Damien T Wojtowicz
email: damien.wojtowicz@irit.fr

Franck Morvan
email: morvan@irit.fr

SLA Definition for Multi-Cloud Queries

Keywords: Database-as-a-Service, Multi-Cloud, Service-Level Agreement

Public data availability in cloud-hosted databases raises interests in systems providing multi-cloud querying capabilities. Since data access in this context induces monetary costs, we suggest a method to compute SLAs for multi-cloud queries. It consists in decomposing queries into a directed graph of maximal sub-queries per provider, and finding a financially cheapest execution plan. This method yields SLAs with monetary costs lower than a download-all approach, granted that inter-provider data transfers are minimised.

INTRODUCTION

Cross-analysis of datasets is of key importance to researchers, as the accumulation of data in fields such as astronomy and biology dramatically increases the opportunities for new discoveries. Various data sharing methods are available. File provision, either through a web interface or in the cloud 1 , is popular and historically ingrained. Sharing can also be achieved by offering public read access to a database, which may be hosted in the cloud following the Database-as-a-Service (DBaaS) model 2 . In this case, users can leverage the services of public cloud providers in order to execute their queries. Alternatively, users can download datasets and run queries on self-administrated databases, implying sufficient skills and resources.

Unfortunately, providers do not offer out-of-the-box multi-cloud querying capabilities, leaving space for systems orchestrating query execution across multiple public databases in a DBaaS fashion. Since access to the providers' datasets is billed, such a service shall let users control their expenditures by means of Service-Level Agreement (SLA). Those contracts are known to play a key role in query optimisation [START_REF] Yin | SLA Definition for Multi-Tenant DBMS and its Impact on Query Optimization[END_REF], hence the importance of well-defined SLAs. In the context of DBaaS, a single performance objective cannot be retained for all queries due to different complexities. In addition, even for the same query, different tenants may have different performance expectations which are partially influenced by their budget (i.e. minimising response time may not be the goal). These challenges should be taken into account in the SLA definition process.

In Section 2, we briefly review literature about multi-cloud database systems. In Section 3, we propose a graph-based SLA definition method for multi-cloud relational queries. Its results are analysed in Section 4. We conclude in Section 5 by mentioning perspectives.

𝑄 𝑆𝑄 1 = Π 𝑖𝑑1,𝑥 𝑅 1 𝑆𝑄 2 = Π 𝑖𝑑1,𝑦 𝑅 2 𝑆𝑄 3 = Π 𝑥,𝑦 𝑆𝑄 1 𝑆𝑄 2 𝑆𝑄 4 = Π 𝑥,𝑦 𝑆𝑄 2 𝑆𝑄 1 𝑷 1 𝑷 2

RELATED WORK

Work on multi-cloud database services has mostly focused on federated databases. Indeed, several query processing systems involving different cloud providers exist. To name a few, SCOPE [START_REF] Rafique | SCOPE: Self-Adaptive and Policy-Based Data Management Middleware for Federated Clouds[END_REF] is the base of a collaborative document editing tool, MetaStorage [START_REF] Bermbach | MetaStorage: A Federated Cloud Storage System to Manage Consistency-Latency Tradeoffs[END_REF] operates as a key-value storage system and SHAMC [START_REF] Wang | SHAMC: A Secure and Highly Available Database System in Multi-Cloud Environment[END_REF] acts as a relational DBMS. Despite their seemingly differences, in terms of context, objectives and data models, they share the same approach.

Indeed, they all own their data, seeking an overall system optimisation, using for example data placement or replication strategies, with respect to an objective (e.g. response time, availability, financial cost) rather than solely focusing on optimising data access. These systems are based on the Infrastructure-as-a-Service (IaaS) model, and their SLAs encompass broader services than querying.

Research has also been carried on multi-objective cost models for multi-cloud queries [START_REF] Gounaris | A Bi-Objective Cost Model for Optimizing Database Queries in a Multi-Cloud Environment[END_REF], aiming at adding a monetary aspect to usual cost models. In this case, query execution is performed on a set of cloud-hosted virtual machines leveraging IaaS capabilities. Those cost models are best suited to scale up or down resources, and are therefore not suitable to help defining SLAs for DBaaS-based multi-cloud systems.

SLA DEFINITION METHOD

In this paper, we focus on the performance objective for a query and its relation to the monetary costs. Therefore, we model the SLA for a query as a couple 𝑆𝐿𝐴 =< C, 𝑅𝑇 >, with C the monetary cost of a query and 𝑅𝑇 its response time. This paper is exemplified by the query 𝑄 = Π 𝑥,𝑦 𝑅 1 𝑅 2 , assuming relations 𝑅 1 𝑖𝑑1, 𝑥 and 𝑅 2 𝑖𝑑2, #𝑖𝑑1, 𝑦 respectively hosted on providers 𝑃 1 and 𝑃 2 (see fares 3 in Table 1a). For the sake of simplicity, network bandwidth in our example is supposed to be constant at 1.0 GB/s.

Our method is a three-step procedure, starting by decomposing the input query, then generating SLAs for sub-queries and ending by aggregating the latter.

Query decomposition

Inspired by query decomposition techniques [START_REF] Gounaris | A Bi-Objective Cost Model for Optimizing Database Queries in a Multi-Cloud Environment[END_REF][START_REF] Wong | Decomposition -a Strategy for Query Processing[END_REF], we suggest that multi-cloud queries can be modelled as a directed acyclic graph (DAG), where vertices are maximal sub-queries involving a minimal set of providers. Query 𝑄 can be decomposed as depicted in Figure 1. 𝑆𝑄 1 and 𝑆𝑄 2 are maximal sub-queries involving a single provider, respectively 𝑃 1 and 𝑃2. 𝑆𝑄 3 and 𝑆𝑄 4 involve data from 𝑃 1 and 𝑃 2 respectively executed on 𝑃 1 and 𝑃 2 .

This decomposition creates two plans, one ending with 𝑆𝑄 3 and the other with 𝑆𝑄 4 . It is worth noticing that both are similar to execution plans in distributed databases. In this context, the best plans usually minimise network transfers [START_REF] Özsu | Principles of Distributed Database Systems[END_REF].

Mitigation of provider miscalculations

For each sub-query 𝑆𝑄 ★ , a SLA is generated as well as an estimation of the output relation's size 𝑆 (see Table 1d). Those might not reflect the actual sub-queries' output relation size due to internal cardinality estimation errors [START_REF] Leis | How Good Are Query Optimizers, Really?[END_REF]. We suggest to keep track of those estimation errors by computing a supposed error ratio for each query submitted to the provider as 𝑟 = 𝑆 (𝑅𝑒𝑎𝑙 ) 𝑆 (𝑆𝐿𝐴) . Those ratios are then used to compute 𝑟 , a sliding average of 𝑟 on the last 𝑛 queries exemplified in Table 1f. While being sensitive to the complexity of the last queries, using a sliding average let our system take into account changes of the provider's estimator.

SLA components aggregation

We use separate methods for each component of the SLA, using corrected values (see Table 1e). Monetary cost C is the sum of all sub-queries costs and all transfers costs (see the latter in Table 1b). Response time 𝑅𝑇 is the sum of the 𝑅𝑇 -maximal path in the DAG, taking into account cross-providers transfer time.

Due to space limitation, we cannot put the cost formulas in the paper. We only show the results for illustration purposes. Table 1c shows the costing for each plan. At the end of the procedure, the SLA that will be presented to the user, in the context of our example query 𝑄, is < 0.184 $, 7.287 𝑠 > stemming from 𝑆𝑄 4 .

RESULT ANALYSIS

Figure 2 depicts the breakdown of each scenario costs. The less expansive one minimises inter-provider transfers, which is unsurprising given the similarity of our setting with distributed systems. Moreover, there is no significant differences between 𝑆𝑄 3 and 𝑆𝑄 4 in storage and processing costs.

𝑆𝑄 4 is 1.8 times cheaper with an acceptable performance degradation (43%) compared to the download-and-process scenario, thus a multi-cloud query appears to be more competitive financially-wise than the download-all approach.

CONCLUSION

Our SLA computation method is a first step towards a middleware enabling multi-cloud querying in a DBaaS fashion. We showed that optimal multi-cloud query execution plans should minimise inter-providers transfers in order to limit costs.

Next steps will involve setting up a SLA-constrained execution engine. Given that sub-queries' output relation size is an estimation, the SLA-based optimal plan might not actually be the best. Hence, methods mitigating estimation errors, such as mobile-agent-based models [START_REF] Arcangeli | Mobile Agents Based Self-Adaptive Join for Wide-Area Distributed Query Processing[END_REF] implementing reinforcement learning, appear to be relevant for the execution of multi-cloud queries.

Figure 1 :

 1 Figure 1: DAG and minimal sub-queries 𝑆𝑄 ★ for query 𝑄. Two execution plans, both involving data transfer between providers 𝑃 1 and 𝑃 2 , are possible with 𝑆𝑄 3 and 𝑆𝑄 4 .

Figure 2 :

 2 Figure 2: Breakdown of each scenario cost. Inter-providers transfers can tremendously increase the price of a scenario.

Table 1 :

 1 Values used for SLA computations 𝒓 𝒓 for 𝑷 1 1.50 0.90 1.20 1.05 1.01 0.95 1.102 𝒓 for 𝑷 2 1.01 1.02 0.95 1.03 1.10 0.99 1.017 (f) Last SLA estimation error ratio for providers and 𝑟 computed using the last 𝑛 = 6 queries for each provider

		𝑷 1	𝑷 2
	Export	0.060 0.075		𝑺𝑸 _ C ($) 𝑹𝑻 (s) 𝑺 (GB)
	Querying 0.006 0.008		𝑆𝑄 1 0.009 1.000	1.000
	Storage	0.003 0.002		𝑆𝑄 2 0.030 2.000	3.000
	(a) Providers' fares ($/GB)		𝑆𝑄 3 0.055 6.000	6.100
	Transfer 𝑆𝑄 1 → 𝑆𝑄 4	Time (s) C ($) 1.102 0.066	𝑆𝑄 4 0.061 5.000 (d) Provider-generated SLA of 6.120 each sub-query
	3.050 (b) Transfer times and fares 0.229 𝑆𝑄 2 → 𝑆𝑄 3	𝑺𝑸 _ C ($) 𝑹𝑻 (s) 𝑺 (GB)
	Scenario	C ($) 𝑹𝑻 (s)	𝑆𝑄 1 0.010 1.102 𝑆𝑄 2 0.031 2.033	1.102 3.050
	𝑆𝑄 3	0.353 10.762	𝑆𝑄 3 0.060 6.610	6.720
	𝑆𝑄 4	0.184 7.287	𝑆𝑄 4 0.062 5.083	6.222
	Download-all 0.335 5.083		(e) Corrected SLAs
	(c) Costs of all scenarios
	Query	1	2	3	4	5	6

See examples of public datasets at Amazon (https://registry.opendata.aws/) or Google (https://cloud.google.com/public-datasets)

See examples at Google (https://cloud.google.com/bigquery/public-data). © 2020, Copyright is with the authors. Published in the Proceedings of the BDA 2020 Conference (October 27-29, 2020, En ligne, France). Distribution of this paper is permitted under the terms of the Creative Commons license CC-by-nc-nd 4.0. © 2020, Droits restant aux auteurs. Publié dans les actes de la conférence BDA 2020 (27-29 octobre 2020, En ligne, France). Redistribution de cet article autorisée selon les termes de la licence Creative Commons CC-by-nc-nd 4.0.

Inspired from BigQuery's pricing policy (https://cloud.google.com/bigquery/pricing)


Angeline Poulon-Quintin
email: angeline.poulon@icmcb.cnrs.fr

Emma Ogden

Axel Large

Mélanie Vaudescal

Christine Labrugère

Michel Bartala

Caroline Bertrand

Chemical surface modification of lithium disilicate needles of a silica-based ceramic after HF-etching and ultrasonic bath cleaning: Impact on the chemical bonding with silane

Keywords:

    Chemical surface modification of lithium disilicate needles of a silica-based ceramic after HF-etching and ultrasonic bath cleaning: Impact on the chemical bonding with silane

Introduction

In recent years, an increasing demand for high performance aesthetic restorations has led to the development of several new ceramics. Heat-pressed or computer-aided design and computer-aided manufacturing (CAD/CAM) fabricated lithium disilicate glass ceramics are preferred over other types of dental ceramics because of these superior characteristics: excellent mechanical properties, accuracy of fit and optical properties that mimic those of natural teeth [START_REF] Shenoy | Dental ceramics: an update[END_REF]. In dentistry, each restoration has two interfaces: tooth/resin and resin/ceramic. In this study, we will focus on the resin/ceramic interface known to be in general weaker that the tooth/resin one. There are many reasons for the dental ceramics to fracture, e.g. mastication, parafunction and intraoral occlusal forces creating repetitive dynamic loading. Thus, the establishment of a durable and reliable bond between a dental ceramic and a resin composite is important in dental practice. This bond is usually created via two mechanisms, micromechanical attachment thanks to HF-etching and chemical bonding thanks to the silane molecules. Silanes are silicon esters that may contain trialkoxysilane groups. There are hybrid functional monomers capable of forming chemical bonds with organic and inorganic surfaces. In general, silanes have non-hydrolysable groups (such as methacrylate) and hydrolysable groups (such as ethoxy), which is why they are chemically bifunctional. They bond dissimilar materials together by forming a branched 3D siloxane (-Si-O-Si-) film.

In addition to their chemical bonding power, silanes are used as surface primer agents for adhesion promotion that can increase the critical surface energy of a substrate. A high critical surface energy and a low surface tension of a liquid are desired because liquids will spread evenly onto the surface.

In order to achieve complete wetting, the adhesive must have low viscosity and the surface tension must be lower than the critical surface tension (␥c) of the substrate surface [START_REF] Matinlinna | An introduction to silanes and their clinical applications in dentistry[END_REF]. An optimal silane application implies the elimination of water, alcohol and other solvents, allowing for the complete condensation reaction to form the siloxane bond and eliminate excess silane oligomers to obtain a monolayer silane [START_REF] Hooshmand | Bond durability of the resin-bonded and silane treated ceramic surface[END_REF][START_REF] Queiroz | Surface characterization of feldspathic ceramic using ATR FT-IR and ellipsometry after various silanization protocols[END_REF]. The stability and effectiveness of this adhesion is also supposed to be improved by heat treatment [START_REF] Roulet | Effects of treatment and storage conditions on ceramic/composite bond strength[END_REF][START_REF] Monticelli | Effect of temperature on the silane coupling agents when bonding core resin to quartz fiber posts[END_REF][START_REF] Shen | Effect of post-silanization drying on the bond strength of composite to ceramic[END_REF][START_REF] Fabianelli | The effect of different surface treatments on bond strength between leucite reinforced feldspathic ceramic and composite resin[END_REF][START_REF] Filho | Effect of different ceramic surface treatments on resin microtensile bond strength[END_REF] The HF-etching is known to provide the dissolution of the glassy and/or crystalline phase [START_REF] Bona | Ceramic surface preparations for resin bonding[END_REF] and to leave residual salts of silicafluoride negatively influencing the resin bond strength [START_REF] Magne | Influence of post-etching cleaning and connecting porcelain on the microtensile bond strength of composite resin to feldspathic porcelain[END_REF][START_REF] Belli | Post-etching cleaning and resin/ceramic bonding: microtensile bond strength and EDX analysis[END_REF]. Several cleaning methods are suggested in literature but the most supported post-etching protocol is the used of ultrasonic distilled water bath for 2 min [START_REF] Bruzi | Post-etching cleaning influences the resin shear bond strength to CAD/CAM lithium-disilicate ceramics[END_REF][START_REF] Magne | Influence of post-etching cleaning and connecting porcelain on the microtensile bond strength of composite resin to feldspathic porcelain[END_REF][START_REF] Peumans | Porcelain veneers bonded to tooth structure: an ultra-morphological FE-SEM examination of the adhesive interface[END_REF][START_REF] Jones | Effect of etching technique on the clinical performance of porcelain veneers[END_REF].

This study proposes to investigate how the HF-etching following by ultrasonic water bath cleaning (recommended protocol to improve the bonding with a composite resin), mod-

Materials and methods

The lithium disilicate glass-ceramic (IPS Emax Press, Ivoclar Vivadent) tested samples were in the form of pellets (diameter: 1.9 cm; thickness: This face was before HF-etched applying 9% hydrofluoric acid (Porcelain Etch ® , Ultradent) for 20 s (recommended action time by Ivoclar-Vivadent) and then rinsed 1 min with water as dentists done. To rinse, no distilled water was used like it can be read in the literature. In the dentist protocol, there is also no control of the amount of HF deposited, the only criterion retained by the practitioner being a covering of the entire surface of the ceramic by the HF acid. To improve the reproducibility of our results, the quantities of acid deposited were weighed. For each sample, the same quantities of HF were applied (25 mg) by a professional dentist with the same gesture as in its dental office for preparation of the restauration surfaces. Other samples were etched and rinsed as the previous ones but also immersed in water ultrasonic bath (WUS bath) for 4 min.

Only the central parts of the samples were studied.

To evaluate the crystallized phases present, XRD analysis was performed with a PANalytical X'Pert Pro device ((Cu K␣ ) = 1.5418 Å, 2Â = 10-80 • , step of 0,01 • ).

SEM images (conventional JSM6360A JEOL, 10 kV, sample beforehand metalized with thin gold layer) were used to follow the evolution of the morphology of the surface.

X-ray Photoelectron Spectroscopy XPS surface analysis were performed using a Thermo-Fisher Scientific K-ALPHA spectrometer with a monochromatized AlK ␣ source (h = 1486.6 eV) and a 200 m X-Ray spot size. A pressure of 10 -7 Pa was reached in the chamber when transferring the HF-etched samples. The full spectra (0-2100 eV) were obtained with a constant pass energy of 200 eV and high resolution spectra (i.e. C1s, O1s, F1s, Li1s, K1s, Si2p, Au3d) at a constant pass energy of 40 eV. Charge neutralization was applied during analysis and sputtering was achieved through low energy Ar + ions. The PHI 710 Auger nanoprobe spectroscope used was equipped with an Argon ion gun and a cylindrical mirror analyser (CMA) with energy resolution varying from 0.1 to 0.5 % with also the advantage of excellent spatial in width and depth resolution resp. (10 nm and 5 nm). The AES analyses were semi-quantitative and particularly sensitive for light elements. Line analyses were performed (10 kV electron beam voltage and and 2 nA for current). Prior to Auger analysis, argon sputtering was done at 2 kV, during 60 s with an ion current of 3 A to remove the carbon contamination and most of the gold metallization. Each elemental line trace consists of 512 data points with their intensity corresponding to the peakto-peak intensity of the element's derivative Auger spectrum (dN/dE versus kinetic energy).

For TEM analyses, thin foils were prepared at the liquid nitrogen temperature using an EM-09100 cryo-ion slicer system (JEOL, Tokyo, Japan). The treated-surfaces were previously coated with gold to clearly visualised the top free surfaces and also avoid charging problem during observations. They were then prepared to observed in cross-section the different interfaces. Observations were carried out on a JEOL 2200FX (JEOL, Tokyo, Japan) operated at 200 kV.

Results

The lithium disilicate glass-ceramic

The XRD pattern of one lithium disilicate glass-ceramic pellet (Fig. 2) allows to identify four crystallized phases: lithium disilicate (Li 2 Si 2 O 5 , ICDD card 01-070-4856), zirconium oxide (ZrO 2 , ICDD card 01-079-1796), zinc oxide (ZnO, ICDD card 00-036-1451) and cristobalite (SiO 2 , ICDD card 00-051-1379).

The other components no detected by XDR could be glassed, imperfectly crystallized, nanosized or in too small quantities to be detected.

Evolution of the surface morphology after HF etching

Fig. 3 shows the morphology evolution of the IPS Emax Press lithium disilicate glass-ceramic pellet thanks to SEM images. Fig. 3a is an image of the mirror polish surface realized in chemical contrast (BSE-SEM). Presence of a darker phase with elongated form and surrounding by a whiter one identified as the glassy phase can be notice. This result is in agreement with the morphology after HF-etching presented on Fig. 3c. Fig. 3b is a SEM image of the Li 2 Si 2 O 5 crystals from Ivoclar Vivadent after HF vapour etching for 30 s of the IPS Emax Press lithium disilicate glass-ceramic. Comparison of this image with the morphology obtained after aqueous HF etching (20 s) following by 1 min water rinsing (Fig. 3c), two major differences appear: (1) the crystals are not clearly observable due to the presence of residues (residual glassy phase and residual salts of silicafluoride) and (2) The crystals seems to be thinner and shorter. When an additional cleaning step using WUS for 4 min is added (Fig. 3d), no more residue is observable on the surface and the reduction of the Li 2 Si 2 O 5 crystals dimensions is confirmed.

Study of the fluorine presence after WUS cleaning

HF-etching is known to leave residual salts of silica fluoride that precipitate and can be identified as a white deposit on the etched ceramic surface [START_REF] Bailey | DICOR ® surface treatments for enhanced bonding[END_REF]. During WUS, the formation of microscopic bubbles causes the detachment of impurities even in inaccessible recesses. This gentle cleaning method eliminates all residues, dirt, oxidation products, deposits etc. and contaminants from all surfaces [START_REF] Leite | Effect of the etching duration and ultrasonic cleaning on microtensile bond strength between feldspathic ceramic and resin cement[END_REF][START_REF] Steinhauser | Micro-shear bond strength and surface micromorphology of a feldspathic ceramic treated with different cleaning methods after hydrofluoric acid etching[END_REF]. TEM observations show that there is a thickness reduction of the HF-affected "layer" (around few m for HF-etched water rinsed 20 s sample and difficult to defined when WUS cleaned) and confirms the departure of the salts but also the removing of the no-well entrapped phases in the glassy materials as Li 2 Si 2 O 5 crystals (Fig. 4). F1s XPS spectra are presented Fig. 5 to validate the efficiency of the WUS cleaning and the departure of the residual salts of silica fluoride or other compounds containing fluorine. The binding energy of fluorine F1s is known to be around 686-689 eV. Comparing the curve (Fig. 5a) of the HF-etched water rinsed sample (in red) with the HF-etched, water rinsed sample and WUS cleaned sample (in dark blue), a significant reduction of the F1s peak is observable. This last sample is etched again during 60 s with argon ions inside the XPS chamber after the first previous analyse. The F1s spectrum is acquired again for the same area (in light blue). The disappearance of a small F1s peak is observable. Same protocol of argon etching is used on the HF-etched water rinsed sample for 60, 120 and 600 s of cumulative argon etching time (Fig. 5b). There is no significant change of the F1s peak. These results are in agreement with the TEM observations: the thickness affected by the HF-etched and water rinsed sample is higher than with the use of an additional WUS cleaning where only the extreme top surface seems to be affected.

Localisation of the fluorine presence after WUS cleaning

Because the XPS analysed surface is around 200 m in diameter and due to the roughness of the samples after WUS cleaning (Fig. 3d is presented Fig. 6. In extreme surface, when fluorine appears, lithium and oxygen are always combined with. The length affected by fluorine is around 100 nm. Even after WUS cleaning, fluorine is still present at the extreme surface of the needles. Quantification in atomic percent of fluorine, lithium and potassium realized on another crystal helps to conclude that the ratio between F and Li varies from 4 to 7 and that sometimes fluorine is not combined with Li or K. When F is present with K, the ratio between K and F is around 3.

Discussion

The evolution of the lithium disilicate glass-ceramic disc surface after HF-etching is conventional with the dissolution of the glassy phase following the identified well known scenario. The acid reacts with the silica matrix to form volatile silicon tetrafluoride SiF 4 and creates a porous structure [START_REF] Barjaktarova-Valjakova | Acid etching as surface treatment method for luting of glass-ceramic restorations, part 1: acids, application protocol and etching effectiveness[END_REF][START_REF] Ho | Insights on ceramics as dental materials. Part II: chemical surface treatments[END_REF]. Then, SiF 4 reacts further with the hydrofluoric acid again to form a soluble complex ion, hexafluorosilicate [SiF6] 2-and hydrogen ions.

4 HF (l) + SiO2 (s) → SiF4 (g) + 2H2O (l) (1)

HF (l)

+ SiF4 (g) → [SiF6] 2-(aq) + 2H + (aq) (2)
The hydrogen ions in the solution react with the hexafluorosilicate complex ion to form a fluorosilicic acid that can be rinsed off:

[SiF6] 2-(aq) + 2H + (aq) → H2SiF6 (l) (3)
Depending on the practitioner techniques, the quantity and the concentration used as the duration of etching performed, the glassy matrix is more or less dissolved. Etching alters surface topography by creating micro-and nano-scale porosities of varying depth and width [START_REF] Ramakrishnaiah | The effect of hydrofluoric acid etching duration on the surface micromorphology, roughness, and wettability of dental ceramics[END_REF]. This porous surface is not only providing more surface area for resin bonding, but also exposes and generates hydroxyl groups on the ceramic surface that are responsible for chemical bonding via silane coupling agents.

However, the HF-etching is also known to react with crystallised phases [START_REF] Bona | Ceramic surface preparations for resin bonding[END_REF][START_REF] Ramakrishnaiah | The effect of hydrofluoric acid etching duration on the surface micromorphology, roughness, and wettability of dental ceramics[END_REF]. The glassy phase dissolves at a faster rate than the crystallized phases but no more information is given about presence of fluorine on the lithium disilicate crystal in literature to our knowledge. Figs. 3c and4 are in agreement with the results found in literature with an affected depth around 6 m (small pores observables on Fig. 4) and a modification of the surface morphology with the presence of pores. On TEM images, Li 2 Si 2 O 5 crystals look like pillars of needles randomly oriented and entrapped in the glassy matrix.

An additional WUS cleaning leads to the departure of the fluorine salts as already observed by other authors [START_REF] Magne | Influence of post-etching cleaning and connecting porcelain on the microtensile bond strength of composite resin to feldspathic porcelain[END_REF][START_REF] Belli | Post-etching cleaning and resin/ceramic bonding: microtensile bond strength and EDX analysis[END_REF][START_REF] Ho | Insights on ceramics as dental materials. Part II: chemical surface treatments[END_REF][START_REF] Ramakrishnaiah | The effect of hydrofluoric acid etching duration on the surface micromorphology, roughness, and wettability of dental ceramics[END_REF]. The efficiency of the distilled water ultrasonic bath is clearly identified in the literature as the best postetching cleaning to positively improves the resin shear bond strength [START_REF] Bruzi | Post-etching cleaning influences the resin shear bond strength to CAD/CAM lithium-disilicate ceramics[END_REF][START_REF] Leite | Effect of the etching duration and ultrasonic cleaning on microtensile bond strength between feldspathic ceramic and resin cement[END_REF][START_REF] Steinhauser | Micro-shear bond strength and surface micromorphology of a feldspathic ceramic treated with different cleaning methods after hydrofluoric acid etching[END_REF]. In this study, a conventional process of HFetching is used whereas the supplier etches with HF vapour for 30 s (Fig. 3b, image from Ivoclar Vivadent). Pillars of plate-like Li 2 Si 2 O 5 crystals and no glassy phase are observable.

After WUS cleaning, all the fluorine salts, residues and no adherent phases are removed from the surface as XPS analyses confirm (Fig. 5a) whereas they are still present at a greater depth when only water rinsing is used (Fig. 5b).

Another key point is the Li 2 Si 2 O 5 crystals morphology changes. They seem to be no more plate-like but more sharpen (Fig. 3d) and rounder (Figs. 6 and7) so named needles. AES local analyses in line on the extreme surface of these needles confirm a localised presence of fluorine on their surface (about 100 nm long) and most of the time combined with lithium and oxygen. Bischoff et al. [START_REF] Bischoff | Phase evolution in lithium disilicate glass-ceramics based on non-stoichiometric compositions of a multi-component system: structural studies bu 29 Si single and double resonance solid state NMR[END_REF] working on lithium disilicate glass-ceramics based on non-stoichiometric compositions, conclude to the absence of lithium ion clustering in the glassy component of the final glass-ceramic and also argue against an epitaxial nucleation process proposed in literature. They suggest the nucleation of Li 2 Si 2 O 5 starts at the disordered lithium phosphate phase (Li 3 PO 4 ) and the glassy matrix. But in the lithium silicate glass system, Puls and Eckert give clear evidence of strong lithium cation clustering providing quantitative support for a model of one dimensional chan- nel structure in (Li 2 O) x (SiO 2 ) 1-x with x ≤0.3 [START_REF] Puls | Spatial distribution of lithium ions in glasses studied by 7 Li{ 6 Li} spin echo double resonance[END_REF]. This study indicates that the structure of low lithium silicate glasses is consistent with the formation of lithium rich quasi-onedimensional channels, in which each Q (3) -silicon species is surrounded by about three lithium ions. Each lithium atom is surrounded by three non-bridging oxygen atoms. The structure of Li 2 Si 2 O 5, layered plate-like crystals is similar to the previously described for the glassy phase. They are composed of [SiO 4 ] tetrahedra sharing corners, Li atoms with presence of channels [START_REF] Denry | Ceramics for dental applications: a review[END_REF]. A representation of the needle free surface and of the Li 2 Si 2 O 5 layered structure of crystal phase is proposed by Upadhyaya et al. [START_REF] Upadhyaya | Glass-ceramic technology[END_REF] without taking into account the presence of fluorine. In these crystals, Q (2) and Q (4) -silicon structure of [SiO 4 ] tetrahedra are also present [START_REF] Kamitsos | Structure-property correlation in glasses by infrared reflectance spectroscopy[END_REF].

In this study, XPS analyses not allow to find information about the types of fluorine chemical bonding and AES about the global composition due the technique protocol itself and also to charging phenomenon for long duration. To try to identify if the fluorine is combined with others elements to create a crystallised compound on the Li 2 Si 2 O 5 needle surface, HRTEM study of the extreme surface of needles localised at the free surface of the WUS cleaned samples is carried out.

Fig. 8a is a bright-field TEM image (BF-TEM) of the free sample surface after HF-etching, water rinsing and WUS cleaning. The compound in extreme surface of the needles is oriented to be in Bragg position to reveal the presence of small platelike particles of about 100 nm length (observable in dark inside red circles, Fig. 8a). Their longer face is always parallel to the free surface of the needles. Their dimensions are coherent with AES results. HRTEM analyses are carried out in order to identify the compound formed based on the previous chemical information collected. These small plate-like particles are identified as SiLi 2 F 6 phase. This result is illustrated on Fig. 8b (original HR-TEM image on left and overlapping of the colorized corresponding inverse FFT images on right. The area of interest is between the two previous images. Fig. 9 proposed an idealized mechanism based on the mechanism proposed by Shrivastava [START_REF] Shrivastava | All ceramic cementation: a key to successful restoration[END_REF] to explain silane bonding when crystalized SiLi 2 F 6 small plate-like particles are present on the Li 2 Si 2 O 5 needles surface. As for the glassy phase, HF-etching of the needles leads to the formation of hexafluorosilicate SiF 62-(aq). The VSEPR predicts for [SiF 6 ] 2-the octahedral shape. Thanks to the removing of silica, hexafluorosilicate complex ions can react with the lithium ions to form a SiLi 2 F 6 solid at the needle surface:

[SiF 6 ] 2-(aq) + 2Li + (s) → SiLi 2 F 6 (s) (4)
A global reaction between lithium disilicate needles and hydrofluoric acid leading to the formation of silica and water is proposed:

Si 2 Li 2 O 5 (s) + 6HF (aq) → SiLi 2 F 6 (s) + SiO 2 (s) + 3 H 2 O (l) (5)
Additional HRTEM observations have to be done to find if orientation relationships exist between the two phases. However, the local modification of the needle roughness is observable due to the formation of the SiLi 2 F 6 plate-like particles.

When the silane is applied over the etched ceramic surface, the molecule is activated by hydrolysis to form silanol groups (-Si-O-Si-) which react with the substrate surface hydroxyl (-OH) groups by a condensation reaction. There is the formation of a branched 3D siloxane (-Si-O-Si-) film [START_REF] Chiang | Fourier transform infrared spectroscopic study of the adsorption of multiple amino silane coupling agents on glass surfaces[END_REF].

As a consequence of the previous chemical reaction, some -Si-O-Siassemblies are broken on the Li 2 Si 2 O 5 needles surface which could facilitate the formation of hydroxyl species around these small plate-like particles. For a same quantity of HF used for etching, an increase of the number of bonds with -OH compare to the number formed on a same needle surface for a water rinsed sample could explain the improvement of the WUS sample bonding properties (to be published) for two reasons: (1) the direct access to the surface of the interlocked needles thanks to the departure of all the salts, residues, etc. and (2) the densification of the 3D siloxane network close to the surface of the needles due to the increase of the hydroxyl groups in surface. The role of the SiLi 2 F 6 small plate-like particles seems also to be the increase of the needle surface nano-roughness. At the moment no evidence of their bonding with silane compound is proved.

Fig. 9 proposed an idealised illustration of how silane compounds chemically bond to inorganic silica when SiLi 2 F 6 small plate-like particles are present on the crystallized Li 2 Si 2 O 5 needles surface after the hydrolysis reaction of ␥methacryloxypropyltrimethoxysilane to form silanol groups (-Si-O-Si-).

Conclusion

In the literature, the protocol to obtain the best bonding to lithium disilicate glass-ceramics is to prepare its surface Thanks to the fine analyze of the Li 2 Si 2 O 5 crystals free surface after the HF-etching, the presence of crystalized small plate-like particles identified as SiLi 2 F 6 is proved. Due to their presence, the improvement of the bonding is explained by the creation of additional nano-roughness on the Li 2 Si 2 O 5 needles at the extreme free surface, the direct access to the clean surface of interlocked needles responsible also of microroughness and the densification of the 3D siloxane network.

  but Bruzi et al. study based on the evaluation of the shear bond strength of composite resin to CAD/CAM lithium disilicate ceramics after different post-etching cleaning and silane treatments, recommends a regular application of silane (applied for 20 s, air dried for 20 s and hot dried at 60 • C for 20 s).The silane most used in dentistry is the MPS (␥methacryloxypropyl trimethoxysilane or C 10 H 20 O 5 Si) coupling molecule (geometry shown in Fig.1).

Fig. 1 -

 1 Fig. 1 -Representation of the MPS (␥-methacryloxypropyl trimethoxysilane or C 10 H 20 O 5 Si) in 2D.

Fig. 2 -

 2 Fig. 2 -XRD pattern obtained on a lithium disilicate glass-ceramic pellet (IPS Emax Press, Ivoclar Vivadent). 4 crystalized phases: Li 2 Si 2 O 5 (ICDD card 01-070-4856), ZrO 2 (ICDD card 01-079-1796), ZnO (ICDD card 00-036-1451) and SiO 2 (ICDD card 00-051-1379).

Fig. 3 -Fig. 4 -

 34 Fig. 3 -Microstructure of IPS e.max Press lithium disilicate glass-ceramic: (a) mirror polished, (b) etched with HF vapour for 30 s (image from Ivoclar Vivadent), (c) HF-etched 20 s and water rinsed 1 min and (d) HF-etched 20 s, water rinsed 1 min and WUS cleaned 4 min.

Fig. 5 -

 5 Fig. 5 -(a) Overlapping graph of XPS F1s curves on 2 samples: (1) HF-etched 20 s and then rinsed with water 1 min (in red), 2) WUS rinsed 4 min (dark blue) and then additionally argon etched 60 s (light blue). (b) XPS F1s curves of the HF-etched 20 s and then rinsed with water 1 min (in red) and then additionally argon etched 60, 120 and 600 s (additional argon etchings on the same area; cumulative time) (For interpretation of the references to colour in the figure legend and text, the reader is referred to the web version of this article.).

Fig. 6 -

 6 Fig. 6 -Secondary electron micrograph of the needle with the position of the horizontal line analysed (in blue) and the corresponding elemental line AES analysis (in intensity) for a HF-etched 20 s, water rinsed 1 min and WUS4 min cleaned sample. (For interpretation of the references to colour in the figure legend, the reader is referred to the web version of this article.)

Fig. 7 -

 7 Fig. 7 -Secondary electron micrograph of a needle with the position of the horizontal line analysed (in blue) and quantitative AES analysis of F, Li and K for a HF-etched 20 s, water rinsed 1 min and WUS 4 min cleaned sample. (For interpretation of the references to colour in the figure legend, the reader is referred to the web version of this article.)

Fig. 8 -Fig. 9 -

 89 Fig. 8 -(a) BF-TEM image of the free surface for a HF-etched and WUS cleaned sample, (b) HRTEM images of the interface between a Li 2 Si 2 O 5 crystallised needle and a small plate-like particle identified as SiLi 2 F 6 (Au used as conductive coating). (For interpretation of the references to colour in the text, the reader is referred to the web version of this article.)

  hydrofluoric-acid HF etching 20 s, water rinsing 1 min and then immersion in water ultrasonic bath for 4 min. A regular application of a silane compound MPS in this study (␥methacryloxypropyl trimethoxysilane or C 10 H 20 O 5 Si) applied for 20 s, air dried for 20 s and hot dried at 60 • C for 20 s is required before the application of the resin.

Please cite this article in press as: Poulon-Quintin A, et al. Chemical surface modification of lithium disilicate needles of a silicabased ceramic after HF-etching and ultrasonic bath cleaning: Impact on the chemical bonding with silane. Dent Mater (2021), https://doi.org/10.1016/j.dental.2021.02.006 by

Acknowledgements

The authors want to thanks Ivoclar Vivadent for the supply of the lithium disilicate glass-ceramic IPS Emax Press pellets. r e f e r e n c e s


Donal Macgrogan

Beatriz Martínez Poveda

Jean-Pierre Desvignes

Leticia Fernandez-Friera

Manuel José Gomez

Eduardo Gil Vilariño

Sergio Callejas Alejano

Pablo Garcia-Pavia

Jorge Solis

Joaquín Lucena

David Salgado

Gwenaelle Collod-Béroud

Emilie Faure

Alexis Théron

Julia Torrents

Jean-François Avierinos

Lorena Montes

Ana Dopazo

Valentín Fuster

Borja Ibañez

Fátima Sánchez-Cabo

Stephane Zaffran

José Luis De La Pompa

Keywords: Human fetal valve, CAVD, inflammation, peripheral blood biomarker, gene signature

Calcific aortic valve disease (CAVD) is a significant cause of illness and death worldwide. Identification of early predictive markers could help optimize patient management. RNAsequencing was carried out on human fetal aortic valves at gestational weeks 9, 13, and 22, and on a case-control study with adult non-calcified and calcified bicuspid and tricuspid aortic valves. In dimension reduction and clustering analyses, diseased valves tended to cluster with fetal valves at week 9 rather than normal adult valves, suggesting that part of the disease program might be due to re-iterated developmental processes. The analysis of groups of coregulated genes revealed predominant immune-metabolic signatures, including innate and adaptive immune responses involving lymphocyte T cell metabolic adaptation. Cytokine and chemokine signaling, cell migration, and proliferation were all increased in CAVD, whereas oxidative phosphorylation and protein translation were decreased. Discrete immune-metabolic gene signatures were present at fetal stages and increased in adult controls, suggesting that these processes intensify throughout life and heighten in disease. Cellular stress-response and neurodegeneration gene signatures were aberrantly expressed in CAVD, pointing to a mechanistic link between chronic inflammation and biological ageing. Comparison of the valve RNA-sequencing dataset with a case-control study of whole blood transcriptomes from asymptomatic individuals with early aortic valve calcification identified a highly predictive gene signature of CAVD and of moderate aortic valve calcification in overtly healthy individuals.

These data deepen and broaden our understanding of the molecular basis of CAVD and identify a peripheral blood gene signature for the early detection of aortic valve calcification.

INTRODUCTION

Calcific aortic valve disease (CAVD) is caused by calcium build-up of the aortic valve leaflet and may be associated with hemodynamic obstruction, leading to different degrees of aortic stenosis (AS). Untreated severe AS leads to heart failure, with valve replacement remaining the only available treatment. Severe AS is preceded by aortic sclerosis, a subclinical form of CAVD that develops over many years without hemodynamic consequence [START_REF] Freeman | Spectrum of calcific aortic valve disease: pathogenesis, disease progression, and treatment strategies[END_REF]. The prevalence of aortic valve disorders increases with age, with 25% of individuals >65 years with aortic sclerosis progressing to AS at a rate of 2% per year (4). Other than age, the principal CAVD risk factor for AS is a congenitally malformed bicuspid aortic valve (BAV), which is present in 0.5-2% of the general population [START_REF] Siu | Bicuspid aortic valve disease[END_REF]. Patients with AS and a BAV are a decade younger at diagnosis than those with normal tricuspid aortic valve (TAV) morphology [START_REF] Beppu | Rapidity of progression of aortic stenosis in patients with congenital bicuspid aortic valves[END_REF]. Premature leaflet thickening and calcification in BAV patients may be caused by abnormal shear stress hemodynamics.

CAVD shares mechanistic features with atherosclerosis [START_REF] Freeman | Spectrum of calcific aortic valve disease: pathogenesis, disease progression, and treatment strategies[END_REF]. The current accepted model implicates initial lipid oxidation, inflammatory infiltration by T-lymphocytes and macrophages, cytokine and pro-fibrotic signaling, and altered extracellular matrix (ECM) secretion, leading to ectopic bone formation and tissue mineralization [START_REF] Hulin | Advances in Pathophysiology of Calcific Aortic Valve Disease Propose Novel Molecular Therapeutic Targets[END_REF][START_REF] O'brien | Pathogenesis of calcific aortic valve disease: a disease process comes of age (and a good deal more)[END_REF]. The factors predicting transition from the "at risk" status to aortic sclerosis and subsequently to severe AS, are not known [START_REF] Otto | Aortic-valve stenosis--from patients at risk to severe valve obstruction[END_REF].

Transcriptional profiling of CAVD has helped to identify hundreds of genes differentially expressed between calcified and normal valves [START_REF] Guauque-Olarte | RNA expression profile of calcified bicuspid, tricuspid, and normal human aortic valves by RNA sequencing[END_REF]. Multi-omics approaches combining transcriptomics and proteomics have provided a high-resolution expression atlas and established potential drivers of disease progression [START_REF] Schlotter | Spatiotemporal Multi-Omics Mapping Generates a Molecular Atlas of the Aortic Valve and Reveals Networks Driving Disease[END_REF]. Despite this progress, thus far no medical therapy has been proven to be effective in preventing progression to severe disease, with randomized clinical trials demonstrating the lack of effect of statins on disease progression [START_REF] Zhao | The effect of statins on valve function and calcification in aortic stenosis: A meta-analysis[END_REF]. It has been suggested that statin treatment would have to begin at an earlier disease stage to have a beneficial effect. Current imaging modalities, especially echocardiography, remain the gold standard for diagnosis of valve disease but are mainly performed when symptoms appear. Non-contrast computed tomography may have significant predictive value in both asymptomatic and symptomatic patients, but remains a source of radiation exposure and may not be available in centers with limited resources.

Biomarkers may complement imaging modalities to detect early changes, especially early calcification. The ideal biomarker would predict disease as well as improve prognosis, subclinical disease management, and risk stratification and reduce disease-associated morbidity and mortality. Already identified potential biomarkers include markers of myocardial injury, cardiac mechanical stretch, inflammation, and hemostasis imbalance; however, with the exception of lipoprotein(a), these are not specific to CAVD or have yet to be validated in large randomized studies [START_REF] Small | Biomarkers of Calcific Aortic Valve Disease[END_REF].

The aortic valve develops from rudimentary endocardial cushions that are subsequently refined into thin leaflets well into postnatal life. This process is dependent on complex cell-ECM interactions and is modulated by hemodynamics, and its spatio-temporal dysregulation results in malformations and maladaptive remodeling [START_REF] Hinton | Heart valve structure and function in development and disease[END_REF]. Several lines of evidence indicate that valve disease at any age is accompanied by some underlying structural defect [START_REF] Roberts | Frequency by decades of unicuspid, bicuspid, and tricuspid aortic valves in adults having isolated aortic valve replacement for aortic stenosis, with or without associated aortic regurgitation[END_REF], suggesting that latent CAVD has a developmental origin. Moreover, developmental pathways may also be reactivated during tissue repair processes in the disease setting. For example, molecular mechanisms controlling heart valve cell differentiation are shared with cartilage, tendon, and bone development (reviewed in [START_REF] Lincoln | Hearts and bones: shared regulatory mechanisms in heart valve, cartilage, tendon, and bone development[END_REF], and are reactivated during endochondral ossification and mature lamellar bone formation taking place in end-stage aortic valve disease [START_REF] Mohler | Bone formation and inflammation in cardiac valves[END_REF]. Remarkably, only end-stage CAVD has been described at the molecular level, whereas the molecular underpinnings of subclinical disease are poorly understood, due in part to difficulties in accessing biological material from early lesions.

To gain mechanistic insight, we performed an RNA-seq analysis of human fetal and adult control and calcified bicuspid (cBAV) and tricuspid valves (cTAV). K-means clustering was used to identify dynamically regulated processes taking place simultaneously at crucial stages of human valve development and in diseased valves. The prevailing gene signatures were associated with immune-metabolic processes and heretofore uncharacterized pathways linked to cellular stress and ageing. A search for genes shared between our gene expression datasets and datasets generated from asymptomatic subjects from the Progression of Early Subclinical Atherosclerosis (PESA) cohort [START_REF] Fernandez-Ortiz | The Progression and Early detection of Subclinical Atherosclerosis (PESA) study: rationale and design[END_REF] with aortic valve calcification and without AS identified marker genes in peripheral blood of asymptomatic individuals. When combined in quantitative RT-PCR assays, these marker genes showed good sensitivity and specificity for predicting CAVD at a subclinical stage.

RESULTS

Diseased valves are transcriptionally more similar to developing valves than to controls

Genome-wide transcriptome profiling was carried out on normal adult control valves (n=8) and severely stenotic calcified BAV (cBAV; n=5) and TAV (cTAV; n=7) valves, as well as on fetal aortic valves at gestational weeks 9 (n=4), 12-13, (n=3) and 22 (n=2) (Supplementary Table S1, see https://figshare.com/s/4357d56025cb3380a0df). These developmental timepoints correspond to mouse embryonic days (E) 13.5-17.5 [START_REF] Krishnan | A detailed comparison of mouse and human cardiac development[END_REF], during which valves are remodeled by morphogenic processes leading to thinning, elongation, and formation of multi-laminar valve structure. To integrate data across studies and generalize our findings, we conducted a metaanalysis with publicly available expression data from normal adult control (n=8), cBAVs (n=10), and cTAVs (n=9) valves (8) (Figure 1A). After correcting for batch effects, Principal Component Analysis (PCA) indicated that sample differences did not correlate with batch origin (Supplementary Fig. S1A), but rather depended on disease or developmental stages (Figure 1B).

The first of the PCA components (PC1) separated developing and adult valve samples according to disease severity, ranging from cTAV (right hand side) to controls (left hand side).

Interestingly, fetal valves tended to cluster with diseased adult valves. There were two control samples (both from heart transplants) and one sample of embryonic samples at 9 weeks showing more heterogeneity than the rest according to the second component (y-axis) but still the range of the axis (-0.6 to 0.4) is very small, confirming the reproducibility of the samples in terms of global transcriptome. In the second PCA component (PC2), the adult control and disease samples were separated from fetal weeks 9 and 13 samples (Figure 1B). Therefore, and considering that the first PCA dimension explains 15.24% of the variance, while the second dimension explains only 7.73%, adult disease samples appeared to be more closely related to fetal than to control samples, suggesting that some of the disease pathogenesis can be ascribed to the recapitulation of developmental processes.

To further support this finding, we used Euclidean distances between overall gene expression profiles to perform hierarchical clustering (HC) of the samples (Figure 1C). Similar to what was observed in the PCA, disease samples cluster further apart from controls and appear under the same branch with embryonic samples at 9 and 13 weeks.

Clustering identifies dynamic gene expression across disease and developmental states

Differential expression analysis was initially carried out between each pair of conditions of interest (Supplementary Table S2

Comparative functional analysis identifies multiple interconnected pathways

Functional enrichment analysis of clusters with IPA identified significant associations to canonical pathways in 28 clusters (Figure 2). Subsequent unsupervised hierarchical clustering of clusters, based on the fraction of shared canonical pathways, identified 6 metaclusters (MC), termed MC1-MC6 (Figure 2). Cluster 10 did not overlap with the others in terms of functional enrichment, and was not further considered. Generally, genes highly expressed in adult disease

showed low expression at gestational week 13 and vice-versa; however, there were exceptions (clusters 9, 12, 23, and 45; Figure 2). Although metaclusters were defined as non-overlapping collections of gene clusters, enriched functions could be shared between several meta-clusters.

For example, canonical pathways associated to clusters 46 and 32 were shared by MC1 and MC2; those associated to cluster 12 were shared by MC2, MC3, MC4, and MC6; those to clusters 12 to 45 were shared by MC2, MC3, and MC4; and those canonical pathways enriched in clusters 5 and 45 were shared by MC4 and MC6 (Figure 2). Therefore, the biological functions represented in the different MCs are interconnected.

Innate and adaptive immune processes are present in valve disease and development

The collections of genes associated to metaclusters MC1-MC6 were reanalysed with IPA.

Enriched canonical pathways associated to each MC are summarized in Figure 3

Identification of circulating biomarkers predicting CAVD

To identify potential biomarkers useful for predicting CAVD, a sub-set of genes from the aortic valve study was compared with whole blood RNA-Seq data from asymptomatic individuals with aortic valve calcification, detected by non-contrast computed tomography in the Progression of Early Subclinical Atherosclerosis (PESA) study [START_REF] Fernandez-Ortiz | The Progression and Early detection of Subclinical Atherosclerosis (PESA) study: rationale and design[END_REF]. PESA participants undergo simultaneous cardiac calcium scoring and blood sampling. Out of the 455 PESA participants for which RNAseq data was available in visit 2 (ie. three years after recruitment), 59 had evidence of valve calcification and 52 of theses had sufficient integrity for qPCR analysis. None of them had valvular stenosis/regurgitation, evidencing an early stage of the disease. Their transcriptome was compared to the transcriptome of PESA individuals without valvular calcification (n=395). From the whole set of genes identified as differentially expressed (p<0.05, Supplementary Table S5 4B). An additional model was built to assess the power of the gene signature to distinguish between individuals without calcification (PESA-CTL) from those with subclinical disease (PESA-SD; AUC=0.71).

This model was then validated in whole blood samples of an independent cohort of CAVD patients (n=16), together with samples from 25 of the 52 PESA-SD individuals and 35 of the 52 PESA-CTL individuals taken three years after visit 2 (Figure 4C). The model achieved AUCs of 0.90 and 0.88 to discriminate CAVD patients from PESA-SD and PESA-CTL individuals, respectively. The AUC of the model to distinguish PESA-SD from PESA-CTL was again 0.71 (Figure 4C). Thus, our model identifies a gene expression signature in blood that is highly predictive of valve disease and of aortic valve calcification levels in otherwise healthy individuals.

1 4

DISCUSSION

This study provides, for the first time, a comprehensive molecular profile of shared and unique cellular activities taking place in aortic valve development and CAVD. The transcriptome of adult disease samples appeared to be more closely related to fetal week 9 than to control samples or weeks 13 and 22, suggesting that some of the disease pathogenesis might be ascribed to the recapitulation of processes taking place in early development. The overall cBAV and cTAV gene expression profiles were essentially identical, confirming previous findings suggesting that valve calcification depends on the same biological pathways, regardless of valve morphology [START_REF] Guauque-Olarte | RNA expression profile of calcified bicuspid, tricuspid, and normal human aortic valves by RNA sequencing[END_REF][START_REF] Padang | Comparative transcriptome profiling in human bicuspid aortic valve disease using RNA sequencing[END_REF].

Therefore, the mechanisms leading to BAV formation are independent of those leading to BAV calcification; in other words, the cause of the embryonic defect is distinct from the resulting calcification process.

Functional analysis provided compelling evidence that innate and adaptive immune interactions take place in the aortic valve throughout life (Supplementary Fig. S10B weeks of gestation, implies that the immune landscape is dynamically regulated in developing valves. These observations are consistent with previous findings of multiphasic changes in shear stress-response, cell-ECM interaction and adaptive immunity pathways from week 14 to week 22 of human aortic valve development [START_REF] Gottlieb Sen | The Transcriptional Signature of Growth in Human Fetal Aortic Valve Development[END_REF].

The predominant T cell gene signature in the RNA-seq dataset strengthens the notion that unresolved immune-inflammatory response is a major CAVD component [START_REF] Mathieu | Innate and Adaptive Immunity in Calcific Aortic Valve Disease[END_REF]. This association is well established, with early immunohistological findings indicating the presence of T 1 5

lymphocytes in immune infiltrates [START_REF] Olsson | Accumulation of T lymphocytes and expression of interleukin-2 receptors in nonrheumatic stenotic aortic valves[END_REF][START_REF] Otto | Characterization of the early lesion of 'degenerative' valvular aortic stenosis. Histological and immunohistochemical studies[END_REF]. More recent studies have revealed the existence of T cell infiltration in BAV and TAV calcific disease, consisting of non-specific chemokine driven polyclonal T cell recruitment, and memory-effector CD8+ T cell clonal expansions [START_REF] Winchester | Circulating activated and effector memory T cells are associated with calcification and clonal expansions in bicuspid and tricuspid valves of calcific aortic stenosis[END_REF]35), supporting the interpretation of an antigen-induced immune response. Moreover, this antigendriven response involved circulating expanded CD8+ and, to a lesser extent, CD4+ T cell clones between circulating blood and valve tissues suggesting a systemic disease [START_REF] Winchester | Circulating activated and effector memory T cells are associated with calcification and clonal expansions in bicuspid and tricuspid valves of calcific aortic stenosis[END_REF]. The gene expression signatures of several other immune-competent cells, including B-lymphocytes, macrophages, and dendritic cells were also present, confirming previous characterization of cellular infiltrates in CAVD [START_REF] Steiner | Calcific aortic valve stenosis: Immunohistochemical analysis of inflammatory infiltrate[END_REF]. Together with a body of literature suggesting a tight inflammatory cell-valve interstitial cell interaction in CAVD [START_REF] Hulin | Advances in Pathophysiology of Calcific Aortic Valve Disease Propose Novel Molecular Therapeutic Targets[END_REF][START_REF] Mathieu | Innate and Adaptive Immunity in Calcific Aortic Valve Disease[END_REF], our data are consistent with the notion that unresolved inflammation ultimately drives myo-fibroblast and osteoblastlike differentiation and leads to heterotopic bone formation. Therefore, targeted therapeutic immunosuppression of calcification might be approachable by better characterizing the various infiltrating immune-cell subtypes.

Functional analysis revealed that OXPHOS metabolism was decreased in CAVD, with coordinated expression reduction of multiple nuclear genes encoding subunits of mitochondrial electron transport chain complexes I, III, IV and V. These changes might be part of ongoing adaptive metabolic reprogramming during T lymphocyte activation [START_REF]Metabolism in T cell activation and differentiation[END_REF], when upon encountering an antigen, T cells proliferate and acquire new functions, switching energy metabolism from OXPHOS to glycolysis. However, both increased and decreased expression of glycolytic pathway components were present, suggesting that decreased OXPHOS metabolism cannot be ascribed solely to T cell activation. Moreover, adult aortic valves showed markedly reduced transcript levels of multiple subunits of the small and large protein ribosomal complexes (RPS and RPL respectively) and eukaryotic translation initiation factors (EIF1-4), possibly reflecting co-ordinate dysregulation of OXPHOS activity and ribosomal biogenesis (19). This occurred in uncalcified adult valves as well (clusters 31, 37, and 12), suggesting declining

1 6
function with age. In contrast signatures for cell death and apoptosis (for example in MC3) and, energy-demanding cell processes such as proliferation, DNA repair, and cell movement (in MC4 and MC6) were increased, possibly reflecting cellular heterogeneity within the calcified valve.

Decreased mitochondrial metabolism and protein translation may be a manifestation of altered physiological stress responses that occur during ageing [START_REF] Schlotter | Spatiotemporal Multi-Omics Mapping Generates a Molecular Atlas of the Aortic Valve and Reveals Networks Driving Disease[END_REF], which is also related to the inability to maintain adaptability to stress and re-establish homeostasis in response to environmental perturbations. Thus, multiple stress-response gene signatures were present in MC2 and to a lesser extent in MC1 and MC5 (Supplementary Fig. S10B, see https://figshare.com/s/d9a68e0d6faa67e4c6c3). These included SIRT, a highly conserved family of NAD-dependent deacetylases, linking transcriptional regulation to mitochondrial function, stress resistance, metabolism, cell survival and organismal longevity (3) and mTOR (Mammalian target of rapamycin), a nutrient sensing system which regulates protein synthesis, to promote cell growth and proliferation (19). Moreover, deficiency of the unfolded protein response (UPR) pathway, which is activated by misfolding of newly synthesized proteins as part of the ER stress response (9) contributes to inflammation, disease and ageing. Gene signatures including amyloid processing and Huntington's disease signaling were also featured implying shared calcific valve and neurodegenerative disease mechanisms (Supplementary Fig. S10B, see https://figshare.com/s/d9a68e0d6faa67e4c6c3). Therefore, chronic inflammation drives metabolic stress and biological ageing in CAVD.

Given the phenomenon of cellular trafficking of T cells between the diseased valve tissue, and circulating blood (34), we reasoned that aortic valve and circulating blood from CAVD patients share common genes that would be absent in subclinical individuals. In the PESA study we identified a subpopulation of asymptomatic individuals in this cohort with > 1 valve calcification detected by imaging. Therefore, we searched for circulating biomarkers by comparing the CAVD transcriptomes and whole blood from datasets of the subclinical PESA study population, and identified six differentially expressed genes, four of which were found to

1 7
predict subclinical and overt valve disease. Therefore ASCC3, ITK, CD28, and LINS were specific to aortic valve calcification, whereas PAG1 and NLRC5 were also associated with coronary artery calcification, suggesting that valve and vascular calcification proceed by shared and separate mechanisms. These mechanisms have yet to be examined, but noteworthy is that five of six genes belong to T cell activation and NF-kB pathways. This gene signature could be used in combination with high-resolution imaging for the early diagnosis of valve calcification.

Study Limitations.

A limitation of our study is the low sample numbers especially for week 22 samples (n=2). Investigational studies involving human fetal issue are limited by ethical and legal considerations on medical abortion. Nevertheless, our bioinformatics analysis showed that the week 22 samples clustered together and separated well from the other groups by PCA and we are confident of the robustness of the results involving this group. Moreover, differential expression analysis was performed using limma, a statistical method developed ad-hoc for RNAseq data with low sample size [START_REF] Ritchie | limma powers differential expression analyses for RNA-sequencing and microarray studies[END_REF].

1 8

METHODS

Study population and aortic valve collection.

Fetuses

RNA isolation

For each valve, total RNA from a single aortic valve cusp was extracted using the TRIzol LS (Invitrogen, Carlsbad, CA, USA) method and digested with RNase free DNase I (Invitrogen).

For PESA and CAVD validation total RNA was using either the PAXgene blood mRNA kit (PreAnalytiX, Hombrechtikon, Switzerland) for manual isolation or the QIAsymphony PAXgene blood RNA kit (PreAnalytiX) for automated isolation using a QIAsymphony SP liquid handling robot (Qiagen, Venlo, Netherlands). RNA purity, concentration, and integrity were assessed by Nanodrop 2000 spectrophotometry (Thermo Scientific) and automated electrophoresis in a 2100

Bioanalyzer (RNA6000 Nano LabChip; Agilent). Samples with a RIN > 6 were selected for reverse transcription.

RNA-seq data generation

RNA-seq was performed on DNase I-treated RNA samples with a RIN > 7. RNA-seq libraries Bioinformatics analysis of RNA-Seq.

Data preprocessing

FASTQ files were pre-processed with cutadapt ( 16) to eliminate Illumina adapter remains and reads were aligned to the human reference transcriptome (GRCh38.78) using RSEM 1.2.31 [START_REF] Li | RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome[END_REF].

Raw data from 27 RNA-seq samples from GSE76718 were downloaded from GEO and preprocessed and aligned using the same pipeline. Control samples were present in all experiments, allowing for batch correction of the data using ComBat [START_REF] Johnson | Adjusting batch effects in microarray expression data using empirical Bayes methods[END_REF]. Data were normalized using TMM with log-ratio trimming=0.3 and sumTrim=0.05 and voom-transformed. Only genes with at least 1 cpm in at least 10 samples were considered for further analysis.

Differential Expression Analysis

Differential expression between groups was performed using mixed models with eBayes, as implemented in the Bioconductor limma package [START_REF] Ritchie | limma powers differential expression analyses for RNA-sequencing and microarray studies[END_REF]. A gene was classified as differentially expressed if its Benjamini-Hochberg adjusted P-value was < 0.05 in any of the contrasts under study (Supplementary Table S2, see https://figshare.com/s/4357d56025cb3380a0df).

Clustering and functional analyses

To identify groups of genes with similar expression profiles across the six conditions under consideration, k-means clustering was performed with the R ComplexHeatmap package on all expressed genes (19,962 genes). The Elbow method was used to estimate the optimal number of clusters, which was set as k=50. The number of genes per cluster was between 215 and 680.

Ingenuity Pathway Analysis (IPA, Qiagen) was then used to identify significant associations 1 between canonical pathways and clusters. Twenty-eight clusters, out of the original collection of 50, were found to be associated to at least one canonical pathway with Benjamini-Hochberg (B-H) adjusted P-value < 0.05 (Supplementary Table S3, see https://figshare.com/s/4357d56025cb3380a0df). Concordance between canonical pathway profiles was then used to identify groups of clusters that could be associated to similar functions.

One cluster (cluster 10) did not share any enriched pathway with any other cluster and was not considered further. The remaining 27 clusters were grouped into six meta-clusters, using the fraction of shared pathways as distance measure. Meta-clusters contained from 332 to 3304 genes (Figure 2). IPA was used again to re-annotate meta-clusters (Figure 3).

Data availability

Data are deposited in the NCBI GEO database under accession number GSE148219.

qRT-PCR for validation studies in whole blood

Selection of candidate genes for qRT-PCR. We used whole blood transcriptomics data generated in the PESA study to identify genes that might potentially serve as predictors of CAVD. The criteria to select these candidate genes were: (i) Genes differentially expressed between individuals with (n=59) and without (n=396) calcification of the aortic valve (p-value<=0.05) but not differentially expressed between individuals with and without calcification in the coronaries (p-value>0.05; Supplementary Table S5, see https://figshare.com/s/4357d56025cb3380a0df).

(ii) Aortic valve-specific differentially expressed genes with a top 10% correlation between their expression level and the level of calcification of the aortic valves. (iii) Their expression levels in the RNA-seq samples of the aortic valves were either up-regulated in disease v.s. no disease samples or they were upregulated in early embryonic valve samples and in the diseased samples but not in the control samples. From the 10 genes fulfilling the three criteria,

Statistical analysis

A linear mixed model was used to assess correlation between the expression of each gene in WB and the degree of calcification in the aortic valve for each disease group (PESA-SD, WB-CAVD) separately. Visualization was performed using the multiplot function and the ggplot2 R package. All samples were jointly analyzed using a linear mixed model with the origin of the samples as random effect. The same analysis was performed to assess the correlation of the gene expression with calcification of the coronaries. Four genes which showed no correlation with the amount of calcium in the coronaries were selected to build a gene expression signature using a multinomial model with interaction for the prediction of the three groups of individuals with different disease severity WB-CAVD>PESA-SD>PESA-CTL. To quantify the predictive power 3

of the gene signature between different groups of individuals a logistic regression model was fit to the data and the corresponding ROC curves were built using the ROCR R package.

FIGURE LEGENDS

  [START_REF] Krishnan | A detailed comparison of mouse and human cardiac development[END_REF], see https://figshare.com/s/d9a68e0d6faa67e4c6c3). The fetal immune cell gene signature in MC3 was notably simpler than in adult control or CAVD samples, with fewer immune cell gene expression signatures present at gestational weeks 9 (3/9 clusters), 13 (1/9 clusters), or 22 (3/9 clusters) than in adult controls (5/9 clusters) or in CAVD (9/9 clusters), suggesting that aortic valve immune cell enrichment in the aortic valve increases over time. Moreover, the presence of only one immune cell gene signature at 13 weeks (cluster 9), compared with three at 9 and 22

  were created using the TruSeq Stranded Total RNA with Ribo-Zero Gold Prep Kit (Fetal 2 0 samples) or TruSeq TM RNA Sample Prep Kit v2 (Patient samples; Illumina, Diego, CA). Final cDNA libraries were checked for quality and quantified in the 2100 Bioanalyzer (Agilent). The libraries were loaded in the flow cell at 8 pM, and clusters were generated in Cbot and sequenced as 50bp single-end reads on a Hiseq 2500 instrument (adult samples) or 75 base single-end reads on a Genome Analyzer IIx instrument (fetal samples; Illumina, Diego, CA). Image analysis and base calling were performed using RTA and CASAVA.

Figure 1 .

 1 FIGURE LEGENDS 729 730

Figure 3 .

 3 Figure 3. Functional analysis of meta-clusters. The six gene metaclusters (MC1-6) described in Figure 2 were annotated with IPA. Barplots on the right-hand side represent enrichment values for the top five IPA Canonical Pathways associated with each meta-cluster (MC), selected according to enrichment significance and having an adjusted Benjamini-Hochberg P<0.05. Metaclusters are identified with colored boxes (MC1 to MC6, in red, blue, green, purple, orange and yellow, respectively) and represent groups of clusters associated to similar functions. Central boxes list all the genes associated with the selection of pathways associated with each metacluster. The heatmap on the left represents normalized expression values for individual genes across biological conditions, on a normalized scale (from -2 to 2, for values below and above the mean, respectively).

Figure 4 .

 4 Figure 4. A gene expression signature predictive of CAVD. (A) Correlation of qPCR data (yaxis) vs aortic-valve calcification levels (x-axis) for the 6 selected genes in whole blood (WB). Each point represents an individual´s level of gene expression (y-axis) and its aortic valve calcification (x-axis). Dots are colored based on the group to which the individual belongs: PESA-CTL (n=52), PESA-SD (n=52) or WB-CAVD (n=12). Solid lines represent the linear model fit for PESA-SD and WB-CAVD patients. Shaded regions represent the confidence of the linear regression estimate for each calcium level. (B) Receiver operating characteristic (ROC) curve of multivariate logistic regression model predicting CAVD (WB-CAVD) vs preclinical aortic-valve calcification (PESA-SD; thick discontinuous line, AUC=0.97) vs valve calcification (PESA-CTL; thick solid line, AUC=0.91). A third ROC curve for the prediction of valve calcification (PESA-SD) vs no calcification (PESA-CTL; thin solid line, AUC=0.71) is shown. The discontinuous diagonal line indicates a fifty-fifty percent chance to predict each group. (C) Follow-up of the model in PESA subgroups after 3 years. Validation in an independent group of CAVD patients (WB-CAVD, n=16) vs a PESA-SD subgroup (n=25; thick discontinuous line, AUC=0.90) vs a PESA-CTL subgroup (n=35; thick solid line, AUC=0.88). A third ROC curve for the prediction of PESA-SD (n=25) vs PESA-CTL (n=35; thin solid line, AUC=0.71) is shown. The thin discontinuous diagonal line indicates a fifty-fifty percent chance to predict each group.

  genes showed some degree of correlation when considered individually (P 0.27; Figure4A, Supplementary TableS7, see https://figshare.com/s/4357d56025cb3380a0df). Given that, NLRC5 and PAG1 were also associated with total calcification of the coronaries (Supplementary

	Fig. S10A, see https://figshare.com/s/d9a68e0d6faa67e4c6c3, Supplementary Table S7, see
	https://figshare.com/s/4357d56025cb3380a0df) and were therefore excluded as potential specific
	markers of aortic valve calcification.
	To determine the utility of ASCC3, ITK, CD28 and LINS1 as potential markers of valve
	calcification, a ROC curve was built to represent the predictive value of the model to classify

, see https://figshare.com/s/4357d56025cb3380a0df), six were selected that showed a diseasespecific pattern in the RNA-seq data of human aortic valves (see Methods for selection criteria; Supplementary Fig.

S1

, see https://figshare.com/s/d9a68e0d6faa67e4c6c3). Five of these genes were related to adaptive innate and adaptive responses (CD28, ITK, PAG1, NLRC5, and ASCC3) and a sixth was related to the WNT pathway (LINS1) (Supplementary Table

S6

, see https://figshare.com/s/4357d56025cb3380a0df). The expression of the six genes was measured in whole blood (WB) on 104 PESA participants [52 cases: asymptomatic individuals with calcification of the aortic valve (PESA-SD); 52 controls: individuals without calcification (PESA-CTL)] and 12 patients with CAVD recruited at Monteprincipe Hospital (WB-CAVD) by qRT-PCR and correlated with the degree of aortic valve calcification (Figure

4A

, Supplementary Table

S4

, see https://figshare.com/s/4357d56025cb3380a0df). Considering all conditions, the 6 1 3 calcific disease patients (WB-CAVD) vs PESA-SD individuals and vs PESA-CTL, yielding areas under the curve (AUCs) of 0.97 and 0.91 respectively (Figure

Table S4

 S4 . Aortic valves from human fetuses were obtained from electively-(weeks 9; n=4 and by the Health Service Ethics Committees (Comité Ético de Investigación Clínica) of participant centers. The investigation conformed to the principles outlined in the Declaration of Helsinki.Whole-blood samples. Genes of interest were quantified by quantitative reverse transcription-

	week 12/13; n=3) or medically (week 22; n=2) -terminated pregnancies after written informed polymerase chain reaction (qRT-PCR) in whole-blood (WB) samples from age-matched case-
	consent in concordance with French legislation (PFS14-011) and prior protocol approval from control males (n=104) of the Progression of Early Subclinical Atherosclerosis (PESA).
	the Subclinical cases (PESA-SD) were defined as participants with aortic-valve calcification score "Agence de la biomédecine" (Supplementary Table S1, see
	https://figshare.com/s/4357d56025cb3380a0df). Aortic valves were recognized by their >1, as assessed by the Agatston method (1). Controls (PESA-CTL) had no calcification of the
	anatomical landmarks under the microscope and leaflets were isolated with minimal aortic wall aortic valves or coronary arteries. Patients with aortic valve stenosis and moderate to severe
	calcification (WB-CAVD; n=12) were recruited from Monteprincipe Hospital (Supplementary
	Calcification was graded as mild (1/3 leaflet area affected) or severe (2/3 leaflet area affected).
	Immediately following surgical removal, a portion of the aortic valve leaflet was placed in sterile
	saline buffer and stored at -80°C until processing. Control valve samples were obtained at

contamination Samples were immediately flash frozen in liquid nitrogen. The investigation conformed to the principles outlined in the Declaration of Helsinki.

Patients. The CAVD study population consisted of BAV (n=5) or TAV (n=7) patients referred for aortic valve or aortic root surgery at Monteprincipe Hospital and Hospital Clínico San Carlos (Madrid, Spain; Supplementary Table

S1

, see https://figshare.com/s/4357d56025cb3380a0df).

Patients were prospectively recruited between December 2009 and February 2013. Patients with a history of rheumatic heart disease, infective endocarditis, or connective tissue disorders were excluded. To determine the morphology and functional state of the aortic valve, all patients underwent clinical evaluation and preoperative 2-dimensional echocardiography according to the American Heart Association and the American College of Cardiology guidelines

[START_REF] Nishimura | AHA/ACC guideline for the management of patients with valvular heart disease: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines[END_REF]

. The aortic valve morphology and the presence of valvular degeneration, including the extent of leaflet calcification, thickening, prolapse, and/or redundancy, were documented at surgery. autopsy of individuals without cardiac problems who suffered a traumatic death (n=6) or at time of transplantation from heart transplant receptors with normal aortic valves (n=2). All patients or close relatives (for autopsy controls) gave written informed consent, and the study was approved 1 9 , see https://figshare.com/s/4357d56025cb3380a0df). For validation, an independent cohort of mainly severe aortic valve stenosis patients (n=16) was obtained from the MRVALVE study. Whole blood was collected in PAXGene tubes (BDBiosciences) and stored at -80 o C before processing.

Acknowledgments

We thank S. Bartlett for English editing. This work was supported by the Ministerio de Ciencia, Innovación y Universidades from Spain, (grants SAF2016-78370-R, CB16/11/00399, CIBER CV, and RD16/0011/0021, TERCEL) and the Fundación BBVA (grant BIO14_298) and Fundación La Marató TV3 (grant 20153431) to J.L.dlP; and the Fondation pour la Recherche Médicale, grant from l'Institut National de la Santé et de la Recherche Médicale (grant DPC20111123002) and l'Association Française contre les Myopathies (grant TRIM-RD) to S.Z. The cost of this publication was supported in part with funds from the ERDF. The CNIC is supported by the Ministerio de Ciencia, Innovación y Universidades (MCNU), the Instituto de Salud Carlos III (ISCIII), and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (grant SEV-2015-0505).

https://figshare.com/s/4357d56025cb3380a0df). Among the nine clusters combined into MC3, five of them had expression profiles characterised by increased gene expression in all adult samples, both control and CAVD (clusters 22, 9, 34, 36 and 2), whereas four clusters had increased expression in CAVD only (clusters 49, 44, 7, and 35; Figure 3). Discrete immune cell gene signatures were present at week 9 (clusters 44 and 35), week 13 (cluster 9), and week 22 (clusters 49 and 22) of development (Figure 3), but involved relatively fewer pathways than those found in control or calcified adult valves. The presence of immune and pro-inflammatory gene signatures during fetal development and in apparently healthy valves is consistent with the progressive nature of the calcified valve disease pathology. 3). In general, MC2 transcripts had peak expression at week 13,

with lower expression at the other developmental stages (Figure 3), suggesting that metabolic processes are dynamically regulated during aortic valve development. Overall, this analysis indicates that CAVD is associated with decreased OXPHOS metabolism and altered proteostasis.

Stress response gene signatures are altered in CAVD

MC1 and MC5 did not overlap significantly with any of the other MCs but included similar biological processes. Genes in MC1 were associated to 7 pathways (Figure 3; Supplementary Fig. S9A, see https://figshare.com/s/d9a68e0d6faa67e4c6c3; Supplementary Table S3, see https://figshare.com/s/4357d56025cb3380a0df), the top 5 being Sphingosine-1-phosphate Signaling, Endocannabinoid Developing Neuron Pathway, Amyloid Processing, Huntington's Disease Signaling, Dermatan Sulfate Degradation (Figure 3; Supplementary Fig. S9B, see https://figshare.com/s/d9a68e0d6faa67e4c6c3; Supplementary Table S3, see https://figshare.com/s/4357d56025cb3380a0df). The expression of MC1 genes was relatively increased in adult controls and decreased in CAVD (Figure 3). MC5 was enriched in a single pathway, Unfolded protein response (Figure 3; Supplementary Fig. S9C, see https://figshare.com/s/d9a68e0d6faa67e4c6c3; Supplementary Table S3, see https://figshare.com/s/4357d56025cb3380a0df). MC5 genes were expressed at decreased levels in controls and increased in CAVD (Figure 3). These data suggest that chronic immune-

Competing interests

The authors declare no competing interests. 1