating lung cancer, or to focus on a subset of lung cancer types associated with a certain gene expression. Fred chooses to expand his search to one of the recommended topics, and receives a new list of lung cancers, drugs and genes. Additionally, INODE explains in NL how results are related. That helps him in selecting experimental drugs for certain gene expressions. After a few such queries, the system visually analyzes the results for Fred to study. Fred learns about the similarity between different types of cancer based on distance metrics that he can choose. In order to enable such data exploration, several different databases need to be integrated and potentially be correlated with findings from research papers.

Use Case 2: Astrophysics (Exploration with SQL-Pipelines). In the era of big data, astronomers need to analyze dozens of databases at a time. With the ever increasing number of publicly available astronomical databases from various astronomical surveys across the globe, it is becoming increasingly challenging for scientists to penetrate deep into the data structure and their metadata in order to generate new scientific knowledge. Sri, an astrophysicist, explores astronomical objects in SDSS, a large sky survey database 2 . Sri would like to examine Green Pea galaxies, first discovered in a citizen science project called 'Galaxy zoo', that recently gained attention in astronomy as one of the potential sources that drove cosmic reionization.

Figure 2 shows a sequence of three consecutive processes of analyzing astrophysics data. Sri relies on selected examples at each step and requests to see comparable ones. In the first query, she asks to find galaxies with similar colors as Green Pea galaxies. She then requests objects with similar spectral properties, like emission line measurements, star formation rates etc., as those returned in the first step. The last query finds similar galaxies in terms of their relative ratios and strength of emission lines. As a result, Sri discovers that green pea emission line ratios are similar to high redshift galaxies.

INODE guides any user in making such new discoveries in an intuitive simpler way, without having to write complicated SQL queries or perform manual analysis of thousands of galaxies. For instance, INODE helps a user choose among similarity dimensions rather than rely on her ability to provide them. Additionally, INODE shows to the user alternative queries to pay attention to, thus increasing the chances of making new discoveries.

Crucially, INODE can be extended with additional resources which requires close interaction with domain scientists. Detailed user guides are in preparation.

CURRENT INODE ARCHITECTURE

The main novelty of INODE is bringing together different data management solutions to enable intelligent data exploration (see Figure 3). Although some of these solutions and research challenges have been tackled previously, they have not been combined into such an end-to-end intelligent data exploration system, which in turn opens up new research challenges.

INODE's major components are as follows: (1) Data Modeling and Linking enables integration of both structured and unstructured data. (2) Integrated Query Processing enables efficient query processing across federated databases leveraging ontologies. (3) Data 2 https://www.sdss.org/ Access and Exploration enables guided data exploration in various modalities such as by natural language, by recommendation, by example or visually. We refer to these as operators. 3 Even though INODE is an integrated system, each of the components can also be used independently. A detailed description of each component is given in the component release 4 . The system is targeted for domain scientists as well as the general public.

Data Modeling and Linking

This component links loosely coupled collections of data sources such as relational databases, graph databases or text documents based on the well-established ontology-based data access (OBDA) paradigm [START_REF] Xiao | Ontology-Based Data Access: A Survey[END_REF]. OBDA uses a global ontology (knowledge graph) to model the domain of interest and provides a conceptual representation of the information in the data sources. The sources are linked to elements in the global ontology through declarative GAV mappings [START_REF] Lenzerini | Data Integration: A Theoretical Perspective[END_REF]. It is well-known that designing OBDA mappings manually is a time-consuming and error-prone task. The Data Modeling and Linking component of INODE aims at automatizing this task by providing two mechanisms: data-driven and task-driven mapping generation.

Data-driven Mapping Generation. This mechanism deals with linking novel data sources to the system. The idea is to rely on mapping patterns that describe well-assessed and sound schematransformation rules usually applied in the design process of relational databases. By analyzing (driven by the patterns) the data sources, it is possible to automatically derive a so-called putative ontology [START_REF] Sequeda | Ultrawrap Mapper: A Semi-Automatic Relational Database to RDF (RDB2RDF) Mapping Tool[END_REF] describing both the explicit entities and relationships constituting the schema and the implicit ones inferrable from the data. From the mapping patterns, one can also automatically derive mappings that link the data sources to the putative ontology.

Task-driven Mapping Generation. This mechanism is applied whenever a task or a query is formulated that uses specific target ontology elements that are not yet aligned with the putative ontology. In such scenario, the semantics of the query are used to automatically generate mappings to align the target ontology with the putative ontology.

Knowledge Graph (KG) Generation. This service transforms unstructured information hidden in large quantities of text (e.g. repositories of scientific papers) to an exploitable structured representation through an NLP pipeline. INODE follows an Open Information Extraction (OIE) approach to convert each sentence of the corpus into a set of relational triples, where each triple consists of a subject, an object, and a predicate (relationship) linking them. We leverage a number of preprocessing techniques, including co-reference resolution and extractive summarization to improve the quality of the extracted relational triples. We combine different OIE methods (rule-based, analytics-based and learning-based) to achieve both high precision and high recall [START_REF] Papadopoulos | A Methodology for Open Information Extraction and Representation from Large Scientific Corpora: The CORD-19 Data Exploration Use Case[END_REF]. The relational triples are further linked with domain-specific ontology concepts before being integrated into the knowledge graph. Note that all tasks of the Data Modelling and Linking component are executed offline and hence do not require interactivity.

Integrated Query Processing

This component is responsible for the execution of queries using Ontop [START_REF] Xiao | The Virtual Knowledge Graph System Ontop[END_REF], a the state-of-the-art OBDA system. Ontop allows the users to formulate queries in terms of concepts and properties of their domain of expertise (represented in knowledge graphs), rather than in terms of table and attribute names used in the actual data sources. Hence, users do not have to be aware of the specific storage details of the underlying data sources in order to satisfy their information needs.

Query Execution. This service provides on-the-fly reformulation of SPARQL queries over the domain ontology to SQL queries over the data sources. An approach based on reformulation has the advantage that the data available in the data sources does not need to be duplicated in the query processing system, but can be kept in the data sources as-is. This means that the Query Execution service is guaranteed even in the common scenario where the user does not own the data nor does have the right to copy them. To produce reformulations that can efficiently be executed over the data, in INODE we use optimization techniques such as self-join elimination for denormalized data [START_REF] Xiao | The Virtual Knowledge Graph System Ontop[END_REF] and optimizations of left-joins [START_REF] Xiao | Efficient Handling of SPARQL Optional for OBDA[END_REF].

Source Federation. The Source Federation service deals with distributing the processing of queries over the available data sources. INODE provides seamless federation over the SQL data sources.

In seamless federation, users send queries against a unified view of the remote endpoints without the need to be aware of the actual vocabularies used in the federated endpoints. The challenge is to automatically detect to which sources which components of the query need to be dispatched, to collect the retrieved results, and to combine them into a coherent answer. We address this challenge by relying on the knowledge about the sources encoded in the OBDA mappings. Note that in a seamless setting, the end-user interacts with the endpoint as usual, and remains unaware of whether the system will perform a federated query to retrieve the answers. Given that efficiency is a crucial requirement, in this, mostly interactive setting, our approach requires a dedicated cost-model able to minimize the number of distributed joins over the federation layer, in order to favor more efficient joins at the level of the sources.

Data Analytics. The data analytics service exploits novel and efficient query reformulation and optimization techniques [START_REF] Xiao | The Virtual Knowledge Graph System Ontop[END_REF] to compute complex analytical functions. Such techniques are based on algebraic transformations of the SPARQL algebra tree, rather than on Datalog transformations as traditionally done in the OBDA literature. This shift of paradigm allows for an efficient implementation of analytical functions such as SPARQL aggregates. It is worth noting that INODE, through Ontop, provides the first open-source reformulation-based system able to support SPARQL aggregates.

Data Access and Exploration Operators

We describe the set of operators currently available individually within INODE. Exploration by Natural Language. For translating a natural language question into SQL or SPARQL, INODE uses pattern-based, graphbased and neural network-based approaches. For translating from NL to SQL, INODE extends the pattern-based system SODA [START_REF] Blunschi | Soda: Generating Sql for business users[END_REF] with NLP techniques such as lemmatization, stemming and POS tagging to allow both key word search queries as well as full natural language questions. In addition, we use Bio-SODA [START_REF] Claudia | Enabling semantic queries across federated bioinformatics databases[END_REF], a graphbased system to enable NL questions over RDF graph databases.

Finally, INODE integrates the neural network-based approach ValueNet, which leverages transformer architectures to translate NL to SQL [START_REF] Brunner | ValueNet: A Natural Language-to-SQL System that Learns from Database Information[END_REF]. The ultimate goal of INODE is to combine all these techniques into an intelligent hybrid approach that improves on the errors of each of the individual systems.

Exploration by Explanation. One of the biggest hurdles in today's exploration systems is that the system provides no explanations of the results or system choices. Nor does the system trigger input from the user, for example, by asking the user to provide more information. In INODE, we enable a conversational setting, where the system can (a) ask for clarifications and (b) explain results in natural language. This interaction assumes that the system is capable of analyzing and understanding user requests and generating its answers or questions in natural language.

One approach used in INODE builds on Template-based Synthesis [START_REF] Kokkalis | Logos: a system for translating queries into narratives[END_REF]. This approach considers the database schema as a graph and a query as a subgraph. We use templates that tell us how to compose sentences as we traverse the graph and we use different traversal strategies that generate query descriptions as phrases in natural language. Furthermore, to generate NL descriptions that use the vocabulary of a particular database, INODE enriches its vocabulary by leveraging ontologies built by the Data Modeling and Linking components. To further improve INODE's explanation capabilities, we are working on an approach to automatically learn templates, which is especially critical for databases with no descriptive metadata, such as SDSS. Essentially, we are using neural-based methods to translate from SQL or SPARQL to natural language.

Exploration by Example and by Analytics. By example is a powerful operator that encapsulates multiple semantics. It takes a set of examples, such as galaxies or patients, and explores its different facets, filters them, finds similar/dissimilar sets, finds overlapping sets, joins them with other sets, finds a superset, etc. Additionally, by-example operators can be combined with by-analytics to find sets that are similar/dissimilar wrt some value distributions.

By-example and by-analytics operators can be represented in the Region Connection Calculus (RCC) [START_REF] Li | Region Connection Calculus: Its models and composition table[END_REF] and are, in their general form, computationally challenging. For instance, by-subset is akin to solving a set cover problem, which has been extensively studied [START_REF] Cormode | Set cover algorithms for very large datasets[END_REF]. Similarly, by-join requires to have appropriate indices. In INODE, we adopt two approaches. One is based on a relational backend in which individual operators are translated into SQL. The other one is an in-memory Python implementation that relies on pre-computing and indexing sets.

Exploration by Recommendation. In a mixed-initiative setting, the system actively guides the user in what possible actions to perform or data to look at next. In INODE, we are interested in recommendations in both cold-start (where the user has not given any input) and warm-start settings (where the user has asked one or more queries but may not know what to do next). In the former case, the goal is to show a set of example or starter queries that the users could use to get some initial answers from the dataset (e.g. [START_REF] Howe | Automatic example queries for ad hoc databases[END_REF]). In the latter case, the system can leverage the user's interactions (queries) to show possible next queries (e.g., [START_REF] Le Guilly | SQL Query Completion for Data Exploration[END_REF]).

A big differentiator is the availability of query logs. In case no query logs are available, the system should still provide recommendations. In INODE we are addressing the recommendation problem from different angles, i.e., generating recommendations: (a) based on data analysis [START_REF] Glenis | PyExplore: Clustering-based SQL query recommendations[END_REF] (b) by NL-based processing and query augmentation techniques leveraging knowledge bases (c) by user log analysis.

Exploration by Visualization. In information retrieval, search queries result in a list of candidates ranked by their matching score [START_REF] Christopher D Manning | Introduction to Information Retrieval[END_REF]. This also holds true for INODE, as most exploration operators generate multiple potential answers. However, results are not individual items such as documents, but data sets (i.e. sets of items) and have to be communicated to the user differently to support their goals. Not only do users have to decide, which data set contains the answer they are looking for, but also to compare the results, to assess redundancies, discrepancies and other surprising or interesting differences in order to draw hints on how to continue the exploration. The goals of the by-visualization data access and exploration interface are two-fold: (1) Enable "explorers" to understand, compare and decide based on the provided results and (2) enable them to interact with the results by enabling indirect query manipulation, identifying and highlighting parts that are of interest for further analysis and guiding them towards interesting regions [START_REF] Steiger | Visual analysis of time-series similarities for anomaly detection in sensor networks[END_REF].

Our processes for user requirements elicitation confirms our goals stated above and is based on the User Centered Design standard [START_REF]gonomics of Human-System Interaction -Part 210: Human-Centred Design for Interactive Systems[END_REF]. In addition to that, users emphasized the importance to compare differences as well as similarities of queries and results. As a baseline, we enabled the visualization of multiple tables with direct manipulation capabilities and currently work on an overview visualization that spans the result data space.

OPEN RESEARCH CHALLENGES BEYOND INODE

A full-fledged data exploration system should learn about data sources, learn about users and queries, and leverage this knowledge to facilitate and guide users. All these challenges constitute new opportunities for ML research to contribute to DM.

Learning about Data Sources

There are several data integration challenges that can be tackled with ML. The first one is to automate data integration [START_REF] Golshan | Data integration: After the teenage years[END_REF], in the spirit of existing work that improves entity matching using transformer architectures [START_REF] Brunner | Entity matching with transformer architectures-a step forward in data integration[END_REF]. The second challenge is to automatically generate knowledge graphs, in the spirit of neural-network systems such as Snorkel [START_REF] Alexander J Ratner | Snorkel: Fast training set generation for information extraction[END_REF]. Another powerful concept to automate knowledge graph construction is to combine user dialogs with graph construction [START_REF] Ba Nguyen | Query-driven on-the-fly knowledge base construction[END_REF]. The idea is to augment the knowledge graphs by learning concepts that are commonly queried but do not exist in the graph. In summary, there has been a large amount of research on automatic knowledge base construction. However, the combination of knowledge base construction with natural language query processing has been largely untapped.

Understanding Users and Queries

Understanding user interests and expertise is a vital component for enabling intelligent data exploration. For instance, the general public interested in black holes has different expectations from an experienced astronomer with a vast knowledge of astrophysics. The challenge is to avoid overwhelming a novice user while providing interesting and relevant information to an expert user.

The system should constantly improve its behavior by learning and adapting to the user from task to task. Our operators are a great opportunity to learn and adapt to users, as they provide the ability to choose between utility and novelty, two dimensions that have not been explored together in the past. Additionally, they enable collecting user feedback at the level of individual operators and of a DEP. While ML methods for learning user profiles exist in the context of individual systems for web search or recommendations, they have not been studied before in the context of determining which operator caters for which user in the next step. A simple start is to use regression methods to determine the weight of utility and novelty when exploring data.

The state-of-the-art NL-to-SQL systems use supervised ML [START_REF] Brunner | ValueNet: A Natural Language-to-SQL System that Learns from Database Information[END_REF][START_REF] Kim | Natural language to SQL: Where are we today?[END_REF]. A new opportunity here is to train a neural network for sequence-to-sequence prediction [START_REF] Brunner | ValueNet: A Natural Language-to-SQL System that Learns from Database Information[END_REF][START_REF] Sutskever | Sequence to sequence learning with neural networks[END_REF] for translating from NLto-SQL and vice versa. The key research challenge is how to use the feedback provided by users to disambiguate queries and feed the gained knowledge back into ML models to improve learning with semantic information for building ML models to tackle disambiguation and context modeling [START_REF] Shekarpour | Question answering on linked data: Challenges and future directions[END_REF].

Generating Data Exploration Pipelines

Understanding queries and users helps to provide guidance in generating DEPs. This challenge can be approached in different ways depending on the availability of logs and on the user's expertise and willingness to provide feedback. DEPs could be given either partially or fully. In both cases, they are either provided by the user or learned. In a scenario where a DEP is given (see example in Section 2), the problem could be cast as finding the right parameters for each query in the DEP. In a scenario where the user is providing the next query, it could be seen as a query completion problem. In a scenario where the user does not write exploration queries and only provides feedback on results, it could be seen as the problem of learning the user's DEP. All these cases result in partially-guided or fully-guided exploration.

Furthermore, since DEPs bring together several data access modalities, which may be initiated by the user (e.g. a user query) or by the system (recommendations or explanations), the system needs to learn how to use its options to help the user in meaningful and unobtrusive ways. While there has been work on each of these capabilities individually (e.g., recommendations or query explanations), these efforts only focus on small parts of the problem lacking a holistic understanding of the behavior and dynamics of a multiaspect system. ML approaches such as Active Learning (AL) and Reinforcement Learning (RL) appear to be relevant to the question of training models that represent DEPs.

Partial Guidance with AL. AL is claimed to be superior to faceted search when the goal is to help users formulate queries. Systems like AIDE [START_REF] Dimitriadou | AIDE: An Active Learning-Based Approach for Interactive Data Exploration[END_REF] and REQUEST [START_REF] Ge | REQUEST: A scalable framework for interactive construction of exploratory queries[END_REF] assist users in constructing accurate exploratory queries, while at the same time minimizing the number of sample records presented to them for labeling. Both systems rely on training a decision tree classifier to build a model that classifies unlabeled records. A bigger challenge is to leverage AL in generating and refining queries that go beyond SQL predicates.

Full-Guidance with RL. In exploratory data analysis, logs are rarely available making RL and Deep RL [START_REF] Milo | Automating Exploratory Data Analysis via Machine Learning: An Overview[END_REF] the methods of choice for training DEPs based on a simulated agent experience [START_REF] Ori | Automatically Generating Data Exploration Sessions Using Deep Reinforcement Learning[END_REF][START_REF] Seleznova | Guided Exploration of User Groups[END_REF]. In [START_REF] Ori | Automatically Generating Data Exploration Sessions Using Deep Reinforcement Learning[END_REF], a Deep RL architecture is used for generating notebooks that show diverse aspects of a data set in a coherent narrative. In [START_REF] Seleznova | Guided Exploration of User Groups[END_REF], we used RL to generate an end-to-end exploration policy to find a set of users in a collection of groups. Both frameworks accept a wide class of exploration actions and do not need to gather exploration logs. In [START_REF] Personnaz | Balancing Familiarity and Curiosity in Data Exploration with Deep Reinforcement Learning[END_REF][START_REF] Personnaz | DORA The Explorer: Exploring Data with Interactive Deep Reinforcement Learning (prototype demonstration)[END_REF], we developed dora the explorer, a first prototype that addresses the problem of generating DEPs by leveraging Deep Reinforcement Learning. An open question is the applicability of this framework to specific data sets and the transferability of learned policies across data sets.

Figure 1 :

 1 Figure1: Natural language query interface with user assistance. Step 1: User enters a natural language query. Step 2: System parses query and matches keywords against the available ontology to enable term disambiguation; the user iterates the process.Step 3: System visualizes various cancer types that are similar to lung cancer. The distance metric between the diseases can be chosen by the user, e.g. by semantic distance.

Figure 2 :

 2 Figure 2: Exploring astrophysics data.

Figure 3 :

 3 Figure 3: Major components of the INODE architecture.

http://www.inode-project.eu/

A prototype implementation of the major system components can be found at: http://www.inode-project.eu/opendatadialog/

The INODE components as of September 2021 http://www.inode-project.eu/blog/ second-component-release/

ACKNOWLEDGEMENTS

This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 863410. We also thank all the remaining members of the INODE Team: http://www.inode-project.eu/consortium/