Bryan E Luu

Etienne Lefai

Sylvain Giroud

Jon E Swenson

Blandine Chazarin

Guillemette Gauquelin-Koch

Jon M Arnemo

Alina L Evans

Fabrice Bertile

Dr Kenneth B Storey
email: kenneth_storey@carleton.ca

MicroRNAs facilitate skeletal muscle maintenance and metabolic suppression in hibernating brown bears

Keywords: atrophy, Mef2a, myomiR, noncoding RNA, ubiquitin ligase, Ursus arctos

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

Introduction

Hibernators are able to endure extreme environmental conditions that cannot be tolerated by most mammals. Typically, for small mammal hibernators, seasonal torpor is characterized by a prolonged state of inactivity, a strong decrease in core body temperature to near-ambient values, and the suppression of basal metabolic rate to levels often less than 10% of euthermic values [START_REF] Heldmaier | Natural hypometabolism during hibernation and daily torpor in mammals[END_REF][START_REF] Jastroch | Seasonal control of mammalian energy balance: Recent advances in the understanding of daily torpor and hibernation[END_REF][START_REF] Ruf | Daily torpor and hibernation in birds and mammals[END_REF]Storey & Storey, 2010). However, the iconic hibernators, bears (Ursidae), stand out from these others in several characteristics. Their large body mass makes it difficult to substantially decrease their body temperature such that core body temperature remains at 30-35°C during hibernation and they have the impressive ability to endure up to 6 months of inactivity without arousing and with no food or water intake, no urination or defecation, no muscle wasting, and no accumulation of nitrogen-containing waste products due to an active recycling program (Berg von [START_REF] Berg Von Linde | Insights from the den: How hibernating bears may help us understand and treat human disease[END_REF]. Previous studies of bears also showed no statistically significant changes in skeletal muscle fiber size between hibernating and non-hibernating animals [START_REF] Hershey | Minimal seasonal alterations in the skeletal muscle of captive brown bears[END_REF][START_REF] Tinker | Protein use and muscle-fiber changes in free-ranging, hibernating black bears[END_REF]. In mammals, weight-bearing activities are crucial for the maintenance of skeletal muscle mass, morphology, and protein composition [START_REF] Baldwin | Invited Review: Effects of different activity and inactivity paradigms on myosin heavy chain gene expression in striated muscle[END_REF]). Yet despite drastically reduced weight-bearing activity during hibernation, the skeletal muscle of bears retains most of its strength, with only a 23% decrease in maximal force (elicited by electrical stimulation) [START_REF] Harlow | Muscle strength in overwintering bears[END_REF], whereas reduction in force and size as high as 60% is common in other non-hibernating mammals [START_REF] Baldwin | Invited Review: Effects of different activity and inactivity paradigms on myosin heavy chain gene expression in striated muscle[END_REF]. In small hibernating mammals, up to 20-40% of muscle protein content is lost at low body temperature [START_REF] Steffen | Morphometric and metabolic indices of disuse in muscles of hibernating ground squirrels[END_REF][START_REF] Wickler | Disuse atrophy in the hibernating golden-mantled ground squirrel, Spermophilus lateralis[END_REF][START_REF] Yacoe | Protein metabolism in the pectoralis muscle and liver of hibernating bats, Eptesicus fuscus[END_REF]. However, hibernating bears lose only 4-11% of muscle protein content, and the size and number of their muscle fibres remain unchanged [START_REF] Tinker | Protein use and muscle-fiber changes in free-ranging, hibernating black bears[END_REF].

In an attempt to better understand these observations, another study investigated the importance of the signalling pathways that underlie the resilience of skeletal muscle in hibernating brown bears (Ursus arctos). The authors challenged the concept that mechanical loading and neural activity of the muscles are the primary factors in maintaining skeletal muscle size [START_REF] Lin | Skeletal muscles of hibernating brown bears are unusually resistant to effects of denervation[END_REF]. Indeed, studies on other non-hibernating mammals have demonstrated that regular neural activation or electrical stimulation of muscles is not enough to curtail muscle atrophy induced by mechanical unloading [START_REF] Haddad | Isometric resistance exercise fails to counteract skeletal muscle atrophy processes during the initial stages of unloading[END_REF][START_REF] Zhang | The effects of low frequency electrical stimulation on satellite cell activity in rat skeletal muscle during hindlimb suspension[END_REF]. Given that there is decreased muscle loading during hibernation, these authors studied the differences between the skeletal muscles (long distal extensor and cranial tibial) of hibernating and non-hibernating U. arctos that had undergone transections of the common peroneal nerve [START_REF] Lin | Skeletal muscles of hibernating brown bears are unusually resistant to effects of denervation[END_REF]. Their results showed that active brown bears with denervation exhibited significantly higher muscle mass loss as compared to hibernating bears with denervation [START_REF] Lin | Skeletal muscles of hibernating brown bears are unusually resistant to effects of denervation[END_REF]. The authors therefore suggested that a third factor is involved, proposing that differential regulation of signalling pathways, such as those that mediate the ubiquitin-mediated catabolism of contractile proteins, must be involved in atrophy resistance [START_REF] Lin | Skeletal muscles of hibernating brown bears are unusually resistant to effects of denervation[END_REF].

The observed resistance to muscle atrophy in hibernating bears could be elicited by signalling pathways that mediate changes in regulatory microRNAs. MicroRNAs are a class of highly conserved small non-coding RNAs that are capable of mediating the fate of mRNA transcripts in response to diverse signalling pathways in cells [START_REF] Bartel | MicroRNAs: Genomics, biogenesis, mechanism, and function[END_REF]Storey & Storey, 2010). As a component of RNA-induced silencing complexes, microRNAs contribute to posttranscriptional regulation by binding to complementary mRNAs to promote their degradation or inhibit their translation into functional proteins. MicroRNAs are differentially regulated in a number of small mammal hibernator models in a tissue specific manner, contributing to metabolic control, antioxidant defenses, and muscle maintenance networks during hibernation [START_REF] Hadj-Moussa | The hibernating South American marsupial, Dromiciops gliroides, displays torpor-sensitive microRNA expression patterns[END_REF][START_REF] Kornfeld | Differential expression of mature microRNAs involved in muscle maintenance of hibernating little brown bats, Myotis lucifugus: a model of muscle atrophy resistance[END_REF][START_REF] Luu | Torpor-responsive expression of novel microRNA regulating metabolism and other cellular pathways in the thirteen-lined ground squirrel, Ictidomys tridecemlineatus[END_REF]Storey & Storey, 2010;Wu, Biggar, & Storey, 2014) but, as yet, have not been evaluated in the context of bear hibernation. One direct regulator of skeletal muscle microRNA (myomiR) expression is the myocyte enhancer factor-2A (MEF2A) transcription factor, and its targets include miR-1, miR-133, and miR-206 [START_REF] Liu | An intragenic MEF2-dependent enhancer directs muscle-specific expression of microRNAs 1 and 133[END_REF][START_REF] Rao | Myogenic factors that regulate expression of muscle-specific microRNAs[END_REF]. The MEF2A transcription factor is known to be crucial for skeletal muscle development, maintenance, and regeneration [START_REF] Taylor | Mef2 and the skeletal muscle differentiation program[END_REF]. Previous studies have demonstrated that MEF2A signalling is activated in the skeletal muscle of thirteen-lined ground squirrels (Ictidomys tridecemlineatus) during hibernation and may contribute to muscle maintenance [START_REF] Tessier | Expression of myocyte enhancer factor-2 and downstream genes in ground squirrel skeletal muscle during hibernation[END_REF]. Likewise, a study that profiled 10 microRNAs known to affect muscle-specific factors revealed that expression of these small non-coding transcripts are also differentially regulated in favour of skeletal muscle maintenance of hibernating bats (Myotis lucifugus) [START_REF] Kornfeld | Differential expression of mature microRNAs involved in muscle maintenance of hibernating little brown bats, Myotis lucifugus: a model of muscle atrophy resistance[END_REF].

The present study explores whether signalling pathways that impede muscle wasting are activated during hibernation in U. arctos. To address this, U. arctos mRNA and microRNA gene transcript sequences were obtained from available genomic data. RNA was isolated from skeletal muscle biopsies of 6 brown bears that were sampled during summer active and winter hibernating conditions. Specifically, mRNA transcript levels of MEF2A were measured by RT-qPCR along with a group of 36 myomiRs that are known to control skeletal muscle regeneration, metabolism and/or be under the control of MEF2A [START_REF] Diniz | Regulation of skeletal muscle by microRNAs[END_REF][START_REF] Horak | Muscle-specific microRNAs in skeletal muscle development[END_REF][START_REF] Lima | Role of microRNAs on the regulation of mitochondrial biogenesis and insulin signaling in skeletal muscle[END_REF]. Furthermore, selected downstream mRNA targets including myostatin (mstn), AMP-activated protein kinase (AMPK) catalytic subunit alpha 1 (prkaa1), paired box protein 7 (pax7), and inhibitor of DNA 2 (id2) that are known targets of the subset of microRNAs assessed herein were also quantified to better understand the potential roles of these microRNAs during bear hibernation.

Materials and Methods

Sampling of bear muscle

Six free-ranging brown bears (Ursus arctos; females; 2-3 years old), fitted with GPS collars, have been followed by the Scandinavian Brown Bear Research Project (SBBRP) in the Dalarna and Gävleborg counties, Sweden. Their immobilization was performed as previously reported [START_REF] Evans | Capture, anesthesia, and disturbance of free-ranging brown bears (Ursus arctos) during hibernation[END_REF], to allow sampling during hibernation in late February 2015 or 2016 and again during the active summer period in early June of the same years. After darting in their den (winter) or from a helicopter (summer), immobilized individuals were moved onto an insulated blanket prior to tissue sampling. Biopsies of the vastus lateralis muscle were collected and immediately frozen on dry ice before transfer to storage at -80°C until analysis. The mean body mass of hibernating bears (46.8 ± 8.7 kg; 25-74 kg) was not significantly different (p=0.062; paired t-test) from that of the same bears when resampled as summer-active animals (54.4 ± 7.3 kg; 37.8-83.6 kg). The study was approved by the Swedish Ethical Committee on Animal Experiment (applications #C212/9, #C47/9, #C7/12, #C268/12, and #C18/15), the Swedish Environmental Protection Agency (NV-0758-14), and the Swedish Board of Agriculture (31-11102/12). All procedures complied with Swedish laws and regulations.

Total RNA isolation

Total RNA was isolated from frozen tissue using TRIzol reagent (Invitrogen, Carlsbad, CA), as previously described [START_REF] Luu | The roles of hippocampal microRNAs in response to acute postnatal exposure to di(2-ethylhexyl) phthalate in female and male rats[END_REF]. Briefly, samples of frozen bear muscle (~50 mg) were homogenized 1:20 w/v in TRIzol reagent and then combined with 300 µL of chloroform. Samples were vigorously shaken by hand and incubated at room temperature for 5 min before centrifugation at 10,000 x g for 15 min at 4°C. The aqueous phase of each sample was then combined with 500 µL isopropanol, incubated at room temperature for 15 min, and then centrifuged at 12,000 x g for 15 min at 4°C to facilitate RNA precipitation. RNA pellets were washed with 70% ethanol and air dried prior to resuspension in autoclaved RNase-free water. To standardize concentrations across samples, RNA concentration and purity (260/280 nm ratio) was assessed using a BioTek Take3 plate in a spectrophotometer (BioTek, Winooski, VT). Integrity of RNA was assessed by the appearance of 28S and 18S ribosomal RNA bands on a 1% agarose gel stained with SYBR Green dye.

RNA polyadenylation and cDNA synthesis

For analysis of microRNAs, samples of bear muscle RNA were polyadenylated and then cDNA synthesis was conducted as previously described (Biggar, Wu, & Storey, 2014) using a Poly(A) polymerase tailing kit (Epicentre, Madison, WI) with a modified adapter primer (Supplementary Table S1) for use with short RNA molecules.

Synthesis of cDNA was performed separately for mRNA quantification as previously described [START_REF] Holahan | Preadolescent phthalate (DEHP) exposure is associated with elevated locomotor activity and reward-related behavior and a reduced number of tyrosine hydroxylase positive neurons in post-adolescent male and female rats[END_REF]. Standard oligo-dT (5'-TTTTTTTTTTTTTTTTTTTTTTV-3'; where V= A, G, or C) was combined with RNA (1 μg) in a total volume of 10 μL of RNase-free water. Samples were incubated at 65°C for 5 min in an Eppendorf thermocycler, and then cooled on ice. Next, 4 μL of 5X first strand buffer, 2 μL of 0.1 M DTT, 1 μL of 25 mM dNTPs, and 1 μL of MMLV reverse transcriptase enzyme (Invitrogen) were combined with the RNA/oligo-dT samples. Reactions were incubated at 42°C for 60 min in a thermocycler to facilitate cDNA synthesis.

Primer design

MicroRNAs investigated in this study were selected from the literature based on their expression in skeletal muscle and their roles in regulating muscle cell processes [START_REF] Diniz | Regulation of skeletal muscle by microRNAs[END_REF][START_REF] Horak | Muscle-specific microRNAs in skeletal muscle development[END_REF][START_REF] Lima | Role of microRNAs on the regulation of mitochondrial biogenesis and insulin signaling in skeletal muscle[END_REF]. In order to determine the equivalent microRNA sequences in U. arctos, Mus musculus pre-microRNA sequences were obtained from miRBase (miRBase v.22; http://www.mirbase.org). Each M. musculus pre-microRNA sequence was aligned against the U. arctos WGS genome (SRA Accession No. SRX156136) using Sequence Read Archive Nucleotide BLAST (NCBI SRA BLAST; https://www.ncbi.nlm.nih.gov/sra). The conserved U. arctos pre-microRNA sequences were then aligned against M. musculus mature microRNA sequences from miRBase using Clustal Omega (EMBL-EBI; https://www.ebi.ac.uk/Tools/msa/clustalo/). From these alignments, the sequences of mature U. arctos microRNA were determined. Primers for relative microRNA quantification were designed as previously described [START_REF] Biggar | High-throughput amplification of mature microRNAs in uncharacterized animal models using polyadenylated RNA and stem-loop reverse transcription polymerase chain reaction[END_REF] and are listed in Supplementary Table S1.

Relative quantification of mRNA levels was performed for genes that are both upstream and downstream of microRNA regulatory pathways. Transcript levels of mef2a, mstn, pax7, prkaa1, and id2 were assessed. In order to design primers for these targets, the coding sequence of each gene was obtained from NCBI Gene (https://www.ncbi.nlm.nih.gov/gene/) for multiple mammals (U. maritimus, H. sapiens, M. musculus, S. scrofa, C. lupus, F. catus, O. aries, and E. caballus. These were aligned with Clustal Omega to determine highly conserved regions, for which primers were designed for gene expression quantification for U. arctos samples. Sequences deemed highly conserved and used for primer design had 100% conservation with at least 4 different mammals. Primers were further analyzed with NCBI Primer-BLAST (https://www.ncbi.nlm.nih.gov/tools/primer-blast/) to demonstrate that they would amplify the target gene in multiple mammals. All mRNA primers used in this study are listed in Supplementary Table S1.

RT-qPCR

Relative quantification of gene transcripts used a BioRad CFX96 Connect System (BioRad, Hercules, CA). Reagents for RT-qPCR were prepared as previously described [START_REF] Pellissier | Lab assembly of a low-cost, robust SYBR green buffer system for quantitative real-time polymerase chain reaction[END_REF]. Briefly, each amplification reaction included 2 μL of 10x buffer (100 mM Tris-HCl, pH 8.5, 500 mM KCl, 1.5% Triton X-100, 20 mM MgCl2, and 2 mM dNTPs), 6 μL of 2 M trehalose (BioShop, Canada), 0.5 μL of 100% formamide (BioShop, Canada), 0.1 μL of 1:100 SYBR green dye (Invitrogen), 1 μL (1U) of Taq polymerase (BioShop, Canada), primers (synthesized by Integrated DNA Technologies, 250 nM final concentration), and autoclaved water to a total volume of 20 μL. Each RT-qPCR reaction was analyzed with a melt curve analysis to ensure that the reaction was not amplifying more than one product and primers that amplified multiple products were not used for quantification. For relative quantification of microRNAs, reactions were brought to 95°C for 3 min, followed by 40 cycles of 95°C for 15 sec and 60°C for 1 min, as previously described [START_REF] Biggar | High-throughput amplification of mature microRNAs in uncharacterized animal models using polyadenylated RNA and stem-loop reverse transcription polymerase chain reaction[END_REF]. Relative quantification of mRNAs used a modified protocol, where thermocycler conditions were 50 cycles of 95°C for 10 sec, 57°C for 20 sec, and 72°C for 20 sec. Standard curves were performed with pooled cDNA samples and primer sets in order to assess the reaction specificity and efficiency for each gene transcript, and the efficiency of the primer pair was always used for quantification calculations.

Statistical analyses

Since sampling of tissues was done in a paired manner where each bear was sampled (and tagged) in the winter and then sampled again the following summer, paired t-tests were used to determine statistically significant differences (p < 0.05) in microRNA and mRNA levels. Amplified transcripts were normalized against the expression of reference genes (U6 small nuclear RNA) for microRNA and gapdh for mRNA). The expression of these reference genes was determined to be stable between summer and winter hibernation conditions, as instructed [START_REF] Schmittgen | Analyzing real-time PCR data by the comparative CT method[END_REF]. The comparative ∆∆Ct method was used for relative expression calculations [START_REF] Schmittgen | Analyzing real-time PCR data by the comparative CT method[END_REF]. Expression values for microRNA and mRNA were presented as mean ± SEM with n = 6 bears for microRNA analysis and n = 5 bears for mRNA analysis). SigmaPlot v.12.5 was used for statistical analysis and bar graph generation (Systat Software Inc., San Jose, CA).

Results

Relative mRNA transcript levels of selected genes involved in the MEF2A signalling pathway were quantified via RT-qPCR to assess their transcriptional state in U. arctos vastus lateralis muscle during hibernation, as compared with the summer active state of the same individuals. Transcript levels of mef2a were 2.35±0.26 fold higher during hibernation, relative to summer controls (Figure 1A). Seven microRNAs under MEF2A control (miR-1a-3p, miR-1a-1-5p, miR-1a-2-5p, miR-1b-5p, miR-133b-5p miR-133a-3p, and miR-206-3p) were also quantified. From this group, the levels of miR-1b-5p, miR-133a-3p, and miR-206-3p increased by 1.43±0.19, 1.40±0.18, and 1.65±0.17 fold during hibernation, respectively (Figure 1B). The other four myomiRs were not responsive to hibernation. Two downstream targets of miR-1 and miR-206 were assessed with RT-qPCR to determine the potential impact of microRNA upregulation during hibernation (J.-F. [START_REF] Chen | microRNA-1 and microRNA-206 regulate skeletal muscle satellite cell proliferation and differentiation by repressing Pax7[END_REF][START_REF] Dey | miR-206 and -486 induce myoblast differentiation by downregulating Pax7[END_REF]. Relative transcript levels of pax7 and id2 decreased to 0.67±0.07 and 0.40±0.06 of summer active levels (Figure 1C).

Eighteen other microRNAs known for their regulatory roles in skeletal muscle [START_REF] Lima | Role of microRNAs on the regulation of mitochondrial biogenesis and insulin signaling in skeletal muscle[END_REF] were also assessed in vastus lateralis muscle of active versus hibernating brown bears. Of these microRNAs, there were six that showed higher levels in hibernation. These were miR-27b-5p, miR-27a/b-3p, miR-29a-5p, miR-29a-3p, miR-29b/c-3p, and miR-33-5p that showed significantly higher levels by 1.65±0.25, 2.31±0.36, 1.30±0.12, 2.18±0.18, 2.60±0.18, and 1.24±0.08 fold, respectively, in hibernating versus summer active bears (Figure 2A). To investigate the potential consequences of these increases in miR-33 [START_REF] Davalos | miR-33a/b contribute to the regulation of fatty acid metabolism and insulin signaling[END_REF][START_REF] Rottiers | MicroRNAs in metabolism and metabolic diseases[END_REF], relative levels of prkaa1 mRNA were quantified in bear muscle, but no changes were found between active and hibernating states (Figure 2B).

A subset of eleven myomiRs that are implicated in the atrophy and regeneration of skeletal muscles [START_REF] Diniz | Regulation of skeletal muscle by microRNAs[END_REF] were also assessed. Specifically,, and miR-31-5p, were analyzed. Three microRNAs, miR-23a-5p, miR-221-3p, and miR-31-5p were elevated during hibernation by 1.32±0.12, 1.31±0.11, and 1.26±0.09 fold, respectively (Figure 3A). Oppositely, miR-199a-5p and miR-223-5p decreased to 0.66±0.04 and 0.70±0.06, respectively, of the levels in summer active bears (Figure 3A). Separately, transcripts of the myostatin gene, mstn, a mRNA target implicated in skeletal muscle maintenance [START_REF] Miretti | Functional effect of mir-27b on myostatin expression: A relationship in piedmontese cattle with double-muscled phenotype[END_REF] and a target of miR-27b (Figure 2A) displayed no changes between summer active and hibernation conditions (Figure 3B).

Discussion

Studies on brown bears (U. arctos) have suggested that the skeletal muscle resilience of these mammalian hibernators is determined by more than just mechanical loading and neural activity [START_REF] Lin | Skeletal muscles of hibernating brown bears are unusually resistant to effects of denervation[END_REF]. A third factor proposed to contribute to the resilience of hibernator skeletal muscles is the modulation of signalling pathways, such as those that may be implicated in muscle catabolism. The present study explores muscle-specific cellular signalling pathways that are highly conserved in mammals. A group of 36 myomiRs known to be crucial for skeletal muscle development, metabolism, and regeneration [START_REF] Diniz | Regulation of skeletal muscle by microRNAs[END_REF][START_REF] Horak | Muscle-specific microRNAs in skeletal muscle development[END_REF][START_REF] Lima | Role of microRNAs on the regulation of mitochondrial biogenesis and insulin signaling in skeletal muscle[END_REF]) and 5 key upstream and downstream mRNA transcripts were investigated in vastus lateralis muscle from winter hibernating versus summer-active bears. The results reveal that microRNAs and mRNAs are differentially regulated in hibernating bears in a manner that would facilitate decreased metabolism, and maintenance of skeletal muscle over the winter months of torpor.

The observed higher levels of mef2a mRNA during hibernation suggest that, in a period when the bear is enduring prolonged inactivity without food or water intake [START_REF] Berg Von Linde | Insights from the den: How hibernating bears may help us understand and treat human disease[END_REF], muscle mass (and strength) is still maintained; ie. little or no atrophy occurs (Figure 1A). Although analyses of MEF2A transcription factor activity were not performed in this study, higher mef2a mRNA (compared with summer) suggests that MEF2A protein abudance would also be higher during hibernation, which could result in more MEF2A-mediated gene transcription. Indeed, the MEF2A transcription factor is well known for its role in skeletal muscle development, maintenance, and regeneration [START_REF] Taylor | Mef2 and the skeletal muscle differentiation program[END_REF], and its upregulation at the mRNA and protein levels have been shown to facilitate skeletal muscle maintenance during hibernation in ground squirrels [START_REF] Tessier | Expression of myocyte enhancer factor-2 and downstream genes in ground squirrel skeletal muscle during hibernation[END_REF], 2012). Therefore, enhanced mef2a transcript levels may be an important underlying mechanism of skeletal muscle resilience in hibernating U. arctos. Downstream targets of the MEF2A transcription factor were also assessed in order to elucidate the activated networks in skeletal muscles of bears during hibernation. MEF2A is known to directly regulate skeletal muscle microRNAs, which includes miR-1, -133, and -206 [START_REF] Liu | An intragenic MEF2-dependent enhancer directs muscle-specific expression of microRNAs 1 and 133[END_REF][START_REF] Rao | Myogenic factors that regulate expression of muscle-specific microRNAs[END_REF] and thereby has important roles in the regulation of skeletal muscle metabolism [START_REF] Horak | Muscle-specific microRNAs in skeletal muscle development[END_REF]. The present study found that miR-1b-5p, miR-133a-3p, and miR-206-3p all increased during hibernation (Figure 1B), which is consistent with the observed increase of mef2a transcripts (leading to elevation of MEF2A protein). Downstream consequences on myogenic signalling pathways by miR-1 and miR-206 include downregulation of the pax7 gene, that encodes a transcription factor that is responsible for upregulating id2 mRNA (J.-F. [START_REF] Chen | microRNA-1 and microRNA-206 regulate skeletal muscle satellite cell proliferation and differentiation by repressing Pax7[END_REF][START_REF] Dey | miR-206 and -486 induce myoblast differentiation by downregulating Pax7[END_REF]. In turn, the ID2 protein is responsible for downregulating the myogenic transcription factor MYOD1 that, similarly to MEF2A, has a role in facilitating skeletal muscle growth and development through broad roles including the expression of miR-1 and miR-206 [START_REF] Horak | Muscle-specific microRNAs in skeletal muscle development[END_REF]. Together, this suggests that activation of the MEF2A-mediated signalling pathway in hibernating bear skeletal muscle occurs through the upregulation of miR-1 and miR-206 and consequent downregulation of pax7 and id2 (Figure 1C).

Analyses of other microRNAs in hibernating U. arctos included a subset of fifteen microRNAs that target metabolic processes (Figure 2A) [START_REF] Lima | Role of microRNAs on the regulation of mitochondrial biogenesis and insulin signaling in skeletal muscle[END_REF]. Of these, six (miR-27b-5p, miR-27a/b-3p, miR-29a-5p, miR-29a-3p, miR-29b/c, and miR-33-5p) were elevated during hibernation, compared with the summer active condition. The increases in miR-27, miR-29, and miR-33 found in hibernating bears suggest that these microRNAs may be potentially facilitating decreased glucose utilization. In a previous study, miR-27 action was shown to decrease glucose consumption when overexpressed in rat myoblast cell lines, and this was associated with decreased GLUT4 protein levels [START_REF] Zhou | Regulation of insulin resistance by multiple MiRNAs via targeting the GLUT4 signalling pathway[END_REF]. Indeed, when antisense sequences were introduced to suppress endogenous miR-27 activity in these cell lines, glucose consumption and uptake were enhanced and GLUT4 protein levels increased [START_REF] Zhou | Regulation of insulin resistance by multiple MiRNAs via targeting the GLUT4 signalling pathway[END_REF]. Thus, it can be postulated that elevated miR-27 in hibernating bears may be downregulating GLUT4 protein in skeletal muscle. In another study, a role for miR-29a/b/c in skeletal muscle glucose metabolism was indicated since these microRNAs were upregulated in a model of diabetic rats [START_REF] He | Overexpression of micro ribonucleic acid 29, highly up-regulated in diabetic rats, leads to insulin resistance in 3T3-L1 adipocytes[END_REF]. Further experiments with cell lines showed that overexpression of miR-29a/b/c inhibited insulin-stimulated glucose uptake [START_REF] He | Overexpression of micro ribonucleic acid 29, highly up-regulated in diabetic rats, leads to insulin resistance in 3T3-L1 adipocytes[END_REF]. These previous studies support the notion that increased miR-27 and miR-29 in hibernating bear muscle facilitate decreased glucose metabolism in part by reducing glucose uptake, which may also contribute to lowering basal metabolic rate. Levels of prkaa1 mRNA (that encodes the catalytic subunit of the nutrient sensor AMPK) were also measured in hibernating U. arctos skeletal muscle since expression of this gene known to be downregulated by miR-33 [START_REF] Davalos | miR-33a/b contribute to the regulation of fatty acid metabolism and insulin signaling[END_REF][START_REF] Rottiers | MicroRNAs in metabolism and metabolic diseases[END_REF]. Skeletal muscle AMPK has several regulatory roles in glucose and lipid metabolism, two of these being to increase glucose uptake and promote fatty acid oxidation as a means of enhancing ATP production under energy-stressed conditions [START_REF] Collier | Metformin counters the insulin-induced suppression of fatty acid oxidation and stimulation of triacylglycerol storage in rodent skeletal muscle[END_REF][START_REF] Jeon | Regulation and function of AMPK in physiology and diseases[END_REF][START_REF] O'neill | AMPK phosphorylation of ACC2 is required for skeletal muscle fatty acid oxidation and insulin sensitivity in mice[END_REF]. As mentioned above, increases in miR-27, miR-29, and miR-33 in bear muscle may suggest that both glucose uptake and fatty acid oxidation are suppressed in hibernating bears. Thus, levels of muscle prkaa1 mRNA were assessed to determine how this downstream gene of microRNA action responded in hibernation. However, mRNA levels of prkaa1 did not change in muscle of hibernating bears, compared with summer active animals, suggesting that AMPK is not differentially regulated in summer vs winter, at least at the transcript level (Figure 2B). AMPK activity increased in some tissues of other hibernating species (e.g. in adipose of hibernating ground squirrels) during hibernation but was unchanged in other tissues including skeletal muscle [START_REF] Horman | Evaluation of the role of AMP-activated protein kinase and its downstream targets in mammalian hibernation[END_REF], an observation that is consistent with the present result for bears. It is important to note that the present study only assessed the mRNA levels of the catalytic subunit of AMPK in bear muscle, so further investigations are warranted to obtain a more complete understanding of the role of this energy/nutrient sensor in hibernating bears. It is still possible that hibernating bears are conserving energy by reducing both usage of glucose and fats. Recent studies have shown that enzymes involved in fatty acid oxidation are decreased in the skeletal muscle of hibernating bears, which is consistent with decreased energy expenditure [START_REF] Chazarin | Metabolic reprogramming involving glycolysis in the hibernating brown bear skeletal muscle[END_REF]. Overall, selected U. arctos myomiRs appear to be regulated in a manner that would facilitate the suppression of both glucose and lipid metabolism by targeting specific signalling pathways during hibernation.

The differentially expressed microRNAs identified in this study also have implications for skeletal muscle regeneration and integrity. In particular, miR-206, miR-221, and miR-31 that were elevated in hibernating bear muscle are known promoters of skeletal muscle regeneration (Figures 1B and3A) [START_REF] Diniz | Regulation of skeletal muscle by microRNAs[END_REF][START_REF] Greco | Common micro-RNA signature in skeletal muscle damage and regeneration induced by Duchenne muscular dystrophy and acute ischemia[END_REF][START_REF] Togliatto | Unacylated ghrelin promotes skeletal muscle regeneration following hindlimb ischemia via SOD-2-mediated miR-221/222 expression[END_REF]. Two of the microRNAs that were reduced during hibernation, miR-199a and miR-223, are associated with inhibited myogenic differentiation, muscle disruption, injury, and inflammation (Figure 3A) [START_REF] Alexander | MicroRNA-199a is induced in dystrophic muscle and affects WNT signaling, cell proliferation, and myogenic differentiation[END_REF][START_REF] Chen | MiR-351 transiently increases during muscle regeneration and promotes progenitor cell proliferation and survival upon differentiation[END_REF]. Similarly to miR-27b and miR-29b (discussed above), miR-23a increases in hibernating bear muscle, and these three myomiRs not only increase in other species during hibernation (e.g. the little brown bat, Myotis lucifugus), but are also associated with muscle atrophy resistance, increased MEF2A activity, and decreased transcript levels of mstn that encodes myostatin, an inhibitor of muscle growth (Figure 2A and 3A) [START_REF] Kornfeld | Differential expression of mature microRNAs involved in muscle maintenance of hibernating little brown bats, Myotis lucifugus: a model of muscle atrophy resistance[END_REF][START_REF] Miretti | Functional effect of mir-27b on myostatin expression: A relationship in piedmontese cattle with double-muscled phenotype[END_REF]. In the present study, mstn mRNA levels were assessed in U. arctos skeletal muscle, but these did not change between summer active and hibernation conditions (Figure 3B). However, a lack of microRNA interaction with mstn mRNA cannot be ruled out since microRNAs function not only to facilitate mRNA degradation, but also manage mRNA storage, during which transcript levels can remain constant but their translation into protein is inhibited [START_REF] Bartel | MicroRNAs: Genomics, biogenesis, mechanism, and function[END_REF]. Indeed, mRNA storage appears to be a common feature of metabolic rate depression in multiple species and in response to a wide variety of environmental stress conditions, including hibernation [START_REF] Tessier | The involvement of mRNA processing factors TIA-1, TIAR, and PABP-1 during mammalian hibernation[END_REF]Tessier & Storey, 2014). MiR-23a also promotes resistance to muscle atrophy by disrupting protein degradation. The mechanism by which this occurs is the direct suppression of two muscle-specific ubiquitin ligases, MAFbx/atrogin-1 and muscle RING-finger 1, that are known to be induced during muscle atrophy and atrophy-associated protein degradation [START_REF] Wada | Translational suppression of atrophic regulators by MicroRNA-23a integrates resistance to skeletal muscle atrophy[END_REF]. Protein levels of ubiquitin ligases such as these are known to be modulated throughout the torpor-arousal cycle of hibernating ground squirrel hearts (Y. [START_REF] Zhang | Transcriptional activation of muscle atrophy promotes cardiac muscle remodeling during mammalian hibernation[END_REF], and therefore, there is potential for them to participate in the skeletal muscle of bears. Together, the results from this study demonstrate how differential myomiR expression in hibernating U. arctos may help to regulate a diverse range of mechanisms that contribute to the bear's resilience to muscle atrophy.

An increase in miR-33 (discussed earlier for its potential role in regulating prkaa1 mRNA) and miR-29 in hibernation, as compared to active summer bears, may have a cytoprotective effect in the skeletal muscle of brown bears through the downregulation of sterol regulatory element-binding protein (SREBP). Overexpression of miR-33 in various cell lines have shown that it directly reduces the protein levels of SREBP-1, whereas miR-33 null mice had higher protein expression of SREBP-1 [START_REF] Horie | MicroRNA-33 regulates sterol regulatory element-binding protein 1 expression in mice[END_REF]. It has also been shown that miR-29 inhibits SREBP-1 expression in cancer cells [START_REF] Ru | Feedback loop regulation of SCAP/SREBP-1 by miR-29 modulates EGFR signaling-driven glioblastoma growth[END_REF]. Although SREBP-1 has mostly been studied in the liver for its roles in metabolic regulation, studies have shown that it is significantly expressed in muscle cells [START_REF] Ducluzeau | Regulation by insulin of gene expression in human skeletal muscle and adipose tissue. Evidence for specific defects in type 2 diabetes[END_REF][START_REF] Guillet-Deniau | Sterol regulatory element binding protein-1c expression and action in rat muscles: insulin-like effects on the control of glycolytic and lipogenic enzymes and UCP3 gene expression[END_REF], and furthermore, SREBP-1 nuclear protein content induces muscle atrophy in both in vitro and in vivo models [START_REF] Lecomte | A New Role for Sterol Regulatory Element Binding Protein 1 Transcription Factors in the Regulation of Muscle Mass and Muscle Cell Differentiation[END_REF]. Many experiments have identified that SREBP regulates many myogenic and metabolic genes in skeletal muscle [START_REF] Abiola | Activation of Wnt/β-catenin signaling increases insulin sensitivity through a reciprocal regulation of Wnt10B and SREBP-1c in skeletal muscle cells[END_REF][START_REF] Lecomte | A New Role for Sterol Regulatory Element Binding Protein 1 Transcription Factors in the Regulation of Muscle Mass and Muscle Cell Differentiation[END_REF][START_REF] Rome | Microarray analyses of SREBP-1a and SREBP-1c target genes identify new regulatory pathways in muscle[END_REF]. Of interest among SREBP targets are myogenic transcription factors MYOD1, MYOG, and MEF2C, which have shown to decrease with SREBP overexpression [START_REF] Dessalle | SREBP-1 transcription factors regulate skeletal muscle cell size by controlling protein synthesis through myogenic regulatory factors[END_REF]. Furthermore, SREBP is believed to induce skeletal muscle atrophy by leveraging the ubiquitin ligase system, in part by upregulating MuRF1 mRNA [START_REF] Lecomte | A New Role for Sterol Regulatory Element Binding Protein 1 Transcription Factors in the Regulation of Muscle Mass and Muscle Cell Differentiation[END_REF]. The present results suggest that a similar effect might occur in bears, where downregulation of SREBP by miR-33 and/or miR-29 facilitates a cytoprotective effect in skeletal muscle.

Conclusion

The present study sought to assess the role of microRNAs as regulatory factors in limiting atrophy of U. arctos skeletal muscle during winter hibernation. Analysis of selected muscle-specific microRNAs and mRNA transcripts indicated broad modulations occurring at both transcriptional levels, which were suggestive of post-transcriptional mechanisms activating myogenic pathways, suppressing glucose and lipid metabolism, and decreasing atrophic signalling during hibernation (Figure 4). The results support a hypothesis made previously that the modulation of cellular signalling pathways is crucial for the resilience of skeletal muscles against atrophy in hibernating brown bears [START_REF] Lin | Skeletal muscles of hibernating brown bears are unusually resistant to effects of denervation[END_REF]. These current results warrant continuing investigation into other regulatory controls on signalling networks (such as SREBP and/or ubiquitin ligases) in order to further our understanding the complex molecular mechanisms involved in muscle maintenance in the hibernating brown bear. MicroRNA and mRNA transcripts were standardized against the expression of U6 and gapdh, respectively. Data are means ± S.E.M., n = 6 for microRNA and n = 5 for mRNA individuals sampled in both winter hibernating and summer active states. Statistically significant differences are denoted with an asterisk (*) and were determined with a paired t-test (p < 0.05).

Figure legends Figure 1 .

 legends1 Figure legendsFigure1. MEF2A-controlled muscle microRNAs are upregulated in skeletal muscle of active versus hibernating brown bears, U. arctos. (A) Relative transcript abundance of mef2a mRNA, (B) Relative abundance of MEF2A-regulated microRNAs, and (C) Downstream mRNA targets of miR-1 and miR-206, pax7 and id2 mRNA. MicroRNA and mRNA transcripts levels were standardized against the expression of U6 and gapdh, respectively. Data are means ± S.E.M., n = 6 for microRNA and n = 5 for mRNA individuals sampled in both winter hibernating and summer active states. Statistically significant differences are denoted with an asterisk (*) and were analyzed with a paired t-test (p < 0.05).

Figure 2 .

 2 Figure 2. Expression of metabolic microRNAs involved in glucose and lipid metabolism and downstream targets in skeletal muscle of hibernating brown bears, U. arctos. (A) Abundance levels of metabolic microRNAs, and (B) transcript levels of a downstream target, prkaa1.MicroRNA and mRNA transcripts were standardized against the expression of U6 and gapdh, respectively. Data are means ± S.E.M., n = 6 for microRNA and n = 5 for mRNA individuals sampled in both winter hibernating and summer active states. Statistically significant differences are denoted with an asterisk (*) and were determined with a paired t-test (p < 0.05).

Figure 3 .

 3 Figure3. Expression of microRNAs linked with skeletal muscle regeneration and maintenance, and one downstream mRNA target, in skeletal muscle of the hibernating brown bear, U. arctos. (A) Relative abundance of microRNAs implicated in muscle maintenance during hibernation, and (B) transcript levels of the downstream target, mstn. MicroRNA and mRNA transcripts were standardized against the expression of U6 and gapdh, respectively. Data are means ± S.E.M., n = 6 for microRNA and n = 5 individuals sampled in both winter hibernating and summer active states. Statistically significant differences are denoted with an asterisk (*) and were determined with a paired t-test (p < 0.05).

Figure 4 .

 4 Figure 4. Proposed microRNA-mediated mechanism facilitating metabolic suppression and skeletal muscle maintenance in U. arctos during hibernation. Perforated boxes denote areas of the model supported by this study. Red font and asterisks denotes targets and areas warranting further investigation.

Acknowledgements

The authors thank J.M. Storey for editorial review of the manuscript. This work was supported by a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery grant

to KBS, a French Space Agency (CNES) grant to FB (#874), and CNRS and Strasbourg University grants to FB (projEX H2E and MyoBears project of the PEPS ExoMod program). KBS holds the Canada Research Chair in Molecular Physiology. BC was supported by a fellowship from the CNES and CNRS. The authors also thank the capture team from the Scandinavian Brown Bear Research Project (SBBRP), whose long-term funding has come primarily from the Swedish Environmental Protection Agency, the Norwegian Environment Agency, the Austrian Science Fund, and the Swedish Association for Hunting and Wildlife Management. This is paper N°283 from the SBBRP (to be edited post-acceptance).

Data availability statement

Research data are not shared.

Supplementary Table

 S1. Primers for quantification of U. arctos mRNA and microRNA.


Brooke Stephenson
email: brooke.stephenson@gipsa-lab.grenoble-inp.fr

Laurent Besacier
email: laurent.besacier@univ-grenoble-alpes.fr

Laurent Girin
email: laurent.girin@gipsa-lab.grenoble-inp.fr

Thomas Hueber
email: thomas.hueber@gipsa-lab.grenoble-inp.fr

What the Future Brings: Investigating the Impact of Lookahead for Incremental Neural TTS

Keywords: incremental speech synthesis, deep neural networks, representation learning

In incremental text to speech synthesis (iTTS), the synthesizer produces an audio output before it has access to the entire input sentence. In this paper, we study the behavior of a neural sequence-to-sequence TTS system when used in an incremental mode, i.e. when generating speech output for token n, the system has access to n + k tokens from the text sequence. We first analyze the impact of this incremental policy on the evolution of the encoder representations of token n for different values of k (the lookahead parameter). The results show that, on average, tokens travel 88% of the way to their full context representation with a one-word lookahead and 94% after 2 words. We then investigate which text features are the most influential on the evolution towards the final representation using a random forest analysis. The results show that the most salient factors are related to token length. We finally evaluate the effects of lookahead k at the decoder level, using a MUSHRA listening test. This test shows results that contrast with the above high figures: speech synthesis quality obtained with 2 word-lookahead is significantly lower than the one obtained with the full sentence.

Introduction

Text-to-speech (TTS) systems have made great strides with the introduction of sequence-to-sequence (seq2seq) neural models, combined with end-to-end trainable architectures [START_REF] Shen | Natural TTS synthesis by conditioning Wavenet on mel-spectrogram predictions[END_REF][START_REF] Arik | Deep voice: Real-time neural text-to-speech[END_REF][START_REF] Sotelo | Char2wav: End-to-end speech synthesis[END_REF][START_REF] Wang | Tacotron: Towards end-to-end speech synthesis[END_REF]. Neural models typically take character as input and learn a direct mapping to spectrogram or waveform output, without the need for feature engineering. However, most of these neural TTS systems are designed to work at the sentence level, i.e. the synthetic speech signal is generated after the user has typed a complete sentence. When processing a given word, the system can thus rely on its full linguistic context (i.e. both past and future words) to build its internal representation. Despite its ability to generate high-quality speech, this synthesis paradigm is not ideal for several applications. For example, when used as a substitute voice by people with severe communication disorders or integrated in a dialog system (e.g. personal assistant, simultaneous speech interpretation, etc.), the system's need to wait until the end of a sentence introduces a latency which might be disruptive to conversational flow and system interactivity. Incremental TTS (iTTS, sometimes called low-latency or online TTS) aims to address these issues by synthesizing speech onthe-fly, that is by outputting audio chunks as soon as a new word (or a few of them) become available. This task is particularly challenging since producing speech without relying on the full linguistic context can result in both segmental (phonetic) and supra-segmental (prosodic) errors [START_REF] Maguer | Evaluation of contextual descriptors for HMM-based speech synthesis in French[END_REF].

Early iTTS systems were developed in the context of HMM-based speech synthesis [START_REF] Baumann | Evaluating prosodic processing for incremental speech synthesis[END_REF][START_REF] Pouget | HMM training strategy for incremental speech synthesis[END_REF][START_REF] Pouget | Adaptive latency for part-of-speech tagging in incremental text-to-speech synthesis[END_REF]. In this paradigm, models are trained on a set of explicit linguistic features (e.g. number of syllables in the next word). The authors of [START_REF] Baumann | Evaluating prosodic processing for incremental speech synthesis[END_REF][START_REF] Pouget | HMM training strategy for incremental speech synthesis[END_REF] developed coping mechanisms to handle missing features when making predictions for iTTS: unknown future context information is replaced with the most common values for these features at inference time in [START_REF] Baumann | Evaluating prosodic processing for incremental speech synthesis[END_REF], whereas uncertainty on those features is explicitly integrated at training time by [START_REF] Pouget | HMM training strategy for incremental speech synthesis[END_REF]. In [START_REF] Pouget | Adaptive latency for part-of-speech tagging in incremental text-to-speech synthesis[END_REF], an adaptive decoding policy based on the online estimation of the stability of the linguistic features is proposed: the synthesis of a given word is delayed if its part-of-speech (POS) is likely to change when additional (future) words are added.

Several strategies have been proposed to reduce the latency of a sequence-to-sequence model with input text for neural machine translation [START_REF] Ma | STACL: Simultaneous translation with implicit anticipation and controllable latency using prefix-to-prefix framework[END_REF][START_REF] Arivazhagan | Monotonic infinite lookback attention for simultaneous machine translation[END_REF][START_REF] Ma | Monotonic multihead attention[END_REF] or incremental speech translation [START_REF] Niehues | Lowlatency neural speech translation[END_REF][START_REF] Oda | Optimizing segmentation strategies for simultaneous speech translation[END_REF][START_REF] Gu | Learning to translate in real-time with neural machine translation[END_REF]. However, only a few studies have attempted to adapt these models for iTTS [START_REF] Yanagita | Neural iTTS: Toward synthesizing speech in real-time with end-to-end neural text-tospeech framework[END_REF][START_REF] Ma | Incremental text-to-speech synthesis with prefixto-prefix framework[END_REF]. The authors of [START_REF] Yanagita | Neural iTTS: Toward synthesizing speech in real-time with end-to-end neural text-tospeech framework[END_REF] proposed an approach that consists in (1) marking three subunits within the training sentences using start, middle and end tags, (2) training a Tacotron 2 TTS model with these tags so it learns intrasentential boundary characteristics, and (3) synthesizing sentences by inputting chunks of length n words (up to half a sentence) with the appropriate middle or end tag. An alternate policy reported in [START_REF] Ma | Incremental text-to-speech synthesis with prefixto-prefix framework[END_REF] (inspired by the prefix-to-prefix framework introduced for translation [START_REF] Ma | STACL: Simultaneous translation with implicit anticipation and controllable latency using prefix-to-prefix framework[END_REF]) consists in having access to a future context of k input tokens while generating speech output. They also rely on the soft attention to learn the relationship between the predicted spectrogram and the currently available source text. These two approaches give promising results but introduce a fixed size (and possibly large) latency.

The goal of the present paper is to pave the way toward an adaptive decoding policy for a neural iTTS. Similarly to the HMM-based iTTS system described in [START_REF] Pouget | Adaptive latency for part-of-speech tagging in incremental text-to-speech synthesis[END_REF], the envisioned neural iTTS is expected to modulate the lookahead (and thus the latency) by the uncertainty on some features due to the lack of future context. However, the gain in naturalness provided by endto-end models (over, e.g., HMM-based systems) is also accompanied by reduced interpretability. Because of the black box nature of the models, studying the importance of missing features is a challenging task. To address this, we analyse the evolution of the encoder representations of a neural TTS (Tacotron 2) when words are incrementally added (i.e. when generating speech output for token n, the system only has access to n + k tokens from the text sequence, k being the lookahead parameter). We also investigate which text features are the most influential on this evolution towards the final encoder representation. Finally, we evaluate the effect of the lookahead at the perceptual level using a MUSHRA listening test.

Methods and Materials

Models and data

For these experiments, we use a sequence-to-sequence neural model which has achieved state-of-the-art results: Tacotron 2 [START_REF] Shen | Natural TTS synthesis by conditioning Wavenet on mel-spectrogram predictions[END_REF]. We use a pretrained model developed by NVIDIA 1 and trained on the LJ Speechset [START_REF] Ito | The LJ speech dataset[END_REF], a collection of non-fiction books read by a single speaker. The corpus contains 24 hours of audio recordings. The encoder takes characters as input, passes them through an embedding layer, three convolution layers and then a bidirectional LSTM layer. The decoder uses the encoder output, an attention module and previous decoder outputs to predict the corresponding log-spectrogram frames, which are converted into a speech waveform using WaveGlow neural vocoder [START_REF] Prenger | Waveglow: A flow-based generative network for speech synthesis[END_REF].

For our analysis, the test sentences used as input sequences are taken from the libriTTS corpus [START_REF] Zen | LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech[END_REF]. We filter 1, 000 utterances with sentence length ranging from 5 to 42 words. We follow the procedure outlined in [START_REF] Kilgarriff | Comparing corpora[END_REF] to verify that word distribution is similar to that of larger general corpora. 2 Our corpus contains 34, 768 tokens and 4, 085 types.

Incremental encoding policy

We consider an input sequence of tokens, where each token can be either a word, a space or a punctuation mark. We define an iTTS system with the following simple policy (similar to [START_REF] Ma | Incremental text-to-speech synthesis with prefixto-prefix framework[END_REF]): the encoder starts by reading k input tokens (k is the lookahead parameter) and then it alternates between generating speech output and reading the next token until the complete input token sequence is consumed. Formally, we use the following notations and definitions (see Table 1 for an example of the listed items):

• N is the length of the input sequence (in number of tokens);

• xn is the token at position n (the "current" token); x1:N is the complete sequence of input tokens; x1:n is the subsequence of input tokens from position 1 to position n;

• yn is the speech output segment corresponding to token xn;

• c(n, k) = min(n + k, N
) is the number of input tokens read when generating yn (recall that k is the lookahead parameter); z n,k n is the corresponding encoder output. 3 In other words, yn is obtained after reading the partial sequence of input tokens x 1:c(n,k) ; z n,k 1:c(n,k) is the sequence of encoder representations obtained so far; Conventional offline encoding (using the full sequence of input tokens x1:N at each position n) is also processed for comparison, and z full 1:N denotes the corresponding encoded sequence. 3 n is used two times in z n,k n since we will see that the value at other positions, e.g. z n,k n-1 , also depends on n and k. In the Tacotron model, input sequences are encoded at the character level. However, in our study, we consider an iTTS decoding policy at the word level; this is because token breaks are a natural trigger for synthesis or evaluation in a practical iTTS system. Consequently, we need to go from character representation to word representation. We start from the encoder's bidirectional LSTM network: forward and backward layers each provide a 256-dimensional vector for each input character. For each new token xn, we extract the output of the forward layer corresponding to the last character of xn. The forward layer continues up to the last character of token x c(n,k) . Then the backward layer goes from the last character of token x c(n,k) to the first character of token x1. We extract the output of the backward layer corresponding to the first character of xn. Both vectors are concatenated to get a 512-dimensional vector representation z n,k n of xn. Note that the input sequence is re-encoded for each new token (i.e., for each increment of n), leading to new values for the sequence z n,k 1:n-1 . Of course, this sequence also depends on k, which is the purpose of this study. In contrast, there is only one single value for the sequence z full 1:N .

From character to word representations

Incremental decoding

We build the iTTS decoder output as follows. For a given value of k, and for the current token xn, we first produce the speech waveform corresponding to the encoded sequence z n,k 1:n . Then, using the Munich Automatic Segmentation system [START_REF] Kisler | Multilingual processing of speech via web services[END_REF] (an automatic speech recognition and forced alignment tool which employs an HMM and Viterbi decoding to find the best alignment between the text and audio), we select the portion yn of the waveform corresponding to xn. Finally we concatenate this speech segment yn to the speech segment resulting from the processing of previous tokens, that we can denote as y1:n-1.

In short, we simply update the generated speech waveform as y1:n = [y1:n-1 yn]. For example, for k = 2, we extract the speech waveform segment y1 corresponding to token x1 from the signal generated from z 1,2 1:3 ; then we extract the speech waveform segment y2 corresponding to token x2 from the signal generated from z 1,2 1:4 ; we concatenate y1 and y2, and we continue this process until the end of the input sequence is read. This process is illustrated in Fig. 1 for k = 1. Segment concatenation is done with a 5-ms cross-fade, a simple and efficient way to prevent audible artefacts in our experiments. Note that the overall encoding and decoding process simulates an effective k-lookahead iTTS system that generates a new speech segment yn when entering the new input token x c(n,k) . Sound examples obtained with this procedure are available online. 4

Analyzing impact of lookahead on encoder representation

Our first goal is to analyze the impact of the lookahead parameter k on the representation of a given token xn at the encoder level. Given the two encoder representations of xn (z n,k n in incremental mode and z full n in offline mode), we compute the cosine distance between them as d(n, k) = 1 -

z n,k n .z full n ||z n,k n ||.||z full n || .
We then average this distance for all tokens of our corpus or all tokens of a given syntactic category. Our analysis consists in investigating which token features could best explain the observed variance in our data (i.e. why are some tokens relatively far from their final representation while others are close at the same value of k?). We did this using random forest (RF) regressors [START_REF] Pedregosa | Scikit-learn: Machine learning in python[END_REF] which optimize cosine distance predictions and can provide information about which input features contribute the most towards these predictions. Our selected features are summarized in Table 2. The RFs were fit using 100 estimators, mean squared error measures and bootstrapping. We followed the following procedure to determine which features are the most significant: (1) we add a column of random variables to our data set; (2) we fit an initial RF and eliminate all variables with a Gini importance lower than the random feature; and (3) we fit a new RF using only the remaining features and then calculate the permutation feature importance (i.e. the drop in R 2 that results from swapping columns in the dataset) [START_REF] Altmann | Permutation importance: a corrected feature importance measure[END_REF].

Analyzing the effect of lookahead on decoder output

We evaluate the perceptual impact of the lookahead k using a MUSHRA listening test [START_REF] Itu-R | Recommandation[END_REF]. To that purpose, we selected 20 sentences and generated each at multiple values of k, namely k = 1, 2, 4, 6. k = 1 corresponds to a lookahead of one space (or one punctuation mark). It was chosen as the baseline and should be considered as the low-range anchor for the test. In general, k = 2 represents a 1-word lookahead, k = 4 a 2-word lookahead and k = 6 a 3-word lookahead, although other cases occasionally happen (e.g. a space followed by an open parenthesis). Note that k = 0 was not selected because the output signal was deemed too unintelligible to warrant evaluation. The reference stimuli were generated with the offline TTS mode, and were used both as reference and as the hidden high-range anchor. 21 participants, all native English speakers, were asked to assess the similarity between the reference and each of the stimuli obtained with the incremental decoding policy (plus the high-range anchor) on a 0-100 scale (100 means that sample and reference are identical). The MUSHRA test was done online, using the Web Audio Evaluation Tool [START_REF] Jillings | Web Audio Evaluation Tool: A browser-based listening test environment[END_REF]. 3 participants were excluded from analysis because they did not give high similarity ratings for the reference and the hidden high-range anchor (which were identical). Statistical significance between different experimental conditions (different values of k and incremental vs. offline synthesis) were assessed using paired t-tests.

Results and Discussion

3.1. Encoder representations Figure 2 displays an example of distance (in log-scale) between encoder representations in incremental versus offline modes for a given sentence and all possible values of k. While the global trend is a movement towards the final (offline) representation as k increases (d(n, k) decreases with k), we also observe some cases where an increment of the context leads to a representation that is farther away from its offline counterpart (see for instance the comma after the word "Hansel"). One possible explanation for this might be that Tacotron interprets the input as the end of an intonational or rhythmic phrase and when further input is received, it reassesses the token representation.

Figure 3 also shows the distance d(n, k) for k = 0 to 8, but this time averaged over all tokens of the 1, 000 test sentences and for the different token categories: punctuation, space, function word and content word. As in Figure 2, increasing the lookahead consistently reduces the distance between the encoder outputs in incremental and in offline mode, on average. Importantly, the most significant decrease is observed between k = 1 and k = 2, that is, when considering a lookahead of one space and one word (in addition to the current word). A slower decrease toward the final representation is observed for k ≥ 2. This is consistent with Figure 2. A series of paired t- This might show that, on average, each new token considered in future context contributes slightly but significantly to the evolution of the encoder representation. We also observe that, while representations of content words are more stable to context variation, those of punctuation, spaces and function words are further away from their final representation in offline mode when not enough context is given (k < 2).

A more fine grain analysis of the factors that impact d(n, k) is provided by the results of the RF analysis, which are summarized in Table 2. For k = 0, the length of xn is the most effective predictor of cosine distance, and for k = 2 the lengths of xn+1 and xn+2 (i.e. the future tokens that the encoder sees when encoding xn) are the most effective predictors. For instance, at k = 2, our model correctly predicts that the token "to" in Sentence A below (lookahead = space + "be") is farther away from its final representation than "to" in Sentence B (lookahead = space + "Kitty"). The cosine distances are 0.135 and 0.057 respectively. A) I suppose, he said, I ought to be glad of that. B) And the Captain of course concluded (after having been introduced to Kitty) that Mrs Norman was a widow. the incremental synthesis closer to the offline one. We also found a significant difference between k = 6 and k = N (offline mode), i.e. with a lookahead of typically 3 words. This is in contradiction with [START_REF] Yanagita | Neural iTTS: Toward synthesizing speech in real-time with end-to-end neural text-tospeech framework[END_REF] who did not report any difference between incremental and offline synthesis for such lookahead. Possible explanations for this include 1) the use of a different experimental paradigm (MUSHRA vs. MOS in [START_REF] Yanagita | Neural iTTS: Toward synthesizing speech in real-time with end-to-end neural text-tospeech framework[END_REF]), 2) duration distortions caused by the concatenation of speech segments or 3) by the fact that contrary to [START_REF] Yanagita | Neural iTTS: Toward synthesizing speech in real-time with end-to-end neural text-tospeech framework[END_REF], we did not retrain the Tacotron 2 on shorter linguistic units (this is left for future work).

Perceptual evaluation of the decoder output

Conclusion

This study presents several experiments which probe the impact of future context in a neural TTS system, based on a sequenceto-sequence model, both in terms of encoder representation and perceptual effect. Reported experimental results allow us to draw the contours of an adaptive decoding policy for an incremental neural TTS, which modulates the lookahead (and thus the overall latency) by potential change in internal representations. Shorter words are more dependent on future context than longer ones. Therefore, in a practical iTTS, if the lookahead buffer is fed a short word, it may be preferable to delay its synthesis because internal representation associated with it is likely to change when additional tokens become available. Also, it may be more useful to define the lookahead parameter in terms of future syllables rather than words. In addition, perceptual evaluation shows that the dynamics between encoder and decoder are such that even if the encoder representation of an individual token changes slightly, the length of the encoder representation sequence will influence the way in which the decoder treats that token. We can conjecture that the decoder is regulating the duration of each segment with respect to sequence length. This will be addressed in future work by examining attention weights the decoder uses when making predictions. Now that the importance of future context has been assessed, we also plan to work on context extension through prediction of future tokens using contextualized language models [START_REF] Devlin | BERT: Pretraining of deep bidirectional transformers for language understanding[END_REF].

Figure 1 :

 1 Figure 1: Illustration of the incremental speech waveform generation process for lookahead parameter k = 1.

Figure 2 :

 2 Figure 2: Change in token representations over time. Each colored line represents a token from the utterance "Grethel wept bitterly, and said to Hansel, What will become of us?" The height of the colored line shows the distance between encoder outputs z n,k n (incremental decoding) and z full n (offline decoding) at different values of k. The vertical grid line where xn (the colored line) first appears represents k = 0 for that token; the next vertical grid line to the right represents k = 1 for xn and k = 0 for xn+1, etc.

Figure 3 :

 3 Figure 3: Distance d(n, k) between encoder representations z full n (offline) and z n,k n (incremental decoding) averaged over all tokens of a given category (punctuation, space, function word, content word), for lookahead parameter k = 0 to 8. Error bars represent standard deviation.

Table 2 :

 2 Influence of text features on the distance d(n, k) estimated by RF regression for k = 0 and 2 (NS=not significant; * = weak effect; ** = medium effect; *** = strong effect). Part of speech of token x n-m NS NS POSNext + m Part of speech of token x n+m NS m=1 * Word Length of Prev + m # of characters in x n-m m = 1 * m = 2 * NS Word Length of Next + m # of characters in x n+m m = 2 * m = 1: *** m = 2: *** m = 4: * tests on d(n, k) (averaged over all test sentences and all token categories) reveals a tiny but systematically significant difference between pairs of consecutive lookaheads (e.g. k = 3 vs. k = 4, k = 7 vs. k = 8) up to the end of the sentence.

  Results of the MUSHRA listening test are presented in Figure 4. First, statistical analyses show significant differences for all pairs of considered lookahead (k = 1 vs. k = 2, k = 2 vs. k = 4, and k = 4 vs. k = 6). This confirms at a perceptual level the tendency observed on the evolution of the encoder representation (see Section 3.1): each additional lookahead brings

Figure 4 :

 4 Figure 4: Perceptual evaluation of the impact of lookahead parameter k using MUSHRA listening test.

Table 1 :

 1 Incremental inputs (for different lookahead k) for sentence "The dog is in the yard." to generate x3 (the word "dog.")

	n k	c(n, k)	Input at c(n, k)	xn
	3 0	3	The dog	dog
	3 1	4	The dog	dog
	3 2	5	The dog is	dog
	3 ... ...	...	dog
	3 8	11	The dog is in the yard	dog
	3 9	N = 12 The dog is in the yard. dog

1 https://github.com/NVIDIA/tacotron2 2 Brown and BNC corpora.

https://tinyurl.com/y3hvl5cn

Acknowledgements

This work was funded by the Multidisciplinary Institute in Artificial Intelligence MIAI@Grenoble-Alpes (ANR-19-P3IA-0003). The authors would like to thank Sylvain Gerber, Fanny Roche and Dave Green for fruitful discussions.