Francisco José Sala De Oyanguren

Nathan E Rainey

Aoula Moustapha

Ana Saric

Franck Sureau

José-Enrique O'connor

Patrice X Petit
email: patrice.petit@inserm.fr

Highlighting curcumin-induced crosstalk between autophagy and apoptosis: A biochemical approach coupling impedancemetry, imaging, and flow cytometry

Keywords: Curcumin, cell death, autophagy, apoptosis, ROS, Calcium, Stress, Mitochondria, Endoplasmic reticulum, cancer

Curcumin, a major active component of turmeric (Curcuma longa, L.), is known to have various effects on both healthy and cancerous tissues. In vitro studies suggest that curcumin inhibits cancer cell growth by activating apoptosis, but the mechanism underlying the anticancer effects of curcumin is still unclear. Since there is a consensus about endoplasmic reticulum (ER) stress being involved in the cytotoxicity of many natural compounds, we investigated by Amnis ® Imaging flow cytometry the mechanistic aspects of curcumin's destabilization of the ER, but also the status of the lysosomal compartment involved in curcumin-associated apoptosis. Curcumin induces ER stress thereby causing an unfolded protein response (UPR) and calcium release which destabilize the mitochondrial compartment and induce apoptosis. These events are also associated with secondary lysosomal membrane permeabilization and activation of caspase-8, mediated by activation of cathepsins and calpains. We previously showed that sequence lead to the generation of truncated tBid and disruption of mitochondrial homeostasis. These two pathways of different intensities and momentum converge towards an amplification of cell death that still needs to be studied in more detail. It has been suggested that it may be possible to exploit autophagy for cancer therapy. There is a complex interplay involving early autophagy as soon as mitochondria produce superoxide anions and hydrogen peroxide. Treatments with 10 µM to 20 µM curcumin induce autophagosome formation, while only early events of cell death are detectable.In the present study, curcumin-induced autophagy failed to rescue all cells since most cells underwent type II cell death following initial autophagic processes. However, a small number of cells blocked in the cell cycle escaped and were rescued to give rise to a novel proliferation phase.

Introduction. Curcumin, a major bioactive compound in turmeric, has a broad spectrum of activity, including antioxidant, anticarcinogenic, and anti-inflammatory properties [START_REF] Gupta | Therapeutic roles of curcumin: Lessons learned from clinical trials[END_REF][START_REF] Anand | Curcumin and cancer: An "old-age" disease with an "age-old" solution[END_REF][START_REF] Marchiani | Curcumin and Curcumin-like Molecules: From Spice to Drugs[END_REF]. Curcumin has a symmetric molecular organization and is defined as a diferuloyl methane [START_REF] Kavirayani | The chemistry of curcumin: From extraction to therapeutic agent[END_REF]. The chemical name of curcumin is (1E,6E)-1,7-bis(4-hydroxy-3-methoxyphenyl)-1,6-heptadiene-3,5-dione (C 21 H 20 O 6 ). Its structure contains three chemical entities: two aromatic ring systems with an omethoxy phenolic group, connected by a seven-carbon linker consisting of an α,β-unsaturated β-diketone moiety. Double bonds inside the molecule account for its participation in many electron transfer reactions. Curcumin is an electron donor and stabilizes its chemical structure by redistribution and resonance of the π electron cloud [START_REF] Kavirayani | The chemistry of curcumin: From extraction to therapeutic agent[END_REF]. Curcumin exhibits UV-visible absorption bands (250-270 nm and 350-450 nm, respectively). So, curcumin fluoresce with a maximum emission at 470 nm in methanol. These optical properties have been used to isolate and purify curcumin by various techniques, as high-performance liquid chromatography (HPLC). Its fluorescence allow to track very low amounts of curcumin and its related metabolites in plasma and urine at concentrations down to 2.5 ng/mL [START_REF] Sharma | Antioxidant activity of curcumin and related compounds[END_REF][START_REF] Lee | Separation and identification of curcuminoids from Asian turmeric (Curcuma longa L.) using RP-HPLC and LC-MS[END_REF][START_REF] Ali | Separation and identification of curcuminoids in turmeric powder by HPLC using phenyl column[END_REF]. Curcumin can also be excited at 488 nm, with a lower fluorescent yield emission in the 500-530 nm range, for detection by flow cytometry and confocal microscopy. Curiously, this has rarely been used for curcumin imaging at the cellular level [START_REF] Moustapha | Curcumin induces crosstalk between autophagy and apoptosis mediated by calcium release from the endoplasmic reticulum, lysosomal destabilization and mitochondrial events[END_REF][START_REF] Gábor | Achiral Mannich-base curcumin analogs induce unfolded protein response and mitochondrial membrane depolarization in PANC-1 cells[END_REF]. Curcumin is a hydrophobic molecule with a log P value of 3.0 at neutral pH [START_REF] Mohammadi | Synthesis and characterization of dual function vanadyl, gallium and indium curcumin complexes for medicinal applications[END_REF]. Therefore, curcumin is not easily soluble in physiological media and exhibits poor distribution and bioavailability [START_REF] Barik | Evaluation of a new copper(II)-curcumin complex as superoxide dismutase mimic and its free radical reactions[END_REF].

The main chemical feature of the curcumin molecule is the presence of o-methoxyphenol group and methylenic hydrogen responsible for the donation of an electron / hydrogen to reactive oxygen species, hence producing antioxidant activity. Curcumin is thought to be as efficient as well-known antioxidants -thiols, vitamin A, vitamin C and vitamin E -in the removal of oxyradicals and mimics the function of a superoxide dismutase [START_REF] Mohammadi | Synthesis and characterization of dual function vanadyl, gallium and indium curcumin complexes for medicinal applications[END_REF]. The unsaturated ketone of curcumin undergoes a nucleophilic addition reaction as an acceptor together with A-OH, A-SH, A-SeH that sustain its interaction with multiple molecules [START_REF] Mohammadi | Synthesis and characterization of dual function vanadyl, gallium and indium curcumin complexes for medicinal applications[END_REF]. Curcumin has also been suggested to change the properties of cell membranes in which they insert and indirectly affect membrane-bound proteins [START_REF] Karewicz | Interaction of curcumin with lipid monolayers and liposomal bilayers[END_REF][START_REF] Fang | Interaction between curcumin and mimetic biomembrane[END_REF]. The interaction of curcumin with artificial mem- brane (DOPC based membranes) show that it thinned the bilayer and weaken its elasticity modulus [START_REF] Sun | The bound states of amphipathic drugs in lipid bilayers: Study of curcumin[END_REF][START_REF] Chin | Membrane-thinning effect of curcumin[END_REF]. As a result, curcumin can insert in proteo-lipidic compartments of the cells and/or binds covalently to various proteins in the cytosol or influence protein functions at the organelle membrane surfaces [START_REF] Helgi | Curcumin is a modulator of bilayer material properties[END_REF]. As an exemple, the conjugation of curcumin with glutathione-SH results in the depletion of glutathione and its association with the antioxidant defense system in cells. In this regard, the depletion of glutathione suggests that curcumin could act as a pro-oxidant contributor in some conditions [START_REF] Prabhakar | Effect of Curcumin and Curcumin Copper Complex (1:1) on Radiation-induced Changes of Anti-oxidant Enzymes Levels in the Livers of Swiss Albino Mice[END_REF]. At low concentrations, curcumin reacts as an antioxidant, but acquires pro-oxidant properties above 20 µM, thus revealing its hormetic behavior [START_REF] Moustapha | Curcumin induces crosstalk between autophagy and apoptosis mediated by calcium release from the endoplasmic reticulum, lysosomal destabilization and mitochondrial events[END_REF][START_REF] Rainey | Curcumin hormesis mediates a cross-talk between autophagy and cell death[END_REF]. Curcumin is a pleiotropic molecule which interacts with multiple targets involved in inflammatory reactions, such as tumor necrosis factor-alpha (TNFα) and interleukins (ILs) [START_REF] Bharat B Aggarwal | Curcumin: an orally bioavailable blocker of TNF and other pro-inflammatory biomarkers[END_REF]. Curcumin also interacts with a number of biomolecules through noncovalent and covalent binding. The hydrogen bonding and hydrophobicity of curcumin, arising from its aromatic and tautomeric structures along with the flexibility of the linker group, are responsible for the noncovalent interactions. Curcumin directly interacts with numerous proteins in the cytoplasm and this may explain its pluripotent and multiple intracellular effects [START_REF] Ajaikumar B Kunnumakkara | Curcumin inhibits proliferation, invasion, angiogenesis and metastasis of different cancers through interaction Sala-de-Oyanguren F. et al. | Curcumin-induced effects and organelle localization November 1, 2019 | bioRχiv | 13 with multiple cell signaling proteins[END_REF][START_REF] Subash C Gupta | Multitargeting by curcumin as revealed by molecular interaction studies[END_REF]. In the present study, we investigated in more detail the intracellular localization of curcumin, since all curcumin-induced cell signaling depends on its precise insertion into well-defined intracellular membranes. Here, we confirm that curcumin mainly targets the endoplasmic reticulum (ER) and is associated with ER swelling and a combi-nation of an unfolded protein response (UPR) response and calcium release. We point out a secondary role for the lysosomal pathway, which is that it enhances the induction of cell death. We investigated the mechanism whereby curcumin modulates oxidative stress-mediated signaling of inflammatory responses leading to autophagy. With the impedancemetry setup xCELLigence, we depict for the first time a situation where two different concentrations of curcumin induce on the one hand successful autophagy and on the other hand classic apoptotic cell death. The link between impedancemetry and flow cytometry helped us understand better the molecular mechanisms of action of curcumin. Detailed knowledge of curcumin-induced autophagy and possible type II cell death and the relationship between the two pathways may lead to the pharmacological development of novel therapeutic approaches, for example, to liver tumorigenesis and neurological disease.

Results.

Uptake of curcumin and invalidation of its putative cytoplasmic membrane/nuclei localization. We previously demonstrated that curcumin enters cells quite rapidly, within seconds, to a final ratio between external added curcumin and the intracellular concentration of 1/20, that correspond to 1 to 2.5 µM as intracellular concentration for 20 and 50 µM added externally [START_REF] Moustapha | Curcumin induces crosstalk between autophagy and apoptosis mediated by calcium release from the endoplasmic reticulum, lysosomal destabilization and mitochondrial events[END_REF]. There is also a linear relationship between external and internal concentrations of curcumin in the range of 0 to 80 µM [START_REF] Moustapha | Curcumin induces crosstalk between autophagy and apoptosis mediated by calcium release from the endoplasmic reticulum, lysosomal destabilization and mitochondrial events[END_REF]. In the present study, we studied intracellular curcumin with two techniques: flow imaging (image flow cytometry, Amnis technology) and flow cytometry (FACS calibur 4C). First, image cytometry confirmed rapid cell staining by 5µM curcumin for 3 h (Figure 1a). Neither the nuclei nor the cytoplasmic membranes appeared to be stained by curcumin (white arrows). More attention paid to regions stained by curcumin together with white light diffraction images also suggested that curcumin is certainly associated with an intracellular compartment of the cytoplasm (Figure 1a). We decided to investigate early curcumin staining by using timeresolved flow cytometry for two usual concentrations of curcumin, i.e. 5 µM and 20 µM (Figure 1b-d). Within 30 seconds, curcumin was taken up by cells and reached a steady state that lasted for hours (Figure 1b,c). Cell autofluorescence was low at 488 nm excitation and baseline gate (R1) mean fluorescence intensity was 3 ± 2, whereas curcumintreated gate R2 (5µM) and R3 (20µM) were 46 ± 23 and 109 ± 25, respectively (Figure 1d). low TMRM signature, corresponding to a few mitochondria, while the curcumin signal was higher than in region 1 (Figure 2c), excluding a direct link between curcumin and mitochondria (Figure 2d). At this point, we observed that there was no curcumin in the extracellular medium (data not shown) and no significant fluorescence associated with the cytoplasmic membrane, nuclear membrane, nuclei or mitochondria. According to previous results and current microscopy, we hypothesized that diffuse curcumin fluorescence is located in the ER network with a large proportion of the ER being in contact with the mitochondrial compartment. We decided then to concentrate our attention on the ER and the lysosomal compartment.

The major fraction of curcumin locates to the endoplasmic reticulum. By using the ER-tracker TM red and curcumin fluorescence, we localized curcumin at the level of the ER network (Figure 3d). Curcumin fluorescence (5 µM for 3 h) coincided widely with the ER-tracker™ red fluorescence (Figure 3c,d). Additional experiments were conducted to follow the status of the ER compartment. ER swelling was evaluated since it is a common marker of stress, and the increased fluorescence of the ER-tracker TM red associated with the increased ER concentration is a coarse measurement of the ER lumen swelling (Figure 3e). The use of 4-phenylbutyric acid (PBA) as an ER stress inhibitor reduced this ER-tracker TM red fluorescence (Figure 3e) and minimized the ER swelling (data not shown), showing that curcumin causes ER stress which is only partially reversed by PBA.

Curcumin localization at the endoplasmic reticulum cause an UPR response and affect calcium status. Increasing curcumin treatment inhibits the SERCA pump (Sarco/endoplasmic reticulum Ca 2+ -ATPase) that is usually responsible of the transfer of calcium from cytosol to ER lumen at the expense of ATP (Figure 3f). On the other side, the UPR stress response features an increase in the activity of C/EBP homologous protein (CHOP) and glucose-related protein (GRP78 / BiP). We investigated by flow cytometry the activity of these proteins after 24-h treatment with various concentrations of curcumin. As the curcumin concentration increases, and especially for 20 and 50 µM, the activity of CHOP and GRP78 increases, showing an active UPR response in Huh7 cells (Figure 3g). Calcium release experiments assessed by Fluo-4AM (cytosolic calcium) showed a fast increase of cytosolic calcium as soon as cells were treated with curcumin (Figure 3h). This increase is true in terms of intensity of staining but also cell population as 95% of the population showed this fast increase in cytosolic calcium. An initial pulse (before 30min) was observed both for cytosolic calcium and curcumin followed by a decrease along time to reach a level that was still above of untreated cell condition (Figure 3h). This permanent raised calcium level is certainly very important for the disruption of cell homeostasis.

A second fraction of curcumin is also lysosomal. Flow imaging was used to locate curcumin within the cell, notably in the lysosomal compartment ( Figure 4). For this purpose, we used the green curcumin fluorescence and the Lysotracker red probe. The histogram of curcumin fluorescence (97.4 % of cells, Figure 4a) together with the histogram of the Lysotracker Red (97.4 % of cells, Figure 4b) gave rise to a biparametric histogram with 87.1 % of the cells being double stained (Figure 4c). Lysosome staining appeared heterogeneous but discrete, while curcumin fluorescence was much more widespread (Figure 4d). So, we performed a similarity analysis on the Amnis ® system to see whether the chosen cells (those presenting double staining) exhibited a similar colocalization of Lysotracker Red and curcumin in lysosomes (Figure 5). The histogram presented in figure 5a shows that similarity scores under 1 correspond to a non-colocalized population (Figure 5b), while similarity score over 2 concerned a significant colocalization signal observed in cells (Figure 5c). This population account for 14.8 % and 23.2 % of the total double-stained population cells, with 20 µM and 50 µM curcumin incubation for 3 h, respectively (Figure 5a). This population went up to 95% after 50 µM for 24 h (Table 1). So, while the ER stains rapidly and homogeneously, the lysosomal compartment is also stained by curcumin but later on and its full staining is only reached with a longer time of incubation or higher concentration (Table 1). This delayed sequence of lysosomal staining point the ER as the main and primary intracellular target of curcumin.

Depicting curcumin-induced autophagy and apoptosis by

impedancemetry combined with flow cytometry. The xCEL-Ligence system allows the follow-up in real time of cell attachment, spread, and proliferation, eventually leading to confluency, medium exhaustion, and cell death. Curcumin was added to the medium during the proliferation phase at different concentrations (Figure 6). At 50 µM curcumin (blue curve), the cell index dropped faster and sooner than under control conditions (black circles), indicating irreversible cell death (Figure 6a). At lower concentration (25 µM; red curve), cells underwent a similar drop in cell index but progressed 24 h after curcumin injection to a novel proliferation phase. These observations could be explained by cell death only or associated with autophagic processes and temporary cell cycle arrest. We then investigated, by flow cytometry, some characteristics of these cells at different timepoints along the proliferation curves. The cell cycle study showed a classic profile for Huh7 control cells with the majority of cells in G0G1 (56%), and a large population in S phase (43%) that relied on the 8% proliferative cells in G2/M. Dead cells in culture were only 3% in a sub-G0G1 phase (Figure 6b). Cells treated with 50 µM curcumin exhibited a 41% dead cell population (sub-G0G1 cells), a low ratio of cells in S phase (20%), and an increase to 17% G2/M phase cells suggesting cell cycle arrest (Figure 6d). Cells treated with lower amounts of curcumin exhibited an intermediate proportion of dead cells (24%), a large population in G0G1 (31%) and S phase (24%), but also a 21% population blocked in G2/M (Figure 6c). The secondary proliferation phase seen in figure 6a showed a G2/M population decrease (15%) and only a 6% population of dead cells at the beginning of the curve (Figure 6e), ending up with a regular profile of cell growth at point 5 (Figure 6f). This toxicity associated with cell cycle arrest and a delay before a novel proliferating phase suggests a successful coping mechanism like autophagy.

Table 1. Interaction of curcumin with the lysosome as a function of its concentration and the incubation time |

The percentage (%) of cells that exhibit both curcumin and LysoTracker fluorescence is given, as is the percentage (%) of cells that exhibit strict colocalization, meaning that curcumin segregates to lysosomes.

Cell viability status along these timepoints was also investigated with YOPRO-1/PI staining membrane permeability assays (Figure 7). Untreated cells and collection at timepoint #5, are both cells that have escaped from death, and exhibited a similar profile with more than 90% of cells intact (Figure 7a,e). At timepoint #6, 96% of the cells were in the process of death and almost 60% of them were already necrotic (Figure 6f). At timepoint #2 (Figure 7b), we observed a 13.2% increase in necrotic cells, while at timepoint #4 there were still 24% of necrotic cells (Figure 7d). More interestingly, the population of YOPRO-1-positive/PI-negative cells increased to 19.9% (Figure 7f), a consequence of the semi-permeability of the cytoplasmic membrane, which can, of course, indicate an apoptotic process, but also a transient state during autophagic processes. If autophagy is successful, it lifts cell cycle blockade and gives a profile like timepoint #5 with a 90.3% viable population (Figure 7e). We further investigated at these timepoints ( Figure 6a) for evidence of apoptosis initiation, with a drop in mitochondrial membrane potential, production of superoxide anions, and an increase in cytoplasmic calcium levels (Table 2) [START_REF] Petit | Alterations in mitochondrial structure and function are early events of dexamethasone-induced thymocyte apoptosis[END_REF][START_REF] Zamzami | Sequential reduction of mitochondrial transmembrane potential and generation of reactive oxygen species in early programmed cell death[END_REF][START_REF] Zamzami | Reduction in mitochondrial potential constitutes an early irreversible step of programmed lymphocyte death in vivo[END_REF]. Timepoint #6 (Table 2f) clearly showed massive cell death, with only 4% of cells detectable with high ∆Ψm, while other variables could not be measured as the cells were too damaged. Timepoints #2 and #3 showed a significant drop in ∆Ψm associated with an increase in ROS and increased calcium levels (Table 2b,c), consistent with previous results (Figure 6,7). Except for ∆Ψm, timepoint #2 displayed less stress response than timepoint #3, which is consistent with the curcumin concentrations used. The population at high ∆Ψm was still low at timepoint #4, but ROS and calcium were back to normal levels and cells were actively proliferating (Table 2d). The interpretation of this profile could be that some cells had the ability to perform enough ROS detoxification and could turn the transient calcium increase back to a functional state. This relies on a fine balance where ROS induces autophagy but stays under a threshold that would cause alterations -e.g. lipid peroxidation and protein carbonylation -leading to cell death.

Table 2. Flow cytometry analysis of curcumin-treated cells from the xCEL-Ligence system |

The percentage (%) provided corresponds to the cells characterized as follows: high mitochondrial membrane potential (viable cells), cells presenting an increase in either superoxide anions or hydroperoxide, and cells with increased cytoplasmic calcium. Xcell: xCELLigence and the numbering #1 (a) to #6 (f) is the same as along the impedance curves. NA: not applicable due to the very low number of viable cells and the high amount of cellular debris.

Number and size of acidic compartment as evidence of au-

tolysosomes formation. To get further evidence of curcumininduced autophagy we analyzed the variation of intracellu-lar acidic compartment of treated cells (20µM curcumin for 16h) by image in flow (Figure 8) and flow cytometry (Figure 9). Dark field images (Channel 1) clearly show an increase of subcellular structures in curcumin-treated cells compare to control (Figure 8a-c). As previously reported, acridine orange probe (AO) accumulates in vesicles and stain acidic compartment in red (AO red) [START_REF] Moustapha | Curcumin induces crosstalk between autophagy and apoptosis mediated by calcium release from the endoplasmic reticulum, lysosomal destabilization and mitochondrial events[END_REF]. A clear increase in AO red staining is observed in curcumin-treated cells, both in terms of intensity and in terms of numbers throughout cells (Channel 4, Figure 8). Moreover, AO red fluorescence double in intensity and reach a maximum only after one hour of 20µM curcumin (Figure 9a). This increase of size and number of acidic compartment suggest an increase in both autophagic vesicles and lysosomes followed by their fusion (Figure 8c-9b). Size of control isolated lyzosomes reach 0,9 ± 0,8 µM while alleged autolysosomes reach 7,85 ± 1,9 µm (Figure 9b). Electron microscopy confirm classical features of stress like swollen mitochondria with translucid cristae devoided of membranes, surrounded by ER also swollen with extensive lumen (Figure 9c,d). Some stress fibers (StF) were also observed as were lysosomes (Lys) and large autophagosomes (Figure 9c-f). Curcumin rapidly mobilized the autophagic capacity of the cells in order to discard unwanted and dysfunctional cellular compartments.

Discussions.

Curcumin to iron interactions. Many studies have investigated the use of curcumin for therapeutic purposes, though few have quantified curcumin uptake or analyzed its intracellular localization. Despite its poor bioavailability, a fraction of curcumin is able to cross the plasma membrane and accumulates in the cell within seconds after its addition to the extracellular medium. The intracellular level of curcumin is linearly related to the extracellular content and the concentration ratio is 20 to 1 [START_REF] Moustapha | Curcumin induces crosstalk between autophagy and apoptosis mediated by calcium release from the endoplasmic reticulum, lysosomal destabilization and mitochondrial events[END_REF] . By combining imaging and flow cytometry, we were able to follow the intracellular curcumin staining in Huh-7 cells. Flow imaging revealed that almost all cells were stained. Despite diffuse intracellular staining, curcumin did not localize at the cytoplasmic membrane or at the nuclear membrane (Figure 1a,4). This staining and the intracellular accumulation were not related to any destabilization of the plasma membrane, since 97.5% of Huh-7 cells were intact in these conditions [START_REF] Moustapha | Curcumin induces crosstalk between autophagy and apoptosis mediated by calcium release from the endoplasmic reticulum, lysosomal destabilization and mitochondrial events[END_REF].

Curcumin do not localize at the mitochondria. Previous publications show that curcumin is involved in mitochondrial destabilization [START_REF] Moustapha | Curcumin induces crosstalk between autophagy and apoptosis mediated by calcium release from the endoplasmic reticulum, lysosomal destabilization and mitochondrial events[END_REF][START_REF] Shilpa Kuttikrishnan | Curcumin Induces Apoptotic Cell Death via Inhibition of PI3-Kinase/AKT Pathway in B-Precursor Acute Lymphoblastic Leukemia[END_REF] and induces mitochondrial biogenesis [START_REF] Roberto De Oliveira | Curcumin, mitochondrial biogenesis, and mitophagy: Exploring recent data and indicating future needs[END_REF]. Hypothetically, if curcumin treatment increases the ratio of Bax/Bcl-2 or results in a leaky mitochondrial membrane, curcumin could be linked to mitochondrial destabilization, apoptosis, and mitophagy [START_REF] Moustapha | Curcumin induces crosstalk between autophagy and apoptosis mediated by calcium release from the endoplasmic reticulum, lysosomal destabilization and mitochondrial events[END_REF][START_REF] Rainey | Curcumin hormesis mediates a cross-talk between autophagy and cell death[END_REF][START_REF] Yang | Curcumin induces apoptosis and protective autophagy in castrationresistant prostate cancer cells through iron chelation[END_REF]. But this hypothesis does not need a direct interaction between curcumin and the mitochondrial membrane. Our microspectrofluorimetry assays indicate that curcumin does not stain the mitochondrial compartment itself, but rather its vicinity (Figure 2), mainly the ER compartment, which is known to be tightly linked to the mitochondrial network [START_REF] Csordás | Endoplasmic Reticulum-Mitochondrial Contactology: Structure and Signaling Functions[END_REF][START_REF] Lee | The Interface Between ER and Mitochondria: Molecular Compositions and Functions[END_REF][START_REF] Stacchiotti | Perspective: Mitochondria-ER Contacts in Metabolic Cellular Stress Assessed by Microscopy[END_REF]. The new concept of "contactology" has been applied to the study of the physical and functional bridge between the ER and mitochondria [START_REF] Csordás | Endoplasmic Reticulum-Mitochondrial Contactology: Structure and Signaling Functions[END_REF]. Another proof that mitochondria are not the primary target of curcumin is that, when needed, curcumin targeting to mitochondria is achieved by curcumin grafting with tetraphenyl-phosphonium (TPP+), which will favor the mitochondrial localization of the construct mediated by the cationic charges and enhanced cytotoxicity [START_REF] Cheruku | Mitochondrial-targeted curcuminoids: a strategy to enhance bioavailability and anticancer efficacy of curcumin[END_REF]. One can also hypothesize that curcumin interacts with transcription factors likely to act on efficient coordinators of mitochondrial biogenesis and perhaps of autophagy, without the need for any direct interaction with mitochondria (26).

Curcumin-induced endoplasmic reticulum alterations and its consequences. Confocal images of curcumin autofluorescence and ER tracker red clearly identified the ER as the main intracellular target of curcumin (Figure 1a-d). This ER localization is concomitant with a discrete increase in ER lumen size (Figure 2e) which may be related to the UPR response. ER stress is a very common feature of many physiological and pathological conditions affecting protein secretory pathway functions. The UPR is an adaptive response to ER stress. When disturbed, ER reacts to the burden of unfolded proteins in its lumen (ER stress) by activating intracellular signal transduction pathways, collectively termed UPR. This UPR response is activated to lower the misfolded or unfolded protein content. When ER stress becomes intense, the UPR response shifts to autophagic and pro-apoptotic signaling capabilities ending in cell death [START_REF] Li | Emodin ameliorates hepatic steatosis through endoplasmic reticulumstress sterol regulatory element-binding protein 1c pathway in liquid fructose-feeding rats[END_REF][START_REF] Zhang | Ratiometric fluorescent pH-sensitive polymers for high-throughput monitoring of extracellular pH[END_REF]. To ascertain fidelity to correct protein folding, cells regulate protein-folding capacity in the ER according to need. The underlying mechanism of lumen swelling we described is partially dependent on the 4-PBA that is commonly used to alleviate ER stress (Figure 2e) [START_REF] Walter | The unfolded protein response: from stress pathway to homeostatic regulation[END_REF]. 4-PBA is supposed to interact with the hydrophobic domains of misfolded proteins thereby preventing their aggregation. Nevertheless, inhibition of the lumen size increase is limited, suggesting that other mechanisms could be at work and that a single curcumin insertion into the membrane inducing ER permeabilization could be enough as a complementary mechanism. Beyond its primarily ER localization, curcumin inhibits Ca 2+ -ATPase (SERCA), thereby inducing an increase in cytoplasmic calcium (Figure 3f). These results support previous reports showing that curcumin inhibits the SERCA Ca 2+ -pump, via inhibition of its Ca 2+ -dependent ATPase activity, with an IC50 value between 7 and 15 µM (external concentration) [START_REF] Jonathan G Bilmen | Inhibition of SERCA Ca2+ pumps by 2-aminoethoxydiphenyl borate (2-APB). 2-APB reduces both Ca2+ binding and phosphoryl transfer from ATP, by interfering with the pathway leading to the Ca2+-binding sites[END_REF][START_REF] J G Bilmen | Inhibition of the SERCA Ca2+ pumps by curcumin. Curcumin putatively stabilizes the interaction between the nucleotide-binding and phosphorylation domains in the absence of ATP[END_REF]. It appears that curcumin acts through inhibition of the Ca 2+ -dependent ATPase and conformational changes (curcumin stabilizes the E1 conformational state), which fully abolishes ATP binding. This observation could be directly linked to intracellular calcium increases (Figure 3h). Furthermore, ER lumen swelling was associated with an increase in the number of cells exhibiting a high calcium level (Figure 3h). The shape of the calcium fluorescence increase induced by curcumin looks like a pulse of calcium followed by a decrease in calcium-associated fluorescence to a level that is somewhat higher than before curcumin addition. The pulse duration lasted almost two hours.

There is also increased activity of CHOP (CCAAT-enhancerbinding protein homologous) (37) and of the 78-kDa glucoseregulated protein, GPR78 (Figure 3g) [START_REF] Li | The unfolded protein response regulator GRP78/BiP is required for endoplasmic reticulum integrity and stress-induced autophagy in mammalian cells[END_REF], an ER molecular chaperone also referred to as BiP and generally associated with the UPR response. Indeed, GRP78/BiP is required for stress-induced autophagy [START_REF] Li | The unfolded protein response regulator GRP78/BiP is required for endoplasmic reticulum integrity and stress-induced autophagy in mammalian cells[END_REF].

Curcumin and secondary lysosomes stained. When its concentration increased, curcumin also stained a portion of the lysosomal compartment that is growing (Figure 4,5 and table 1). In this context, it is well known that curcumin is able to bind directly to TFEB (transcription factor EB) [START_REF] Zhang | Ratiometric fluorescent pH-sensitive polymers for high-throughput monitoring of extracellular pH[END_REF], a master regulator of autophagy and lysosomal biogenesis. The curcumin functional groups suitable for interaction with other macromolecules include the α,β-unsaturated β-diketone moiety, carbonyl and enolic groups of the β-diketone moiety, methoxy and phenolic hydroxyl groups, and the phenyl rings (4). The current literature shows that the subcellular localization of TFEB is lysosomal and that its delocalization is very sensitive to lysosomal stress [START_REF] Roczniak-Ferguson | The transcription factor TFEB links mTORC1 signaling to transcriptional control of lysosome homeostasis[END_REF][START_REF] Settembre | A lysosome-to-nucleus signalling mechanism senses and regulates the lysosome via mTOR and TFEB[END_REF][START_REF] Palmieri | Corrigendum: mTORC1-independent TFEB activation via Akt inhibition promotes cellular clearance in neurodegenerative storage diseases[END_REF]. As 10 to 20 µM curcumin activates lysosomal destabilization and induces autophagy, lysosomal stress secondarily activated by curcumin may also activate lysosomal biogenesis and autophagy.

Successful curcumin-induced autophagy detected by impedancemetry coupled to multiparametric flow cytometry

analysis. The xCELLigence impedancemetry setup yielded unexpected results (Figure 6a). We observed a classic proliferation curve with untreated cells, i.e. a proliferation phase until reaching a plateau, before cells started to condense (lower cell index CI) and detach (no CI) as they undergo cell death due to nutrient deprivation in wells. The use of two concentrations of curcumin, i.e., 20 and 50 µM, gave us two distinct curves. The 50 µM curve is typical of induction of cell death (Figure 6a), whereas the 20 µM curve is very surprising: after a proliferation slope similar to the one at 50 µM curcumin during the first 16 hours following curcumin administration, the curve changed shape and the CI stopped decreasing and then increased again in a second proliferation curve (Figure 6a point 4). The cell cycle analysis provided a more precise idea of what happened to the cells. At 20 µM curcumin, the cells clearly escaped cell death and one may suspect successful autophagy allowing the cells to proliferate again. Cells (Figure 6c) first exhibited a cell cycle similar to that of dying cells (50 µM curcumin treatment) (Figure 6d), except that dead cells in sub-G0G1 were less abundant (24% versus 41%), whereas cells in G0G1, S phase (55% versus 42%), and those blocked in G2/M were more abundant. Most importantly, cells in Figure 6e and 7 exhibited the characteristics of cells re-entering the cell cycle before completely recovering and being engaged in a new proliferation step. Many publications assume that curcumin inhibits the proliferation of several types of cancer cells [START_REF] Choudhuri | Curcumin induces apoptosis in human breast cancer cells through p53-dependent Bax induction[END_REF]. The arrest in cell-cycle progression is generally positioned at the G2/M phase of the cell cycle [START_REF] Sahu | Activation of ATM/Chk1 by curcumin causes cell cycle arrest and apoptosis in human pancreatic cancer cells[END_REF]. This is the case with curcumin-treated MCF-7 cells [START_REF] Simon | Inhibitory effect of curcuminoids on MCF-7 cell proliferation and structure-activity relationships[END_REF]. The curcumin arrest at the G2/M phase might be explained by the fact that curcumin was found to perturb microtubule-kinetochore attachment and also activated the mitotic checkpoint, resulting in delayed mitosis [START_REF] Conly | Stuck in division or passing through: what happens when cells cannot satisfy the spindle assembly checkpoint[END_REF]. It has also been demonstrated that treatment of MCF-7 cells with higher curcumin concentrations (over 20 µM) increased the number of cells in the sub-G0G1 phase related to increased apoptosis [START_REF] Choudhuri | Curcumin induces apoptosis in human breast cancer cells through p53-dependent Bax induction[END_REF][START_REF] Banerjee | Curcumin suppresses the dynamic instability of microtubules, activates the mitotic checkpoint and induces apoptosis in MCF-7 cells[END_REF]. Monitoring of 50 µM curcumin-treated cells tells us that the important loss of cell viability fits with that of cells undergoing classic cell death (Figure 7c,f). The cells (20 µM treatment) that entered a new proliferation step after transition (Figure 7b,d,e) never presented a similar decrease in cell viability. There were 58.1% of viable cells plus as few as 19.9% of them going through a permeability state (non-permanent loss of viability) that is reversible. Indeed, 78% (58.1% + 19.9%) of cells maintained more or less their plasma membrane in an almost impermeable state (these cells did not become PI-positive) (Figure 7d). Moreover, if we follow the cells along the impedance curve during their proliferation state (CI increase) and/or their curve of CI decrease, and measure their characteristics in terms of early events associated with cell death induction [START_REF] Petit | Alterations in mitochondrial structure and function are early events of dexamethasone-induced thymocyte apoptosis[END_REF][START_REF] Zamzami | Sequential reduction of mitochondrial transmembrane potential and generation of reactive oxygen species in early programmed cell death[END_REF], we can draw a clear picture of their behavior. Usually, early cell death is detected through a drop in mitochondrial potential (∆Ψm) associated with the immediate generation of ROS (superoxide anions and hydrogen peroxides) associated with an increase in intracellular calcium [START_REF] Gendron | Oxidation of pyridine nucleotides during Fas-and ceramide-induced apoptosis in Jurkat cells: correlation with changes in mitochondria, glutathione depletion, intracellular acidification and caspase 3 activation[END_REF][START_REF] Kroemer | The biochemistry of programmed cell death[END_REF]. This is the situation recorded for the 50 µM curcumin-treated cells (Table 2), whereas it is not the same when cells are treated with 20 µM curcumin. These cells undergo some change in ∆Ψm, but the number of cells with a low ∆Ψm is not a majority (38%) (Table 2d), and the cells exhibited increased ROS production and calcium when compared with cells undergoing apoptosis, where 50% of cells showed a drop in ∆Ψm. More significant is the fact that the proportion of cells with ROS and calcium is approximately the same ( 45%) (Table 2c). This means that curcumin's induction of the cellular antioxidant system and/or efficient mitophagy associated with restoration of ∆Ψm may push almost 50% of cells to go through the cell cycle and escape cell death. It is quite interesting to hypothesize that the alteration of lysosomes and activation/delocalization of TFEB may lead to mitochondrial biogenesis [START_REF] Zhang | Ratiometric fluorescent pH-sensitive polymers for high-throughput monitoring of extracellular pH[END_REF]. In parallel, this is also associated with an increased level of mitophagy and overall this results in a restored quality of mitochondrial functions [START_REF] Zhang | Ratiometric fluorescent pH-sensitive polymers for high-throughput monitoring of extracellular pH[END_REF]. On the other hand, the anti-inflammatory action of curcumin has two aspects: the first is mediated through its ability to inhibit cyclooxygenase-2 (COX-2), lipoxygenase (LOX), and inducible nitric oxide synthase (iNOS) (49)(59, 60), and the second is mediated through the ability of curcumin to activate directly antioxidant enzymes, i.e., catalase (CAT), superoxide dismutase (SOD), and glutathione peroxidase (GSH-PX) [START_REF] Zhang | Ratiometric fluorescent pH-sensitive polymers for high-throughput monitoring of extracellular pH[END_REF][START_REF] Miranda | Potential application of non-flavonoid phenolics in diabetes: antiinflammatory effects[END_REF]. Low-and medium-concentration curcumin decreased malondialdehyde (MDA) and ROS levels and Nrf2 KEAP-1 induction [START_REF] Li | Resveratrol protects against oxidative stress by activating the Keap-1/Nrf2 antioxidant defense system in obese-asthmatic rats[END_REF]. Therefore, our results suggest that curcumin, which drives successful autophagy (mitophagy), not only protects cells (after a first step of toxicity-induced events) but also reverses the mitochondrial damage and dysfunction induced by oxidative stress. Evidence in previous publications clearly shows that curcumin is rapidly internalized within cells even if the extra-/intracellular concentration ratio is 20 to 1. So, working within 0 to 50 µM curcumin implies that intracellular curcumin concentration is in the range of 0 to 2.5 µM. Internalized curcumin localizes first at the ER and then at the lysosomes (Lys). In both cases, curcumin destabilizes these compartments inducing an UPR response and a lysosomal stress. Originally, we used impedancemetry to investigate the fact that curcumin can induce both autophagy and cell death. Flow cytometry based on cells from xCELLigence really impeded our capability to depict sequential events produced by curcumin. At 25 µM, curcumin first induced cells to enter cell death processes that rely on apoptosis and necrosis, but also started autophagic processes that eventually -if successful -allowed cells to recover, escape G2/M blockade, and proliferate again (Figure 6). In contrast, if curcumin is used at higher concentration, the insults at both the ER and lysosomes are too great to be overcome by coping mechanisms like autophagy and full type II cell death signaling occurs, with no possible return. These results confirm that the mechanisms of curcumin operate at different levels of the cell, but also at different times and intensities, as summarized in Figure 10, a paradigm first described with previous data [START_REF] Moustapha | Curcumin induces crosstalk between autophagy and apoptosis mediated by calcium release from the endoplasmic reticulum, lysosomal destabilization and mitochondrial events[END_REF][START_REF] Rainey | Curcumin hormesis mediates a cross-talk between autophagy and cell death[END_REF].

Methods.

Chemicals and reagents. Calcein Cells. Human hepatoma-derived Huh-7 cells (RIKEN BioResource Center, Tsukuba, Japan) were grown in the presence of 5% CO2 with Dulbecco's modified Eagle's medium (DMEM) containing high glucose (25 mM Sigma-Aldrich, St. Louis, MI) with 10% Fetal Bovine Serum (FBS, Hyclone, Logan, UT) completed with 1% penicillin-streptomycin, HEPES NaOH 1 mM, Na-pyruvate 1 mM and 1% nonessential amino acids (MEAM, GIBCO).

Microspectrofluorimetry. The UV-visible confocal laser microspectrofluorimeter prototype was built around a Zeiss UMSP80 UV epifluorescence microscope (Carl Zeiss, Inc., Oberkochen, Germany), optically coupled by UV reflecting mirrors to a Jobin-Yvon HR640 spectrograph (ISA, Longjumeau, France) [START_REF] Sureau | Microspectrofluorimetric study of the kinetics of cellular uptake and metabolization of benzo(a)pyrene in human T 47 D mammary tumor cells: evidence for cytochrome P1450 induction[END_REF]. The 351nm UV line of an argon laser (model 2025; Spectra-Physics, Mountain View, CA) was used for either drug or fluorochrome excitation. The diameter of the laser beam is first enhanced through a doublelens beam expander in order to cover the entire numerical aperture of the microscope's optics. The laser beam is then deflected by the epi-illumination system (dichroic mirror or semireflecting glass) and focused on the sample through the microscope objective (X63 Zeiss Neofluar water-immersion objective; numerical aperture = 1.2) on a circular spot 0.5 µm in diameter. The excitation power is reduced to less than 0.1 mW by neutral optical density filters. The objective was immersed in the culture medium, and a circular area 0.8 µm in diameter was selected at the sample level, by interposing a field diaphragm on the emission pathway of the microscope, to selectively collect the fluorescence signal from the nucleus or a specific cytoplasmic area. Confocal conditions are met when the image of this analysis field diaphragm through the microscope objective perfectly coincides with the focus of the laser beam on the sample. Under these conditions, the experimental spatial resolution, measured on calibrated latex beads (2, 0.6, and 0.16 µm in diameter) labeled with the fluorescent probe fluorescein, is 0.5 µm for the directions X, Y, and Z. Finally the fluorescence spectra were recorded after spectrograph dispersion, in the 380-630 region on a 1024 diode-intensified optical multichannel analyzer (Princeton Instruments, Inc., Princeton, NJ) with a resolution of 0.25 nm/diode. Each fluorescence emission spectrum was collected from 1 to 10 s. Data were stored and processed on an 80286 IBM PS/2 microcomputer using the Jobin-Yvon "Enhanced Prism" software. It should be noted that, in order to avoid any possible fluorescence from a plastic or glass support during analysis with near-UV excitation, cells were grown on quartz plates that were then placed on the microscope stage in 50-mm thermostated Petri dishes, filled with 5 ml of phosphate-buffered saline. A uranyl glass bar was used as a fluorescence standard to control laser power and instrumental response and to enable quantitative comparison between spectra recorded on different days. Sample heating, photobleaching, and photo damage were assessed empirically and found to be negligible under our experimental conditions. In particular, cells always remained viable after repeated fluorescence determinations, as controlled by phase-contrast microscopy.

Imaging Flow Cytometry. Imaging flow cytometry combines the strengths of microscopy and flow cytometry in a single system for quantitative image-based cellular assays in large and heterogeneous cell populations. The Amnis ® ImageStream100 instrument (Amnis, Merck Millipore) acquires up to six images of each cell in different imaging modes. The system is equipped with 3 lasers (405 nm, 488 nm and 640 nm) and cells can be magnified by a 20, 40 or 60x objective allowing a wide range of applications. The six images of each cell comprise: a side scatter (SSC) or darkfield image, a transmitted light (brightfield) image and four fluorescence images corresponding to the FL1, FL2, FL3 and FL4 spectral bands of conventional flow cytometers [START_REF] Bharat B Aggarwal | Curcumin: an orally bioavailable blocker of TNF and other pro-inflammatory biomarkers[END_REF].

Light is quantified for all the pixels in the image and identifies both the intensity and location of the fluorescence. Each pixel has an X coordinate, a Y coordinate, and an intensity value that corresponds to the amount of light captured at that location. Validation and quality control of the cytometer were performed by daily running of SpeedBeads™ reagent (Amnis, Merck Millipore) and using INSPIRE™ 2.2 software (Amnis ® ). Data analysis was performed using IDEAS TM 6.0 software (Amnis ® ) and single color controls were used to create a compensation matrix that was applied to all files to correct for spectral crosstalk.

Cell viability, mitochondrial membrane potential (∆Ψm), ROS and calcium levels . 2 millions Huh-7cells were seeded on 6-well plates and maintained with 25 µM of curcumin for a given period of time ranging from 0 to 48h depending on the experiments. After treatment, cells were trypsinized, harvested, washed and then resuspended together with their supernatants in PBS. Before flow cytometry, various staining were used : DiOC 6 (3) was added at 40 nM final concentration for ∆Ψm determination, DCFH-DA at 5 µM for hydrogen peroxide detection, MitoSOX at 1 µM for superoxide anion detection. Most of the time a double staining is realized in order to simultaneously assess cell viability, with propidium iodide (PI; stock at 1mg.mL -1 ) in the case of DiOC 6 (3), DCFH-DA and Fluo4-AM and with 2 µg/ml TO-PRO-3 iodide (stock at 1mg.mL -1 ) for MitoSOX. A supplemental double staining was used for the distinction between viable, apoptotic and necrotic cells with YO-PRO-1 / PI in parallel with a Annexin-V / PI staining realized with the Annnexin-V-FITC when needed (Immunotech, Beckman-Coulter). All samples were measured and analyzed on a FACS Calibur 4C as previously described [START_REF] Zamzami | Sequential reduction of mitochondrial transmembrane potential and generation of reactive oxygen species in early programmed cell death[END_REF][START_REF] Gendron | Oxidation of pyridine nucleotides during Fas-and ceramide-induced apoptosis in Jurkat cells: correlation with changes in mitochondria, glutathione depletion, intracellular acidification and caspase 3 activation[END_REF].

Cell cycle analysis by flow cytometry. Huh-7 cells were taken from the xCELLigence wells at defined times along the different proliferation curves and their position in the cell cycle was evaluated by measuring 5-bromo-2-deoxyuridine (BrdU) incorporation using the APC BrdU Flowkit (catalogue number 552598, BD Pharmingen). Cells were incubated with BrdU (10 mM) for 1 h at 37µC, washed, trypsinized, and fixed with cytofix/cytosperm buffer. BrdU staining was done following the kit procedure. DNA was stained with 7aminoactinomycin D (7-AAD) and cells were analyzed using FACS Calibur 4C (Becton Dickinson) with the FL-1 channel (band pass 530 ± 30 nm) for BrdU and the FL-3 channel (long pass 670 nm) for 7-ADD. The sub-G0G1 peak represented the dead cells within the samples.

Control of ROS production by antioxidants.. To test the effects of ROS scavengers, N-acetylcysteine (NAC; 1 mM), MitoQ10 (5 µM) and SkQ1 (500 nM) cells were preincubated for 3 h with the antioxidants before treatments. Cell viability (PI) together with ∆Ψmm by DiOC 6 (3) was measured.

Control of calcium efflux by calcium chelators or inhibition

of the mitochondrial calcium uniport. For cytosolic calcium determination, Fluo4-AM (1 mM stock solution, Molecular Probes) was used at 5 µM final concentration, as previously described [START_REF] Petit | Alterations in mitochondrial structure and function are early events of dexamethasone-induced thymocyte apoptosis[END_REF][START_REF] Gendron | Oxidation of pyridine nucleotides during Fas-and ceramide-induced apoptosis in Jurkat cells: correlation with changes in mitochondria, glutathione depletion, intracellular acidification and caspase 3 activation[END_REF]. Ca 2+ titration of Fluo4-AM was performed by reciprocal dilution of Ca 2+ -free and Ca 2+saturated buffers prepared by using a Ca 2+ kit buffer (Invitrogen) on cells poisoned with DPBS supplemented with carbonyl cyanide m-chlorophenylhydrazone (mClCCCP, 10 µM) and ionomycin (5 µM) [START_REF] Basiji | Cellular Image Analysis and Imaging by Flow Cytometry[END_REF]. Most of the time double staining was done to assay simultaneously cell viability with propidium iodide (PI) at 1 µg.mL -1 final concentration. Control analysis was done with curcumin at different concentrations and was found to be negligible when Fluo4-AM was added (since the fluorescence of the probes is substantial compared with the fluorescence of curcumin at the chosen setting).

Analysis of cathepsin and calpain activities by flow cytom-

etry. Cathepsin and calpain activities in live cells were determined with the use of specific substrates as previously described [START_REF] Moustapha | Curcumin induces crosstalk between autophagy and apoptosis mediated by calcium release from the endoplasmic reticulum, lysosomal destabilization and mitochondrial events[END_REF].

Analysis of CHOP and GRP78 activities by flow cytometry. Flow cytometry analysis of CHOP and GRP78 activities. Caco-2 cells were treated with different cocktails of pollutants or pollutants alone for 6 or 12 h and tested for GRP78 and CHOP activities. Cells were grown in 6-well plates, trypsinized and then fixed with 4% paraformaldehyde at 4°C for 40 min and rinsed several times with PBS. Nonspecific binding sites were blocked for 2 h at room temperature with 5% normal SVF (Gibco, ThermoFisher Scientific, France) in 0.1% Triton X-100-PBS. Caco-2 cells were incubated overnight at 4°C with primary antibodies (1:100 dilutions with blocking buffer) for GRP78 (Cell Signaling, MA, USA) or CHOP (Santa Cruz, CA, USA). Cells were then incubated with a mixture of fluorescein isothiocyanate and tetramethyl rhodamine isothiocyanate-conjugated secondary antibodies (BD Biosciences, France) for 2 h at 4°C. Cells were analyzed by flow cytometry using the green (FL1 at 525 ± 30 nm) or red (FL-2 at 585 ± 42 nm) channels. Each experiment was repeated at least four times in duplicate.

Caspase activation & fluorimetric assays. Isolated Huh-7 cells were washed and suspended in calcium-free buffer solution (140mM NaCl, 1.13mM MgCl 2 , 4.7mM KCl, 10mM glucose, 0.1 M EDTA, and 10mM HEPES, pH 7.2). Cells were then loaded at room temperature for 30 min with fluorescent indicator-linked substrates for activated caspase-8 (10 µM Z-IETD-R110; Molecular Probes), caspase-9 (10 µM Z-LEHD-R110; Molecular Probes), or caspases 3/7 (Caspase-3/7 Green ReadyProbes™ reagent with a DEVD sequence, Molecular Probes).

Impedancemetry with xCELLigence for the measurement of cell proliferation. All impedance measurements were performed with the xCELLigence RTCA MP instrument (ACEA Biosciences). First, 50 µL of Huh-7 cell culture medium was added to each well of 16-well E-Plates (ACEA Biosciences) and the background impedance was measured and displayed as cell index. In parallel, the xCELLigence system allowed us to choose a specific timeframe according to the impedance curve, detach quadruplicate wells, pool them, and run flow cytometry on cells of interest.

Measurement of Ca2+-ATPase activity. Sarco-endoplasmic reticulum ATPase (SERCA) activity was measured with a Ca 2+ -ATPase assay kit according to the manufacturer's protocols (Abcam). Inorganic phosphate (Pi), which was generated from hydrolysis of ATP by ATPase, can be measured by a simple colorimetric reaction. Ca 2+ -ATPase activities were expressed in units of micromolar inorganic phosphate produced (mM Pi/mg protein/h).

Electron microscopy. Huh-7 cells were fixed in 1.25% glutaraldehyde buffered with 0.1 M sodium phosphate (pH 7.4) for 24 h at 4°C, dehydrated with ethanol at 4°C, and immersed in a 1:1 mixture of propylene oxide and Epon. They were embedded in Epon by polymerization at 60°C for 48 h and examined under the electron microscope.

Fig. 1 .

 1 Fig. 1. Qualitative analysis of curcumin staining of cells | a. Image flow cytometry of curcumin uptake by Huh7 cells. White arrows indicate either nuclei (n) or cytoplasmic membrane (Cm) | b,c. Cell fluorescence over time before and after 5 µM curcumin (b) or 20 µM curcumin (c) was added to the tube | d. Curcumin fluorescence intensity of different regions of previous kinetic measurements. The mean fluorescence values of curcumin in R2 and R3 are directly noted on the histograms.

2 |Fig. 2 .

 22 Fig. 2. Intracellular curcumin is not found in the mitochondrial network | a. Fluorescence microscopy of the mitochondrial specific dye, TMRM, at 585 ± 42 nm after excitation at 488 nm | b. Fluorescence microscopy of curcumin at 530 ± 30 nm after excitation at 488 nm. White arrows indicate regions with high curcumin fluorescence, yellow arrows indicate regions with a high density of mitochondria| b,c. Microspectrofluorimetric spectra at selected regions 1 and 2 (shown in a,b). Panels (c and d) are related to the areas named 1 and 2 in a and b. The relative values of curcumin and TMRM in areas 1 and 2 are evaluated at the points of maximum emission of curcumin (530 nm) and TMRM (585 nm)| e. Microspectrofluorimetric spectra of curcumin or TMRM alone showed peaks around 530 nm and 585, respectively, in their free forms .

3 Fig. 3 .

 33 Fig. 3. Localization of intracellular curcumin at the endoplasmic reticulum (ER) and associated signaling events | a,b,c,d. Confocal analysis of the double staining with curcumin (in green) and ER-tracker red, (in red). Arrows indicate the ER compartment; c and d panels are enlargements of the two boxes numbered 1 and 2 of the a and b pictures | e. Volume of the ER compartment assayed by the ER tracker red fluorescence in flow cytometry with different concentrations of curcumin with or without the ER stress inhibitor 4-phenylbutyric acid (PBA) | f. Ca 2+ -ATPase measurements illustrating SERCA inhibition by different curcumin concentrations| g. Histogram representation of CHOP and P38 measurements following incubation with different curcumin concentrations illustrating the UPR response| h. Flow cytometry analysis of curcumin and calcium contents over time (in hours), by curcumin autofluorescence and Fluo4-AM, respectively.

Fig. 4 .

 4 Fig. 4. Image cytometry (Amnis ® ) of the double staining: LysoTracker Red and curcumin | a. Curcumin green fluorescence for 24h incubation with 20 µM curcumin | b. LysoTracker Red fluorescence analysis for 10min incubation with 100 nM LysoTracker Red | c. Fluorescence histogram of curcumin and LysoTracker green fluorescence | d.A selection of 6 cells analyzed with Amnis ® where it is possible to see the widespread intracellular staining of 5 µM curcumin for 3 h and the punctate staining with LysoTracker Red.

Fig. 5 .

 5 Fig. 5. Similarity analysis for correlation between curcumin fluorescence and LysoTracker Red | a.Analysis of the colocalization of curcumin and LysoTracker red (lysosomes) by using the Similarity Score included in IDEAS 6.0 software™ (Amnis ® ). This score, a log-transformed Pearson's correlation coefficient between the pixels of two image pairs, provides a measure of the degree of colocalization by measuring the pixel intensity correlation between the curcumin and LysoTracker images. Analysis performed on 542 cells. Cells that were permeable to TO-PRO-3 iodide and/or debris were excluded from the analysis together with cellular aggregates. This corresponds to 5 µM curcumin and 3h incubation plus 10min staining at 37°C with 100 nM LysoTracker Green | b. Selection of some images that correspond to cells (from the histogram in a) where there is no strict correlation between curcumin and LysoTracker Red. | c. Selection of some images corresponding to the cells presenting full colocalization of the two probes from the histogram in a .

Fig. 6 .

 6 Fig. 6. Combined xCELLigence and flow cytometry analysis of the Huh-7 cell cycle at 20 and 50 µM curcumin | Cells were treated with 20 µM (red line) or 50 µM (blue line) curcumin. The control was imaged with circles after the injection position since it was correlated with either the red or blue line in the first 24 h (we did not want superimposition of the control line with the two others). Circled numbers (1-6) indicate the times when cells (4 wells each time) were taken up from the xCELLigence set-up to be stained and analyzed by flow cytometry of the cell cycle. The percentage of cells in each part of the cell cycle (G0G1, S, G2/M) is indicated, and the sub-G0G1 indicates dead cells (DNA concentration and less staining with PI). Cells are from positions 1-6 along the curve and are labeled a-f in biparametric analysis and tables.

Fig. 7 .

 7 Fig. 7. Detailed analysis of the change in plasma membrane permeability of Huh-7 cells using the xCELLigence device | a.YOPRO-1/PI staining was used and three cell populations could be distinguished: YO-PRO-1 -/PI -for viable cells, YO-PRO-1 + /PI -or YO-PRO-1 + /PI intermediate for apoptotic cells and YO-PRO-1 + /PI ++ for necrotic cells. Cells are from positions 1-6 along the curve and are labeled a-f in biparametric analysis and tables.

Fig. 8 .

 8 Fig. 8. Image in flow of the acidic compartment of Huh-7 cells treated with 5 µM curcumin for 16 h | a. Images presented are dark field (SSC, Ch01), bright field (Ch02) and the red channel (Ch04) for red (aggregates in low pH) nonyl acridine orange (AO) fluorescence . Control Huh-7 cells exhibit few spots of lysosome staining but cells treated with curcumin largely increase their staining both coming from lysosomes and acidic autophagic vacuoles| b,c. Same as in panel a, exhibiting 6 distinct cell staining experiments for 16h treatment with 5 µM curcumin.

8 |Fig. 9 .

 89 Fig. 9. Detection of the acidic vesicles after curcumin treatment and electron microscopy | a. Flow cytometry analysis of acridine orange (AO) staining of the acidic vesicles in samples treated with curcumin for 16 h. The histogram presents the fast increase in AO internalization in low pH vesicles| b. Image cytometry measurements presented in Figure 8. The sizes of the vesicles are given in µm and the number of vesicles is indicated | c,d,e,f. Electron microscopy picture of a sample of Huh-7 cells treated with 20 µM curcumin for 16 h. In c, essentially mitochondria (Mito.) with swollen ER (SER) and stress fibers (StF). In d, essentially round, translucent mitochondria defective in cristae membranes with swollen ER (SER) and stress fibers (StF): in e and f, essentially lysosomes (Lys) and autophagolysosomes (AutoP). With one mitochondrion (Mito) in f .

9 Fig. 10 .

 910 Fig. 10. Schematic representation of the various events involved in curcumin-induced signaling | Lipophilic curcumin penetrates the cells and interacts with some of the membrane compartments. Curcumin primarily provokes ER stress and secondarily lysosomal membrane destabilization. Calcium released from the ER enters the mitochondria and alters the electron transport chain, resulting in a ∆Ψm drop and superoxide generation. Altered mitochondria are taken up by mitophagy. The lysosomal pathway also converges towards mitochondria since calpain and cathepsin are likely to activate caspase-8 and cleave Bid (giving rise to tBid and mitochondrial membrane permeabilization). Huge autophagic processes are induced at low curcumin concentration in parallel to cell death events until beclin-1 is cleaved and cell death dominates. Interestingly, bafilomycin A1 inhibition of autophagy leads to more pronounced cell death..

10 |

 10 bioRχiv | November 1, 2019 Sala-de-Oyanguren F. et al. | Curcumin-induced effects and organelle localization

  Sala-de-Oyanguren F. et al. | Curcumin-induced effects and organelle localization November 1, 2019 | bioRχiv | 11

  Eugene, OR, cat. no. L7528) was dissolved in PBS (50 nM) and stored at 4°C. Aliquots of the stock solutions of the dyes were added directly to culture media. Prior to imaging, cells were incubated for 15 min with the dye at 4 µM (AO) or 100 nM (LyR).

	BRL (Invitrogen, Grand Island, NY, USA). Calcein-AM,
	2,7-dichlorodihydrofluorescein diacetate (DCFH-DA), 3,3'-
	dihexyloxacarbocyanine iodide [DiOC 6 (3)] and N-[4-[6-
	[(acetyloxy) methoxy]-2,7-dichloro-3-oxo-3H-xanthen-9-
	yl]-2-[2-[2-[bis[2[(acetyloxy) methoxy]-2-oxyethyl] amino]-
	5-methyl-phenoxy]	ethoxy]phenyl-	N-[2-[(acetyloxy)
	methoxy]-2-oxyethyl]-(acetyloxy) methyl ester (Fluo-4/AM)
	were from Molecular Probes (Invitrogen, Eugene, OR,
	USA). M. Murphy (Medical Research Council Dunn Human
	Nutrition Unit, Wellcome Trust/MRC Building, Hills Road,
	Cambridge CB2 2XY, United Kingdom) and VP Sku-
	lachev (Belozersky Institute of Physico-Chemical Biology,
	Lomonosov Moscow State University, Vorob'evy Gory
	1/40, 119992 Moscow, Russia) provided us with chemical
	products and advice regarding the mitochondrially targeted
	anti-oxidants MitoQ10 and SKQ1, respectively. Acridine
	orange (AO ; Molecular Probes, Eugene, OR, cat. no.
	A1301) stock solution was made in water (1 mg/mL) and
	stored at 4°C, and LysoTracker Red DN 99 (LyR ; Molecular
	Probes,
	-AM, curcumin, propidium
	iodide (PI), N-acetylcysteine (NAC) and ruthenium red were
	from Sigma-Aldrich Chemical Co. (St. Louis, MO, USA).
	Culture medium RPMI-1640, fetal bovine serum (FBS),
	penicillin-streptomycin and L-glutamine were from GIBCO

| bioRχiv | November 1, 2019 Sala-de-Oyanguren F. et al. | Curcumin-induced effects and organelle localization

| bioRχiv | November 1, 2019 Sala-de-Oyanguren F. et al. | Curcumin-induced effects and organelle localization

| bioRχiv | November 1, 2019 Sala-de-Oyanguren F. et al. | Curcumin-induced effects and organelle localization

ACKNOWLEDGEMENTS

As this manuscript is made with L A T E X we thank the whole T E X community and the work of Dr. Ricardo Henriques's lab for the initial available template.

AUTHOR CONTRIBUTIONS

These contributions follow the Contributor Roles Taxonomy guidelines: https://casrai.org/credit/. Conceptualization: J.E.O, P.X.P; Data curation: P.X.P; Formal analysis: F.J.S.O, N.E.R, A.M, J.E.O, P.X.P; Funding acquisition: P.X.P; Investigation: F.J.S.O, N.E.R, A.M, A.S, F.S, P.X.P; Methodology: F.J.S.O, N.E.R, J.E.O, P.X.P; Project administration: P.X.P; Resources: J.E.O, P.X.P; Software: N/A; Supervision: P.X.P; Validation: P.X.P; Visualization: N/A; Writing -original draft: N.E.R, J.E.O, P.X.P ; Writingreview & editing: All authors.

COMPETING INTERESTS

The authors declare no competing interests.


Mark Abraham

Rossen Apostolov

Jonathan Barnoud

Paul Bauer

Christian Blau

Alexandre M J J Bonvin

Matthieu Chavent

John Chodera

Sharing Data from Molecular Simulations

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Sharing Data from Molecular Simulations

Mark Abraham [START_REF] Abraham | GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers SoftwareX[END_REF] , Rossen Apostolov 2 , Jonathan Barnoud 3, † , Paul Bauer 1 , Christian Blau 1 , Alexandre M.J.J. Bonvin [START_REF] Stone | GPU-Accelerated Molecular Modeling Coming of Age[END_REF] , Matthieu Chavent 5,* , John Chodera [START_REF] Jo | CHARMM-GUI 10 Years for Biomolecular Modeling and Simulation[END_REF] , Karmen Čondić-Jurkić [START_REF] Jo | CHARMM-GUI 10 Years for Biomolecular Modeling and Simulation[END_REF][START_REF] Marrink | Computational Modeling of Realistic Cell Membranes[END_REF] , Lucie Delemotte [START_REF] Abraham | GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers SoftwareX[END_REF] , Helmut Grubmüller 8 , Rebecca J. Howard [START_REF] Im | Challenges in Structural Approaches to Cell Modeling[END_REF] , E. Joseph Jordan [START_REF] Im | Challenges in Structural Approaches to Cell Modeling[END_REF] , Erik Lindahl [START_REF] Im | Challenges in Structural Approaches to Cell Modeling[END_REF] , O. H. Samuli Ollila [START_REF] Chavent | Molecular Dynamics Simulations of Membrane Proteins and Their Interactions: From Nanoscale to Mesoscale[END_REF] , Jana Selent [START_REF] Elofsson | Ten Simple Rules on How to Create Open Access and Reproducible Molecular Simulations of Biological Systems[END_REF] , Daniel G. A. Smith [START_REF] Berman | The Protein Data Bank at 40: Reflecting on the Past to Prepare for the Future[END_REF] , Phillip J. Stansfeld 32 , Johanna K.S. Tiemann [START_REF] Hinsen | ActivePapers: a Platform for Publishing and Archiving Computer-Aided Research[END_REF] , Mikael Trellet [START_REF] Stone | GPU-Accelerated Molecular Modeling Coming of Age[END_REF] , Christopher Woods [START_REF] Barba | Terminologies for Reproducible Research[END_REF] , Artem Zhmurov 1

AUTHOR ADDRESS

Introduction

Molecular simulations have become increasingly powerful and accessible in recent years, due in part to the rise of HPC [START_REF] Abraham | GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers SoftwareX[END_REF][START_REF] Lagardère | a Massively Parallel Molecular Dynamics Package for Multiscale Simulations of Large Complex Systems with Advanced Point Dipole Polarizable Force Fields[END_REF][START_REF] Jung | Scaling Molecular Dynamics Beyond 100,000 Processor Cores for Large-Scale Biophysical Simulations[END_REF] and GPU-powered clusters and powerful desktop computers [START_REF] Stone | GPU-Accelerated Molecular Modeling Coming of Age[END_REF] as well as the development of user-friendly software to set-up simulations [START_REF] Doerr | High-Throughput Molecular Dynamics for Molecular Discovery[END_REF][START_REF] Jo | CHARMM-GUI 10 Years for Biomolecular Modeling and Simulation[END_REF] . The underlying physical models and methods have also improved over the years to address ever more complex biological and chemical questions [START_REF] Marrink | Computational Modeling of Realistic Cell Membranes[END_REF][START_REF] Bottaro | Biophysical Experiments and Biomolecular Simulations: a Perfect Match[END_REF] . Finally, the number of users and available tools is continuously increasing, as is the amount and complexity of workflows and produced output [START_REF] Im | Challenges in Structural Approaches to Cell Modeling[END_REF][START_REF] Chavent | Molecular Dynamics Simulations of Membrane Proteins and Their Interactions: From Nanoscale to Mesoscale[END_REF] Here, we would like to focus on the term reproducibility. The definition of reproducibility and its distinction from replicability can vary between disciplines [START_REF] Plesser | Reproducibility vs. Replicability: a Brief History of a Confused Terminology[END_REF][START_REF] Hinsen | ActivePapers: a Platform for Publishing and Archiving Computer-Aided Research[END_REF][START_REF] Barba | Terminologies for Reproducible Research[END_REF] , but in this context, we will broadly define reproducibility as the ability to re-implement the workflows of published work and obtain similar behavior for observables of interest as well as define the appropriate way to measure/calculate and report these observables [START_REF] Grossfield | Best Practices for Quantification of Uncertainty and Sampling Quality in Molecular Simulations [Article v1.0[END_REF] . Reproducibility is a long-standing issue for molecular modeling [START_REF] Walters | Informatics, and the Quest for Reproducibility[END_REF] and a key step toward better reproducibility and improved collaboration is making data more accessible and workflows interoperable. This can help reduce the entry barrier for the newcomers, but it could also help the existing practitioners to focus on answering scientific questions rather than wasting time in redeveloping existing sets of parameters or translating files formats to pass from one software to another. To reach this goal, it is now necessary to overcome several difficulties:

• First, there is now a multitude of package-specific file formats and object models.

This variety, although increasing the efficiency for each package, introduces limitations in the interoperability and creates friction for users juggling with various software to generate and analyze their data.

• Second, there is still a lack of exhaustive documentation related to new software development. The proliferation of various libraries and toolkits definitely opens up new avenues of research, but documenting the entire workflow from building a molecular model and parameterization to data analysis and visualization has become more complex. The method sections in publications often lack sufficient details to successfully re-implement the protocol or repeat the study from scratch, and default parameters to run a simulation may vary from one software version to another.

• Last but not least, there is no consensus to share data. The recent years have seen developments of different open data platforms, but the (ever-increasing) size of the generated trajectories makes it difficult to share simulation data efficiently. The absence of appropriate infrastructure, guidelines, and incentives further complicate the situation [START_REF] Graham | Molecular Models Should Not Be Published Without the Corresponding Atomic Coordinates[END_REF][START_REF] Romero | Silico Atomistic Coordinates and Molecular Dynamics Simulation Trajectories of the Glucocerebrosidase-Saposin C Complex[END_REF] .

In general, we are witnessing a growing effort to make science more open by researchers themselves and increasingly so by funders and journals 20,21 . Soon, it may be mandatory to share data and deposit models obtained from hybrid/integrative approaches combining molecular modeling and experimental results [START_REF] Burley | PDB-Dev: a Prototype System for Depositing Integrative/Hybrid Structural Models[END_REF] . Finding a way to consistently share data, workflows, and protocols will be thus necessary to ensure an efficient information exchange. Defining best practices and coming up with solutions should be a community effort to achieve the best outcome for everyone involved. In an effort to start a discussion around

Standardization of file formats

While in structural biology the established PDB file format was stable for decades [START_REF] Berman | The Protein Data Bank at 40: Reflecting on the Past to Prepare for the Future[END_REF] , the MD simulations field has a tendency to produce a multitude of input/output formats each related to one MD package [START_REF] Phillips | Scalable Molecular Dynamics with NAMD[END_REF][START_REF] Brooks | CHARMM: the Biomolecular Simulation Program[END_REF][START_REF] Abraham | GROMACS: High Performance Molecular Simulations Through Multi-Level Parallelism From Laptops to Supercomputers[END_REF][START_REF] Ferrer | An Overview of the Amber Biomolecular Simulation Package[END_REF][START_REF] Rackers | Tinker 8: Software Tools for Molecular Design[END_REF][START_REF] Eastman | OpenMM 4: a Reusable, Extensible, Hardware Independent Library for High Performance Molecular Simulation[END_REF] . With the rapid growth in complexity, size, and number of macromolecular structures led by advances in experimental techniques, even the canonical PDB format is now evolving to allow rendering and analyzing larger files with a gain in performance [START_REF] Bradley | MMTF-an Efficient File Format for the Transmission, Visualization, and Analysis of Macromolecular Structures[END_REF] . This evolution may also encourage the MD community to update its file formats to deal with larger and more heterogeneous data.

A new jointly developed format would need to be modular and flexible enough to not only take into account current but also catch future needs. Here arises a first question: What are the current and future needs of the MD community for such format? While particle coordinates are the current main feature both for input and output standards, other features need to be discussed such as physical/chemical descriptions of the model, experimental data used to create the model, technical details related to the simulation (such as algorithms used, sampling method, and forcefield). Different formats may be used as templates such as MMTF [START_REF] Bradley | MMTF-an Efficient File Format for the Transmission, Visualization, and Analysis of Macromolecular Structures[END_REF] , MMCIF [START_REF] Bourne | Macromolecular Crystallographic Information File[END_REF] , JSON (http://www.json.org/), TNF [START_REF] Lundborg | An Efficient and Extensible Format, Library, and API for Binary Trajectory Data From Molecular Simulations[END_REF]

Streamlining molecular simulations data

The MD simulation is often not a means and an end in itself but instead is run as part of a larger workflow. Such workflows involve joining together the output of many independent programs, such as those used for parameterizing molecules, those for performing molecular dynamics, and those for trajectory analysis. Managing the data movement between different programs in this workflow is challenging for several reasons:

1. The file formats used by different programs in the workflow may be incompatible, thereby preventing certain combinations of tools from being used together.

2. The features and forcefields supported by different programs in the workflow may be incompatible, thereby forcing researchers to choose algorithms and forcefields based on software compatibility rather than for good scientific reasons.

3. Different programs may implement features or forcefields in different ways, thereby meaning that the results of running the workflow will depend on the exact combination of programs (and possibly program versions) used. It is generally not possible to mixand-match different programs and get the same results.

These challenges have forced researchers to develop workflows using specific software packages and specific forcefields. This creates divisions within the community and makes it difficult to write workflows that function equally well across a number of forcefields and a number of different software packages.

One of the solutions to this problem is the development of programs that convert/handle molecular information between the different file formats such as VMD [START_REF] Humphrey | Visual Molecular Dynamics[END_REF] , cpptraj [START_REF] Roe | PTRAJ and CPPTRAJ: Software for Processing and Analysis of Molecular Dynamics Trajectory Data[END_REF] , MDAnalysis [START_REF] Michaud-Agrawal | MDAnalysis: a Toolkit for the Analysis of Molecular Dynamics Simulations[END_REF][START_REF] Gowers | MDAnalysis: a Python Package for the Rapid Analysis of Molecular Dynamics Simulations[END_REF] , mdtraj [START_REF] Mcgibbon | a Modern Open Library for the Analysis of Molecular Dynamics Trajectories[END_REF] , LOOS [START_REF] Romo | an Extensible Platform for the Structural Analysis of Simulations[END_REF][START_REF] Romo | Lightweight Object Oriented Structure Analysis: Tools for Building Tools to Analyze Molecular Dynamics Simulations[END_REF] and many others for trajectory analysis and TopoGromacs [START_REF] Vermaas | TopoGromacs: Automated Topology Conversion From CHARMM to GROMACS Within VMD[END_REF] , CHARMM-GUI [START_REF] Lee | CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field[END_REF] , CHAMBER [START_REF] Crowley | Comprehensive Support for CHARMM Force Fields Within the AMBER Software[END_REF] , ParmEd (http://parmed.github.io/ParmEd/html/index.html# ), InterMol [START_REF] Shirts | Lessons Learned From Comparing Molecular Dynamics Engines on the SAMPL5 Dataset[END_REF] (https://github.com/shirtsgroup/InterMol), and others for topology generation and editing. The aim of these programs is to translate as much information as possible from one molecular file format into another. One recent example is BioSimSpace (https://biosimspace.org/), which provides wrappers that simplify the generation of the command files that are used to control the running of simulations. This allows researchers to write workflows that are independent of the choice of the underlying packages used to perform the simulation. BioSimSpace aims to run all stages of the workflow using the simulation software installed on the researcher's computer that is compatible with the forcefield chosen for the specific calculation.

While translators and program wrappers like ParmEd and BioSimSpace solve some of these problems, they are not a universal solution. They do not solve the issue that different simulation programs use different algorithms (or interpretations of algorithms, for example, different implementations of thermostats or integrators), or that different programs store and represent molecular information in different ways (e.g. SHAKE information for constraining bonds is represented in the molecular topology in GROMACS, while it is a simulation command parameter in NAMD and AMBER). This means MD properties/observables computed with one package will be systematically different by an often small but statistically significant amount from those computed with a different package as shown for free energy calculations [START_REF] Loeffler | Reproducibility of Free Energy Calculations Across Different Molecular Simulation Software Packages[END_REF] . Thus, the version and name of the MD program used to produce a simulation result will affect that result, and must be reported accordingly. Furthermore, MD simulations outputs are mainly trajectories which (1) represent ensemble averages (2) are chaotic in that small differences in initial conditions cause large differences in the subsequent dynamics ('butterfly effect'). This adds another layer of complexity and needs also a consensus on how to further analyze/process these trajectories to provide the final quantities of interest.

The recordings of this session can be found here:

• Introduction to the topic by John Chodera (https://youtu.be/6xOfN0y_uoQ)

Tools for trajectory file sharing

The benefits of sharing data together with the peer-reviewed publication, preprint or as a selfstanding research output seem to be many -from receiving additional credit for one's work to improving reproducibility, reusability or offering potentially new avenues of research 20,[START_REF] Woelfle | Open Science Is a Research Accelerator[END_REF] . Some disciplines, such as protein crystallography or genomics, have open data practices well integrated into their workflow, with metadata being collected throughout the workflow, and those practices are a de facto standard in scholarly communication. However, data sharing in the MD community still has not become widely adopted because best practice guidelines or journal recommendations on how to share MD simulations are yet to be established and adopted by the whole community. Making data sharing a standard practice in the field faces both technical and cultural challenges, although these are currently being tackled by some ongoing initiatives and solutions 20,[START_REF] Botan | Toward Atomistic Resolution Structure of Phosphatidylcholine Headgroup and Glycerol Backbone at Different Ambient Conditions[END_REF][START_REF]Promoting Transparency and Reproducibility in Enhanced Molecular Simulations[END_REF] . Thus, the development of best practices and guidelines for simulation data sharing will be of tremendous value, especially if created with the FAIR principles in mind [START_REF] Wilkinson | The FAIR Guiding Principles for Scientific Data Management and Stewardship[END_REF] . To do so, we need to address several important questions regarding what data should be shared, how and where.

Answering to the what data question would need longer discussions not limited to a small group of individuals but involving the whole community and especially all the MD packages (another workshop will be held soon to help starting to answer to this question: https://molssi.org/2019/07/29/molssi-workshop-molecular-dynamics-software-interoperability/

). The emergence of dedicated tools is now helping to answer to the how question. Software such as MDsrv [START_REF] Tiemann | Viewing and Sharing Molecular Dynamics Simulations on the Web[END_REF] , HTMoL [START_REF] Carrillo-Tripp | Full-Stack Solution for Remote Access, Visualization, and Analysis of Molecular Dynamics Trajectory Data[END_REF] , Mol* (https://molstar.org), Molmil 50 are now taking advantage of the WebGL API for sharing trajectories through interactive visualization on the web [START_REF] Hildebrand | Bringing Molecular Dynamics Simulation Data Into View[END_REF] .

Other fields of research can help us to answer to the where question. Existing databanks, such as wwPDB [START_REF] Berman | Announcing the Worldwide Protein Data Bank[END_REF] and Galaxy (https://usegalaxy.org), have been recognized by the scientific community. However, the establishment of an analogous, specialized platform for MD data, poses a great challenge, given the current lack of long-term support for the infrastructure projects of this kind. It is not clear yet who should be responsible for building such platform and how this infrastructure could be funded in a sustainable way, preferably without relying on short-term research grants, to cover the costs of development, maintenance and data hosting. In the meantime, community-driven, special-purpose platforms like the GPCRmd (http://www.gpcrmd.org), Lipidbook [START_REF] Domański | Lipidbook: a Public Repository for Force-Field Parameters Used in Membrane Simulations[END_REF] and NMRlipids [START_REF] Botan | Toward Atomistic Resolution Structure of Phosphatidylcholine Headgroup and Glycerol Backbone at Different Ambient Conditions[END_REF] (http://nmrlipids.blogspot.com), Ligandbook [START_REF] Domański | Ligandbook -an Online Repository for Small and Drug-Like Molecule Force Field Parameters[END_REF] , MoDEL [START_REF] Meyer | Molecular Dynamics Extended Library): a Database of Atomistic Molecular Dynamics Trajectories[END_REF] and BIGNASim 56 lead the way, providing specialized platforms for deposition and analysis of G protein-coupled receptors (GPCR), lipids, small molecules, proteins, and nucleic acids, respectively. General data sharing resources like Zenodo (https://zenodo.org), FigShare (https://figshare.com), Open Science Framework (https://osf.io) and others, also provide an opportunity for every researcher to deposit their simulation files and trajectories. Nevertheless, those resources may not provide sometimes enough space to sustainably store MD simulations outputs (with file size limits ranging between 5 GB and 50 GB).

To establish an efficient sharing culture, a systematic approach to developing tools and sharing guidelines is necessary, with the participation of the entire community in such activities and efforts. An open and inclusive discussion about best practices in data sharing, identification of short-term solutions based on the currently available frameworks and tools, as well as developing a strategy and requirements for future solutions bespoke to MD community and their needs is necessary. More details about the discussions taking place at the workshop can be found in the following videos:

• Introduction to the topic by Daniel Smith (https://youtu.be/mvesL9Y_9xU)

• Preliminary talk I by Johanna Tiemann (https://youtu.be/VOT6fEc7Iuc)

• Preliminary talk II by Jana Selent (https://youtu.be/TVS75j48mQ8)

• Breakout discussions presented by John Chodera, Karmen Čondić-Jurkić, Samuli

Olllila and Lucie Delemotte (https://youtu.be/UIs1isntUPY)

Reproducibility of molecular simulations

MD simulations are chaotic and as such, the definition of reproducible results is nontrivial. First, the distinction between repeatability (by the same team and the same computational setup), replicability (by a different team and the same computational setup) and reproducibility (by a different team, and with a different experimental setup) should be made [START_REF] Hinsen | ActivePapers: a Platform for Publishing and Archiving Computer-Aided Research[END_REF] . Differences in outputs from these three perspectives may indicate different types of errors (bugs in software, human errors, or different choices along the workflow -choice of code, force field, system setup and more). The variability of parameters and dependence of the final results on both software and hardware makes it complicated (but also often unnecessary) to achieve the exact replication/repetition of any given setup, and untangling all the effects would be a difficult task. Focusing on a set of observables that can be calculated and preferably validated against experiments might be a better way of approaching reproducibility in this particular field. Similarly, focusing on observables which, despite the underlying chaoticity of the detailed dynamics, are reproducible without too large variation might be beneficial.

Reaching an agreement on which observables we should aim to reproduce and how to properly calculate and report these values is thus desirable. For this, educational efforts are needed: best practice dissemination in terms of calculating statistical properties, for example, are crucial [START_REF] Grossfield | Best Practices for Quantification of Uncertainty and Sampling Quality in Molecular Simulations [Article v1.0[END_REF] . Coming up with standard benchmarks would also help, where the performance of different software/forcefield combinations for selected tasks could be compared.

In practice, data sharing would help with replicability and reproducibility. Practical challenges come from the size of data sets. However, one can envision sharing at least minimal data sets to improve As may be noted by the reader, these discussions have not solved the issues about sharing data that our field is facing. Of course, this has never been the goal of such a small workshop. This workshop was intended to start asking relevant questions. Thus, this document (and the videos associated) can be seen as a road map for future developments. It is now crucial to build a community responsible for transforming these ideas into actions. This community needs to represent a diversity of perspectives by including both MD users and developers, newcomers and more seasoned practitioners, PhD students and postdocs, who are performing MD simulations on a daily basis, and PIs, who may hold the bigger picture views. As a community building effort, we are planning to regularly organize

  these questions, we organized a BioExcel workshop on Sharing Data from Molecular Simulations (SDMS) in Stockholm, November 2018. In this paper, we present a summary of discussions broadly focused on 4 topics: • Standardization of file formats • Streamlining molecular simulations data • Tools for trajectory file sharing • Reproducibility of molecular simulations Each topic was introduced by 2 researchers and then openly discussed by all participants. All the presentations and the discussions were recorded and are accessible here: https://bioexcel.eu/sdms18-recordings/. The slides for the majority of the talks can be found here: https://doi.org/10.5281/zenodo.2652703 .

  . At this workshop we all agreed that it would be a great advance if this new standard can follow the FAIR principle 32 : Findable, Accessible, Interoperable, and Reproducible/Reusable. Many details remain to be discussed and the standardization question cannot be solved in one workshop with only a small sample of the MD community but need to be discussed by all main software developers joined with users to ensure usability. To do so another workshop will be held soon in New York to further

	discuss	the	question	of	file	format	and	MD	packages	interoperability:
	https://molssi.org/2019/07/29/molssi-workshop-molecular-dynamics-software-interoperability/
	.
	For further details and discussions interested readers can watch associated videos from the
	2018 workshop:

• Introduction of the topic by Mark Abraham (https://youtu.be/2S3qjBIE6Y4) • Preliminary talk I by Erik Lindahl (https://youtu.be/Hvy8-gyTmj8) • Preliminary talk II from Alexandre Bonvin (https://youtu.be/48Eb2MLHoYU) • Breakout discussions presented by Phillip Stansfeld, Mikael Trellet, Daniel Smith and Johanna Tiemann (https://youtu.be/4fnV5EFXDpc )

  inferential reproducibility: share among other analysis code, pipeline/workflow and example used.Inspiration can be found in other research fields (e.g. Genomics[START_REF] Kaye | Data Sharing in Genomics -Re-Shaping Scientific Practice[END_REF] or Proteomics[START_REF] Martens | A Golden Age for Working with Public Proteomics Data[END_REF] ) and existing dedicated initiatives, like MemProtMD 59 (http://memprotmd.bioch.ox.ac.uk ), NMRlipids project (www.nmrlipids.blogspot.fi) and GPCRmd (http://www.gpcrmd.org), show that small groups of people focused on a narrow topic can create the necessary structure to share even large datasets in an efficient way. For further details and discussions interested readers can watch associated videos: • Introduction to the topic by Karmen Čondić-Jurkić (https://youtu.be/lUTQgOXDEP8) • Preliminary talk I by Helmut Grubmüller (https://youtu.be/cliVmGlrKag) • Preliminary talk II by Samuli Ollila (https://youtu.be/46s33SonsiU) • Breakout discussions presented by Mikael Trellet, Alexandre Bonvin, Mark Abraham and Christopher Woods (https://youtu.be/ex0_bqmJwE8) This article summarizes the discussions started during the workshop held in Stockholm in November 2018.

	• results and
	• methods reproducibility: provide sufficient details to replicate the study; this is
	in principle already done in publications, but authors, reviewers, and editors
	should pay special attention to the question, and sharing directly all input files
	should be mandatory,
	• raw data reproducibility: share a minimum amount of data in the form of MD
	simulation snapshots, or even better whole trajectories, on existing data
	sharing repositories -Zenodo, Figshare, OSF, and

Acknowledgement

This work was supported by BioExcel Centre of Excellence (www.bioexcel.eu) funded by the European Union contracts H2020-INFRAEDI-02-2018-823830 and H2020-EINFRA-2015-1-675728. MC acknowledges support from CNRS-MITI grants PEPS MPI 2018 and "Modélisation du vivant" 2019. This work was supported by grants from the Gustafsson Foundation and Science for Life Laboratory to LD. HG has been supported by Max Planck Society and the German Research Foundation (DFG), Cluster of excellence Multiscale Imaging and the DFG priority programmes 1648, 755, and 803. OHSO acknowledges financial support from Academy of Finland (315596). DGAS thanks the National Science Foundation for support under Grant No. ACI-1547580. PJS would like to thank Wellcome [208361/Z/17/Z], the BBSRC [BB/P01948X/1, BB/R002517/1, BB/S003339/1 and BB/I019855/1] and MRC [MR/S009213/1] for funding. JKST acknowledges support from the Deutsche Forschungsgemeinschaft (DFG) HI1502/1-2. CW acknowledges support from the EPSRC (EP/N018591/1). We thank Oliver Beckstein and David Mobley for their careful reading and their comments.

Link to the SDMS18 recordings: https://bioexcel.eu/sdms18-recordings/ Discussions from Twitter: can be retrieved/extended by using the hashtag #SDMS18 Several participants from this workshop can be contacted/followed on Twitter: @the_mabraham, @jbarnoud, @amjjbonvin, @Matth_Chavent, @jchodera, @karmecon, @DelemotteLab, @CompBioPhys, @eriklindahl, @NMRlipids, @dga_smith, @pstansfeld, @j0kaso, @chryswoods

Funding Sources

The workshop was supported by BioExcel Centre of Excellence (www.bioexcel.eu).

more specific workshops aiming to address some of the issues raised in this article or to expand the scope of newly recognized problems. Of course, the structure of the workshops limits the number of participants, but care will be taken to ensure the aforementioned diversity of perspectives and roles in the field. In an effort to include as many users as possible in this discussion, the best practices guidelines that will emerge from these workshops will be submitted to the Living Journal of Computational Molecular Science (http://www.livecomsjournal.org/). This journal "... provides a venue where authors can submit living documents that are updated on an ongoing basis as websites or Wikipedia articles could be, but which still have clear authorship and provide a mechanism for authors to get publication credit for their work." [START_REF] Mobley | Why We Need the Living Journal of Computational Molecular Science[END_REF] Hence, researchers interested to help us shape new practices to share data will be able to provide their feedback or directly contribute to the forthcoming document (as per the general idea laid out here: https://livecomsjournal.github.io/about/paper_code/).

We hope that our work will act as a first step in a community-driven process of defining best practices for tool development and application in the molecular dynamics field.

AUTHOR INFORMATION

Corresponding Author

*Correspondence: matthieu.chavent@ipbs.fr , @Matth_Chavent

Author Contributions

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.