I Michaud-Soret

C Fauquant

C Sageot

A Chan

A Petit

M Chevallet

S Ollagnier De Choudens

Nathalie Herlin-Boime

Pierre-Henri Jouneau

Interference between titanium from TiO2 nanoparticles and iron

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This paper is originally published with the following reference : Nanoscale. 2014 Jun 7;6 (11):6102-14. doi: 10.1039/c4nr00319e Analysis of cellular responses of macrophages to zinc ions and zinc oxide nanoparticles: a combined targeted and proteomic approach Sarah Triboulet 1, 2, 3 , Catherine Aude-Garcia 1, 2, 3 , Lucie Armand 4 , Adèle Gerdil 5 , Hélène Diemer 6 , Fabienne Proamer 7 , Véronique Collin-Faure 1, 2, 3 , Aurélie Habert their possible role in nanomedicine (e.g. in 1 ), they are also widely used in sunscreens 2 or as antibacterials 3 . However, zinc oxide nanoparticles are known to be toxic in man, as they are for example one of the causal agents of the metal fume fever 4 . There are also several reports on zinc oxide toxicity on different cellular models [5][6][7][8][9][10][11] . Although some reports show a clear role for the nanoparticles themselves 7,12 , several reports demonstrate a clear role of dissolved zinc ion in the toxicity of zinc oxide nanoparticles [13][14][15][16] . As to the role of dissolved zinc ion, some reports suggest a major role for extracellular dissolution 11,15,17 , while other reports suggest that intracellular dissolution in lysosomes after uptake of nanoparticles is predominant 7,12,13,18 .

As to the toxicity mechanisms of zinc oxide nanoparticles, most of the studies imply the oxidative stress pathway 5,9,19 , leading to DNA damage 5,8 , although interference with ion homeostasis has also been implicated 9,20 . However, these are mostly targeted studies investigating classical mechanisms in nanoparticles toxicity, and studies using wider and less oriented approaches are still rare 21 .

Due to their known implication in diseases related to particulate matter such as asbestosis 22,23 , and the well-known inflammatory dimension of the zinc oxideinduced metal fume fever 24,25 , myeloid cells and especially macrophages are a typical target cell type when investigating the toxicity of zinc oxide nanoparticles. Thus, myeloid cells have been used in several studies dealing with zinc oxide toxicity 6,11,21,26,27 . Most of these studies have concentrated on the classical chronic inflammation scheme, and investigated the production of pro-inflammatory cytokines upon treatment with zinc oxide. The results are however conflicting, some studies describing the production of some cytokines 26,27 while others do not describe any cytokine production 6 .

However, a wide scope approach aiming at finding the key responses of macrophages to zinc oxide nanoparticles is still missing, despite the recent transcriptomic work recently described 21 . This is why we undertook a combined targeted and proteomic study of the responses of the mouse macrophage cell line RAW264 to zinc ion and to two different types of zinc oxide nanoparticles (coated and uncoated). RAW264 cells are one of the classical macrophage models and have been often used for studying the effects of nanoparticles in macrophages (e.g. in 26,[28][29][30][31][32][33] . They are also one of the few cell lines able to produce NO upon LPS stimulation 34 , a feature absent from the other classical human macrophage model THP-1. The rationale of this comparison was to check if the coating can modify not only the toxicity of the nanoparticle 19 , but can also alter the cellular responses to the nanoparticles.

RESULTS

Nanoparticles characterization

The two commercial nanoparticles were characterized by several methods, and the results are summarized in Table 1 and in supplementary figures 1 to 3. The zinc oxide nanoparticles were rod shaped, while the cationic coated zinc oxide was more spherical.

The XRD diagrams conformed to those of nominal zinc oxide, showing no crystalline impurities. The zeta potential was positive for both nanoparticles, with the cationic coating accounting for an increase in the positive surface charge. Finally, the nanoparticles could be dispersed in complete culture medium as aggregates of ca. 200 nM in diameter.

In order to verify that the nanoparticles were not degraded prior to their contact with the cells, we checked their dissolution in the 50mg/ml intermediate nanoparticules dispersion (corresponding to 625mM zinc). Only 1mM dissolved zinc was measured after 7 days.

Determination of the effective doses

For carrying out a proteomic study, we had to determine the dose for which we would obtain the best compromise between viability and biological effect. We therefore decided to use a LD20, i.e. a dose leading to a cell mortality of 20%, knowing that the mortality of a control culture is around 5%.

The LD20 was obtained at 8 µg/ml for the ZnO nanoparticles, 7µg/ml for the coated cationic ZnO nanoparticles, and at 120µM for zinc ion (supplementary figure 4). These concentrations were used for the subsequent studies.

Determination of nanoparticles fate in cells

Transmission electron microscopy was performed to determine the fate of the nanoparticles when in presence of macrophages. These experiments (Figure 1) showed that nanoparticles were present in vacuolar structures identified as multivesicular bodies and as lysosomes. These results were in line with what can be expected from professional phagocytes. Moreover, these results demonstrate that macrophages seem to ingest both nominal nanoparticles and nanoparticles aggregates present in the culture medium and to break them down in the endosomes-lysosomes. Indeed, eroded nanoparticles can easily be seen (Figure 1B and 1C), demonstrating this intracellular erosion phenomenon.

In order to gain further insights into the intracellular zinc concentration, we measured the zinc concentration that could be released from a post nuclear supernatant prepared from cells exposed to zinc ion or ZnO nanoparticles. The results, shown in Table 2, demonstrate that zinc is concentrated in cells compared to the original input. For example, the values obtained with zinc ion convert to an intracellular concentration of 187± 12.5 µM, while the input is only 100µM zinc in the culture medium. These values are orders of magnitude higher than those recently published for the concentrations of free zinc ion after exposure to zinc salts and ZnO nanoparticles 20 . It should be emphasized, however, that the two studies do not measure the same parameter. The study using zinc-sensitive fluorescence probes 20 measures only free zinc, and thus does not measure zinc complexed to macromolecules (e.g. proteins) whereas our study implements a strong protein denaturation step to release protein-bound zinc and to measure all intracellular zinc ion. This demonstrates in turn that most intracellular zinc is not free but protein-bound, which prompted us to analyze zinc-binding proteins.

Proteomic analyses

Two sets of proteomic analyses were performed. The first one dealt with the quantitative analysis of the proteome on whole cell extracts prepared from control cells vs. cells exposed to zinc ion or to both types of ZnO nanoparticles. This analysis pointed to the cellular responses to the presence of excess zinc in terms of protein quantitative changes. The second type dealt with zinc-binding proteins, providing a list of putative targets in case of a zinc overload. This list does not necessarily superimpose to the list of zinc-containing proteins under normal zinc load. As the extract used in the affinity enrichment is prepared under normal growth conditions, it is highly likely that the bona fide zinc binding proteins will be already loaded with zinc and will not bind to the extra zinc ion presented via the affinity column. Conversely, we expect to retain on the column the proteins that do not necessarily bind zinc under normal conditions, but that can bind it when present in excess, i.e. in the conditions prevailing during zinc overload.

The proteomic analysis of whole cell extracts is detailed in Figure 2 and Table 3 . With 2780 reproducibly detected spots, and using an average of 3 spots per protein 35 this proteomic screen probed the proteome to a depth of ca 900 gene products, i.e. ca.15% of the total proteome. Despite this rather limited depth, we could detect reproducible modulations forproteins belonging to several functional classes. The median coefficient of variations of spots was 28%, i.e. marginally higher than in typical 2D DIGE experiments, where coefficient of variations range from 18 to 28%, depending on the sample [36][37][38][39] . The protein changes were detected through the use of a variance-based screen 40 . Compared to a fold-change screen, this process compensates automatically for the variance of each spot. This excludes automatically spots with a high coefficient of variation, but enables to take into account small but reproducible changes when the coefficient of variation is low, thus avoiding the arbitrary exclusion of changes that can be biologically meaningful 40 . As an example, the endoplamic reticulum protein ERP29 is selected as significantly modulated, although the fold change is only 0.8 fold. This is due to the fact that the coefficient of variation of this spot is only 8% in the control group and 4% in the zinc ion-treated group.

Metabolic enzymes, cytoskeletal proteins and proteins implicated in the proteasomal degradation are strongly represented in the list of modulated proteins. Moreover, more than a third of these modulated proteins (17/45) are significantly changed only by the zinc ion, but not by the nanoparticles. Very few proteins (4) are significantly changed by the three treatments, and very few are specific of one type of nanoparticles (4 for zinc oxide and 3 for cationic zinc oxide). It is also interesting to compare our proteomic results to the transcriptomic results recently published on immune cells and zinc oxide 21 . Only two proteins (ERP29, spot F1, and major vault protein, spot C8) are found in common between our list of modulated proteins and the list of modulated mRNAs in macrophages, an additional four proteins being found in common with modulated RNAs in Jurkat cells (ADSS spot N2 , DLAT spot M3, GCLM spot O2, and calcyclin-binding protein spot D2).

The proteomic analysis of zinc binding proteins is detailed in Figure 3 and Table 4. First, it should be noted that the background given by an "empty" resin (i.e. without a bound metal) was extremely low, as a basically empty 2D gel was obtained (data not shown). However, to minimize any possible bias, the experiments were carried out on three different biological replicates and only the proteins significantly enriched in the zinc column compared to the starting extract (p≤ 0.05) were further analyzed and listed in Table 2. Here again, metabolic enzymes and cytoskeletal proteins were strongly represented. The two tables show a low overlap, however, pointing out the complementarity of the two analyses.

Validation studies

The inclusion of small but reproducible protein changes means in turn that these changes cannot be validated easily by classical biochemical techniques at the protein expression level. For example, protein blotting often shows a technical variability well above 20%, and a response curve often lower than the one of 2D electrophoresis (e.g. in 38 ), making this technique unsuitable for the validation of small fold changes. This renders functional validation even more necessary 41 , to confirm the biological relevance of the proteomically detected protein modulations.

Characterization of the oxidative stress response

Oxidative stress is now used as a toxicological marker for almost all toxicants, and zinc ion and zinc oxide are no exception to this rule 5,14 . However, we did not detect any change in the classical oxidative stress response proteins, as opposed to what was observed with copper oxide nanoparticles 42 . Nonetheless, we detected an increase in two non classical proteins that can be involved in the antioxidant response, namely flavin reductase (spot O3) and glutamate cysteine ligase (GCLM, spot O2). The latter protein is of interest, as it controls the limiting step in glutathione biosynthesis. Furthermore, glutathione depletion has been suggested as an important mechanism in zinc toxicity 43 .

We therefore measured the intracellular levels of reduced glutathione by a monochlorobimane conjugation approach 44 . The results, shown on Figure 4A, demonstrate a moderate but reproducible increase in GSH levels, validating the increase of GCLM detected via proteomics (fold change 1.6 to 1.8 in Table 3). This suggests that macrophages are able to counteract the GSH chelation due to zinc ion by de novo synthesis of glutathione, a situation quite different from the one observed in neurons 43 .

Another interesting protein detected via our proteomic screen and involved in the oxidative stress response is the flavin/biliverdin reductase (spot O3), which reduces biliverdin into bilirubin. This bilirubin/biliverdin cycle has been implied in hydrogen peroxide destruction 45 . As this protein has a detectable enzymatic activity, we decided to confirm the proteomic result by a direct assay of the biliverdin reductase activity (Table 5). A significant induction was observed, as detected by the proteomic screen.

Characterization of the phagocytic response

As we observed several changes in proteins involved in the actin (e.g. ARP proteins spots C5 and C9, lsp1 protein spot C3, glia maturation factor spot C1, twinfilin spot C4 or gelsolin spots C7) and tubulin cytoskeleton (MAPRE1spot C2 and TCP1 spot C6), we could anticipate perturbations in the phagocytic capacity of zinc-treated macrophages.

We thus assessed this important function of macrophages upon treatment with zinc ion or ZnO nanoparticles. The results, presented on Figure 4B, show that macrophages are still highly phagocytic even when treated with zinc, as long as the concentrations remain non toxic.

DNA damage

As several nanoparticles have been shown to induce DNA damage 8,[46][47][48] , we decided to test whether zinc-based nanoparticles can induce DNA damage, using an alkaline comet assay. The results, shown on Figure 5, demonstrate that zinc ion, zinc oxide but not cationized zinc oxide, are able to induce DNA damage even at the moderately toxic LD20 dose. This also confirms previous results , e.g. on normal nasal mucosa cells 8 .

Metabolic perturbations

Our proteomic screens highlighted several proteins implicated in energy metabolism, and more specifically in the glycolysis and pentose phosphate pathways (spots E1 to E5). These proteins were found in both screens, i.e. proteins responding to the zinc stress and zinc-binding proteins. In fact, several important energy metabolism enzymes have been shown to bind zinc and be inhibited by it [49][50][51] .

We proceeded to a two step validation. First, the enolase and 6-phosphogluconate dehydrogenase activities were measured (Table 5). Here again, the enzymatic activities confirmed the proteomic results, showing an increase upon treatment of the cells with zinc. As the 6-phosphogluconate dehydrogenase was also found in the zinc binding proteins, we hypothesized that it could be inhibited by zinc in the context of a complete cell extract, and not only as a purified protein 52 . We therefore assayed the 6phosphogluconate dehydrogenase activity in the presence of added zinc acetate, and found an IC50 close to 12µM. As this concentration is much lower than the one detected in our cell extracts upon zinc treatment, it can be reasonably hypothesized that the pentose phosphate pathway is impaired during zinc overload. This could contribute to the observed oxidative stress, as the pentose phosphate pathway is a major source of NADPH, required by many reductases implied in the defense against oxidative stress (e.g. glutathione reductase, biliverdin reductase and thioredoxin reductase). In contrast, GAPDH was marginally inhibited by zinc, with only 30% inhibition at 100µM zinc (data not shown).

Finally, we tested the final metabolite of the pentose phosphate and glycolysis pathways, i.e. pyruvate. First, zinc stress induced a higher level of pyruvate in cells, as shown in Figure 6A. This is in line with the activation of the glycolytic metabolism observed in occupational medicine 53 , and suggested that pyruvate is a critical metabolite for survival in presence of a zinc stress, as in neuronal cells 54,55 . Our results demonstrate that pyruvate acts as a survival factor upon zinc stress, both for zinc ion (Figure 6B), zinc oxide (Figure 6C) and cationized zinc oxide (Figure 6D). Thus, energy metabolism is one of the crucial determinants in resistance to zinc.

Our proteomic screens also highlighted several proteins of the proteasomal degradation pathway such as proteasome subunits (spots D4 and D6) and proteins implicated in the ubiquitination step (spots D5 and D1 to D3). In order to investigate the putative implication of this pathway in the response to zinc, we assessed the survival to zinc treatment after proteasome inhibition with the inhibitor MG132. The results, shown on Figure 7, demonstrate that proteasome inhibition increases zinc toxicity, which can be attributed to a synergistic effect between the inhibition of the proteasome by MG132 and the known inhibition of the proteasome by zinc 56 .

DISCUSSION

When taken together, the results of both the proteomic and targeted experiments produce a landscape of the cellular response to zinc. Opposite to what has been observed with copper 42 , zinc ion and zinc oxide do not induce by themselves a strong oxidative stress. This is consistent with the fact that zinc, unlike copper, iron or manganese, cannot undergo redox cycles between ions of different valency.

Thus, the almost generic oxidative stress that has been observed with zinc oxide 5,14 , can be attributed to a more general perturbation leading to an imbalance in cellular redox cycles and thus to ROS production.

From our results, a key determinant in zinc toxicity appears to be a general metabolic perturbation. Many carbohydrate catabolic enzymes are either induced upon zinc treatment (Table 1) or bind to zinc (Table 2), and the overall result is both an increase in pyruvate production and the evidence that pyruvate is a critical metabolite for cell survival during zinc treatment. This is in line with previous studies on neuronal cells 51 , and suggests that an important determinant in zinc toxicity is a metabolic impairment leading to an "energy shortage" in zinc-treated cells, leading eventually to cell death.

Along a closely related line, the number of proteins able to bind zinc ions and the importance of a correct proteasome function also suggests an unfolded protein response. Indeed, if taking an average Mw of 50,000 for proteins, a 5mg/ml solution of proteins represents a 100µM protein concentration, to be compared with the 30µM zinc detected in these extracts upon zinc overload. Consequently, many proteins binding zinc when present in excess may become ill-folded due to zinc binding, and therefore degraded through the proteasomal pathway. This would explain in turn why proteasome function is also an important parameter in zinc tolerance. This role of the proteasome may also explain why zinc, acting as a proteasome inhibitor also synergizes with prooxidants 57 , which also induce a strong unfolded protein response.

Furthermore, our results also shed light on the controversy between the roles of extracellular and intracellular dissolution of the zinc oxide nanoparticles to explain the toxic mechanisms observed. The responses to the zinc oxide nanoparticles are included in the responses to zinc ion, which induces further specific responses. This supports the hypothesis that the main determinant of zinc oxide toxicity is the zinc ion resulting from nanoparticles dissolution. However, the fact that zinc oxide nanoparticles are still visible in the cells after 24 hours of treatment suggests that uptake of nanoparticles and intracellular dissolution within the cells may be a key determinant of zinc toxicity, at least in macrophages. The situation may be indeed quite different in cells with a very low endocytic/phagocytic activity, such as T lymphocytes 17 .

Last but not least, when comparing in detail the results obtained on copper oxide and on zinc oxide in the RAW264 cell line, very few common features emerge, although both types of nanoparticles show close toxicity. This clearly demonstrates, if further needed, that each nanoparticle will induce different toxic mechanisms and different cell responses. In this context, zinc seems to act as a general toxicant, acting on central cellular pathways, so that cells are metabolically impaired but still functional as long as they are alive. Conversely, copper seems to act as a more subtle toxicant, leading to cells that are still alive but functionally impaired.

CONCLUSIONS

When trying to understand the toxicity of zinc nanoparticles in macrophages, the fact that nanoparticles are detected in the cells argues in favor of a model where the nanoparticles are first internalized, then intracellularly dissolved, releasing zinc ion that is the real toxicant. As cells seem to be relatively permeable to zinc, the molecular effects of the nanoparticles can also be reproduced with zinc ion, even when dealing with large scale screens such as proteomic analyses. Our analyses also show that zinc toxicity is produced by a strong uptake of zinc in a protein-bound form up to sub-micromolar concentrations, leading to impairment of central metabolism and thus to energy shortage in the cell, and also very likely to an unfolded protein response. As proteasome is also inhibited by zinc, the proteasome pathway becomes an important determinant of zinc resistance.

EXPERIMENTAL SECTION

Most of the experiments were performed essentially as previously described 42 and the detailed procedures can be found in the supplementary material and methods

Nanoparticles

Zinc oxide nanoparticles, coated or not, were purchased from Sigma-Aldrich (catalog numbers 721077 and 677450 respectively). The aminopropyl silane coated particles, representing a product used in solar screens, were used as purchased. The uncoated zinc oxide nanoparticles were dispersed in water as a 5% w/v suspension by sonication for 60 minutes in a cup-horn instrument (BioBlock Scientific, Strasbourg, France) , under a 5°C thermostated water circulation. The actual size of the particles was determined after dilution in water or in complete culture medium by dynamic light scattering, using a Wyatt Dynapro Nanostar instrument. A Malvern HS 3000 instrument was also used to determine the zeta potential. The crystalline phases were identified using a powder diffractometer from Bruker and the Match software (crystal impact). The morphology of samples was observed by SEM (Scanning Electron Microscopy) and TEM (Transmission Electron Microscopy) after dispersion of the nanoparticles in water using an ultrasonic bath and deposition of a droplet on 200 mesh carbon lacey grids. The weight loss (from about 10mg of sample) was evaluated from annealing under air using a Thermogravimetric Analysis device (Setaram). The temperature cycle was heating at a rate of 10°C/min up to 1000°C followed by a dwelling time of 30 minutes and natural cooling.

To measure nanoparticles dissolution in the 50 mg/ml intermediate dispersion used prior to addition in the cell cultures, the dispersion was centrifuged at 270,000g for 45 minutes to sediment the nanoparticles 58 .

The dissolved zinc concentration in the supernatant was then determined, after the appropriate dilution in water, by a xylenol orange colorimetric assay 59 , using 0.1M MES buffer pH 6 and 60µM xylenol orange as an indicator.

Nanoparticles dissolution in cells

For the studies on cell extracts, the cells were treated with zinc oxide nanoparticles for 24 hours prior to harvest. The cells were then recovered by scraping, collected by centrifugation, washed three times with PBS, and the volume of the cell pellet was determined. A cell lysis solution (Hepes 20 mM pH 7.5, MgCl2 2 mM, KCl 50 mM, tetradecyldimethylammonio propane sulfonate (SB 3-14) 0.15% w/v) was added at a ratio of 5 volumes of solution per volume of cell pellet, and the cells were left to lyse on ice for 20 minutes. The suspension was then centrifuged at 1000g for 5 minutes, the supernatant collected, and recentrifuged at 270,000g for 45 minutes to sediment the nanoparticles 58 .

Zinc concentration was then determined using an adapted xylenol orange colorimetric assay 59 . Briefly, 1 ml of medium/cell extract was first cooled in an ice bath for 30 minutes. TCA was added to a 5% w/v final concentration, and the mixture was left for 1 hour on ice to denature the proteins and release complexed zinc ion.

The mixture was centrifuged for 10 minutes at 15,000g, the supernatant recovered and its volume measured. A neutralizing solution (2 M MES-Na salt) was added (0.25 volume/volume of supernatant) was added, followed by 0.03 volume of a 2 mM xylenol orange solution in water. The color was left to develop for 5 minutes and the absorbance measured at 565 nm. Under these conditions, no interference was observed with magnesium, calcium or iron. As the extract is diluted 5fold compared to the initial cell volume, the concentration read in the assay is multiplied by 5 to obtain the intracellular zinc concentration.

Cell culture

The mouse macrophage cell line RAW 264.7 was obtained from the European Cell Culture Collection (Salisbury, UK). The cells were cultured in RPMI 1640 medium + 10% fetal bovine serum. Cells were seeded at 200,000 cells/ml and harvested at 1,000,000 cells per ml. For treatment with zinc ion or zinc oxide nanoparticles, cells were seeded at 500,000 cells/ml. They were treated with zinc on the following day and harvested after a further 24 hours in culture. Cell viability was measured by the neutral red uptake assay 60 . All experiments were carried out at least in triplicate on independent cultures.

Phagocytosis activity measurement

The phagocytic activity was measured after treatment with zinc using fluorescent latex beads (1µm diameter, green labelled, catalog number L4655 from Sigma) and flow cytometry, essentially as described in 6142 .

Transmission Electron Microscopy

Transmisson electron microscopy was carried out as previously described 42 on cells fixed with glutaraldehyde, included in Epon and post stained with lead citrate and uranyl acetate. Energy dispersive X-ray (EDX) analysis was performed on sample sections. Spectra were obtained using a large angle SDD-EDS attached to a Jeol 2010 TEM.

Intracellular glutathione measurements

Intracellular glutathione levels were analyzed by the monochlorobimane technique 44 , with some modifications 6242 .

Pyruvate assay

The pyruvate content of cells was determined using the pyruvate assay kit from Sigma (ref # MAK071), used according to the manufacturer instructions.

Enzyme assays

The enzymes were assayed according to published procedures. Enolase was assayed by the direct conversion of 2-phosphoglycerate into phosphoenolpyruvate, detected at 240nm 63 .6-phosphogluconate dehydrogenase was assayed by a coupled assay using Nitro blue tetrazolium as the final acceptor and phenazine methosulfate as a relay 64 . Biliverdin reductase was assayed directly for the NADPHdependent conversion of biliverdin into bilirubin, followed at 450nm 45 .

The cell extracts for enzyme assays were prepared by lysing the cells for 20 minutes at 0°C in Hepes 20 mM pH 7.5, MgCl2 2 mM, KCl 50 mM, EGTA 1 mM, SB 3-14 0.15% w/v, followed by centrifugation at 15,000g for 15 minutes to clear the extract.

The protein concentration was determined by a dye-binding assay 65 .

For the zinc inhibition studies, the cell extracts were first diluted in the assay buffer (final protein concentration 0.2 mg/ml), supplemented with defined concentrations of zinc acetate. The resulting solutions were incubated at 37°C for 30 minutes to allow zinc binding. The substrates and cofactors were then added and the activity measured spectrophotometrically.

Comet assay

The comet assay was performed essentially as described in Jugan et al. 46 . Briefly, microscope slides were coated with 1% normal melting point agarose (NMA) and allowed to dry. Around 10,000 cells (75 µL of each cell suspension) were mixed with 0.6 % low melting point agarose (LMPA) and deposited over the agarose layer, and the LMPA/cells mix was allowed to solidify on ice. The slides were immersed overnight in cold lysis solution (2.5 M NaCl, 100 mM EDTA, 10 mM Tris, 1% SDS, 10% DMSO, 1 % Triton X-100) at 4°C. DNA was then allowed to unwind for 30 min in alkaline electrophoresis solution (300 mM NaOH, 1 mM EDTA, pH > 13).

Electrophoresis was performed in a field of 0.7 V/cm and 300 mA current for 30 min.

Slides were then neutralized with 0.4 M Tris pH 7.5 and stained with 50 µL of 20 µg/ml ethidium bromide. At least 50 comets per slide were analyzed under a fluorescence microscope (Zeiss) equipped with a 350-390 nm excitation and a 456 nm emission filter at ×20 magnification. Comet length and intensity were measured by using Comet IV software (Perceptive Instruments, Suffolk, UK).

Proteomics

Sample preparation, 2D gel analysis and mass spectrometry were performed essentially as previously described 42 and the detailed procedures can be found in the supplementary material and methods. Thus, only the specific methods will be described in detail in this section Sample preparation: zinc-binding proteins Zinc-binding proteins were prepared by an affinity enrichment, using zinc ion loaded onto iminodiacetic agarose (Sigma#I4510). One milliliter of gel suspension was diluted with one milliliter of gel suspension buffer (Hepes 20 mM pH 7.5, KCl 50 mM), incubated for 10 minutes in a cold room on a rotating wheel, and centrifuged at 10,000g for 10 minutes. The gel pellet was then resuspended in 1.5 ml of gel suspension buffer, and the agitation-centrifugation cycle was repeated. The gel pellet was then resuspended in 1.5 ml of gel suspension buffer, to which zinc acetate was added at a final concentration of 0.1 M. The suspension was agitated on the rotating wheel (cold room) for 30 minutes, then centrifuged as described above. Five rinsing cycles (suspension in 1.5 ml of gel suspension buffer, rotating wheel for 15 minutes, centrifugation) were then applied. The gel was ready for use after the last centrifugation.

The native cell extract was prepared as follows: Cells were collected by scraping, centrifuged (200g for 5 minutes), and rinsed three times with PBS. The cell pellet volume was estimated, a cell lysis solution (Hepes 20 mM pH 7.5, MgCl2 2mM, KCl 50 mM, tetradecyldimethylammonio propane sulfonate (SB 3-14) 0.15 % w/v, phenylmethylsulfonyl fluoride1 mM and iodoacetamide 1 mM) was added at a ratio of 5 volumes of solution per volume of cell pellet, and the cells were left to lyse on ice for 20 minutes. The suspension was then centrifuged at 1000 g for 5 minutes, the supernatant collected, and the protein concentration determined by a dye-binding assay 65 .

One milligram of proteins was loaded onto 0.2 ml of zinc-loaded iminodiacetic agarose, and the final volume was brought to 1.5 ml with gel suspension buffer. The proteins were left to adsorb overnight (rotating wheel, cold room). The gel was collected by centrifugation (10,000g 10 minutes), and six rinsing cycles (suspension in 1.5 ml of gel suspension buffer, rotating wheel for 15 minutes, centrifugation) were then applied. SB 3-14 (0.1% final concentration) was added to the rinsing buffer for the first four cycles. After the final centrifugation, four volumes (respective to the gel volume) of concentrated lysis buffer (urea 8.75 M, thiourea 2.5 M, 3-[3-Cholamidopropyl]-dimethylammoniopropane-1-sulfonate (CHAPS) 5% w/v, tris(carboxyethyl)phosphine-HCl 6.25 mM, spermine base 12.5 mM) were added and the solution was let to extract at room temperature for 1 hour. The gel was removed by centrifugation (15,000g, 20 minutes) and the protein concentration in the supernatant was determined by a dye-binding assay. Carrier ampholytes (Pharmalytes pH 3-10) were added to a final concentration of 0.4% (w/v), and the samples were kept frozen at -20°C until use.

2D gel electrophoresis

Isoelectric focusing was carried out on home made 160 mm long 4-8 linear pH gradient gels 66 , cast according to published procedures 67 , and rehydrated overnight with the sample in 7 M urea, 2 M thiourea, 4% CHAPS, 0.4% carrier ampholytes (Pharmalytes 3-10) and 100 mM dithiodiethanol 68 , 69 .

IEF strips were run for 60-70 kVh (see supplementary material and methods for details), equilibrated for 20 minutes in Tris 125mM, HCl 100mM, SDS 2.5%, glycerol 30% and urea 6 M 70 , and transferred on top of the SDS gels.

Ten percent gels (160x200x1.5 mm) were used for the second dimension. The Tris taurine buffer system 71 was used and operated at a ionic strength of 0.1 and a pH of 7.9. Detection was carried out by fast silver staining [START_REF] Chevallet | Nucleoside diphosphate kinase A P15532 17208 1.84/4.5 76% 26 Adenylyl cyclase-associated protein 1 P40124 51565.1 1.20/34 63% 27 Adenylyl cyclase-associated protein 1[END_REF] .

Image analysis

The gel images were analyzed using the Delta 2D software (v 3.6). Three gels coming from three independent cultures were used for each experimental group. Spots that were never expressed above 100 ppm of the total spots were first filtered out. Then, significantly-varying spots were selected on the basis of their Student T-test p-value between the treated and the control groups. Spots showing a p-value lower than 0.05 were selected.

Mass spectrometry

The spots selected for identification were analyzed by NanoLC-MS/MS analysis, performed using a nanoLC-QTOF-MS system and a nanoLC-IT-MS system operated as described previously 42 (details can also be found in the supplementary material).

The MS/MS data were interpreted using MASCOT 2. A similar internalisation was also observed for the cationic coated zinc oxide nanoparticles (data not shown) A: analysis of the total post nuclear supernatant B: eluate from the Zn-iminodiacetic sepharose column

The arrows point to spots that show a reproducible and statistically significant increase in the column eluate compared to the starting extract. Spot numbering according to Table 4.

5 ,France 4 :

 54 Jean-Marc Strub 6 , Daniel Hanau 7 , Nathalie Herlin 5 , Marie Carrière 4 , Alain Van Dorsselaer 5 , Thierry Rabilloud 1, 2, 3 * 1: Univ. Grenoble Alpes, Laboratory of Chemistry and Biology of Metals, Grenoble, France 2: CEA Grenoble, iRTSV/CBM, Laboratory of Chemistry and Biology of Metals, Grenoble, France 3: CNRS UMR 5249, Laboratory of Chemistry and Biology of Metals, Grenoble, UMR E3 CEA-Université Joseph Fourier, Service de Chimie Inorganique et Biologique, Laboratoire Lésions des Acides Nucléiques (LAN), Grenoble 5: CEA-DSM, IRAMIS, SPAM-LFP (URA CEA CNRS 2453), Saclay 6: Laboratoire de Spectrométrie de Masse BioOrganique (LSMBO), Université de Strasbourg, IPHC, 25 rue Becquerel 67087 Strasbourg, France. CNRS, UMR7178, 67037 Strasbourg, France. 7: UMR_S949, INSERM-UdS, EFS-Alsace, 10, rue Spielmann, 67065 Strasbourg *: to whom correspondence should be addressed: Laboratoire de Chimie et Biologie des Métaux, UMR CNRS-CEA-UJF 5249, iRTSV/LCBM, CEA Grenoble, 17 rue des martyrs, F-38054 Grenoble Cedex 9, France INTRODUCTION Zinc oxide nanoparticles are among the most widely used nanoparticles. Apart from

  4.0 (Matrix Science, London, UK) against UniProtKB/SwissProt (version 2012_08, 537,505 sequences). The search was carried out in all species. A maximum of one trypsin missed cleavage was allowed. Spectra from Qtof were searched with a mass tolerance of 15 ppm for MS and 0.05 Da for MS/MS data and spectra from Ion Trap were searched with a mass tolerance of 0.3 Da in MS and MS/MS modes. Carbamidomethylation of cysteine residues and oxidation of methionine residues were specified as variable modifications. Protein identifications were validated with at least two peptides with Mascot ion score above 20. AUTHOR CONTRIBUTIONS ST performed the proteomics experiments with nanoparticles, the phagocytosis, NO experiments, and pyruvate assay. CAG also performed phagocytosis, glutathione and NO experiments, and helped in drafting the manuscript. LA performed the comet assays, and critically read the manuscript. MC helped to perform the comet assays, helped in designing the whole study and in drafting the manuscript, and critically revised the manuscript. HD, JMS and AVD performed and interpreted the mass spectrometry identification in the proteomics experiments, and helped in drafting the manuscript. FP and DH performed the transmission microscopy experiments on cells, including the EDX experiments. AG, AH and NH performed and interpreted all the characterization experiments of the nanomaterials. VCF performed the proteomics experiments on zinc ions and the glutathione dosage experiments. TR conceived and designed the whole study, took part in the proteomics experiments (especially metalloproteomics), performed the zinc dissolution studies, the proteasome inhibition experiments, the enzymatic measurements, and drafted the manuscript. All authors read and approved the manuscript.

Figure 1 :

 1 Figure 1: Transmission electron microscopy analysis of nanoparticles-treated cells (A) TEM image of a RAW 264 cell exposed to zinc oxide. (B) A higher magnification of the boxed area in A is shown ( flipped 180°) and the arrow points to nanoparticles concentrated in multivesicular bodies. (C) Electron-dense particles resembling zinc oxide were observed in a multivesicular body by TEM and (D) the corresponding EDX spectrum, indicating the presence of zinc (arrows) which appears overlapping with a background generated from other elements present in the sample (copper for the grids and osmium for the cell post-fixation step).

Figure 2 :Figure 3 :

 23 Figure 2: Proteomic analysis of total cell extracts by 2D electrophoresis

Figure 4 :Figure 5 : 001 Figure 6 :

 450016 Figure 4: Functional alterations of macrophages upon zinc treatment

Figure 7 :

 7 Figure 7: Role of proteasome as a survival factor during zinc treatment RAW 264 cells were pretreated with 100 nM MG132 (circles) or not (squares) for 6 hours before treatment for a further 18 hours with zinc as indicated. Cell survival is indicated as the percentage of untreated cells. A: zinc ion; B: zinc oxide; C: cationic zinc oxide. Experiments were carried out in quadruplicate. Star denotes statistically significant differences (p ≤ 0.05 in the Mann Whitney U test)

Table 1 :

 1 Characterization of the two zinc oxide nanoparticles used in this study

	ZnO	Cationic coated ZnO

Table 2 :

 2 intracellular uptake of zinc under the different conditions

	Input in cell culture	Concentration	Concentration
		in cell extract	in cells
	Cationic zinc oxide (90µM)	27±7µM	135±35 µM
	Zinc acetate (100µM)	37.5±2.5 µM	187.5±12.5 µM
	Zinc oxide (100µM)	30±1µM	150±5 µM

Table 3 :

 3 Differentially-expressed proteins identified in the proteomic screen

	spot protein name	accession ZnOcat /ctrl ZnO /ctrl	Zn ion/ctrl	nb. of sequence
	ID	number* fold/T-test	fold/T-test	fold/T-test	unique coverage
						peptides
	F: folding
	F1 Endoplasmic reticulum resident protein 29	P57759	1.003/98	1.008/92	0.808/4		24%
	F2 Prefoldin 3	P61759	1.111/59	0.599/9	0.645/8.4		21%
	C: cytoskeleton
	C1 Glia maturation factor gamma	Q9ERL7 0.753/19	0.486/18	0.620/7.4		39%
	C2 Microtubule-associated protein MPREB1	Q61166	0.969/73	0.872/14	0.809/3.4		40%
	C3 Lymphocyte-specific protein 1	P19973	0.815/10	0.824/5	0.755/3		21%
	C4 Twinfilin-2	Q9Z0P5	0.835/36	0.832/36	0.755/3		22%
	C5 Actin-related protein 2	P61161	0.855/8	0.841/12	0.862/9	10	29%
	C6 T-complex protein 1 subunit gamma	P80318	1.022/84	0.988/99	0.618/2		12%
	C7a Gelsolin	P13020	1.26/41	1.50/5	1.28/39		8%
	C7b Gelsolin	P13020	1.22/44	1.51/6	1.22/41		8%
	C7c Gelsolin	P13020	1.28/31	1.65/2	1.35/22		7%
	C7d Gelsolin	P13020	1.45/37.2	1.876/6	1.569/18	11	17%
	C7e Gelsolin	P13020	1.72/25	2.3/4	17/18		8%
	C8 Major vault protein	Q9EQK5 1.643/4.1	1.599/7	1.469/10	12	17%
	C9 Actin-related protein 3	Q99JY9	1.23/3	1.19/11	1.25/18		15%
	D: degradation
	D1 Ubiquitin-conjugating enzyme E2 N	P61089	0.734/19	0.600/3	0.741/1.3		27%
	D2 Calcyclin-binding protein	Q9CXW3 1.111/67	1.072/22.4 0.862/5		30%
	D3 Ubiquitin carboxyl-terminal hydrolase L3 Q9JKB1 0.667/7	1.024/88	0.58/10		14%
	D4 Proteasome activator complex subunit 2	P97372	0.943/33	0.98/86	0.832/1.3		37%
	D5 Ubiquitin carboxyl-terminal hydrolase L5 Q9WUP7 0.972/73	0.908/8.4	0.495/2		32%
	D6 26S protease regulatory subunit 7	P46471	0.761/13	0.762/11.3 0.518/2.2		8%
	S: signalling
	S1 Growth factor receptor-bound protein 2	Q60631	0.622/14.5	0.666/3	0.466/2		16%
	S2 G nucleotide-binding protein beta-2	P62880	0.866/34	0.892/13	0.638/1.6		13%
	S3 G nucleotide-binding protein alpha-2	P08752	0.878/19	0.848/12	0.712/2.4		21%
	S4 GNB protein subunit beta-2-like 1	P68040	1.719/3.2	1.674/7.9	1.54/8.1	11	42%
	S5 PP2A 2A 65 kDa scaffold subunit A alpha	Q76MZ3 1.087/52	1.246/4	1.177/15		19%
	N: nucleotide metabolism
	N1 Adenine phosphoribosyltransferase	P47957	0.86/7	0.918/2	0.943/54		54%

Table 5 :

 5 enzyme acitivities measured in control cell extracts and in extracts prepared from cells treated for 24 hours with either 8µg/ml zinc oxide, or 7µg/ml cationic zinc oxide, or 100µM zinc acetate. The activities are expressed in units/mg protein, the unit being defined as 1µmole of substrate converted per minute

	enzyme	enolase	6PGDH	biliverdin
				reductase
	ctrl	55.3±4.2	50.1±6.3	1.53±0.16
	Cationic ZnO 84.15±8.9 59.7±4.6	2.05±0.02
	ZnO	98±9.7	94.6±0.2	2.01±0.14
	Zn acetate 87±2.2	92.7±4.0	2.17±0.09
		6PGDH
	0µM Zn	50.5±5.5
	10µM Zn	33±2
	15µM Zn	20±1
	20µM Zn	9.5±0.75
	25µM Zn	4.75±0.5

ACKNOWLEDGEMENTS

ST thanks the Université Joseph Fourier for a PhD fellowship. The financial support of the CEA toxicology program (Nanobiomet and Nanostress grants) and of ANSES (Innimunotox project) is also gratefully acknowledged. Finally the support of the Labex SERENADE (11-LABX-0064) and of ARMESA is also acknowledged.

C: gel obtained from cells treated with zinc oxide (8 µg/ml, 24 hours) D: gel obtained from cells treated with zinc ion (120 µM, 24 hours)

The arrows point to spots that show reproducible and statistically significant changes between the control and zinc-treated cells. Spot numbering according to Table 3


Damien Serant
email: serant@recherche.enac.fr

Lionel Ries

Paul Thevenon

Olivier Julien

Christophe Macabiau

Mathieu Dervin

Marie-Laure Boucheret

Use of OFDM-based Digital TV for Ranging: Tests and Validation on Real Signals

Keywords: signal-of-opportunity, positioning, TV, OFDM, DVB-T I Correlator Frequency (subcarriers index) Time (OFDM symbols)

This article presents results of tests on real signal of a method of positioning using a digital TV DVB-T signal-ofopportunity which is based on an OFDM modulation. OFDM and DVB-T standard are presented and the positioning method using the DVB-T signal is detailed. The result on real signal are analyzed qualitatively and quantitatively in static and dynamic scenarios and indoor and outdoor environments. In static scenarios, the standard deviation of pseudorange error is about 8 cm outdoor and 0.7 m indoor. In dynamic scenarios the pseudorange estimation is considerably disturbed and additional experiments are necessary to quantify the performance in this case.

INTRODUCTION

Global Navigation Satellite System (GNSS) has grown significantly in recent years thanks to the development of many mass-market applications, such as car navigation, asset management or mobile positioning. However, in difficult environments such as dense urban or indoor areas, GNSS shows only limited performance in terms of accuracy and availability due to masking and/or reflections and/or diffraction of the direct signal. This compromises the use of GNSS as a standalone mean for robust positioning in challenging environments. Fortunately, urban areas have the advantage to have dense telecommunication networks to provide mobile phone, TV, and all sorts of services to the user. The use of these signals is now strongly considered as a way to provide a rough estimation of the user position when GNSS is not available, or to assist GNSS in those environments. This is why these signals are often referred to as signals of opportunity. They include Wi-Fi, WiMAX, TV signals, mobile phone signals, etc… Several commercial positioning services based on signal of opportunity already exist : Rosum uses Time Difference of Arrival (TDOA) with ATSC digital TV signals [START_REF]Rosum FemtoSynch In-Building Timing and Location for Femtocells[END_REF], Skyhook uses fingerprinting with Wi-Fi signals [START_REF]SKYHOOK Wireless[END_REF], etc…

The study presented in the paper targets the use of the European Digital Video Broadcast -Terrestrial (DVB-T) signals for positioning. This digital TV standard, although developed in Europe, has been widely accepted worldwide [START_REF]DVB -Digital Video Broadcasting -DVB Worldwide[END_REF] and is the basis for other standards targeting handheld receivers, DVB-H (Handheld) and DVB-SH (Satelitte-to-Handheld). The DVB-T signal uses an Orthogonal Frequency Division Multiplexing (OFDM) air interface. The advantages of TV signals for our purpose, especially when designed for handheld devices, are:  the received signal power level is high enough to authorize an indoor or dense urban service area reception (which is often impossible with GNSS signals). Moreover, it is easy to understand that the typical received Signal to Noise Ratio (SNR) threshold for a communication service will likely be significantly higher than that required for ranging due to the necessity of demodulating the communication message with a low Bit Error Rate (BER). Therefore, as the broadcasting network has been designed to provide at least one emitter with power above the demodulation threshold, it will be likely that several emitters of this same network will be "available" for ranging.

 the wide bandwidth of TV signals (between 6 and 8 MHz), larger than current mass-market GNSS signals, can potentially offer a better synchronization capability than the current GPS C/A.

 DVB-T signals (and all OFDM signals) contain pilot symbols that are used for equalization and can be used for synchronization. Those pilots can be used to construct a Delay Lock Loop (DLL) [START_REF] Kaplan | Understanding GPS: Principles And Applications[END_REF].

 emitters are fixed which facilitates the position computation (emitter position monitoring is not required as in GNSS)

A ranging technique using DVB-T signals and its performances in an AWGN channel was introduced in earlier work [START_REF] Serant | Development and validation of an OFDM/DVB-T sensor for positioning[END_REF]. This paper aims at assessing the proposed pseudorange estimation technique through experimental results using the French digital TV terrestrial network based on DVB-T standard. This paper is organized as follow:

 The first section describes the principles of OFDM modulation and its related processing. This section also presents the DVB-T standard, including the signal parameters and the pilot structures.

 The second section deals with the software DVB-T receiver architecture used to process the recorded real signal, including the classical functions of a DVB-T receiver and the functions dedicated to positioning (i.e. delays acquisition and tracking).

 The third section gives a description of the test bench including the hardware devices used for the signal digitization and the software used to record the signal.

 The last section presents results of the tests on real signal including static and dynamic scenarios and indoor or outdoor environments.

II. OFDM MODULATION AND DVB-T STANDARD

A. Orthogonal Frequency Division Multiplexing

The concept of OFDM [START_REF] Bingham | Multicarrier modulation for data transmission: An idea whose time has come[END_REF] consists in transmitting in parallel complex data symbols over orthogonal narrowband subcarriers (1 subcarrier carries 1 symbol). The width of these subcarriers is chosen narrow enough so that the channel frequency response can be considered as flat over the subcarrier bandwidth. The consequence is that channel equalization becomes very simple even in case of dense multipath environment. Thanks to the orthogonality of the subcarriers, their spectra can overlap without interfering with each other, allowing an excellent spectral efficiency and no Inter-Carrier Interference (ICI) when the receiver is synchronized.

An OFDM useful symbol is obtained by passing the complex data symbols through an inverse-Fast Fourier Transform (iFFT) operator. The useful part of the OFDM symbol is thus composed of samples. To take advantage of the FFT algorithm, is generally chosen to be a power of two. Conversely, the demodulation of an OFDM symbols is performed by a direct FFT.

Additionally, a guard interval of samples is inserted before the useful OFDM symbols in order to avoid Inter-Symbol Interference (ISI). In the case of DVB-T, this guard interval, referred to as Cyclic Prefix (CP) is a replica of the last samples of the OFDM symbol. By doing so, any demodulation of the OFDM symbol that is done with an Nsample FFT starting in the CP will only result in a phase rotation of each subcarrier proportional to its frequency offset, thus easily equalized. An OFDM symbol (useful symbol + CP) is thus composed of samples.

Fig. 1 illustrates an OFDM transmission/reception block diagram. The sampled received signal is affected by several impairments due to the propagation channel and imperfections of the emitter and receiver:  Carrier Frequency Offset (CFO), noted that occurs when the receiver local oscillator (LO) used for down conversion does not match with the carrier of the received signal. It results in two factors: frequency mismatch between emitter and receiver LO, and the resulting Doppler effect due to the movement of the receiver and/or the emitter. It is usually normalized by the frequency separation between 2 subcarriers (1/ ). is also the duration of the useful part of an OFDM symbol ( samples). This CFO can be decomposed into an integer and a fractional part so that , with and .

Modulator

  k p d   k p d ~  Sampling Clock Offset (SCO), denoted
, is due to the sampling clock frequency mismatch between emitter and receiver and the Doppler effect. Its value is expressed in part per million (ppm), which corresponds to a clock drift of 1 µs per second.

 Timing Offset, denoted

, corresponding to the time difference between the estimated and the real beginning of one OFDM symbol normalized by the sampling period. This means that the FFT made by the receiver is not perfectly synchronized with the OFDM useful symbol.

 Distortion of the channel with a transfer function and an impulse response .

 Additive White Gaussian Noise (AWGN) .

Thus the received signal is:

(

This received signal is sampled at the sampling period , where denotes the required sampling period, and grouped by samples to form the vectors .

Taking into account all these impairments and assuming that the FFT windows taken by the receiver begin within the CP and after the end of the last replica of the previous OFDM symbol (i.e. no Inter-Symbol-Interference), the p-th demodulated subcarrier of the k-th received OFDM symbol is [START_REF] Chiueh | OFDM Baseband Receiver Design for Wireless Communications[END_REF]: [START_REF]DVB -Digital Video Broadcasting -DVB Worldwide[END_REF] where:



the subcarrier index circularly rotated as a result of the integer CFO.



is the demodulated symbol over the p-th subcarrier of the k-th OFDM symbol,



is the noise on the p-th subcarrier of the k-th OFDM symbol,



is the channel frequency response over the p-th subcarrier of the k-th OFDM symbol,



is the complex attenuation due to SCO and CFO,  is the phase rotation due to the SCO and CFO and depending on the OFDM symbol number , and  ICI the inter-carrier interference According to (3) a demodulated symbol is equal to the transmitted one if the integer part of the CFO is equal to zero (if not, a different transmitted symbol is demodulated instead), and if it is correctly phase-compensated by some complex factors dependent on the different aforementioned impairments. The integer CFO needs to be estimated and corrected in order to recover the transmitted symbols in the right order. The complex factor can be easily corrected by the channel equalization module. However fractional CFO and SCO also induce ICI which can be considered as noise. Consequently those errors need to be corrected before demodulation in order to limit the noise on the demodulated symbols. In AWGN channel and for a high SNR, the degradation of SNR due to ICI (rising from the uncompensated CFO and SCO) is [START_REF] Pollet | The BER performance of OFDM systems using non-synchronized sampling[END_REF][9]:

(4)
The fractional CFO may often reach its maximum value of 0.5 and then the SNR degradation can be several tens of dB. Thus the estimation and the correction of the fractional CFO need to be done before the demodulation in order to limit the induced ICI. The classical values of the SCO (about 10 ppm) induces a limited SNR degradation (a few dB). Thus, the estimation of the SCO can be done after demodulation. Moreover for small values of SCO (less than about 5 ppm) the correction is not necessary since the SNR degradation is negligible.

B. Digital Video Broadcasting -Terrestrial

The DVB-T [START_REF]Digital Video Broadcasting (DVB); Framing structure, channel coding and modulation for digital terrestrial television[END_REF] is a European standard based on an OFDM air-interface for digital TV broadcasting to fixed receivers in the VHF and UHF bands. DVB-H (Handheld) and DVB-SH (Satellite-to-Handheld), both based on DVB-T, target mobile TV receivers and other potential frequency bands. These standards define a family of OFDM signals which depends on three parameters: the FFT size ( ), the ratio between the Cyclic Prefix length and the useful OFDM symbol length (

) and the sampling period (

). Table I shows different possible values defined in the DVB-T standard for each parameters.

The subcarriers of the useful OFDM symbol have different natures:

 Null subcarriers which are located on the edges of the signal spectrum and have a zero value. They serve as guard bands to avoid out-of-band emissions of the OFDM signal.  Data subcarriers which are QAM-modulated and have their amplitude normalized in such a way that their variance is unitary.

 Transmission

Parameter Signaling (TPS) subcarriers which are BPSK-modulated and carry information about the actual transmission parameters.

 Pilot subcarriers which are BPSK-modulated. Their value is given by a known Pseudo-Random Binary Sequence (PRBS). Their amplitude is boosted by a factor of 4/3 compared to Data and TPS subcarriers. Pilot subcarriers are used for several synchronization functions and for channel estimation.

The pilot subcarriers can be of two types: continuous pilots and scattered pilots. Continuous pilots are always located on the same subcarriers on every OFDM symbols. On the contrary scattered pilots are inserted every 12 subcarriers and the first pilot subcarrier index takes four different values (3, 6, 9 or 12) depending on the OFDM symbols number. Therefore the scattered pilot pattern repeats every 4 OFDM symbols. This organization is illustrated in Fig. 2 where only non-null subcarriers are represented.

A. Decription of the Communication Part

The communication part of the developed DVB-T software receiver is composed of 3 main functions:  Timing and Frequency synchronization: based on the Van De Beek algorithm [START_REF] Van De Beek | ML estimation of time and frequency offset in OFDM systems[END_REF], this function gives an estimation of the timing offset and of the fractional part of the frequency offset .

 Estimation of the integer part of frequency offset [START_REF] Chiueh | OFDM Baseband Receiver Design for Wireless Communications[END_REF].

 Estimation of the scattered pilot sequence related to the OFDM symbol number [START_REF] Chiueh | OFDM Baseband Receiver Design for Wireless Communications[END_REF].

B. Description of the Navigation Part

The navigation part is composed of 2 main functions: delay acquisition and delay tracking. Both of them require the computation of a correlation function. The terrestrial channel induces the presence of several strong multipaths in the received signal. The developed tracking strategy, introduced in [START_REF] Thevenon | S-band air interfaces for navigation systems: a focus on OFDM signals[END_REF], consists in trying to acquire and track a large number of multipaths in order to keep only the shortest one that is hoped to be the direct path. Tracking several signals also allows to go through some strong fading affecting certain signals. In addition it is necessary to periodically restart the delay acquisition in order to be able to track appearing signals potentially closer to the direct path.

1) The correlation function

The correlation function is computed by correlating the received signal with a local replica generated only with the pilot subcarriers. Fig. 3 illustrates this principle. Where:



is the correlation delay normalized by the sampling period,



is the value of the correlation function at for the k-th OFDM symbol,



is the demodulated symbol over the p-th subcarrier of the k-th OFDM symbol,



is the value (-1 or +1) of the pilot over the p-th subcarrier of the k-th OFDM symbol,



is the set of scattered pilot indexes for the k-th OFDM symbol,



the number of scattered pilot subcarriers.

As demonstrated in [START_REF] Serant | Development and validation of an OFDM/DVB-T sensor for positioning[END_REF], in an AWGN channel with an attenuation , the resulting absolute value of the correlation function for small delay values expressed in [START_REF] Bingham | Multicarrier modulation for data transmission: An idea whose time has come[END_REF], is plotted on

Coherent and non-coherent summations

In order to reduce noise effect it is possible to use coherent and non-coherent summations of the correlator outputs. Let be the number of coherent summations and the number of non-coherent summation. Then averaged correlation is expressed as: [START_REF] Chiueh | OFDM Baseband Receiver Design for Wireless Communications[END_REF]

Windowed Correlation

The goal of windowed correlation is to limit the amplitude of secondary lobes of the classical correlation and thus reduces their role in the well-known near-far problem [START_REF] Thevenon | S-band air interfaces for navigation systems: a focus on OFDM signals[END_REF]. The expression of the windowed correlation, denoted is presented on [START_REF] Pollet | The BER performance of OFDM systems using non-synchronized sampling[END_REF]. [START_REF] Pollet | The BER performance of OFDM systems using non-synchronized sampling[END_REF] where:

 for the rectangular window (i.e. no windowing),  for hamming window,  for the blackman-harris window,  and is the number of non-null subcarriers in an OFDM symbol. It can be seen that Hamming and Blackman-Harris windows reduce considerably the amplitude of secondary lobes in the correlation function. The counterpart is that the main lobe width is increased thus reducing the precision of the delay estimate.

2) Delays Acquisition

The correlation between the incoming signal and the local replica will be an estimation of the Channel Impulse Response (CIR). This CIR will be composed of a sum of delayed, attenuated and phase-modified windowed sinc function (plus noise and interference).

The first step towards the tracking of multiple paths is the acquisition of the delays of each individual path. This is done by identifying each peak of the estimated CIR, each peak corresponding to one delay of a multipath component of the received signal. As explained in details in [START_REF] Thevenon | Pseudo-Range Measurements Using OFDM Channel Estimation[END_REF], this is done by using the Matching Pursuit algorithm [START_REF] Cotter | Sparse channel estimation via matching pursuit with application to equalization[END_REF]. It consists in decomposing the absolute value of the estimated CIR in a sum of delayed and attenuated absolute reference functions (in the present case, one of the correlation functions plotted on Fig. 5 is the reference function) by iteratively (1) estimating the delay and amplitude of the highest peak of the remaining CIR and (2) subtracting the corresponding absolute sinc function (applying the estimated delay and amplitude) from the remaining CIR. By using a threshold for the minimum desired amplitude of the signals of interest, or by setting a maximum number of signals to estimate, this decomposition provides an estimation of the delay and absolute amplitude of the main signal components of the received signal. The drawback of this technique is that it uses the absolute value of the correlation function and is cumbersome. However, it was showed to work sufficiently well for the present purpose.

3) Delays Tracking

All the delays acquired thanks to the Matching Pursuit algorithm are tracked using a classical delay lock loop (DLL). This DLL uses a normalized Early-minus-Late-Power (EMLP) discriminator and a 2 nd order loop filter. Three criterions are defined to avoid to track uselessly a delay. A delay tracking is stopped when:

 the estimated SNR of the tracked peak passes under the tracking threshold (defined in [START_REF] Serant | Development and validation of an OFDM/DVB-T sensor for positioning[END_REF]); More details on this DLL can be founded in [START_REF] Serant | Development and validation of an OFDM/DVB-T sensor for positioning[END_REF] and [START_REF] Thevenon | Pseudo-Range Measurements Using OFDM Channel Estimation[END_REF].

The overall tracking techniques was shown to work efficiently with simulated signals reproducing a realistic propagation channel [START_REF] Serant | Development and validation of an OFDM/DVB-T sensor for positioning[END_REF] [START_REF] Thevenon | Pseudo-Range Measurements Using OFDM Channel Estimation[END_REF]. The following sections will show the first tests realized using real signals.

IV. DESCRIPTION OF THE TEST BENCH

The real signal acquisition is done thanks to two devices from Ettus LLC [15] called USRP2 and WBX. These devices are controlled by an open source software called GnuRadio [16].

USRP2, the motherboard, allows to digitize an analog baseband signal with a bandwidth up to 100 MHz at a maximum sampling frequency of 100 MS/s (Mega-samples per second). The baseband signal is quantized on 14 bits on I&Q channels. The digitized signal is sent to a PC through a gigabit Ethernet interface. Due to the limitation of Ethernet bandwidth, the actual maximum sampling frequency is 25 MS/s. WBX, the daughterboard, plugged directly onto the USRP2, allows down-converting a received RF signal into an analog baseband signal which can be subsequently digitized by the USRP2. The WBX is capable to downconvert any kind of signal in the 50MHz -2.2 GHz frequency range. An on-board adjustable amplifier from 0 dB to 31 dB can be used.

The goal of the experiment was to record a French digital TV signal, located in the 482 MHz -826 Mhz frequency range, which occupies a bandwidth of approximately 8 MHz. Consequently, the couple USRP2/WBX was used in reception and the down-converted signal is sampled at the sampling frequency of MS/S (about 8.33 MS/s ).

A commercial omnidirectional TV antenna is connected to the WBX input to capture the TV signals. It is amplified with a gain of 20 dB and a noise factor of 3.5 dB.

In addition, in order to have a stable sample frequency, the USRP2 is locked on a 10 MHz external reference coming from a GPS receiver Novatel OEM4. Thus, the 10 MHz is synchronized with the GPS time (with the GPS receiver precision). Fig. 6 summarizes this architecture. The URSP2 can be controlled by an open-source program called GNU Radio. GNU Radio is a software dedicated to the development of software-defined radio systems. It provides signal processing blocks, written in C++ and the code structure to connect those blocks, in Python.

V. RESULTS OF REAL SIGNAL TESTS

Tests use the signal of an emitter located at about 80 km from the lab at an altitude of 1312 meters emitting at the central frequency of 554 MHz with an EIRP of 5kW. A preliminary study showed that this particular emitter was synchronized to GPS time, which seems not to be the case of the other emitters of the region.

In the following are presented results of static outdoor, static indoor and dynamic indoor experiments. The correlation uses a rectangular windows and the loop bandwidth of the DLL is 1 Hz.

A. Static scenario

For this first test, the antenna is placed on the roof of the lab at approximately 10 meters above the ground. Fig. 7 shows the correlation image which represents the evolution of the CIR over the duration of the record. It is obtained by plotting, every 100 OFDM symbols, the correlation power over a large delay range between -3000 meters (~ -10µs) and +6000 meters (~ 20µs) around the rough delay estimate of the main signal path obtained by the Van-de-Beek algorithm (see section III). The color scale shows the correlation power in dB relative to the maximum power during all the simulation. This figure shows two powerful peaks (about 20-25 dB above the noise floor) spaced by about 4000 meters corresponding to the direct path and a strong multipath. Secondary lobes of the sinc correlation function and/or close multipath clearly appear in light blue. The standard deviation of the pseudorange error (the real pseudorange variation being null since the emitter and receiver antennas are fixed) is about 8 cm for the two estimates. This is quite good even if the theoretical expression (presented in [START_REF] Serant | Development and validation of an OFDM/DVB-T sensor for positioning[END_REF]) predicts a standard deviation of about 3 cm. Since theoretical expression were obtained in an ideal case (no multipaths, no synchronization errors), it seems reasonable to have this difference. Because the two pseudorange errors are uncorrelated, they are not due to clock error (which should produced the same error), but more likely to noise and/or multipath effects. An SNR estimation is obtained thanks to the amplitude of the tracked peak. The estimated SNR for each tracked peak are about -3.5 dB for the shortest one and -9 dB for the other one.

In the second example, the antenna is located in a light indoor environment in the second floor of the building near a window.

The correlation image, plotted on Fig. 9, presents more short and strong multipaths and a higher noise floor than in the outdoor scenario. However the two strongest paths of the outdoor case are always presents. Fig. 10 shows the pseudorange error corresponding to the tracking of the two same peaks as in the static outdoor scenario. The blue curve has a standard deviation of 0.7 meters and the green one of 2.3 meters. For information, at the estimated SNRs (-13 dB for the first peak and -25 dB for the second), the theoretical expression published in [START_REF] Serant | Development and validation of an OFDM/DVB-T sensor for positioning[END_REF] gives a standard deviation of respectively 0.1 meters and 0.5 meters in an ideal case. The differences between theoretical and practical results are surely due to the omnipresence of strong multipaths in indoor environment.

B. Dynamic Scenario

In the following, dynamic indoor experiment the movement is divided into a succession of static and dynamic phases as follow : 15 s static, then 15 s dynamic, followed by 15 s static, then 12 s dynamic and finally 15 s static. The beginning of the test was near a window, while the rest of the test can be considered as deep indoor. Fig. 11 illustrates the correlation image obtained in this experiment. Static and dynamic phase are easily recognizable: in the static phases the correlation is stable whereas in the dynamic phases correlation is highly disturbed. Here the fast fading phenomenon well known of urban and indoor channels is clearly observable. This observation is also very clear when observing the estimated SNR on Fig. Fig. 13 shows the variation of the estimated pseudorange. In this case, this plot does not represent the pseudorange error since no reference position was available and receiver was moving. However it can be seen that during the dynamic phase the estimated pseudorange is strongly disturbed and that a stabilization appears during static phases. This can be understood when looking at Fig. 13 that shows a clear drop in the estimated SNR values during dynamic phases. Unfortunately, because of real test constraints, a reference position was unavailable and the covered distance was limited. As a consequence a quantitative performance of the technique in a dynamic scenario can't be provided but it is clear that dynamic induces an important degradation of pseudorange estimation performance compared to static case.

VI. CONCLUSIONS AND FUTURE WORK

A pseudorange computation method using digital-TV signal based on DVB-T standard, using OFDM modulation, has been introduced.

Tests of this method on real signal from a French digital TV emitter have been presented. Static scenarios present a small pseudorange error (<1 meter), even in indoor environment. Those results are fairly close to theoretical predictions [START_REF] Serant | Development and validation of an OFDM/DVB-T sensor for positioning[END_REF] although slightly degraded by the presence of numerous and strong multipaths. In dynamic scenarios the pseudorange error is strongly increased, especially because of fast fading phenomenon typical of urban and indoor propagation channels. Unfortunately the lack of measurements in this scenario does not permit to give a quantitative conclusion. Future work will consist in a better characterization of the pseudorange error in dynamic scenarios by using a more complex test setup using a reference position (thanks to GPS for example) and increasing the travelled distance during the experiment (the real pseudorange variation has to be an order larger than the typical pseudorange variation due to fast fading to be representative). For this purpose a mobile and autonomous test bench is being developed.

Figure 1 .

 1 Figure 1. OFDM transmission/reception block diagram

Figure 2 .

 2 Figure 2. DVB-T pilot organization

Figure 3 .

 3 Figure 3. Correlation principle To take advantage of the efficient FFT used for OFDM demodulation, the correlation function computation involves the demodulated symbols and the pilot values, as expressed in (5).

Fig. 4 .Figure 4 .

 44 Figure 4. Absolute value of correlation function

Fig. 5

 5 Fig. 5 compares the absolute values of the correlation function for those three windows.

Figure 5 .

 5 Figure 5. Absolute correlation function for Rectangular (blue), Hamming(green) and Blackman-Harris(red) windows

  relative to the sample period)Presented at NAVITEC 2010, Noordwijk  two tracked delay converge (then one of them is stopped); a delay estimate diverge unrealistically.

Figure 6 .

 6 Figure 6. Architecture of the test bench

Figure 7 .

 7 Figure 7. Correlation image -Static outdoor scenario Fig. 8 represents the pseudorange error resulting from the tracking of the two powerful peaks of the correlation function (the blue curve corresponding to the shortest peak and the green to the other one).

Figure 8 .

 8 Figure 8. Pseudorange error -Static outdoor scenario

Figure 9 .

 9 Figure 9. Correlation image -Static indoor scenario

Figure 10 .

 10 Figure 10. Pseudorange error -Static indoor scenario

12

 12

  .

Figure 11 .

 11 Figure 11. Correlation image -Dynamic indoor scenario

Figure 13 .

 13 Figure 13. Pseudorange variation -Dynamic outdoor scenario

TABLE I

 I

	.	DVB-T PARAMETERS
	Parameters	Possible values
	2048 (Mode 2K), 4096 (Mode 4K) and 8192 (Mode 8K)
	1/32, 1/16, 1/8, 1/4
	7/64 μs (8 MHz channels), 1/8 μs (7 MHz), 7/48 μs (6
	MHz) and 7/40 µs (5 MHz)

I would like to thank CNES and Thales Alenia Space France for funding this work