Massamba M Ndiaye

Ha Phuong Ta

Giovanni Chiappetta

Joëlle Vinh
email: joelle.vinh@espci.fr

On-chip sample preparation using a ChipFilter coupled to nanoLC-MS/MS for bottom-up proteomics

Keywords: Proteomics, Microfluidic, ChipFilter, CFP, filter-assisted proteolysis, sample processing

Sample preparation is a crucial step in bottom-up proteomics. Analytical performances of bottom-up proteomics can be improved by the miniaturization of sample preparation. Many microfluidic devices have been designed in the field of proteomics, but many of them are not capable of handling complex samples and do not integrate the processing and digestion steps. We propose a ChipFilter Proteolysis (CFP) microfluidic device as a proteomics reactor for the miniaturization of protein sample processing and digestion steps, whose design is closely related to the experimental setup of Filter Aided Sample Processing (FASP), even if no denaturing surfactant is required. The microchip has two reaction chambers of 0.6 µL volume separated by a protein filtration membrane in regenerated cellulose (10kD cut-off) that will concentrate or retain large polypeptides and will release small molecules. Cell lysis, protein concentration, and rapid chemical or enzymatic treatment can be performed in the ChipFilter. Complex proteomic samples like yeast protein extract or whole human cells proteome have been successfully analyzed with our microchip. Compared to the membranebased commercial ultracentrifugation cartridge, our microfluidic device offered a better proteome coverage with ten times less starting material and eight times faster protocol duration.

INTRODUCTION

Mass spectrometry has become the method of choice for the direct detection of bio-organic molecules. It takes a primordial place in the analysis of proteins. Among different strategies, bottom-up proteomics relies on proteolytic peptides separation and sequencing by liquid chromatography coupled to mass spectrometry (LC-MS) after protein digestion. It is widely used because of its ease of use and robustness compared to top-down proteomics. However, protein processing for the bottom-up approach remains sample and time-consuming. In fact, after the recovery of the protein sample, it requires disulfide bonds reduction, cysteine residue alkylation to avoid bridge reformation, proteolysis of the proteins with an endoprotease, recovery of the proteolytic peptides before chromatographic separation and tandem mass spectrometry analysis. The experimental time is even more important when aiming at posttranslational modifications characterization. Sample-loss is also another key issue with several millions of cells routinely required for the bottom-up approach in order to recover at least 10 µg of protein extract. Indeed, the cytoplasm volume of Hela cells is about 0.94 pL [START_REF] Fujioka | Dynamics of the Ras/ERK MAPK Cascade as Monitored by Fluorescent Probes[END_REF] and a protein concentration of 1E10 per cell [START_REF] Milo | What Is the Total Number of Protein Molecules per Cell Volume? A Call to Rethink Some Published Values: Insights & Perspectives[END_REF] gives a theoretical value of 150 µg total protein with 1E6 Hela cells for a 100% extraction yield. The experimental values are estimated to be closer to 1E9 proteins per cell [START_REF] Zeiler | A Protein Epitope Signature Tag (PrEST) Library Allows SILAC-Based Absolute Quantification and Multiplexed Determination of Protein Copy Numbers in Cell Lines[END_REF] , and this 10-fold mismatch has been associated with both losses during sample handling and approximation during the calculation of the absolute concentration using pure proteomics signals [START_REF] Milo | What Is the Total Number of Protein Molecules per Cell Volume? A Call to Rethink Some Published Values: Insights & Perspectives[END_REF] . The loss of sample during protein extraction, adsorption, protein or peptide purification, and detergent removal creates a gap between the high sensitivity of LC-MS for peptide analysis and the minimum threshold required for the starting material [START_REF] Milo | What Is the Total Number of Protein Molecules per Cell Volume? A Call to Rethink Some Published Values: Insights & Perspectives[END_REF][START_REF] Zeiler | A Protein Epitope Signature Tag (PrEST) Library Allows SILAC-Based Absolute Quantification and Multiplexed Determination of Protein Copy Numbers in Cell Lines[END_REF][START_REF] Xu | Benchtop-Compatible Sample Processing Workflow for Proteome Profiling of < 100 Mammalian Cells[END_REF] .

It is acknowledged that sample preparation and digestion steps in bottom-up proteomics need to be improved. Microfluidics is a logical strategy to address the sample and time-consumption problems since it has been well described for more than a decade [START_REF] Lion | Why the Move to Microfluidics for Protein Analysis?[END_REF] that miniaturization is strongly correlated with an improvement of reaction kinetics [START_REF] Demello | Control and Detection of Chemical Reactions in Microfluidic Systems[END_REF] . Reinhoudt's group [START_REF] Brivio | Miniaturized Continuous Flow Reaction Vessels: Influence on Chemical Reactions[END_REF] reports that the downscaling process increases the surface to volume ratio, the mass and heat transfer.

Several microfluidic applications dedicated to proteomics have been developed in the past years, but unfortunately, these applications were not as successful as they were in genomics. This is explained by the complexity of the proteomic sample and analysis process. The objectives are to reduce the sample quantity threshold, accelerate sample processing and digestion. Freire et al. [START_REF] Freire | Proteome-on-a-Chip: Mirage, or on the Horizon?[END_REF] pointed out the fact that most microchip devices did not integrate the whole sample preparation process for bottom-up proteomics. Individual modules for on-chip digestion, pre-concentration, and clean-up, peptide or protein separation were separately available. Since that review, the situation has barely changed. Digestion is an important and unavoidable step in bottom-up proteomics but remains routinely time-consuming. Several microfluidic devices have been realized to reduce the time necessary for protein digestion.

Microfluidic has been usually combined with immobilized enzyme reactors. Immobilized trypsin, for example, is reputed to have high digestion efficiency, low auto-digestion, and reusability [START_REF] Li | High-Efficiency Nano/Micro-Reactors for Protein Analysis[END_REF] . A number of microfluidic devices dedicated to proteomic analysis has incorporated a solid phase with immobilized trypsin. This resulted in extremely fast digestion time from 10 minutes for cytochrome C 10 to 15 seconds for a two standard protein mixture (Mb-BSA) [START_REF] Liuni | A Microfluidic Reactor for Rapid, Low-Pressure Proteolysis with on-Chip Electrospray Ionization: A Simple Proteolytic Chip for ESI-MS[END_REF] with a sequence coverage from 80 to 95%. More recently, Kecskemeti et al. presented a PDMS microchip with immobilized trypsin on to silica particles allowing rapid proteolysis in less than 10 seconds [START_REF] Kecskemeti | Preparation and Characterization of a Packed Bead Immobilized Trypsin Reactor Integrated into a PDMS Microfluidic Chip for Rapid Protein Digestion[END_REF] . Trypsin has also been immobilized directly on the PDMS walls for the digestion of tear proteins in 10 minutes [START_REF] Kecskemeti | The Application of a Microfluidic Reactor Including Spontaneously Adsorbed Trypsin for Rapid Protein Digestion of Human Tear Samples[END_REF] . PMMA surface can be functionalized with trypsin-agarose for rapid digestion of standard protein in less than 4 seconds [START_REF] Liuni | A Microfluidic Reactor for Rapid, Low-Pressure Proteolysis with on-Chip Electrospray Ionization: A Simple Proteolytic Chip for ESI-MS[END_REF] .

Unfortunately, these microchips perform only the digestion step. The other steps involving sample denaturation, reduction of disulfide bonds, alkylation to prevent artefactual disulfide bonds had to be performed off-chip. Furthermore, no protein pre-concentration is associated with these microchips that enables the sample to have high protein concentration and low contaminant levels for proper protein identification. However, some microfluidic devices are dedicated only to sample pre-concentration and clean-up. Those microchips are generally based on sample stacking, solid-phase extraction, and membrane filtration. Zhang et al 14 integrated a nanocapillary array in a PDMS microchip dedicated to concentrate large molecules according to their charge. After preconcentration, analytes can be recovered for further processing or detected directly. Gasilova et al. [START_REF] Gasilova | Microchip Emitter for Solid-Phase Extraction-Gradient Elution-Mass Spectrometry[END_REF] used C18 functionalized magnetic beads integrated into a microfluidic device coupled to ESI-MS for peptide preconcentration. Microfluidic devices integrating a functionalized solid phase are promising devices for digestion, preconcentration or separation: Ethier et al. [START_REF] Ethier | The Proteomic Reactor: A Microfluidic Device for Processing Minute Amounts of Protein Prior to Mass Spectrometry Analysis[END_REF] used SCX solid phase to make a proteomic reactor that allowed protein extraction, clean-up, preconcentration, and sample digestion. Mouse cells analyzed by the SCX proteomic reactor showed increased proteome coverage than in solution analyses.

Unfortunately, such microfluidic devices encountered two main challenges. First, the incorporation of solid phase (particles) in the micro-system. Indeed, special designs have to be conceived to specifically introduce and retain solid supports. Second, the physico-chemical properties of the solid support should allow the retention of all the proteins in a sample despite the proteome complexity.

An interesting non-microfluidic strategy was reported for bottom-up proteomics, relying on filter-assisted proteolysis (FAP) [START_REF] Manza | Sample Preparation and Digestion for Proteomic Analyses Using Spin Filters[END_REF] . FAP method is based on the use of a molecular filtration membrane that retains high molecular weight (proteins) to allow easy removal of impurities and buffer component by centrifugation and has been widely used in the so-called Filter Aided Sample Preparation (FASP) [START_REF] Wiśniewski | Universal Sample Preparation Method for Proteome Analysis[END_REF] . While this method addressed the issues of sample loss and proteome coverage, it did not address the time-consumption limitation or the need to downscale samples in experiment [START_REF] Sielaff | Evaluation of FASP, SP3, and IST Protocols for Proteomic Sample Preparation in the Low Microgram Range[END_REF] . Recently, Zougman et al. described S-traps or suspension traps [START_REF] Zougman | Suspension Trapping (STrap) Sample Preparation Method for Bottom-up Proteomics Analysis[END_REF] approach that allows the digestion of complex samples in 30 min. It provides the best balance of time, cost and performance when compared to in-solution and FASP digestion method [START_REF] Ludwig | Comparison of In-Solution, FASP, and S-Trap Based Digestion Methods for Bottom-Up Proteomic Studies[END_REF] .

In the present work, we developed a straightforward microfluidic device integrating a molecular filtration membrane called a ChipFilter. It has been often contested that mixing is required to take full advantage of such a miniaturized device and agreeable modelization of enzymatic proteolysis kinetics has been proposed for such experimental conditions [START_REF] Elagli | Diffusion Based Kinetic Selectivity Modulation of Enzymatic Proteolysis in a Microfluidic Reactor: Experimental Analysis and Stochastic Modeling[END_REF] . In our study, we designed a continuous flow mixing reactor which enables us to also increase the concentration-to-volume ratio thanks to the insertion of a 10kDa cut-off membrane.

Our system takes advantage of filter-assisted proteolysis (FAP) methods for easy preconcentration, purification and detergent removal steps; and from microfluidic systems for fast reaction kinetics, capacity to integrate multiple processes, minute sample processing, and automation. The filtration membrane allows the retention of high molecular weight species that includes the great majority of proteins. The membrane integration in the microchip during the microfabrication is easier than solid-phase packing into microfluidic devices which can be a very tedious task. Using a filtration membrane, every single step of protein processing for bottom-up proteomics can be done in one microfluidic device using the ChipFilter Proteolysis (CFP) strategy using a minimum of 1µg starting material. Centrifugal Filter Units with Ultracel-10 membrane were purchased from Merck Millipore.

MATERIALS AND METHODS

Materials

FilterChip Design and Microfabrication:

The PDMS microfluidic device is composed of two superimposed reaction-chambers (inner diameter 4 mm, height 50 µm, volume 0,6 µL each) assembled under a microscope. They are separated by a filtration membrane (diameter 4.3 mm) slightly larger than the reaction chamber. 150 µm diameter pillars inside the two chambers maintain the filtration membrane (see Figure 1). Fluorescence and transmission microscopy imaging was obtained with a confocal TCS SP8 confocal setup equipped with a Leica DMi8 inverted microscope (Leica microsystems) using dry objective *10 in XYZ-scan mode. The laser excitation was set to a band of 10 nm centered on 488 nm and the emission measured at 520 nm. The images were analyzed with the Leica software LASX. A 3D master mold is used to ensure proper integration of the membrane. The patterns were designed with CleWin5 software, printed at high resolution (25,400 dpi) on a photosensitive film by a photoplotter FilmStar-PLUS: 2 masks for each reaction chamber, with a 2 steps lithography for 3D molds. The mold is composed of 2 layers of negative photoresist on the silicon wafer. The first layer is a SU-2007 resin (8 µm thickness, spin-coated at 2000 rpm), dedicated to the structure to incorporate the filtration membrane and to avoid any leakage of the reaction chamber (MIS, Figure 1B). After baking at 95°C for 2 minutes, the first mask features were transferred onto the wafer by photolithography using a UV-KUB3 aligner (LED, 40 mW/cm 2 , 160 mJ/cm 2 , 5 seconds). After insolation through the mask 1 and post baked at 95°C for 2 minutes, a second photoresist is spin-coated on the first one. The second layer is a SU-2050 resin (50 µm thickness, spin-coated at 4080 rpm) dedicated to reaction chambers and pillars. The mask 2 is aligned to the patterns of the mask 1 using a UV-KUB3 aligner. The wafer is then exposed at LED light (40 mW/cm2, 160 mJ/cm2, 4 s). Finally, the developer removed the non-exposed part of the photoresist and reveal the 3D patterns. PDMS elastomeric polymer has been used to replicate the features from the mold with high precision.

PDMS Sylgard 184 is used for the replica molding. Using a two-component mix (base/curing agent, 10:1 (w/w)), the liquid pre-polymer is poured on the mold and cured at 70°C for 1 h.

After being exposed to an air plasma (20 W, 8 sccm O2 flow and 0.13 mbar pressure) for 1 min, the 2 PDMS parts are put in contact with the membrane between them. The membrane is deposited on the first chamber before the 2 PDMS parts assembly. It covers the first reaction chamber and the alignment of the two chambers is obtained under a microscope using alignment marks. A high-pressure contact between the 2 PDMS parts will allow a tight sealing around the filtration membrane. The whole setup is placed at 90°C for 15 minutes to enhance bonding.

Yeast extract. The cellular culture was maintained in a synthetic defined liquid media. The proteins were extracted from the Y252 yeast stem by mechanical agitation (beating) with glass beads [START_REF] Nandakumar | Comparison of Lysis Methods and Preparation Protocols for One-and Two-Dimensional Electrophoresis of Aspergillus Oryzae Intracellular Proteins[END_REF] . The proteins were extracted after cell lysis in 20% (v/v) TCA (600 µL) solution and 500 µL silica beating beds. Five 30s duration beatings (Vortex-T Genie 2, Scientific Industries) spaced by 1 min (cooling on ice) was done to enhance the homogenization of the sample in a 2 mL microfuge tube. The supernatant was collected and centrifuged for 1 hour at 13,500 rpm.

Proteins were rinsed with cold acetone and solubilized in urea 6 M, β-octanoylglucopyranoside 1%, protease inhibitor, and Tris-HCl 150 mM pH=8.8. Protein quantification was done by BCA assay.

Epithelial cells: HT29 colon adenocarcinoma cells (human intestinal epithelial cells) were

obtained from the European Collection of Cell Cultures (Wiltshire, UK). Cells are cultured at 37°C in a 5% CO2/air atmosphere in a Dulbecco's Modified Eagle Medium (DMEM, Fisher Scientific). After incubation, cells are washed with 5 mL PBS 1x and incubated at 37°C with trypsin-EDTA (Gibco) for 10 minutes. After centrifugation (3 min, 0.9 rcf), cells are washed three times with 3 mL NaCl 0.9%. During the 3rd wash, cells are counted on Malassez lamella (1/10 dilution). They are stored at -20 o C in 100 µL of distilled water.

HT29 protein extracts were obtained by sonication in 10% aqueous TCA (final concentration). After centrifugation (13,500 rpm, 1h, 4°C), the supernatant was removed and the protein pellet was rinsed with cold acetone and the proteins were solubilized in urea 6 M, β-octanoylglucopyranoside 1%, protease inhibitor, and Tris-HCl 150 mM pH=8.8.

For CFP analysis, the protein extraction of HT29 human cells has been realized inside the ChipFilter. First, the cells were loaded inside the ChipFilter. Second, 40 µL 10% TCA solution was added at a flow rate of 2 µL/min and then 40 µL of lysing buffer (0.1 M DTT, urea 6 M, β-octanoylglucopyranoside 1%, protease inhibitor, and Tris-HCl 150 mM pH=8.8) at the same flow rate. The protein proteolysis was finally performed in the same ChipFilter.

FAP proteolysis:

The protein extract in urea 6 M, β-octanoylglucopyranoside 1%, protease inhibitor, Tris 150 mM and DTT 20 mM was added in a Microcon and incubated for 2 h at 37°C. The sample was then centrifuged at 14,000 g. IAM 200 mM was added and incubated for 1 h at 37°C. The sample was then rinsed with 1mL ABC 50 mM by ultracentrifugation (14,000 g). Trypsin digestion was performed at a ratio of 1:20 E/S in 150 µL ABC 50 mM, pH 8 for overnight digestion. The proteolytic peptides were then recovered by ultracentrifugation at 14,000g for 1 hour.

Proteolysis automation: A flow-EZ pressure module, a flow controller, an M-switch (Fluigent) and the soft MAT (Microfluidic Automation Tool, Fluigent) were implemented for complete automation of the digestion protocol. The ChipFilter proteolysis protocols for mammalian cells and yeast protein extract are presented in tables 1 and 2 The Kyte-Doolittle hydrophobicity index has been calculated from a published package on R-Journal [START_REF] Osorio | Peptides: A Package for Data Mining of Antimicrobial Peptides[END_REF] .

Protein assay: BSA concentration was estimated using a UV spectrophotometer Nanodrop 1000 module proteinA280 (ThermoFisher Scientific) by reading the absorbance at 280 nm. A sample volume of 2 µL has been used for BSA measurement. Yeast protein extract concentration was determined by micro BCA protein assay kit (Fisher Scientific).

The quantities indicated in this document correspond to the starting material quantities. They are expressed in cell number when the protein extraction is performed in the ChipFilter and in µg of protein extract when the extraction is performed outside the ChipFilter. Before the nanoLC MS/MS analysis, proteolytic peptides are concentrated on a C18 media with an estimated maximal retention capacity of 5µg (Zip-Tip pipette tips, Millipore). The eluted solutions are dried (SpeedVac concentrator) and peptides resuspended in 2 µL aqueous TFA 0.1% (v/v). The 2µL volume is then injected for nanoLC MS/MS analysis. This is valid for FAP_10µg, FAP_1µg, and CFP_100µg. For CFP_1µg, the ChipFilter was coupled to nanoLC with the total amount of proteolytic peptides directly trapped and analyzed.

RESULTS AND DISCUSSION

Validation of the experimental setup: CFP method is composed of several steps starting from sample loading (protein extract or whole cells) into the microchip by pressure-driven flow, followed by retention of proteins on the membrane while small molecules and salts are washed off, i.e., concentrate and desalt the sample. Protein extraction and/or proteolysis was easily performed inside the microfluidic device since the ChipFilter holds up to 150 °C even in acidic solutions. Moreover, protein reduction, alkylation, and labeling were to be done by adding the corresponding reagents (DTT for reduction and IAM for carbamidomethylation). As the molecular masses of the reagents were below 10 kDa cut-off of the filtration membrane, the reagents in excess were completely removed by rinsing the proteins with a washing buffer.

Proteolytic digestion was finally performed by flowing the trypsin solution into the reaction chamber. Like proteins, intact trypsin molecules were retained on the membrane so the digestion could be performed in a small volume above the membrane (0.6 µL). Sample and trypsin confined in such a small volume allowed rapid and effective digestion of the complex protein extract. The proteolysis aims at covering as much as possible proteome of the sample and ultimately the sequence of each protein, thus lowering the risk of false-negative and falsepositive results. Since the sizes of generated tryptic peptides in bottom-up proteomics are between 500 and 3000 Da, they passed through the 10 kDa filtration membrane to be recovered for LC-MS analysis (Erreur ! Source du renvoi introuvable.A, (a) and (b)).

The design of the CFP device integrated a regenerated cellulose molecular membrane into a PDMS microchip. PDMS presents the advantage to irreversibly seal to itself after plasma exposure. This advantage was crucial for the ChipFilter sealing to avoid any sample leak and for passage of the sample through the filtration membrane and not around. Fluid transport and flow-rate inside the microchip were controlled by an external pump and a fluid controller.

Different flow-rate values were applied depending on the steps of the digestion protocol. A maximum flow rate of 3 µL/min and a maximum pressure of 500 mbar could be used reliably.

To check if proteins are efficiently retained in the reaction chamber by the membrane, the protein concentration of BSA standard solution (500 µL, 1 mg/mL) was monitored before and after passing through the ChipFilter membrane by UV absorption. The absorbance of the solutions was measured at 280 nm using a Nanodrop1000. No UV absorbance was detected at the exit of the device confirming the absence of leaks. When the solution in the reaction chamber was recovered by reverse percolation, an UV absorbance slightly lower (89%) than the starting solution was detected (see Suppl Figure S1). We performed the same protocol using 1ug of Bovine Serum Albumin-fluorescein isothiocyanate conjugate (BSA-FITC) to test the proper sealing and recovery of the ChipFilter. Through the transparent PDMS volume, the fluorescence of BSA-FITC demarcated the upper reactor chamber and the 12 pillars and demonstrated the effective sealing between the two PDMS parts (Figure 1C). The filter is embedded in the PDMS interface and is detected as an opaque material. The dark spot in the center is the punched hole for the inlet capillary. Fluorescence confocal microscopy allowed the localization of BSA-FITC during sample loading (Suppl. Figure S2) and highlighted a strong limitation due to non-negligible sample loss during specific sample handling for recovery (Suppl. Figure S3).

The proteolysis of BSA in the ChipFilter was tested for different amounts of starting material to evaluate the limit of detection in off-line conditions (see Table 3). In this specific test, CFP was performed off-line and from the total elution volume of 15 µL. In parallel FAP was performed on 100 µL of BSA at 1µg/µL. For each nanoLC-MS/MS analysis (with analytical triplicates), 2 µL out of the total elution volume were directly injected. The results were compared in terms of percentage of BSA sequence coverage and the total number of distinct sequences, regardless of any chemical or post-translational modification. First of all, CFP was validated and demonstrated interesting performances, when compared to FAP for equivalent injected amounts. Then we could suspect a threshold between 400 ng and 100 ng total protein starting amount because the sequence coverage and the number of identified proteolytic peptides dropped. Different rinsing conditions were tested with ABC 50 mM, milli-Q water, and a detergent solution (β-octanoyl glucopyranoside, 1%). None of them affected the efficiency of recovery.

However, we found that the pressure and the flow rate may affect protein retention on the membrane. The maximum flow rate and pressure reliability were found at 3µL/min and at 500 mbar respectively. The experimental conditions are detailed in Table 2 for protein extract. They have been adapted from whole-cell analysis in Table 1. Higher values of the flow rate or the pressure could lead to a reduced presence of proteins in the flow-through.

Automation of CFP:

Since CFP was proven to be fully operational with BSA, it was further applied to the characterization of the total extract of yeast proteins and compared with the FAP method. The microchip was then interfaced for automation using a flow-EZ pressure module, flow controller and M-switch ("Figure 2). This setup was operated using the software "MAT" S1"

(

Comparison of CFP and FAP using the proteome of the Y252 strain:

To assess the sample loss in our experimental setup, different amounts of raw yeast cell protein extracts were processed and analyzed. According to our validation criteria, starting with 10 µg of raw protein extract, 2406 proteins were unambiguously identified, while 1485 proteins were identified on average (n=3) starting from 1 µg of protein extract, and 1185 proteins from 500 ng (see Supplementary Figure S14).

The results obtained with 1 µg of protein extract were then compared to those of the classical FAP method using 1 and 10 µg of protein extract respectively. In total 1815 proteins were unambiguously identified from 1µg of protein extract as a starting material by CFP method and was compared with 1652 proteins identified from 10µg and 95 proteins identified from 1µg of starting material by FAP method. We decided to compare the proteome and sequence coverages between the two most comparable results obtained with CFP from 1µg of protein and FAP from 10 µg of protein ("Figure 3). One major factor is in the digestion time: we decided to consider 1h-incubation for CFP and rapid FAP, and 18h for conventional FAP. We will also discriminate between the peptides that are generated during the first hour of incubation and the peptides that are generated overnight after a 1h-incubation. All the experiments were performed in triplicate using three separate extracts for each series of experiments and each digestion was realized in an independent and new device. All at once, CFP allowed decreasing drastically both the sample processing duration and the amount of required starting material that led to improved proteome coverage. Even if the absolute number of identifications with only 1 peptide is similar between the CFP (1μg) and FAP (10μg) protocols, our results suggested a trend toward a higher percentage of proteins identified in FAP with only 1 peptide (p-value=0.07 for Student-test with 273 proteins or 18.3% in CFP, and 278 proteins or 20.3% in FAP). On the contrary, the number of proteins identified with more than 10 peptides was unambiguously higher for the CFP (p-vale=2.10 -4 for Student test, 338 proteins or 22.8% instead of a maximum of 217 proteins or 15.9% for FAP), leading to more robust identifications, wherein protein coverage is a key parameter to differentiate proteoforms and subclasses of proteins or to detect endogenous proteolysis and protein maturation.

"Figure 3: Comparison between CFP (on-chip digestion) and FAP methods for yeast protein extract: Averaged number of protein identifications (n=3) is according to the number of sequenced proteolytic peptides in CFP vs. FAP, that uses CFP with 1µg of extract incubated for 1h (CFP_1ug_1H), FAP with 10µg of extract incubated overnight (FAP_10ug), FAP with 10 µg of extract incubated for 1h (FAP_10ug_1H) and new identifications obtained from 10 µg of extract incubated overnight after removal of proteolytic peptides by filtration after incubation for 1h, and addition of 100µL of buffer ABC (FAP_10ug_1H-ON)"

To further evaluate our setup, we compared the percentage of detected peptides with 0, 1 or 2 miscleavages between CFP and FAP. Overnight FAP provided higher sample coverage among the FAP methods. CFP analyses showed that 65% of the detected peptide had no miscleavage while 29% and 6% had 1 and 2 miscleavages respectively. Overnight FAP analyses showed that 85% had no miscleavage while 14% and 1% had 1 and 2 miscleavages respectively ("Figure 4). CFP produced in percentage more peptides with miscleavages and this could be related to the shorter digestion times. This effect could be a double edge sword for protein identifications as it leads to the detection of short sequence portions usually missed because of their low mass and specificity, but improves protein sequence coverage. Despite this observation regarding the relative number of identifications, it should be noted that the absolute number of identified peptides is globally higher with CFP in any number of miscleavages.

However, the peptide mass distribution remained similar to the two protocols (see Suppl.

Figures S6 and S7).

"Figure 4: Comparison of the number of identified peptides with 0, 1 and 2 miscleavage(s)

between CFP with 1 µg yeast extract and FAP with 10 µg yeast extract. (n=3)"

We then evaluated the protein sequence coverage considering the peptides identified with 0, 1 and 2 miscleavages in each method. Looking at the differences in terms of the number of identifications, it was then combined with the physicochemical properties of validated peptides. Indeed, the PDMS surface is hydrophobic and is prone to non-specific adsorption of proteins and hydrophobic peptides. To attenuate this effect, the PDMS device was treated by O2 plasma oxidation, as its effect has been reported to last for a few hours [START_REF] Trantidou | Hydrophilic Surface Modification of PDMS for Droplet Microfluidics Using a Simple, Quick, and Robust Method via PVA Deposition[END_REF] . The PDMS surface of the ChipFilter had not received any other hydrophilic treatment than the plasma exposition during the bonding step. For our experiments, the ChipFilter was fabricated extemporaneously just before the sample processing to take advantage of the hydrophilic property of plasma treated-PDMS lasting for a few hours just after O2 plasma exposition. To evaluate the efficiency of PDMS plasma pre-treatment we compared some features of the peptides identified with or without miss cleavages in CFP and FAP. In total 4565 peptides without miscleavages were exclusively identified by CFP, while 2477 were exclusively identified by the FAP method ("Figure 5). Only 44% of the peptides were common. Looking at the protein level, however, 70% of the proteins were identified with both protocols (see Suppl. Considering the Kyte-Doolittle hydrophobicity index, the hydrophobic properties of peptides identified exclusively in each method have been compared ("Figure 6A). A two-sample t-test (Student) showed that the mean of hydrophobicity index for peptides exclusively identified by CFP is greater than the mean of those exclusively identified by the FAP method. This is not the case when considering the complete set of peptide identifications (common or specific to one method, see Suppl Figure S8). The absence of permanent hydrophilic PDMS pre-treatment did not bias the selectivity toward the hydrophobic peptides compared to the FAP method. We also evaluated if CFP induced a bias in the molecular mass range of identified peptides. For this we compared the molecular mass of the peptides identified with CFP and/or FAP. We observed that if the number of peptides identified with CFP is greater in every mass interval as compared to the FAP method, the distributions are slightly biased towards the high mass range in CFP ("Figure 6B). This has to be correlated with the higher number of miscleavages detected in CFP as it has been discussed previously.

A B

"Figure 6 proteins in each cellular compartment than the FAP method, but the distribution is similar in both protocols (Supp. Figure S5, Table S3).

On-chip cell lysis CFP nanoLC MS/MS:

To minimize sample loss, sample handling should be limited and the direct approach for that is to include the cell lysis and protein extraction steps in the same reactor. If this is not easily implemented with yeast cells due to the structure of their membrane, the smooth membrane of mammal cells such as human colon adenocarcinoma cell line HT29 can be disrupted using chemical cell lysis. The protein extraction and proteolysis have been implemented inside the ChipFilter using 28,000 HT29 cells leading to 4081 protein identifications (n=3). The cell lysis and protein processing realized both in the ChipFilter lead to a bigger proportion of peptides with miscleavages compared to protein processing realized in the ChipFilter with the same digestion (Supp. Figure S9). The subcellular compartments are evenly represented when compared to a classical protein extraction outside the ChipFilter (Supp. Figure S10). The on-chip cell lysis leads to a peptide mass distribution shift towards the higher mass range as shown in "Figure 7, which compares the normalized distribution of peptides identified with CFP_1µg, FAP_10µg, and on-chip cell lysis, but with the Kyte-Doolittle index distribution unaffected (Suppl. Figure S12). An improved proteome coverage is observed when protein processing is realized inside the ChipFilter compared to FAP (Suppl. Figure S11). This could be explained by the larger volumes used for peptides' recovery and the additional step for preconcentration. That improvement is even better when the cell lysis is performed inside the ChipFilter. The reproducibility of CFP has been evaluated using identified protein intensities, by scatter plot function (Perseus). Pearson correlation coefficient has been estimated for 0.5 µg, 1 µg and 28,000 cells starting materials (n=3). We observed a good reproducibility (r≈0.94)

with protein extracts or cells as starting material (Suppl. Figure S13). This is an illustration of the advantages of sample preparation integration in the field of proteomics.

CONCLUSION

We propose the ChipFilter Proteolysis (CFP) method as a simple device for rapid protein processing including at least five main steps: cell lysis, preconcentration, sample clean-up, reduction, alkylation and enzymatic proteolysis in the same reactor. The design for microfabrication of the ChipFilter allows easy and robust integration of a molecular filtration membrane in PDMS microchip, using soft lithography and replica molding. The efficient protein retention and proteolysis on the membrane have been validated with the BSA standard solution. Then, CFP has been applied to yeast protein extract. In comparison to the classical overnight FAP method, CFP allowed reaching better proteome and protein sequence coverage from at least ten times less starting material to work in one hour. The digestion protocol with ChipFilter reduced significantly the amount of sample and the required time for such in depth proteome analysis. CFP is an example of miniaturization that can handle integrated complex sample processing. The on-chip cell lysis has been implemented in order to minimize sample loss. This allows a good proteome coverage from 28,000 epithelial cells. It also proves the efficiency of chemical lysis inside the ChipFilter which will contribute to improving the sensitivity of the device. Moreover, the ChipFilter could be directly coupled to nano LC-MS/MS for the proteolytic peptides to be fractionated and sequenced in an integrated and miniaturized automated platform.

Further work may be done to reach a higher sensitivity such as decreasing the inner diameter of reaction chambers. More efficient and stable PDMS surface treatment could be used to enhance sample recovery. On-site peptide trapping can be also implemented just below the 10kDa membrane to minimize sample loss by nonspecific adsorption. Another very promising application is the possibility to perform sample derivatization for specific moieties targeting or to adapt our device for functionalized membranes.

SUPPORTING INFORMATION:

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [START_REF] Manza | Sample Preparation and Digestion for Proteomic Analyses Using Spin Filters[END_REF] partner repository with the project accession: PXD018066 and PXD016742.

The following supporting information is available free of charge at ACS website http://pubs.acs.org:

:

  PolyDiMethylSiloxane (PDMS) Sylgard 184 was purchased from Dow Corning, the SU-2000 photoresist from Microchem and the silicon wafer from Entegris. The following chemicals were selected at the highest available quality from the mentioned supplier. Iodoacetamide (IAM), dithiothreitol (DTT) and BSA protein standard were purchased from Sigma Aldrich. Bovine Serum Albumin-fluorescein isothiocyanate conjugate (BSA-FITC) was purchased from Fisher. Trypsin sequencing grade from bovine pancreas was purchased from Roche. LC-MS quality grade Optima acetonitrile (ACN) was purchased from Fisher Scientific. Trifluoroacetic acid (TFA), trichloroacetic acid (TCA), formic acid (FA), ammonium bicarbonate (ABC) and urea were purchased from Fluka. The Milli-Q water was produced by an IQ 7000 from Merck Millipore. The filtration membrane inside the ChipFilter was cut from ultrafiltration PLGC cellulosic (regenerated cellulose) disks, MWCO 10,000 Da, Millipore. Glass beads used during yeast protein extraction by mechanical agitation and βoctanoylglucopyranoside have been purchased from Sigma Aldrich. Microcon-10kDa

Figure 1 :

 1 Figure 1: ChipFilter design. A) Picture of a ready to use ChipFilter with PEEK capillaries

3 )

 3 loading Cell loading in the ChipFilter D = 2 µL/min, V = sample volume According to sample volume 2) Cell rinsing Cell rinsing with acidic solution TCA 10% D = 2 µL/min, V = 40 µL 20 Cell lysis and disulfide bridges reduction 0.1 M DTT in lysing buffer D = 2 µL/min, V=40 µL then D = 1µ/min, V=10 µL 30 4) Alkylation 50 mM IAM in lysing buffer D = 2µL/min, V=40 µL then D = 1 µL/min, V=10 µL 30 5) Rinsing ABC 50 mM 2 µL/min, V = 30 µL 15 6) Trypsin loading Trypsin solution 0,1 µg/µL in TFA 0.1% D = 2 µL/min, V = 10 µL 5 7) Trypsin activation ABC 50 mM D = 2 µL/min, V = 20 µL 10 8) Digestion and Elution ABC 50 mM D = 0.4 µL/min, V = 24 µL then D = 2µL/min, V = 15 µL 67 Table 2: ChipFilter proteolysis protocol applied to protein extract Steps Description Flow (D) et Volume (V) Time (min) 1) Sample loading Protein extract solution loading in the ChipFilter D = 2 µL/min, V = sample volume According to sample volume 2) Disulfide bridges reduction 20 mM DTT solution in ABC 50 mM D = 2 µL/min, V=40 µL then D = 1µ/min, V=10 µL 30 3) Alkylation 50 mM IAM in ABC 50 mM D = 2µL/min, V=40 µL then D = 1 µL/min, V=10 µL 30 D = 0.4 µL/min, V = 24 µL then D = 2µL/min, V = 15 µL 67 NanoLC MS/MS: The proteolytic peptides were recovered in a trapping column (C18 Pepmap, 300 µm i.d x 5mm length, ThermoFisher Scientific ) and analyzed in nanoLC MS/MS with a 2-hour gradient elution from 1 to 40%B (eluant A 2% ACN in 0.1% aq. FA, eluant B 90% ACN in 0.1% aq. FA v/v) on a 75µm i.d. x 50cm length C18 column (Pepmap, ThermoFisher Scientific) using an RSLC U3000 system coupled to a nanoESI Q-Exactive HF mass spectrometer (ThermoFisher Scientific). The nanoESI (1.5kV) MS acquisition is operated in positive DDA mode using a Top20 method with automated switching between MS (350-1500 m/z, resolution 60k) and MS/MS mode (resolution 15k). The data were analyzed with the MaxQuant software version 1.6.2.10 on Uniprot Yeast database (Uniprot, February 9th, 2013, 6651 entries) or on Homo sapiens (Uniprot, September 19th, 2018, 73.118 entries), with first search peptide tolerance at 20 ppm and main search peptide tolerance at 4.5 ppm, using trypsin specificity with up to 2 miscleavages and 3 variable modifications: carbamidomethylation (C), oxidation (M), deamidation (NQ). The proteins identified following MaxQuant research have been filtered by Perseus 24 software to remove those only identified by site, reverse and potential contaminant.

"Figure 2 :

 2 see Material and Methods) for complete automation of the protocol. The outlet of the ChipFilter was connected to the sample loop of the LC autosampler to perform the online nano LC-MS/MS analysis of the peptide mixture generated by the CFP. Automated setup for CFP coupled to nanoLC MS/MS. Using the microfluidic pump (Flow-EZ) the sample, the dithiothreitol (DTT), the iodoacetamide (IAM), the trypsin (TRP)and the ABC buffer were selected with the M-Switch before injection in the ChipFilter under controlled flow rates using a feedback loop (Flow Unit). Eluted peptides are loaded in an 80µL loop fitted in the injection valve of the nanoLC autosampler before injection and concentration on a C18 precolumn for subsequent nanoLC MS/MS analysis. All the capillaries used in this work are PEEK (PolyEtherEtherKetone). The capillaries (L1 to L5) dimensions can be found in Supp. Table

Figure S4). "Figure 5 :

 5 Distribution of identified peptides sequences without miscleavage from CFP 1µg and FAP 10 µg -method BioVenn 21 "

25 "

 25 : A) Kyte-Doolittle Hydrophobicity Index of peptides exclusively identified by CFP_1µg and FAP_10µg; B) Molecular mass distribution of peptides identified by CFP_1µg and FAP_10µg. The Kyte-Doolittle index has been calculated from a published package on R-Journal Looking at the gene ontology for cellular compartment classification demonstrated that the cytoplasm, the mitochondrion, phosphoproteins, and ribosomal proteins were the most enriched compartments. The CFP digestion allowed the identification of a larger number of

"Figure 7 :

 7 Normalized distribution of the number of peptides identified by CFP_1µg, FAP_10µg, CFP_28000_HT29 cells, and CFP_100µg_HT29 according to their mass"

Figure S1 :

 S1 Figure S1: Test for protein retention efficiency using BSA assay

Figure S14 :

 S14 Figure S14: ChipFilter Proteolysis yeast protein extract with different amounts of starting

Table 1 :

 1 ChipFilter proteolysis protocol applied to mammalian cells

Table 3 :

 3 BSA proteolysis ChipFilter. The maximum injected amount in equivalent BSA is estimated according to the ratio injection to elution volumes and the starting amount of undigested BSA

	Protocol	Starting	Elution vol.	% Sequence	# distinct	Max injected
		amount (µg)	(µL)	coverage	sequences	amount (equ. BSA)
	CFP	10	15	90	93	1.3 µg
		0.4		80	82	53 ng
		0.1		30	18	13 ng
		0.03		20	12	4.0 ng
	FAP	100	100	72	52	2.0 µg

Table S1 :

 S1 Capillaries dimensions

Table S2 :

 S2 Detailed description of the microfluidics experimental setup

Table S3 :

 S3 Gene ontology cellular compartments comparison

Table S4 :

 S4 GO cellular compartment of proteins exclusively identified by CFP or FAP treatment

Table S5 :

 S5 Gene ontology cellular compartment of proteins identified in HT29 cell protein extract C Table S6.00 _Supplementary_tables_description.xlsx Table S6.01_CFP_1ug_Proteins.xlsx: protein identifications using CFP_1µg method Table S6.02_CFP_1ug_Peptides.xlsx: peptide identifications using CFP_1µg method Table S6.03_CFP_500ng_Proteins.xlsx: protein identifications using CFP_500ng method Table S6.04_CFP_500ng_Peptides.xlsx: peptide identifications using CFP_500ng method Table S6.05_FAP_1ug_Proteins.xlsx: protein identifications using overnight FAP 1µg method Table S6.06_FAP_1ug_Peptides.xlsx: peptide identifications using overnight FAP1µg

	Table S6.12_FAP_10ug_1H_Peptides.xlsx: peptide identifications using 1hou FAP10µg
	method
	Table S6.13_FAP_10ug_1H-ON_Proteins.xlsx: protein identifications using overnight/1h
	FAP10µg method
	Table S6.14__FAP_10ug_1H-ON_Peptides.xlsx: peptide identifications using overnight/1h
	FAP10µg method
	Table S6.15_FAP_10ug_ON_Proteins.xlsx: protein identifications using overnight
	FAP10µg method
	Table S6.16__FAP_10ug_ON_Peptides.xlsx: peptide identifications using overnight
	FAP10µg method
	method
	Table S6.07_CFP_10ug_Proteins.xlsx: protein identifications using overnight CFP 10µg
	method
	Table S6.08_CFP_10ug_Peptides.xlsx: peptide identifications using overnight CFP10µg
	method
	Table S6.09_CFP_28000cells_Proteins.xlsx: protein identifications using CFP_28000cells
	method
	Table S6.10_CFP_28000cells_Peptides.xlsx: peptide identifications using CFP_28000cells
	method
	Table S6.11_FAP_10ug_1H_Proteins.xlsx: protein identifications using 1hour FAP10µg
	method

ACKNOWLEDGMENTS

MMN received a doctoral fellowship from Labex IPGG pour la microfluidique (IPGG 9117), mass spectrometry equipment was subsidized by Conseil Régional d'Île-de-France (Sesame 2010 N°10022268) and access to IPGG for microfluidics platform (Equipex CNRS UMS3750).

The authors thank Olivier Lesage, Guillaume Laffite, and Bertrand Cinquin from IPGG CNRS UMS3750 for their help in microfluidics design, and Ranjith Kumar from SMBP CNRS FRE2032 for the final proofreading

TABLE OF CONTENTS GRAPHIC

For TOC Only


Fiorella Faienza

Matteo Lambrughi

Salvatore Rizza

Chiara Pecorari

Paola Giglio

Juan Salamanca Viloria

Maria Francesca Allega

Giovanni Chiappetta

Joelle Vinh

Francesca Pacello

Joëlle Vinh

Andrea Battistoni

Andrea Rasola

Elena Papaleo
email: elenap@cancer.dk

Giuseppe Filomeni
email: filomeni@bio.uniroma2.it.

S-nitrosylation affects TRAP1 structure and ATPase activity and modulates cell response to apoptotic stimuli

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Tumor necrosis factor type 1 receptor-associated protein 1 (TRAP1) is a molecular chaperone belonging to the Hsp90 family, which shares 34% identity and 60% similarity with the other orthologs [START_REF] Ho Yeong Song | Identification of a protein with homology to hsp90 that binds the type 1 tumor necrosis factor receptor[END_REF]. Unique in the Hsp90 family, TRAP1 shows a predominant, but not exclusive mitochondrial localization [START_REF] Leskovar | The ATPase Cycle of the Mitochondrial Hsp90 Analog Trap1 * □[END_REF]. Indeed, it is the only member showing a 59 aa mitochondrial import sequence, which is cleaved in the organelle [START_REF] Kang | TRAP1 regulation of mitochondrial life or death decision in cancer cells and mitochondria-targeted TRAP1 inhibitors[END_REF].

TRAP1 acts as a homodimer, although it has recently been proposed it is mostly present under tetrameric form, with this quaternary structure modulating mitochondrial oxidative phosphorylation [START_REF] Joshi | The mitochondrial HSP90 paralog TRAP1 forms an OXPHOSregulated tetramer and is involved in maintaining mitochondrial metabolic homeostasis[END_REF]. Each subunit presents a N-terminal domain (NTD) domain, involved in ATP binding and hydrolysis; a middle-domain (MD), that contains that contains part of the ATP-binding pocket [START_REF] Lavery | Structural Asymmetry in the Closed State of Mitochondrial Hsp90 (TRAP1) Supports a Two-Step ATP Hydrolysis Mechanism[END_REF], and the binding site for client proteins; a C-terminal domain (CTD) that constitutes the interface for homodimerization [START_REF] Masgras | The Chaperone TRAP1 As a Modulator of the Mitochondrial Adaptations in Cancer Cells[END_REF]. Differently form the other Hsp90 orthologs, TRAP1 lacks the linker domain between MD and CTD, and exhibits a N-terminal extension that acts as thermal regulator of its chaperone activity [START_REF] Partridge | A novel Nterminal extension in mitochondrial TRAP1 serves as a thermal regulator of chaperone activity[END_REF].

The ATPase cycle of TRAP1 has been characterized in detail, with both protomers assisting the folding of the target proteins through structural modifications associated with repeated cycles of ATP binding, hydrolysis and release [START_REF] Leskovar | The ATPase Cycle of the Mitochondrial Hsp90 Analog Trap1 * □[END_REF][START_REF] Lavery | Article Structural Asymmetry in the Closed State a Two-Step ATP Hydrolysis Mechanism[END_REF][START_REF] Sung | Mitochondrial Hsp90 is a ligand-activated molecular chaperone coupling ATP binding to dimer closure through a coiled-coil intermediate[END_REF]. During the ATPase cycle, TRAP1 can be present under three different states: i) an open conformation (called apo); ii) a close conformation with the NTD placed between the two protomers; iii) an intermediate coiled-coil conformation with both NTD in close proximity. In the absence of substrates, TRAP1 is present in an open state, while the binding of two 2 molecules of ATP induces structural changes leading to a closed asymmetric conformation [START_REF] Lavery | Article Structural Asymmetry in the Closed State a Two-Step ATP Hydrolysis Mechanism[END_REF], this being distinctive of TRAP1 among Hsp90 family members. ATP binding provides the energy required for substrate remodelling which takes place by a two-step reaction. The hydrolysis of the first ATP produces symmetric changes in protomers, establishing a structural rearrangement in the substrate binding site. The hydrolysis of the second ATP leads to an ADP-bound state. This conformation is associated with a compact structure of the chaperon and is preparatory for the release of the remodeled substrate along with ADP [START_REF] Lavery | Structural Asymmetry in the Closed State of Mitochondrial Hsp90 (TRAP1) Supports a Two-Step ATP Hydrolysis Mechanism[END_REF][START_REF] Masgras | The Chaperone TRAP1 As a Modulator of the Mitochondrial Adaptations in Cancer Cells[END_REF][START_REF] Lavery | Article Structural Asymmetry in the Closed State a Two-Step ATP Hydrolysis Mechanism[END_REF][START_REF] Elnatan | Symmetry broken and rebroken during the ATP hydrolysis cycle of the mitochondrial Hsp90 TRAP1[END_REF].

Mitochondrial TRAP1 expression differs among tissues (e.g. brain, skeletal muscle, liver, kidney, heart and pancreas) [START_REF] Ho Yeong Song | Identification of a protein with homology to hsp90 that binds the type 1 tumor necrosis factor receptor[END_REF] and -with the exception of the brain -it is kept low [START_REF] Yoshida | Molecular chaperone TRAP1 regulates a metabolic switch between mitochondrial respiration and aerobic glycolysis[END_REF], whereas it is found abnormally increased in tumors [START_REF] Amoroso | TRAP1 revisited: Novel localizations and functions of a "next-generation" biomarker (Review)[END_REF][START_REF] Rasola | Mitochondrial oxidative phosphorylation TRAP(1)ped in tumor cells[END_REF]. In the last few years it has become clear that, besides mitochondrial proteostasis [START_REF] Chae | Control of Tumor Bioenergetics and Survival Stress Signaling by Mitochondrial HSP90s[END_REF], TRAP1 plays key roles also in several cellular processes, such as cell death regulation [START_REF] Costantino | TRAP1 , a novel mitochondrial chaperone responsible for multi-drug resistance and protection from apoptotis in human colorectal carcinoma cells[END_REF], antioxidant response [START_REF] Kang | Regulation of Tumor Cell Mitochondrial Homeostasis by an Organelle-Specific Hsp90 Chaperone Network[END_REF]; mitochondrial transmembrane potential control [START_REF] Amoroso | TRAP1 revisited: Novel localizations and functions of a "next-generation" biomarker (Review)[END_REF][START_REF] Rasola | Mitochondrial oxidative phosphorylation TRAP(1)ped in tumor cells[END_REF] and metabolism tuning [START_REF] Sciacovelli | The mitochondrial chaperone TRAP1 promotes neoplastic growth by inhibiting succinate dehydrogenase[END_REF]. In regards to the last process, it has been demonstrated that TRAP1 regulates the metabolic rewiring from oxidative phosphorylation to aerobic glycolysis (the so-called Warburg effect) through its inhibitory activity on complex IV and, mostly, on complex II of the electron transport chain [START_REF] Amoroso | TRAP1 revisited: Novel localizations and functions of a "next-generation" biomarker (Review)[END_REF], this finally leading to the stabilization of hypoxia-inducible factor 1α (HIF1α) [START_REF] Selak | Succinate links TCA cycle dysfunction to oncogenesis by inhibiting HIF-α prolyl hydroxylase[END_REF]. TRAP1 activity is regulated by posttranslational modifications. In particular, phosphorylation events mediated by PTEN induced kinase 1 (PINK1) have been reported to be instrumental for antioxidant and anti-apoptotic response [START_REF] Pridgeon | PINK1 protects against oxidative stress by phosphorylating mitochondrial chaperone TRAP1[END_REF], whereas those induced by the extracellular signal-regulated kinase 1 and 2 (ERK1/2) regulate TRAP1 ability to modulate cancer metabolism [START_REF] Masgras | Absence of Neurofibromin Induces an Oncogenic Metabolic Switch via Mitochondrial ERK-Mediated Phosphorylation of the Chaperone TRAP1[END_REF]. Similarly, SIRT3-mediated deacetylation has been very recently proposed to have a role in rewiring glioblastoma stem cell metabolism [START_REF] Park | Interplay between TRAP1 and Sirtuin-3 Modulates Mitochondrial Respiration and Oxidative Stress to Maintain Stemness of Glioma Stem Cells[END_REF]. In addition to phosphorylation and (de)acetylation, it has been also demonstrated that TRAP1 undergoes S-nitrosylation at Cys501, which is preparatory for its degradation via the proteasome [START_REF] Rizza | S-nitrosylation of the mitochondrial chaperone TRAP1 sensitizes hepatocellular carcinoma cells to inhibitors of succinate dehydrogenase[END_REF]. C501S mutant of TRAP1 is, indeed, more stable in conditions of excessive S-nitrosylation, e.g., in cells lacking the denitrosylase S-nitrosoglutathione reductase (GSNOR).

Here we predict using biomolecular simulations that S-nitrosylation modifies the functional motions of TRAP1, as well as its intra-and intermolecular interaction network and activity.

We also observe that TRAP1 S-nitrosylation at Cys501 affects mitochondrial homeostasis and modulates response to staurosporine-induced cell death, indicating a possible biological role for this modification.

MATERIALS AND METHODS

The scripts, inputs and output files for the computational parts of the work are freely available in a GitHub repository associated with our publication (https://github.com/ELELAB/TRAP1_activity) and the trajectories have been deposited in OSF (https://osf.io/wytg2).

Molecular dynamics simulations

We used the full-length Danio rerio TRAP1 (PDB entry: 4IPE, resolution 2.29 Å, [START_REF] Lavery | Structural Asymmetry in the Closed State of Mitochondrial Hsp90 (TRAP1) Supports a Two-Step ATP Hydrolysis Mechanism[END_REF]) as a starting structure for the molecular dynamics (MD) simulations, following the preparation of the structure used in a recent work by Colombo's group [START_REF] Moroni | The Structural Asymmetry of Mitochondrial Hsp90 (Trap1) Determines Fine Tuning of Functional Dynamics[END_REF]. We selected this structure since it is the only full-length structure available for TRAP1 and the one used in structural studies carried out so far. Moreover, we used MODELLER 9.15 [START_REF] Webb | Comparative Protein Structure Modeling Using MODELLER[END_REF] to generate a model of each individual protomer of TRAP1 using as template the full-length D. rerio structure (chain A and B from 4IPE) in which we modeled the missing residues T153, D240-A241, V370-G375, T567-E587, R617, T639-I652, K718-H719 in chain A and A149-D152, A201-A208, E273-S376, T389-T392, Q640-Q651, K718-H719 in chain B. We removed the AMP-PNP molecule from the crystallographic structure before carrying out the simulations to keep our model system the simplest possible. This strategy is also based on the notion that functional dynamics patterns are intrinsic to protein structures [START_REF] Eisenmesser | Intrinsic dynamics of an enzyme underlies catalysis[END_REF]. Cofactors or binding of other biomolecules generally promote population shift of already existing conformational states of a protein ensemble [START_REF] Ma | Conformational footprints[END_REF]. The orthologous protein in D. rerio has an 80% sequence identity when compared to the human TRAP1 variant and no structures are available for the full-length human variant to the best of our knowledge.

Moreover, due to the known TRAP1 structural asymmetry [START_REF] Lavery | Structural Asymmetry in the Closed State of Mitochondrial Hsp90 (TRAP1) Supports a Two-Step ATP Hydrolysis Mechanism[END_REF][START_REF] Elnatan | Symmetry broken and rebroken during the ATP hydrolysis cycle of the mitochondrial Hsp90 TRAP1[END_REF], we performed simulations of both the dimeric assembly and of each individual protomer (chain A and B from 4IPE, respectively). For each individual protomer, we removed their first 23 residues (T85-A107) corresponding to the N-terminal strap that wraps around the other protomer in the dimeric assembly to avoid that the floppy terminal disordered regions engaged in nonnative interactions with the rest of the protomer. Chain A and B correspond to the buckled and straight protomers, respectively, as defined in [START_REF] Elnatan | Symmetry broken and rebroken during the ATP hydrolysis cycle of the mitochondrial Hsp90 TRAP1[END_REF]. For each protomer, we carried out MD simulations of the unmodified (TRAP1 SH ) and S-nitrosylated variant on cysteine 516 (TRAP1 SNO ). The SNO variant was obtained by Vienna-PTM [START_REF] Margreitter | Vienna-PTM web server: a toolkit for MD simulations of protein post-translational modifications[END_REF]. Dimer simulations were performed only for the SH variant. The details of the simulations used in the study are reported in Table 1.

We performed the simulations using GROMACS version 4.6 [START_REF] Hess | GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation[END_REF], employing the force fields ff99SB*-ILDN [START_REF] Robustelli | Developing a molecular dynamics force field for both folded and disordered protein states[END_REF] and GROMOS 54a7 [START_REF] Schmid | Definition and testing of the GROMOS force-field versions 54A7 and 54B7[END_REF] for which parameters for S-nitrosylation were available [START_REF] Petrov | A Systematic Framework for Molecular Dynamics Simulations of Protein Post-Translational Modifications[END_REF][START_REF] Han | Force field parameters for S-nitrosocysteine and molecular dynamics simulations of S-nitrosated thioredoxin[END_REF]. The protein was solvated in a dodecahedral box of water molecules in periodic boundary conditions at a concentration of 150 mM NaCl, neutralizing the net charge of the system. We used Single Point Charge (SPC) or the TIP3P solvent models for GROMOS and AMBER force fields, respectively. We defined the protonation state of each histidine using ProPka and the PDB2PQR server (https://academic.oup.com/nar/article/35/suppl_2/W522/2920806) [START_REF] Dolinsky | PDB2PQR: expanding and upgrading automated preparation of biomolecular structures for molecular simulations[END_REF] and modeled all the histidine in the Nε tautomeric state with the exception of H326 and H573 in the Nδ state.

The system was equilibrated through a series of energy minimization, solvent equilibration, thermalization and pressurization steps (see GitHub repository associated with the publication for details). We performed the productive MD simulations in the canonical ensemble at 298 K. We collected 150 and 500 ns for monomers and dimers, respectively.

We used the Particle-mesh Ewald summation scheme and we truncated Van der Waals and short-term Coulomb interactions at 9 Å. For more details, we provided the parameter input files iin the GitHub repository associated with the publication the parameter input files.

Protein Structure Network

We used a contact-based protein structure network (PSN) to analyze the MD ensemble of the TRAP1 SH dimer, as implemented in PyInteraph [START_REF] Tiberti | PyInteraph: A Framework for the Analysis of Interaction Networks in Structural Ensembles of Proteins[END_REF]. For the contact-based PSN, we selected a 5 Å distance cutoff to define the existence of a link between the nodes, which is a suitable compromise between an entirely connected and a sparse network, according to our recent work on PSN cutoffs [START_REF] Viloria | An optimal distance cutoff for contact-based Protein Structure Networks using side-chain centers of mass[END_REF]. The distance was estimated between the side-chain center of masses (excluding glycines). Since MD force fields have different mass definitions, we used the PyInteraph mass databases compatible with the two force fields selected for this study. We retained in the PSN for analysis only those edges which were present in at least 20% of the simulation frames to remove spurious interactions, as previously applied to other case studies [START_REF] Tiberti | PyInteraph: A Framework for the Analysis of Interaction Networks in Structural Ensembles of Proteins[END_REF]. Prior to the analysis, we evaluated the time evolution of the root mean square deviation (rmsd) of the NTD, MD and CTD, respectively.

We observe that approximately 120 and 50 ns were needed to have a stable rmsd profile over time (see GitHub repository for the rmsd analyses). We thus carried out the PSN analyses only on the portion of the MD trajectories with stable rmsd profiles.

We defined as hubs those residues of the network with at least three edges, as commonly done for networks of protein structures [START_REF] Papaleo | Integrating atomistic molecular dynamics simulations, experiments, and network analysis to study protein dynamics: strength in unity[END_REF]. We applied a variant of the depth-first search algorithm to identify the shortest path of communication between the SNO site of each monomer (C516) and all the other residues of the protein (testing different cutoffs for the path lengths to be sure we cover also very distal effects, i.e. 15, 25 and 40). We focused on the analyses of the paths with a maximal path length of 25 since increasing this value to 40 did not provide additional target residues. We defined the shortest path as the path in which the two residues were non-covalently connected by the smallest number of intermediate nodes. We did not include in the path analyses, the residues in the regions 429-485 and 543-550 of the protomer where the source SNO site was located since they contained several residues within 8 Å of distance from C516 (i.e., not suitable as distal sites). All the PSN and path calculations have been carried out using the PyInteraph suite of tools [START_REF] Tiberti | PyInteraph: A Framework for the Analysis of Interaction Networks in Structural Ensembles of Proteins[END_REF].

FoldX calculations

We employed the FoldX energy function [START_REF] Guerois | Predicting Changes in the Stability of Proteins and Protein Complexes: A Study of More Than 1000 Mutations[END_REF] to estimate the impact of mutation to serine at the C516 site on structural stability. The calculations resulted in an average ΔΔG (differences in ΔG between mutant and wild-type variant) for each mutation over five independent runs performed using the same structure used for MD simulations (PDB entry 4IPE), along with some of the available structures for the TRAP1 human variant (i.e. PDB codes here 4Z1F, 4Z1G, 4ZIH, and 5HPH). For the X-ray structures of TRAP1 dimers (4IPE and 5HPH), we estimated the ΔΔG values the isolated protomers, i.e. chain A and chain B. The typical prediction error of FoldX is about 0.8 kcal/mol [START_REF] Guerois | Predicting Changes in the Stability of Proteins and Protein Complexes: A Study of More Than 1000 Mutations[END_REF]. Twice the prediction error (i.e., 1.6 kcal/mol) was used as a threshold to discriminate between neutral and deleterious mutations in the analyses.

Principal Component Analysis (PCA)

We employed a dimensionality reduction technique based on Principal Component Analysis (PCA) [START_REF] Amadei | Essential dynamics of proteins[END_REF] to represent and compare in a conveniently simplified two-dimensional (2D) space the conformational space explored by the two different TRAP1 variants 10 (TRAP1 SH and TRAP1 SNO ). PCA, applied to MD trajectories, allows to calculate the eigenvectors (principal components, PCs) of the mass-weighted covariance matrix of the atomic positional fluctuations. We calculated the covariance matrix of only Cα atoms of the protein for the region 108-716. We did not include the unstructured regions at the N-and C-terminal of the protein, whose pronounced motions might otherwise hide important differences in the rest of the protein. We aligned the structures from the concatenated trajectories, before the covariance matrix calculation, on a subset of Cα atoms that do not undergo pronounced conformational changes during the simulations. In details, we selected for the alignment the following residues: 591-608, 611-614, 618-626, 630-638, 656-659, 664-675, 677-695 in the CTD of the buckled protomer.

We used a common essential subspace derived from a PCA of the concatenated trajectories of TRAP1 SH and TRAP1 SNO . In details, we used two concatenated trajectories, each of them including all the MD simulations of TRAP1 SH and TRAP1 SNO protomers and performed with one force field (ff99SB*-ILDN or GROMOS 54a7). The first two principal components explains more than 77.6 and 83.9 % of the total variance for ff99SB*-ILDN and GROMOS 54a7, respectively. We thus obtained a 2D density plots along the first two principal components, using an in-house R script that performs 2D kernel density estimation, employing the kde2d function from the MASS package and the stat_density_2d function from ggplot2 package. We collected the conformations of TRAP1 in the concatenated trajectories corresponding to the most densely populated areas identified on the PCA 2D plots.

Plasmids

TRAP1 WT and C501S were cloned in pET-26b(+) plasmid for bacterial expression and in pcDNA3.1(+)-C-eGFP plasmid for mammalian expression using the Gene Synthesis & DNA Synthesis service from GeneScript Biotech.

not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

The copyright holder for this preprint (which was this version posted November 30, 2019. ; https://doi.org/10.1101/859629 doi: bioRxiv preprint 11 2.6. Purification of recombinant proteins TRAP1 WT and TRAP1 C501S recombinant proteins were produced in BL21(DE3) E. coli cells after 3 h-induction with 1 mM IPTG (Isopropil-β-D-tiogalattopiranoside, VWR). Protein purifications were performed using Ni-NTA resin (Quiagen) according to manufacturer's instructions. Proteins release from the resin packed in a FPLC column was achieved using a linear imidazole gradient generated by mixing buffer A (50 mM Phosphate Buffer, 250 mM NaCl, 10 mM imidazole, 10 mM β-mercaptoethanol, pH 7.8) and B (buffer A with 250 mM imidazole). Eluted fractions containing recombinant proteins were then collected and combined. After purification the proteins were dialyzed in the storage buffer (50 mM Tris-HCl, 150 mM NaCl, 1 mm DTT, 1 mM EDTA, pH 7.5) and stored into aliquots.

S-nitrosylation in vitro

Purified WT and C501S forms of TRAP1 (TRAP1 WT and TRAP1 C501S ) were dialyzed in storage buffer. Before treatments, DTT was removed by Zeba Spin Desalting Columns (Thermo Fisher) and proteins transferred in reaction buffer (NaCl 150 mM, TRIS 50 mM, pH 7.5). S-nitrosylation and denitrosylation were performed at 37°C in the dark by incubations with S-nitroso-N-acetilpenicilamine (SNAP -Enzo Life Sciences, Inc) 500 μM for 4 h and with ascorbate (Sigma-Aldrich) 20 mM for 30 min, respectively.

TRAP1 ATPase assay

TRAP1 ATPase activity was measured by by quantifying the release of inorganic phosphate as PO 4 nmoles/TRAP1 nmoles/min. The ATPase assay was performed at 37°C for 1 h, incubating 5, 10, and 15 μg of proteins with 200 μM ATP and 10 mM MgCl 2 , in reaction buffer, in the dark. The amount of inorganic phosphate (PO 4

3-) released due to the activity of TRAP1 was measured using the Malachite Green Phosphate Assay Kit (Sigma-Aldrich), by detecting the increase in absorbance at 650 nm with a plate reader (Tecan).

Cell culture, treatments and transfections

HeLa cell line was obtained from the American Type Culture Collection (ATCC) and grown in DMEM (Thermo-Fisher Scientific) supplemented with 10% FBS (Thermo-Fisher Scientific) and 1% Penicillin/Streptomycin mix (EuroClone). Cells were maintained at 37°C in an atmosphere of 5% CO 2 .

Cells were transfected using polyethylenimine (PEI) (Sigma-Aldrich) in Opti-MEM medium (Thermo-Fisher Scientific), and incubated twice with plasmids and PEI with a 8 h-release between one transfection and the other. After the second transfection cells were incubated in DMEM and treated with 250 μM of the NO donor DETA-NONOate (Enzo Life Sciences) for 22-24 h and/or 200 nM Staurosporine (STS) (Sigma-Aldrich) for 4h.

Analysis of cell viability and cell death.

Cell death was analyzed with the direct count of dead cells upon Trypan Blue staining (Sigma-Aldrich).

Analyses of mitochondrial transmembrane potential (Δψ m ).

Mitochondrial Δψ m was analyzed by incubating the cells with 200 nM tetramethylrhodamine methyl ester (TMRM, Thermo-Fisher Scientific) for 30 min in serumfree DMEM. Stained cells were washed twice with cold PBS, collected and analyzed by flow cytometry (FACs Verse, BD-biosciences).

Western blot.

Samples preparation and acquisition of Western blots were performed as previously reported [START_REF] Rizza | S-nitrosylation of the mitochondrial chaperone TRAP1 sensitizes hepatocellular carcinoma cells to inhibitors of succinate dehydrogenase[END_REF]. Protein concentration was determined by the method of Lowry [39]. Western blots shown are representative of at least n=3 independent experiments giving similar results. We used the following primary antibodies: anti-HSP75 (TRAP1) and anti-Actin from Santa Cruz Biotechnology; anti-Vinculin from Sigma-Aldrich; anti-PARP1 from Enzo

Life Sciences; anti-Caspase 3 from Cell Signaling.

Statistical analyses.

Values are expressed as means ± SEM and statistical significance was assessed by Student's t-test or one-way ANOVA test using Prism 8.0 (GraphPad Software, Inc.) in order to determine which groups were significantly different from the others.

RESULTS AND DISCUSSION

The S-nitrosylation site establishes a long-range communication with the active site

As above introduced, the chaperone TRAP1 exploits its function as a homodimer, with each monomer consisting of three distinct domains: a N-terminal domain (NTD, residues 101-308) that harbors the ATP binding site, a middle domain (MD, residues 311-571)

where the binding site for client proteins resides, and a C-terminal domain (CTD, 587-719) important for dimerization (Figure 1A).

The available crystal structures of TRAP1 and biophysical experiments carried out in D.

rerio variant [START_REF] Lavery | Structural Asymmetry in the Closed State of Mitochondrial Hsp90 (TRAP1) Supports a Two-Step ATP Hydrolysis Mechanism[END_REF][START_REF] Elnatan | Symmetry broken and rebroken during the ATP hydrolysis cycle of the mitochondrial Hsp90 TRAP1[END_REF] revealed asymmetry between the two protomers with differences especially at the interface between MD and CTD, where the client protein binds. The two protomers have been defined as buckled and straight [START_REF] Lavery | Structural Asymmetry in the Closed State of Mitochondrial Hsp90 (TRAP1) Supports a Two-Step ATP Hydrolysis Mechanism[END_REF][START_REF] Elnatan | Symmetry broken and rebroken during the ATP hydrolysis cycle of the mitochondrial Hsp90 TRAP1[END_REF], which are illustrated in Figure 1B for sake of clarity. The SNO site is located at approximately 60 Å away from the active site in the middle domain of TRAP1 (Fig. 1A).

We thus employed a method to predict structural perturbations that can be propagated over long distances from one site to other sites of a protein, based on network theory [START_REF] Tiberti | PyInteraph: A Framework for the Analysis of Interaction Networks in Structural Ensembles of Proteins[END_REF][START_REF] Viloria | An optimal distance cutoff for contact-based Protein Structure Networks using side-chain centers of mass[END_REF]. The approach is based on the collection of an ensemble of conformations for the protein of interest using MD simulations, followed by the analysis of the conformational ensemble with a contact-based protein structure network (PSN). On the resulting network, an algorithm to search for the shortest path of communications between the site of interest is applied, according to the knowledge that in protein structures communication of structural effects among distal sites is preferred along the shortest roads [START_REF] Viloria | An optimal distance cutoff for contact-based Protein Structure Networks using side-chain centers of mass[END_REF]. In this work, we collected the MD simulations with two different force fields to assess the robustness of the results, namely the physical models used to describe the protein and its environment during the simulation. We used the D. rerio structure of TRAP1, as detailed in the method, where the S-nitrosylation site is the C516 (conserved in the human variant at position 501, C501). We noticed that the SNO site was in a region surrounded by hubs residues (see GitHub repository associated with the publication), which are important residues to organize the global network structure and transmit information from one site to the other.

The SNO site is also included in the largest connected component of the PSN in both the simulations of the TRAP1 SH dimer (Fig. 1C in red). We calculated all the paths of longrange communication from each SNO site to the rest of the protein. We observe that each SNO site has the potential to exert distal effects within the corresponding protomer (i.e., C516 of the buckled protomer to distal residue of the buckled protomer and vice versa, Fig. 1D). The communication extends to regions in the CTDs of both protomers and the NTD of the cognate protomer, through localized hotspots at the dimerization interface (Fig.

1D).

In summary, the TRAP1 cysteine which undergoes S-nitrosylation, shows a propensity to promote conformational changes to distal sites in the NTD, and at the interface between the two protomers, i.e., in the MD and CTD. Our analysis suggests that S-nitrosylation of C516 (human C501) might modulate the activity of the chaperone and not only the propensity of the protein to be degraded by the proteasome, as we previously reported [START_REF] Rizza | S-nitrosylation of the mitochondrial chaperone TRAP1 sensitizes hepatocellular carcinoma cells to inhibitors of succinate dehydrogenase[END_REF].

S-nitrosylation alter protein flexibility and inhibit the functional movements of the chaperone

In light of the PSN results, we wonder if we could identify changes in the dynamics of the TRAP1 SH and TRAP1 SNO variants that might be related to the conformational changes that the chaperone needs to undergo during the catalytic cycle. As a simple model to address these points, we used MD simulations of the individual protomers for the two TRAP1

variants. This allowed us to see possible motions of opening and closing of the different domains in short simulations of few hundred ns. We compared the simulations using a dimensionality reduction technique based on Principal Component Analysis, highlighting, in such a way, large amplitude motions in the protein [START_REF] Amadei | Essential dynamics of proteins[END_REF]. We used the 2D projections of the MD structures along the first and second principal components (Fig. 2A). Each point in this essential subspace represents a structure of the original MD trajectories. We notice that the S-nitrosylation of C516 induced a stiffness in the structure, reducing opening/closing motions and the exploration of a smaller portion of the 2D subspace (Fig. 2B). This behavior is especially evident in the TRAP1 SNO buckled protomer where the conformational transition between the two states observed in the corresponding TRAP1 SH protomer is impaired (Fig. 2C).

S-nitrosylation at Cys501 affects TRAP1 ATPase activity

Based on results so far obtained, we moved to in vitro systems, i.e. recombinant human TRAP1, to verify in silico predictions about the effects induced by S-nitrosylation on TRAP1 activity. To this end, we measured ATPase activity by Malachite green phosphate assay upon 4 h incubation with the NO donor SNAP. Colorimetric analyses indicated that SNAP treatment decreased TRAP1 ATPase activity by about 25-30% (Fig. 3A). Next, to avoid that the observed phenomenon was related to other possible S-nitrosylationindependent reactions induced by NO (e.g. nitration), after SNAP treatment, we dialyzed TRAP1 and incubated it for 1 h with 20 mM ascorbate, a specific SNO reductant. Results

shown in Fig. 3B indicated that ascorbate addition rescued the activity of the protein, confirming that S-nitrosylation was the driving event affecting TRAP1 activity. On the basis of these results, we decided to substitute the nitrosylable Cys (conserved in human TRAP1 at position 501) with a Ser, in order to maintain steric hindrance and polarity.

Before mutagenesis, we predicted the effects on protein stability induced by Cys-to-Ser substitution in the form of ΔΔGs. We used the different available crystallographic structures of human and D. rerio TRAP1 variants for the calculation and the FoldX energy function (Table 2), as we and others showed that this biophysical measurement correlates well with the cellular protein expression levels of protein mutant variants [START_REF] Abildgaard | Computational and cellular studies reveal structural destabilization and degradation of MLH1 variants in Lynch syndrome[END_REF][START_REF] Scheller | Toward mechanistic models for genotype-phenotype correlations in phenylketonuria using protein stability calculations[END_REF][START_REF] Nielsen | Predicting the impact of Lynch syndrome-causing missense mutations from structural calculations[END_REF]. The advantage of using multiple structures of the same protein for this calculation is to overcome the inherent limitation of the FoldX method, which performs only local conformational sampling around the input structure. We noticed that in all the cases, the C516S -and, by homology, C501S -mutation is not predicted to change protein stability, with low ΔΔG values always below the threshold of 1.6 kcal/mol. These calculations suggest that we could use the C501S mutant variant for experimental studies without risking to affect protein expression levels. This conclusion was confirmed by the amount of TRAP1 C501S obtained upon purification, which did not differ significantly from TRAP1 WT (Fig. 4A). TRAP1 variants were than analyzed for their capability to hydrolyze ATP.

Malachite green assays showed that TRAP1 C501S exhibited a higher ATPase activity than TRAP1 WT (Fig. 4B) but, most importantly, this was not affected by SNAP addition, confirming that Cys501 nitrosylation affects TRAP1 activity.

TRAP1 S-nitrosylation at Cys501 modulates staurosporin-induced cell death

In order to understand the effects of Cys501 nitrosylation in a biological context, we overexpressed TRAP1 WT and TRAP1 C501S in HeLa cells (Fig. 5A) and analyzed whether the expression of TRAP1 C501S could modulate apoptotic response, as this has been reported as one of the processes regulated by TRAP1 [START_REF] Kang | TRAP1 regulation of mitochondrial life or death decision in cancer cells and mitochondria-targeted TRAP1 inhibitors[END_REF][START_REF] Costantino | TRAP1 , a novel mitochondrial chaperone responsible for multi-drug resistance and protection from apoptotis in human colorectal carcinoma cells[END_REF][START_REF] Kang | Regulation of Tumor Cell Mitochondrial Homeostasis by an Organelle-Specific Hsp90 Chaperone Network[END_REF][START_REF] Landriscina | Mitochondrial Chaperone Trap1 and the Calcium Binding Protein Sorcin Interact and Protect Cells against Apoptosis Induced by Antiblastic Agents[END_REF]. As inducer, we selected staurosporine (STS), as it has been already shown it activates the intrinsic (mitochondrial) pathway of apoptosis [START_REF] Malsy | Staurosporine induces apoptosis in pancreatic carcinoma cells PaTu 8988t and Panc-1 via the intrinsic signaling pathway[END_REF][START_REF] Manns | Triggering of a novel intrinsic apoptosis pathway by the kinase inhibitor staurosporine: activation of caspase-9 in the absence of Apaf-1[END_REF]. Direct cell count upon Trypan blue exclusion indicated that TRAP1 C501S -expressing cells were more resistant than those carrying the TRAP1 WT variant (Fig. 5B). This difference was maintained (even increased) upon treatment with NO donors (i.e., DETA-NONOate), that we carried out to induce inhibition TRAP1 WT via S-nitrosylation. This result was also confirmed by Western blot analyses of caspase 3 and PARP1 cleavage (Fig. 5C), which are well-established markers of apoptosis, indicating that the non-nitrosylable form of TRAP1 was protective under these experimental conditions.

CONCLUSIONS

S-nitrosylation of Cys501 is a redox modification associated with TRAP1 degradation via proteasome [START_REF] Rizza | S-nitrosylation of the mitochondrial chaperone TRAP1 sensitizes hepatocellular carcinoma cells to inhibitors of succinate dehydrogenase[END_REF]. However, no molecular mechanisms, neither possible structural changes or biological relevance have been provided being related to this, if not the evidence that TRAP1 degradation indirectly causes an increased in SDH expression/activity [START_REF] Rizza | S-nitrosylation of the mitochondrial chaperone TRAP1 sensitizes hepatocellular carcinoma cells to inhibitors of succinate dehydrogenase[END_REF]. In this study we used molecular dynamics simulations and methods based on graph theory and dimensionality reduction to investigate the effects induced by S-nitrosylation of Cys501.

Our results suggest that the cysteine has an intrinsic propensity to transmit conformational changes to distal sites that are important for the conformational changes of the chaperone during the catalytic cycle or for interaction with the client proteins. Its posttranslational modification is thus likely to perturb the functional dynamics of TRAP1, as we showed using principal component analyses of the simulations. The buckled protomer of the asymmetric TRAP1 structure seems the most sensitive to the modification in our model system and S-nitrosylation causes a stiffness of the structure and impairment of motions.

This restrains the elements in the C-terminal and middle domain that can work as hinges for these conformational changes. Our data are in good agreement with recent findings from another computational study where the region surrounding the SNO site has been shown with a pronounced sensor signature by perturbation scanning analyses [START_REF] Verkhivker | Dynamics-based community analysis and perturbation response scanning of allosteric interaction networks in the TRAP1 chaperone structures dissect molecular linkage between conformational asymmetry and sequential ATP hydrolysis[END_REF], making the area a suitable candidate for allosteric communication. In another computational study using all-atom MD simulations, the authors identified a cross-talk and coupling between the nucleotide site and distal client binding sites, also in the proximity of the S-nitrosylation site [START_REF] Moroni | The Structural Asymmetry of Mitochondrial Hsp90 (Trap1) Determines Fine Tuning of Functional Dynamics[END_REF].

The inhibitory effects of Cys501 nitrosylation on ATPase activity support the in vivo evidence arguing for a protective role of TRAP1 C501S to apoptotic stimuli, mostly if these are associated with NO fluxes. Our data, together with those previously published [START_REF] Rizza | S-nitrosylation of the mitochondrial chaperone TRAP1 sensitizes hepatocellular carcinoma cells to inhibitors of succinate dehydrogenase[END_REF],

reinforce the role of Cys501 in maintaining the stability and ensuring the activity of TRAP1. This is in agreement with the higher TRAP1 activity of the mutant, but moves the question on: "why the mutant should be more active?" Work is still ongoing in our laboratory to exactly clarify how S-nitrosylation interferes with TRAP1 activity and functions. However, it is reasonable to speculate that, as observed in this work, TRAP1 SNO cannot ever reach the "stiff" conformation and, in turn, ever decrease the chaperone efficiency in a population of TRAP1 molecules.

Evolutionarily, TRAP1 is considered the ancestral member of HSP90 family [START_REF] Ho Yeong Song | Identification of a protein with homology to hsp90 that binds the type 1 tumor necrosis factor receptor[END_REF] and, for this reason, its activity could probably be more responsive to ROS and NO as hypothesized for many ancestral proteins (i.e., the bacterial transcription factor OxyR) [START_REF] Stomberski | Protein S-Nitrosylation: Determinants of Specificity and Enzymatic Regulation of S-Nitrosothiol-Based Signaling[END_REF][START_REF] Seth | Endogenous Protein S-Nitrosylation in E. coli: Regulation by OxyR[END_REF] not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Figure 1 .

 1 Figure 1. TRAP1 S-nitrosylation site modulates propensity for intra-and

Figure 2 .

 2 Figure 2. TRAP1 S-nitrosylation results in structural stiffness and impairs

Figure 3 .

 3 Figure 3. Human recombinant TRAP1 S-nitrosylation results in loss of ATPase activity of. A) ATPase activity of recombinant TRAP1 was measured as PO 4 3-release by Malachite Green Phosphate Assay following the absorbance at 650 nm in the presence

Figure 4 .

 4 Figure 4. TRAP1 C501S mutant is insensitive to NO-mediated inhibition of ATPase

Figure 5 .

 5 Figure 5. TRAP1 C501S mutant exerts protective role towards staurosporine-

Table 1 . Summary of the MD simulations collected in this study.

 1 . Therefore, this new regulation of TRAP1 could find a rationale if seen from this angle. Whether Cys501 can also act as general "redox antenna" (e.g. whether it can react with H 2 O 2 and other electrophiles) is still far from being elucidated. However, the evidence that Cys501 is placed in close proximity to another cysteine (Cys527) might suggest the presence of a peroxiredoxin-like intra-molecular redox switch able to function as novel signaling mechanism. Work is in progress in our lab to elucidate this issue and others dealing with the role of S-nitrosylation and redox mechanism underlying TRAP1 regulation.

	In this regard, preliminary studies suggest that S-nitrosylation is a posttranslational
	modification broadly modulating TRAP1 functions. In particular, mass spectrometry
	analyses, performed in human TRAP1, indicate that Cys573 (which is not present in
	TRAP1 from D. rerio) can also undergo S-nitrosylation, with this event being probably
	implicated in regulating TRAP1 activity and structure by further, still unknown,
	mechanisms.

Table 2 . Prediction of changes in protein structural stability upon mutations of C516/C501.

 2 The average ΔΔG values and standard deviations associated with protein stability are reported for each TRAP1 crystallographic structure. C516 and C501 refer to the Danio rerio and human TRAP1 variants, respectively. For dimeric structures, the results for each of the monomers (i.e., chain A or B in the PDB file) are reported.

	MUTATION	PDB	ΔΔG (kcal/mol)
	C516S	4IPE, Chain A	1 (0.077)
	C516S	4IPE, Chain B	1.62(0.0056)
	C501S	4Z1F	1(0.095)
	C501S	4ZIG
			0.56 (0.099)
	C501S	4Z1H	-1.41 (0.10)
	C501S	5HPH, Chain A	0.60 (0.00001)
	C501S	5HPH, Chain B	0.85 (0.097)

ACKNOWLEDGEMENTS

We would like to thank Giorgio Colombo's group to provide the structure of the TRAP1 dimer to use in our simulations and Laila Fisher for secretary assistance. This work has been supported by Danish Cancer Society Grant Kraeftens Bekaempelses Videnskabelige Udvalg (KBVU) Grant [R146-A9414] (to G.F. and E.P.); Associazione Italiana per la Ricerca sul Cancro (AIRC) Grant [IG20719] (to G.F.); the DECI-PRACE 13 th HPC Grant CHAPREDO for computational time on Archer (to E.P.); and a LISA CINECA HPC Grant for computational time on Marconi and Pico (HPL13PEJU2, to E.P.); AIRC IG 2017-20749 (to A.R.) and "The neurofibromatosis therapeutic acceleration program" (to A.R.); the 20 Danish National Research Foundation (DNRF125) as fund to the Center of Excellence in Autophagy, Recycling and Disease (CARD).

Author contributions: F.F., C.P., P.G., and G.C. performed the experiments in vitro and cellular experiments, and interpreted the data; F.F., S.R., A.R., G.F., E.P., conceived and designed the study, M.L. J.S.V., M.F.A. and E.P. performed molecular dynamics and in silico studies, and interpreted the data; F.P. and A.B. produced the recombinant proteins; F.F., M.L., S.R., J.V., A.B., A.R., E.P. and G.F., critically commented the manuscript and conceived the figures; E.P. and G.F. wrote the manuscript.

Conflicts of Interest:

The authors state that no conflict of interests does exist.