Marie Couturier

Simon Ladeveze

Gerlind Sulzenbacher

Luisa Ciano

Mathieu Fanuel

Céline Moreau

Ana Villares

Bernard Cathala

Florence Chaspoul

Kristian E Frandsen

Simon Ladevèze

Aurore Labourel

Isabelle Herpoël-Gimbert

Sacha Grisel

Mireille Haon

Nicolas Lenfant

Hélène Rogniaux

David Ropartz

Gideon Davies

Marie-Noëlle Rosso

Paul H Walton

Bernard Henrissat

Jean-Guy Berrin
email: jean-guy.berrin@inra.fr

Lytic xylan oxidases from wood-decay fungi unlock biomass degradation

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Introduction

Wood is the most abundant organic source of biomass on Earth, with an annual production of about 5.64 × 10 10 tons of carbon [START_REF] Field | Primary production of the biosphere: integrating terrestrial and oceanic components[END_REF] . Its widespread nature has allowed humans to use it in many contexts, most notably as a building material due to its exceptional mechanical properties and resistance to decay. In bio-based industries, the utilization of wood is taking on a new importance as it constitutes the most promising source for advanced biofuels and plant-derived products. Notwithstanding its potential, however, the cost-effective conversion of woody feedstocks is limited by a single key factor, the recalcitrance of the lignocellulosic matrix to degradation by enzyme cocktails 2 . To overcome this recalcitrance, biorefineries utilize energydemanding pretreatment processes to solubilize the inaccessible biomass components before enzymatic saccharification. The recalcitrant fraction reflects its heteroxylan content which is known to be particularly resistant to xylanases due to extensive decoration and because these xylans can adopt a flat conformation with their chains solidly adhering via hydrogen-bonds to the surface of cellulose microfibrils [START_REF] Biely | Towards enzymatic breakdown of complex plant xylan structures: State of the art[END_REF][START_REF] Simmons | Folding of xylan onto cellulose fibrils in plant cell walls revealed by solid-state NMR[END_REF] . Finding sustainable means of overcoming this resistance to degradation is one of the main challenges faced by modern biorefineries. Indeed, the xylan problem is so severe that consideration is being given to engineering energy crops modified to contain fewer recalcitrant xylans [START_REF] Loqué | Engineering of plant cell walls for enhanced biofuel production[END_REF] .

In nature, fungi play a vital role in the terrestrial carbon cycle and dominate wood decomposition in boreal forests [START_REF] Hibbett | Analysis of character correlations among wood decay mechanisms, mating systems, and substrate ranges in homobasidiomycetes[END_REF] . Wood-decaying basidiomycetes classified as white-rot and brown-rot fungi, naturally degrade cellulose and hemicelluloses using a large diversity of carbohydrate-active enzymes (CAZymes; www.cazy.org) [START_REF] Lombard | The carbohydrateactive enzymes database (CAZy) in 2013[END_REF] and Fenton-type chemistry [START_REF] Riley | Extensive sampling of basidiomycete genomes demonstrates inadequacy of the whiterot/brown-rot paradigm for wood decay fungi[END_REF] . In this context, understanding of plant-cell wall deconstruction was recently overturned by the discovery of lytic polysaccharide monooxygenases (LPMOs) enzymes which cleave polysaccharides through an oxidative as opposed to hydrolytic mechanism [START_REF] Vaaje-Kolstad | An oxidative enzyme boosting the enzymatic conversion of recalcitrant polysaccharides[END_REF][START_REF] Quinlan | Insights into the oxidative degradation of cellulose by a copper metalloenzyme that exploits biomass components[END_REF][START_REF] Kracher | Extracellular electron transfer systems fuel cellulose oxidative degradation[END_REF] . Such is their importance, that industrial enzyme mixtures for the conversion of agricultural residues to biofuels now incorporate cellulose-active LPMOs [START_REF] Johansen | Discovery and industrial applications of lytic polysaccharide mono-oxygenases[END_REF] , helping biorefineries move towards environmental and economic sustainability. Despite the significant efficiencies that LPMOs have brought to biomass degradation, industrial enzyme cocktails are still unable to degrade woody biomass completely and there is a major need to identify new enzymes capable of effecting this breakdown. From this perspective, there are three fungal LPMO families (termed AA9, AA11 and AA13 in the CAZy classification) [START_REF] Lombard | The carbohydrateactive enzymes database (CAZy) in 2013[END_REF] , which were discovered from genome sequences by virtue of their modular structure where the catalytic LPMO domain is sometimes appended to known substrate-targeting carbohydrate-binding modules (CBMs). Each fungal LPMO family is associated with the oxidative cleavage of distinct polysaccharides with AA9 acting mainly on cellulose and xyloglucan [START_REF] Quinlan | Insights into the oxidative degradation of cellulose by a copper metalloenzyme that exploits biomass components[END_REF] , AA11 on chitin [START_REF] Hemsworth | Discovery and characterization of a new family of lytic polysaccharide monooxygenases[END_REF] and AA13 on starch [START_REF] Vu | A family of starch-active polysaccharide monooxygenases[END_REF][START_REF] Lo Leggio | Structure and boosting activity of a starch-degrading lytic polysaccharide monooxygenase[END_REF] : a solely xylan-acting LPMO is conspicuous by its absence.

Using comparative post-genomic approaches among fungal wood decayers, we identified the existence of a previously unknown family of LPMO. This new family to be termed AA14 in the CAZy classification differs phylogenetically and structurally from the previous AA9, AA10, AA11 and AA13 families. The first characterized members from the white-rot basidiomycete fungus Pycnoporus coccineus target xylan chains covering wood cellulose fibers thus unlocking the enzymatic degradation of wood biomass.

Results

Discovery of the AA14 family among fungal wood decayers

The white-rot basidiomycete Pycnoporus coccineus is an efficient degrader of both hardwood and softwood [START_REF] Couturier | Enhanced degradation of softwood versus hardwood by the white-rot fungus Pycnoporus coccineus[END_REF] . While studying the effect of different types of biomass on P. coccineus growth using transcriptomics and secretomics, we identified a gene encoding a protein of unknown function that was highly up-regulated on pine and poplar as compared to control [START_REF] Couturier | Enhanced degradation of softwood versus hardwood by the white-rot fungus Pycnoporus coccineus[END_REF] . The corresponding protein (JGI ID 1372210; GenBank ID #KY769370) was secreted only during growth on pine and poplar suggesting a role in wood decay. A BLAST search against public sequence databases identified more than 300 proteins with significant similarity to #KY769370 from P. coccineus, many of which from well-known saprotrophic fungi. Sequence alignment revealed a conserved N-terminal histidine (Supplementary Fig. 1), commensurate with a copper-binding histidine brace active site [START_REF] Quinlan | Insights into the oxidative degradation of cellulose by a copper metalloenzyme that exploits biomass components[END_REF] , which is a hallmark of known LPMOs. A phylogenetic analysis shows that the newly identified sequences strongly cluster together with high bootstrap values and are very distant from AA9, AA10, AA11 and AA13 sequences (Supplementary Fig. 2), thereby defining a new LPMO family designated AA14 in the CAZy database. AA14 members are found in all well-known white-rot (Pleurotus ostreatus, Phanerochaete chrysosporium, Trametes versicolor) and brown-rot (Serpula lacrymans, Coniophora puteana, Postia placenta) basidiomycetes and in some wood-inhabitants ascomycetes within the Xylariaceae and Hypocreaceae families. A slight gene family expansion is observed in wood-decaying basidiomycetes (average number per species 3.35 in basidiomycetes and 1.28 in ascomycetes) (Fig. 1; Supplementary Data Set 1). None of the AA14 members identified in fungal genomes harbors a carbohydrate-binding module (CBM) explaining why this family was not previously discovered together with AA11 and AA13 through the "module walk" approach [START_REF] Hemsworth | Discovery and characterization of a new family of lytic polysaccharide monooxygenases[END_REF][START_REF] Lo Leggio | Structure and boosting activity of a starch-degrading lytic polysaccharide monooxygenase[END_REF] .

Expression and biochemical characterization of PcAA14

Two P. coccineus proteins, PcAA14A (#KY769369) and PcAA14B (#KY769370), displaying 65% sequence identity were produced to high yield in Pichia pastoris, purified to homogeneity and biochemically characterized (Supplementary Table 1; Supplementary Fig. 3 and4). We confirmed the correct processing of the native signal peptide, which exposed the N-terminal histidine residue at position 1 in the mature polypeptide chain (Supplementary Table 1). Mass spectrometry analyses revealed that both proteins contained ~ one copper atom per protein molecule and treatment with EDTA led to partial apo forms (~0.1 copper atom per protein molecule). PcAA14A and PcAA14B were both able to produce hydrogen peroxide in the presence of ascorbate, cysteine or gallate as electron donors (Supplementary Table 2).

Crystal structure of PcAA14

The structure of PcAA14B was solved by multiple-wavelength anomalous dispersion data recorded at the gadolinium edge, and refined at 3.0 Å resolution. The core of the protein folds into a largely antiparallel immunoglobulin-like -sandwich (Fig. 2a), a fold globally similar to that seen in LPMOs from other families. The active site of PcAA14B constituted by His1, His99 and Tyr176 forming the canonical histidine brace is exposed at the surface (Fig. 2b). In contrast to the flat substrate-binding surfaces observed in AA9 LPMOs [START_REF] Frandsen | The molecular basis of polysaccharide cleavage by lytic polysaccharide monooxygenases[END_REF] , the PcAA14B surface has a rippled shape with a clamp formed by two prominent surface loops (Supplementary Fig. 5).

Both loops are located in the N-terminal half of PcAA14B, and are equivalent to the L2 and L3 loop regions in AA9 LPMOs. Conventionally, the N-terminal part of AA9 LPMOs upstream of the L2 loop region makes up a β-strand segment (single β-strand or a β-hairpin). No equivalent β-strands are found in the PcAA14B structure, which, in contrast, forms loop segments immediately after the N-terminal His (Supplementary Fig. 5). The PcAA14B structure also reveals a cystine (Cys67-Cys90) in the L3-equivalent region, which borders an extension not present in AA9 LPMOs (Supplementary Fig. 5). It is highly interesting to note that the two loops making up the clamp in PcAA14B correspond to modified L2 and L3 loop regions, as these have been shown to be involved in LPMO-substrate interactions [START_REF] Frandsen | The molecular basis of polysaccharide cleavage by lytic polysaccharide monooxygenases[END_REF] . For AA9 LPMOs a conserved Tyr has been shown to be involved in substrate interactions at the active site surface [START_REF] Frandsen | The molecular basis of polysaccharide cleavage by lytic polysaccharide monooxygenases[END_REF] .

Interestingly, PcAA14B possesses equally a conserved tyrosine residue at the edge of the substrate-binding surface, Tyr240, albeit located on a different loop region, which could potentially make substrate interactions. Overall the crystal structure of PcAA14B reveals novel features within its putative substrate binding site, which may suggest differences in terms of substrate specificity compared to known LPMOs.

EPR spectroscopic analysis of the copper site of PcAA14

Multi-frequency Electron Paramagnetic Resonance (EPR) analysis was carried out on both PcAA14A and PcAA14B to determine the nature of the copper active site (Figure 2C; Supplementary Fig. 6). The spin Hamiltonian parameters (Supplementary Table 3) displayed axial parameters (g x ≈ g y < g z ) with a d(x 2 y 2 ) SOMO, placing the copper active site squarely within a type 2 Peisach-Blumberg classification [START_REF] Peisach | Structural implications derived from the analysis of electron paramagnetic resonance spectra of natural and artificial copper proteins[END_REF] . Simulations required the addition of two (I=1) nitrogen atoms (coupling in the range of 30 to 36 MHz), as would be expected from the coordinating histidine side chains. Overall, these spin-Hamiltonian parameters are similar to those obtained for AA9 LPMOs confirming the presence of the copper(II) ion within the histidine brace coordination environment [START_REF] Garajova | Single-domain flavoenzymes trigger lytic polysaccharide monooxygenases for oxidative degradation of cellulose[END_REF] . These data support the hypothesis that PcAA14s display LPMO characteristics and that copper is their native metal cofactor.

Substrate specificity of PcAA14

Activity assays were initially carried out with PcAA14A and PcAA14B on a wide range of polysaccharides including cellulose and xylans in the presence of ascorbic acid, which is widely used as electron donor for LPMOs. Using standardized methods previously employed to characterize AA9 LPMOs [START_REF] Bennati-Granier | Substrate specificity and regioselectivity of fungal AA9 lytic polysaccharide monooxygenases secreted by Podospora anserina[END_REF] , no activity could be detected on these polysaccharides. Next, we performed saccharification assays on pretreated biomass including poplar, pine and wheat straw using a Trichoderma reesei CL847 cocktail mainly composed of cellulases and xylanases [START_REF] Herpoël-Gimbert | Comparative secretome analyses of two Trichoderma reesei RUT-C30 and CL847 hypersecretory strains[END_REF] . A boost of glucose release from poplar and pine was observed upon addition of either of the AA14 enzymes to the cocktail (Fig. 3a). When the reactions were conducted in absence of a reductant the boost effect was maintained (Supplementary Fig. 8), suggesting that one of the components from the biomass (e.g. lignin) may act as an electron donor [START_REF] Westereng | Enzymatic cellulose oxidation is linked to lignin by long-range electron transfer[END_REF] . This improvement in glucose release was dose-dependent yielding up to ~100% increase on pretreated softwood (Fig. 3b).

However, no significant boost was observed on wheat straw (Supplementary Fig. 8), which differs in terms of hemicellulose composition compared to wood, indicating that AA14 enzymes specifically target one of the components of woody biomass. In a finding with important consequences for biorefinery use of woody biomass as feedstock, the T. reesei CL847 cocktail enriched in AA9 LPMO acting on cellulose was also boosted by PcAA14A, suggesting that AA9 and AA14 enzymes may act on different regions within the lignocellulosic matrix (Supplementary Fig. 8). Because AA14 members do not harbor any CBM module, we artificially attached a fungal CBM1 module targeting crystalline cellulose to PcAA14A. The resulting modular PcAA14A-CBM1 enzyme performed less efficiently than the catalytic module alone (Supplementary Fig. 8), suggesting that AA14 enzymes may not require specific binding to the flat crystalline cellulose surface.

To discern which polymer was attacked by AA14 enzymes, we used birchwood cellulosic fibers, consisting of 79% cellulose and 21% xylan, as a substrate. After incubation with PcAA14A or PcAA14B, wood fibers were disrupted (Fig. 4a) uncovering cellulose structures visualized at different scales using transmission electron microscopy and atomic force microscopy (Supplementary Fig. 9). These observations suggest a weakening of the cohesive forces that link the wood fibers together in a manner similar to that previously described with AA9 enzymes [START_REF] Villares | Lytic polysaccharide monooxygenases disrupt the cellulose fibers structure[END_REF] . Samples treated with AA14 enzymes were further analyzed using solid-state Cross-Polarization Magic Angle Spinning 13 C Nuclear Magnetic Resonance ( 13 C CP/MAS NMR). The impact of AA14 enzymes on the fibers was different to that recently observed for AA9 LPMOs [START_REF] Villares | Lytic polysaccharide monooxygenases disrupt the cellulose fibers structure[END_REF] . In the case of PcAA14 enzymes, no meaningful change was observed on cellulose signals (Fig. 4b; Supplementary Fig. 10). Interestingly, however, significant changes in signal areas corresponding to hemicelluloses located at 101 ppm and 82 ppm were observed when the NMR spectra were deconvoluted in the C-1 and the C-4 regions (Supplementary Fig. 10).

These results suggest that AA14 enzymes act on xylans bound to cellulose, which have a rigidity and a conformation similar to that of the underlying cellulose chains [START_REF] Simmons | Folding of xylan onto cellulose fibrils in plant cell walls revealed by solid-state NMR[END_REF] . The specific attack of PcAA14 on xylan substrates differentiates this new class of enzymes from all other LPMOs [START_REF] Frommhagen | Discovery of the combined oxidative cleavage of plant xylan and cellulose by a new fungal polysaccharide monooxygenase[END_REF][START_REF] Fanuel | The Podospora anserina lytic polysaccharide monooxygenase PaLPMO9H catalyzes oxidative cleavage of diverse plant cell wall matrix glycans[END_REF] , none of which has previously been reported to oxidize xylan in such a selective and efficient manner.

To further substantiate the idea that AA14 enzymes act on xylan bound to cellulose, we performed synergy assays of AA14 enzymes in combination with a fungal GH11 xylanase using birchwood cellulosic fibers. Addition of PcAA14A to a GH11 xylanase significantly increased by 40% the release of xylo-oligomers from birchwood cellulosic fibers (Figure 4C; Supplementary Fig. 11). Additionally, no improvement of xylan conversion was observed on birchwood cellulosic fibers when the xylanase was combined with a cellulose-acting AA9 LPMO (Fig. 4c).

We further investigated the nature of soluble products generated after synergistic action of PcAA14A and the GH11 xylanase. Using ionic chromatography, a range of oligosaccharides eluted at similar retention time to C1-oxidized oligosaccharides (Supplementary Fig. 11). Mass spectrometry analyses performed on the same samples allowed the identification of several putative oxidative species with masses corresponding to C1-oxidized xylotriose (X3ox) and C1oxidized xylotetraose (X4ox) and non-oxidized xylo-oligosaccharides substituted with glucuronic acid (X 3 MeGlcA, X 4 MeGlcA, X 5 MeGlcA) (Supplementary Fig. 12). The structure of the C1-oxidized xylotriose with an aldonic acid on the reducing end (Fig. 4d) was confirmed by fragmentation of the species observed at 429 m/z by tandem MS (MS/MS) (Supplementary Fig. 12). The identification of oxidative products demonstrates that AA14 enzymes are LPMOs.

Discussion

Our findings that xylans are susceptible to AA14 oxidative cleavage only when adsorbed onto crystalline cellulose and not when in solution are supported by reports that showed that xylans exist in different contexts within the cell wall [START_REF] Simmons | Folding of xylan onto cellulose fibrils in plant cell walls revealed by solid-state NMR[END_REF][START_REF] Mccartney | Differential recognition of plant cell walls by microbial xylan-specific carbohydrate-binding modules[END_REF] . Recalcitrant xylans bound to cellulose microfibrils display a two-fold screw axis conformation aligned parallel to the cellulose chain direction [START_REF] Simmons | Folding of xylan onto cellulose fibrils in plant cell walls revealed by solid-state NMR[END_REF] that is compatible with the proper orientation of the carbohydrate H1 and H4 atoms with respect to the LPMO catalytic center [START_REF] Westereng | Enzymatic cellulose oxidation is linked to lignin by long-range electron transfer[END_REF] . Unravelling the substrate specificity of AA14s has been challenging as these enzymes are not active on xylans in solution most probably due to the three-fold helical screw conformation of the substrate [START_REF] Nieduszynski | Structure of beta-D-(1-->4')xylan hydrate[END_REF] . Using multidisciplinary approaches, we reveal that AA14 LPMOs probably target specifically the protective shield made by heteroxylans that cover cellulose microfibrils in wood. The conformation of xylan in this context contributes to wood recalcitrance and glycoside hydrolases are not able to access such a sterically restricted substrate. The cleavage of these rare motifs by AA14 LPMOs unlocks the accessibility of xylan and cellulose chains to glycoside hydrolases therefore improving the overall saccharification of woody biomass. These results not only greatly enhance our knowledge of wood superstructure, they also contribute to understand and better exploit biomass deconstruction by fungal saprotrophs. with simulation (red) of PcAA14A. More data are presented in Supplementary Figures 5 and6. xylanase. The fragmentation pattern corresponds to a C1 oxidized species with an aldonic acid at the reducing end. : water losses. : H 2 CO losses. An expanded view of the spectrum is provided in Supplementary Figure 12.

On-line Materials and Methods

Transcriptomics and secretomics of Pycnoporus sp.

Transcriptomic and proteomic data of three-days-old cultures of Pycnoporus coccineus BRFM 310 and Pycnoporus sanguineus BRFM 1264 grown on cellulose (Avicel), wheat straw, pine and aspen are described in 16;28 .

Bioinformatic analysis of AA14 LPMOs

P. coccineus AA14 sequences (Genbank ID KY769369 and KY769370) were compared to the NCBI non redundant sequence database using BlastP [START_REF] Altschul | Basic local alignment search tool[END_REF] in February 2016. Blast searches conducted with AA14 did not retrieve AA9s, AA10s, AA11s or AA13s with significant scores, and vice-versa. MUSCLE [START_REF] Edgar | MUSCLE: multiple sequence alignment with high accuracy and high throughput[END_REF] was used to perform multiple alignments. To avoid interference from the presence or absence of additional residues, the signal peptides and C-terminal extensions were removed. Bioinformatic analyses were performed on 286 fungal genomes sequenced and shared by JGI collaborators. Protein clusters are available thanks to the JGI (https://goo.gl/ZAa2NX) for each of these fungi. A phylogenetic tree has been inferred using 100 cleaned and merged alignments of proteins from selected clusters of proteins. Those clusters are present, as much as possible, in all fungi in 1 copy in order to maximize the score ∑1/n (with n, the number of copy in the genome). Sequences from clusters were aligned with Mafft [START_REF] Katoh | MAFFT multiple sequence alignment software version 7: improvements in performance and usability[END_REF] , trimmed with Gblocks 32 and a phylogenetic tree was built with concatenation of alignments with Fasttree [START_REF] Price | FastTree 2--approximately maximum-likelihood trees for large alignments[END_REF] . The tree is displayed with Dendroscope 34 and Bio::phylo [START_REF] Vos | Phylophyloinformatic analysis using perl[END_REF] .

Production of P.coccineus AA14 LPMOs

The sequences corresponding to PcAA14A (Genbank ID KY769369) and PcAA14B (Genbank ID KY769370) genes from P. coccineus BRFM310 were synthesized after codon optimization for expression in P. pastoris (GenScript, Piscataway, USA). The region corresponding to the native signal sequence was kept while the C-terminal extension region was removed. Synthesized genes were further inserted with into a modified pPICZαA vector (Invitrogen, Cergy-Pontoise, France) using BstBI and XbaI restriction sites in frame with the (His) 6 -tag located at the C-terminus of recombinant proteins. Fusion of PcAA14A with CBM1 was carried using the CBM1 domain of PaLPMO9E, which was added to PcAA14A at the end of the catalytic module using the linker sequence of PaLPMO9E 20 . Constructs without (His) 6 -tag sequence were also designed by adding a stop codon at the end of the AA14 catalytic module. P. pastoris strain X33 and the pPICZαA vector are components of the P. pastoris Easy Select Expression System (Invitrogen), all media and protocols are described in the manufacturer's manual (Invitrogen).

Transformation of competent P. pastoris X33 was performed by electroporation with PmeIlinearized pPICZαA recombinant plasmids and zeocin-resistant P. pastoris transformants were screened for protein production as described in [START_REF] Haon | Recombinant protein production facility for fungal biomass-degrading enzymes using the yeast Pichia pastoris[END_REF] . The best-producing transformants were grown in 2 L of BMGY medium containing 1 mL.L -1 Pichia trace minerals 4 (PTM 4 ) salts in shaken flasks at 30°C in an orbital shaker (200 rpm) to an OD 600 of 2 to 6. Cells were then transferred to 400 mL of BMMY medium containing 1 ml.L -1 of PTM 4 salts at 20°C in an orbital shaker (200 rpm) for 3 days, with supplementation of 3% (v/v) methanol every day.

Bioreactor productions were carried out in 1.3-L New Brunswick BioFlo  115 fermentors (Eppendorf, Hamburg, Germany) following the P. pastoris fermentation process guidelines (Invitrogen). Recombinant enzymes were secreted up to ~1 g.L -1 (Supplementary Figure 13).

Purification of PcAA14 LPMOs

The culture supernatants were recovered by pelleting the cells by centrifugation at 2,700 g for 5 min, 4°C and filtered on 0.45 µm filters (Millipore, Molsheim, France). For (His) 6 -tagged enzymes, the pH was adjusted to 7.8 and the supernatants were loaded onto 5 ml His Trap HP columns (GE healthcare, Buc, France) connected to an Akta Xpress system (GE healthcare).

Prior to loading, the columns were equilibrated in 50 mM Tris HCl pH 7.8; 150 mM NaCl (buffer A). The loaded columns were then washed with 5 column volumes (CV) of 10 mM imidazole in buffer A, before the elution step with 5 CV of 150 mM imidazole in buffer A.

Fractions containing the protein were pooled and concentrated with a 3-kDa vivaspin concentrator (Sartorius, Palaiseau, France) before loading onto a HiLoad 16/600 Superdex 75 Prep Grade column (GE Helthcare) and separated in 50 mM sodium acetate buffer pH 5.2. Gel filtration analysis showed that both PcAA14 proteins are monomeric in solution. For enzymes without (His) 6 -tag, salts contained in the culture media were diluted ten-fold in 20 mM Tris-HCl pH 8, then culture supernatants were concentrated with a Pellicon-2 10-kDa cutoff cassette (Millipore) to a volume of approx. 200 mL and loaded onto a 20-mL High Prep DEAE column (GE Helthcare). Proteins were eluted using a linear gradient of 1 M NaCl (0 to 700 mM in 200 mL). Fractions were then analyzed by SDS PAGE and those containing the recombinant protein were pooled and concentrated. The concentrated proteins were then incubated with one-fold molar equivalent of CuSO 4 overnight before separation on a HiLoad 16/600 Superdex 75 Prep Grade column in 50 mM sodium acetate buffer pH 5.2.

Biochemical analysis of AA14 LPMOs

Concentration of purified proteins was determined by using the Bradford assay (Bio-Rad, Marnes-la-Coquette, France) or using a nanodrop ND-2000 device with calculated molecular mass and molar extinction coefficients derived from the sequences. Proteins were loaded onto 10% SDS-PAGE gels (Thermo Fisher Scientific, IL, USA) which were stained with Coomassie Blue. The molecular mass under denaturating conditions was determined with reference standard proteins (Page Ruler Prestained Protein Ladder, Thermo Fisher Scientific). Native IEF was carried out in the Bio-Rad gel system, using pI standards ranging from 4.45 to 8.2 (Bio-Rad).

N-terminal amino acid sequence determination

The N-terminal amino acid sequences of purified PcAA14A and PcAA14B were determined according to the Edman degradation. Samples were electroblotted onto a polyvinylidene difluoride membrance (iBlot, Life Technologies). Analyses were carried out on a Procise Sequencing System (Thermofisher).

Matrix-assisted laser desorption ionization/mass spectrometry

Matrix-assisted laser desorption ionization mass spectra analyses were performed on a Microflex II mass spectrometer (Bruker Daltonics). One μL of matrix [10 mg of 2,5-dihydroxybenzoic acid in 1 mL of CH 3 CN/H 2 O 50/50 (v/v), 0.1% formic acid (v/v)] was added to 1 μL of intact PcAA14A or PcAA14B protein sample (100 pmoles) in the same solution. Then, mixtures were allowed to dry at room temperature. Data acquisition was operated using the Flex control software. External mass calibration was carried out on Peptide calibration standard (Bruker Daltonics).

Deglycosylation assays

To remove N-linked glycans, purified enzymes were treated with EndoHf (New England Biolabs, Ipswich, MA) under denaturing conditions according to the manufacturer's instructions.

Briefly, 10 µg of protein were incubated in 0.5% SDS and 40 mM DTT and heated for 10 min at 100°C for complete denaturation. Denaturated samples were subsequently incubated with 1,500 units of EndoHf in 50 mM sodium acetate pH 6.0 for 1 h at 37°C. Deglycosylated and control samples were analyzed by SDS-PAGE.

Amplex Red assay

A fluorimetric assay based on Amplex Red and horseradish peroxidase was used as described previously [START_REF] Kittl | Production of four Neurospora crassa lytic polysaccharide monooxygenases in Pichia pastoris monitored by a fluorimetric assay[END_REF] . The reaction (total volume 100 μL, 30°C, 30 min) was measured in 50 mM sodium acetate buffer pH 6.0 containing 50 μM Amplex Red (Sigma-Aldrich, Saint-Quentin Fallavier, France), 7.1 U.mL -1 horseradish peroxidase, 0.2 to 4 μM enzyme, and 50 μM reductant, i.e. ascorbate, p-coumaric acid, caffeic acid, cinapic acid, vanillic acid, menadione, L-cysteine, tannic acid, syringic acid, gallic acid, 3-hydroxyanthranilic acid (3-HAA) and epigallocatechin gallate in water and fluorescence was detected using an excitation wavelength of 560 nm and an emission wavelength of 595 nm using a Tecan Infinite M200 plate reader (Tecan, Männedorf, Switzerland). The specific activity was counted from H 2 O 2 calibration curve, and the slope (13,227 counts.μmol -1 ) was used to convert the fluorimeters' readout (counts.min -1 ) into enzyme activity.

ICP/MS Analysis

To obtain apo enzymes, 100 mM EDTA treatment was performed overnight. Prior to the analysis, samples were mineralized in a mixture containing 2/3 of nitric acid (Sigma-Aldrich, 65% Purissime) and 1/3 of hydrochloric acid (Fluka, 37%, Trace Select) at 120°C. The residues were diluted in ultra-pure water (2 mL) before ICP/MS analysis. The ICP-MS instrument was an ICAP Q (ThermoElectron, Les Ullis, France), equipped with a collision cell. The calibration curve was obtained by dilution of a certified multi-element solution (Sigma-Aldrich). Copper concentrations were determined using Plasmalab software (Thermo-Electron), at a mass of interest m/z=63.

Saccharification assays

Wheat straw, pine and poplar biomass were pretreated under acidic conditions. Sugar composition was determined using the alditol acetate method. [START_REF] Englyst | Improved method for measurement of dietary fiber as nonstarch polysaccharides in plant foods[END_REF] Wheat straw consisted of 51.98 ± 2.02 % (w/v) glucose, 5.70 ± 0.23 % (w/v) xylose and 0.46 ± 0.04 % (w/v) arabinose. Pine consisted of 43.25 ± 1.34 % (w/v) glucose, 0.24 ± 0.01 % (w/v) xylose and 0.15 ± 0.02 % (w/v) arabinose. Poplar consisted of 50.85 ± 0.91 % (w/v) glucose, 0.39 ± 0.01 % (w/v) xylose and 0.07 ± 0.01 % (w/v) arabinose. The enzymatic treatments were carried out in sodium acetate buffer (50 mM, pH 5.2) in a final volume of 1 ml at 0.5% consistency (w d.m./v). The LPMO treatment was carried out sequentially with a CL847 T. reesei enzyme cocktail [START_REF] Herpoël-Gimbert | Comparative secretome analyses of two Trichoderma reesei RUT-C30 and CL847 hypersecretory strains[END_REF] provided by IFPEN (Rueil-Malmaison, France). Each PcAA14 enzyme was added to the substrate at a concentration of between 0.1 and 1 µM in the presence or absence of 1 mM ascorbic acid for 72 h, followed by addition of 1 mg.g -1 dry matter (d. m.) substrate of commercial cellulases from T. reesei for 24 h. Enzymatic treatments were performed in 2-ml tubes incubated at 45°C and 850 rpm in a rotary shaker (Infors AG, Switzerland). Then, samples were centrifuged at 14,000 g for 5 min at 4°C and the soluble fraction was heated for 10 min at 100°C to stop the enzymatic reaction. Glucose was quantified by high performance anion exchange chromatography coupled with amperometric detection (HPAEC-PAD) as described in [START_REF] Bennati-Granier | Substrate specificity and regioselectivity of fungal AA9 lytic polysaccharide monooxygenases secreted by Podospora anserina[END_REF] .

Polysaccharides cleavage assays

Avicel was purchased from Sigma-Aldrich and lichenan (from Icelandic moss), curdlan, starch, barley -1,3/1,4-glucan, konjac glucomannan, wheat arabinoxylan, tamarind xyloglucan were purchased from Megazyme (Wicklow, Ireland). PASC was prepared from Avicel as described previously [START_REF] Bennati-Granier | Substrate specificity and regioselectivity of fungal AA9 lytic polysaccharide monooxygenases secreted by Podospora anserina[END_REF] in 50 mM sodium acetate buffer pH 5.2. A similar protocol was used to prepare swollen squid pen chitin provided by Dominique Gillet (Mahtani Chitosan, India).

Glucuronoxylans were extracted from birchwood as described previously [START_REF] Westbye | The effect of molecular composition of xylan extracted from birch on its assembly onto bleached softwood kraft pulp[END_REF] .

All the cleavage assays contained between 0.5 and 1 μM of PcAA14s in the presence of 1 mM ascorbate and 0.1% (w/v) polysaccharides. The enzyme reactions were performed in 2-mL tubes and incubated in a thermomixer (Eppendorf, Montesson, France) at 45°C and 850 rpm. After 16 h of incubation, samples were heated for 10 min at 100°C to stop the enzymatic reaction and then centrifuged at 14,000 g for 15 min at 4°C to separate the soluble fraction from the remaining insoluble fraction before determination of soluble products using HPAEC as described above with oligosaccharides standards (Megazyme).

Microscopy

Aqueous dispersions of Kraft birchwood cellulosic fibers (kindly provided by Sandra Tapin, FCBA, Grenoble, France) were adjusted to pH 5.2 with acetate buffer (50 mM) in a final reaction volume of 5 mL. Each PcAA14 enzyme was added to the fibers at a final concentration of 20 mg.g -1 in the presence of 1 mM of ascorbic acid. Enzymatic incubation was performed at 40 ºC under mild agitation for 48 h. Samples were then dispersed by a Polytron PT 2100 homogenizer (Kinematica AG, Germany) for 3 min, and ultrasonicated by means of a QSonica Q700 sonicator (20 kHz, QSonica LLC., Newtown, USA) at 350 W ultrasound power for 3 min as described previously [START_REF] Villares | Lytic polysaccharide monooxygenases disrupt the cellulose fibers structure[END_REF] . The reference sample was submitted to the same treatment but it did not contain the PcAA14 enzyme. Birchwood cellulose fibers (reference and PcAA14-treated) were deposited onto a glass slide and observed by a BX51 polarizing microscope (Olympus France S.A.S.) with a 4× objective. Images were captured by a U-CMAD3 camera (Olympus For the C1-region, this approach used three Lorentzian lines for the crystalline part (Cr (I) and Cr (I)) and one Gaussian line for the less ordered cellulose (para-crystalline cellulose, PCr). For the C-4 region, four lines for the crystalline part corresponding to crystalline and para-crystalline (PCr) cellulose and three Gaussian lines for the amorphous part (accessible surfaces, AS, and inaccessible surface, IAS) were used. The cellulosic fibers contained xylan, which was considered in the spectral decomposition: in the C-1 region with one line at 101.4 ppm and in the C-4 region with one broad line centered at 81.6 ppm.

Synergy assays with xylanase

Assays were run on the birchwood cellulose fibers used in microscopy and NMR experiments.

Fibers were grinded (< 0.18 mm particle size) and hydrated in water under stirring for 48 h prior to enzymatic assays. One mL reaction volumes containing 0.5% (w/v) birchwood fibers were incubated with 1 μM of PcAA14s and 0.1 µM of GH11 xylanase M4 (Aspergillus niger) from Megazyme (reference E-XYAN4) in 10 mM sodium acetate pH 5.2 supplemented or not with 1 mM L-cysteine. Prior to the reaction, the GH11 xylanase was buffer exchanged with 10 mM sodium acetate pH 5.2 using a PD-10 column (GE Helthcare) to remove any trace of ammonium sulfate. Enzymatic reactions were performed in 2-mL tubes and incubated in a thermomixer (Eppendorf, Montesson, France) at 45°C and 850 rpm for 24 h. Samples were then centrifuged at 14,000 g for 5 min at 4°C to separate the soluble fraction from the remaining insoluble fraction.

Proteins were removed from the soluble oligosaccharides fraction by filtering the supernatants using Nanosep 3K Omega centrifugal devices (Pall corporation). Soluble oligosaccharides generated were analyzed by HPAEC as described previously and mass spectrometry (see below) using non-oxidized xylo-oligosaccharides (Megazyme) as standards. Corresponding C1-oxidized standards (from DP2 to DP4) were produced from non-oxidized xylo-oligosaccharides by using purified PaCDHB prepared as described previously. [START_REF] Bennati-Granier | Substrate specificity and regioselectivity of fungal AA9 lytic polysaccharide monooxygenases secreted by Podospora anserina[END_REF] All assays were carried out in triplicate.

Electrospray mass spectrometry (ESI-MS and MS/MS)

Experiments were performed on a Synapt G2Si high-definition mass spectrometer (Waters Corp., Manchester, UK) equipped with an Electrospray ion (ESI) source. Two types of mass measurements were performed on the samples: firstly, a mass profile was done on a mass range of 300-2000 m/z (M/S). Ions of interest were further isolated and fragmented by collisioninduced dissociation in the transfer cell of the instrument (MS/MS). In these experiments, ion mobility (IM) was activated to reduce interference from sample impurities. IM was performed in a travelling-wave ion mobility (TWIM) cell. The gas flows were held at 180 mL.min -1 He in the helium cell and at 90 mL.min -1 N 2 in the mobility cell. The IM traveling wave height was set to 40 V and its wave velocity was set to 480 m.s -1 for positive ionization mode and 500 m.s -1 for negative ionization mode. Samples were diluted 10-fold in MeOH/H 2 O (1:1, v/v) and infused at a flow rate of 5 μL.min -1 in the instrument. The instrument was operated in positive or negative polarity, and in "sensitivity" mode.

Crystallization, data collection, structure determination and refinement

All crystallization experiments were carried out at 20°C by the sitting-drop vapour-diffusion method using 96-well crystallization plates (Swissci) and a Mosquito ® Crystal (TTP labtech) crystallization robot. Reservoirs consisted of 40 µL of commercial screens and crystallization drops were prepared by mixing 100 nL reservoir solution with 100, 200 and 300 nL of protein solution. An initial hit was obtained after 1 week from a condition of the AmSO 4 screen (Qiagen) consisting of 2.4 M (NH 4 ) 2 SO 4 and 0.1 M citric acid pH 4.0. This condition was further optimized to obtain diffraction-grade crystals by mixing protein solution at 28 mg mL -1 with precipitant solution consisting of 2.4 M (NH 4 ) 2 SO 4 and 0.1 M citric acid pH 4.4 at a volume ratio of 3:1. PcAA14B crystals grew to dimensions of 0.15×0.15×0.05 mm in one week. Crystals belong to space group P4 1 2 1 2 with cell axes 204×204×110 Å and two molecules per asymmetric unit.

Crystals of PcAA14B were soaked for 5 min in a solution where 2.4 M (NH 4 ) 2 SO 4 of the mother liquor was replaced by 2.4 M Li 2 SO 4 for the sake of cryoprotection prior to flash-cooling in liquid nitrogen. As X-ray fluorescence scans on native crystals did not reveal a significant presence of copper within the crystals, a heavy atom derivative was prepared by soaking the crystals in reservoir solution supplemented with 55 mM of the gadolinium complex gadoteridol prior to cryo-cooling. Native diffraction data were collected on beamline ID23-1, while a MAD dataset at wavelengths of 1.711 and 1.698 Å for peak/inflection and remote energies, was collected on beamline ID30B at the European Synchrotron Radiation Facility (ESRF), Grenoble, France. Data were indexed and integrated in space group P4 1 2 1 2 using XDS 41 and subsequent processing steps were performed with the CCP4 software suite [START_REF] Winn | Overview of the CCP4 suite and current developments[END_REF] . Determination of the Gd 3+ substructure and subsequent phasing combined with solvent flattening were carried out with SHELXC/D/E 42 , leading to a pseudo-free correlation coefficient of 71.8%. Starting from experimental phases, an initial model comprising 526 residues (out of 584), was automatically built with Buccaneer [START_REF] Cowtan | The Buccaneer software for automated model building. 1. Tracing protein chains[END_REF] and manually completed with Coot [START_REF] Emsley | Features and development of Coot[END_REF] . This initial model was used for rigid body refinement followed by restrained refinement against native data with the program Refmac [START_REF] Murshudov | Refinement of macromolecular structures by the maximum-likelihood method[END_REF] . A random set of 5% of reflections was set aside for cross-validation purposes. Model quality was assessed with internal modules of Coot [START_REF] Emsley | Features and development of Coot[END_REF] and using the Molprobity server [START_REF] Chen | MolProbity: all-atom structure validation for macromolecular crystallography[END_REF] . Figures representing structural renderings where generated with the PyMOL Molecular Graphics System For all statistics, n = 3 values were used to calculate the standard error of the mean. Values resulted from independent experiments. For all representative results, experiments were repeated at least two times and at least 20 images were collected for microscopy analyses.

Accession codes

PcAA14A and PcAA14B sequences were deposited in GenBank under accession numbers KY769369 and KY769370, respectively. The X-ray structure of PcAA14B was deposited in the Protein Data Bank with accession 5NO7. Raw EPR data are available on request through the Research Data York (DOI: 10.15124/8758d712-1e67-467e-b0f0-f0dd99f0232a).

Figure 1 .

 1 Figure 1. Phylogeny of the AA14 family of LPMOs. Phylogenetic tree of 283 fungi analyzed

Figure 2 .

 2 Figure 2. Structure of AA14 LPMO and organization of the copper active site. (a) Overall

Figure 3 .

 3 Figure 3. Contribution of PcAA14 enzymes to the saccharification of biomass. (a) Glucose

Figure 4 .

 4 Figure 4. Enzymatic activity of PcAA14 LPMOs. (a) Morphology of birchwood cellulosic

Acknowledgements. We thank the European Synchrotron Radiation Facility (Grenoble), and the synchrotron Soleil (Gif-sur-Yvette) for beam time allocation and assistance. We thank S. Tapin for providing cellulose fibers, E. Bonnin and J. Vigouroux for compositional analyses, G. Toriz and P. Gatenholm for providing purified wood xylan, L. Foucat and X. Falourd for their valued assistance with treatments of the NMR data, E. Perrin for the excellent technical support for TEM images, B. Seantier for the access and assistance to AFM facilities, D. Hartmann and E. Bertrand for their help with enzyme production in bioreactor, and D. Navarro and G. Anasontzis for insightful discussions. MC was funded by a Marie Curie International Outgoing Fellowship within the 7 th European Community Framework Program (328162). SL, MNR and JGB were funded by the Microbio-E A*MIDEX project (ANR-11-IDEX-0001-02). AL was funded by a Marie Curie Individual Fellowship within the Horizon 2020 Research and Innovation Framework Programme (748758). This work was supported in part by the CNRS and the French Infrastructure for Integrated Structural Biology (FRISBI) ANR-10-INSB-05-01. NL and BH were supported by Agence Française de l'Environnement et de la Maîtrise de l'Energie (1201C102). PHW, GJD and LC thank the UK Biotechnology and Biological Sciences Research

Data Availability Statement

All data generated or analysed during this study are included in this published article (and its supplementary information files).

Competing Financial Interests Statement. The authors declare no competing financial interests. Japan). For the atomic force microscopy (AFM) experiments, samples were deposited onto mica substrates from fiber solutions at 0.1 g L -1 , and allowed to dry overnight. Topographical images on mica were registered by a Nanoscope III-A AFM (Brukernano, Santa Barbara, US). The images were collected in tapping mode under ambient air conditions (temperature and relative humidity) using a monolithic silicon tip (RFESP, Brukernano) with a spring constant of 3 N m -1 , and a nominal frequency of 75 kHz. Image processing was performed with the WSxM 5.0 software. For transmission electron microscopy (TEM) experiments, fiber solutions at 0.1 g L -1 in water were deposited on freshly glow-discharged carbon-coated electron microscope grids (200 mesh, Delta Microscopies, France) and the excess of water was removed by blotting. The sample was then immediately negatively stained with uranyl acetate solution (2%, w/v) for 2 min and dried after blotting. The grids were observed with a Jeol JEM 1230 TEM at 80 kV.

NMR spectroscopy

Solid state [START_REF] Hemsworth | Discovery and characterization of a new family of lytic polysaccharide monooxygenases[END_REF] C NMR experiments were performed on a Bruker Avance III 400 spectrometer operating at a 13 C frequency of 100.62 MHz using a 4 mm double-resonance (H/X) magic angle spinning (MAS) probe. Samples were dialyzed against ultrapure water (MWCO 12-14000) for 7 days to remove buffer, ascorbate and released soluble sugars. Experiments were conducted at room temperature at a MAS frequency of 9 kHz using a cross-polarization sequence (CP/MAS).

The [START_REF] Hemsworth | Discovery and characterization of a new family of lytic polysaccharide monooxygenases[END_REF] C chemical shift was referenced using the carbonyl signal of glycine at 176.03 ppm. The cross polarization pulse sequence parameters were: 3.2 μs proton 90° pulse, 2.50 ms contact time at 67.5 kHz, and 10 s recycle time. Typically, the accumulation of 5,120 scans was used. All spectra obtained were processed and analyzed using Bruker Topspin version 3.2. To determine the crystallinity and the general cellulose's morphology of the C-1 and C-4 region of the samples, we used the sophisticated approach [START_REF] Larsson | A CP/MAS 13 C NMR investigation of molecular ordering in celluloses[END_REF] that is described in details in our previous work [START_REF] Villares | Lytic polysaccharide monooxygenases disrupt the cellulose fibers structure[END_REF] .

(DeLano, W.L. The PyMOL Molecular Graphics on http://www.pymol.org/). Atomic coordinates and structure factors have been deposited within the Protein Data Bank http://www.rcsb.org [START_REF] Berman | Announcing the worldwide Protein Data Bank[END_REF] . Data collection and refinement statistics are summarized in Supplementary Table 4.

EPR

Continuous wave (cw) X-band frozen solution EPR spectra of a 0.2 to 0.3 mM solution of Cu(II)-PcAA14A and PcAA14B, prepared and copper loaded as described above, in 10% v/v glycerol at pH 5.2 (50 mM sodium acetate buffer) and 165 K were acquired on a Bruker EMX spectrometer operating at ~9.30 GHz, with modulation amplitude of 4 G, modulation frequency 100 kHz and microwave power of 10.02 mW (4 scans). Both enzymes showed identical EPR spectra. Cw Q-band frozen solution spectra of 1.0 mM solution of Cu(II)-PcAA14A at pH 5.2 (50 mM sodium acetate buffer) and 113 K were acquired on a Jeol JES-X320 spectrometer operating at ~34.7 GHz, with modulation width 1 mT and microwave power of 1.0 mW (8 scans).

Spectral simulations were carried out using EasySpin 5.0.3 [START_REF] Stoll | a comprehensive software package for spectral simulation and analysis in EPR[END_REF] . Simulation parameters are given in Supplementary Table 3. g z and |A z | values were determined accurately from the absorptions at low field. It was assumed that g and A tensors were axially coincident. Accurate determination of the g x , g y , |A x | and |A y | was obtained by simultaneous fitting of both X and Q band spectra.

The superhyperfine coupling values for the nitrogen atoms could not be determined accurately, although it was noted that satisfactory simulation could only be achieved with the addition of two nitrogen atoms with coupling in the range 30-36 MHz.

Statistics


Mustafa Corekci
email: mcorekci@gsu.edu.tr

Ozgun Pinarer
email: opinarer@gsu.edu.tr

Sylvie Servigne

DESIGNING A NOVEL SMART HOME MONITORING SYSTEM WITH THE INTEGRATION OF BIM AND WSN

Keywords: Pervasive environment, Building information modelling, Sensor technology, Sensor integration, Wireless sensor networks

In the recent years, the Building Information Modelling (BIM) has become one of the hottest research area in computer science. It attracts many researchers from huge various research domains. Not only the researchers from computer science but also researchers from environmental health, archeology and cultural heritage started working in BIM and its integration with Wireless Sensor Networks (WSN). In this paper, we focus on the integration of BIM and WSN. We tackle one of the major challenges of this integration: how to manage sensor streams by using a digital model of the environment and how to integrate sensor data with the environmental data. Even there are few studies that manage multi sensor data streaming, these approaches are often fitted to a single application and mostly they are independent from the building model. To overcome this problem, we propose a novel real-time environmental monitoring system based on the BIM principles. In this study, we present a multi-application monitoring system architecture. As a testbed environment, the university campus is preferred and for the prototype chosen offices and classrooms are equipped with sensor devices. Real data are enriched with the simulated data to get a large database. The 3D digital model introduces the numerical model of the building and a sensor integration is achieved on the given digital model.

INTRODUCTION

We live in an era where our lives are surrounded by highly advanced technological devices. In recent years, there has been a great trend to work on the field of Building Information Modelling (BIM) and the integration of wireless sensor networks (WSN) into digital model of a building. It is considered that to build a robust smart city, a well-connected smart building has a crucial role.

Over the last few decades, popular attention to smart building has increased. Smart building and sustainability are intertwined. Since traditional buildings are primary consumers of a significant portion of energy resources, we need smart buildings that are designed based on sustainable construction standards to consume less energy than traditional buildings and to minimize their impacts on the natural environment. Thus, smart buildings became one of the major application domains of smart environments such as smart cities. Smart building technology brings in some nice features such as security, comfort and accessibility. It can also enhance the energy consumption of the buildings.

Advanced technology based smart devices form an environment (the environment we live in) in which each device may interact with the other devices and with the human. This interaction opens a new era in the technology and a research domain so called Internet of Things (IoT). It is considered as the next wave in the era of computing [START_REF] Gubbi | Internet of Things (IoT): A vision, architectural elements, and future directions[END_REF]. A generic description commonly adopted by the researchers is that the IoT is the network of objects that surround us with the understanding that information and communication systems are invisibly embedded in the environment around us. Objects may embed various sensors, actuators, UI devices, etc. This invisible network covers smart connectivity, cloud computing, context-aware computation, etc.

We spend most of our life in the buildings and enhancing living environment through the use of smart technology has a crucial role to improve the ambience in buildings and also working environments. To reduce energy consumption, management of the building (monitoring via control based wireless sensor networks and actuators) is required [START_REF] Wu | Gis-bim based virtual facility energy assessment(vfea)-framework development and use case of california state university, fresno[END_REF], even taking into account energy consumption of the monitoring system itself [START_REF] Pinarer | Dynamic energy-aware sensor configuration in multiapplication monitoring systems[END_REF].

BIM has become a powerful technology based on an object-oriented approach, which fulfills all the functions of traditional 3D CAD/CAM techniques. With BIM technology, a virtual building model is built numerically, which can be very useful for planning, design, pre-build and post-build processes [START_REF] Azhar | Building information modelling (BIM): now and beyond[END_REF]. The BIM is generally visible in 3D, but it also includes data that can be used by other analysis applications, such as energy consumption simulation, cost estimation, natural lighting, etc. Figure 1 from a market report presents the possible market usage of BIM. BIM is an emerging trend in the Architecture, Engineering and Construction (AEC) sector, which can be used to improve building life cycle performance and productivity throughout the design process, construction, operation and maintenance. BIM is an innovative methodology that can be used to improve the performance and productivity of an asset owner's construction, operation and maintenance processes. BIM provides a multicollaborative platform for sharing semantic and geometric information within the project management team throughout the asset lifecycle. It allows us a standardized means of sharing and exchanging information about building data. It provides a virtual model of a building with all minor details prior to its physical construction.

There are several definitions of BIM. The National Institute of Building Sciences of the United States (NIBS) defines BIM as "a digital representation of the physical and functional characteristics of an installation. [START_REF] Eastman | BIM handbook: A guide to building information modeling for owners, managers, designers, engineers and contractors[END_REF] defines BIM as a modeling technology and a set of associated processes for generating, communicating, and analyzing building models. [START_REF] Isikdag | Building Information Models: An Introduction[END_REF] defines BIM as the digital representation of a building containing semantic information about building elements. BIM also defines an information management process based on the collaborative use of semantically rich 3D digital building models at all stages of the project and building lifecycle. A BIM is defined by its object model schema. IFC -Industry Foundation Classes is currently the most widely used BIM standard. According to [START_REF] Azhar | Building information modelling (BIM): now and beyond[END_REF], BIM is one of the emerging developments in the architectural, engineering and construction industries.

In this study, we construct a 3D model of a building (a building of our university as a prototype) and deploy the wireless sensor devices in the offices and classrooms to acquire temperature, humidity, emission of CO2, occupancy and luminosity data. A database system is constructed in order to register all stream sensor data into database. This database is served to other applications that require sensor data. We consider that every sensor device individually is a service provider (from the user point of view) serves physical measure (such as temperature, humidity etc.) All the deployed sensor devices are virtualized in the 3D model that called BIM.

Via BIM, we can manage the locations of each sensor device. Afterwards, we developed a user interface to manage the BIM and the sensor environment. By using this interface, based on the user preferences on building-floor-room and available services in that room, a subscription to that sensor devices are made and sensor data stream is launched.

The remainder of this paper is organized as follows: related works are given in Section 2. Section 3 presents the architecture of the proposed real-time smart home monitoring system. Finally, we conclude in Section 4.

RELATED WORK

The literature review shows that there is a big trend on working on the integration of BIM and WSN and the existing works in this area are divided in three main sub researches: using BIM during the construction phase of a building, using BIM for the management of the smart home system and using BIM in cultural heritage.

Benefits of BIM in early stages of construction

With the recent improvements in architecture, softwares and platforms specialized in architecture are now capable of integrating other systems such as sensor networks. This gives a huge opportunity to users to manage the area even during the construction phase of the building. In such systems, the researchers benefit the sensor technology to monitor the construction environment where the field is occupied by the workers. Such systems are called environmental health where the sensor data is used to get information about the environment such as temperature, humidity etc. Although many appropriate data acquisition technologies are available and used in other sectors, applications in the construction sector are not very common [Vaha, 2013]. [Riaz, 2014] offers a solution based on WSN and BIM for environmental monitoring in confined spaces. The authors propose a prototype titled Cosmos to monitor the physical condition and safety of operators. The presented system includes a notification mechanism to alert building supervisors and the responsible to take the necessary action if needed/in case of emergency.

[ Kiani, 2014] introduces a prototype system for visualization of sensor data. It is used for the real-time environmental monitoring, visualization and reporting of the construction. The study uses the standard BIM software and modifies it to view and manage sensor data in real time in its native environment.

[ [START_REF] Lee | A BIM-and sensor-based tower crane navigation system for blind lifts[END_REF] highlights a major problem for tower crane operators. They often operate a tower crane with blind spots. To solve this problem, video camera systems and collision avoidance systems are often deployed. However, current video camera systems do not provide accurate distance and do not include the vicinity of the crane. A collision detection system provides location information only as digital data. The authors present a tower crane navigation system providing three-dimensional information about the building and its surroundings, as well as the position of the lifted object in real time, using various sensors and a BIM. The proposed system indicates the location of an object raised in the context of a built building and its surroundings, in the manner of an automobile navigation system, indicating the location of an automobile in the context of roads and of historic buildings.

Benefits of BIM in smart building management

Besides the construction phase, BIM mainly is used in smart home monitoring and management systems. The studies in this part focus on managing sensor data flows and the integration of BIM and the actuators in the environment. [START_REF] Liu | Requirements and evaluation of standards for integration of sensor data with building information models[END_REF] introduces that a digital model of a building provides a useful information about the spatial context information. The authors tackle the lack of integration of sensor information and BIM. Authors declare that all information about the environment can be achieved via analyzing sensor data and integration sensor metadata to building model. [START_REF] Park | A wireless tracking system integrated with bim for indoor construction applications[END_REF] offers a tracking system. The study proposes an integrated approach that merges information from Bluetooth beacons and motion sensors, extracts map knowledge information from a BIM, and integrates aggregated sensor data with map knowledge to estimate position of a target. The tracking system consists of three main components (BLE -Bluetooth Low Energy -sensors, motion sensors and BIM). BLE sensors were used to estimate real-time position information, motion sensors were used to obtain relative motion information, and a BIM was used as a geometric constraint. The BIM used in this study can detect and correct false movements, such as moving through a wall, and viewing the BIM on a mobile device. Building geometry and object information are extracted into an XML-readable file and imported into the system. This information serves as a geometric constraint. Relevant BIM data is converted to a format compatible with a mobile device and used for real-time visualization of the model. [START_REF] Hu | Optimized Selection and Placement of Sensors using Building Information Models (BIM)[END_REF] focuses on the advanced lighting system. Since improper sensor placement can compromise system performance, inconvenience occupants and reduce savings, the placement and orientation of occupancy sensors and photoelectric sensors are essential in these systems. Thus, the authors present a method for automating the deployment of the detection device. Since the BIM contains a detailed geometry of building elements, the proposed system can accept sensor parameters and user constraints for sensor placement. With all entries, code compliance and performance requirements are verified. The proposed approach is an optimization in which if the solutions are found to be non-compliant, the process goes backwards to evaluate alternative solutions.

[ [START_REF] Kim | Integrated energy monitoring and visualization system for Smart Green City development: Designing a spatial information integrated energy monitoring model in the context of massive data management on a web based platform[END_REF] offers an energy management system called EnerISS (System for planning and integrated urban management of energy). The platform middleware retrieves geospatial data from a GIS database and data from each sensor installed on the city and provides the browser-based client with the appropriate hosted information to display the location-specific geolocation characteristics. respective energy. The study introduces a new urban data structure model for monitoring and visualizing aggregated and real-time reports of various energy uses.

Benefits of BIM for cultural heritage

In the area of architectural risk management, many cultural heritage organizations have conducted research on methods and tools for assisting conservation planning and management decisions. Risk management can be defined as the process of identifying, assessing and analyzing potential damage or establishing a strategy to reduce damage to cultural heritage. However, the risk management of architectural heritage is more complicated due to the combination of structural instability and component degradation. Thus, new methods and tools are needed to assess, analyze and implement a damage reduction strategy for architectural heritage risk management.

[ [START_REF] Lee | Context-aware risk management for architectural heritage using historic building information modeling and virtual reality[END_REF] proposes a new form of metadata and a framework for Risk management using historical building information modeling (HBIM) data in a virtual environment for architectural heritage risk management is suggested. To this end, it is important to share enriched data between those who monitor and diagnose inheritance and those who recognize the context of the information.

The existing studies in the literature propose a local solution for environmental health and monitoring. However most of the proposed approaches are not at level of being prototype and are not suitable for every environment.

Besides, multi sensor data management are not handled in these studies and they are bounded by predefined building applications. Even there are few studies that manage multi sensor data streaming, these approaches are often fitted to a single monitoring application. In this study, we present a multi-application monitoring system architecture where multiple users are allowed to get served by multiple services.

ARCHITECTURE

Architecture

BIM is currently widely used in the design and engineering phases, but very little effort has been expended to consider the combination of sensor technology and BIM. A full integration of BIM and WSN is extremely required to real time smart home monitoring systems and to stay connected to the environment/ stay connected to other systems.

In this study, we construct a 3D model of a building (a building of our university as a prototype) and deploy the wireless sensor devices in the chosen offices and classrooms to acquire temperature, humidity, emission of CO2, occupancy and luminosity data. Due to having limited electronic components, we enrich the real data with the simulated data. Thus, in database, a huge number of sampled data are obtained. A database system (using Open Source MySQL platform) is constructed in order to register all stream sensor data into database.

It is a continuous sensor data streaming that each sensor device measures the physical measure periodically and sends to the sink (base station). This database is served to every application that requires sensor data. Once a database structure is well constructed, every sensor device individually becomes a service provider (from the user point of view) that serves physical measure. The architecture of our proposed system is illustrated in Figure 2.

We used Autodesk Revit software architecture platform to model the building and its environment. Since model of building gives a very detailed information about the environment, the descriptions of the deployed sensor devices and the locations are covered by the digital model as well. Thus, the XML file (export the model as a XML file) gives all the relevant information about the sensors. A XML parser is developed in Python 3 to read that file and understand the information about the sensor devices such as which sensor devices are located in which room, which services are available from each sensor etc.

Hardware Setup and Implementation

For a prototype, chosen offices and classrooms are equipped with ESP8266 and relevant sensor devices. As sensor devices, temperature, humidity, emission of CO2, presence and luminosity sensors are used. The acquired data is sent to the base station via ESP8266 wifi module (by using MQTT). Another ESP8266 stands for a base station and gather all acquired data from all the sensor devices. Through a serial port, sampled and sent data are inserted to the database. Instead of using commercial sensor devices, we prefer to build our own electronic circuits to avoid having hardware constraints.

All the deployed sensor devices are virtualized in the 3D model that called the BIM. Via the BIM, the users are not obliged to have knowledge about the sensors or their locations. A user simply chooses the buildingfloor and the room form where he wants to get information (physical condition). Once the user submits his preferences, system based on the BIM and the sensor data stored in the model, proposes the available services from that room. Finally, the user chooses the available services from the list and the system makes the subscription to the chosen services. Here, the subscription stands for the retrieving the sensor stream data that goes to database in actual condition. The samples from the developed user interface (in Python 3) are represented in Figure 3.

CONCLUSION AND DISCUSSION

In this paper, we point at a major challenge of smart building monitoring systems: using the 3D model of the building to manage sensor network for real time monitoring systems. Here, we focus on the integration of BIM and WSN in smart home environments and connected systems. By relying on declarative PEMS principles, we present a sustainable declarative monitoring architecture to process massive raw data from sensors assisted by digital model of the building. In the further phases of this study, we are planning to integrate a continuous query structure to retrieve sensor stream data to fulfill user requirements successfully. Besides, the researchers in this domain have difficulties to find a working physical testbeds to run and test their approaches, algorithms.

We are working on converting our system in a public testbed where users can connect remotely and deploy their novel approaches.

Figure 1 .

 1 Figure 1. Product scope of BIM and Market Opportunities (Global Building Information Modeling (BIM) Market Report)

Figure 2 .

 2 Figure 2. Architecture of The Proposed System

Figure 3

 3 Figure 3. a. Choosing a building from the list. b. After choosing the building and a floor, a reliable plan of the floor is fetched to present the rooms on that floor. In the chosen room, presence and humidity services are available. c. Streaming data from the chosen services

ACKNOWLEDGEMENT

This work is supported by Galatasaray University Research Foundation under the Grant No. 19.401.002 and Consortium of Galatasaray University.