David A Clément

Muriel Gelin

Clarisse Leseigneur

Valérie Huteau

Lou Mondange

Jean-Luc Pons

Olivier Dussurget

Corinne Lionne

Gilles Labesse
email: gilles.labesse@cbs.fr

Sylvie Pochet
email: sylvie.pochet@pasteur.fr

‡ Lou Mondange

Synthesis and structure-activity relationship studies of original cyclic diadenosine derivatives as nanomolar inhibitors of NAD kinase from pathogenic bacteria

Keywords: Inhibitor, macrocyclic compound, NAD kinase, crystal structure, Staphylococcus, Listeria

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

Introduction

Antibacterial multidrug resistance is a major public health problem requiring urgent development of new antibiotics and identification of original bacterial targets, as pointed out by the World Health Organization [1]. We have initiated a decade ago the characterization of such a new target involved in NAD biosynthesis: the nicotinamide adenine dinucleotide kinase. NAD kinases (NADK,EC 2.7.1.23) catalyse the phosphorylation of NAD to NADP, which is subsequently reduced to NADPH [START_REF] Mcguinness | NAD+ kinase-A review[END_REF][START_REF] Magni | Structural and functional properties of NAD kinase, a key enzyme in NADP biosynthesis[END_REF]. As the sole known enzyme producing NADP de novo, NADK plays a crucial role in controlling the intracellular balance of NAD(H) and NADP(H) in many metabolic pathways [START_REF] Kawai | Structure and Function of NAD Kinase and NADP Phosphatase: Key Enzymes That Regulate the Intracellular Balance of NAD(H) and NADP(H)[END_REF][5][START_REF] Murata | Polyphosphate-dependent nicotinamide adenine dinucleotide (NAD) kinase: A novel missing link in human mitochondria[END_REF]. NADK enzymatic properties have been known for decades [START_REF] Kornberg | On the structure of triphosphopyridine nucleotide[END_REF], but the genes encoding the NADK were identified more recently, leading to the discovery of orthologs in nearly all living organisms [START_REF] Murata | Polyphosphate-dependent nicotinamide adenine dinucleotide (NAD) kinase: A novel missing link in human mitochondria[END_REF][START_REF] Kawai | Inorganic Polyphosphate/ATP-NAD kinase of Micrococcus flavus and Mycobacterium tuberculosis H37Rv[END_REF][START_REF] Lerner | Structural and Functional Characterization of Human NAD Kinase[END_REF]. The NADKs belong to a superfamily of kinases, including 6phosphofructokinases (PFKs), soluble diacylglyceride kinases, and sphingosine kinases [START_REF] Labesse | Diacylglyceride kinases, sphingosine kinases and NAD kinases: distant relatives of 6-phosphofructokinases[END_REF]. In line with a low overall sequence similarity (~20% sequence identity), the human cytoplasmic and mitochondrial enzymes exhibit substantial differences compared to their bacterial counterparts [START_REF] Murata | Polyphosphate-dependent nicotinamide adenine dinucleotide (NAD) kinase: A novel missing link in human mitochondria[END_REF][START_REF] Kawai | Inorganic Polyphosphate/ATP-NAD kinase of Micrococcus flavus and Mycobacterium tuberculosis H37Rv[END_REF][START_REF] Lerner | Structural and Functional Characterization of Human NAD Kinase[END_REF]. Because of the essential role of NADK in all bacteria tested so far, such as Bacillus subtilis [START_REF] Mary | Crystal structure of human NADK2 reveals a dimeric organization and active site occlusion by lysine acetylation[END_REF], Mycobacterium tuberculosis [START_REF] Kobayashi | Essential Bacillus subtilis genes[END_REF][START_REF] Sassetti | Genes required for mycobacterial growth defined by high density mutagenesis[END_REF] and Staphylococcus aureus [START_REF] Chaudhuri | Comprehensive identification of essential Staphylococcus aureus genes using Transposon-Mediated Differential Hybridisation (TMDH)[END_REF]15], NADK is considered as an attractive target for the development of novel antibiotics with an original mode of action [5,[START_REF] Gerdes | From Genetic Footprinting to Antimicrobial Drug Targets: Examples in Cofactor Biosynthetic Pathways[END_REF][17][START_REF] Depaix | NAD Analogs in Aid of Chemical Biology and Medicinal Chemistry[END_REF].

During the last decade, we have developed a series of adenosine derivatives as potent inhibitors of recombinant Listeria monocytogenes and S. aureus NADKs [15,[START_REF] Poncet-Montange | NAD kinases use substrateassisted catalysis for specific recognition of NAD[END_REF][START_REF] Gelin | Screening and In Situ Synthesis Using Crystals of a NAD Kinase Lead to a Potent Antistaphylococcal Compound[END_REF][START_REF] Paoletti | 8-Thioalkyl-adenosine derivatives inhibit Listeria monocytogenes NAD kinase through a novel binding mode[END_REF]. The most potent molecules were found to bind to the NAD-binding site of NADK in a restricted conformation as compared to NAD thanks to the propargyl linker. One of these compounds became the first NADK inhibitor (NKI1, Figure 1) active in mice infected with S. aureus, including antibiotic-resistant strains [15]. We next described the synthesis of a series of functionalized NKI1 derivatives as chemical tools to gain detailed knowledge about the mechanism of action of this lead compound [START_REF] Clement | New Chemical Probe Targeting Bacterial NAD Kinase[END_REF]. Two of these new compounds harbor some chemical substitutions suggesting that macrocyclization could be envisioned to strengthen further their affinity and specificity for NADKs. The peculiar binding mode of NAD and its analogues into the active site of NADKs suggested that a more constrained compound would gain both affinity for those NADKs and specificity over a wide range of NAD(P) binding proteins [START_REF] Kho | A path from primary protein sequence to ligand recognition[END_REF][START_REF] Fujii | Novel concept of enzyme selective nicotinamide adenine dinucleotide (NAD)-modified inhibitors based on enzyme taxonomy from the diphosphate conformation of NAD[END_REF]. Indeed, macrocyclization has been demonstrated to provide ligands with higher affinity due to entropy gain upon binding [START_REF] Marsault | Macrocycles Are Great Cycles: Applications, Opportunities, and Challenges of Synthetic Macrocycles in Drug Discovery[END_REF][26][27]. This was successfully implemented for several therapeutic targets [28-31], but not yet applied to NADKs.

Recent progress have been made to develop cyclization strategies for macrocyclization of peptides [26],

nucleosides [32] and oligonucleotides [START_REF] Lietard | New strategies for cyclization and bicyclization of oligonucleotides by click chemistry assisted by microwaves[END_REF] or to generate libraries of macrocyclic compounds [START_REF] Sangouard | Picomole-Scale Synthesis and Screening of Macrocyclic Compound Libraries by Acoustic Liquid Transfer[END_REF].

However, the synthesis of macrocycles is generally challenging since the ring-closing reaction efficiency depends on the size and geometry of the bridging linker. Thus, cyclization reactions do not favor the formation of large rings. Moreover, inter-instead of intra-molecular reaction is a major issue, in particular for the head-to-tail cyclization of peptides [START_REF] Bechtler | Macrocyclization strategies for cyclic peptides and peptidomimetics[END_REF]. Therefore, most cyclizations need to be conducted in dilute solutions to favor macrocyclization over side product formation (dimerization or oligomerization). The ringclosing step can be achieved by a variety of chemical reactions, including amide coupling, ring-closing metathesis, nucleophilic substitution, palladium-catalyzed cross coupling reaction. The careful choice of ring disconnection and appropriate precursors can improve yields and reduce side reactions [27,[START_REF] Amrhein | Synthetic Opportunities and Challenges for Macrocyclic Kinase Inhibitors[END_REF].

In the present study, we describe the synthesis of six original macrocycles (MC1-MC6, Figure 2) inspired by linear derivatives of compound NKI1 [15,[START_REF] Clement | New Chemical Probe Targeting Bacterial NAD Kinase[END_REF]. These cyclic molecules differ in the functional group introduced at the 5' position of one adenosine moiety (sulfonamide, amide, urea) and in the length of the small linking alkyl chain (5 to 8 atoms long). In particular, the X-ray structure reveals a distance of 3.4 Å between the amino group at the 5' end and the oxygen atom of the propargyl linker of compound 1 (Figure 3B). We hypothesized that joining these two positions (by an appropriate linker) would restrict the conformational flexibility of the molecule and provide more intra-and inter-molecular interactions. These properties were expected to provide more potent NADK inhibitors than 1 or its parent compound NKI1.

Structure-based design of novel cyclic diadenosines

From the crystal structures of LmNADK1 with various nucleosidic compounds and especially the lead compound NKI1, we deduced possible substitutions at the 5' end of the free ribose (i.e.

, not yet linked to the propargyl moiety). The best possible cyclization paths from that 5' position to the polar head of the propargyl were first deduced from the position occupied by substitutions of the same ribose in various mono-adenosines previously screened by X-ray crystallography [15,[START_REF] Gelin | Screening and In Situ Synthesis Using Crystals of a NAD Kinase Lead to a Potent Antistaphylococcal Compound[END_REF][START_REF] Clement | New Chemical Probe Targeting Bacterial NAD Kinase[END_REF]. Beside the sulfonamide group as in compound 1 (PDB ID 8A9V), an amide group will also fit well according to a recent complex (PDB ID 6Z65) [START_REF] Clement | New Chemical Probe Targeting Bacterial NAD Kinase[END_REF]. From these two structures, a urea group was also envisioned to bring a stabilizing internal hydrogen bond. On the propargyl side, the main constraints learned from previous structures come from the knowledge of the orientation of the methyl group on an amino propargyl linker [15] and that of a small linear alkyl chain (PDB ID 6Z64) [START_REF] Clement | New Chemical Probe Targeting Bacterial NAD Kinase[END_REF]. The resulting molecules were built virtually and docked into the binding site of LmNADK1. The conformational constraints on the macrocycles were carefully evaluated after energy minimization or docking. For the most relaxed conformations, the interactions with the protein were checked. Chemical feasibility was then considered to focus on the most readily accessible compounds (illustrated in Figure 2).

Chemistry

The general synthetic route to macrocycles MC1-6 is based on intramolecular cyclization of unprotected linear (acyclic) precursors through an amide bond formation. Amide formation was selected for the final ring-closure reaction, as it is compatible with the functionalities of nucleosidic components. The necessary carboxylic acid and amine functional groups were introduced at a determined position of the linking alkyl chain (retrosynthetic disconnection, red dotted lines on Figure 2) to reduce the number of building blocks to be prepared while expecting reasonable cyclization yields. Functionalized linear precursors were obtained via a Sonogashira coupling reaction between two adenosine derivatives bearing either a terminal alkyne or a bromide (as depicted in Scheme 1).

Synthesis of the target compound MC1

The synthesis of MC1 is depicted in Scheme 1. The key intermediate 2b was obtained in two steps from 2',3'-O-protected adenosine via Staudinger reduction of the 5'-azido-derivative 2a [START_REF] Paoletti | 8-Thioalkyl-adenosine derivatives inhibit Listeria monocytogenes NAD kinase through a novel binding mode[END_REF][START_REF] Clement | New Chemical Probe Targeting Bacterial NAD Kinase[END_REF]. Further reaction of 2b with commercially available 2-phtalimidoethanesulfonyl chloride, followed by hydrazinolysis of phthalimide 3a, N-Boc protection and C-8 bromination, resulted in the formation of bromide 4. On the other hand, alkyne 6 was obtained starting from 2b by selective 5'-N-propargylation via the nosyl strategy [START_REF] Kan | Ns strategies: a highly versatile synthetic method for amines[END_REF], followed by reaction of In addition, we synthesized the truncated ligand MC1T to evaluate the contribution of the second adenosine moiety to the inhibitory potency (Scheme 2). Starting from bromide 4, Sonogashira coupling reaction with ethyl 2-N-propargylaminoacetate gave compound 9. Removal of all protecting groups, followed by cyclization of crude 10 under our previously optimized conditions, resulted in MC1 in 11% yield from 9. As observed for compound 8, rapid degradation of 10 occurred under HPLC purification conditions.

2a R = N 3 2b R = NH 2 a 3a R = Nphth 3b R = NH 2 3c R = NHBoc

Synthesis of cyclic molecules MC2 and MC3

We previously showed that the functionalization of NKI1 with an alkyl amide at the 5' end has a little impact on the inhibitory activity against LmNADK1 [START_REF] Clement | New Chemical Probe Targeting Bacterial NAD Kinase[END_REF]. We thus decided to synthesize two cyclic molecules having an amide bond in place of sulfonamide with two lengths for the linker. The synthetic access is

Synthesis of cyclic compound MC4

To evaluate the contribution of the amide bond within the linker to the binding affinity, we synthesized compound MC4 that contains an inverted amide bond as compared to compound 4 (CH 2 CO-NH in place of NH-COCH 2 ) while keeping the same ring size. Sonogashira reaction between bromide 17 and alkyne 18

Synthesis of cyclic compounds MC5 and MC6

We also synthesized two macrocycles having a 5'-urea functional group, compounds MC5 and MC6, that differ by two carbon atoms within the ring. As outlined in Scheme 5, Sonogashira reaction between alkyne

Effects of MC1-6 on LmNADK1 activity

The inhibitory potency of the synthesized macrocyclic compounds (MC1-6, MC1T) and the corresponding linear intermediates were determined in vitro on recombinant LmNADK1 as described previously [15]. The inhibition efficiency (K i ) of macrocycles and their acyclic precursors are compared to that of NKI1, which demonstrated a K i value of 17 ± 1 µM on LmNADK1 (Table 1).

The best inhibitor was MC1, harboring a sulfonamide group, with a K i of 54 nM. The sulfonamide moiety increased the inhibition efficiency 10-fold compared to the amide containing macrocycle of same length, MC3 (Table 1). Increasing the length of the linker seems to favor the inhibition as suggested by the comparison of K i values of MC2 (linker length of 5 atoms) with MC3 (6 atoms) or K i values of MC5 (7 atoms) with MC6 (8 atoms). The orientation of the central amide seems to be crucial as suggested by the comparison of the K i values of MC3 and MC4. The lower inhibition with MC4 (compared to MC3) may be due to the less favorable positioning of this amide bond in the active site of the enzyme (see below). In this series, the most potent inhibitor is the longest one, MC6 (K i of 177 nM), suggesting that a too constrained cycle is not so favorable. However, for most compounds, cyclization reaction led to a higher inhibition of LmNADK1 enzymatic activity (Table 1). It should be noted that the absence of the adenosine moiety (truncated MCT1) decreases dramatically the inhibitory activity compared to MC1 (K i of MC1T > 125 µM).

Effects of compounds MC1-6 on L. monocytogenes and S. aureus growth

Since most of the macrocyclic compounds had better inhibitory potency on recombinant NADKs than NKI1, we next evaluated their ability to affect growth of L. monocytogenes and S. aureus in vitro. As previously reported [15], growth of S. aureus was inhibited by NKI1 or levofloxacin (Figure 4A). However, no significant growth reduction was observed upon treatment with the macrocyclic compounds. Similarly, while L.

monocytogenes growth was severely impaired by levofloxacin, it was not significantly affected by macrocyclic compounds (Figure 4B). Of note, NKI1 did not inhibit growth of L. monocytogenes. Together, these results indicate that while displaying better inhibitory properties on recombinant NADKs, the macrocyclic compounds fail to inhibit bacterial growth in vitro in our experimental conditions. Since deficient bacterial cell penetration could mask antibacterial activity of compounds targeting cytoplasmic targets, we next determined the effect of MC1-6 on growth of L. monocytogenes and S. aureus in vitro in presence of membrane permeabilizing polymyxin B. Interestingly, S. aureus was sensitized to the 6 macrocyclic compounds when incubated with 40 µg/mL polymyxin B (Figure S1). In contrast, MC1-6 were not active against L. monocytogenes irrespective of the concentration of polymyxin tested (Figure S2).

These results suggest that limited access to the target, along with other factors, could contribute to some extent to the absence of activity of macrocycles in our experimental conditions.

X-ray structures of LmNADK1 bound to macrocyclic compounds

The crystal structures of 12 distinct complexes with LmNADK1 were solved and refined to 1.54-2.41 Å resolution (Table S1, Figure 5). Most of these structures were solved after ligand soaking into preformed LmNADK1 crystals. Surprisingly, we could not obtain the crystal structure in complex with the best compound MC1, using the same strategy as for the other macrocycles. Instead, a co-crystallization followed by an additional soaking was necessary to solve the structure of this particular complex (Figure 5B).

Nevertheless, MC1 is accommodated in a very similar fashion as the other compounds and especially MC3

in agreement with their shared length.

These structures provided insight into the precise conformation of six linear precursors and their corresponding macrocycles (Figure 5A-L). First, the two adenosine moieties occupied the same positions as previously observed for other inhibitors such as NKI1. Of note, the whole molecules were clearly visible in the electron densities including the most flexible parts of the linear compounds but for the very terminal amino groups.

In most of the linear compounds (numbered 8, 15, 16, 25 and 26) the carboxylic acid attached to the Npropargyl moiety points toward the solvent and the groove made by the glycine G43 and histidine H223

(which is frequently flipped out in these structures). The amino alkyl chain at the same position in compound 20 points also outward and in the same groove (Figures 5G). The linear precursors of the urea and amide series showed a similar orientation stabilized by one or two hydrogen bonds with the neighboring ribose moiety but for the compound 15 (see Figures 5 and S3A and S3C). In this particular case, the hydrogen bond involved, instead, the amino atom of the propargyl linker. Meanwhile, the short amino alkyl chains differ and point into distinct conformations but the locally weaker electron densities suggest some flexibility. The unique sulfonamide precursor 8 showed a distinct orientation of its 5' extension compared to the other linear compounds, with no hydrogen bond to the second ribose (nor the propargyl nitrogen atom).

The newly formed macrocycles were clearly visible in all the crystal structures in agreement with conformation constraints created by the additional covalent bond and the rigid amide, urea and/or sulfonamide groups present along the short connectors.

Interestingly, it is not the longest ones that showed the major rearrangements upon macrocyclization (see Figures S3 andS4). Indeed, shorter macrocycles led to some reorientation even for the group directly attached to the 5' end. This feature indicates that in some cases, some of the conformation constraints were too strong to be compatible with the good intra-or inter-molecular interactions observed in the linear precursors (such as the hydrogen bonds to the ribose moiety). This is illustrated by the position of the amide group in MC2 (compared to its linear precursor 15). In the linear precursor 15, a weak internal hydrogen bond involves the nitrogen atom of the 5' amide group with the nitrogen atom of the propargyl linker (distance 3.5 Å). In the macrocycle MC2, this hydrogen bond is broken and substituted by a slightly shorter one (3.4 Å) between the amide nitrogen and the oxygen atom O4' of the second ribose (Figures 5C and5D). Still, macrocyclization improves affinity 7-fold in that case. Another type of rearrangement is observed upon macrocyclization of compound 16 into MC3 (Figures 5E and5F). In both compounds, the oxygen atom O4' from the second ribose is involved in two hydrogen bonds but either with the nitrogen atom the 5' amide group (distances: 3.1 Å and 3.4 Å in compound 16) or the nitrogen atom of the newly form amide group (distances: 3.1 Å and 2.7 Å in MC3). Overall, this macrocyclization brings a 40-fold improvement. In the macrocycle MC4, which harbors an inverted amide group (compared to MC3), only one hydrogen bond is kept, and the inverted amide group does not bring any favorable interactions (either with the protein or intramolecularly). In this series, the increase in efficacy seems to correlate with some strengthening of the hydrogen bond network.

In the urea-based series, the shortest macrocycle, MC5, shows a constrained conformation preventing any hydrogen bond involving the urea while its linear precursor did form such a hydrogen bond to the O4' oxygen of the second ribose. This macrocyclization to MC5, with the second longest linker, yields a very modest gain in potency of 2.4. One the contrary, in the one-carbon longer macrocycle MC6, the closed chain and its linear version adopts a similar conformation. In both inhibitors (Figures 5K and5L), two similar hydrogen bonds are formed between the urea nitrogen atoms and the O4' oxygen of the second ribose (distances 3.4 Å and 3.3 Å in MC6 instead of 3.0 Å and 2.9 Å in compound 26). In addition, favorable van der Waals contacts (distances: 3.8 Å and 3.9 Å) between two carbon atoms of the linking alkyl chain and the adenine of the second adenosine (Figure 5L) can be observed. Of note, the one-atom shorter MC5 showed no such contact (Figure 5J). Hence, proper orientation of the urea group seemed to require a slightly longer alkyl chain of 3 carbon atoms instead of 2. While shorter macrocycles were expected to provide higher entropy gain, local constraints due to rigid groups along the closed chain (e.g.: urea, amide) led to counterintuitive results. The lower gain in potency were mainly associated with the loss of intramolecular interactions (mostly hydrogen bonds). Finally, we analyzed the conformation of MC1, the best NADK inhibitor ever described so far (54 nM), while its precursor is one of the weakest inhibitors described here (0.09 mM). The linear and open compound 8

showed no internal hydrogen bond and no compensatory interaction with the protein in agreement with its poor activity. On the contrary, the circular inhibitor MC1 showed a conformation stabilized by two intramolecular hydrogen bonds. One hydrogen bond involved an oxygen atom of the sulfonamide group and the nitrogen of the newly formed amide. Meanwhile, the nitrogen atom of the sulfonamide group is oriented toward the O4' of the second ribose moiety and forms a second hydrogen bond. This partially resembles the situation observed in second best inhibitor, MC6, which harbors a urea group and a twoatom longer linker. Of note, the two oxygen atoms of the sulfonamide do not attract a nearby arginine (R148) that also showed no contact with the carbonyl in LmNADK1-MC6 complex. Therefore, the better affinity of MC1 over MC6 and the other macrocycles would be explained by an internal hydrogen bond between the nitrogen atom of the closing amide and an oxygen atom of the sulfonamide group (Figure 5B).

The sulfonamide group may also be more constrained than the planar urea and this could bring extra entropy gain for its bound conformation. This would contribute to the 9-fold improvement in potency over the compound MC3 of identical length.

While the compounds showed no detectable activity on whole L. monocytogenes bacteria, some activity were detected against S. aureus in presence of polymyxin B. However, ranking of the macrocyclic compounds differs in this assay and in the enzymatic inhibition performed on purified LmNADK1 (SaNADK could not by purified to homogeneity in our hands). Although it might come from some distinct solubility and permeability properties, it might arise also from some variations (T133 and F193 instead of P132 and Y192) in the active site of SaNADK compared to that of LmNADK1. Indeed, one substitution, T133, brings a polar residue in the vicinity of one amide group within the macrocyclization chain. The hydroxyl of the side chain of T133 may form a hydrogen bond with a carbonyl oxygen of this amide group. This could account for the (slightly) better activity of MC2, MC3 and MC5 against S. aureus compared to that of MC1 and MC6, the two most potent inhibitors of LmNADK1. This particular feature may be further exploited to improve the specificity and the potency against staphylococcal NADKs.

Conclusion

New compounds were designed from the various crystal structures of linear diadenosine derivatives bound to LmNADK1, synthesized, and tested in vitro against this still orphan drug target. A series of adenosinebased macrocyclic compounds were successfully obtained through ring closure reaction involving an amide bond formation. The acyclic precursors that contained the complementary functional groups (carboxylic acid and amine) were designed to minimize the building blocks required for generating these 16-to 19membered rings. The macrocycles were formed from unprotected precursors by intramolecular amide bond formation and isolated in acceptable yields (ranging from 8 to 23% depending on the nature of the linking chain). Macrocycles MC1-6 and acyclic precursors were tested in vitro as inhibitors of recombinant NADK from L. monocytogenes (LmNADK1). Macrocyclization was shown to improve the potency against the purified enzyme with up to a 1685-fold improvement in affinity (MC1 versus 8). The 55 nM active compound MC1 (a 10,000 better binder than NAD) represents a novel and promising inhibitor of NADK, the tightest binder described to date. Crystal structures explain the stabilizing features provided by the new linkers. This will help guiding the design of compounds also efficient in vivo.

The resulting macrocycles appeared inactive in vitro against the two pathogenic Gram (+) bacteria, L.

monocytogenes and S. aureus, which express a similar NADK. We cannot exclude lower affinity for the SaNADK, although, comparative docking suggested no major issue. Instead, solubility and cell penetration could be important issues to solve to turn such compounds into new drugs. Indeed, activity of MC1-6 could be revealed by treatment of S. aureus with membrane permeabilizing polymyxin B. In contrast, MC1-6 were not active against L. monocytogenes in presence of polymyxin B. Several factors could account for this difference, such as specific envelop composition, metabolic properties and presence of a second NADK in L.

monocytogenes. Vectorization or other strategies to improve their cell penetration will be implement in the future to fully benefit for their potent inhibition of the NADKs from Gram (+) pathogens [START_REF] Benedetto Tiz | Overcoming problems of poor drug penetration into bacteria: challenges and strategies for medicinal chemists[END_REF][START_REF] Prajapati | How to Enter a Bacterium: Bacterial Porins and the Permeation of Antibiotics[END_REF][START_REF] Zeiders | Antibiotic-cell-penetrating peptide conjugates targeting challenging drug-resistant and intracellular pathogenic bacteria[END_REF].

The observed conformations of these new inhibitors and the orientation of the surrounding residues provide new information in order to derive additional chemical modifications to improve further this chemical series. While considered as highly promising for improve pharmacological properties and inhibitory potencies, macrocycles are still seldom characterized structurally and too few examples have been described at atomic details despite seminal examples (see [START_REF] Mallinson | Macrocycles in new drug discovery[END_REF] for macrocyclic natural products) and now some protein-kinase inhibitors [START_REF] Amrhein | Synthetic Opportunities and Challenges for Macrocyclic Kinase Inhibitors[END_REF][START_REF] Kurz | Illuminating the Dark: Highly Selective Inhibition of Serine/Threonine Kinase 17A with Pyrazolo[1,5-a]pyrimidine-Based Macrocycles[END_REF]. This study also revealed, in very precise details, the contradicting constraints in the design of macrocycles and the difficulties to anticipate the outcome of ring closure. This should be very helpful for the design of potent ligands beyond the scope of the NADK inhibition and it may constitute an interesting gold standard to calibrate theoretical methodologies dedicated to drug design by macrocyclization.

Materials and methods

Structure-based design of novel cyclic diadenosines

Virtual molecules were designed by derivation from the compound NKI1. 3D conformers were generated from SMILE strings using Grade (http://grade.globalphasing.org/). The resulting compounds were docked in the electron density of NKI1 and minimized in various conformations compatible with the environment using COOT [START_REF] Emsley | Features and development of Coot[END_REF]. Virtual docking was also performed using PLANTS [START_REF] Korb | Application of Ant Colony Optimization to Structure-Based Drug Design[END_REF]. The results were compared to extract the most plausible conformations. Interactions with the neighboring residues were analyzed and some reorientations of nearby side-chains were also modeled. From this focused screen, a selection of optimal paths for connecting the two 5' ends of our di-adenosine lead-compound were scrutinize for synthetic feasibility.

X-ray structures of LmNADK1 bound to cyclic compounds

The soaking procedure described previously [15] was used to get crystals of the target bound to the synthesized macrocycles MC2-6. For MC1, direct soaking failed and instead co-crystallization at alkaline pH was attempted and showed partial occupancy of the macrocycle into the active site. Full occupancy was achieved by soaking more compound prior to mounting and freezing the corresponding crystals. The X-ray structures were solved by molecular replacement using PDB ID 6RGC [15] as a starting template and refined to 2.2-2.4 Å resolution using the software Phenix [START_REF] Adams | PHENIX: a comprehensive Python-based system for macromolecular structure solution[END_REF]. Manual rebuilding in the electron densities were performed in COOT [START_REF] Emsley | Features and development of Coot[END_REF]. A few rounds of optimization rapidly led to convincing refinements for all the structures.

Chemistry

Chemicals and General Methods

Commercially available reagents and solvents, unless otherwise stated, were purchased from commercial suppliers, and used as received. All air and water sensitive reactions were carried out under an argon atmosphere. Analytical thin-layer chromatography (TLC) was performed on TLC plates pre-coated with silica gel 60 F 254 . Compounds were visualized with UV light (254 nm) and by spraying with a mixture of ethanol/anisaldehyde/sulfuric acid/acetic acid (90/5/4/1), followed by heating. Reactions were also monitored using an HPLC system (Agilent 1200 equipped with a C18 reverse phase column) coupled to a mass spectrometer (ESI source). Flash chromatography was performed with silica gel 60 (230-400 mesh).

HPLC purification was carried out on an Agilent system (1100 Series) equipped with a diode array detector using a C18 reverse phase column (Kromasil, 5 μm, 100 Å, 250×10 mm) and a linear gradient of acetonitrile To a solution of 3c (0.60 g, 1.17 mmol) in a mixture of dioxane/acetate buffer pH 5.3 (8.2 mL/4.7 mL) was slowly added dropwise bromine (0.12 mL, 2.34 mmol). After stirring for 4 h at room temperature, the reaction was quenched by addition of saturated aqueous solution of Na 2 S 2 O 3 and the mixture was

In vitro enzymatic activity inhibition assays

NADK1 of L. monocytogenes (EC 2.7.1.23) with a 6-His tag at the C-terminus was expressed and purified as described previously [START_REF] Poncet-Montange | NAD kinases use substrateassisted catalysis for specific recognition of NAD[END_REF]. The enzymatic reaction was followed at 30°C in a CLARIOstar plate reader (BMG LABTECH) by measuring the absorbance at 340 nm (OD 340 ) using an enzymatic coupled system involving glucose-6-phosphate dehydrogenase (G6PDH).

Antibacterial activity

Overnight cultures of either S. aureus Xen36 (Caliper Life Science) or L. monocytogenes EGDe (BUG1600) in BHI at 37°C were diluted 1:100 into BHI broth. For S. aureus growth inhibition assay, BHI broth was supplemented with 25% PBS/10% DMSO (negative control), 10 μg/mL levofloxacin (positive control), NKI1 [15] or cyclic compounds (this study) in 25% PBS/10 % DMSO. For L. monocytogenes growth inhibition assay, BHI broth was supplemented with 25% PBS/1% DMSO (negative control), 10 μg/mL levofloxacin (positive control), NKI1 or cyclic compounds in in 25% PBS/1% DMSO. Growth inhibition was also determined for each cyclic compounds with polymyxin B at 20, 40, 60 and 80 µg/mL in BHI supplemented with 25% PBS/10% DMSO (for S. aureus) or 25% PBS/1% DMSO (for L. monocytogenes). Bacterial suspensions were incubated in 96-well plates, with shaking at 200 rpm at 37°C. Growth was monitored with a microplate reader (Glomax Discover, Promega).

Comparative modeling

The structure of the NADK from S. aureus was modeled based on various conformation of LmNADK1 using the webserver @TOME-2 [START_REF] Pons | @TOME-2: a new pipeline for comparative modeling of protein-ligand complexes[END_REF]. These models were superposed to the new complexes of LmNADK1 solved in this study to evaluate the specific protein-ligand interactions due to some residue substitutions observed in the active site (especially T133 in SaNADK instead of P132 in LmNADK1).

Figure 1 :

 1 Figure 1: Chemical structures of NAD and NKI1

Figure 2 : 1 .Figure 3 :

 213 Figure 2: Chemical structures of macrocycles MC1-6 (amide disconnection in red dotted lines)

Scheme 2 :

 2 Scheme 2: Synthesis of truncated ligand MC1T Reagents and conditions: (a) Pd(PPh 3 ) 4 , CuI, TEA, THF, 60°C, 4 h, 75%; (b) (1) 2N NaOH, dioxane, rt, 3 h; (2) 50% aq. TFA, 0°C to rt, 5 h; (c) PyBOP, DIEA, DMF, rt, overnight, 11% from 9 after HPLC purification.

4

 4

outlined in Scheme 3 .Scheme 3 :

 33 Scheme 3: Synthesis of MC2 and MC3 Reagents and conditions: (a) Pd(PPh 3 ) 4, CuI, TEA, THF, 60°C, 3 h, 60-35%; (b) (1) 2N NaOH, dioxane, rt, 3 h; (2) 50% aq. TFA, 0°C to rt, 5 h, 75-63%; (c) PyBOP, DIEA, DMF, rt, overnight, 23% from 15 and 15% from 16, after HPLC purification.

Scheme 4 :

 4 Scheme 4: Synthesis of MC4 Reagents and conditions: a) Pd(PPh 3 ) 4 , CuI, NEt 3 , THF, 60 °C; b) TFA/H 2 O, 22% in two steps; c) PyBOP, DIEA, DMF, rt, overnight, 8% after HPLC purification.

6Scheme 5 :

 5 Scheme 5: Synthesis of MC5 and MC6 Reagents and conditions: (a) Pd(PPh 3 ) 4, CuI, TEA, THF, 60°C, 3 h, 60-35%; (b) (1) 2N NaOH, dioxane, rt, 3 h; (2) 50% aq. TFA, 0°C to rt, 5 h, 75-63%; (c) PyBOP, DIEA, DMF, rt, overnight, 23% from 25 and 15% from 26.

Figure 4 :

 4 Figure 4: Macrocyclic compounds do not affect growth of S. aureus or L. monocytogenes in vitro. A) Percentage of S. aureus growth after 6h of culture in BHI broth at 37°C supplemented with PBS/10% DMSO, 10 μg/mL levofloxacin, 0.01 μg/mL NKI1 or macrocyclic compounds. Bars indicate the standard errors of the means of biological replicates (n=6). Comparison of data was performed using one-way analysis of variance (****p < 0.0001, **p < 0.01, ns: non-significant). B) Percentage of L. monocytogenes growth after 6h of culture in BHI broth at 37°C supplemented with PBS/1% DMSO, 10 μg/mL levofloxacin, 0.01 μg/mL NKI1 or macrocyclic compounds. Bars indicate the standard errors of the means of biological replicates (n=6). Comparison of data was performed using one-way analysis of variance (****p < 0.0001, ns: non-significant).

Figure 5 :

 5 Figure 5: Crystal structures of linear precursors and corresponding macrocycles bound to LmNADK1. (A-L) Macrocycles MC1-6 and their precursors are shown as sticks in CPK colors (with green carbons) in the active site of LmNADK1. The protein is drawn as grey ribbons. The electron density is shown as a green surface around the ligand, which is the view of the 2Fo-Fc electron density map contoured at 0.9 σ. Black dashed lines indicate intramolecular hydrogen bonds. The figures were drawn using Pymol (www.pymol.org).

  in 10 mM triethylammonium acetate (TEAA) buffer over 15 or 20 min at a flow rate of 4 mL/min. Retention time (t R ) and gradient are specified. NMR spectra were recorded on Bruker Avance 400 ( 1 H at 400.13 MHz and13 C at 100.62 MHz). Chemical shifts (δ) are reported in parts per million (ppm) relative to the solvent signals. Coupling constants (J values) are reported in Hz and splitting patterns are indicated as follows: s = singlet, d = doublet, t = triplet, q = quartet, p = pentet, m = multiplet, br = broad signal. The complete assignment of 1 H and13 C signals was performed by analysis of the correlated homonuclear 1 H,{ 1 H}-COSY and heteronuclear 1 H,{ 13 C}-HMBC, 1 H,{13 C}-HSQC spectra. High-resolution mass spectra (HRMS) were recorded with a Q-Tof Micro mass spectrometer under electrospray ionization in positive ionization mode using a mobile phase of acetonitrile/water with 0.1% formic acid. The purity of all tested compounds was greater than 97% as determined using NMR spectroscopy and HPLC analysis (C18 Kromasil) using a linear gradient of acetonitrile in 10 mM TEAA buffer over 15 or 20 min at a flow rate of 1 mL/min. Synthetic procedures for intermediate building blocks (outlined in Scheme S1-S4) and compounds MCT1, MC2, MC3, MC4 and MC5 are included in Supplementary information. Synthesis of MC1 and MC6 is given below.

  The reaction mixture was made in a half area 96-well microplate (Greiner Bio-One Clear UV-Star). Buffer used were 50 mM Bis-Tris pH 7.0, 2 mM NaCitrate Tribasic Dihydrate, 1 mM MgCl 2 . Reaction mixtures contained 25 nM LmNADK1, 0.1-2 mM NAD (12 different concentrations), 4 mM MgATP, 5 mM glucose-6-phosphate, 1 U/mL G6PDH and 4-8 different concentrations of inhibitors in the range of the K i values. Buffer, NAD and inhibitor solutions (total volume 25 µL, concentration 4×) were dispensed into the microplate using an OT-2 lab robot (Opentrons). 25 µL of a mixture containing G6PDH, MgATP and glucose-6-phosphate at a 4× concentration were dispensed using a manual repeating pipette. Finally, the reaction was started at time 0 by adding and mixing 50 µL of LmNADK1 at a 2× concentration using a multichannel pipette. OD 340 were measured every 32 s during 32 min. The values of the inhibition constants were determined by global fittings of the steady state rate constants, k ss , at different concentrations of NAD and inhibitor, with a competitive inhibition equation using GraFit software (version 7.0.3, Erithacus Software Ltd). The inhibition assays were carried out in independent triplicates.

  5 with ethyl bromoacetate. Sonogashira reaction of 4 with 6 gave the coupling product 7 in moderate yield. Two-step global deprotection resulted in the diadenosine 8 as TFA salt.

	Scheme 1: Synthesis of MC1
	Reagents and conditions: (a) PPh 3 , pyridine, 3 h, then NH 4 OH, 4h, 92%; (b) PhthNCH
	Attempts to further purify the crude product by reverse phase HPLC using 10 mM TEAA buffer (pH 6)
	resulted in the formation of unknown compounds. Intramolecular cyclization into MC1 was achieved by
	reaction of crude 8 with PyBOP in the presence of DIEA in DMF under high-dilute condition.

2 CH 2 SO 2 Cl, TEA, DMF, rt, 1 h; (c) Hydrazine hydrate, EtOH, rt, 5 h, 49% from 2b; (d) Boc 2 O, DMF, rt, 18 h, 84%; (e) Br 2 , dioxane/acetate buffer, rt, 4 h, 86%; (f) (1) NsCl, pyridine, 88%; (2) Propargylbromide, K 2 CO 3 , DMF, rt, 3 h; then PhSH, rt, overnight, 89%; (g) ethyl bromoacetate, DIEA, DMF, rt, overnight, 86%; (h) Pd(PPh 3 ) 4, CuI, TEA, THF, 60°C, 4 h, 35%; (i) (1) 2N NaOH, dioxane, rt, 3 h; (2) 50% aq. TFA, 0°C to rt, 5 h; (j) PyBOP, DIEA, DMF, rt, overnight, 8% from 7 after HPLC purification.

Table 1 :

 1 Inhibitory activities of linear intermediates and macrocyclic compounds on LmNADK1 and effect of cyclization expressed as potentiation factor.

	Ligand	X	Y	K i LmNADK1 a (µM)	Cyclization potential (fold)
	1		O	5.5 ± 1.5	N/A
	8			91 ± 4
					1685
	MC1			0.054 ± 0.006
	15			104 ± 2
					6.9
	MC2			15 ± 2
	16			19 ± 4
					38
	MC3			0.504 ± 0.070
	20			53 ± 6
					7.8
	MC4			6.8 ± 2.0
	25			18 ± 4
					2.4
	MC5			7.6 ± 2.5
	26			16 ± 6
					90
	MC6			0.177 ± 0.017

a Data represent the mean ± standard error of the mean (SEM) values of at least three independent experiments. Compounds were tested at different concentrations chosen on either side of the K i value.

Acknowledgments

This work was funded by the Agence Nationale de la Recherche (NADKiller, ANR-17-CE18-0011-02) and supported by Institut Pasteur, the Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM) and University of Montpellier. C.L. and D.A.C. acknowledge ANR for financial support. This work was also supported by the French Infrastructure for Integrated Structural Biology (FRISBI, ANR-10-INSB-05-01). We acknowledge the experimental assistance from the staff of the European Synchrotron Radiation Facility (ESRF, Grenoble, France, beamlines ID23-2, ID30-A1, ID30-A3) and the synchrotron ALBA (Barcelona, Spain, beamline BL13-XALOC) during data collection. The authors would like to thank Frédéric Bonhomme (CNRS, UMR3523, Institut Pasteur) for assisting with HRMS analysis.

Appendix A Supporting information/ Supplementary data

Supplementary data related to this article can be found at http://

Accession Codes

The crystal structures of LmNADK1 bound to the macrocycles MC1-6 are deposited under accession codes PDB: 8B47, 7ZZH, 7ZZJ, 7ZZF, 7ZZG and 7ZZE, respectively. The complexes of LmNADK1 with the acyclic precursors are available in the PDB under the accession codes: 7ZZ7, 7ZZ9, 7ZZA, 7ZZB, 7ZZC, 7ZZD and 8A9V.


Alex Limin Wang

Sanjeev Bista
email: sanjeev.bista@iplesp.upmc.fr

Arnaud Can
email: arnaud.can@univ-eiffel.fr

Basile Chaix
email: basile.chaix@iplesp.upmc.fr

Alex Limin
email: limin.wang@iplesp.upmc.fr

Personal noise exposure during daily commutes and subjectively reported stress: A trip stage level analysis of MobiliSense data

Keywords: Noise exposure, transport, stress

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

Introduction

Noise is a pervasive component of day-to-day life and is associated with both auditory and nonauditory health effects like cardiovascular diseases, diabetes, anxiety and depression [START_REF] Basner | Auditory and nonauditory effects of noise on health[END_REF][START_REF] Ising | Health effects caused by noise : Evidence in the literature from the past 25 years[END_REF][START_REF] Lusk | Acute Effects of Noise on Blood Pressure and Heart Rate[END_REF][START_REF] Mette | Long-Term Exposure to Road Traffic Noise and Incident Diabetes: A Cohort Study[END_REF][START_REF] Dzhambov | Self-reported occupational noise may be associated with prevalent chronic obstructive pulmonary disease in the us general population Dzhambov AM, Dimitrova DD -Noise Health[END_REF].

Existing studies on the causal relationship between noise exposure, noise perception and health have yielded inconsistent findings which may be attributed to conceptual and methodological issues [START_REF] Matthews | The Salience of Neighborhood: Some Lessons from Sociology[END_REF]. Only a small number of studies have attempted to examine how momentary perceived noise can influence people's subjective perceptions. Bild et al. postulates that a sound is determined to be a "noise" if it interferes with an individual's daily activities and social interactions [START_REF] Bild | Public Space Users' Soundscape Evaluations in Relation to Their Activities. An Amsterdam-Based Study[END_REF]. Other factors, like the time of day when the noise occurs, as well as the duration of the occurrence can also affect noise perception [START_REF] Stansfeld | Noise and Health in the Urban Environment[END_REF][START_REF] Levy-Leboyer | Neighbourhood noise annoyance[END_REF]. Existing studies in the literature also tend to focus on individuals' chronic noise exposure to one specific type of noise from a specific source (e.g. road traffic, railway, or aircraft) usually at one particular geographic location (e.g. school, home, or workplace) [START_REF] Mette | Long-Term Exposure to Road Traffic Noise and Incident Diabetes: A Cohort Study[END_REF][START_REF] Seidler | Aircraft, road and railway traffic noise as risk factors for heart failure and hypertensive heart disease-A case-control study based on secondary d[END_REF][START_REF] Willich | Noise burden and the risk of myocardial infarction[END_REF][START_REF] Selander | Long-Term Exposure to Road Traffic Noise and Myocardial Infarction[END_REF]. It has also been emphasized that studies tend to focus on noise in residential areas and ignore exposure in public urban areas [START_REF] Jiang | Valuing transport noise impacts in public urban spaces in the UK: Gaps, opportunities and challenges[END_REF]. Indeed, as participants are dynamic in their daily movements (e.g. running errands, buying groceries), they are exposed to multiple sources of noise over various geographic locations for varying amounts of time [START_REF] Kwan | From place-based to people-based exposure measures[END_REF][START_REF] Kwan | Reconceptualizing Sociogeographic Context for the Study of Drug Use, Abuse, and Addiction[END_REF]. Neglecting the dynamics of individuals' daily movements can lead to a substantial misclassification of the overall exposure [START_REF] Yu | Quantifying the impact of daily mobility on errors in air pollution exposure estimation using mobile phone location data[END_REF]. Therefore, taking dynamic spatiotemporal data of how individuals interact with their environment helps to establish a more accurate assessment of the relationship between environmental noise exposure and stress [START_REF] Matthews | The Salience of Neighborhood: Some Lessons from Sociology[END_REF][START_REF] Perchoux | Conceptualization and measurement of environmental exposure in epidemiology: Accounting for activity space related to daily mobility[END_REF][START_REF] Rainham | Conceptualizing the healthscape: Contributions of time geography, location technologies and spatial ecology to place and health research[END_REF][START_REF] Srivastava | On the temporal variation of human activity spaces[END_REF].

This study therefore proposes a novel mobility survey strategy based on Global Positioning System (GPS) receivers which decomposes trips into trip stages, and utilizes location information and participants' confirmation to identify the modes of transportation taken. It also incorporates noise exposure for each trip stage. Based on this novel methodology, the empirical aim of this study was to utilize the accuracy gains offered by this mobility survey to assess (i) how personal exposure to noise (overall and low-and high-frequency noise) varied according to activity patterns and during personal transport activities (i.e. by transportation mode); and (ii) whether personal exposure to noise during trips was associated with the reported stress levels of participants.

Methods

Population

Participants included adults of both genders from the Mobilisense cohort which was recruited using a two-stage stratified sampling design. In the first stage, neighbourhood sampling took place through the random selection of local neighbourhoods in the Metropolitan Area of Paris (Grand Paris). Neighbourhoods were stratified by quartiles of area-level household income as well as by quartiles of road traffic density (using the traffic model from the Ministry of Infrastructure). Within each income area stratum, 30 neighbourhoods were randomly selected in each of the two extreme quartiles of traffic density, resulting in 60 neighbourhoods in each area income quartile and 240 neighbourhoods overall. In the second stage, census information collected by the French National Institute of Statistics and Economic Studies (Insee) was used to sample dwelling units in each of the selected neighbourhoods. This sampling design was useful to maximize disparities in exposure to air pollutants and noise, while allowing us to document deviations from representativity to the background population of the Grand Paris. Overall, 33,501 dwellings were selected from the 240 previously identified neighbourhoods based on the 2013-2014 censuses. Demographic and sociodemographic information on these dwellings was also obtained from the census. Each dwelling was contacted twice by postal mail. Finally, 282 eligible participants aged from 30 to 64 years old were included between May 2018 and March 2020. This age range was selected to reflect a segment of the general adult population in Grand Paris that is both likely to be integrated in professional life (as we wanted to look at trip-level exposure during commutes) and is potentially affected by the onset of chronic diseases. We applied the following inclusion criteria: speaking French, being free of cardiovascular, cerebrovascular or specific contagious pulmonary diseases and glaucoma, not wearing an implanted device, not wearing an auditive device and not having audition problems, not being pregnant and not breastfeeding a child, not being a smoker and not living in a smoking household (for the proper functioning of air pollution sensors), not intending to move outside the Grand Paris area during the 2-year duration of the study, not being a night worker, not working outside the region 4 days or more per week, and not being cognitively impaired.

The participants were recruited at their home after signing an informed consent letter. Prior to sensor-based assessment, participants were guided through pre-study computerized questionnaires which collected information pertaining to dimensions including but not limited to: socioeconomic status, occupational history over 2 years, health-related behaviour (e.g., physical activity, past smoking, etc.), resources for transport (e.g., driving license, public transport pass, etc.), perceptions related to air pollution and noise, etc. Participants carried sensors over a 6 day period and then underwent a GPS-based mobility survey along with a post-questionnaire during a follow-up phone survey. The GPS-based mobility survey targeted the period where the GPS receiver and other sensors where worn. The sampling and data collection protocol of MobiliSense was approved by the National Council for Statistical Information, the French Data Protection Authority, and the Ethical Committee of Inserm.

Sensor based protocol and mobility survey

Participants wore a Class I equivalent dosimeter SV 104A on their belt with the microphone secured near the ear and on top of clothing for 4 days during their commutes and were instructed to recharge it overnight. Noise dosimeters had three filters available to provide three distinct measurement profiles for sound [START_REF]SV104 Noise Dosimeter[END_REF]. The three filters (or weightings) A, C, and Z are as follows:

A approximates the range of sounds heard by the human ear; C is a standard weighting of the audible frequencies but focuses more on the effect of low-frequency sounds as well as peak sounds resulting from sudden or brief noises (i.e. crashes or bangs) [START_REF]Pulsar Instruments. Understanding A-C-Z noise frequency weightings | Environmental XPRT[END_REF] ; Z represents the actual noise that is made with no weighting (Z stands for zero) and ranges from 8Hz to 20kHz. . Personal dosimeters recorded noise level measurements between 20Hz and 10kHz per second and were calibrated before and after use by each participant following the manufacturer's instructions. In order to account for the natural scope of human hearing which ranges from 20Hz to 20kHz with reduced sensitivity to low and high frequency sounds (below 1kHz and above 4kHz) [START_REF] Wereski | The Threshold of Hearing[END_REF], sound level measurements were "weighted" using Aand C-weightings to produce one second intervals of A-and C-weighted measurements (noted as LAeq,1s and LCeq,1s respectively). In order to take into account noise level fluctuations over a period of time, the "average energy" or Leq value is calculated to produce an energetic mean. The Leq is not a simple arithmetic average as decibels are measured in logarithmic values. It reflects the constant noise level that would have been produced with the same energy rather than the noise actually perceived during the given period.

For each trip stage window, several acoustic indicators were computed. First, the equivalent continuous sound level (Leq) with A-and C-weightings was calculated. Second, we determined so-called spectral indicators for bands of low frequency (20Hz to 125Hz), medium frequency (160Hz to 2kHz) and high frequency (2.5kHz to 20kHz). For each frequency interval, based on the one second A-weighted equivalent continuous sound levels, an energetic mean was calculated for each trip stage to produce LAeq[20Hz-125Hz],T LAeq[160Hz-2kHz],T and LAeq[2.5kHz-20kHz],T These are henceforth referred to as low frequency, medium frequency, and high frequency noises; in this case, the subscript "T" refers to the time period during which the measurement was taken. Third, we calculated LCeq,1s-LAeq,1s noted CA,1s. This difference accounts for low frequency sounds below 1kHz. The average of such difference at the second level was calculated for each trip stage.

Participants also wore a GPS receiver and were asked to complete a travel diary on the places visited and modes of transportation taken as supporting information for the mobility survey, which was carried out a few days after the end of data collection. In order to assess participants' transportation modes and stress reports per trip, GPS tagged commutes were uploaded to Tripbuilder Web, an online application which integrates Google Maps and which automatically generates trip stages from location data and identifies the mode of transportation taken for each trip stage. According to Heshner and Button, trips often involve different modes of transportation (e.g. walking and public transportation), while trip stages refer to the unimodal portions of trips (segments of trips which are based on a unique mode of transportation [START_REF] Hensher | Handbook of Transport Modelling: 2nd Edition[END_REF]. A purely unimodal trip occurs only if the same mode of transportation is taken from the departure place to the destination without the use of any other modes (including walking).

Once data collection was completed, research assistants used the GPS data and identified trips and transportation modes displayed in the Tripbuilder web mapping application to conduct, with minimal delays after the GPS follow-up, a telephone interview with the participants (mobility survey). Only research assistants had access to the application screen while participants were sent detailed paper screenshots of their GPS trips via postal mail. In addition, the research assistants considered the paper travel diary as supportive information during the telephone mobility survey. Participants were walked through the different days and through each trip stage taken to help facilitate recall. Research assistants confirmed or if necessary, manually edited the type of transportation taken at each trip stage. Participants were also successively asked to report a posteriori the level of stress experienced for each trip stage and whether any particular trip stage over that day was more stressful than the others. The data collection steps as well as the type of data collected in each stage are detailed in Figure 1. Stress level was coded on a scale of 1 to 7 for each trip stage. The stress scale corresponds to the following: 1 -Conditions are perfect, no stress; 2/3/4 -Objectively stressful conditions were present, which however did not bother the participant, with a gradation of 2 (mild), 3 (intermediate), and 4 (significant); 5/6/7 -Objectively stressful conditions which stressed the participants slightly (=5), intermediately (=6), and significantly (=7).

It was specified that the sources of discomfort and stress must be linked either to the transport conditions themselves (e.g. traffic jam, cancelled train, crowded metros, etc.) or to the circumstances of the trip (e.g. the person was late, he fell, etc.). A summary sentence was then used to identify stressful trip stages and to pinpoint whether the travel conditions themselves were really the source of stress, such as: "That day, when you took the metro in the morning to go to X, or in the afternoon to go to Y or in the evening to go to Z, were the travel conditions particularly stressful? For example, was the metro crowded? Were there any delays or were you running late?" By the responses given by the participants, research assistants placed the conditions on the stress scale.

Classification of trips

Based on the travel modes indicated in each trip stage, we discerned the following categories: walking only; other active modes (biking, rollerblading, skateboarding, etc.); personal motorized transport (driver); personal motorized transport (passenger); RER/TER/SNCF; bus; metro; tram; and other. RER/TER/SNCF incorporates the RER (higher speed trains travelling to and from the suburbs), TER (trains from Paris towards suburbs or adjacent regions), and SNCF standard suburban trains. Personal motorized vehicles (as a passenger or driver) include both fourwheeled motorized vehicles (including taxis) and two-wheeled motor vehicles. The "other" category incorporates miscellaneous modes like other long-distance trains, plane trips, boats, etc.

Sociodemographic and time-related covariates

Age was coded in 3 categories. Education was coded into 4 categories: no education/primary education/lower secondary education; higher secondary and lower tertiary; intermediate tertiary; and upper tertiary education. Employment status was classified into 4 categories: stable job; unstable or temporary job; unemployed; and other (e.g. retired). Household income levels were calculated by standardizing average household income by family size (one unit per member ≥14 years, 0.5 unit otherwise) and then divided into quartiles.

With regards to time, our analysis distinguished weekends from weekdays, as well as time of the day (morning: 1:00 am -9:59 am; day: 10:00 am -6:59 pm; evening: 7:00 pm 0:59 am).

Statistical analysis

The dataset consisted in the trip stage windows for each participant with corresponding information on the mode of transportation, amount of stress experienced, and the appropriate acoustic indicators.

Objective 1 involved assessing the relationship between transportation modes and noise indicators, using linear mixed effect models. Noise exposure was measured according to the various indicators. Both individual random effect and temporal autocorrelation [AR(1) structure] were included in the linear model. All analyses were performed in R version 1.2.5042 [START_REF] Team | RStudio | Open source & professional software for data science teams[END_REF].

Mixed models were estimated using the nlme package version 2.1-2.131, while plots were made using the ggplot2 package version 2.2.1.

Objective 2 was the assessment of the relationship between noise and stress. A quasi-Poisson model was used to assess the relationship between noise exposure and stress. The quasi-Poisson model was chosen over a Poisson model as there was evidence of overdispersion of the outcome.

The stress variable was transformed into "stress-minus-1" in order to obtain a count variable ranging from 0 to 6 rather than from 1 to 7. Since the outcome was log transformed, the regression coefficients were exponentiated back. This can then be interpreted as multipliers of the stress level for a particular variable. In order to take repeated measurements into account, individual random intercepts were incorporated in the model. Temporal autocorrelation between the repeated measurements within each participant was taken into account by using an autoregressive AR(1) function which assigns a covariance structure with correlation that decreases with increases in time intervals separating the measures for a participant [START_REF] Kincaid | Guidelines for Selecting the Covariance Structure in Mixed Model Analysis[END_REF][START_REF]Theory and Computational Methods for Linear Mixed-Effects Models[END_REF].

Quasi-Poisson models were run using the GLMMadaptive package version 0.6-8 and the MASS package version 7.3-51.4. Root mean square error (RMSE) values were used to compare the fit of models.

Results

Descriptive information on the sample

Among our 282 participants, the noise data collection failed for 17 participants, and another 12 participants lacked noise frequency band data. These participants were excluded, leaving 253 participants for the present analysis. Of the 12994 stages of trips identified for these participants, 4264 were made on days where the protocol did not include a noise data collection. We further excluded 916 trip stages for which noise data were missing, and 14 trip stages for which there was less than 50% of the noise data. We analysed data on 7800 trip stages from 253 participants. Among this final sample, 58% were women; 73% had a permanent job, 13% were retired, and 4% unemployed; and 50% had three or more years of university education.

Overall, 6522 (83.6%) trip stages were reported as being "1" on the stress scale while only 24 (0.31%) were reported as "6" and 14 (0.18%) as "7". In the distribution of trip stages, 59.3% were entirely walked trips; 5.1% using bikes/roller-skates/skateboards; 2.6% were with buses/coaches, 7.5% with metros, 4.1% with suburban trains, and 1.2% with tramways; and 16.8% and 3.2% involved personal motorized vehicles as the driver or passenger, respectively.

The mean duration of trip stages was 12.1 minutes, with the median duration being 6.5 minutes (standard deviation 20.1). The range of the duration of trip stages was 0.05 minutes to 567 minutes.

Modes of transportation and noise exposure

As shown in Figure 2, the median LAeqT exposure was highest for trips involving the metro As shown in Table 1, mixed effects models were generated for each noise indicator as the outcome, and included participant random effect and temporal autocorrelation, as well as sociodemographic variables as a way of correcting for the sociodemographic distortions in the sample.

Regarding LAeq, individuals in the study experience on average a 5.5 dB(A) (95% CI 4.7, 6.3) increase in noise exposure when taking the metro compared to walking as well as a 4.0 dB(A) (95% CI 3.0, 5.1) increase when taking suburban trains (RER/TER/SNCF) compared to walked trips. Although to a lower extent, there were also indications of a higher LAeq noise exposure in the bus, in the tram, when driving a car, and with other active modes compared to walking. Furthermore, when compared to walking, all transport modes were associated with a higher exposure to low frequency noise, especially those involving taking the bus and driving a car. All transport modes except for taking the tram or driving a car were related to a higher exposure to high frequency noise when compared to walking, and this is especially true for using the metro.

Relationship between noise exposure and stress

Table 2 reports how the potential confounders were related to the reported stress in trips in quasi-Poisson models. Sociodemographic covariates showed no association. Weekend trips were associated with a lower reported stress while morning trips were related to a higher stress.

Certain modes of transportation resulted in higher reported levels of stress. Compared to walked trips, taking the bus/coach led to 1.859 times (95% CI 1.518, 2.277) higher stress levels; metro 1.862 times (95% CI 1.639, 2.114); and RER/TER/SNCF 2.022 times (95% CI 1.724, 2.370) higher stress levels. Using a car as a driver or passenger was also associated with higher levels of stress than walking. However, it is for the other active modes that the reported stress level was the highest, with a ratio of 2.796 (95% CI 2.338, 3.343).

Table 3 presents the associations between noise exposure (6 different indicators) and subjective stress, adjusted for the variables reported in Table 2. In order to test whether noise indicators were nonlinearly associated with stress, quadratic terms were also added into the model. However, as there was no evidence of quadratic patterns, the squared noise indicator terms were dropped from the models. In 1-indicator models, for every 10 dB(A) increase in LAeqT, the level of reported stress was shown to increase by 1.118 times (95% CI 1.067, 1.172). Similar trends were observed for the remaining acoustic indicators (except for CA), possibly due to the high correlation between noise indicators (see Figure 3). As a result, r-squared values and the root mean square error (RMSE) indicators among the different models were relatively similar.

We then estimated models for the trip-level stress outcome including the LAeq and each time one additional indicator (bottom of Table 3). In these models, only LAeq remained associated with stress, while all of the other indicators lost their association with the outcome.

Discussion

Summary of findings and interpretation

This study analysed the association between several noise indicators and stress in daily commutes within a real-life setting. We documented disparities in noise exposure across transportation modes. We controlled for sociodemographic factors due to the related distortions in our sample: people from different social and demographic backgrounds typically use different transportation modes and participants included in our small sample may not be representative of their sociodemographic groups in terms of place of residence and transportation modes used. Thus, it was necessary to control for these variables to avoid sample distortions which would affect the transportation modes' effects on exposure and stress. We also controlled for the day of the week and the time of day. Previous studies have indeed found that the subjectivity of noise perception is influenced by the time of day during which the noise occurs [START_REF] Stansfeld | Noise and Health in the Urban Environment[END_REF] and that noise is less likely to be perceived as being "normal" when it occurs at night (between 10:00 p.m. and 8:00 a.m.) compared to when it occurs during the day [START_REF] Levy-Leboyer | Neighbourhood noise annoyance[END_REF].

A higher overall level of noise exposure was documented in metros and in suburban trains, and to a lower extent in other modes compared to walking. However, our study which assessed personal exposure to low and high noise frequencies brought novel information by showing that exposure to particular noise frequencies also varied by transportation modes. For instance, while taking the bus and driving were associated with particularly higher exposure to low frequency noise compared to walking, using the metro is especially associated with higher exposure to high frequency noise. Contrary to the metro, taking the tram or driving a car were associated with a lower exposure to high frequency noise than walking. The noise at these high frequencies (>2.5kHz) heard in the tram or in a car comes mainly from the outside of the vehicle, which filters out some of the noise. This is different to the metros, which produce a lot more of high frequency, squealing/screeching noises.

In our innovative approach assessing the stress effects of noise by frequency bands, all indicators of noise exposure showed a positive association with trip-level self-reported stress. We controlled for transportation modes and sociodemographic characteristics because the two likely causally influence the noise exposure in trips and also influence stress through other pathways than noise. We did not find evidence that any of our refined noise indicators accounting for frequency bands were associated with stress beyond overall noise. This is in opposition with our a priori expectation that high frequency noise may be particularly stressful.

Strengths and limitations

Regarding strengths, the present study uses newly developed methodologies for the joint collection and processing of GPS, mobility survey, and personal noise exposure data to present an accurate assessment of noise exposure in daily commutes.

In a previous study of ours where we ranked acoustic indicators on the basis of their predictive ability [START_REF] Aarbaoui | The short-term association between exposure to noise and heart rate variability in daily locations and mobility contexts[END_REF], C-weighted acoustic indicators tended to outperform their A-weighted counterparts, despite the fact that A-weighted indicators are the most commonly used for summarizing sound exposure. However, this previous study did not measure sounds by frequency bands, in contrast to the present work. Thus, the present use of both A-and C-weighted indicators as well as CA, and low-, medium-, and high-frequency noise indicators allowed for a more comprehensive examination of the relationship between specific frequencies of sound and resulting stress levels, although it led to a negative finding.

Compared to most previous studies which focused on individuals' chronic exposures to specific sources of noise at static locations [START_REF] Basner | Auditory and nonauditory effects of noise on health[END_REF], the present study considers individuals' personal exposures to different sources of noise at multiple geographic locations and during travel between these locations. The main strength of this study lies in the particular GPS-based mobility survey methodology which enabled the identification of transportation modes and start and end times of trip stages. This detailed information was used in conjunction with the timestamped noise data. A related strength lies in the large number of observations collected which translated to 7800 trip stages. This is considerably more than previous studies looking at personal noise exposure and stress. Our GPS-based mobility survey also allowed us to innovatively collect information on stress during trips at the trip stage level. Overall, the use of wearable sensors and innovative survey methodologies for trip conditions allowed for the accurate and continuous measurement of personal exposure over time as well as its effects on stress. This enabled the investigation of the noisestress association in a "real-life" context while moving away from previously-used laboratory environments.

Regarding limitations, although participants were instructed to wear the noise dosimeters over 4 full days with the exception of sleep, dosimeter data contain missing periods. An additional challenge lies in the fact that we had to align two separately collected sources of data for the same trips (GPS / mobility survey data and noise data). This was addressed through the use of timestamps indicating the start and end points of each trip, as well as points of change when it came to modes of transportation. Although the start and end times of trip stages were derived from an algorithmic processing of GPS data and were confirmed during the phone mobility survey with the participants, challenges to this approach lie in the fact that the accuracy of the starts and ends of trips as well as the changes in mode of transportation during trips can be reduced for a number of reasons. For example, accuracy is reduced when GPS data is lacking and approximated timestamps must be assigned manually during the mobility survey. Furthermore, "transfers" between trip stages can occur in an underground environment in the Paris region, especially for public transportation. As a result, geolocation data is often not available for these transfer episodes, although they do have associated start and end times. Also, certain modes of transportation (i.e., tram) consisted of a small number of measurements (only 91 out of 7800 trip stages), which made the corresponding associations less reliable than others.

Regarding noise, due to the considerable amount of data collected, automated processes were required for the filtering of data. These processing steps leading up to the calculation of the noise parameters could potentially be a source of measurement bias. It was also noted that 100 trip stages out of 7800 (1.3%) had an average LAeq below 30 dB(A), which might involve measurement error, as these levels are abnormally low.

In addition, most previously conducted studies use self-reported recall questionnaires and retrospective assessments to examine participants' stress [START_REF] Douglas | Source-based subjective responses to sleep disturbance from transportation noise[END_REF][START_REF] Evans | Community noise exposure and stress in children[END_REF]. Similarly, our study assessed stress in trips a posteriori during the mobility survey, with a delay between the end of data collection and the survey. Although attempts were made to perform the mobility survey with as minimal of a delay as possible, this approach is susceptible to bias as psychological stress is highly dynamic and a posteriori reinterpretation is possible. Therefore, we cannot exclude measurement error and recall bias related to specific participants or specific trips. These issues indicate the need for an integrated assessment approach which incorporates ecological momentary assessment (EMA), i.e., in situ surveys of stress using smartphones, with GPS receivers and wearable noise sensors to produce accurate assessments of short-term effects of noise on stress [START_REF] Schwartz | Strategies for analyzing ecological momentary assessment data[END_REF].

An additional limitation is whether the study participants are representative of a larger population. Originally, a sample of around 49000 individuals were selected from the census as being eligible for participation in the study. Participants' interest in engaging in the study was gauged through postal mail. Therefore, although the final group of participants was not a convenience sample, it is also not a "representative sample" given the small final sample size.

However, individual and contextual determinants of participation in the study have been investigated, and it should allow us to address potential selection biases.

Another limitation is related to the fact that this study mainly focuses on the relationship between measured noise over the short term and acute rather than chronic psychological stress. It is important to note, however, that the impacts of noise on individuals' psychological stress may stem from a cumulative exposure over time and may only become apparent after a certain period (time-lagged and accumulated effect). The long-term portion of the Mobilisense study will compare participants' health outcomes after a one or two-year follow-up period, permitting for the investigation of the relationship on a longer temporal scale.

Conclusion

Our future research will evaluate the interactions between mobility contexts and sound levels in their association with psychological stress. Furthermore, our future work will have to take into account the subjectivity of noise perception which can be affected by non-acoustic factors like the personal sensitivity to noise, levels of mental arousal, the meaning of and the predictability of sound levels, as well as perceived control over the sound source. Moreover, future studies should give more consideration to the behavioural consequences of noise, for example in terms of physical activity, as research suggests noise affects whether people choose to exercise or not [START_REF] Sorensen | Transport, noise, and health | DORA Empa[END_REF]. Some of these aspects could be incorporated into future research through the use of an enhanced version of the mobility survey. Future work should also take into account potentially unmeasured confounders which may also affect stress, such as air pollution, vibration, etc. as studies have shown that it is difficult to separate these effects from those of noise [START_REF] Sorensen | Transport, noise, and health | DORA Empa[END_REF]. These stressors can be accounted for through the use of additional sensors such as the air pollution monitors that were used in the MobiliSense study.

The combination of GPS and noise dosimeter data collection along with the use of a GPS-based mobility survey allowed us to better explore the relationship between noise exposure and stress at an unprecedented level of accuracy: at the level of trip stages for which exact transportation modes were identified. The associations that were found between noise indicators and subjectively reported stress indicated that overall noise levels rather than particular noise frequencies contributed to stress. Although perhaps only applicable to cities with a similar urban and transport infrastructure, our results suggest that noise exposure varies depending on the modes of transportation taken. We are also able to conclude that noise at levels typically encountered in transport poses a threat to human wellbeing by increasing stress. These findings suggest that noise is an important concern in the daily lives of urban residents which urgently needs to be addressed through engineering and adequate urban planning transformations related to transportation systems.

[

  median 74.7 dB(A), interdecile range: 65.1, 80.2 dB(A)] while it was lowest for entirely walked trips [median 68.7 dB(A), interdecile range: 54.0, 76.9 dB(A)]. Personal motorized vehicle trip stages taken as the passenger showed the largest range in terms of LAeqT exposure [median 68.9 dB(A), minimum 25.7, maximum 96.9 dB(A)].

Figure 1 :Figure 2 :Figure 3 :

 123 Figure 1: Flow chart showing study stages and the data collected from each stage

Table 1 :

 1 Associations between mode of transportation and indicators of noise exposures from random effect linear models controlling for time autocorrelation and sociodemographic variables, the

	Mobilisense Study
		LAeqT	LCeqT	CA	Low Freq	Medium Freq	High Freq
	Transportation mode
	Walking	Ref.	Ref.		Ref.	Ref.	Ref.
	Other active	3.514 (2.411, 4.617)	4.921 (3.816, 6.026)	1.399 (0.815, 1.983)	5.718 (4.572, 6.865)	3.475 (2.324, 4.625)	3.606 (2.598, 4.613)
	Bus/Coach	2.728 (1.433, 4.022)	10.360 (9.063, 11.658) 7.655 (6.955, 8.355)	8.690 (7.345, 10.035)	2.574 (1.224, 3.923)	2.188 (1.005, 3.371)
	Metro	5.475 (4.695, 6.256)	6.079 (5.296, 6.862)	0.580 (0.158, 1.002)	5.973 (5.161, 6.784)	5.661 (4.847, 6.476)	4.055 (3.341, 4.768)
	RER/TER/SNCF	4.033 (2.989, 5.077)	5.083 (4.037, 6.129)	1.046 (0.482, 1.610)	5.494 (4.409, 6.578)	4.308 (3.219, 5.397)	1.407 (0.453, 2.361)
	Tram	2.307 (0.414, 4.199)	5.264 (3.367, 7.160)	2.914 (1.891, 3.938)	5.393 (3.426, 7.359)	2.513 (0.539, 4.486)	-1.810 (-3.539, -0.080)
	Personal motorized (driver)	2.201 (1.576, 2.826)	10.491 (9.865, 11.117) 8.360 (8.026, 8.693)	9.576 (8.927, 10.226)	1.786 (1.135, 2.438)	-0.577 (-1.148, -0.007)
	Personal motorized (passenger) -0.300 (-1.469, 0.869)	7.049 (5.877, 8.220)	7.333 (6.703, 7.964)	6.407 (5.193, 7.622)	-0.745 (-1.964, 0.474)	-3.254 (-4.322, -2.185)
	Education level
	Primary, lower secondary	Ref.	Ref.		Ref.	Ref.	Ref.
	Higher secondary	-3.805 (-7.666, 0.056)	-2.421 (-6.255, 1.413)	1.398 (0.007, 2.789)	-2.615 (-6.666, 1.437)	-4.100 (-8.137, -0.062)	-4.354 (-7.812, -0.895)
	Intermediate tertiary	-4.693 (-8.662, 0.723)	-4.268 (-8.209, -0.326) 0.457 (-0.972, 1.886)	-4.353 (-8.518, -0.188) -4.999 (-9.150, -0.849)	-4.736 (-8.292, -1.181)
	Upper tertiary	-2.084 (-5.902, 1.734)	-1.629 (-5.421, 2.162)	0.466 (-0.908, 1.840)	-1.550 (-5.556, 2.457)	-2.297 (-6.289, 1.695)	-2.306 (-5.726, 1.114)
	Employment
	Unstable job	Ref.	Ref.		Ref.	Ref.	Ref.
	Stable job	-0.595 (-4.769, 3.580)	-0.778 (-4.921, 3.365)	-0.201 (-1.676, 1.275)	-1.252 (-5.633, 3.129)	-0.539 (-4.903, 3.826)	-1.140 (-4.877, 2.596)
	Unemployed	-0.052 (-5.987, 5.883)	-0.893 (-6.785, 4.999)	-0.894 (-3.007, 1.219)	-2.564 (-8.793, 3.666)	-0.085 (-6.291, 6.121)	-0.392 (-5.706, 4.922)
	Retired	-1.739 (-6.777, 3.300)	-1.042 (-6.044, 3.960)	0.700 (-1.095, 2.494)	-1.610 (-6.898, 3.678)	-1.865 (-7.134, 3.404)	-1.930 (-6.441, 2.582)
	Other	-0.198 (-5.016, 4.620)	-0.795 (-5.578, 3.987)	-0.638 (-2.349, 1.073)	-1.324 (-6.380, 3.733)	-0.160 (-5.198, 4.878)	-0.413 (-4.726, 3.900)
	Relationship (couple vs. not)	-1.407 (-3.638, 0.825)	-2.129 (-4.345, 0.087)	-0.740 (-1.544, 0.063)	-1.214 (-3.556, 1.127)	-1.346 (-3.679, 0.987)	-1.034 (-3.033, 0.965)
	Household income
	NA	-2.547 (-8.373, 3.279)	-1.378 (-7.165, 4.410)	1.141 (-1.024, 3.307)	-1.209 (-7.319, 4.901)	-2.474 (-8.565, 3.616)	-1.626 (-6.850, ,3.598)
	1 st tertile	Ref.	Ref.		Ref.	Ref.	Ref.
	2 nd tertile	-0.762 (-3.013, 1.489)	-1.629 (-3.864, 0.605)	-0.801 (-1.606, 0.004)	-1.255 (-3.618, 1.107)	-0.732 (-3.086, 1.621)	0.050 (-1.966, 2.065)
	3 rd tertile	-1.598 (-4.092, 0.896)	-0.695 (-3.288, 1.897)	-0.012 (-0.906, 0.883)	-1.254 (-3.871, 1.363)	-1.624 (-4.231, 0.984)	-1.254 (-3.487, 0.980)
	Female (vs. male)	0.021 (-1.694, 1.736)	-0.048 (-1.751, 1.654)	-0.055 (-0.671, 0.560)	0.629 (-1.171, 2.429)	0.025 (-1.768, 1.818)	-0.366 (-1.902, 1.170)
	Age group
	30-44	Ref.	Ref.		Ref.	Ref.	Ref.
	45-60	0.469 (-1.583, 2.522)	0.509 (-1.529, 2.547)	0.073 (-0.661, 0.808)	0.580 (-1.574, 2.734)	0.472 (-1.675, 2.618)	0.599 (-1.239, 2.438)
	≥60	0.386 (-2.515, 3.286)	-0.300 (-3.180, 2.580)	-0.629 (-1.669, 0.410)	-0.475 (-3.519, 2.569)	0.441 (-2.592, 3.474)	0.100 (-2.498, 2.698)
	Weekend vs. weekdays	1.271 (0.656, 1.887)	0.784 (0.167, 1.401)	-0.440 (-0.768, -0.112)	1.486 (0.846, 2.126)	1.317 (0.674, 1.959)	1.282 (0.719, 1.844)
	Time
	Morning	0.276 (-0.251, 0.804)	0.853 (0.324, 1.381)	0.577 (0.292, 0.862)	0.155 (-0.395, 0.705)	-1.556 (-2.406, -0.705)	0.956 (0.474, 1.438)
	Day	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.
	Evening	-0.470 (-0.962, 0.021)	-0.215 (-0.707, 0.278)	0.270 (0.004, 0.535)	-0.572 (-1.084, 0.059)	-5.500 (-6.295, -4.704)	-0.337 (-0.786, 0.112)

Table 2 :

 2 Quasi-Poisson models for the relationship between noise exposure and stress, adjusted for sociodemographic variables and adjusted or not for transportation modes: ratios (95% confidence intervals)

Table 1 :

 1 Counts and percentages for the number of days of delay between the end of the study period and the mobility survey

	Number of Days of Delay	Count	Percentage
	0	1	0.403
	1	9	3.629
	2	35	14.113
	3	40	16.129
	4	52	20.968
	5	38	15.323
	6	28	11.290
	7	16	6.452
	8	5	2.016
	9	5	2.016
	10	7	2.823
	11	2	0.806
	12	1	0.403
	14	2	0.806
	15	1	0.403
	16	1	0.403
	22	1	0.403
	23	1	0.403
	28	1	0.403