Anne Felim

Amparo Urios

Anne Neudörffer

Guadalupe Herrera

Manuel Blanco

Martine Largeron
email: martine.largeron@univ-paris5.fr

An Efficient Tandem for Evaluating the Ability of Catechol-Thioether Metabolites of MDMA ("Ecstasy") to Induce Toxic Effects through Redox-Cycling

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Introduction ()-3,4-methylenedioxymethamphetamine (MDMA, also known as "ecstasy") is a synthetic psychoactive drug possessing stimulant and hallucinogenic properties. The recreational use of MDMA is prevalent, mainly among young people, despite warnings of irreversible damage to the central nervous system [START_REF] Green | The pharmacology and clinical pharmacology of 3,4-methylenedioxymethamphetamine (MDMA, "ecstasy[END_REF][START_REF] Cole | Altered states: the clinical effects of ecstasy[END_REF][START_REF] Lyles | Methylenedioxymethamphetamine (MDMA, Ecstasy) neurotoxicity: cellular and molecular mechanisms[END_REF][START_REF] Morton | Ecstasy: pharmacology and neurotoxicity[END_REF][START_REF] Ricaurte | Recognition and management of complications of new recreational drug use[END_REF][START_REF] Baumann | 3,4-methylenedioxymethamphetamine (MDMA) neurotoxicity in rats: a reappraisal of past and present findings[END_REF]. Furthermore, MDMA consumption produces a variety of systemic and organspecific adverse effects [START_REF] Milroy | Pathology of deaths associated with "ecstasy" and "eve" misuse[END_REF][START_REF] Walubo | Fatal multi-organ failure after suicidal overdose with MDMA, "ecstasy": case report and review of the literature[END_REF][START_REF] Fineschi | MDMA) and Eve (MDA) misuse : an immunohistochemical study on three fatal cases[END_REF]. Consequently, MDMA abuse has the potential to give rise to a major public health problem.

There is a general consensus that MDMA metabolism, resulting in the formation of highly redox active metabolites, is required for the expression of MDMA-induced neurotoxicity (10-18), hepatotoxicity [START_REF] Carvalho | The toxicity of N-methyl--methyldopamine to freshly isolated rat hepatocytes is prevented by ascorbic acid and N-acetylcysteine[END_REF][START_REF] Carvalho | Hepatotoxicity of 3,4-methylenedioxyamphetamine and -methyldopamine in isolated rat hepatocytes: formation of glutathione conjugates[END_REF], nephrotoxicity [START_REF] Carvalho | Role of metabolites in MDMA (ecstasy)-induced nephrotoxicity: an in vitro study using rat and human renal proximal tubular cells[END_REF] and cardiotoxicity [START_REF] Carvalho | Metabolism is required for the expression of Ecstasy-induced cardiotoxicity in vitro[END_REF]. However, the mechanism by which these metabolites initiate and propagate the toxic events remains to be thoroughly clarified.

In humans, MDMA is essentially metabolized, via o-demethylenation, into 3,4dihydroxymethamphetamine (HHMA), while N-demethylation into 3,4-methylenedioxyamphetamine (MDA) constitutes a minor metabolic pathway (Scheme 1) [START_REF] Segura | 3,4-Dihydroxymethamphetamine (HHMA). A major in vivo 3,4-methylenedioxymethamphetamine (MDMA) Metabolite in Humans[END_REF]. Because of its catechol moiety, HHMA can be easily oxidized to the corresponding o-quinone species Q1, which can enter redox cycles through its semiquinone radical SQ1 . , leading to the formation of reactive oxygen species (ROS) including superoxide, hydrogen peroxide and, ultimately, the hydroxyl radical. The o-quinone intermediate is also a Michael acceptor, which can be conjugated with thiol nucleophiles such as GSH, to form 5-glutathion-S-yl-N-methyl--methyldopamine. This GSH conjugate remains redox active, being readily oxidized to the quinone-thioether species Q2, which, after the reductive addition of a second molecule of GSH, yields the 2,5-bis-glutathion-S-yl conjugate, also oxidizable to the quinone-thioether species Q3 (Scheme 1).

Taking all together, the ability of HHMA and its catechol-thioether conjugates to undergo redox cycling and produce ROS, might provide a rationale for the role of these metabolites in MDMA-induced toxicity (15, [START_REF] O'shea | 3, 4methylenedioxymethamphetamine induces monoamine release, but not toxicity, when administered centrally at a concentration occurring following a peripherally injected neurotoxic dose[END_REF][START_REF] De La Torre | Response to Sprague and Nichols : Contribution of metabolic activation to MDMA neurotoxicity[END_REF]. This prompted us, first, to develop a straightforward one-pot electrochemical procedure allowing the synthesis of several catechol-thioether metabolites of MDMA 1-5 (Figure 1) and, second, to evaluate their cytotoxicity profiles through simple Escherichia coli plate assays, which allowed the discrimination of toxicity mediated by ROS (ROS-TOX) from that induced by the reaction of o-quinones with cellular nucleophiles (Q-TOX) [START_REF] Bolton | Role of quinones in toxicology[END_REF].

We have recently shown that OxyR -cells, deficient in the OxyR function, are adequate targets for the detection of ROS-TOX, a toxicity sensitive to inhibition by antioxidant defenses [START_REF] Martínez | Mutagenicity of 80 chemicals in Escherichia coli tester strains IC203, deficient in OxyR, and its oxyR + parent WP2 uvrA/pKM101: detection of 31 oxidative mutagens[END_REF][START_REF] Urios | Nitric oxide promotes strong cytotoxicity of phenolic compounds against Escherichia coli: the influence of antioxidant defenses[END_REF][START_REF] Urios | A convenient approach for evaluating the toxicity profile of in vitro neuroprotective alkylaminophenol derivatives[END_REF]. The OxyR protein is a redox-sensitive transcriptional regulator, that mediates the induction of genes encoding antioxidant enzymes such as catalase and peroxiredoxin alkylhydroperoxide reductase [START_REF] Christman | Positive control of a regulon for defenses against oxidative stress and some heat-shock proteins in Salmonella typhimurium[END_REF][START_REF] Zheng | DNA microarray-mediated transcriptional profiling of the Escherichia coli response to hydrogen peroxide[END_REF].

Consequently, this induction is prevented in OxyR protein-deficient cells.

Toxicity arising from binding of quinones with cellular nucleophiles (Q-TOX) is not inhibited by antioxidant enzymes and can be detected in OxyR + cells [START_REF] Urios | Nitric oxide promotes strong cytotoxicity of phenolic compounds against Escherichia coli: the influence of antioxidant defenses[END_REF]. Detection of Q-TOX has been improved by adding to the assay nitric oxide, which promoted quinol oxidation [START_REF] Urios | Nitric oxide promotes strong cytotoxicity of phenolic compounds against Escherichia coli: the influence of antioxidant defenses[END_REF][START_REF] Urios | A convenient approach for evaluating the toxicity profile of in vitro neuroprotective alkylaminophenol derivatives[END_REF]. In the present work, the two-electron oxidation of quinols by tyrosinase was used to promote quinone formation.

At this point, it should be noted that catechol-thioether metabolites of MDMA are known to stimulate dopamine transport into cells that express the human serotonin transporter, and hence, can potentiate ROS generation through dopamine deamination and/or autoxidation [START_REF] De La Torre | Response to Sprague and Nichols : Contribution of metabolic activation to MDMA neurotoxicity[END_REF]. So, for comparison, the same bacterial plate assays have also been applied to dopamine (DA) and its related catechol-thioether conjugates 6-10 (Figure 1).

In this paper, we demonstrate that HHMA, and most of its catechol-thioether conjugates 1-5, behave as potent inducers of toxicity mediated by ROS, with a greater efficiency than DA and its related thioether conjugates 6-10. In addition, the results suggest an important role of the o-quinone species as redox active centers generating ROS.

Experimental Procedures

Chemistry. All reagents and solvents (HPLC grade) were commercial products of the highest available purity and were used as supplied. Dopamine hydrochloride (DA, HCl) was purchased from

Aldrich. (±)-N-methyl--methyl-dopamine hydrobromide (N-Me--Me-DA, HBr) was synthesized in four steps from commercially available 3,4-dimethoxy-benzaldehyde and nitroethane through procedures previously reported [START_REF] Borgman | -Methyldopamine derivatives. Synthesis and pharmacology[END_REF][START_REF] Morgan | Synthesis of some N-oxygenated products of 3,4dimethoxyamphetamine and its N-alkyl derivatives[END_REF][START_REF] Cannon | N-alkyl derivatives of ()--methyldopamine[END_REF][START_REF] Milhazes | Synthesis and cytotoxic profile of 3,4-methylenedioxymethamphetamine ("Ecstasy") and its metabolites on undifferentiated PC12 cells: a putative structure-toxicity relationship[END_REF].

HPLC was carried out using a Waters system consisting of a 600E multisolvent delivery system, a

Rheodyne-type loop injector, and a 2487 dual-channels UV-visible detector set at 254 and 278 nm. A mixture of three solvents constituted the mobile phase: acetonitrile (MeCN), solvents A and B. Solvent A was prepared by adding 1‰ concentrated trifluoroacetic acid (TFA) to deionized water. Solvent B was prepared by adding 0.5‰ TFA to a mixture of equivalent volumes of deionized water and methanol. Semi-preparative reversed-phase HPLC was performed using a 250 x 20 mm -5 m-Kromasil C18 column and a 2 mL loop injector, while, for analytical reversed-phase HPLC, a 250 x 4.6 mm -5 m-Kromasil C18 column, together with a 50 L loop injector, were used.

1 H NMR and 13 C NMR spectra were performed on a Brucker AC-300 spectrometer operating at 300 and 75 MHz, respectively. Chemical shifts are expressed as  units (part per million) downfield from TMS (tetramethylsilane). The measurements were carried out using the standard pulse-sequences. The carbon type (methyl, methylene, methine or quaternary) was determined by DEPT experiments. 1 H NMR spectra of all catechol-thioether conjugates 1-10 are included in the supporting information as a proof of their identity. Controlled-potential electrolyses were carried out using a DEA 332 electrochemical analyser (Radiometer, Copenhagen), in a cylindrical three-electrode divided cell (9 cm diameter). In the main compartment, a cylindrical platinum grid (area = 60 cm 2 ) served as the anode (working electrode). A platinum sheet was placed in the concentric cathodic compartment (counter-electrode), which was separated from the main compartment with a glass frit. The reference electrode was as above described.

Electrochemistry

5-(glutathion-S-yl)-N-methyl--methyl-dopamine (1). General procedure A:

A solution of (±)-Nmethyl--methyl-dopamine hydrobromide (N-Me--Me-DA, HBr) (65.5 mg, 0.25 mmol), in 0.2 M HCl (250 mL), was oxidized under nitrogen, at room temperature, at a platinum grid whose potential was fixed at + 1.0 V versus Ag/AgCl. After the consumption of 2 electrons per molecule, 2 equiv of glutathione (157 mg, 0.50 mmol) were added to the pale yellow solution which turned colorless. The reaction mixture was immediately frozen at -80°C and then freeze-dried. The residue was subdivided in fractions of about 25 mg. Each fraction was dissolved in 2 mL of water, and then purified by semipreparative reversed-phase HPLC, using a mixture of solvent A/solvent B 70/30 as the eluent (flow rate:

3 mL min -1 ). Fractions containing 5-(glutathion-S-yl)-N-Me--Me-DA 1 were collected, immediately frozen at -80°C, and then freeze-dried. Compound 1 was isolated as a white solid (78 mg, 0.16 mmol), in 64% yield. Its degree of purity (99%) was determined by analytical HPLC (eluent: solvent A/ MeCN 94/6 -flow rate: 1 mL min -1 ).

2,5-bis-(glutathion-S-yl)-N-methyl--methyl-dopamine (4). General procedure B: A solution of

N-Me--Me-DA, HBr (65.5 mg, 0.25 mmol) in 0.2 M hydrochloric acid (250 mL), was oxidized under nitrogen, at room temperature, at a platinum grid (E = + 1.0 V versus Ag/AgCl). After the consumption of 2 electrons per molecule, the electrolysis was stopped and 3 equiv of glutathione (235 mg, 0.75 mmol) were added. When the yellow electrolysis solution became colorless, it was oxidized again at + 1.0 V versus Ag/AgCl. After exhaustive electrolysis (n = 5.8, that is 2.0 for the first step and 3.8 for the second one), 2 equiv of glutathione (157 mg, 0.50 mmol) were added to the reaction mixture, which was immediately frozen at -80°C and then freeze-dried. The residue was subdivided in fractions of about 25 mg. Each fraction was dissolved in 2 mL of water and then purified by semi-preparative reversed-phase HPLC, using a mixture of solvent A/ solvent B 72/28 as the eluent (flow rate: 3 mL min -1 ). Fractions were collected individually, immediately frozen at -80°C and then freeze-dried. Compound 4 was isolated as the major product in 50% yield (pale pink solid, 99 mg, 0.13 mmol), along with compound 1 as the minor one, in 22% yield (26.5 mg, 0.055 mmol). The degree of purity for compound 4 (99%) was determined by analytical HPLC (eluent: solvent A/ MeCN 94/6 -flow rate: 0.8 mL min -1 ). Spectroscopic data were identical to those previously reported [START_REF] Zhang | Influence of glutathione on the oxidation chemistry of the catecholaminergic neurotransmitter dopamine[END_REF][START_REF] Zheng | Inhibition effects of 5-S-Glutathionyl-N--alanyl-L-dopa analogues against Src protein Tyrosine Kinase[END_REF][START_REF] Picklo | Endogenous catechol thioethers may be pro-oxidant or antioxidant[END_REF].

2,5-bis-(N-acetylcystein-S-yl)-N-methyl

5-(N-acetylcystein-S-yl)-dopamine (7). The general procedure A, replacing N-methyl--methyl-

dopamine hydrobromide by dopamine hydrochloride (DA, HCl) (47.5 mg, 0.25 mmol), and glutathione by N-acetylcysteine (83 mg, 0.50 mmol), afforded, after semi-preparative reversed-phase HPLC (eluent: solvent A/ solvent B 88/12 -flow rate: 11 mL min -1 ), 5-(N-acetylcystein-S-yl)-DA 7 as a white solid (43 mg, 0.137 mmol), in 55% yield. Its degree of purity (99%) was determined by analytical HPLC (eluent: solvent A/ MeCN 94/6 -flow rate: 0.8 mL min -1 ). Spectroscopic data were identical to those previously reported [START_REF] Picklo | Endogenous catechol thioethers may be pro-oxidant or antioxidant[END_REF][START_REF] Xu | Characterization of products from the reactions of dopamine quinone with N-acetylcysteine[END_REF].

5-(cystein-S-yl)-dopamine (8).

The general procedure A, replacing (±)-N-methyl--methyl-dopamine hydrobromide by dopamine hydrochloride (DA, HCl) (47.5 mg, 0.25 mmol), and glutathione by cysteine (62 mg, 0.50 mmol), afforded, after semi-preparative reversed-phase HPLC (eluent: solvent A/ solvent B 88/12 -flow rate: 3 mL min -1 ), 5-(cystein-S-yl)-DA 8 as a white solid (43 mg, 0.16 mmol) in 63 % yield. Its degree of purity (99%) was determined by analytical HPLC (eluent: solvent A/ MeCN 95/5 -flow rate: 0.6 mL min -1 ). Except for 13 C NMR spectrum which was not described, spectroscopic data were identical to those previously reported [START_REF] Zhang | Effects of L-cysteine on the oxidation chemistry of dopamine: new reaction pathways of potential relevance to idiopathic Parkinson's disease[END_REF]. 13

2,5-bis-(glutathion-S-yl)-dopamine (9)

. The general procedure B, replacing (±)-N-methyl--methyldopamine hydrobromide by dopamine hydrochloride (DA, HCl) (47.5 mg, 0.25 mmol) afforded, after exhaustive electrolysis (n = 5, that is 2 for the first step and 3 for the second one) and semi-preparative reversed-phase HPLC (eluent: solvent A/ solvent B 78/22 -flow rate: 2.4 mL min -1 ), 2,5-(glutathion-Syl)-DA 9 as a white solid (122 mg, 0.16 mmol) in 64% yield, along with compound 6 as the minor one, in 24% yield (28 mg, 0.06 mmol). The degree of purity for compound 9 (99%) was determined by analytical HPLC (eluent: solvent A/ MeCN 94/6 -flow rate: 0.7 mL min -1 ). Except for 13 C NMR spectrum which was not described, spectroscopic data were identical to those previously reported [START_REF] Zhang | Influence of glutathione on the oxidation chemistry of the catecholaminergic neurotransmitter dopamine[END_REF][START_REF] Zheng | Inhibition effects of 5-S-Glutathionyl-N--alanyl-L-dopa analogues against Src protein Tyrosine Kinase[END_REF]. 13

2,5-bis-(N-acetylcystein-S-yl)-dopamine (10).

The general procedure B, replacing (±)-N-methyl-methyl-dopamine hydrobromide by dopamine hydrochloride (DA, HCl) (47.5 mg, 0.25 mmol) and glutathione by N-acetylcysteine (208 mg, 1.25 mmol, overall quantity), afforded, after exhaustive electrolysis (n = 5.5, that is 2 for the first step and 3.5 for the second one) and semi-preparative reversed-phase HPLC (eluent: solvent A/ solvent B 80/20 -flow rate: 6 mL min -1 ), 2,5-bis-(Nacetylcystein-S-yl)-DA 10 in 62% yield (white solid, 74 mg, 0.16 mmol), along with compound 7 in 18% yield (14.2 mg, 0.045 mmol). The degree of purity for compound 10 (99%) was determined by analytical HPLC (eluent: solvent A/ MeCN 91/9 -flow rate: 0.9 mL min -1 ). Except for 13 C NMR spectrum which was not described, spectroscopic data were identical to those previously reported [START_REF] Shen | Synthesis, redox properties, in vivo formation, and neurobehavioral effects of N-acetylcysteinyl conjugates of dopamine: possible metabolites of relevance to Parkinson's disease[END_REF][START_REF] Xu | Characterization of products from the reactions of dopamine quinone with N-acetylcysteine[END_REF]. 13 curves obtained in the assays, a parameter indicating the cytotoxic potency, the TC50 value, was calculated as the concentration (mol/plate) needed to reduce the survival to 50%.

Results and Discussion

Chemistry. Synthesis of catechol-thioether mono-adducts 1-3. A simple method for synthesizing a catechol-thioether mono-adduct involves the oxidation of the catechol with mushroom tyrosinase, in the presence of a thiol. The reaction proceeds via formation of the o-quinone intermediate (43), followed by 1,6-Michael addition reaction of thiol. This method has been recently used for the synthesis of the catechol-thioether metabolite 2 using N-acetylcysteine as the thiol, and HHMA as the starting catechol (yield not given) [START_REF] Jones | Serotonergic Neurotoxic Metabolites of Ecstasy Identified in Rat Brain[END_REF]. To the best of our knowledge, the syntheses of catechol-thioether mono-adducts 1 and 3 have never been reported, although similar conjugates, isolated from the enzymatic oxidation of 3,4-dihydroxyamphetamine (10,18), dopamine or dopa [START_REF] Zhang | Influence of glutathione on the oxidation chemistry of the catecholaminergic neurotransmitter dopamine[END_REF][START_REF] Zheng | Inhibition effects of 5-S-Glutathionyl-N--alanyl-L-dopa analogues against Src protein Tyrosine Kinase[END_REF][START_REF] Ito | A facile one-pot synthesis of cysteinyldopas using mushroom tyrosinase[END_REF][START_REF] Ito | Identification of 5-S and 2-Scysteinyldopamine and 5-S-glutathionyldopamine formed from dopamine by high-performance liquid chromatography with electrochemical detection[END_REF], are known. However, the enzymatic procedure is not adapted for routine syntheses, because it is too expensive and not suitable to yield substantial amounts of catechol-thioether mono-conjugates.

When applied to HHMA, existing chemical methods reported for the synthesis of catechol thioether mono-conjugates of dopamine or dopa, which use diverse oxidizing agents such as hydrobromic acid, silver oxide and ceric ammonium nitrate [START_REF] Ito | Novel reaction of cysteine with phenolic amino-acids in hydrobromic acid: reversible formation of 3-cystein-S-yltyrosine and cystein-S-yldopas[END_REF][START_REF] Ito | Synthesis and antitumor activity of cysteinyl-3,4-dihydroxyphenylalanines and related compounds[END_REF][START_REF] Chioccara | A convenient one step synthesis of 5-cysteinyl-S-yldopa using ceric ammonium nitrate[END_REF], only gave low yields of the corresponding conjugates.

Furthermore, the workup and isolation procedures were not suitable for the synthesis of metabolites in high purity grade (99%) as required for biological evaluation, because the products were separated by column chromatography on Dowex using HCl as the eluent.

For a long time, electrochemical oxidation has proved to be an efficient tool for the generation of highly electrophilic quinones species, which can be further scavenged by diverse entities such as amines [START_REF] Largeron | Synthesis and in vitro evaluation of new 8-amino-1,4-benzoxazine derivatives as neuroprotective antioxidants[END_REF][START_REF] Largeron | Oxidative deamination of benzylamine by electrogenerated quinonoid systems as mimics of amine oxidoreductases[END_REF], enamines (51) or thiols. Accordingly, unstable o-quinone species have been electrogenerated from dopamine [START_REF] Zhang | Influence of glutathione on the oxidation chemistry of the catecholaminergic neurotransmitter dopamine[END_REF][START_REF] Shen | Synthesis, redox properties, in vivo formation, and neurobehavioral effects of N-acetylcysteinyl conjugates of dopamine: possible metabolites of relevance to Parkinson's disease[END_REF][START_REF] Xu | Characterization of products from the reactions of dopamine quinone with N-acetylcysteine[END_REF][START_REF] Zhang | Effects of L-cysteine on the oxidation chemistry of dopamine: new reaction pathways of potential relevance to idiopathic Parkinson's disease[END_REF], norepinephrine [START_REF] Shen | Oxidation chemistry of (-)-norepinephrine in the presence of Lcysteine[END_REF] or N-acetyldopamine [START_REF] Huang | Electrochemical oxidation of N-acyldopamines and regioselective reactions of their quinones with Nacetylcysteine and thiourea[END_REF], and then scavenged by thiol residues to afford the corresponding catechol-thioether adducts. Consequently, we turned our attention toward these methods for producing the catechol-thioether conjugates 1-3.

The mono-adducts 1-3 have been prepared following a procedure close to that previously reported in the case of dopamine [START_REF] Zhang | Influence of glutathione on the oxidation chemistry of the catecholaminergic neurotransmitter dopamine[END_REF][START_REF] Shen | Synthesis, redox properties, in vivo formation, and neurobehavioral effects of N-acetylcysteinyl conjugates of dopamine: possible metabolites of relevance to Parkinson's disease[END_REF][START_REF] Xu | Characterization of products from the reactions of dopamine quinone with N-acetylcysteine[END_REF][START_REF] Zhang | Effects of L-cysteine on the oxidation chemistry of dopamine: new reaction pathways of potential relevance to idiopathic Parkinson's disease[END_REF], with some modifications [START_REF] Largeron | Biomimetic electrochemical synthesis of quinol-thioether conjugates: their implication in the serotoninergic neurotoxicity of amphetamine derivatives[END_REF]. We found that the anodic oxidation of HHMA had to be conducted in the absence of thiol substrate, because the catechol-thioether monoadduct was also electroactive at the applied potential, giving unwanted additional oxidation products.

The cyclic voltammogram of HHMA (1 mM), in deaerated 0.2 mM phosphate buffered aqueous solution (pH 7.

3), at room temperature, showed, at a platinum anode, an oxidation peak Pa at + 200 mV versus Ag/AgCl, due to a two-electron process, the sweep rate being 200 mV s -1 (Figure 2a). On the reverse sweep, the lack of peak Pc illustrated the irreversibility of the overall two-electron oxidation process, indicating that the electrogenerated o-quinone species was reactive enough to be engaged in a fast follow-up chemical reaction. As previously described in the case of dopamine [START_REF] Zhang | Effects of L-cysteine on the oxidation chemistry of dopamine: new reaction pathways of potential relevance to idiopathic Parkinson's disease[END_REF], the putative oquinone species underwent rapid intramolecular cyclization, leading to a redox active indoline derivative (as shown by the presence of the redox system (Pa',Pc') in figure 2a) which, after subsequent oxidation reaction, rapidly afforded insoluble melanin-like polymers. Consequently, to circumvent this problem, the anodic electrolysis of HHMA was performed under acidic conditions. Then, the protonation of the amino group prevented the nucleophilic attack at the origin of the formation of the indoline species.

Accordingly, the cyclic voltammogram of HHMA (1 mM), in deaerated 0.2M HCl aqueous solution, at room temperature, showed, at a platinum anode, an oxidation peak Pa at + 560 mV versus Ag/AgCl, due to a two-electron process, the sweep rate being 10 mV s -1 . As can be seen in Figure 2b, a cathodic peak Pc appeared on the reverse sweep at + 470 mV versus Ag/AgCl, illustrating the quasireversibility of the two-electron transfer that could be assigned to the o-quinol/o-quinone redox couple.

When the controlled potential of the platinum anode was fixed at + 1.0 V versus Ag/AgCl, which is at a potential following the anodic peak observed in cyclic voltammetry, a coulometric value of 2.0  0.1 was found for the number of electrons (n) involved in the oxidation of one molecule of HHMA into the transient o-quinone species. The latter was rather stable under the aforementioned experimental conditions, indicating that the combination of the acidic aqueous conditions, together with the low concentration of HHMA (1mM), prevented both cyclization to indoline derivatives and polymerization side-reactions. Subsequent addition of 2 equiv of thiol derivative resulted in the immediate discoloration of the yellow solution, due to the formation of the catechol-thioether conjugate. Treatment of the electrolysis solution afforded, after semi-preparative reversed-phase HPLC (See Experimental Section), catechol-thioether mono-adducts 1-3, in yields ranging from 50 to 66% (Table 1).

Synthesis of catechol-thioether bi-adducts 4 and 5.

The synthesis of bis-conjugates, resulting from two consecutive 1,6-addition reaction of thiol, has received only scant attention, most of the previous works being restricted to the synthesis of catechol-thioether mono-adducts [START_REF] Zhang | Influence of glutathione on the oxidation chemistry of the catecholaminergic neurotransmitter dopamine[END_REF][START_REF] Zheng | Inhibition effects of 5-S-Glutathionyl-N--alanyl-L-dopa analogues against Src protein Tyrosine Kinase[END_REF][START_REF] Picklo | Endogenous catechol thioethers may be pro-oxidant or antioxidant[END_REF][START_REF] Shen | Synthesis, redox properties, in vivo formation, and neurobehavioral effects of N-acetylcysteinyl conjugates of dopamine: possible metabolites of relevance to Parkinson's disease[END_REF][START_REF] Xu | Characterization of products from the reactions of dopamine quinone with N-acetylcysteine[END_REF][START_REF] Zhang | Effects of L-cysteine on the oxidation chemistry of dopamine: new reaction pathways of potential relevance to idiopathic Parkinson's disease[END_REF]. The corresponding bis-catechol-thioether conjugate was then isolated as a by-product, in very low yields ranging from 1 to 10%. To the best of our knowledge, only a report described the synthesis of 2,5-bis-(glutathion-S-yl)-methyldopamine from the oxidation of 5-(glutathion-S-yl)--methyldopamine, using sodium periodate in aqueous solution of 10% formic acid (yield not given) (12). Consequently, we decided to adapt our aforementioned electrochemical procedure to the synthesis of bi-adducts 4 and 5, using catecholthioether conjugates 1 and 2 as the starting materials, respectively. Our initial attempts to prepare the expected bi-adducts were disappointing, leading to low yields of the desired products (20%). As the electrolysis solution became dark purple, we attributed these results to the instability of the electrogenerated quinone-thioether species. As a consequence, subsequent addition of 2 equiv of thiol derivative did not result in the discoloration of the dark purple solution, in agreement with the formation of melanin-like polymers. So, to circumvent this problem, we decided to oxidize the catechol-thioether mono-adduct in the presence of thiol derivative. Then, we thought that the produced unstable quinonethioether species would be trapped by thiol derivative before its decomposition.

However, to markedly simplify the synthesis of the bis-catechol-thioether conjugates 4 and 5, we envisioned a two-step one-pot electrochemical procedure, which allowed the preparation of the biadduct, using HHMA as the starting material, without the need to isolate the mono-adduct intermediate (Scheme 2). For the synthesis of the bi-adduct 4 for example, the first experimental step consisted of the generation of the o-quinone species and consecutive addition of 2 equiv of glutathione, under the aforementioned experimental conditions. After the electrolysis solution became colorless, the catecholthioether mono-adduct 1 was not isolated, but a third equiv of glutathione was added and, in a second step, the resulting solution was oxidized again at + 1.0 V versus Ag/AgCl. Then, the solution became pale yellow and a coulometric value of 3.8 was found for n. To maintain a reducing medium during work-up, two additional equiv of glutathione were added at the end of the anodic oxidation. Finally, treatment of the electrolysis solution afforded, after semi-preparative reversed-phase HPLC (See Experimental Section), the catechol-thioether bi-adduct 4, in 50% yield, together with 22% of the monoadduct 1 (Scheme 2).

The same one-pot procedure allowed the isolation of bis-catechol-thioether conjugate 5, in 44% yield, along with 22% of catechol-thioether mono-adduct 2, isolated as a minor product (Scheme 2). In all cases, the presence of the catechol-thioether mono-adduct as a minor product could not be avoided, probably because redox interchange occurred between the redox systems present in the electrolysis solution.

Synthesis of catechol-thioether conjugates of dopamine 6-8, 9 and 10. When applied to dopamine, the aforementioned procedure used for the synthesis of catechol-thioether conjugates 1-3 led to the mono-adducts 6-8, in yields ranging from 55 to 65%. Although the electrochemical preparation of conjugates 6-8 had been previously reported, the described procedure was poorly adapted for routine synthesis because the amount of isolated products was very low [START_REF] Zhang | Influence of glutathione on the oxidation chemistry of the catecholaminergic neurotransmitter dopamine[END_REF][START_REF] Shen | Synthesis, redox properties, in vivo formation, and neurobehavioral effects of N-acetylcysteinyl conjugates of dopamine: possible metabolites of relevance to Parkinson's disease[END_REF][START_REF] Xu | Characterization of products from the reactions of dopamine quinone with N-acetylcysteine[END_REF][START_REF] Zhang | Effects of L-cysteine on the oxidation chemistry of dopamine: new reaction pathways of potential relevance to idiopathic Parkinson's disease[END_REF]. When applied to dopamine, the above procedure reported for the synthesis of bis-catechol-thioether conjugates 4 and 5 led to the biadducts 9 and 10 in 64 and 62% yield respectively, along with the corresponding mono-adducts 6 and 7, isolated in 24 and 18% yields respectively. To date, these bi-adducts have been only described as byproducts, in yields lower than 10%, in the course of the electrolysis of mono-conjugates [START_REF] Zhang | Influence of glutathione on the oxidation chemistry of the catecholaminergic neurotransmitter dopamine[END_REF][START_REF] Shen | Synthesis, redox properties, in vivo formation, and neurobehavioral effects of N-acetylcysteinyl conjugates of dopamine: possible metabolites of relevance to Parkinson's disease[END_REF].

Biological evaluation. Cytotoxicity profiles in OxyR -and OxyR + cells. The cytotoxicity profiles of

HHMA and its catechol-thioether conjugates 1-5 were characterized using simple Escherichia coli plate assays, and compared with those of dopamine and its related catechol-thioether conjugates 6-10. The results are presented in Table 2.

The toxicity mediated by ROS (ROS-TOX) was detected in OxyR -cells deficient in the OxyR function, because they are unable to exert antioxidant defenses. In contrast, in OxyR + cells, ROS-TOX was inhibited due to the elimination of hydrogen peroxide by catalase and alkylhydroxyperoxide reductase, both enzymes encoded by OxyR-regulated genes, so that the toxicity arising from binding of quinones with cellular nucleophiles (Q-TOX) could then be evidenced. Moreover, detection of Q-TOX was improved by adding to the assay tyrosinase, which promoted quinone formation.

As shown in Table 2, HHMA gave a positive response in the OxyR -assay (TC50 = 0.75 mol/plate),

indicating high ROS-TOX induction, which was dose-dependent (Figure 3a). Furthermore, an increase in ROS-TOX (TC50 = 0.5 mol/plate) was observed in the presence of tyrosinase which catalyzed the oxidation to o-quinone (Figure 3a), suggesting that the oxidation of HHMA to the corresponding oquinone opens the way for redox cycling, leading to the formation of ROS. Similarly, induction of ROS-TOX by HHMA was also enhanced in the presence of a nitric oxide-releasing agent promoting quinone formation (data not shown), a result in favor of an important role of the o-quinone species as redox active center generating ROS.

In contrast, the negative response in OxyR + assay, even in the presence of tyrosinase, was indicative of the absence of Q-TOX resulting from the high susceptibility of quinones toward endogenous nucleophiles. Note that the detection of ROS-TOX promoted by quinone redox cycling (OxyR - /tyrosinase assay) probably required the absence of Q-TOX (OxyR + assay/tyrosinase), which could mask the toxicity mediated by ROS. Accordingly, for catechol, tyrosinase induced potent Q-TOX to OxyR + cells (TC50 = 0.1 mol/plate), which prevented the detection of ROS-TOX in OxyR -/tyrosinase assay. Conversely, the tyrosinase-promoted induction of ROS-TOX by L-tyrosine, the amino-acid precursor of catecholamines, was easily detected because of the absence of Q-TOX induction (Table 2).

As HHMA did, catechol-thioether mono-conjugates 1 and 2 produced high ROS-TOX (TC50 = 0.8 mol/plate), which was enhanced in the presence of tyrosinase (Figure 3b), suggesting that the generated o-quinone-thioether can enter redox cycles leading to the formation of ROS, while no toxicity was evidenced with 5-cystein-S-yl mono-adduct 3 (Table 2). In this case, the redox activity of the generated quinone-thioether would be suppressed consecutively to a rapid intramolecular cyclization reaction: the cysteinyl amino group would condense with the quinone carbonyl to give a benzothiazolyllike compound (dihydrobenzothiazine), as previously suggested by Monks et al (10)(11)(12)(13)16).

Although markedly less toxic than the corresponding mono-adduct 2 (TC50 = 0.8 mol/plate), 2,5-Nacetylcystein-S-yl bis-conjugate 5 was a good inducer of ROS-TOX (TC50 = 1.50 mol/plate). Once again, the significant increase in ROS-TOX promoted by tyrosinase (TC50 = 0.8 mol/plate) suggested ROS generation via the redox cycling of the generated quinone-thioether species (Figure 3b). To a lesser extent, the same conclusion could be drawn with the slightly toxic 2,5-glutathion-S-yl bis-conjugate 4, for which the toxicity appeared only in the presence of tyrosinase (TC50 = 2.0 mol/plate).

Conversely to HHMA and its related catechol-thioether mono-adducts 1 and 2, dopamine and its corresponding catechol-thioether mono-conjugates 6 and 7 were found to be poor inducers of ROS-TOX (TC50 = 4.0 mol/plate), while no Q-TOX was detected in the OxyR + assay (Table 2 and Figure 3). However, if these compounds were not very prone to autoxidation, catalytic oxidation by tyrosinase induced a significant increase of ROS-TOX (TC50 reached 1.5 mol/plate for mono-adduct 7),

indicating that the generated o-quinone or o-quinone-thioether could also enter redox cycles leading to the formation of ROS (Figure 3). As compound 3 did, 5-cystein-S-yl monoadduct 8 was toxic, neither to the OxyR -cells, nor to the OxyR + cells. However, in contrast to 3, a weak induction of ROS-TOX was observed in the presence of tyrosinase (TC50 = 3.5 mol/plate). This could be explained by a difference in the rate of intramolecular cyclization reaction involving the cysteinyl amino group and the quinone carbonyl to give the dihydrobenzothiazine derivative. In other words, the quinone-thioether species generated from mono-adduct 6 would be more stable than that arising from the oxidation of monoadduct 2.

2,5-N-acetylcystein-S-yl bis-conjugate 10 was found to be a weak inducer of ROS-TOX, the toxicity being detected only in the presence of tyrosinase (Figure 3b), while no Q-TOX could be evidenced in the OxyR + assay (Table 2). Once again, a lower toxicity was found for the bi-adduct in comparison to that exhibited by the corresponding mono-adduct 6. An unexpected behavior was observed with 2,5glutathion-S-yl bis-conjugate 9 as it showed toxic effects at high doses (3 and 4 mol/plate), irrespective of the presence or not of tyrosinase, whereas a low toxicity to OxyR + cells was observed.

Due to these results, further assays are needed to elucidate the toxicity profile of bis-conjugate 9 and, consequently, the data obtained from 9 are not reported in Table 2.

At this stage, we sought to justify the higher toxicity of HHMA and its related catechol-thioether conjugates 1-5 versus that of dopamine and its related catechol-thioether conjugates 6-10. A comparison of the toxic effects with the corresponding oxidation potentials showed that there was no correlation between the detected toxicity and the oxidation potentials. For example, HHMA was found to be a much better inducer of ROS-TOX than dopamine, although they exhibited the same oxidation potential (+ 200 mV versus Ag/AgCl, in 0.2 mM phosphate buffered aqueous solution pH = 7.3). To date, we suspect that structural features mainly might account for the difference of toxicity between HHMA (or its related catechol-thioether conjugates) and dopamine (or its corresponding conjugates). In particular, HHMA possesses, on its side chain, a methyl group which creates an asymmetric center, together with a terminal secondary amine. No doubt these two methyl groups are sufficient to modify, not only the overall reactivity of this catecholamine, but also its affinity for cellular constituents through change in physicochemical parameters such as pKa and Log P. Accordingly, it would be interesting to synthesize HHMA and its related catechol-thioether conjugates in their enantiomerically and diastereomerically pure forms. These syntheses are currently in progress in our laboratory.

Conclusion

The alarming increase in the recreational use of MDMA, and the multitude of adverse effects resulting from its misuse, require a complete understanding of the pharmacology and toxicology of this amphetamine derivative.

To date, there is a general consensus that MDMA metabolism, resulting in the formation of highly redox active metabolites, is required for the expression of MDMA-induced toxicity. However, the mechanism by which these metabolites initiate and propagate the toxic events remains to be thoroughly clarified.

This prompted us to develop a straightforward one-pot electrochemical procedure allowing the synthesis of catechol-thioether conjugates of MDMA 1-5, in good yields and high degree of purity (99%), under environmentally friendly conditions. The utilization of electricity as energy instead of oxidative reagents, aqueous media instead of organic solvents, room temperature, one-pot operation and high atom economy, are of preeminent green advantages. To the best of our knowledge, at the exclusion of compound 2, these catechol-thioether conjugates have never been prepared as yet. No doubt the synthesis of conjugates 1-5 will permit a comprehensive examination, both in vitro and in vivo, of their exact role in the diverse toxic effects of ecstasy.

To get an insight into the possible participation of the catechol-thioether conjugates 1-5 in the toxic events of MDMA, we have used simple bacterial assays, which could be predictive of the toxicological effects associated with these compounds against different targets in vivo. Furthermore, these assays could provide additional informations about the mechanism by which these metabolites could exert their toxic effects. In particular, the cytotoxicity profiles characterized by positive responses in OxyR -assay, which were reinforced in the presence of tyrosinase, supported the hypothesis of an oxidative mechanism dependent on the oxidation of HHMA and its catechol-thioether conjugates 1-5 to the corresponding o-quinone species. These could enter redox cycles, leading to the formation of ROS. The lack of positive responses in OxyR + assay corroborated this hypothesis as these quinonoid species failed to induce Q-TOX arising from binding with cellular nucleophiles. Dopamine and its corresponding quinol-thioether conjugates 6-10 showed a low susceptibility to spontaneous oxidation. However, tyrosinase, which promoted quinone formation, was able to enhance ROS-TOX, in agreement with oquinone species acting as redox active centers generating ROS.

From a general toxicological point of view, the catecholamine derivatives constitute a specific class of quinol derivatives because simple catechol derivatives are rather prone to Q-TOX, due to the high susceptibility of the generated o-quinone to give Michael addition reaction with cellular nucleophiles.

Recently, we have also described another behavior of quinol derivatives possessing a pyrogallol arrangement, for which high ROS-TOX was detected in OxyR -cells. However, in the presence of agents promoting quinone formation such as nitric oxide, a significant decrease of ROS-TOX was observed, suggesting that the nitric oxide promoted two-electron oxidation process would compete with the oneelectron oxidation reactions initiating ROS-TOX phenomena. In this case, the results in the OxyR -assay showed a good correlation with those given by the MTT assay performed in vitro on murine HT-22 hippocampal cell cultures, thus allowing to approach the toxicity mechanism of neuroprotective agents [START_REF] Urios | A convenient approach for evaluating the toxicity profile of in vitro neuroprotective alkylaminophenol derivatives[END_REF]. Obviously, the amino side chain of catecholamine derivatives plays a major role in the development of ROS-TOX involving quinonoid species. This point would merit thorough further investigations.

Finally, the last question that remains is the relevance of our in vitro results to the in vivo situation.

Despite the fact that HHMA and some catechol-thioether conjugates are present in vivo [START_REF] Jones | Serotonergic Neurotoxic Metabolites of Ecstasy Identified in Rat Brain[END_REF][START_REF] Segura | 3,4-Dihydroxymethamphetamine (HHMA). A major in vivo 3,4-methylenedioxymethamphetamine (MDMA) Metabolite in Humans[END_REF], they have not been undoubtedly established as in vivo toxic metabolites of MDMA. Furthermore, several other pathways could also contribute to the overall MDMA toxicity, such as mechanism-based inhibition of CYP2D6, the main P450 enzyme responsible for the demethylenation of MDMA. In addition, the fraction of HHMA that undergoes oxidation to the o-quinone species, relative to the fraction that undergoes catechol o-methyltransferase (COMT) mediated o-methylation is unknown [START_REF] Jones | Serotonergic Neurotoxic Metabolites of Ecstasy Identified in Rat Brain[END_REF].

However, catechol estrogens undergo significant oxidation coupled to GSH conjugation (28-50%), even under conditions optimized for COMT-catalyzed o-methylation (presence of ascorbate) [START_REF] Butterworth | 17-Estradiol metabolism by hamster hepatic microsomes: comparison of catechol estrogen o-methylation with catechol estrogen oxidation and glutathione conjugation[END_REF]. Taking all together, it can be suggested that HHMA, and catechol-thioether conjugates 1-5, could participate in the toxic effects of MDMA through the efficiency of redox active quinonoid centers generating ROS. a Reagents and conditions: HHMA or DA = 1mM, RSH = 2 mM, 0.2 M HCl, rt, Pt anode (E = + 1.0 V vs Ag/AgCl), 30 min. b Yields refer to isolated products (after semi-preparative reversed-phase HPLC, degree of purity: 99%). NAC: N-acetylcysteine. DA:dopamine

.

  Electrochemical measurements were made with a Radiometer-Tacussel PRG 5 multipurpose polarograph, which was used only as a rapid-response potentiostat. For cyclic voltammetry, triangular waveforms were supplied by a Tacussel GSTP 4 function generator. Currentpotential curves were recorded on a Schlumberger SI 8312 instrument. The cell was a Radiometer-Tacussel CPRA water-jacketed cell working at a temperature of 25°C. The working electrode was a platinum disk, carefully polished before each voltammogram with an aqueous alumina suspension. The counter electrode was a platinum electrode Tacussel Pt 11. The reference electrode was an Ag/AgCl electrode, to which all potentials quoted are referred.

containing 2 , 5 -

 25 bis-(glutathion-S-yl)-N-Me--Me-DA (4) and 5-(glutathion-S-yl)-N-Me--Me-DA[START_REF] Green | The pharmacology and clinical pharmacology of 3,4-methylenedioxymethamphetamine (MDMA, "ecstasy[END_REF]

  --methyl-dopamine[START_REF] Ricaurte | Recognition and management of complications of new recreational drug use[END_REF]. The general procedure B, replacing glutathione by N-acetylcysteine (208 mg, 1.25 mmol, overall quantity), afforded, after exhaustive electrolysis (n = 5.5, that is 2 for the first step and 3.5 for the second one) and semipreparative reversed-phase HPLC (eluent: solvent A/ solvent B 93/17 -flow rate: 14 mL min -1 ), 2,5-bis-(N-acetylcystein-S-yl)-N-Me--Me-DA 5 in 44% yield (white solid, 55 mg, 0.11 mmol), along with compound 2 in 22% yield (19 mg, 0.055 mmol). The degree of purity for compound 5 (99%) was determined by analytical HPLC (eluent: solvent A/ MeCN 92/8 -flow rate: 0.8 mL min -1 ).1 H NMR (D2O)  1.08 (d, J = 6.5 Hz, 3H), 1.75 (d, J = 3.5 Hz, 3H), 1.80 (d, J = 4.5 Hz, 3H), 2.59 (s, 3H), 2.80 (m, 1H), 3.09 and 3.25 (m, 6H), 4.25 (m, 2H), 6.84 (s, 1H). 13 C NMR (D2O)  14.8, 21.5(2), 30.0, 34.5, 34.7, 37.0, 52.6, 53.5, 56.2, 119.4, 121.0, 126.1, 131.6, 143.9, 146.6, 173.8 (4) .MS (ES+), m/z: 504 [M+H] + , 526 [M+Na] + .5-(glutathion-S-yl)-dopamine (6).The general procedure A, replacing (±)-N-methyl--methyldopamine hydrobromide by dopamine hydrochloride (DA, HCl) (47.5 mg, 0.25 mmol) afforded, after semi-preparative reversed-phase HPLC (eluent: solvent A/ solvent B 95/5 -flow rate: 11.5 mL min -1 ), 5-(glutathion-S-yl)-DA 6 as a white solid (74.5 mg, 0.16 mmol) in 65% yield. Its degree of purity (99%) was determined by analytical HPLC (eluent: solvent A/ MeCN 97/3 -flow rate: 1.0 mL min -1 ).

( 10 )

 10 Miller, R.T., Lau, S., and Monks, T.J. (1995) Metabolism of 5-(glutathion-S-yl)-methyldopamine following intracerebroventricular administration to male Sprague-Dawley rats. Chem. Res. Toxicol. 8, 634-641. (11) Miller, R.T., Lau, S., and Monks, T.J. (1996) Effects of intracerebroventricular administration of 5-(glutathion-S-yl)--methyldopamine on brain dopamine, serotonin and norepinephrine concentrations in male Sprague-Dawley rats. Chem. Res. Toxicol. 9, 457-465. (12) Miller, R.T.; Lau, S.; Monks, T.J. (1997) 2,5-bis-(glutathion-S-yl)--methyldopamine, a putative metabolite of (±)-3,4-methylenedioxy amphetamine, decreases brain serotonin concentrations. Eur. J. Pharmacol. 323, 173-180. (13) Bai, F.B., Lau, S.S., and Monks, T.J. (1999) Glutathione and N-acetylcysteine conjugates of neurotoxicity. Chem. Res. Toxicol. 12, 1150-1157. (14) Bai, F., Jones, D.C., Lau, S.S., and Monks, T.J. (2001) Serotonergic neurotoxicity of 3,4-(±)methylenedioxyamphetamine and 3,4-(±)-methylenedioxymethamphetamine (ecstasy) is potentiated by inhibition of -glutamyl transpeptidase. Chem. Res. Toxicol. 14, 863-870. (15) Jones, D.C., Lau, S.S., and Monks, T.J. (2004) Thioether metabolites of 3,4methylenedioxyamphetamine and 3,4-methylenedioxymethamphetamine inhibit human serotonin transporter (hSERT) function and simultaneously stimulate dopamine uptake into hSERT-expressing SK-N-MC cells. J. Pharmacol. Exp. Ther. 311, 298-306. (16) Monks, T.J., Jones, D.C., Bai, F., and Lau, S.S. (2004) The role of metabolism in 3,4-()methylenedioxyamphetamine and 3,4-()-methylenedioxymethamphetamine (Ecstasy) toxicity.Ther. Drug Monit.26, 132-136.

Figure 1 . 10 Figure 2 .

 1102 Figure 1. Chemical Structures of Synthesized Catechol-Thioether Conjugates 1-10

Figure 3 . 2 , 5 , 7 Scheme 1 . 4 :Scheme 2 .

 3257142 Figure 3. Dose-response curves: survival of OxyR -cells after exposure a) to HHMA and dopamine;

N-acetylcystein-S-yl)-N-methyl--methyl-dopamine (2). The

	HPLC (eluent: solvent A/ solvent B 80/20 -flow rate: 16.5 mL min -1 ), 5-(N-acetylcystein-S-yl)-N-Me-
	-Me-DA 2 as a white solid (56 mg, 0.165 mmol), in 66% yield. Its degree of purity (99%) was
	determined by analytical HPLC (eluent: solvent A/ MeCN 93/7 -flow rate: 1 mL min -1 ). 1 H NMR
	(D2O)  1.10 (d, J= 6.5 Hz, 3H), 1.79 (s, 3H), 2.55 (s, 3H), 2.60 (m, 1H), 2.77 (m, 1H), 3.05 (m, 1H),
	3.27 (m, 2H), 4.26 (m, 1H), 6.64 (s, 1H), 6.73 (s, 1H). 13 C NMR (D2O)  15.4, 22.2, 30.5, 35.2, 38.5,
	53.1, 56.9, 117.8, 120.1, 126.8, 128.8, 144.6, 145.1, 174.4 ( 2). MS (ES+), m/z 343 [M+H] + .
	5-(
	1 H NMR (D2O)  1.12 (d, J = 6.5 Hz, 3H), 2.01 (m, 2H), 2.36 (t, J = 7.5
	Hz, 2H), 2.56 (s, 3H), 2.62 (m, 1H), 2.78 (m, 1H), 3.06 (m, 1H), 3.26 (m, 2H), 3.70 (s, 2H), 3.81 (m,
	1H), 4.28 (m, 1H), 6.66 (s, 1H), 6.75 (s, 1H). 13 C NMR (D2O) 15.5, 26.6, 30.6, 31.9, 35.4, 38.6, 42.1,
	53.8, 54.4, 57.0, 118.0, 120.1, 126.8, 129.0, 144.7, 145.2, 172.9, 174.0, 175.2 ( 2). MS (ES+), m/z 487
	[M+H] + .
	5-(general procedure A, replacing
	glutathione by N-acetylcysteine (83 mg, 0.50 mmol), afforded, after semi-preparative reversed-phase

cystein-S-yl)-N-methyl--methyl-dopamine (3). The

	general procedure A, replacing glutathione
	by cysteine (62 mg, 0.50 mmol), afforded, after semi-preparative reversed-phase HPLC (eluent:
	solvent A/ solvent B 90/10 -flow rate: 11.5 mn min -1 ), 5-(cystein-S-yl)-N-Me--Me-DA 3 as a white
	solid (38 mg, 0.13 mmol), in 50% yield. Its degree of purity (99%) was determined by analytical HPLC
	(eluent: solvent A/ MeCN 97/3 -flow rate: 0.8 mL mn -1 ). 1 H NMR (D2O)  1.14 (d, J = 6.5 Hz, 3H),
	2.57 (s, 3H), 2.65 (m, 1H), 2.80 (m, 1H), 3.33 (m, 3H), 3.86 (m, 1H), 6.71 (s, 1H), 6.85 (s, 1H). 13 C
	NMR (D2O)  15.5, 30.6, 35.1, 38.6, 53.4, 56.9, 118.4, 119.3, 127.3, 129.1, 145.1, 145.4, 171.6. MS
	(ES+), m/z 301 [M+H] + .

  which were incubated at 37ºC, for 24 or 48 h. % Survival = [colony forming units (CFU) per plate at each dose divided by CFU per plate at zero dose] x 100. Data were the mean of at least three experiments. Error bars represent SD and are shown when larger than 10%. From the dose-response

	containing approximately 800 test cells (from a dilution, in E buffer, of an overnight broth culture) and
	3x10 7 filler cells (from an undiluted overnight broth culture) were added: 100 L of a suitable dilution
	of the test compound, and 2.5 mL of molten top agar. Where indicated, mushroom tyrosinase (EC

C NMR (D2O) 21.5 ( 2),

31.2, 34.5, 34.8, 40.3, 52.8, 53.6, 119.1, 121.2, 125.6, 132.5, 143.7, 146.7, 173

.9, 174.0.

Bacterial plate cytotoxicity assays. The E. coli strains were IC5282 (a trp + derivative of strain WP2 uvrA trpE65/pKM101), IC5204 (as IC5282 but carrying the oxyR30 mutation (42)), and IC2880 (a trp::Tn10 derivative of strain WP2 uvrA trpE65). Cultures were prepared by inoculating 100 L from frozen permanents into 10 mL of nutrient broth (Oxoid Nutrient Broth No. 2), and incubating overnight at 37ºC. Solid minimal E4 medium contained 15 g Difco agar and 4 g glucose per liter of Vogel-Bonner E buffer (containing MgSO4  7 H2O (10 g), citric acid monohydrate (100 g), K2HPO4 (500 g) and NaHNH4PO4 , 7 H2O (175 g) per liter of distilled water). Top agar contained 6 g Difco agar and 5 g NaCl per liter of distilled water. In the assays, test cells (from IC5282 and IC5204 strains) were exposed to the compound in the presence of trp -filler cells (from IC2880 strain), unable to growth in the minimal E4 medium, which was used to obtain a background lawn. To 100 L of the bacterial mix, 1.14.18.1; Sigma) was added to the assay (100 L of a suitable dilution from a starting solution in 0.1 M sodium phosphate pH 7.0). The mixture was poured on plates containing solid E4 medium (about 35 mL),

Table 1 .

 1 One-pot synthesis of catechol-thioether mono-adducts 1-3 and 6-8 a

	HHMA or DA
	-2e, -2H +
		RSH
	R 1 = Me or H				1-3: R 1 = Me
					6-8: R 1 = H
	R 1	RSH	Product yield% b
	Me	Glutathione	1	64
	Me	NAC		2	66
	Me	Cysteine		3	50
	H	Glutathione	6	65
	H	NAC		7	55
	H	Cysteine		8	63

Acknowledgment. We thank Dr. M.-B. Fleury, Emeritus Professor at the Paris Descartes University, for fruitful discussions. We thank also Carmen Navarro for her technical assistance. This Research was made possible thanks to the joint financial support of the Mission Interministérielle de Lutte contre la Drogue et la Toxicomanie (MILDT) and the Institut National de la Santé et de la Recherche Médicale (INSERM). (Appel à projets commun 2004 MILDT-INSERM « Recherche sur les drogues et la toxicomanie »). A.F. thanks MILDT together with INSERM for a PhD grant.

Supporting Information Available. Analytical HPLC chromatograms together with 1 H NMR spectra for all catechol-thioether conjugates 1-10. This information is available free of charge via the Internet at http://pubs.acs.org.


Nicolas Roehri

Samuel Medina

Aude Jegou

Bruno Colombet

Bernard Giusiano

Aurélie Ponz

Fabrice Bartolomei

Christian-George Bénar

Transfer, collection and organisation of electrophysiological and imaging data for multicentre studies

Keywords: Multicenter, Data Management, BIDS, Electrophysiology, Neuroimaging

Multicentre studies are of utmost importance to confirm hypotheses. The lack of established standards and the ensuing complexity of their data management often hamper their implementation. The Brain Imaging Data Structure (BIDS) is an initiative for organizing and describing neuroimaging and electrophysiological data. Building on BIDS, we have developed two software programs: BIDS Manager and BIDS Uploader. The former has been designed to collect, organise and manage the data and the latter has been conceived to handle their transfer and anonymisation from the partner centres. These two programs aim at facilitating the implementation of multicentre study by providing a standardised framework.

Introduction

Multicentre research is carried out by two or more research institutions which share resources such as techniques, methods or data to answer a common question. Such studies are crucial to validate hypotheses, models or theories and provide many benefits over single-centre studies such as increased sample size and better generalisability of the results [START_REF] Cheng | Conducting multicenter research in healthcare simulation: Lessons learned from the INSPIRE network[END_REF][START_REF] Dashevsky | Multicenter Research Studies in Radiology[END_REF]. They are however accompanied by several challenges. The major challenge is to coherently transfer and collect data in a robust and error minimizing way, which seamlessly blend in the workflow of the researchers conducting the study [START_REF] Irving | Challenges to Conducting Multicenter Clinical Research[END_REF]. The lack of standards is another barrier as it leads to a waste of time in developing specific scripts, programs and interfaces.

The Brain Imaging Data Structure (BIDS) [START_REF] Gorgolewski | The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments[END_REF] is an initiative aiming at establishing a common standard to describe data and its organisation on disk for both neuroimaging and electrophysiological data [START_REF] Niso | MEG-BIDS, the brain imaging data structure extended to magnetoencephalography[END_REF][START_REF] Holdgraf | iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial electrophysiology[END_REF][START_REF] Pernet | EEG-BIDS, an extension to the brain imaging data structure for electroencephalography[END_REF]. Datasets following BIDS are composed of three folders: raw, sourcedata and derivatives which contain the data in an open file format, original format and processes form, respectively. Standardised metadata are stored in either dictionary (key/value) JSON files or tabulation-separated value (TSV) files. Each file is named according to a strict but explicit convention in a series of key-values (sub-01 ses-01 task-rest run-01 ieeg.vhdr). Notably, the dataset description.json and participants.tsv files at the root of the dataset contain information about the dataset (e.g. name, project, author) and details about the participants (e.g. identifier, age, sex, group), respectively. This standard does however not provide features for a data management practice, namely, a consistent identification and tracking system, and fixed naming variables with associated values [START_REF] Schmitt | Data management practices for collaborative research[END_REF]).

An identifier (ID) which uniquely and consistently identifies a subject is critical to ensure data quality and follow the progression of data collections, especially when handling anonymised data. This is of utmost importance for clinical data as a great care is required to protect the patient identity. Such environment may be more prone to missing elements and variability (unit specific vocabulary and differences in electrode placing and naming), hence the importance of storing naming variables and allowed values in a master file [START_REF] Schmitt | Data management practices for collaborative research[END_REF]. For instance, there is no standard naming of the intracranial EEG electrodes and their name is entered manually (sometimes several times for the same patient) which may lead to inconsistent naming across recording sessions and unnoticed error during analysis of the data.

In this work, we proposed a standardised framework for multicentre studies handling multimodal dataset. Building on the concept of the Brain Imaging Data Structure, we have developed, within the PHRC SPREAD and RHU EPINOV (two ongoing multicentre studies gathering eleven epilepsy centres in France), two software programs to tackle these issues. These programs aims to provide: an interface to anonymise and securely send the data, a unique and consistent ID for the subjects, a BIDS converter and an interface for BIDSrelated data management and issue solving. BIDS Manager has been designed to collect, organise and manage the data in the investigating centre and BIDS Uploader has been conceived to handle the transfer of data from the partner centres.

Methodology

Context

Our team was in charge of developing a solution to transfer clinical electrophysiological and imaging data from a dozen French epilepsy units for two multicentre clinical studies (one being a clinical trial). Both studies had similar constraints, namely the transfer and storage of pseudo-anonymised data which must be readily accessible for analysis pipeline to generate a report returned back to the epilepsy unit for surgical decision-making. The emphasis was put on the ability to track the patient's data while preserving his or her identity in a reversible manner so that the clinicians of the corresponding epilepsy unit could link the report to the patient. The type of data handled within these studies are anatomical magnetic resonance imaging (T1-, T2-weighted, FLAIR), computerized tomography scans, diffusion weighted imaging and intracranial recording -stereoelectroencephalography and electrocorticography -(which are all covered by BIDS standards [START_REF] Gorgolewski | The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments[END_REF][START_REF] Niso | MEG-BIDS, the brain imaging data structure extended to magnetoencephalography[END_REF][START_REF] Holdgraf | iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial electrophysiology[END_REF][START_REF] Pernet | EEG-BIDS, an extension to the brain imaging data structure for electroencephalography[END_REF]). Rather than developing tools specific to these projects, we conceived flexible programs relying on a configurable JavaScript Object Notation File (JSON) file (see Systematic nomenclature). Also, we focused on graphical interfaces in order for the software to blend in the research routine. BIDS Uploader and BIDS Manager are two python-based programs available for Windows, Linux, and Mac OS X platforms. BIDS Uploader is installed in each partner centre, which allows the user to select, tag and send files of interest via a secured File Transfer Protocol (FTP) to the server of the analysing centre. Its interface has been designed in a sequential fashion so that each of its sections can only be accessed once the previous section has been completed, in order to reduce the risks of missing values. BIDS Manager permits to explore and dynamically organise the data according to the BIDS standard whilst tracking the subject data readiness and integrity. BIDS Manager depends on two pieces of software to convert the electrophysiological and imaging data: AnyWave1 [START_REF] Colombet | AnyWave: A crossplatform and modular software for visualizing and processing electrophysiological signals[END_REF] and dicm2nii2 (by Xiangrui Li ) respectively. Figure 1 illustrates a possible implementation of the programs within different centers.

Results

Illustrative Example

Let us use a toy example to describe our tools. The main objective of this example project is to study seizure recordings and the ancillary one is to compare a biomarker of epilepsie between resting and sleep. The researchers thus want to collect at least one seizure recorded with intracranial EEG (iEEG) as well as an hour of resting state and slow wave sleep data along with structural T1 weighted imaging before and after electrode implantation. Some characteristics about the subjects also need to be stored, such as age, sex and handedness (optional) as well as the centre in which their data were acquired. Data acquired before the implantation will be stored in the session labeled 'preop', and for those acquired after in the session labeled 'postimp'. The BIDS required field 'task' is used to describe the type of iEEG recording with the following values: 'seizure', 'sleep' and 'rest'. To store the subject information, we add columns in the participants.tsv file. The BIDS folder corresponding to this dataset is shown in Fig. 2.

Systematic nomenclature

To ensure a systematic nomenclature across centres, we designed a key/values file named requirements.json (our master file), based on the strict naming conventions proposed by BIDS, which characterises the dataset of the given study. This file is stored in the 'code' folder at the root of the dataset directory and describes the subject and modality types. Under "Subject", "keys" lists the subject attributes (e.g. the age, sex and handedness) followed by their allowed values ("F" or "M" for the sex) and "required keys" states whether an attribute is required. The dictionaries following the modality keys (e.g. "Anat" or "Ieeg") list the required files (e.g. the iEEG seizure recording). The following dictionaries describe the different modality types. Particularly, the attributes must be keys which are allowed by BIDS. There are no "required keys" for the modality types as these are defined in the BIDS specifications3 . Below is the requirements.json file corresponding to our example. Notably, only the seizure files are required for the iEEG as the others are in the ancillary study.

"Subject": { "keys": {"age": 0, "sex": ["F","M"], "handedness": ["R","L"], "centre": ["Mars", "Lyon", "Stras", "Paris"]} , "required_keys": ["age", "sex", "centre"], "Anat": [ {"type": {"ses": "preop", "modality": "T1w"}, "amount": 1}, {"type":

{"ses": "postimp", "modality": "T1w"}, "amount": 1} ], "Ieeg": [ {"type": {"ses": "postimp", "task": "seizure", "run": "_", "modality": "ieeg"}, "amount": 1}] }, "Anat": { "keys": {"ses": ["preop","postimp"]} }, "Ieeg": { "keys": { "ses": ["postimp"], "task": ["seizure","rest","sleep"] } }

BIDS Uploader

Prior to selecting the files to be transmitted using BIDS Uploader (Fig. 3), the user must first fill in the information concerning the subject: surname, first name, sex and date of birth plus all the other information written in the requirements.json specific to the project (Fig. 3a). BIDS Uploader only proposes to the user to select values from within the corresponding list in the requirements.json (e.g. for handedness, it proposes "R" or "L" and not "right" or "left", see Systematic nomenclature). Once these elements have been entered, the program assigns a unique and specific twelve-alphanumeric character code to the subject (the UID in Fig. 2) by encrypting (using the SHA-256 algorithm) the surname, first name, date of birth and a protocol specific keyword (kept secret). Every time the user types in the information of the same subject, let us say John Doe born on 01/09/1983 with the protocol specific keyword 'test', the unique and specific identifier will always be 3a8573a45f91. The program then invites the user to select the files to be imported (Fig. 3b) according to the modalities described in the requirements.json (i.e. in this example anatomical data ("Anat") and intracranial recordings ("Ieeg")) and tag them. Similarly, the software only proposes allowed tags from the previously chosen key list. Prior to pseudo-anonymisation, BIDS Uploader verifies whether the identity of the subject input by the user matches the information in the selected files. If mismatches are found, the program asks the user to either modify the subject information, remove the file or force the validation in case the data were already anonymised (Fig. 3c). The definition of pseudo-anonymisation used here is the one of the General Data Protection Regulation (GDPR, a regulation in EU law on data protection). 4 Once all files have passed the validation stage, the software copies in a temporary folder the data, anonymises them, writes a metadata file (data2import.json), containing the different tags, anonymised subject information and file names, and sends them to the server of the analysing centre via a secure FTP (Secure Shell, SSH). During the anonymisation procedure, the software removes all sensitive fields from the header of the imaging and electrophysiological data (e.g. first name, family name, date of birth, address of the patient). When the transfer is finished, the interface is reset and the temporary folder is deleted. An example of the data2import.json file is given below. It illustrates a case of a transfer of data, namely a preoperative T1w image and an iEEG recording containing a seizure acquired in Paris, of a right-handed 43 -year-old male patient identified as f766f46be929 and intended for the protocol YourProtocol. Notably, the first name and surname of the patient is not present in this file 4 GDPR defines in Article 3 pseudo-anonymisation as "the processing of personal data in such a way that the data can no longer be attributed to a specific data subject without the use of additional information." Such "additional information" must be "kept separately and subject to technical and organisational measures to ensure non-attribution to an identified or identifiable person."

only the SHA-identifier ensuring anonymity. Importantly, BIDS Uploader complies with GDPR criterion "Subject": [ { "sub": "f766f46be929", "Anat": [ { "sub": "f766f46be929", "ses": "preop", "acq": "", "ce": "", "rec": "", "run": "01", "mod": "", "modality": "T1w", "fileLoc": "preop_01_T1w", "AnatJSON": {} } ], "Ieeg": [ { "sub": "f766f46be929", "ses": "postimp", "task": "seizure", "acq": "", "run": "01", "proc": "", "modality": "ieeg", "fileLoc": "CR1.TRC", "IeegJSON": {}, "IeegChannelsTSV": [], "IeegEventsTSV": [] } ], "age": "43", "sex": "M", "handedness": "R", "center": "Paris" } ], "Derivatives": [], "DatasetDescJSON": { "Name": "YourProtocol", "BIDSVersion": "1.0.1", }

BIDS Manager

Basic functionalities BIDS Manager allows the user to prepare a folder for a new BIDS dataset using a user-friendly interface. Via this interface, the user can create the dataset description.json file which describes the dataset (required by BIDS standard) and the requirements.json (described in Systematic nomenclature). The user can explore the dataset and open files without going though all the nested folders. Moreover BIDS Manager stores the state and metadata of the dataset in a key/value file with the corresponding date (parsing dataset yyyymm-ddTHH-MM-DD.json.gz in Fig. 2), in order to monitor any modification. Any action is stored in a log file in a specific folder in the BIDS dataset. To import data which were transferred by BIDS Uploader, BIDS Manager first verifies three key points (Fig. 4):

Importing data

1. data2import.json file verification: the metadata sent by BIDS Uploader must have all the BIDS required keys, all files to be imported must have been verified manually (e.g. for file corruption or missing slices in DICOM folder) and the folder must be intended for the protocol of the chosen BIDS directory otherwise the program raises an error.

2. current subject verification: If the subject is not already in the BIDS folder, the participants.tsv is updated with the subject information. If the subject is already present, the software checks that the characteristics of the subject to be imported match the characteristics of the subject already stored in the BIDS folder (e.g. sex, age and centre). If mismatches are found, an error is raised. 3. current file verification: The session should correspond to previous stored sessions, the source file name should be different from the source file names already present and there should not be a file with the same name in the raw folder. Otherwise an error is raised.

If no error was generated, BIDS Manager launches AnyWave and dicm2nii.

AnyWave converts the electrophysiological files into BrainVision Core Data Format (one format accepted by both BIDS-iEEG [START_REF] Holdgraf | iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial electrophysiology[END_REF] and BIDS-EEG [START_REF] Pernet | EEG-BIDS, an extension to the brain imaging data structure for electroencephalography[END_REF]) and dicm2nii converts the imaging data into Nifti [START_REF] Cox | A (Sort of) New Image Data Format Standard: {NIfTI-1}[END_REF] format. Both programs create all required sidecar file containing the metadata which are also imported and renamed according to BIDS standard. If errors were raised, these are stored in a JSON file named issue yyyy-mm-ddTHH-MM-DD.json with the corresponding date in the log folder of the BIDS directory (Fig. 2). The user can solve these issues via the interface by either correcting the mismatches with the correct values proposed by BIDS Manager or removing the file from the list of files to be imported. If the user needs to contact the sending centre, the issues can be solved in another session as they are stored in the specific issue file.

Tracking system and electrode naming integrity BIDS Manager adds new columns to the participants.tsv file in order to allow researchers to track the subject's data readiness and, in the specific case of iEEG datasets, guarantee the concordant naming of electrodes. Following our example, the software adds the 'Anat ready', 'Ieeg ready', 'Ieeg integrity' and 'Subject ready' columns (Fig. 5).

Whenever data are imported, BIDS Manager compares the list of files within the subject folder with the required files from the requirements.json (see Systematic nomenclature). When all data of a modality type have been imported for a given subject, BIDS Manager updates the column corresponding to the modality type in the participants.tsv. For instance, when the seizure recording of a given participant is imported, the value in column 'Ieeg ready' turns from 'False' to 'True'. To ensure the same electrode naming in the iEEG recordings of a given subject, we use the BIDS electrodes.tsv file as reference which contains the electrode coordinates, additional information (e.g. material and hemisphere), and the electrode names. To create this file, we have modified our in-house program Fig. 5 Widget of BIDS Manager which displays the participants.tsv and the completeness of the data. For instance, the first three subject's data are not ready as anatomical, electrophysiological data are missing or some channels are mislabelled. The last participant's data are ready to be analysed.

GARDEL 5 (Medina Villalon et al., 2018) which extracts the electrode coordinates from anatomical imaging data. If discrepancies between the electrode names in the iEEG files and the reference eletrodes.tsv are found, BIDS Manager stores these mismatches in the issue file (described in Importing data) and proposes the user to rename the electrodes with an allowed names or change its type if the electrodes is not an iEEG electrodes. BIDS Manager turns the value in 'Ieeg integrity' to 'True' when no mismatch remains. Finally, when all flags have turned 'True', the value in 'Subject ready' is updated to 'True'.

Discussion

We propose a framework to facilitate the implementation of multicentre research handling human electrophysiological and neuroimaging data. Specifically, we provide a standardised way of transferring and organising data. This framework is based on the Brain Imaging Data Structure and two developed software packages: BIDS Uploader and BIDS Manager which work conjointly.

The decision of building it on BIDS (a standardised structured dataset) rather than on a database format is twofold. First, scientific data management platforms such as XNAT [START_REF] Marcus | The Extensible Neuroimaging Archive Toolkit An Informatics Platform for Managing, Exploring, and Sharing Neuroimaging Data[END_REF] and COINS [START_REF] Scott | Coins: An innovative informatics and neuroimaging tool suite built for large heterogeneous datasets[END_REF] have specifically been developed for neuroimaging data (and have now been extended to other modalities) but do not permit to store iEEG data with standardised metadata. On the other hand, BIDS has been widely adopted and extended to electrophysiological data (iEEG-, EEG-and MEG-BIDS (Holdgraf et al., 2019;[START_REF] Pernet | EEG-BIDS, an extension to the brain imaging data structure for electroencephalography[END_REF][START_REF] Niso | MEG-BIDS, the brain imaging data structure extended to magnetoencephalography[END_REF]) offering standardised metadata and file naming for both electrophysiological and imaging data (the aforementioned platforms are planning to support BIDS as an export and/or import option). Secondly, a dedicated application programming interface should have been developed to get data from these databases. Most available tools [START_REF] Oostenveld | FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data[END_REF][START_REF] Gramfort | MEG and EEG data analysis with MNE-Python[END_REF][START_REF] Litvak | EEG and MEG data analysis in SPM8[END_REF][START_REF] Delorme | EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics[END_REF][START_REF] Tadel | Brainstorm: A user-friendly application for MEG/EEG analysis[END_REF][START_REF] Colombet | AnyWave: A crossplatform and modular software for visualizing and processing electrophysiological signals[END_REF][START_REF] Gorgolewski | BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods[END_REF] are however already BIDS compatible, making previous analysis pipeline readily applicable.

While BIDS already has a strict naming convention, we have appended a second layer which ensures a systematic nomenclature for a given project thanks to the requirements.json file. Once the collaborators have chosen the naming variables as well as their allowed values, the requirements.json file storing these information is used by the two programs to only propose values from these lists. This drastically decreases the risks of entry errors [START_REF] Atkinson | Accuracy of data transfer: Double data entry and estimating levels of error[END_REF][START_REF] Paulsen | Quality of data entry using single entry, double entry and automated forms processing-an example based on a study of patient-reported outcomes[END_REF][START_REF] Kupzyk | Data Validation and Other Strategies for Data Entry[END_REF], use of vague terms, homonyms or synonyms [START_REF] Schmitt | Data management practices for collaborative research[END_REF] for both the files and the participant characteristics. Moreover, this file can be specifically adjusted for the needs of any study which makes these controls generic. In case of pseudo-anonymised data, the risk of subject re-identification increases with the number of subject characteristics stored in participants.tsv file. The researchers should therefore only store participant characteristics which are essential to the study and use generalised characteristics such as age rather than date of birth.

The creation by BIDS Uploader of a unique and specific twelve-alphanumeric character subject code permits to track the subject's data while preserving its anonymity as his or her surname and first name are not send with the data. This also allows removing data from participants requesting that their data be no longer archived. The protocol specific keyword used to produce this code avoids identifying the same subject in independent studies. Importantly, each time the same subject information are input the same code is generated. This makes it easy for the user to send the data at different time points and is especially suitable for longitudinal studies. Encrypting these information based on manual entries is at risk as one simple typing error yields an entirely different code due to the avalanche effect [START_REF] Feistel | Cryptography and Computer Privacy[END_REF]. To control that, BIDS Uploader matches the subject information in the selected files (as described in BIDS Uploader ). This also ensure that the data to be sent correspond to the chosen subject and not another one as this is impossible to recover after anonymisation.

The tracking system implemented in BIDS Manager allows the researchers to follow the progress (or stagnation) of data collection and is a crux of data management practices [START_REF] Schmitt | Data management practices for collaborative research[END_REF]. The consecutive stages of verification at import protect the data from being undesirably overwritten or wrongly labeled. As iEEG electrode names are manually typed at recording, they may differ between files. This type of silent error may have dramatic consequences in the analysis process. Indeed, computing the average rate of a given activity for each electrode across files with inconsistent electrode naming yields absurd and unusable results which could be carried on throughout the pipeline. BIDS Manager, by ensuring the cohesion of the electrode names across subject's files, assuredly reduces these silent errors.

The proposed framework does not intend to replace the case report form (CRF) of clinical research [START_REF] Latha | Basics of case report form designing in clinical research[END_REF] as their goal is different but may share common information (e.g. participant characteristics contained in both the CRF and the participants.tsv ). Moreover, we did not address the open-access sharing of the finalised dataset since multicentre studies correspond more to a controlled-access model of sharing [START_REF] Keerie | Data sharing in clinical trials -practical guidance on anonymising trial datasets[END_REF]. As the dataset is already structured in BIDS it requires a minimal amount of development to modify the data to reach the appropriate level of anonymisation. Furthermore, we did not detail how the data were stored. Each BIDS dataset with personal data should be stored on encrypted disks with limited access to the data (handled by the competent personnel of the institution).

The current programs handle all imaging and iEEG data. Further development will include EEG-and MEG-BIDS extensions, and the possibility to handle analysis pipelines.

Information Sharing Statement

BIDS Manager (RRID:SCR 019228) is available to download from https: //github.com/Dynamap/BIDS_Manager or as a Windows 10 executable from https://doi.org/10.6084/m9.figshare.11728872. The software is free and distributed under the terms of the GNU General Public Licence as published by the Free Software Foundation. BIDS Manager is written for Python 3.7 or above, and needs two pieces of software (AnyWave available to download from http://meg.univ-amu.fr/wiki/AnyWave and a compiled version of dicm2nii from https://github.com/Dynamap/BIDS_Manager). To install our program, the user has to copy BIDS Manager folder on its computer disk and run the setup.py file for the python package. To run the software, the user can either double-click on the executable icon or launch it with python. We have also made available a example dataset for the user to test the software: https://doi.org/10.6084/m9.figshare.11687064. For further information on how to use the software, please see BIDS Manager online manual: https://github.com/Dynamap/BIDS_Manager or the tutorial video: https://youtu.be/HvJjr6WZNQA

ImplementationFig. 1

 1 Fig. 1 Illustration of the implementation of BIDS Uploader and BIDS Manager in one partner center and the analysing center.

Fig. 2

 2 Fig.2Illustration of the BIDS structured dataset corresponding to the example. This dataset contains the data and sidecar files (.json and .tsv files containing metadata) required by the BIDS standard as well as some files -written in bold -specific to our proposed framework.

Fig. 3

 3 Fig. 3 Interface of BIDS Uploader. Information concerning the subject's identity is first entered in frame a. Once filled, frame b is accessible and the user can select the type of data to be imported and tag them. The type of data and the values of the tags are proposed according to the requirements.json file. Frame c lists the selected files and corresponding tags. After clicking on the 'Validate and format' button, BIDS Uploader changes the color of each line in red in presence of identity mismatch in the file or in green in absence of mismatch.

Fig. 4

 4 Fig.4Flow chart of the importation procedure. Once a new folder has been sent, it goes through three verification stages before being added to the dataset.

available here: http://meg.univ-amu.fr/wiki/AnyWave

https://mathworks.com/matlabcentral/fileexchange/42997-xiangruili-dicm2nii

https://bids-specification.readthedocs.io/en/stable/

available at: http://meg.univ-amu.fr/wiki/GARDEL:presentation

Acknowledgements NR was supported by the French national clinical project SPREAD [PHRCN-16-0685 sponsored by University Hospitals of Strasbourg, IDRCB No. 2017-A00497-46]. This work was partially supported by the RHU EPINOV [A*MIDEX project (ANR-17-RHUS-0004) funded by the 'Investissements d'Avenir' French Government]. AJ has received funding from the European Union's Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreement No. 785907 (Human Brain Project SGA2

). We would like to thank Dr Olivier David who shared the code of the uploader used in the F-TRACT project (f-tract.eu) which served as the starting point of BIDS Uploader described above.

Author contributions

NR drafted the manuscript. NR, SMV, BC, BG, AP, FB and CGB designed the framework. NR developed BIDS Manager and SMV developed BIDS Uploader. AJ contributed in programming BIDS Manager. All the authors contributed to reviewing and editing the manuscript.

Competing interests

The authors declare no competing interests.