Fanfan Sun

Jie Zhao

Huajie Zhang

Qihui Shi

Yan Liu

Anne Robert

Qiong Liu
email: liuqiong@szu.edu.cn

Bernard Meunier
email: bernard.meunier@lcc-toulouse.fr

Proteomics Evidence of the Role of TDMQ20 in the Cholinergic System and Synaptic Transmission in a Mouse Model of Alzheimer's Disease

Keywords: Alzheimer iTRAQ proteomics, cholinergic neurotransmission, oxidative stress Highlights

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

copper from Cu-Aβ1-16 complex and to restore cognitive and behavioral deficits in AD mouse models. For a better understanding of the mechanism of action of TDMQ20, we decided to investigate the change of profile of proteins expressed in 5xFAD mice after an oral treatment of TDMQ20 (dose = 10 mg/kg, once every two days during 3 months, in total 45 times). Clioquinol (CQ), a non-specific chelator, has been used as comparator.

Here, we report the proteomic alterations in the cortex of 5xFAD mice using iTRAQ (isobaric tags for relative and absolute quantification) proteomics technology. The results indicated that 178 differentially expressed proteins (DEPs) have been identified in AD mouse group with respect to wild type (WT) animals (AD/WT). After treatment by TDMQ20, 35 DEPs were common in AD/WT and TDMQ20/AD groups in an opposite up-or down-regulated manner, respectively.

In addition, among the 35 DEPs mentioned above, 10 common target proteins have been identified in AD/WT, TDMQ20/AD and CQ/AD groups, among which three target proteins were successfully validated by Western blot analysis. In particular, the expression level of ChAT and CHRM4 is significantly increased upon TDMQ20 treatment with respect to 5xFAD mice, while CQ did not significantly change the expression of these proteins. Our study suggests the involvement of this copper chelator TDMQ20 on the cholinergic system, a feature which may explain the improved cognitive and behavioral performance in AD mice upon oral treatment by TDMQ20.

INTRODUCTION

Among the 55 million of persons currently living with dementia worldwide, 60-70% of them are due to Alzheimer's disease (AD), the most common type of dementia [START_REF] Who | Key Facts[END_REF]. At the moment, AD is the largest unmet medical need in neurology.

In AD brains, the main pathology is featured by protein deposits of aggregated amyloid-β (Aβ) and hyperphosphorylated form of tau protein (p-tau), appearing as amyloid plaques and neurofibrillary tangles, respectively, distributed in several brain regions like cortex, hippocampus, olfactory bulb, amygdala and basal forebrain [START_REF] Hampel | The amyloid- pathway in Alzheimer's disease[END_REF][START_REF] Knopman | Alzheimer disease[END_REF]. At the moment, there is still a lack of efficient drugs able to stop the development AD in the early stages and/or prevent the cognitive decline and behavioral changes induced by the disease. Since the approval of memantine in 2003, and despite huge research efforts made by universities or pharmaceuticals companies, the level of failures in clinical trials of drug-candidates is close to 100% [START_REF] Cummings | Alzheimer's disease drug-development pipeline: few candidates, frequent failures[END_REF]. GV-971, a sodium oligomannate, and aducanumab, an antibody targeting amyloids, have been approved by China National Medical Products Administration (NMPA) in 2019 and 2021, respectively [START_REF] Wang | Sodium oligomannate therapeutically remodels Running title: Proteomics pattern of TDMQ20 anti-AD treatment in 5xFAD mice Ms for ACS Chem. Neurosc. cn-2022-00455[END_REF][START_REF] Cummings | Aducanumab produced a clinically meaningful benefit in association with amyloid lowering[END_REF]. However, this biopharmaceutical has been rejected by the European Agency in December 2021 [START_REF] Mahase | Aducanimab: European agency rejects Alzheimer's disease drug over efficacy and safety concerns[END_REF]. Therefore, it is still urgent to explore other potential targets to enlarge the drug spectrum with different mechanisms, due to the complexity of the etiology of AD.

Consequently, several research groups, including our own, decided to develop different metal chelators as drug candidates for the treatment of AD [START_REF] Barnham | Biological metals and metal-targeting compounds in major neurodegenerative diseases[END_REF][START_REF] Kepp | Alzheimer's disease: how metal ions define amyloid function[END_REF][START_REF] Ceccom | Copper chelator induced efficient episodic memory recovery in a nontransgenic Alzheimer's mouse model[END_REF][START_REF] Robert | Regulation of copper and iron homeostasis by metal chelators as possible chemotherapy for Alzheimer's disease[END_REF]. The driving force of this research domain is the rupture of the homeostasis of redox active metal ions such as copper and iron in AD brains and their accumulation in amyloid plaques [START_REF] Lovell | Copper, iron and zinc in Alzheimer's disease senile plaques[END_REF][START_REF] Guilloreau | Redox chemistry of copper-amyloid-beta: the generation of hydroxyl radical in the presence of ascorbate is linked to redox potentials and aggregation state[END_REF]. In the presence of endogenous reductants, copperamyloid complexes are able to catalyze the reduction of dioxygen and to produce reduced, reactive oxygen species (ROS) responsible for neuron death [START_REF] Sharma | Linking oxidative stress and proteinopathy in Alzheimer's disease[END_REF]. In addition, -amyloid oligomers induce mitochondrial damage in a copper catalyzed nitric oxide mediated process [START_REF] Cho | S-Nitrosylation of Drp1 Mediates -Amyloid-Related Mitochondrial[END_REF].

Based on our previous studies on copper chelators [START_REF] Ceccom | Copper chelator induced efficient episodic memory recovery in a nontransgenic Alzheimer's mouse model[END_REF] and our understanding of the failure of non-specific copper chelators [START_REF] Nguyen | Structures of the copper and zinc complexes of PBT2, a chelating agent evaluated as potential drug for neurodegenerative diseases[END_REF], we designed a new series of tetradentate ligands based on a 8-aminoquinoline specific for copper chelation named TDMQ (17)[START_REF] Zhang | Preparation of new tetradentate copper chelators as potential anti-Alzheimer agents[END_REF][START_REF] Zhang | N4-Running title: Proteomics pattern of TDMQ20 anti-AD treatment in 5xFAD mice Ms for ACS Chem[END_REF][START_REF] Liu | Metals ions in Alzheimer's disease: a key role or not?[END_REF]. One of these chelators, TDMQ20, has been selected as drug-candidate and evaluated on three different mouse AD-models, two models based on non-transgenic mice, and one being the classical transgenic 5xFAD mice. TDMQ20 was able to reduce memory impairments in all three AD-models and was also able to reduce the brain level of malondialdehyde, a marker of oxidative stress [START_REF] Zhao | TDMQ20, a specific copper chelator, reduces memory impairments in Alzheimer's disease mose models[END_REF].

We should keep in mind that the rupture of the homeostasis of copper ions generates an excess of this metal ion in some proteins and a reduction of its availability for other ones. For example, copper is a regulator of muscarinic acetylcholine receptor in the brain of rats and its deficiency has a dramatic effect on both receptor number and binding of antagonists to this receptor [START_REF] Farrar | Characterization of muscarinic cholinergic receptors in the brains of copper-deficient rats[END_REF]. This fact might create a link between copper homeostasis and three of the few approved drugs (donepezil, galantamine and rivastigmine) acting as acetylcholinesterase (AChE) inhibitors [START_REF] Hampel | The cholinergic system in the pathology and treatment of Alzheimer's disease[END_REF]. Cholinergic network in the central nervous system is essential for learning capacity and memory, with most of cell bodies sitting in basal forebrain region and innervations projecting to areas like cortex and hippocampus [START_REF] Ballinger | Basal forebrain cholinergic circuits and signaling in cognition and cognitive decline[END_REF]. Cholinergic neurons are lost in AD patients, with dramatically reduced levels of acetylcholine (ACh), a key neurotransmitter for many neurophysiological processes [START_REF] Whitehouse | Alzheimer disease: evidence for selective loss of cholinergic neurons in the nucleus basalis[END_REF]. Choline acetyltransferase (ChAT), a key enzyme for the biosynthesis of ACh, was significantly reduced in temporal lobes of patient brains [START_REF] Wilcock | Alzheimer's disease: correlation of cortical choline acetyltransferase activity with the severity of dementia and histological abnormalities[END_REF] and in dementia induced by hypoxia [START_REF] Bowen | Neurotransmitterrelated enzymes and indices of hypoxia in senile dementia and other abiotrophies[END_REF]. For all these different reasons, it is essential to understand the origin of the degradation of cholinergic neurons, and in general the neuron death that has been related to the accumulation of copper ions within amyloid proteins when these copper-amyloid complexes are activated by endogenous reductants [START_REF] Kepp | Alzheimer's disease: how metal ions define amyloid function[END_REF][START_REF] Liu | Metals ions in Alzheimer's disease: a key role or not?[END_REF][START_REF] Lei | The essential elements of Alzheimer's disease[END_REF]. So, the different possible modes of action of the TDMQ20 copper regulator may involve reduction of the oxidative stress created by Cu-amyloids, but also a copper-dependent regulation of several proteins involved in the redox metabolism, resulting in normalization of the activity of cholinergic neurons.

Here we report proteomics data obtained with transgenic 5xFAD mice that received by oral route 10 mg/kg of TDMQ20 once every two days during 3 months.

The reported results suggest that this drug-candidate has a positive effect on the cholinergic system. Consequently, TDMQ20 should be considered as having a bimodal mechanism of action: the first one as inhibitor of ROS production due to its capacity to extract copper from copper amyloid and the second as provider of copper to copper-deprived cholinergic neurons. This regulation of copper homeostasis, i.e. the capacity for a ligand to extract copper from amyloid and to release it for a copper carrier, has been suggested by "in chimico" experiments [START_REF] Nguyen | Transfer of copper from an amyloid to a natural copper-carrier peptide with a specific mediating ligand[END_REF].

RESULTS

Relative quantification of proteins in the brain of 5xFAD mice was achieved using iTRAQ based mass spectrometry proteomics analysis. Using the statistical assay of t-test, DEPs between two mouse groups were screened with fold change higher than 1.2 or lower than 0.8 as the threshold, and p value < 0.05 as significance criterion. A total of 3243 DEPs were identified and classified in four categories: proteins identified in (i) 5xFAD mice compared to wild type mice (AD/WT), (ii) 5xFAD mice treated with TDMQ20 compared to untreated 5xFAD mice (TDMQ20/AD), (iii) 5xFAD mice treated with clioquinol compared to untreated 5xFAD (CQ/AD) and (iv) common DEPs among the above three categories.

DEPs between 5xFAD mice compared to WT

Among total identified proteins in AD/WT comparison, 178 DEPs were identified, 101 being up-regulated (57%) and 77 down-regulated (43%) (Figure 1A and1B) indicating that there is no significant protein loss in AD mouse brain tissue (raw data: Supplementary 1.xlsx, sheet AD vs WT). Gene Ontology analysis (GO) of biological process (BP), cell component (CC) and molecular function (MF) of these proteins is shown in Figure 1C-E AD related pathways associated with these differentially expressed proteins were identified using KEGG (Kyoto Encyclopedia of Genes and Genomes) bioinformatics tool (Figure 2A). The majority of DEPs were involved in organismal systems (Figure 2A, red bars), human diseases (violet), environmental information processing (green) and, in a minor way, in metabolism (blue) and genetic information processing (orange). Among all the enriched pathways, the synaptic vesicle cycle pathway had the lowest p value (p=2.69e-04), with 5 enriched proteins: Ap2a1, Atp6v1b2, Syt1, Cacna1a and Slc18a2 (Supplementary Figure 1). Besides, important pathways also included choline metabolism in cancer, endocrine and other factor-related calcium, dopaminergic synapse, cysteine and methionine metabolism, serotonergic synapse, thyroid hormone synthesis, ErbB signaling pathway, hepatitis B, Wnt signaling, protein processing in endoplasmic reticulum, axon guidance, pancreatic secretion, insulin resistance (Figure 2A). Protein-protein interaction (PPI) network analysis (Figure 2B) confirms that these AD affected proteins are associated with synaptic vesicle cycle and also with metabolism, protein processing, choline metabolism, neurotransmitters and signaling, these pathways are closely related with each other, and expressional disturbance are expected to influence many functions.

DEPs between TDMQ20-treated 5xFAD mice compared to untreated animals

Compared to untreated 5xFAD AD group, 183 proteins were altered after TDMQ20 treatment, 72 proteins being up-regulated (39%) and 111 proteins being down-regulated (61%) (Figure 3A and 3B) (raw data: Supplementary 1.xlsx, sheet TDMQ20 vs AD). Gene Ontology analysis of BP, CC and MF of these proteins is shown in Figure 3C-E Ywhae, Hsp90ab1, Ywhaz, Ywhag, Hras, Cdc37, Rac1, Gnb4, Tsc1, Tnc and Ppp2r5b (Supplementary Figure 2). PPI network of TDMQ20/AD is depicted in Figure 4B. Putting together with the above analysis, these DEPs were found to be associated with the following pathways which were closely related with each other: PI3K-Akt signaling pathway and synaptic vesicle cycle, as well as protein processing in endoplasmic reticulum, regulation of actin cytoskeleton, focal adhesion, hippo signaling pathway, and inositol phosphate metabolism.

DEPs between CQ-treated 5xFAD mice compared to untreated animals

When compared to the untreated AD group, 226 proteins were altered after CQ treatment of AD mice (Figure 5). Among them, 73 proteins (32%) were upregulated and 153 proteins (68%) were down-regulated (Figure 5A andB Protein localization, regulation transport, establishment of protein localization, protein complex subunit organization, amide transport, peptide transport, protein complex assembly protein complex biogenesis and nucleobase containing small molecule metabolic process were also altered to a lesser extent (Figure 5D). Cellular components significantly linked to DEPs of CQ/AD groups included cytoplasm, cytoplasmic part, vesicle, extracellular region, extracellular region part, membrane-bounded vesicle, extracellular exosome, extracellular vesicle, extracellular organelle, extracellular membrane-bounded organelle, neuron part, cell projection, neuron projection and cell body (Figure 5E). Molecular function mainly included protein binding and binding, and also enzyme binding, small molecule binding, purine ribonucleoside binding, purine nucleoside binding and several other binding functions. KEGG pathway analysis (Figure 6A) showed that the majorities of DEPs were involved in metabolism, genetic information processing, environmental information processing, cellular processes, organismal systems, human diseases. Among all the enriched pathways, the endocrine and other factor-regulated calcium reabsorption pathway had the lowest p value, and accounted for 8 proteins as Ap2b1, Plcb1, Atp2b1, Atp1b1, Atp1b2, Calb1, Prkca and Adcy6. PPI network is shown in Figure 6B. Putting together with the above analysis, these proteins were found to be associated with fatty acid degradation, cysteine and methionine metabolism, carbon metabolism, thyroid hormone synthesis, phospholipase D signaling pathway, endocrine and other factorregulated calcium reabsorption, synaptic vesicle cycle and GABAergic synapse, and cholinergic synapse.

Common DEPs in AD/WT, TDMQ20/AD and CQ/AD categories

We further analyzed the commonly altered DEPs in the AD/WT, TDMQ20/AD and CQ/AD series. Among the 35 common DEPs (Supplementary Figure 4) identified in AD/WT and TDMQ20/AD categories, and 30 common DEPs (Supplementary Figure 5) identified in AD/WT and CQ/AD categories, 10 proteins were found common in all three AD/WT, TDMQ20/AD and CQ/AD categories, with DEPs in AD/WT that were reversed in TDMQ20/AD and CQ/AD (Figure 7, panels A and B). Five proteins which were down-regulated in AD/WT, namely Chrm4, Chat, Rin1, Lgalsl, and Mrps9 displayed a consistent up-regulated pattern both in TDMQ20/AD and CQ/AD categories. Conversely, proteins Plp1, Vps29, Rac1, Ptprz1 and Cmpk1, which were overexpressed in AD/WT were downregulated in both TDMQ20/AD and CQ/AD (Figure 7B). According to PPI network analysis, these 10 common DEPs are mainly involved in pathways related to cognition, behavior, learning and memory, anterograde trans-synaptic signaling as well as neuron projection development (Figure 7C). Most significantly regulated proteins with gene names Chat, Rin1, Chrm4, Ptprz1, Rac1 and Plp, were associated with these pathways.

To further validate the proteomic results, the expression of seven proteins with the gene names Chrm4, Chat, Rin1, Lgalsl, Mrps9, Ptprzl and Cmpkl, were evaluated by Western blot analysis in WT, AD, TDMQ20 and CQ mice. Results are reported in Figure 8. Among these seven proteins, three proteins, which are significantly downregulated in AD mice with respect to control animals (ChAT, CHRM4 and Lgalsl) were found up-regulated after treatment of AD mice with TDMQ20 with respect to untreated AD mice (Figure 8A, C-E). Specially, the significantly increased level of ChAT and CHRM4 (Figure 8C and 8D, respectively) clearly suggests a role of TDMQ20 in the cholinergic system. This result is consistent with improved memory and behavioral performance of TDMQ20 treated 5xFAD mice that was previously reported [START_REF] Zhao | TDMQ20, a specific copper chelator, reduces memory impairments in Alzheimer's disease mose models[END_REF]. Conversely, treatment by CQ failed to efficiently reverse the low level of ChAT and CHRM4 in AD mice. The level of Lgalsl, downregulated in AD mice, was efficiently reversed by treatment with TDMQ or CQ (Figure 8E). Ptprz1 level was somewhat lower in AD mice than in control WT, and its level trends to be reversed by TDMQ20 and CQ. However, these differences indicate a tendency but are not statistically significant (Figure 8G). RIN1, Mrps9 and Cmpk1 levels were found similar in WT control mice and in 5xFAD mice. So, these proteins cannot be considered as relevant of AD targets in this animal model (Figure 8F,8H,8I).

Immunofluorescence staining of Aβ plaques in the cortex

As one of the important pathological hallmarks in AD brains, we additionally quantified amyloid plaques in the mouse cortex of each group. The amyloid plaque load of each mouse group was quantified as the percentage of area covered by 6E10-immunoreactivity (Figure 9) in each image sample, acquired using a florescent microscope. According to the result, the cortex of mice treated with TDMQ20 exhibited a drastically reduced plaque load compared to untreated AD mice (roughly -70% compared to AD mice), suggesting a lower amount of uncontrolled copper ions promoting plaque formation in the brain of treated mice. This result is consistent with a lower concentration of malondialdehyde, a marker of oxidative stress, in the cortex of AD mice treated with TDMQ20 we reported in a previous study [START_REF] Wang | Quantitative proteomics reveals the mechanism of oxygen treatment on lenses of Alzheimer's disease model mice[END_REF]. The reduction of plaque load upon treatment with CQ was also significant (-57%), which is also consistent with previous report [START_REF] Tice | Distribution of muscarinic receptor subtypes in rat brain from postnatal to old age[END_REF].

DISCUSSION

TDMQ20 is a highly specific copper chelator able to extract Cu from Cu-Aβ complex, thus inhibiting Cu-Aβ induced activation of dioxygen and ROS production, and to release Cu from Cu-TDMQ20 into normal physiological circulation [START_REF] Zhang | N4-Running title: Proteomics pattern of TDMQ20 anti-AD treatment in 5xFAD mice Ms for ACS Chem[END_REF]. Furthermore, oral treatment of TDMQ20 on 5xFAD mice had a clear beneficial effect the short-term memory and cognitive function of this transgenic model of Alzheimer's disease [START_REF] Zhao | TDMQ20, a specific copper chelator, reduces memory impairments in Alzheimer's disease mose models[END_REF]. In order to investigate further the underlying mechanism of action of TDMQ20, and specially the possible interaction with the copper dependent cholinergic system, the current study was designed as an exploration based on proteomic and bioinformatic analysis. By using iTRAQ, proteomic alterations in the cortex of 5xFAD mice with respect to WT animals were investigated, leading to identification of DEPs in AD with respect to healthy animals. The fate of these DEPs proteins in AD mice treated by TDMQ20 (or CQ used as comparator) was then investigated in order to evaluate the relationship between (i) the pattern of protein modifications induced by TDMQ20 treatment, (ii) copper regulation in brain and (iii) ability of this drug to reverse AD impairment.

Proteomics of AD 5xFAD mice compared to healthy WT mice (AD/WT)

Amyloid precursor protein (APP) was highly upregulated with a fold change of 6.8 in our 5xFAD mice with respect to WT (raw data: Supplementary 1.xlsx, sheet AD Running title: Proteomics pattern of TDMQ20 anti-AD treatment in 5xFAD mice Ms for ACS Chem. Neurosc. cn-2022-00455p (Accepted September 26 th , 2022) 13 vs WT), result consistent with plaque accumulation in AD condition (Figure 9). More importantly, Rin1 (Ras and Rab interactor1), known to take part in the longterm potentiation [START_REF] Dhaka | The RAS effector RIN1 modulates the formation of aversive memories[END_REF] and neuronal plasticity [START_REF] Szíber | Ras and Rab interactor 1 controls neuronal plasticity by coordinating dendritic filopodial motility and AMPA receptor turnover[END_REF], is downregulated with a fold change of 0. [START_REF] Whitehouse | Alzheimer disease: evidence for selective loss of cholinergic neurons in the nucleus basalis[END_REF][START_REF] Strada | Decreased choline acetyltransferase mRNA expression in the nucleus basalis of Meynert in Alzheimer disease: an in situ hybridization study[END_REF][START_REF] Davies | Selective loss of central cholinergic neurons in Alzheimer's disease[END_REF], is consistent with low Ach level and degeneration of cholinergic neurons in AD brain [START_REF] Giacobini | Cholinergic function and Alzheimer's disease[END_REF]. The significant lower level of Chrm4 (fold change = 0.2) in our 5xFAD mice with respect to healthy animals confirms impairment of cholinergic system at this stage of AD.

Proteomics of AD mice treated with TDMQ20 compared to untreated AD mice (TDMQ20/AD)

After treatment of AD mice by TDMQ20, 183 DEPs, with 72 up-regulated and 111 down-regulated with respect to untreated animals (39% and 61%, respectively). Among these 183 DEPs, 35 DEPs were found common in AD/WT and TDMQ20/AD comparisons and are analyzed below. Most of these 35 DEPs were involved in PI3K-Akt signaling pathway involved in cellular survival and proliferation. In addition, several DEPs link to PI3K-Akt signaling pathway, with the gene names Hsp90ab1, Ywhaz, Ywhag, and Rac1 also present properties in synaptic plasticity, such as axon extension [START_REF] Benitez | Hsp90 activity is necessary to acquire a proper neuronal polarization[END_REF], synapse formation (39), dendritic spine enlargement and morphogenesis [START_REF] Raynaud | Rho-GTPase-activating protein interacting with Cdc-42-interacting protein 4 homolog 2 (Rich2): a new Ras-related C3 botulinum toxin substrate 1 (Rac1) GTPase-activating protein that controls dendritic spine morphogenesis[END_REF][START_REF] Sarowar | Enlarged dendritic spines and pronounced neophobia in mice lacking the PSD protein RICH2[END_REF]. The downregulation in AD of another protein involved in PI3K-Akt pathway, galectin-related protein Lgalsl (= HSPC159) (42) was also reversed upon treatment by TDMQ20.

Regarding nervous system, TDMQ20/AD DEPs also showed a significant connection with the synaptic vesicle cycle. Cplx1 and Cplx3, which regulate synaptic vesicle exocytosis and neurotransmitter secretion [START_REF] Gaudet | Phylogeneticbased propagation of functional annotations within the Gene Ontology consortium[END_REF][START_REF] Abderrahmani | Complexin I regulates glucose-Running title: Proteomics pattern of TDMQ20 anti-AD treatment in 5xFAD mice Ms for ACS Chem. Neurosc. cn-2022-00455p[END_REF], were significantly up-regulated in TDMQ20 treated AD mice. The above-mentioned downregulation of Rin1, involved in synaptic plasticity in AD, was significantly reversed upon treatment by TDMQ20. Specially, the muscarinic receptor Chrm4 was up-regulated with a high fold change value of 6.6, with respect to untreated AD mice. Chrm4 is localized at the pre-synapse of axon terminals and on dendritic membranes at the post-synapse [START_REF] Hersch | Distribution of m1-m4 muscarinic receptor proteins in the rat striatum: light and electron microscopic immunocytochemistry using subtype-specific antibodies[END_REF], with high density in striatum, cortex and hippocampus [START_REF] Tice | Distribution of muscarinic receptor subtypes in rat brain from postnatal to old age[END_REF], and alterations of Charm4 have been reported in AD as early as 1982. This confirms the very likely role of TDMQ20 in regulation of cholinergic signaling in the brain, possibly through modulation of synaptic neurotransmission as indicated by biological process from GO analysis.

Proteomics of AD mice treated with CQ compared to untreated AD mice (CQ/AD)

Despite clioquinol (CQ) was reported to exhibit beneficial effects on cognition in a transgenic mouse model of AD [START_REF] Zhang | Efficacy and toxicity of clioquinol treatment and A-beta42 inoculation in the APP/PSI mouse model of Alzheimer's disease[END_REF], it was discarded due to severe neurotoxicity due to its lack of metal selectivity [START_REF] Zhang | Efficacy and toxicity of clioquinol treatment and A-beta42 inoculation in the APP/PSI mouse model of Alzheimer's disease[END_REF]. As historical prototype of copper chelator, it was used as comparator in the present study. AD mice treated with CQ exhibited 73 (32%) upregulated and 153 (68%) downregulated DEPs with respect to untreated AD mice. DEPs were mostly related to establishment of localization, transport and single-organism localization in biological process and mostly link to cytoplasmic (Cell Components). Unlike TDMQ20 treated mice, KEGG pathway analysis showed a very high percent of DEPs involved in metabolic pathways which might be related to detrimental side-effects of CQ in clinics. Other pathways like endocytosis, adrenergic signaling in cardiomyocytes, pancreatic secretion, carbon metabolism, purine metabolism, are also involved. Among all the analyzed pathway, the endocrine and other factor-regulated calcium reabsorption pathway showed the most significance, with eight proteins including Ap2b1, Plcb1, Atp2b1, Atp1b1, Atp1b2, Calb1, Prkca and Adcy6. In the nervous system, the effect of CQ on several types of synapse pathways. Rin1 displayed significant higher level in CQ treated mice than in untreated AD animals. In cholinergic synapse pathway, similarly to TDMQ20, Chrm4 was highly upregulated with respect to AD. Other proteins related to cholinergic system: Map2k1, Gng3 and Kcnq2 were downregulated.

Differentially expressed proteins in common to AD/WT, TDMQ20/AD and CQ/AD categories

Ten target proteins were found common to these three groups, with similar expression pattern either upregulated or downregulated from one to another.

Compared to AD/WT category, five proteins Chrm4, Chat, Rin1, Lgalsl, Mrps9 displayed a consistent up-regulated pattern, while five proteins Plp1, Vps29, Rac1, Ptprz1 and Cmpk1 were downregulated in both TDMQ20/AD and CQ/AD categories. Based on the KEGG pathway analysis, these common DEPs were mainly involved in cognition, learning and memory, behavior, anterograde transsynaptic signaling and neuron projection development. According to the commercial availability of the antibodies of these common DEPs, seven proteins with the gene names Chrm4, Chat, Rin1, Lgalsl, Mrps9, Ptprzl and Cmpkl were selected and validated by Western blot analysis. Results showed that three proteins, with the gene names Chrm4, Chat and Lgalsl, were found significantly upregulated after the treatment of TDMQ20, and the decreased level of Lgalsl in AD mice was also reversed by CQ treatment.

Among the ten proteins reversed to normal levels by both TDMQ20 and CQ, three of them were successfully validated by Western blot analysis for TDMQ20 treatment. Specially, the relative abundance levels of ChAT and CHRM4 (impaired in 5xFAD mice) were significantly increased upon TDMQ20 treatment (with fold changes of 4.3 and 6.6, respectively), while CQ did not induce significant changes of these protein levels (Figure 8), suggesting a specific action of TDMQ20 on these two masterpiece proteins of the cholinergic system.

Plaque load in the mouse cortex

Amyloid plaque, consisting of misfolded and aggregated amyloid β peptides is one of the well-known hallmarks of AD pathological states. In our present study, we calculated the plaque load in mouse cortex as the ratio of area covered by the anti-Aβ antibody 6E10 to the entire imaged area acquired by a fluorescent microscope.

Treatment by TDMQ20 remarkably reduced plaque load in the cortex by about 70%, suggesting a beneficial role of TDMQ20 in clearing pathological Aβ deposits in AD mice. CQ treatment also contributed to an approximately 57% reduction of plaque load in mouse cortex, though not as much as TDMQ20 did. These results further consolidate TDMQ20 as a good candidate for AD treatment.

EXPERIMENTAL PROCEDURES

Experimental design and statistical rationale

5xFAD mice (genetic background: C57BL/6 x SJL) were used as AD model. These mice express human APP and PSEN1 transgenes with a total of five AD-linked mutations: the Swedish (K670N/M671L), Florida (I716V), and London (V717I) mutations in APP, and the M146L and L286V mutations in PSEN1. Starting when they were two-month-old, these AD mice were orally treated either with TDMQ20

or CQ and compared with untreated mice. The expression of proteins in the cortex of these three AD mouse groups and in the healthy control WT C57BL/6 x SJL mice was compared using iTRAQ labeling and bioinformatics analysis described below. GraphPad Prism 8.0.2 software was used for statistical analysis. Data were expressed as mean ± SEM unless otherwise indicated, with P < 0.05 defining differences as statistically significant (*p < 0.05; **p < 0.01; ***p < 0.001).

Differences among means were determined using the two-tailed t test.

Materials and animals

Animal grouping and treatment

Two-month-old mice were separated into four groups and drug-treated as indicated in Table 1. Drugs were delivered to stomach of mice once every two days (oral administration by gavage, volume of each gavage = 100 L, of a dose = 10 mg/kg) for three months (in total 45 times), and followed with behavioral tests as conducted previously [START_REF] Zhao | TDMQ20, a specific copper chelator, reduces memory impairments in Alzheimer's disease mose models[END_REF]. WT (C56BL/6J control) 0.9% NaCl 0 AD (5xFAD mice) 0.9% NaCl 0 TDMQ20 (5xFAD treated with-) TDMQ20 in 0.9% NaCl 10 CQ (5xFAD treated with-) Clioquinol in DMSO 10

Sample preparation

Fourteen days after the end of treatment (5.5 months old mice), mice were killed by cervical dislocation. Brains were rapidly excised and placed on an ice-cold plate for dissection of cortices. Six cortex tissue samples were taken from each group of mice for proteomics analysis. Tissue samples were lysed in lysis buffer containing

Trypsin digestion and iTRAQ labeling

For proteomics analysis, equal amounts of above protein samples from 6 mice cortex (random selection) in each group were pooled separately to yield two distinct pools in each group. Protein digestion and iTRAQ labeling were performed as previously reported [START_REF] Wang | Quantitative proteomics reveals the mechanism of oxygen treatment on lenses of Alzheimer's disease model mice[END_REF][START_REF] Iqbal | Effect of sodium selenate on hippocampal proteome of 3xTg-AD mice -Exploring the antioxidant dogma of selenium against Alzheimer's disease[END_REF]. In brief, approximately 100 µg of each processed tissue protein sample was reduced with 10 mM DTT (Sigma Aldrich, Missouri, USA) for 1 h at 37 °C, followed by alkylation using 50 mM iodoacetamide (IAA, Sigma Aldrich, Missouri, USA) for 15 min at room temperature in the dark. The samples were then desalted and buffer-changed three times with 100 μL of 0. The labeled samples were mixed together and lyophilized. After labeling, the peptides were subjected to reverse-phase chromatography at pH 10, to remove the excess of labeling reagents and salts by HPLC system (Agilent Technologies, Santa Clara, CA, USA). Peptides were eluted and combined into 16 batches. They were then lyophilized and stored at -80 °C.

Nano LC-MS/MS (Mass Spectrometry) analysis and iTRAQ quantification

Nano LC-MS/MS analysis was conducted as previously described [START_REF] Wang | Quantitative proteomics reveals the mechanism of oxygen treatment on lenses of Alzheimer's disease model mice[END_REF][START_REF] Iqbal | Effect of sodium selenate on hippocampal proteome of 3xTg-AD mice -Exploring the antioxidant dogma of selenium against Alzheimer's disease[END_REF]. Briefly, the 16 pooled batches were reconstituted in 30 μL of 0.1% formic acid and 2% acetonitrile, among which 8 μL of each batch was added to a Triple TOF 6600 system fitted with a NanosprayIII source (AB Sciex). Data were obtained with a 2.4 kV ion spray voltage, 30 psi curtain gas, 5 psi nebulizer gas, and an interface heater temperature of 150 °C. TOF-MS was conducted with a scan scope of 350-1500, and for MS/MS was 400-1250. Smart information-dependent acquisition was activated with automatic collision energy and automatic MS/MS accumulation.

The protein was identified and quantified by ProteinPilot v5.0 (AB Sciex) with the Paragon algorithm against the Uniprot "complete proteome" mouse proteins database. Based on a 95% confidence level, at least one unique peptide per protein group was required for identifying proteins, and two quantified peptides were required for quantifying proteins. The cutoff values of 1.2-fold for up-regulated and 0.8-fold for down-regulated proteins, p value <0.05, were considered as significant results. This significant change was further determined by conversion of log2 ratios, followed by data normalization and performance of a statistical t test by using OMICSBEAN online tools (http://www.omicsbean.cn) [START_REF] Shen | Proteomics Study of Peripheral Blood Mononuclear Cells (PBMCs) in Autistic Children[END_REF].

Bioinformatics analysis

Bioinformatics analysis was performed by using OMICSBEAN online tools and STRING (Search Tool for the Retrieval of Interacting Genes/Proteins, version 9.1, http://string-db.org/) database. Differentially expressed proteins (DEPs) between the different mouse groups were analyzed using Gene Ontology annotation (GO), Kyoto Encyclopedia of Genes and Genomes pathways (KEGG) and proteinprotein interaction (PPI) networks.

Western Blot Analysis

The validation of targeted proteins was performed by Western blot analysis.

Proteins were extracted from the cortex tissue after homogenizing and their concentrations were measured by Pierce

Immunofluorescence staining and image acquisition

Mouse brains were removed after cervical dislocation and were prepared as paraffin-embedded slices cut with ~4 μm thickness. Six slices from each group

  . As shown in Figure 1D, fifteen enriched BP found in GO analysis linked DEPs of AD/WT category mainly included localization, cellular component organization or biogenesis, transport, establishment of localization, single-organism localization, single-organism transport and regulation of biological quality. While establishment of localization in cell, cell-cell signaling, single-organism intracellular transport, chemical synaptic transmission, anterograde trans-synaptic signaling, trans-synaptic signaling, synaptic signaling, and modulation of synaptic transmission were affected to a lesser extent. Regarding to structural stability, fifteen enriched CC processes in GO analysis displayed in Figure 1E mainly linked to DEPs related to intracellular part, cytoplasm, cytoplasmic part and, to a lesser extent, to vesicle, cytosol, extracellular region part, membrane-bounded vesicle, extracellular exosome, extracellular vesicle, extracellular organelle, extracellular membranebounded organelle, cell projection, cell body and neuronal cell body. MF DEPs included protein binding, enzyme binding, identical protein binding, binding, kinase binding, anion binding and purine ribonucleoside binding, indicating that these DEPs related to molecular processes were disrupted under AD pathological conditions.

.

  As shown in Figure3D, BP linked to DEPs in the TDMQ20/AD category mainly included cellular component organization or biogenesis, cellular component organization, and nervous system development. Then follows in order of diminishing impact: protein complex subunit organization, nitrogen compound transport, generation of neurons, neuron development, neuron projection development, response to organonitrogen compound, chemical synaptic transmission, anterograde trans-synaptic signaling, trans-synaptic signaling, synaptic signaling and modulation of synaptic transmission.Cellular components linked to DEPs of TDMQ20/AD comparison included cytoplasm and cytoplasmic part and, to a lesser extent, vesicle, cytosol, extracellular region part, extracellular exosome, extracellular vesicle, extracellular organelle, extracellular membrane-bounded organelle, cell projection, neuron part, somadendrites, cell body and neuronal cell body (Figure3E). Molecular function included protein binding, binding and, to a much lesser extent, nucleoside-, ribonucleoside-purine ribonucleoside-binding, identical protein binding (Figure3C).

  Running title: Proteomics pattern of TDMQ20 anti-AD treatment in 5xFAD mice Ms for ACS Chem. Neurosc. cn-2022-00455p (Accepted September 26 th , 2022) 9 KEGG pathway analysis (Figure4A) showed that the majorities of DEPs were involved in the human diseases (brown bars), then in cellular processes (Figure4A, red bars) and environmental information processing (green bars), organismal systems (violet bars), genetic information processing (orange bars) and metabolism (blue bars). Among all the enriched pathways, the PI3K-Akt signaling pathway had the highest significantly (lowest p value, p = 3.88e-04), with 11 enriched proteins:

  ) (raw data: Supplementary 1.xlsx, sheet CQ vs AD). As shown in Figure 5C-D, biological process of GO analysis linked to DEPs of CQ/AD groups mainly included establishment of localization, transport, single-organism localization, single-organism transport, cellular localization and regulation of localization.

  3 in 5xFAD mice. Several other DEPs identified in AD/WT were involved in synaptic vesicle cycle: proteins Slc18a and Cacna1a were upregulated, and Ap2a1, Syt1 and Atp6v1b2 were down-regulated, indicating alterations of synaptic signaling and plasticity that are likely linked to impairment of learning, memory and cognitive functions. Another important set of DEPs are related to choline metabolism pathway: ChAT, Chrm4, Slc44a1 and Plpp3 were downregulated, and Prkcg was upregulated. Reduced expression of ChAT (fold change = 0.2), already reported

TDMQ20 was synthesized according to Reference 18 .

 18 Clioquinol (5-chloro-7iodo-8-hydroxyquinoline, abbreviated as CQ) was purchased from Sigma (Sigma-Aldrich Co., St. Louis, USA). All other reagents were supplied by Sigma-Aldrich, AB Sciex or Promega, except otherwise stated. Thirty specific pathogen-free (SPF) 5xFAD mice and 10 non-transgenic C57BL/6 x SJL control mice (male/female ratio = 1/1, weighing 20-25 g, 8 weeks) have been used. Animals were housed by groups in standard laboratory conditions: air-conditioned room (20-25ºC), 12 hours light/dark illumination cycle, free access to food and water. Food was provided by Guangdong Medical Experimental Animal Center. All protocols were implemented in accordance with NIH guidelines (NIH publication No. 85-23, revised 1985) for animal care and welfare. All animal care and experiments were approved by the Institutional Animal Care and Use Committee of the Institute for Nutritional Sciences in China and approved by the Regional Ethical Committee for Animal Experimentation at Shenzhen University (Permit Number: AEWC-20140615-002) and performed according to the animal protection law.

7 M urea, 2 M

 2 thiourea, 4 w/v% 3-[(3-cholamidopropyl)dimethylammonio]-1propanesulfonate hydrate (CHAPS), 40 mM dithiothreitol (DTT), and 40 mM Tris base), sonicated 10 times for 5 s with 10 s pause in an iced water bath, and centrifuged for 10 min at 12 000 rpm, 4 °C. The supernatant was withdrawn and stored at -80 °C until use.

  were selected and deparaffinized by immersing in xylene (2 × 5 min), and then rehydrated by immersing in the following solutions for 3 min each: 100% ethanol, 95% ethanol, 85% ethanol, 75% ethanol, 50% ethanol, 30% ethanol and then deionized water. Slices were then washed in PBS for 2 × 5 min. After drawing a circle around each slice with a hydrophobic barrier pen, permeabilization buffer containing 0.8% Triton X-100 in PBS was added to each slice for 30 min. Slices were further incubated in blocking solution containing 10% normal goat serum (Solarbio, Beijing, China) at RT for 2 h, and then with primary antibodies (anti-6E10, mouse polyclonal, Biolegend, San Diego, USA; anti-MAP2, chicken polyclonal, Invitrogen, MA, USA) overnight at 4 °C. Slices were washed 3 × 10 min with PBS and then incubated with the secondary antibodies (goat anti-chicken Alexa Fluor 488, goat anti-mouse Alexa Fluor 568, Life Technologies GmbH) for 2 h at RT in the dark. After 3 × 10 min washing in PBS, slices were further stained with DAPI (10 μg/mL, Beyotime, Shanghai, China) for 15 min in the dark. Then slices were washed for 3 × 10 min with PBS before mounting on glass coverslips with Dako fluorescence conserving medium (Dako, Hamburg, Germany). The dilution of antibodies used here were 1:500 in blocking solution. Images were acquired by using Olympus BX53 fluorescence microscope under a 20 × objective, with resolution of 1920 × 1900 pixels. CONCLUSIONS Our current study has been designed as an investigation of protein-based molecular mechanism underlying the improved cognitive and behavioral performance in a transgenic 5xFAD mouse model of Alzheimer's disease upon treatment with the copper chelator TDMQ20. Abnormally expressed proteins in the cortex of AD mice are closely involved in several key functional brain pathways, such as choline metabolism, trans-synaptic signaling, chemical synaptic transmission, and synaptic vesicle cycle.Among DEPs common to AD/WT, TDMQ20/AD categories, ChAT and CHRM4 were further validated by Western blot analysis, suggesting a recovery of the cholinergic system functioning induced by TDMQ20, a feature which may account for improved cognitive, behavioral performances and spatial memory of AD mice upon treatment by TDMQ20. Especially, after treatment of AD mice by TDMQ20, ChAT level was similar to that observed in healthy WT mice. Downregulation of Lgalsl observed in AD mice was also fully reversed upon TDMQ20 treatment. These results suggest that, beside "traditional" targeting of AChE, ChAT and Chrm4 involved in cholinergic system also deserve consideration as potential targets for AD chemotherapy. Noteworthy, cortex of mice treated with TDMQ20 do not present the panel of metabolic DEPs that might to be responsible for CQ toxicity.Moreover, the load of amyloid plaques in mouse cortex was drastically reduced after treatment by TDMQ20, suggesting a lower amount of uncontrolled copper ions promoting plaque formation in the brain of treated mice. This result is consistent with the decreased level of malondialdehyde observed in the three different AD mouse models upon TDMQ20 treatment in our previous study. The simultaneous reversal of the deprivation of proteins involved in the copper dependent cholinergic system, suggests the capacity of TDMQ20 to act as regulator of copper homeostasis in vivo. The correlation of such a mechanism of action of TDMQ20 and drastic improvement of the cognitive performances of treated animals validates copper homeostasis as a priority target and TDMQ20 as a drug candidate.Author ContributionsB.M., Q.L., and A.R. designed the research; Y.L. and colleagues performed the chelator synthesis; F.S., J.Z., and H.Z. performed biological assays, animal interactor 1; SFP, specific pathogen free; TDMQ, tetradentate monoquinoline chelator.

Figure 1 .

 1 Figure 1. Proteomic and bioinformatic analysis of DEPs in AD/WT category. (A) Volcano-diagram of DEPs. (B) Number of up-and down-regulated DEPs. (C) GO (Gene Ontology) analysis of DEPs including BP (biological process), CC (cell component), MF (molecular function). (D) Biology process. (E) Cell component.

Figure 2 .

 2 Figure 2. Bioinformatic analysis of DEPs in AD/WT category. (A) KEGG pathway analysis. (B) PPI network of DEPs.

Figure 3 .

 3 Figure 3. Proteomic and bioinformatic analysis of DEPs in TDMQ20/AD category. (A) Volcano-diagram of DEPs. (B) Number of up-and down-regulated DEPs. (C) GO (Gene Ontology) analysis of DEPs including BP (biological process), CC (cell component), MF (molecular function). (D) Biology process. (E) Cell component.

Figure 4 .

 4 Figure 4. Bioinformatic analysis of DEPs in TDMQ20/AD category. (A) KEGG pathway analysis. (B) PPI network of DEPs.

Figure 5 .

 5 Figure 5. Proteomic and bioinformatic analysis of DEPs in CQ/AD category. (A) Volcano-diagram of DEPs. (B) Number of up-and down-regulated DEPs. (C) GO (Gene Ontology) analysis of DEPs including BP (biological process), CC (cell component), MF (molecular function). (D) Biology process. (E) Cell component.

Figure 6 .

 6 Figure 6. Bioinformatic analysis of DEPs in CQ/AD category. (A) KEGG pathway analysis. (B) PPI network of DEPs.

Figure 7 .

 7 Figure 7. Common DEPs in AD/WT, TDMQ20/AD and CQ/AD categories. (A)

Table 3 .

 3 List of top 10 common DEPs reversed by treatment by TDMQ20 or CQ

					TDMQ:AD	AD:WT	CQ:AD
	No.	UniProt ID	Gene names	Protein names	Fold	Fold	Fold
					change	change	change
				Muscarinic
	1	P32211	Chrm4	acetylcholine receptor	6.6	0.2	5.6
				M4 (Mm4 mAChR)
				Choline O-
				acetyltransferase
	2	Q03059	Chat	(CHOACTase) (ChAT)	4.3	0.2	4.8
				(Choline acetylase) (EC
				2.3.1.6)
				Ras and Rab interactor
	3	Q921Q7	Rin1	1 (Ras interaction/interference	3.1	0.3	4.0
				protein 1)
				Galectin-related protein
				(Galectin-related
	4	Q8VED9	Lgalsl	protein A) (Lectin	2.3	0.5	1.7
				galactoside-binding-like
				protein A)
				28S ribosomal protein
	5	Q9D7N3	Mrps9	S9, mitochondrial	1.9	0.6	1.5
				(MRP-S9) (S9mt)
				Vacuolar protein
	6	D3Z645	Vps29	sorting-associated protein 29 (Vesicle	0.7	1.4	0.6
				protein sorting 29)
				Ras-related C3
	7	Q3TLP8	Rac1	botulinum toxin	0.7	1.3	0.5
				substrate 1
				Receptor-type tyrosine-
	8	B9EKR1	Ptprz1	protein phosphatase zeta (R-PTP-zeta) (EC	0.8	1.3	0.8
				3.1.3.48)
				UMP-CMP kinase (EC
				2.7.4.14)
				(Deoxycytidylate
				kinase) (CK) (dCMP
	9	A0A0R4J093	Cmpk1	kinase) (Nucleoside-	0.8	1.2	0.8
				diphosphate kinase) (EC
				2.7.4.6) (Uridine
				monophosphate/cytidine
				monophosphate kinase)

Table 1

 1 Mice grouping and drug administration

	Group	Drug	Drug dose (mg/kg)

Table 2

 2

	. iTRAQ labeling of the samples
	Group	TDMQ20	CQ	WT	AD
	iTRAQ label	113/114	115/116	117/118	119/121

experiments, and analyzed the data; F.S., Q.L., A.R., and B.M., wrote the manuscript.

Conflicts of Interest

None.

Supporting Information


Romain Dejeante
email: romain.dejeante@cefe.cnrs.fr

Marion Valeix

Simon Chamaillé-Jammes

Time-varying habitat selection analysis: a model and applications for studying diel, seasonal and post-release changes

Keywords: habitat selection, migration, post-release, resource selection function, seasonality, space use, temporal variation, time-varying effects

Resource selection functions are commonly employed to evaluate animals' habitat selection, e.g. the disproportionate use of habitats relative to their availability. While environmental conditions or animal motivations may vary over time, sometimes in an unknown manner, studying changes in habitat selection usually requires a priori time discretization. This limits our ability to precisely answer the question 'when is an animal's habitat selection changing?'. Here, we present a straightforward and flexible alternative approach based on fitting dynamic logistic models to used/available data. First, using simulated dataset, we demonstrate that dynamic logistic models performed well to recover temporal variations in habitat selection. We then show real-world applications for studying diel, seasonal, and post-release changes in habitat selection of blue wildebeest (Connochaetes taurinus). Finally, we provide the relevant R scripts to facilitate the adoption of the method by ecologists. Dynamic logistic models allow to study temporal changes in habitat selection in a framework consistent with resource selection functions, but without the need to discretize time, which can be a difficult task when little is known about the process studied, or may obscure inter-individual variability in timing of change.

These models should undoubtedly find their place in the movement ecology toolbox.

Introduction

Changes in environmental conditions consistently challenge animals in their lives, leading them to regularly adjust their behavior. One of the important way animals do so is by using the landscape they live in differently, i.e. by relocating themselves or selecting habitats differently.

This may be most clearly exemplified by migrations, which occur in response to seasonal changes in weather and/or resource availability [START_REF] Dingle | Migration: Definition and Scope[END_REF]. Changes at smaller time-scales also occur, such as when animals shift habitats in response to forage depletion or day/night alternation in predation risk [START_REF] Courbin | Zebra Diel Migrations Reduce Encounter Risk with Lions at Night[END_REF]. Naturally, discovering and understanding such changes in space use and habitat selection is a key goal of ecologists.

Over time, habitat selection analysis (HSA) conducted using the resource selection function (RSF) approach [START_REF] Boyce | Evaluating Resource Selection Functions[END_REF] has become the standard framework to study changes in animals' habitat selection. RSF analyses statistically compare the environmental characteristics of used locations collected over a period of time with the characteristics of locations available during that period. As such, RSF estimates the average strength of selection for the various habitats considered over the period of interest. How this period is defined is up to the researcher, but strongly affects the results and the associated interpretations [START_REF] Mayor | Habitat Selection at Multiple Scales[END_REF]). As the within-period variability in selection is averaged, finer-scale temporal dynamics (e.g. day/night changes when the period covers weeks or months) in selection are overlooked, and the mean selection strength estimated might represent an average that is not meaningful. This would be the case if, for instance, the study period encompasses two different phases in an animal's habitat selection behavior without the researcher being aware of it.

Discretizing time to define biologically relevant periods over which to conduct HSA may be difficult, and often involves somewhat arbitrary decisions with unknown consequences. This is true for even apparently obvious periods like seasons [START_REF] Basille | Ecologically Based Definition of Seasons Clarifies Predator-Prey Interactions[END_REF] or day-night periods [START_REF] Richter | So Close and yet so Different: The Importance of Considering Temporal Dynamics to Understand Habitat Selection[END_REF]. Starts and ends of seasons vary between years, and can only be roughly defined without ancillary data. Some seasons like spring or fall are also clearly periods of environmental changes during which patterns of habitat selection are unlikely to be constant.

Animal needs and motivations, and thus habitat selection [START_REF] Roever | The Pitfalls of Ignoring Behaviour When Quantifying Habitat Selection[END_REF] may also change at unpredictable (for the researcher) times, such as when they disperse [START_REF] Delgado | Changes of Movement Patterns from Early Dispersal to Settlement[END_REF]). This again makes discretization of time difficult, or even irrelevant if one is interested in the dynamics of the change itself. This issue has been recognized before and various suggestions have been offered, from using a combination of movement metrics and habitat use information to define periods (but without estimating habitat selection) [START_REF] Basille | Ecologically Based Definition of Seasons Clarifies Predator-Prey Interactions[END_REF], to integrating time directly into the RSF predictor (but with a constraint on the shape of the time-dependence) [START_REF] Picardi | Individual Variation in Temporal Dynamics of Post-Release Habitat Selection[END_REF] or using continuous-time movement models (but with a complex implementation) [START_REF] Hooten | Temporal Variation and Scale in Movement-Based Resource Selection Functions[END_REF]. Generally, we feel that ecologists are still lacking an easy and flexible tool to answer the question 'when is an animal's habitat selection changing?', learning from the data with no assumption about when changes should happen.

Here, we present how dynamic logistic regression models allow one to easily estimate the temporal dynamics of habitat selection without a priori discretization of time. Dynamic logistic regression models are commonly used to analyze binary time series in survival analysis [START_REF] Martinussen | Dynamic Regression Models for Survival Data[END_REF], but can be applied to other data sources [START_REF] Fahrmeir | Posterior Mode Estimation by Extended Kalman Filtering for Multivariate Dynamic Generalized Linear Models[END_REF]. First, we use simulated movement data to demonstrate that dynamic logistic regression models can adequately recover time-varying habitat selection coefficients. We also highlight the influence of parameters, whose values are under the researcher's control, on the estimation process. Second, we illustrate the usefulness of the approach by applying time-varying HSA on blue wildebeest (Connochaetes taurinus) tracking data, showing how one can describe temporal variations of animal habitat selection such as diel, seasonal and post-release changes. The relevant R scripts are provided to facilitate the adoption of the method by ecologists.

Methods

Dynamic logistic models for time-varying HSA

General principles of dynamic logistic models

Here, we briefly describe the discrete-time state space model developed by [START_REF] Fahrmeir | Posterior Mode Estimation by Extended Kalman Filtering for Multivariate Dynamic Generalized Linear Models[END_REF] to estimate time-varying coefficients from generalized linear models, and especially logistic models. Generally, discrete-time state space models relate observations over time to hidden parameters, with hidden parameters following a Markovian transition model [START_REF] Auger-Méthé | A Guide to State-Space Modeling of Ecological Time Series[END_REF]. Once applied to dynamic logistic regressions in the context of time-varying HSA, such relationships can be simplified as (1) and ( 2):

(1) 𝑙𝑜𝑔𝑖𝑡(𝑦 𝑡 ) = 𝛽 𝑡,0 + 𝛽 𝑡,1 𝑥 𝑡,1 +. . . +𝛽 𝑡,𝑛 𝑥 𝑡,𝑛

(2) 𝛽 𝑡 = 𝑇 𝑡 𝛽 𝑡-1 + 𝑣 𝑡 with 𝑦 𝑡 a binary response variable (i.e. used/available), (𝛽 𝑡,1 , . . . , 𝛽 𝑡,𝑛 ) the hidden parameters (i.e. time-varying selection coefficient), (𝑥 𝑡,1 , . . . , 𝑥 𝑡,𝑛 ) the covariates (i.e. environmental layers), 𝑣 𝑡 the error process with 𝑣 𝑡 ~𝑁(0, 𝑄 𝑡 ), 𝑄 𝑡 and 𝑇 𝑡 the covariance and transition matrices of the Markov chain at time t. The transition matrix 𝑇 𝑡 is estimated by the model. The covariance matrix 𝑄 𝑡 is a diagonal matrix with initial values (at 𝑡 0 ) chosen by the researcher, and then automatically updated over time during the fitting process. Hereafter, for simplicity, we call Q the initial value used by the researcher to fill in the 𝑄 𝑡 diagonal matrix, and as it can greatly affect the wiggliness of the model (see Results), we also refer to Q as the wiggliness parameter. Time-varying coefficients (𝛽 𝑡,1 , . . . , 𝛽 𝑡,𝑛 ) are estimated by maximization of the posterior densities, following a partial Bayesian approach. Basically, the generalized extended Kalman filter and smoother algorithm described by [START_REF] Fahrmeir | On Kalman Filtering, Posterior Mode Estimation and Fisher Scoring in Dynamic Exponential Family Regression[END_REF] recursively iterates prediction, correction and smoothing steps to approximate posterior mode estimation in dynamic generalized linear models.

Implementation in the context of HSA

To conduct time-varying HSA using dynamic logistic models, the following steps are required: first, as in RSF analyses, a sample of locations that could be considered 'available' is drawn. This can be, for instance, locations sampled randomly within the animal home range. Each used location (𝑦 𝑡 = 1) obtained at time t is paired with N random locations (𝑦 𝑡 = 0). Each used and available location is then characterized using environmental variables or any other variable of interest (𝑥 𝑡,1 , . . . , 𝑥 𝑡,𝑛 ). Finally, the time-varying parameters (𝛽 𝑡,1 , . . . , 𝛽 𝑡,𝑛 ) are estimated by fitting a dynamic logistic model, with type of location (used vs. available) as response variable 𝑦 𝑡 and the covariate time series 𝑥 𝑡,𝑛 as predictors. Here, to fit the dynamic logistic model, we used the "ddhazard" function from the dynamichazard R package by [START_REF] Christoffersen | Dynamichazard: Dynamic Hazard Models Using State Space Models[END_REF], which implements the method described by [START_REF] Fahrmeir | Posterior Mode Estimation by Extended Kalman Filtering for Multivariate Dynamic Generalized Linear Models[END_REF]. We provide the R script needed to run a time-varying HSA on a simulated trajectory (Appendix S1).

Evaluation of the accuracy of dynamic logistic models for time-varying HSA

To assess the ability of our approach to detect shift in habitat selection patterns, we (1) simulated animal trajectories emerging from time-varying selection for one environmental variable, and (2) fitted dynamic logistic models on the simulated data to compare the estimated coefficients with the theoretical values used in the simulations.

Landscape and movement simulation

For simplicity, animal trajectories were simulated on one habitat layer (500 × 500 cells), with values that did not vary over time. To mimic patchy landscapes, we used spatially correlated Gaussian random fields, which attribute a continuous value ranging from 0 to 1 to each cell, using the localGibbs R package [START_REF] Michelot | Linking Resource Selection and Step Selection Models for Habitat Preferences in Animals[END_REF]. Following [START_REF] Michelot | Linking Resource Selection and Step Selection Models for Habitat Preferences in Animals[END_REF], we then simulated animal trajectories over 500 time steps using a local Gibbs movement model. For each time t, 1000 potential locations were uniformly generated within a 100-pixel radius around the current location, and the location at time t+1 was sampled among them with probabilities proportional to the strength of selection for potential locations. This strength of selection was determined by the value of the habitat layer at these locations, and by the model coefficient describing how the strength of selection changes with values of the habitat variable. An important benefit of using a local Gibbs model is that the coefficient of habitat selection used in the simulation model is theoretically equal to the one that should be estimated by a RSF fitted on the data [START_REF] Michelot | Linking Resource Selection and Step Selection Models for Habitat Preferences in Animals[END_REF].

Scenarios of temporally-changing habitat selection

To be able to test whether changes in habitat selection strength could be robustly recovered by dynamic logistic models, we built scenarios that differed in terms of how often the model's coefficient of habitat selection changed over time. We did so by randomly sampling, in the [-5, 5] range, and either every 20 steps (referred as "frequent change" scenario) or every 250 steps (referred as "rare change" scenario), the model's habitat selection coefficient. To avoid having sudden, step-like, changes in habitat selection, we then used spline regressions to smooth the variations of habitat selection over time. For each scenario, we generated 100 trajectories per simulated landscape, and replicated this on 100 different landscapes. We then tested our ability to recover the temporal changes in the model's habitat selection coefficient by fitting a dynamic logistic regression model to each trajectory as presented above, drawing 100 available locations, at each time-step, within the 99% utilization distribution location-based kernel of each simulated trajectory. We also assessed to what extent the model's estimation was affected by the value of the wiggliness parameter Q. We did so by fitting, for each dataset of each scenario, a set of models with different values of Q, ranging from 0.01 (i.e. low wiggliness) to 2 (i.e. high wiggliness). For each value of Q, we then averaged the estimated coefficients over each set of 100 simulated trajectories per landscape, and fitted a linear regression with the mean estimated coefficient as response and theoretical coefficients which were used in the simulations as predictors, adding a random intercept with replication number. A slope near 1 would indicate that a dynamic logistic model is able to estimate the temporal changes in the habitat selection coefficient correctly, and an intercept near 0 would indicate that the estimations are not biased.

Time-varying HSA: applications

To illustrate some applications of time-varying HSA, we analyzed wildebeest movement datasets collected in Hluhluwe-iMfolozi Park (South Africa). There, surface-water availability, which is high during the wet season (October-March) as temporary waterholes are filled up by the rains, becomes low in the dry season (April to September), with water remaining available only in a few rivers. Since wildebeests are water-dependent grazers that preferentially forage in open grasslands, we used the distance to the closest main river and the habitat openness as relevant habitat variables to demonstrate the use of dynamic logistic models. For each study below, we fitted the models with a high-value for Q (Q=2), as simulations showed that high wiggliness in the estimates lead to better results (see Results section, or Appendix S2).

Short-term temporal variations: diel changes in habitat selection

We used tracking data collected on one wildebeest over one month in the dry season, at a fix rate of one location every 15 minutes. Following the practical implementation of time-varying HSA described above, we estimated the habitat selection of this wildebeest by (1) generating, for each time t, 100 random locations within its home range, (2) extracting the environmental characteristics of used and random locations, and (3) fitting a dynamic logistic regression to compare the habitat openness and the distance to the closest main river between the used and available locations over time. A preliminary visual inspection of the spatial data suggested that day/night and longer, few-days, changes in locations occurred (Appendix S3: Figure S1). To check whether these changes were associated with changes in habitat selection, we estimated the temporal auto-correlation of the time-varying habitat selection coefficients.

Long-term temporal variations: seasonal changes in habitat selection

We used tracking data collected on one wildebeest over one year, at a fix rate of one location every 15 minutes, and subsampled to one location per hour. A preliminary visual inspection of the spatial data showed that the individual moved mostly westward and then eastward during the period (Appendix S3: Figure S2). Hence, in addition to habitat openness and distance to the closest river, we added longitude to the model's predictors. We generated 10 random locations, per time t, within its home range to fit the time-varying HSA. To provide an example of how one can subsequently delineate temporal periods that are homogeneous in terms of habitat selection, we further segmented the time series of each model's coefficients, using the segmentation method described by [START_REF] Patin | Identifying Stationary Phases in Multivariate Time Series for Highlighting Behavioural Modes and Home Range Settlements[END_REF]) and implemented in the segclust2d R package.

Event-based variations: post-release changes in habitat selection

We used the tracking data of three wildebeest simultaneously introduced in the park in October 2020. Data were collected over the 100 days following the release date, at a fix rate of one location every hour. Similarly to previous applications, we estimated wildebeest's habitat selection drawing 100 random locations, for each time t, within their individual home ranges. To incorporate the dispersal of the released individuals into HSA, we added longitude and latitude to model's predictors. Hence, we fitted a dynamic logistic model for each individual, using habitat openness, distance to the closest river, longitude and latitude as predictors.

Results

Theoretical evaluation

In general, we found that dynamic logistic models allowed us to adequately recover the temporal changes in the coefficients of habitat selection used in the simulations. When the true coefficients did not change often ('rare change' scenarios), the wiggliness parameter had little impact and estimations were always good (Fig. 1a-b; Appendix S2: Figure S1). When the true coefficients did change often ('frequent change' scenarios), it became critical to use high values of the wiggliness parameter to obtain estimates matching the theoretical coefficients (Fig. 1c-d; Appendix S2: Figure S1). Importantly, the effect of the wiggliness parameter tend to stabilize at large values of Q (Appendix S2: Figure S2), making it safe to use large values when investigating large and frequent changes in habitat selection.

Short-term temporal variations: diel changes in habitat selection

A time-varying HSA conducted with a dynamic logistic model very clearly revealed that the wildebeest's selection for open habitats and rivers varied greatly over the month of the study (Fig. 2a-b). In particular, the wildebeest's selection for open habitats clearly changed across the day/night cycle, as the auto-correlation period of the coefficient was approximately 24h (Appendix S3: Figure S3). Open habitats were strongly selected during nighttime, but were not selected, and sometimes even avoided, during daytime (Fig. 2a). We did not find such a pattern for the selection of areas close to rivers (Fig. 2b), but there were variations over periods of 3 or 4 days. Contrary to the diel variations in the selection of open habitats, such variations would be hard to detect using the common HSA approach based on time discretization.

Long-term temporal variations: seasonal changes in habitat selection

The time-varying HSA showed clear seasonal changes in the wildebeest' habitat selection, which could then be separated in several periods according to the segmented time-series (Fig. 2c-d).

The existence and timing of some these periods were unpredictable a priori. For example, this wildebeest maintained the same overall strength of selection for open habitats from November to August, whereas this period covers months from both the dry and wet seasons (Fig. 2c). Also, we note that during the dry season (April to June) this wildebeest did not preferentially use areas close to rivers, but selected areas close to rivers consistently from July to mid-August (green segment) and likely did back-and-forth trips to and away from the rivers from mid-August to October (yellow segment) (Fig. 2d).

Event-based variations: post-release changes in habitat selection

After their release, the three wildebeest generally selected open habitats, but their level of selection differed between individuals (Appendix S3: Figure S4), particularly towards the end of the study period (Fig. 3a). Differences in the selection for areas close to rivers (Fig. 3b) or in the longitude (Fig. 3c) and latitude (Fig. 3d) of the park also became apparent at the end of the first month after release. Then, although the three wildebeest established in different areas (see difference in selection for longitude and latitude), the selection of areas close to rivers remained and was similar for two wildebeests (colored in green and yellow), whereas the third one avoided the areas close to rivers (colored in purple).

Discussion

There is obvious evidence that animals' habitat selection changes regularly and at different timescales, from diel to seasonal shifts, or during key life-history events such as dispersal.

Unfortunately, ecologists have had limited and often unsatisfactory options to study these changes. Most commonly, an a priori discretization of time periods of apparent biological relevance is made, although this discretization can sometimes be difficult to justify, let alone to validate. The alternative approach of simply integrating time as predictor in a RSF has limitations (see discussion in Picardi et al. ( 2021)), and more statistically complex approaches [START_REF] Hooten | Temporal Variation and Scale in Movement-Based Resource Selection Functions[END_REF]) are unlikely to be broadly used. In this work, we propose a new approach based on dynamic logistic models [START_REF] Fahrmeir | Posterior Mode Estimation by Extended Kalman Filtering for Multivariate Dynamic Generalized Linear Models[END_REF] to easily estimate temporal changes in habitat selection, in a framework consistent with RSF. We demonstrated, using simulations, its general validity, while highlighting a point of attention (parameter Q). We also showcased its use for the study of diel, seasonal and post-release changes in habitat selection. In addition, we provided the necessary R scripts to make the use of time-varying HSA accessible to the ecologist community.

With this time-varying HSA approach, one can simultaneously estimate both the timing and the amplitude of habitat selection changes. Estimation of the timing of change from the data is what makes this approach novel and attractive. Many times, a priori discretization of time is a guesstimate from expert knowledge or is based on ancillary data (e.g. climate data) whose relevance for a particular dataset is not warranted. 'Letting the data speak' allows revealing the actual pattern of change. This may be of particular importance, for instance, in the study of inter-individual variability, as the timing of change can be one of the differencing variables, as evidenced in our post-release study case. As recognized by [START_REF] Picardi | Individual Variation in Temporal Dynamics of Post-Release Habitat Selection[END_REF], time-varying HSA opens a new avenue to broaden the scope of the studies of inter-individual differences in space use, which has so far focused on movement characteristics or habitat selection strength. More generally, even when the relevance of an a priori discretization of time is easier to ascertain, such as when comparing daytime to night-time habitat selection, timevarying HSA allows one to immediately identify unusual periods (e.g. night of the 27 th of August, when the wildebeest did not increase its selection for open habitat). These unusual periods may either be of interest (in such case, one would have to conduct one standard HSA per night to have discovered this), or be 'noise' that should not affect the estimation of habitat selection strength during more 'usual' periods (conversely to what occurs in a standard HSA).

Importantly, data-driven estimation of the timing of change in habitat selection makes it possible to derive 'segments' of homogeneous habitat selection, and open the way to estimate specific habitat selection 'modes' of animals defined by the strength of habitat selection. Until now, behavioral modes in space use could be defined by movement characteristics such as speed and turning angles [START_REF] Patin | Identifying Stationary Phases in Multivariate Time Series for Highlighting Behavioural Modes and Home Range Settlements[END_REF][START_REF] Mcclintock | MomentuHMM: R Package for Generalized Hidden Markov Models of Animal Movement[END_REF], but could not integrate information about habitat selection, although it was likely to strongly contribute to define these modes. The use of segmentation-clustering algorithm such as segclust2d or hidden-Markov models on the time-series coefficients obtained by time-varying HSA will allow one to do this, and to estimate the duration and frequencies of such modes. This could complement very recent works developing behavioral-mode detection approaches based on step-selection functions [START_REF] Prima | Multi-Mode Movement Decisions across Widely Ranging Behavioral Processes[END_REF][START_REF] Klappstein | Flexible Hidden Markov Models for Behaviour-Dependent Habitat Selection[END_REF].

Results from the time-varying approach proposed here are to some extent sensitive to the model's wiggliness. In particular, but without surprise, model allowing for little wiggliness (small values of Q) can provide a poor fit to the data when habitat selection often changes. Models allowing for high wiggliness generally performed much better, especially if habitat selection often changed. There was no obvious evidence of an optimum value of Q to look for, as correlations plateaued when increasing Q values. Therefore, running a time-varying HSA with a high value for Q appears a safe way to conduct robust analyses.

In conclusion, we think dynamic logistic models offer an easy yet powerful approach to conduct time-varying HSA, for both exploratory and inferential studies. Our work, in which we show real-world applications and provide R scripts, aims to facilitate the appropriation of the method by ecologists and enriches their statistical toolbox. Novel questions about how animals time their response to environmental changes can now be addressed.

FigureFigure 1 .

 1 Figure

Figure 2 .

 2 Figure 2. Estimations of short (a-b) and long-term (c-d) temporal variations of wildebeest's

Figure 3 .

 3 Figure 3. Estimations of post-release variation of wildebeest's selection for open habitats, areas

  Figure 1 393

Figure S2 .

 S2 Figure S2. Relationship between the value of the model wiggliness parameter Q and the

Figure S2 .

 S2 Figure S2. Illustration of the wildebeest's locations recorded during one year, at a fix rate of one

Figure S3 .

 S3 Figure S3. Temporal autocorrelation of the selection coefficient for open habitat 𝛽(𝑡)𝛽(𝑡 +

Figure S4 .

 S4 Figure S4. Distribution of the habitat selection coefficients for (a) habitat openness, (b) distance

Acknowledgements

We are grateful to Ezemvelo for providing the authorization to conduct this research, and to its ecologist D.J. Druce for his continuous administrative and field support, as well as for a review of the manuscript. We acknowledge the field efforts of B. Gehr and R. Leeming and anyone involved in the wildebeest captures. G. Fradin created the layer for habitat openness. This work was partially funded by the French National Research Agency (program ANR-16-IDEX-0006) through the i-Site MUSE (project REPOS).

Conflict of interest statement

The authors declare no competing interests.

Supplementary information

Appendix S1

Available in the figshare digital repository: https://doi.org/10.6084/m9.figshare.c.6365415.v3