Sophie Bouillet
email: bouillet.sophie@outlook.fr

Dallel Arabet

Cécile Jourlin-Castelli

Vincent Méjean

Chantal Iobbi- Nivol

Regulation of σ factors by conserved partner switches controlled by divergent signaling systems

Keywords: Environmental signal and stress responses, gene regulation, anti-sigma factor, chemosensory systems, phylogeny

Partner-Switching Systems (PSS) are widespread regulatory systems, each comprising a kinaseanti-σ, a phosphorylatable anti-σ antagonist and a phosphatase modules. The anti-σ domain quickly sequesters or delivers the target σ factor according to the phosphorylation state of the anti-σ antagonist induced by environmental signals. The PSS components are proteins alone or merged to other domains probably to adapt to the input signals. PSS are involved in major cellular processes including stress response, sporulation, biofilm formation and pathogenesis. Surprisingly, the target σ factors are often unknown and the sensing modules acting upstream from the PSS diverge according to the bacterial species. Indeed, they belong to either twocomponent systems or complex pathways as the stressosome or Chemosensory Systems (CS).

Based on a phylogenetic analysis, we propose that the sensing module in Gram-negative bacteria is often a CS.

Introduction

Bacteria have to face constantly many environmental changes in their ecological niches. Their ability to rewire rapidly the expression of genes involved in the response to these alterations is crucial for their survival and is mediated by the use of different alternative σ factors. The σ factors target the core RNA polymerase to the promoter of genes required to adapt to new cell surroundings. All bacteria possess a primary housekeeping σ factor (σ 70 ) that insures the transcription of the majority of genes, but most bacteria own additional σ factors that coordinate the transcription of many genes involved in response and adaptation of bacteria to changing environmental or cellular conditions. The number of these alternative σ factors varies depending on the lifestyle of the bacteria (Österberg et al., 2011).

Except for Escherichia coli σ 54 and homologs, all σ factors belong to the σ 70 family, divided itself into four subfamilies based on phylogenetic and structural properties [START_REF] Paget | The sigma70 family of sigma factors[END_REF][START_REF] Helmann | Regulation by Alternative Sigma Factors[END_REF]. The group 1 of σ 70 family is composed of the whole housekeeping σ factors, σ 70 also called σ A in Gram-positive bacteria. The group 2 gathers σ factors that resemble to that of group 1 except that they are not essential in laboratory conditions, as σ S factor involved in the General Stress Response (GSR) of proteobacteria [START_REF] Helmann | Regulation by Alternative Sigma Factors[END_REF][START_REF] Battesti | The RpoS-mediated general stress response in Escherichia coli[END_REF]. The group 3 contains more diverse σ factors, which have roles in various cellular processes as σ 32 that regulates genes involved in response to heat shock, σ 28 in flagella synthesis and also σ E , σ F , σ G , σ K , of Bacillus subtilis that control different stages of sporulation. The group 4 contains very diverse σ factors called σ ECF (for ExtraCytoplasmic Function) that have been classified into at least 56 subgroups [START_REF] Staroń | The third pillar of bacterial signal transduction: classification of the extracytoplasmic function (ECF) sigma factor protein family[END_REF][START_REF] Huang | Environmental Sensing in Actinobacteria: a Comprehensive Survey on the Signaling Capacity of This Phylum[END_REF][START_REF] Sineva | Themes and variations in gene regulation by extracytoplasmic function (ECF) sigma factors[END_REF].

Sigma factors play crucial roles to conduct the cellular processes and in turn they have to be tightly regulated. In many cases, sigma factors are controlled at several levels, from transcription to post-translation. At the protein level, we can distinguish three main modes of regulation, the proteolysis of the sigma factor as for σ S in E. coli [START_REF] Becker | Regulation of RpoS proteolysis in Escherichia coli: The response regulator RssB is a recognition factor that interacts with the turnover element in RpoS[END_REF][START_REF] Hengge | Proteolysis of sigmaS (RpoS) and the general stress response in Escherichia coli[END_REF][START_REF] Hengge | The General Stress Response in Gram-negative Bacteria[END_REF][START_REF] Battesti | The RpoS-mediated general stress response in Escherichia coli[END_REF]; the cleavage of the σ factor from an inactive to an active form, including pro-σ E and pro-σ K in B. subtilis [START_REF] Hilbert | Compartmentalization of gene expression during Bacillus subtilis spore formation[END_REF][START_REF] Higgins | Recent progress in Bacillus subtilis sporulation[END_REF][START_REF] Fimlaid | Diverse mechanisms regulate sporulation sigma factor activity in the Firmicutes[END_REF] and the inactivation of the sigma factor activity by an anti-σ factor [START_REF] Hughes | The anti-sigma factors[END_REF][START_REF] Helmann | Anti-sigma factors[END_REF][START_REF] Österberg | Regulation of alternative sigma factor use[END_REF][START_REF] Feklístov | Bacterial sigma factors: a historical, structural, and genomic perspective[END_REF][START_REF] Paget | Bacterial Sigma Factors and Anti-Sigma Factors: Structure, Function and Distribution[END_REF]. The anti-σ factors sequester their target σ factor, disabling it to interact with the core RNAP and thus to transcribe genes of its regulon [START_REF] Campbell | Crystal Structure of the Bacillus stearothermophilus Anti-σ Factor SpoIIAB with the Sporulation σ Factor σF[END_REF][START_REF] Sorenson | Crystal structure of the flagellar sigma/antisigma complex sigma(28)/FlgM reveals an intact sigma factor in an inactive conformation[END_REF][START_REF] Feklístov | Bacterial sigma factors: a historical, structural, and genomic perspective[END_REF]. In doing so, anti-σ factors could protect their σ factor partner from proteolysis [START_REF] Barembruch | Cellular levels and activity of the flagellar sigma factor FliA of Escherichia coli are controlled by FlgM-modulated proteolysis[END_REF][START_REF] Mao | Dual positive feedback regulation of protein degradation of an extra-cytoplasmic function σ factor for cell differentiation in Streptomyces coelicolor[END_REF][START_REF] Mao | Proteasome involvement in a complex cascade mediating SigT degradation during differentiation of Streptomyces coelicolor[END_REF][START_REF] Bouillet | Protection of the General Stress Response σ(S) by CrsR Allows a Rapid and Efficient Adaptation of Shewanella oneidensis[END_REF]. Thus, anti-σ factors enable the cell to maintain a pool of σ factor molecules that can be rapidly released to act without de novo synthesis when suddenly required. Most of the σ ECF are regulated by anti-σ factors and are usually co-transcribed into the same transcriptional unit to keep a 1:1 stoichiometry of the two proteins [START_REF] Brooks | Signaling mechanisms for activation of extracytoplasmic function (ECF) sigma factors[END_REF][START_REF] Campagne | Extra Cytoplasmic Function sigma factors, recent structural insights into promoter recognition and regulation[END_REF]. The release of the σ factor is also precisely driven to ensure the transcription of its regulon in response to specific signals. Different strategies are thus employed including cell-surface signaling (regulation of σ 24 also called σ E in E. coli) [START_REF] Brooks | Signaling mechanisms for activation of extracytoplasmic function (ECF) sigma factors[END_REF][START_REF] Ho | Extra cytoplasmic function σ factor activation[END_REF], the secretion of the anti-σ factors (FliA/FlgM in E. coli) [START_REF] Hughes | Sensing structural intermediates in bacterial flagellar assembly by export of a negative regulator[END_REF][START_REF] Kutsukake | Excretion of the anti-sigma factor through a flagellar substructure couples flagellar gene expression with flagellar assembly in Salmonella typhimurium[END_REF][START_REF] Smith | Deciphering bacterial flagellar gene regulatory networks in the genomic era[END_REF], the direct sensing of redox state by cysteine residues of the anti-σ factors (RsrA/σ R in Streptomyces coelicolor) [START_REF] Ilbert | Zinc center as redox switch--new function for an old motif[END_REF][START_REF] Jung | Determinants of redox sensitivity in RsrA, a zinc-containing anti-sigma factor for regulating thiol oxidative stress response[END_REF] as well as the involvement of an additional protein called anti-σ factor antagonist in a mechanism known as the partner-switching system (PSS).

Partner-switching systems

The term "partner-switch" has been defined by Alper and colleagues in 1994 to describe the mechanism that regulates σ F , crucial in the sporulation process of B. subtilis [START_REF] Alper | An adenosine nucleotide switch controlling the activity of a cell type-specific transcription factor in B. subtilis[END_REF].

The nature of the PSS components as well as their function is a common feature among typical PSS. Indeed, PSS are made up with an anti-σ factor having a serine kinase activity (HATPase domain), a phosphorylatable STAS anti-σ antagonist, a PP2C serine phosphatase and a target σ factor [START_REF] Alper | An adenosine nucleotide switch controlling the activity of a cell type-specific transcription factor in B. subtilis[END_REF][START_REF] Mittenhuber | A phylogenomic study of the general stress response sigma factor sigmaB of Bacillus subtilis and its regulatory proteins[END_REF]. Anti-σ factors are constituted of a dimerization interface and of a HATPase domain. The latter harbors conserved motifs for phosphorylation and is found in many kinase families [START_REF] Dutta | GHKL, an emergent ATPase/kinase superfamily[END_REF]. STAS stands for Sulfate Transporter and Anti-Sigma antagonist and proteins containing this domain play a role in either of these two processes and are phosphorylatable on a specific serine residue [START_REF] Sharma | STAS domain structure and function[END_REF]. The PP2C domain characterizes a family of serine/threonine phosphatases that need metallic ion for their activity [START_REF] Shi | Serine/threonine phosphatases: mechanism through structure[END_REF][START_REF] Pereira | Eukaryote-like serine/threonine kinases and phosphatases in bacteria[END_REF][START_REF] Bradshaw | A widespread family of serine/threonine protein phosphatases shares a common regulatory switch with proteasomal proteases[END_REF].

In a partner-switching mechanism, the anti-σ factor binds to its target σ factor disabling it to recruit the core RNAP. The release of the σ factor is mediated by the anti-σ factor antagonist, also called anti-anti-σ factor. Indeed, when no specific signal is transduced to the regulatory system, the anti-σ factor binds to the σ factor and phosphorylates its partner the anti-σ factor antagonist. When the specific signal arises, the PP2C-type phosphatase is activated and dephosphorylates the anti-σ factor antagonist that becomes thus efficient to interact with the anti-σ, leading to the release of the σ factor by a competition effect. Anti-σ factors have usually a better affinity for the unphosphorylated form of the anti-anti-σ factor than for the σ protein allowing the binding of the σ factor only when the anti-σ factor antagonist is phosphorylated but not when it is dephosphorylated [START_REF] Duncan | SpoIIAA governs the release of the cell-type specific transcription factor sigma F from its anti-sigma factor SpoIIAB[END_REF][START_REF] Masuda | Crystal Structures of the ADP and ATP Bound Forms of the Bacillus Anti-σ Factor SpoIIAB in Complex with the Anti-anti-σ SpoIIAA[END_REF][START_REF] Bouillet | The General Stress Response σS Is Regulated by a Partner Switch in the Gram-negative Bacterium Shewanella oneidensis[END_REF].

A 3D-structure of the anti-σ factor SpoIIAB bound to its partners showed that two monomers of anti-σ factors interact with only one monomer of σ F factor but with two monomers of anti-σ factor antagonist SpoIIAA [START_REF] Campbell | Crystal Structure of the Bacillus stearothermophilus Anti-σ Factor SpoIIAB with the Sporulation σ Factor σF[END_REF][START_REF] Masuda | Crystal Structures of the ADP and ATP Bound Forms of the Bacillus Anti-σ Factor SpoIIAB in Complex with the Anti-anti-σ SpoIIAA[END_REF]. This mode of binding is probably similar for other PSS. Moreover, anti-σ proteins contain a dimerization interface suggesting their ability to dimerize. However, additional biochemical characterization of the interactions of the PSS partners is needed to confirm their mode of action.

Numerous PSS have been discovered in many bacteria including Gram-negative bacteria whereas they were initially thought to be restricted to Gram-positive bacteria [START_REF] Shi | The icfG Gene Cluster of Synechocystis sp. Strain PCC 6803 Encodes an Rsb/Spo-Like Protein Kinase, Protein Phosphatase, and Two Phosphoproteins[END_REF][START_REF] Mittenhuber | A phylogenomic study of the general stress response sigma factor sigmaB of Bacillus subtilis and its regulatory proteins[END_REF][START_REF] Kozak | Interactions between partner switcher orthologs BtrW and BtrV regulate type III secretion in Bordetella[END_REF][START_REF] Morris | Control of biofilm formation and colonization in Vibrio fischeri: a role for partner switching?[END_REF][START_REF] Houot | A bacterial two-hybrid genome fragment library for deciphering regulatory networks of the opportunistic pathogen Pseudomonas aeruginosa[END_REF]Morris and Visick, 2013b;[START_REF] Eshghi | A putative regulatory genetic locus modulates virulence in the pathogen Leptospira interrogans[END_REF][START_REF] Mercer | Identification of a predicted partner-switching system that affects production of the gene transfer agent RcGTA and stationary phase viability in Rhodobacter capsulatus[END_REF][START_REF] Lambert | Gene inactivation of a chemotaxis operon in the pathogen Leptospira interrogans[END_REF][START_REF] Thompson | The Rsb Phosphoregulatory Network Controls Availability of the Primary Sigma Factor in Chlamydia trachomatis and Influences the Kinetics of Growth and Development[END_REF][START_REF] Bouillet | The General Stress Response σS Is Regulated by a Partner Switch in the Gram-negative Bacterium Shewanella oneidensis[END_REF][START_REF] Gebhardt | GigA and GigB are Master Regulators of Antibiotic Resistance, Stress Responses and Virulence in Acinetobacter baumannii[END_REF]. Although PSS comprises four components (an anti-σ factor, an anti-σ factor antagonist, a phosphatase and a σ factor), the domain organization of the partners is highly diversified as depicted in Figure 1. While the anti-σ antagonist usually remains as a one-domain protein, the anti-σ factor can be a domain of a complex protein. Indeed, it can be associated with other domains including receiver domain of typical response regulator, PP2C-type phosphatase domain or unknown function domain.

Interestingly, the phosphatase is usually associated with a signaling domain such as receiver, HAMP or detection domains. These data show that PSS have evolved probably according to the detected stresses and to the target σ factor.

In conclusion, although the core domain composition is conserved, the domain organization frequently varies and, consequently, PSS often comprise additional domains (Figure 1) [START_REF] Mittenhuber | A phylogenomic study of the general stress response sigma factor sigmaB of Bacillus subtilis and its regulatory proteins[END_REF][START_REF] Galperin | Structural Classification of Bacterial Response Regulators: Diversity of Output Domains and Domain Combinations[END_REF].

Nevertheless, the presence of PSS encoded within bacterial genomes remains yet poorly studied.

Phylogenetic analyses of PSS are thus needed to evaluate how widespread are these systems in particular in Gram-negative bacteria. In fact, the presence of HATPase and STAS domains in other proteins and complex domain organizations make difficult the characterization of anti-σ and anti-anti-σ factors in bacteria by using basic bioinformatics.

However, anti-σ factor antagonist and phosphatase homologs have been found in Gram-positive bacteria, as well as cyanobacteria, Deinococcus species and proteobacteria including Pseudomonas, Vibrio and Shewanella species [START_REF] Mittenhuber | A phylogenomic study of the general stress response sigma factor sigmaB of Bacillus subtilis and its regulatory proteins[END_REF]. For example, B. subtilis and B. cereus possess 16 σ factors and two known PSS targeting σ F and σ B , M. tuberculosis harbors 13 σ factors, one of which is subjected to two PSS (σ F is submitted to UsfX/RsfAB and to Rv1364c regulation). In S. oneidensis, 10 σ factors are present with a known and a putative PSS.

Surprisingly, some species including E. coli do not seem to possess PSS. In contrast, the Grampositive bacterium S. coelicolor is one of the organisms that contain the highest number of σ factors (60 to 65) and its chromosome has been predicted to encode many PSS partners with 45 anti-σ factors, 18 anti-anti-σ factors and 44 PP2C proteins [START_REF] Bentley | Complete genome sequence of the model actinomycete Streptomyces coelicolor A3(2)[END_REF][START_REF] Mittenhuber | A phylogenomic study of the general stress response sigma factor sigmaB of Bacillus subtilis and its regulatory proteins[END_REF][START_REF] Martínez | Osmoregulation in Streptomyces coelicolor: modulation of SigB activity by OsaC[END_REF]. Among them, few have been identified but their study appears complicated because of cross-talks between several PSS, and anti-σ factor antagonists could have more than one associated anti-σ factor. If we consider that each anti-σ factor antagonist is the output protein from distinct sensory modules, this suggests that the release and thus the activation of the targeted σ factor might be induced by many transducing pathways in response to various signals.

Role of the Partner-switching systems

The role of PSS is to control the availability of specific σ factors. However, various PSS have been discovered in many bacteria but the targeted σ factor has not been found in some cases (Figure 1).

PSS signal transduction pathways seem to be implicated in various major cellular processes including the control of type III secretion system synthesis, virulence, chemotaxis, biofilm formation, exoprotein production, stress responses and also metabolism [START_REF] Shi | The icfG Gene Cluster of Synechocystis sp. Strain PCC 6803 Encodes an Rsb/Spo-Like Protein Kinase, Protein Phosphatase, and Two Phosphoproteins[END_REF][START_REF] Mattoo | Regulation of type III secretion in Bordetella[END_REF][START_REF] Kozak | Interactions between partner switcher orthologs BtrW and BtrV regulate type III secretion in Bordetella[END_REF][START_REF] Bordi | Regulatory RNAs and the HptB/RetS signalling pathways fine-tune Pseudomonas aeruginosa pathogenesis[END_REF][START_REF] Bhuwan | Histidine-containing phosphotransfer protein-B (HptB) regulates swarming motility through partner-switching system in Pseudomonas aeruginosa PAO1 strain[END_REF][START_REF] Houot | A bacterial two-hybrid genome fragment library for deciphering regulatory networks of the opportunistic pathogen Pseudomonas aeruginosa[END_REF]Morris andVisick, 2013b, 2013a;[START_REF] Eshghi | A putative regulatory genetic locus modulates virulence in the pathogen Leptospira interrogans[END_REF][START_REF] Mercer | Identification of a predicted partner-switching system that affects production of the gene transfer agent RcGTA and stationary phase viability in Rhodobacter capsulatus[END_REF][START_REF] Lambert | Gene inactivation of a chemotaxis operon in the pathogen Leptospira interrogans[END_REF][START_REF] Bouillet | The General Stress Response σS Is Regulated by a Partner Switch in the Gram-negative Bacterium Shewanella oneidensis[END_REF][START_REF] Gebhardt | GigA and GigB are Master Regulators of Antibiotic Resistance, Stress Responses and Virulence in Acinetobacter baumannii[END_REF]. For instance, the spore formation of B. subtilis is a complex multi-step mechanism under the control of many regulators [START_REF] Higgins | Recent progress in Bacillus subtilis sporulation[END_REF][START_REF] Fimlaid | Diverse mechanisms regulate sporulation sigma factor activity in the Firmicutes[END_REF]. Notably, four main σ factors act successively during the sporulation process. Each of them is thus tightly regulated but has to be also quickly freed to make sure that all genes are correctly expressed in time. σ F is active during the first stage of sporulation only in the forespore compartment. The anti-σ factor SpoIIAB interacts with σ F disabling it to recruit the core RNAP. The release of σ F is permitted by the anti-σ factor antagonist SpoIIAA. When no signal is transduced to the regulatory system, SpoIIAB binds to σ F and also phosphorylates SpoIIAA. When a signal is launched, the PP2C-type phosphatase SpoIIE, which is a membrane-anchored protein that perceives the signal dephosphorylates specifically SpoIIAA. The anti-σ antagonist becomes thus efficient to interact with SpoIIAB, leading to the release of σ F [START_REF] Diederich | Role of interactions between SpoIIAA and SpoIIAB in regulating cell-specific transcription factor sigma F of Bacillus subtilis[END_REF][START_REF] Duncan | SpoIIAA governs the release of the cell-type specific transcription factor sigma F from its anti-sigma factor SpoIIAB[END_REF][START_REF] Magnin | Contribution of partner switching and SpoIIAA cycling to regulation of sigmaF activity in sporulating Bacillus subtilis[END_REF][START_REF] Campbell | Crystal Structure of the Bacillus stearothermophilus Anti-σ Factor SpoIIAB with the Sporulation σ Factor σF[END_REF][START_REF] Masuda | Crystal Structures of the ADP and ATP Bound Forms of the Bacillus Anti-σ Factor SpoIIAB in Complex with the Anti-anti-σ SpoIIAA[END_REF][START_REF] Levdikov | Structure of the phosphatase domain of the cell fate determinant SpoIIE from Bacillus subtilis[END_REF].

The Rsb partner-switching system regulating the availability of the σ B factor of Bacillales is another PSS that has been extensively studied. σ B , the general stress response (GSR) σ factor, is inhibited during growth conditions without stress by the anti-σ factor RsbW and σ B becomes active during stress conditions due to the binding of the dephosphorylated anti-σ factor antagonist RsbV on RsbW (Figures 1 and2) [START_REF] Price | The general stress response in Bacillus subtilis and related Gram-positive bacteria[END_REF].

Beside Gram-positive bacteria PSS models, it has been recently shown in Shewanella oneidensis, an aquatic bacterium from the γ-proteobacteria class, σ S , the GSR-σ factor, is post-translationally regulated by a PSS (Figures 1 and2E). This system is composed of the two main proteins CrsA and CrsR, an anti-σ factor antagonist and a three-domain response regulator, respectively. CrsR contains a N-terminal receiver, a central PP2C and a C-terminal HATPase domains. The latter is able to phosphorylate efficiently CrsA whereas the phosphatase domain dephosphorylates it. In addition, direct interactions of CrsR-CrsA and of CrsR-σ S combined with in vivo data have revealed that σ S is the target of the PSS. The CrsRA-σ S is the first system that links GSR regulation to PSS in a γ-proteobacterium as it is the case in many other bacterial species [START_REF] Bouillet | The General Stress Response σS Is Regulated by a Partner Switch in the Gram-negative Bacterium Shewanella oneidensis[END_REF][START_REF] Bouillet | Protection of the General Stress Response σ(S) by CrsR Allows a Rapid and Efficient Adaptation of Shewanella oneidensis[END_REF].

In P. aeruginosa, the PSS protein HsbR comprises a receiver, a PP2C phosphatase and a HATPase domains. It has been proposed that the last domain could constitute a simple kinase and not an anti-σ factor (Figure 1). The anti-σ factor antagonist HsbA can bind to the anti-σ factor FlgM, inducing the release of the σ factor FliA (or σ 28 ) [START_REF] Bhuwan | Histidine-containing phosphotransfer protein-B (HptB) regulates swarming motility through partner-switching system in Pseudomonas aeruginosa PAO1 strain[END_REF]. Furthermore, HsbA is subjected to phosphorylation control by HsbR that, consequently, modulates the activity of HsbA. Contrarily to typical PSS, HsbA acts thus downstream HsbR. This system controls swimming motility through the regulation of the flagella synthesis mediated by FliA.

Furthermore, HsbA interacts with the diguanylate cyclase HsbD stimulating therefore the c-di-GMP production [START_REF] Valentini | The Diguanylate Cyclase HsbD Intersects with the HptB Regulatory Cascade to Control Pseudomonas aeruginosa Biofilm and Motility[END_REF]. This original partner-switch has thus crucial functional implications in both motility control and biofilm development.

The Syp system formed by SypE and SypA of Vibrio fisheri as well as the Btr system composed of BtrW and BtrV of Bordetella pertussis have important roles in biofilm formation and pathogenesis through type III secretion system control, respectively (Figure 1). The target σ factor and the mechanism of action have not been unraveled. Interestingly, as for HsbR of P. aeruginosa, the HATPase domains of SypE and RsbW could act solely as a kinase and not as an anti-σ factor [START_REF] Kozak | Interactions between partner switcher orthologs BtrW and BtrV regulate type III secretion in Bordetella[END_REF]Morris andVisick, 2013b, 2013a).

Usually, PSS regulate the activity of alternative σ factors but one example of a housekeeping sigma factor regulation by a PSS has been recently brought to light (Figure 1). Indeed, the primary σ factor σ 66 of Chlamydia trachomatis is controlled by PSS partners [START_REF] Hua | Core of the partner switching signalling mechanism is conserved in the obligate intracellular pathogen Chlamydia trachomatis[END_REF][START_REF] Thompson | The Rsb Phosphoregulatory Network Controls Availability of the Primary Sigma Factor in Chlamydia trachomatis and Influences the Kinetics of Growth and Development[END_REF].

Atypical partner-switching modules

In E. coli, σ 70 activity is also modulated by the anti-σ factor Rsd and the histidine phosphorylatable HPr that acts as an anti-σ factor antagonist. However, even though the mechanism of sequestration and release of the σ factor is alike that of PSS, the two partners Rsd and HPr are not homologous to typical PSS proteins with HPr playing a primary role in the translocation of several sugars across the membrane [START_REF] Mitchell | The Escherichia coli Regulator of Sigma 70 Protein, Rsd, Can Up-Regulate Some Stress-Dependent Promoters by Sequestering Sigma 70[END_REF][START_REF] Yuan | Rsd family proteins make simultaneous interactions with regions 2 and 4 of the primary sigma factor[END_REF][START_REF] Hofmann | The E. coli anti-sigma factor Rsd: studies on the specificity and regulation of its expression[END_REF][START_REF] Park | HPr antagonizes the anti-σ70 activity of Rsd in Escherichia coli[END_REF][START_REF] Park | Structural basis for the sequestration of the anti-σ(70) factor Rsd from σ(70) by the histidine-containing phosphocarrier protein HPr[END_REF].

In Gram-negative α-proteobacterial species, the σ factor controlling the GSR called σ ECFG , RpoE or SigT depending on the bacterium, is controlled by an atypical PSS (Figure 2F) [START_REF] Francez-Charlot | Sigma factor mimicry involved in regulation of general stress response[END_REF][START_REF] Staroń | The third pillar of bacterial signal transduction: classification of the extracytoplasmic function (ECF) sigma factor protein family[END_REF][START_REF] Campagne | Structural basis for sigma factor mimicry in the general stress response of Alphaproteobacteria[END_REF][START_REF] Kaczmarczyk | Complex twocomponent signaling regulates the general stress response in Alphaproteobacteria[END_REF][START_REF] Kim | The Brucella abortus virulence regulator, LovhK, is a sensor kinase in the general stress response signalling pathway[END_REF][START_REF] Francez-Charlot | The general stress response in Alphaproteobacteria[END_REF][START_REF] Fiebig | General Stress Signaling in the Alphaproteobacteria[END_REF][START_REF] Herrou | Structured and Dynamic Disordered Domains Regulate the Activity of a Multifunctional Anti-σ Factor[END_REF][START_REF] Francez-Charlot | Multiple σEcfG and NepR Proteins Are Involved in the General Stress Response in Methylobacterium extorquens[END_REF]. Indeed, the PSS partners are not homologous to canonical PSS proteins. Nevertheless, they use a similar mechanism of sequestration and release of the σ factor as typical PSS. The PSS module of GSR regulation is mostly shared by α-proteobacterial species with little divergences. In all cases, two proteins are involved: the anti-σ factor NepR and the two-domain PhyR. The latter acts as an anti-σ factor antagonist and contains a N-terminal σ-like factor and a C-terminal receiver domains (Figure 2F). NepR possesses homologies with σ ECF factors. Phosphorylation of the receiver domain increases the affinity between the σ-like domain of PhyR and NepR so that the σ ECF is freed whereas unphosphorylated PhyR has almost no affinity for NepR that in turn binds to σ ECFG . The phosphorylation state of PhyR is controlled by various histidine kinases that detect and transduce signals including blue light and osmolytes. As for GSR regulation of Gram-positive bacteria, the composition and the number of the sensory inputs vary greatly from a bacterium to another depending on their lifestyle [START_REF] Fiebig | General Stress Signaling in the Alphaproteobacteria[END_REF][START_REF] Francez-Charlot | The general stress response in Alphaproteobacteria[END_REF].

Activation of partner-switching systems

Diversified sensory and transducing modules appear to converge to PSS, although the mechanisms of transduction have not been unraveled in most cases. Input modules could be two-component systems, chemosensory systems or might be directly integrated in PSS modules via the addition of sensing domain(s) in PSS proteins (Figures 1 and2) [START_REF] Hsu | Characterization of the histidinecontaining phosphotransfer protein B-mediated multistep phosphorelay system in Pseudomonas aeruginosa PAO1[END_REF]Morris and Visick, 2013a;[START_REF] Lambert | Gene inactivation of a chemotaxis operon in the pathogen Leptospira interrogans[END_REF][START_REF] Norsworthy | Signaling between two interacting sensor kinases promotes biofilms and colonization by a bacterial symbiont[END_REF][START_REF] Chambonnier | The Hybrid Histidine Kinase LadS Forms a Multicomponent Signal Transduction System with the GacS/GacA Two-Component System in Pseudomonas aeruginosa[END_REF].

In the Gram-positive bacteria Bacillus, Listeria and Staphyloccoccus, the stress responsive σ factor σ B is submitted to a PSS. The PSS module formed by the anti-σ factor RsbW and the anti-σ factor antagonist RsbV is conserved but the RsbV phosphatases as well as the mechanisms of signal transduction to the PSS module diverge from one species to another (Figure 2A and2B).

In B. subtilis, two sensing modules linked to two independent PP2C-containing phosphatases (RsbU and RsbP) converge to the PSS module. Environmental stresses including blue light, heat shock and osmolytes are detected by a protein complex called the stressosome that subsequently transduces signals to the PP2C phosphatase RsbU. The energetic level of the cell is perceived by the PAS domain containing phosphatase RsbP associated with the hydrolase RsbQ (Figure 2A) [START_REF] Benson | Regulation of sigma B levels and activity in Bacillus subtilis[END_REF][START_REF] Boylan | Stress-induced activation of the sigma B transcription factor of Bacillus subtilis[END_REF][START_REF] Voelker | Reactivation of the Bacillus subtilis antisigma B antagonist, RsbV, by stress-or starvation-induced phosphatase activities[END_REF][START_REF] Kim | In vivo phosphorylation of partner switching regulators correlates with stress transmission in the environmental signaling pathway of Bacillus subtilis[END_REF][START_REF] Marles-Wright | Molecular architecture of the "stressosome," a signal integration and transduction hub[END_REF][START_REF] Marles-Wright | The stressosome: molecular architecture of a signalling hub[END_REF][START_REF] Price | The general stress response in Bacillus subtilis and related Gram-positive bacteria[END_REF][START_REF] Gaidenko | Genetic evidence for a phosphorylation-independent signal transduction mechanism within the Bacillus subtilis stressosome[END_REF][START_REF] Guldimann | Resilience in the face of uncertainty: sigma B fine-tunes gene expression to support homeostasis in Grampositive bacteria[END_REF]. RsbU, RsbP and a stressosome are absent from other Bacillales as B. cereus. They are replaced by the RsbY protein composed of a receiver fused to a PP2C domain and the complex histidine kinase RsbK, which is able to detect internal and environmental stresses and thus transduces the signal to RsbY (Figure 2B) [START_REF] Van Schaik | Analysis of the Role of RsbV, RsbW, and RsbY in Regulating σB Activity in Bacillus cereus[END_REF][START_REF] De Been | A novel hybrid kinase is essential for regulating the sigma(B)-mediated stress response of Bacillus cereus[END_REF][START_REF] De Been | Novel sigmaB regulation modules of Gram-positive bacteria involve the use of complex hybrid histidine kinases[END_REF]. Interestingly, genes coding for a stressosome-like complex are also found in many species in particular among the proteobacteria, cyanobacteria and actinobacteria phyla [START_REF] Pané-Farré | The RsbRST stress module in bacteria: a signalling system that may interact with different output modules[END_REF][START_REF] Jia | An O2-sensing stressosome from a Gram-negative bacterium[END_REF]. Likewise, homologs of the histidine kinase RsbK have been found in many species including the proteobacterial genus Vibrio, Pseudomonas, Magnetoccocus or Myxococcus but their roles are still unknown [START_REF] De Been | Novel sigmaB regulation modules of Gram-positive bacteria involve the use of complex hybrid histidine kinases[END_REF].

Streptomyces coelicolor PSS regulation of σ B resembles that of B. subtilis but possesses an additional PSS absent from other species [START_REF] Lee | Regulation of σB by an Anti-and an Anti-Anti-Sigma Factor in Streptomyces coelicolor in Response to Osmotic Stress[END_REF]): the Osa system that regulates σ B under "back to normal" conditions after an osmotic shock. OsaA is a RsbK homolog that may detect signals from a GAF domain, OsaB is a two-domain protein with a N-terminal receiver and an unknown C-terminal domain, and OsaC contains an anti-σ factor, a PAS, two GAF and a PP2Ctype phosphatase domains (Figure 2C) [START_REF] Martínez | Osmoregulation in Streptomyces coelicolor: modulation of SigB activity by OsaC[END_REF][START_REF] Price | The general stress response in Bacillus subtilis and related Gram-positive bacteria[END_REF]. The regulatory cascade that regulates σ B availability is still unknown but the domain composition of OsaC suggests a direct additional signal sensing by the phosphatase protein. In the Actinomycetales Mycobacterium tuberculosis, the GSR σ factor called σ F is also mediated by two PSS [START_REF] Demaio | Mycobacterium tuberculosis sigF is part of a gene cluster with similarities to the Bacillus subtilis sigF and sigB operons[END_REF]. Notably, the protein Rv1364c is a PSS module organized in four domains corresponding to a PAS, a phosphatase, an anti-σ factor and an anti-σ factor antagonist domain [START_REF] Parida | Interactions of anti-sigma factor antagonists of Mycobacterium tuberculosis in the yeast two-hybrid system[END_REF][START_REF] Sachdeva | Loss of kinase activity in Mycobacterium tuberculosis multidomain protein Rv1364c[END_REF][START_REF] Greenstein | Interdomain communication in the Mycobacterium tuberculosis environmental phosphatase Rv1364c[END_REF][START_REF] Malik | Interactions of the M. tuberculosis UsfX with the cognate sigma factor SigF and the anti-anti sigma factor RsfA[END_REF][START_REF] Jaiswal | Role of a PAS sensor domain in the Mycobacterium tuberculosis transcription regulator Rv1364c[END_REF][START_REF] King-Scott | Structural characterization of the multidomain regulatory protein Rv1364c from Mycobacterium tuberculosis[END_REF]. It has been shown that its anti-σ factor domain can bind to σ F whereas its antiσ factor antagonist domain antagonizes the action of the anti-σ domain. However, the complex network of σ F post-translational regulation has not been completely unraveled yet, but Rv1364c seems to detect signals itself without upstream sensing module.

The PSS composed of HsbR and HsbA from P. aeruginosa is activated by a complex phosphocascade. The histidine phosphotransfer protein HptB constitutes the module activating HsbR. HptB acts in the GacA-GacS two-component pathway regulating sRNA involved in P. aeruginosa biofilm formation and pathogenesis [START_REF] Lin | Identification of an HptB-mediated multi-step phosphorelay in Pseudomonas aeruginosa PAO1[END_REF][START_REF] Hsu | Characterization of the histidinecontaining phosphotransfer protein B-mediated multistep phosphorelay system in Pseudomonas aeruginosa PAO1[END_REF][START_REF] Bordi | Regulatory RNAs and the HptB/RetS signalling pathways fine-tune Pseudomonas aeruginosa pathogenesis[END_REF][START_REF] Bhuwan | Histidine-containing phosphotransfer protein-B (HptB) regulates swarming motility through partner-switching system in Pseudomonas aeruginosa PAO1 strain[END_REF][START_REF] Houot | A bacterial two-hybrid genome fragment library for deciphering regulatory networks of the opportunistic pathogen Pseudomonas aeruginosa[END_REF][START_REF] Chambonnier | The Hybrid Histidine Kinase LadS Forms a Multicomponent Signal Transduction System with the GacS/GacA Two-Component System in Pseudomonas aeruginosa[END_REF]. Moreover, other histidine kinases have been shown to detect specific signals and transduce them by phosphorylating HptB that in turn phosphorylates the receiver domain of HsbR, activating the phosphatase domain of HsbR [START_REF] Hsu | Characterization of the histidinecontaining phosphotransfer protein B-mediated multistep phosphorelay system in Pseudomonas aeruginosa PAO1[END_REF].

Activation of partner-switching modules in various proteobacteria: a role for chemosensory systems

Chemosensory systems (CS) are complex signal transduction pathways mainly involve in the regulation of the flagella rotation necessary for swimming motility in most bacteria. Since then, many CS that do not control bacterial motility have been spotted in various bacteria. They were shown to play a role in the cellular differentiation of Myxococcus xanthus and Rhodospirillum centenum, in the production of molecules involved in biofilm formation as c-di-GMP or EPS in P. aeruginosa and Azospirillum brasilense or in the type IV pili based motility in P. aeruginosa. In fact, a large number of bacteria possess more than one CS coding locus in their genome, but their roles are still largely unknown [START_REF] Kirby | Chemotaxis-like regulatory systems: unique roles in diverse bacteria[END_REF][START_REF] Wuichet | Origins and Diversification of a Complex Signal Transduction System in Prokaryotes[END_REF][START_REF] He | Chemosensory signaling systems that control bacterial survival[END_REF].

In S. oneidensis, the genes coding for the two PSS proteins CrsA and CrsR are located in the che1 locus, composed of 10 genes with 8 of them coding for classical CS components (Che proteins). This system is not involved in swimming motility and could form a chemosensory system [START_REF] Armitano | Aerotaxis governs floating biofilm formation in Shewanella oneidensis[END_REF]. This gene organization strongly suggests that the chemosensory system Che1 contains the signal sensing machinery that regulates the activity of the CrsR-CrsA PSS.

Interestingly, it appears that chemosensory machinery adapted to regulate a partner-switch is a common feature of aquatic proteobacteria (Figure 3). Indeed, crsR gene homologs are widespread among those bacteria and an analysis of the genes surrounding crsR in 59 bacterial genomes was carried out for this review [START_REF] Bouillet | Protection of the General Stress Response σ(S) by CrsR Allows a Rapid and Efficient Adaptation of Shewanella oneidensis[END_REF]. This phylogenetic analysis clearly shows that the CrsR-CrsA PSS is most of the time genetically related to a che locus. As shown in Figure 3, the crsR-crsA genes are located in the vicinity of a central histidine kinase cheA gene in 71% of these bacteria (32 out of 45), or of two-component histidine kinase(s). This indicates that the sensing modules of the CrsR-CrsA partner-switch could predominantly be a chemosensory system. Interestingly, two genes coding for detectors MCP (Methyl-accepting Chemotaxis Proteins) are comprised in the che1 locus of S. oneidensis. One of them is predicted to be anchored to the membrane whereas the other seems to be located in the cytosol and possesses two PAS domains (Figure 2). The two MCP could thus allow the detection of different kinds of signals: one from outside and the other from inside the cell cytoplasm. Interestingly, many PSS controlling the GSR including those in B. cereus and in some alpha-proteobacterial species often comprise two sensory detectors presenting similar sensing domains as those found in MCP (Figure 2 and Figure 3). It has been shown that the RsbK histidine kinase from B. cereus that controls the downstream RsbVWY PSS is subjected to the methylation by the methyltransferase RsbM, as usually seen in MCP [START_REF] Chen | Interplay of RsbM and RsbK controls the σB activity of Bacillus cereus[END_REF][START_REF] Chen | Methylatable Signaling Helix Coordinated Inhibitory Receiver Domain in Sensor Kinase Modulates Environmental Stress Response in Bacillus Cereus[END_REF]. The methylation of RsbK by RsbM leads to the inhibition of σ B . As the che1 locus of S. oneidensis contains a gene encoding the methyltransferase CheR1, we propose that σ S sequestration by CrsR could also be modulated by the methylation level of the two MCP.

PSS imbedded in a CS operon has been recently described in Leptospira interrogans. This operon contains genes encoding a response regulator composed of a receiver and an anti-σ factor domains, an anti-σ factor antagonist and chemosensory proteins (CheA, CheY, CheW, CheD, CheB, MCP). This PSS that controls a still unknown σ factor could be regulated by the chemosensory system [START_REF] Eshghi | A putative regulatory genetic locus modulates virulence in the pathogen Leptospira interrogans[END_REF][START_REF] Lambert | Gene inactivation of a chemotaxis operon in the pathogen Leptospira interrogans[END_REF]. Two-component systems or signal transduction coding genes have also been found in the neighborhood of crsR homologs and could thus be the sensor that detects signals and transduces them to the PSS. Genes encoding Hpt proteins are also found near to crsR-crsA genes in Hahella ganghwensis and Marinobacter lipolyticus (Figure 3). This illustrates that the sensing modules acting upstream PSS diverge from one species to another.

The case of GSR regulation in bacteria

The main strategy commonly developed by bacteria to respond, defend and adapt to general stresses is to modify its transcriptional program in order to express appropriate genes. This ability is mediated by the use of a specific σ factor. Gram-positive bacteria as Bacillales and Actinomycetales possess a type-3 σ factor named σ B or σ F , whereas β-and γ-proteobacteria hold the type-2 σ factor σ S and α-proteobacteria use a type-4 σ ECF often called σ ECFG [START_REF] Boylan | Stress-induced activation of the sigma B transcription factor of Bacillus subtilis[END_REF][START_REF] Battesti | The RpoS-mediated general stress response in Escherichia coli[END_REF][START_REF] Hengge | The General Stress Response in Gram-negative Bacteria[END_REF][START_REF] Price | The general stress response in Bacillus subtilis and related Gram-positive bacteria[END_REF][START_REF] Fiebig | General Stress Signaling in the Alphaproteobacteria[END_REF][START_REF] Francez-Charlot | The general stress response in Alphaproteobacteria[END_REF]. Despite the fact that all these σ factors are not homologous and present large differences in terms of sequence and structure, they control analogous processes in the cells [START_REF] Alvarez-Martinez | A Caulobacter crescentus Extracytoplasmic Function Sigma Factor Mediating the Response to Oxidative Stress in Stationary Phase[END_REF][START_REF] Sauviac | An extracytoplasmic function sigma factor acts as a general stress response regulator in Sinorhizobium meliloti[END_REF][START_REF] Gourion | The PhyRsigma(EcfG) signalling cascade is involved in stress response and symbiotic efficiency in Bradyrhizobium japonicum[END_REF][START_REF] Martínez-Salazar | Role of the extracytoplasmic function sigma factor RpoE4 in oxidative and osmotic stress responses in Rhizobium etli[END_REF][START_REF] Britos | Regulatory response to carbon starvation in Caulobacter crescentus[END_REF][START_REF] Hengge | The General Stress Response in Gram-negative Bacteria[END_REF][START_REF] Foreman | The LovK-LovR Two-Component System Is a Regulator of the General Stress Pathway in Caulobacter crescentus[END_REF][START_REF] Jans | Canonical and non-canonical EcfG sigma factors control the general stress response in Rhizobium etli[END_REF][START_REF] Kim | The Brucella abortus General Stress Response System Regulates Chronic Mammalian Infection and Is Controlled by Phosphorylation and Proteolysis[END_REF][START_REF] Landini | sigmaS, a major player in the response to environmental stresses in Escherichia coli: role, regulation and mechanisms of promoter recognition[END_REF][START_REF] Guldimann | Resilience in the face of uncertainty: sigma B fine-tunes gene expression to support homeostasis in Grampositive bacteria[END_REF]. Moreover, although their global regulation is highly divergent, the presence of a PSS is a relatively common feature for their post-translational regulation (Figure 2). Strikingly, although B. cereus and S. oneidensis are distant species and use the two nonhomologous GSR σ factors σ B and σ S , respectively, the pathways that control their posttranslational activity are similar. Conversely, E. coli and S. oneidensis are both γ-proteobacteria and both possess σ S to regulate GSR, but their post-translational regulations of σ S are entirely different [START_REF] Battesti | The RpoS-mediated general stress response in Escherichia coli[END_REF][START_REF] Price | The general stress response in Bacillus subtilis and related Gram-positive bacteria[END_REF]. This strongly suggests that these regulatory pathways can be more related between bacteria that share common lifestyle than phylogenetic proximity.

Concluding Remarks

Since a couple of years, novel PSS have been detected in many bacteria. These PSS play a key role in major cellular processes although the partner σ factor has not been determined in several cases. PSS are common post-translational regulators for the control of the response to general stress but they are also involved in crucial cell processes as motility, biofilm formation, virulence, and cell differentiation as sporulation. Other physiological roles of PSS will be undoubtedly discovered in the next future.

In fact, based solely on basic bioinformatics, it is quite difficult to find out PSS genes within bacterial genomes mainly because their HATPase domain is common to other types of proteins.

Furthermore, each species has to adapt to its own environment, doing this, it has set up dedicated regulatory pathways. PSS and the sensing modules have thus evolved to adapt to their target σ factors and to the signals this σ factor has to be responding to. As a consequence, the domain organization of the PSS (Figures 1 and2) and the composition of the sensing modules vary greatly (Figures 2 and3). In other words, a common signal transduction pathway like PSS can be activated by a large range of sensing machineries.

In conclusion, recent results have revealed that PSS are found not only in Gram-positive but also in Gram-negative bacteria. Moreover, many PSS are governed by complex signaling pathways including two-component and chemosensory systems. PSS are clearly very efficient and rapid ways to trigger or stop specific σ factor responses. Therefore, future studies will most probably reveal novel module architectures for PSS and their regulatory pathways to better respond to the numberless environmental signals encountered by bacteria. Searches for homologous proteins to S. oneidensis CrsR were performed using the bioinformatics BLAST tool from the NCBI database (NCBI Resource Coordinators, 2016) and the sequences were assembled using the program "Phylogeny" [START_REF] Dereeper | Phylogeny.fr: robust phylogenetic analysis for the non-specialist[END_REF]. Among the Gammaproteobacteria, CrsR homologs are found in Alteromonadales, Chromatiales, Methylococcales,

Figure 1 :

 1 Figure 1: Domain organization of PSS modules of chosen bacterial species. The color code is: red for σ factors, purple for anti-σ factor domains, green for anti-σ factor antagonists (anti-anti-σ factors) and yellow for phosphatase domains. HAMP domains (present in Histidine kinases, Adenyl cyclases, Methyl-accepting proteins and Phosphatases) are linkers possessing a role in signal transduction. Transmembrane domains of membranous proteins are mentioned by "TM" (in black), other proteins are cytoplasmic. REC stands for Receiver domain. Sensing domains comprise: CACHE (CAlcium channels and CHEmotaxis receptors), PAS for Per (Period Circadian Protein), Arnt (Aryl hydrocarbon Receptor Nuclear Translocator protein), Sim (Single-Minded Protein) and GAF (for cGMP-specific phosphodiesterases, Adenylyl cyclases and FhlA). When known, the physiological role of PSS is indicated as well as its sensing modules. The domain organizations appear on the right part of the figure.

Figure 2 :

 2 Figure 2: Conserved PSS modules regulating GSR σ factors are controlled by various signal transduction systems. The signal transduction pathways are divided in three major steps: 1(blue) -Signal sensing. Modules involved in the signal detection present a large range of mechanisms with various level of complexity. This step is the most diversified and bacteria have been very creative to detect signals and transduce them to the PSS modules. 2 (green) -Intermediate signal transduction from the input (signal sensing) to the output (σ factors). The partner-switching mechanism is highly conserved. The rule is that according to the environmental conditions, the σ factor should be sequestered or released to hamper or allow its regulatory activity, respectively. 3 (red) -The σ factor involved in the GSR. The release of the σ factor leads to the expression of the genes belonging to the σ factor regulon. Question marks (?) indicate that the steps have not been experimentally demonstrated. * means that the components are not conserved in all alpha-proteobacterial species. A green arrow represents a phosphorylation and a red arrow a dephosphorylation event. "HK-CA" corresponds to the domains HisKA and HATPase involved in receiver (REC) phosphorylation. Histidine kinases from two-component systems and the CheA1 kinase from the Che1 chemosensory system are represented. Protein names are indicated inside the drawing except if the protein harbors multiple domains, in this case the name is written above. The cytoplasmic membrane is symbolized in dark. The colors of the σ factors and the PSS components are those of figure 1. Protein hampering is indicated by a line ending by a small horizontal line. ασ and αασ stand for anti-sigma factor and anti-anti-sigma factor (or anti-sigma factor antagonist).

Figure 3 :

 3 Figure 3: Occurrence and synteny of CrsR homologs in bacteria

Acknowledgements

We would like to thank the anonymous reviewers for helpful suggestions, members of the group, in particular Olivier Lemaire for fruitful discussions and Olivier Genest for discussions and reviewing the manuscript. We also thank David Redelberger for assistance with drawings. This work was supported by the Centre National de la Recherche Scientifique and Aix-Marseille Université (AMU). S.B. was supported by a Ministère de l'Enseignement Supérieur et de la Recherche fellowship and AMU.

Conflict of interest: The authors declare that they have no conflicts of interest with the contents of this article.


Chuang Yu

Adriana Tapus
email: adriana.tapus@ensta-paristech.fr

Interactive Robot Learning for Multimodal Emotion Recognition

Keywords: Interactive robot learning, Multimodal emotion recognition, Human-robot interaction

Interaction plays a critical role in skills learning for natural communication. In human-robot interaction (HRI), robots can get feedback during the interaction to improve their social abilities. In this context, we propose an interactive robot learning framework using multimodal data from thermal facial images and human gait data for online emotion recognition. We also propose a new decision-level fusion method for the multimodal classification using Random Forest (RF) model. Our hybrid online emotion recognition model focuses on the detection of four human emotions (i.e., neutral, happiness, angry, and sadness). After conducting offline training and testing with the hybrid model, the accuracy of the online emotion recognition system is more than 10% lower than the offline one. In order to improve our system, the human verbal feedback is injected into the robot interactive learning. With the new online emotion recognition system, a 12.5% accuracy increase compared with the online system without interactive robot learning is obtained.

Introduction

In the past decade, emotion detection during social interaction attracted more and more attention with the rapid advances in the field of robotics. Research studies have found human emotion perception as a fundamental component of communication that plays a significant role in successful human-human interaction [START_REF] Keltner | Social functions of emotions at four levels of analysis[END_REF]. Emotion recognition during human-robot interaction can help robots to understand user's state and exhibit a natural social interaction. However, human emotion recognition is challenging. Many researchers use multimodal information to address emotion recognition [START_REF] Sebe | Multimodal approaches for emotion recognition: A survey[END_REF]. In our paper, we also use a multimodal system combining the information from thermal face images and gait information during human-robot interaction. Even though many researchers study emotion recognition models for robots, it is very expensive and time-consuming to label and annotate large databases by hand. Interactive Robot Learning (IRL) can Supported by ENSTA Paris. address this problem. During human-robot interaction, robots can get verbal feedback from humans to label or relabel the data extracted from the interaction [START_REF] Katagami | Interactive classifier system for real robot learning[END_REF]. IRL is very useful in the long-life learning situation where there is no large-scale data for emotion recognition. Robots can record the emotion-related features and obtain its label from the interaction with humans. Currently, most of the researches focus on offline emotion analysis. However, the online recognition capability is more challenging. The robot interactive learning can also improve flexibility of emotion recognition model in human-robot interaction.

In the last decades, numerous studies attempted the multimodal method with the visual, verbal, and physiological signals and the natural language in order to get a better ability of emotion recognition. Caridakis et al. [START_REF] Caridakis | Multimodal emotion recognition from expressive faces, body gestures and speech[END_REF] made use of the multimodal Bayesian classifier with features extracted from face images, gestures, body action, and speech information to classify 8 emotions namely anger, despair, interest, pleasure, sadness, irritation, joy, and pride. In addition, they employed the fusion methods at feature-level and decision-level. Regarding IRL, authors in [START_REF] Hanheide | Active memory-based interaction strategies for learningenabling behaviors[END_REF] described what was IRL with mixed-initiative and how the memory-based human-robot interaction strategies worked in the learning environment. Lutkebohle et. al [START_REF] Lutkebohle | The curious robot-structuring interactive robot learning[END_REF] developed an IRL system with the human speech feedback in the dialog loops to help a curious robot learn skills in the grasping task. The human feedback from the human-robot interaction was proved significantly useful during robot learning.

Moreover, to the best of our knowledge, no studies focus on the fusion of gait and thermal facial features together to detect human emotion in social robotics context. A very common scenario is when the human walks towards the social robot and stops in front of it to interact with it. In our work, we developed a multimodal emotion recognition method by using thermal facial images during human-robot interaction and gait data during walking towards the robot to recognize four emotional states (i.e., neutral, happy, angry, and sad). The offline emotion recognition is widely developed by researchers. However, the online testing of emotion recognition model is challenging in real-time HRI context. In this paper, we developed a new method based on Random Forest (RF) model and confusion matrices of two individual Random Forest models by using the data from the face thermal images and gait. In addition, the IRL method is used with the verbal human feedback in the learning loops in order to improve the performance of real-time emotion recognition. The experiment results shows the effectiveness of IRL in multimodal emotion recognition.

The rest of the paper is structured as follows: Section 2 describes the methodology; Section 3 shows the experimental setup. The experimental results are summarized in Section 4. The conclusions and discussions are part of Section 5.

Methodology

Multimodal database

In our study, we collected the data (i.e., the thermal facial images and gait data) from multiple modalities. The database was built up with the data from the human-robot interaction experiments. Here, we use it for the training of the offline emotion recognition model. Many multimodal databases for affective computing exist in the literature. However, to the best of our knowledge, there are no open multimodal databases with face thermal images and gait data. In our past research, we conducted 300 human-robot interaction experiments with 15 participants to build up our database. During the experiments, each participant run 20 experiments (i.e., 5 times each emotion). The gait data was extracted from the RGB-D camera during human walking towards the robot and included 3D skeleton data with the positions of 25 joints and related timestamps of each frame. The trajectories of 3D skeletons are shown in Fig. 1. The thermal images with the human upper body were recorded from the thermal camera Optris Pi during human-robot interaction for 10 seconds. Then, we selected the suitable data where the participants' heads did not move much. The time duration of all data in the thermal database is from 5 seconds to 10 seconds. The examples of the thermal database are shown in Fig. 2.

Features extraction

From the RGB-D camera, we extracted the 3D positions of 25 joints. The index of the 25 joints in the human body is shown in Fig. 3. In this paper, 4 joint angles and 4 joint angular velocities are calculated as features including the left knee joint angle (LKJA) and its velocity (LKJAV), the right knee joint angle (RKJA) and its velocity (RKJAV), the left hip joint angle (LHJA) and its velocity (LHJAV), the right hip joint angle (RHJA) and its velocity (RHJAV). The definition of the joint angles is shown in Fig. 2.2. LHJA is calculated from 3D positions of joints 1, 0, 12, and 13. LKJA is calculated from 3D positions of joints 12, 13, and 14. RHJA is calculated from 3D positions of joints 1, 0, 16, and 17. RKJA is calculated from 3D positions of joints 16, 17, and 18. LHJAV, LKJAV, RHJAV, and RKJAV are just calculated through a subtraction operation of the current angle and the previous angle and the following division operation on the sampling time gap. The more detailed description of the calculation of the 8 parameters was presented in one of our past work [START_REF] Chuang | Multimodal emotion recognition with thermal and rgb-d cameras for human-robot interaction[END_REF]. In this study, we use Power Spectral Density (PSD) as the feature of the gait data, which describes the energy of the specific frequency or frequency range. Then, Welch method is applied to calculate PSD features [START_REF] Rahi | Analysis of power spectrum estimation using welch method for various window techniques[END_REF]. Many researchers have found that human emotions are correlated to the facial areas (e.g., left cheek, right cheek, and nose [START_REF] Sugimoto | A method for detecting transitions of emotional states using a thermal facial image based on a synthesis of facial expressions[END_REF] [10]). In this paper, we use these 3 facial Region of Interest (ROI) for human emotion detection [START_REF] Agrigoroaie | Physiological parameters variation based on the sensory stimuli used by a robot in a news reading task[END_REF] (see Fig. 3 (b)). The average value and variance of these 3 facial regions are used as thermal facial features.

Fusion of multimodal classifiers

Previously, we have tested many machine learning models for offline emotion recognition including Hidden Markov Model (HMM) with thermal data, HMM model with gait data, and Random Forest (RF) model with thermal data, RF model with gait data, Controversial Neural Network (CNN) model with gait data, Support Vector Machine (SVM) model with thermal data, SVM model with gait data. In addition, CNN model with gait feature has only an offline testing accuracy of 55%. The offline accuracy of HMM, SVM and RF with gait data are 65%, 65% and 70%, respectively. SVM and RF have the accuracy of 55% and 60%, respectively. SVM and RF with both features obtain 70% and 80%, respectively. Hence, we found that RF had the best emotion recognition performance with the highest accuracy with respect to the other models. So, this paper applies RF as the basic machine learning model for emotion recognition.

The individual emotion classifier only with gait data and the one only with thermal face data have different recognition abilities for the 4 different emotions.

During online testing, we found that the two individual models show distinct emotion recognition performance with different accuracy in each emotion situation. Hence, the decision-level fusion of the two individual models is necessary to make a better recognition accuracy. In this paper, we developed a new decisionlevel fusion method with two RF classifiers for online emotion recognition and the framework is as shown in Fig. 4. The integration method is based on the modified confusion matrix and the probability vector of each emotion class. We use the confusion matrix information to build up the decision-level hybrid model for emotion recognition. In the confusion matrix all the elements represent probabilities. The row of the confusion matrix represents the instances in a predicted class while the column represents the instances in the real class. Then, the elements of each column in the confusion matrix is divided by the total amount of instances in each column, respectively to get the modified confusion matrix. In the modified one, each column shows the probability of each real class in the predicted class situation. An example of our modified confusion matrix for the thermal emotion recognition model is shown in Fig. 5. For example, in column two corresponding to the predicted class angry in Fig. 5, T C 02 , T C 12 , T C 22 , and T C 32 represent the probabilities of neutral, happy, angry, and sad emotions, respectively when the predicted class is the angry emotion.

During every online testing, the class probabilities of the thermal model make up the vector T P M 1×4 , as shown in Fig. 6. For example, the thermal emotion recognition model gets the prediction result happy. From the above matrix, the predicted happy is neutral with probability T C 01 , happy with probability T C 11 , angry with probability T C 21 , and sad with probability T C 31 . Therefore, in the Fig. 5. Modified confusion matrix of thermal emotion recognition model happy prediction situation, the probability vector of the 4 emotions is equal to T P 1 × T C 1 . Similarly, we can get the probability vectors of the 4 emotions in other 3 prediction situations. The process is as shown in Fig. 6.

Fig. 6. Class probability calculation with modified confusion matrix

The calculation of T EP 1×4 for the thermal model, of GEP 1×4 for the gait model, of F U EP 1×4 for the two new vectors, and of F RR for the final recognition result is as indicated in Eqs. 1, 2, 3, and 4, respectively.

T EP 1×4 = T P M 1×4 × T CM 4×4 (1)
GEP 1×4 = GP M 1×4 × GCM 4×4 (2)
F U EP 1×4 = T EP 1×4 + GEP 1×4 (3)
F RR = argmax(F U EP 1×4 ) (4)

Robot interactive learning

We apply an interactive learning method during the interaction in order to boost the emotion recognition performance of the robot. An overview of IRL system architecture is illustrated in Fig. 7. During the IRL experiments, if the predicted emotion does not match the real one, the gait and the thermal facial features are restored to retrain the emotion recognition models. The updated thermal and gait models will test the saved features again to get the new predicted results, respectively. The new predicted results will help update the two confusion matrices. If the predicted emotion is equal to the real one, we only update the two confusion matrices and the models are not retrained. For the emotion elicitation methods, static images and films clips are considered extensively as stimuli to elicit different emotions in the laboratory and films are one of the most effective ways to elicit emotions [START_REF] Deng | A new standardized emotional film database for asian culture[END_REF]. In our experiments, for emotion elicitation, we used open film clips database-FilmStim [START_REF] Schaefer | Assessing the effectiveness of a large database of emotion-eliciting films: A new tool for emotion researchers[END_REF], in which there are English and French videos that can elicit emotional states including amusement, anger, sadness, tenderness, fear, disgust, and neutral state. In our research, we only focus on 4 emotions: neutral, amusement, anger, and sadness. Hence, the film clips of 4 emotions in the database are used in our research, and more precisely: 6 clips for neutral emotion (3 English, 3 French), 13 clips for happy emotion (3 English, 10 French), 16 clips for angry emotion (9 English, 7 French), and 17 clips for sad emotion (7 English, 10 French). The film clips for each emotion were randomly chosen for emotion elicitation. After emotion elicitation experiments, we applied Pick-A-Mood (PAM) [START_REF] Desmet | Mood measurement with pick-a-mood: review of current methods and design of a pictorial self-report scale[END_REF] as a tool of emotional state measure.

Each experiment is composed of three parts: (1) the emotion elicitation part, (2) the walking part for emotional gait data, and (3) the standing part for thermal facial images. In the emotion elicitation part, the participant randomly selects and watches one or more film clips for one or more times for a specific emotion. Then, the participant walks towards the robot with that specific emotion and stops before the robot to start the interaction. The details of IRL part are described in the following steps. For the other two parts, before and after IRL, there is no step [START_REF] Hanheide | Active memory-based interaction strategies for learningenabling behaviors[END_REF]. The steps are as described below: [START_REF] Agrigoroaie | Physiological parameters variation based on the sensory stimuli used by a robot in a news reading task[END_REF] The participant completes EPQ to determine his/her personality traits (one time); (2) The participant randomly selects and watches the film clips from FilmStim for a specific emotion; (3) The participant selects the emotional state from PAM for mood measurement; (4) The robot instructs the participant to walk towards it with the specific emotional gait; [START_REF] Desmet | Mood measurement with pick-a-mood: review of current methods and design of a pictorial self-report scale[END_REF] The participant walks towards the robot and stops before it for 5 seconds; [START_REF] Hanheide | Active memory-based interaction strategies for learningenabling behaviors[END_REF] The robot asks, how do you feel? The robot records the emotion result and gait/thermal data into a database for retraining the emotion recognition model; [START_REF] Katagami | Interactive classifier system for real robot learning[END_REF] Repeat steps from 2 to 6 to complete eight or more experiments.

Experimental results

Before online testing for emotion recognition, the offline RF models were tested in order to get modified confusion matrix, which is used for fusion of our emotion recognition models. There were 80 testing conducted in this part. The modified confusion matrix shows the probability distribution relation of the four emotions as shown in Table 1 andTable 2, respectively. In the modified confusion matrix, each column sums up to 100% (if without rounding) instead of each row in the original confusion matrix. And, the primary diagonal elements in the confusion matrix represent accuracy of each emotion while modified confusion matrix's diagonal elements just indicate a conditional probability instead of accuracy of each emotion. In Table 2, the ratio with 91.67% is significantly high when the real class and the predicted class both are sad one. That ratio in the modified confusion matrix only represents the probability of "sad" when predicted class is "sad", instead of the accuracy of "sad" (only 55%) in the confusion matrix. For the integration of the model with gait features and the one with thermal facial features, we applied a decision-level method with the modified confusion matrix. Before IRL experiments, we conducted the online testing experiments with the decision-level model. The paper compared the recognition performance of the single models with gait features or thermal facial features and the multimodal model with both, which are with the accuracy of 54.6875% (gait model), 59.375% (thermal model), and 65.625% (fusion model), respectively. The results indicate a higher accuracy for the hybrid model than for any single models.

During IRL, when hybrid model recognition result does not match the real one, the data are saved for model retraining. The confusion matrices are updated with the testing results on the two individual classification models, which are retrained. After IRL, the online testing was conducted again with the updated hybrid model. At last, an online testing accuracy of 78.125% is obtained after IRL, which is an increase of more than 10% than the one before interactive learning (i.e., 65.625%), which demonstrates that the IRL method is useful for emotion recognition with gait and thermal facial data.

Conclusion and Discussion

In our paper, we developed a multimodal emotion recognition model with gait and thermal facial data, which is based on RF model and the modified confusion matrices of two individual models. Through comparison between the individual RF models and the decision-level hybrid model, we found that our integration method is useful to classify the emotion during human-robot interaction. In addition, we have conducted 192 experiments including three parts, namely online testing experiments before IRL, IRL experiments, and online testing after IRL. In these experiments, we compared the emotion recognition performance before and after IRL to find out that the interactive robot learning is useful, with an

Fig. 1 .

 1 Fig. 1. The trajectories of skeleton

Fig. 2 .

 2 Fig. 2. Examples of thermal images from the database

Fig. 3 .

 3 Fig. 3. Data: (a) Joint index and joint angle definition and (b) 3 facial ROIs

Fig. 4 .

 4 Fig. 4. The fusion framework of multimodal classifiers

Fig. 7 .Fig. 8 .

 78 Fig. 7. Overview of the IRL architecture

Table 1 .

 1 Modified confusion matrix of offline testing of gait model

	Gait model		Predicted class
	(Offline testing) Neutral Happy Angry Sad
		Neutral 75%	16%	0% 6.67%
	Real class	Happy Angry 10% 0%	52% 30% 6.67% 20% 65% 0%
		Sad	15%	12%	5% 86.67%

Table 2 .

 2 Modified confusion matrix of offline testing of thermal models

	Thermal model	Predicted class
	(Offline testing) Neutral Happy Angry Sad
		Neutral 52%	25% 5.26% 0%
	Real class	Happy 12% Angry 16% 20.83% 57.89% 0% 50% 21.05% 8.33%
		Sad	20% 4.17% 15.79% 91.67%

increase of more than 10% of the accuracy of multimodal emotion recognition with gait and thermal data. Our IRL architecture could potentially be used in robot long life learning in human-robot interaction scenarios.


Camille Quilgars

Sandrine S Bertrand
email: sandrine.bertrand@u-bordeaux.fr

ACTIVITY-DEPENDENT SYNAPTIC DYNAMICS IN MOTOR CIRCUITS OF THE SPINAL CORD

Keywords: spinal cord, activity-dependent synaptic plasticity, motor functions, postactivation depression, H-reflex, STDP

HAL is

A major feature of the central nervous system (CNS) is its ability to be reshaped throughout life. This CNS adaptability, or plasticity, is the basis of processes such as learning and memory, adaptation to morphological changes during development or ageing as well as functional recovery after injury. The word "plasticity" is a catch-all term covering all the variety of mechanisms endowing the CNS with adaptive properties ranging from the macroscopic level such as the remodeling of cortical maps [START_REF] Kole | Cellular diversity of the somatosensory cortical map plasticity[END_REF] to subcellular aspects such as synaptic receptor trafficking [START_REF] Penn | Hippocampal LTP and contextual learning require surface diffusion of AMPA receptors[END_REF]. Among this wide range of processes, a large body of research has focused on the capability of chemical synapses to amplify (potentiation) or decrease (depression) their connectivity strength for short or long periods of time depending on their previous activity: the so-called activity-dependent synaptic plasticity (ADSP).

Activity versus activity: a matter of definition. When considering ADSP in the ventral spinal cord networks that generate locomotor and postural commands, the term "activity" may lead to some confusion as it could refer to "movement" or "training" instead of the "rate of action potential firing" in presynaptic neurons. Several recent reviews have reported the numerous plastic changes (morphological changes, axonal sprouting, …etc) induced by motor activities as well as by motor task learning in the spinal motor networks under both physiological and pathophysiological conditions (see for examples [START_REF] Smith | A Review on Locomotor Training after Spinal Cord Injury: Reorganization of Spinal Neuronal Circuits and Recovery of Motor Function[END_REF][START_REF] Tahayori | Activity-dependent plasticity of spinal circuits in the developing and mature spinal cord[END_REF][START_REF] Norton | Acquisition, maintenance, and therapeutic use of a simple motor skill[END_REF]). However, although interacting functionally through overlapping elements, a distinction should be made between the overall impact of the neuronal activity generated during training or learning and the continuous and dynamic integration of firing rate that occurs in synaptic connections. For example, a synapse that expresses long-term depression (LTD) following a 1 Hz presynaptic spike train, will express more, less or the same degree of depression or another form of ADSP in response to the same train after motor skill learning or training depending on how the network to which the synapse belongs has been reconfigured. This second order of plasticity, the plasticity of plasticity is termed metaplasticity [START_REF] Abraham | Metaplasticity: tuning synapses and networks for plasticity[END_REF] and reflects the functional adaptation of the synaptic dynamics to the global network changes. In this review, we will present recent data on ADSP in the context of its first definition whereby the term "activity" refers to the action potential firing frequency of presynaptic neurons that in turn conditions the expression of long or short term depression (LTD, STD) or potentiation (LTP, STP) in synaptic terminals in spinal motor networks (see also [START_REF] Bertrand | Activity-dependent Synaptic Plasticity and Metaplasticity in Spinal Motor Networks[END_REF]). Surprisingly, while ADSP has been and is still mainly investigated using in vitro preparations in supraspinal areas, the vast majority of recent data collected on ADSP in spinal motor circuits has been obtained in vivo, with substantial amount of work performed in humans. As we will see below, this is mainly due to the fact that ADSP in the spinal cord is not studied per se but is used as a means to probe spinal network excitability and to promote functional recovery after injury.

Metaplasticity of the H-reflex post-activation depression. The H-reflex, the analogue of the mechanically-induced stretch reflex, corresponds to the reflex muscle contraction observed after the electrical stimulation of the corresponding sensory nerve (Fig. 1A). In contrast to the stretch reflex, the H reflex bypasses muscle spindle activation and therefore enables direct examination of the monosynaptic transmission between I A afferents and motoneurons (MN). H-reflex responses exhibit post-activation depression (PAD) when trains or paired-pulse stimulations of sensory afferents are applied at stimulation rates ranging from 0.1 to 10 Hz [START_REF] Oza | Modulation of H-Reflex Depression with Paired-Pulse Stimulation in Healthy Active Humans[END_REF][START_REF] Crone | Methodological implications of the post-activation depresssion of the soleus H-reflex in man[END_REF] (Fig. 1A). The H-reflex PAD is similar to the homosynaptic depression described in supraspinal structures and is characterized by a decreased probability of glutamatergic release in I A afferent synaptic terminals following their repetitive activation [START_REF] Hultborn | On the mechanism of the post-activation depression of the H-reflex in human subjects[END_REF]. Over the last decade, numerous investigations performed primarily in humans, have used the H-reflex PAD as a tool to evaluate spinal cord functions in both physiological and pathophysiological conditions [START_REF] Burke | Clinical uses of H reflexes of upper and lower limb muscles[END_REF]. In healthy subjects, PAD varies according to the activity of the sensory system, with an increase in depression occurring after motor training [START_REF] Meunier | Spinal usedependent plasticity of synaptic transmission in humans after a single cycling session[END_REF], but a PAD decreased after immobilization [START_REF] Lundbye-Jensen | Immobilization induces changes in presynaptic control of group Ia afferents in healthy humans[END_REF], upslope walking [START_REF] Sabatier | Slope walking causes short-term changes in soleus H-reflex excitability[END_REF] or during ageing [15 but see,16]. Interestingly, H-reflex PAD vanishes following the disuse of the monosynaptic loop after spinal cord injury (SCI) as patients progress from the acute to the chronic state [START_REF] Schindler-Ivens | Low frequency depression of H-reflexes in humans with acute and chronic spinal-cord injury[END_REF][START_REF] Grey | Post-activation depression of soleus stretch reflexes in healthy and spastic humans[END_REF] and is also strongly reduced in patients or in animal models after stroke [START_REF] Lee | Weakened rate-dependent depression of Hoffmann's reflex and increased motoneuron hyperactivity after motor cortical infarction in mice[END_REF][START_REF] Kawaishi | Postactivation depression of soleus H-reflex increase with recovery of lower extremities motor functions in patients with subacute stroke[END_REF]. These alterations in the I A -MN synaptic depression with a resultant increase in stretch reflex amplitude are now well acknowledged to contribute to the development of the spasticity expressed in patients after stroke or SCI. Indeed, a direct link has been established between the degree of spasticity and the alterations in H-reflex PAD expression [START_REF] Lamy | Impaired efficacy of spinal presynaptic mechanisms in spastic stroke patients[END_REF][START_REF] Ryu | The swimming test is effective for evaluating spasticity after contusive spinal cord injury[END_REF]. In healthy subjects, compressive loading of the hind limb decreases PAD of the soleus H-reflex [START_REF] Tseng | Limb Segment Load Inhibits the Recovery of Soleus H-Reflex After Segmental Vibration in Humans[END_REF][START_REF] Tseng | Limb segment load inhibits post activation depression of soleus H-reflex in humans[END_REF].

In contrast, compressive loading of limb segments fails to modulate PAD in SCI patients, revealing a decreased capability of spinal segment below the lesion to respond to short episodes of sensory stimulation [START_REF] Tseng | Limb compressive load does not inhibit post activation depression of soleus H-reflex in indiviudals with chronic spinal cord injury[END_REF]. However, an ever-growing body of evidence suggests that the reactivation of sensorimotor pathways for example by passive cycling movements, treadmill training, physical therapy or robotic-assisted gait training improve functional motor recovery and restore PAD expression in both humans and animals after SCI or stroke [START_REF] Kawaishi | Postactivation depression of soleus H-reflex increase with recovery of lower extremities motor functions in patients with subacute stroke[END_REF][START_REF] Trompetto | Postactivation depression changes after robotic-assisted gait training in hemiplegic stroke patients[END_REF][START_REF] Yen | Vibration training after chronic spinal cord injury: Evidence for persistent segmental plasticity[END_REF][START_REF] Bose | Altered Patterns of Reflex Excitability, Balance, and Locomotion Following Spinal Cord Injury and Locomotor Training[END_REF]. The changes in PAD rate observed after motor mobilization requires the integrity of the sensory afferent pathways [START_REF] Ollivier-Lanvin | Proprioceptive neuropathy affects normalization of the H-reflex by exercise after spinal cord injury[END_REF] and are accompanied by improvement in balance control [START_REF] Kawaishi | Postactivation depression of soleus H-reflex increase with recovery of lower extremities motor functions in patients with subacute stroke[END_REF]. Pharmacological compounds such as the GABA B receptor agonist, baclofen or the neurotoxic protein, botulinum toxin, two standard treatments for spasticity have also been shown to modify the amplitude of the H-reflex PAD after stroke [START_REF] Lee | Weakened rate-dependent depression of Hoffmann's reflex and increased motoneuron hyperactivity after motor cortical infarction in mice[END_REF][START_REF] Kerzoncuf | Plastic changes in spinal synaptic transmission following botulinum toxin A in patients with poststroke spasticity[END_REF]. In the same way, pharmacological or functional genetic manipulations of the potassium chloride cotransporter KCC2, whose downregulation parallels the development of spasticity after SCI, have been found to alter the amount of depression expressed by the H-reflex [START_REF] Boulenguez | Down-regulation of the potassium-chloride cotransporter KCC2 contributes to spasticity after spinal cord injury[END_REF].

Altogether, these data show that the solicitation of I A -MN synapses in normal and pathophysiological conditions changes the strength of the connection and restores part of the physiological dynamics of the reflex loop.

TBS and STDP-inspired-protocols. ADSP was for a long time studied using paired pulse stimulations and high or low frequency stimulus trains. In both animal models and humans, more physiological stimulations, the theta burst stimulations (TBS, 4-7 HZ) based on the theta burst rhythm recorded during exploratory behaviors in rodents are now frequently used to induce ADSP in different brain areas [see for examples : 32,33]. In the spinal cord, TBS stimulations (classically consisting of high frequency bursts of 3 or 4 stimuli delivered at 200-300 ms intervals) induce dynamics changes in H-reflex responses and motor evoked potentials [START_REF] Jimenez | Effects of patterned peripheral nerve stimulation on soleus spinal motor neuron excitability[END_REF][START_REF] Yeh | Intensity sensitive modulation effect of theta burst form of median nerve stimulation on the monosynaptic spinal reflex[END_REF][START_REF] Jung | Quadri-Pulse Theta Burst Stimulation using Ultra-High Frequency Bursts -A New Protocol to Induce Changes in Cortico-Spinal Excitability in Human Motor Cortex[END_REF] and reduce spasticity in humans [START_REF] Nardone | Effects of intermittent theta burst stimulation on spasticity after spinal cord injury[END_REF].

Another type of electrical stimulation paradigm is now commonly used to enhance plasticity in motor spinal systems: spike-timing dependent plasticity (STDP) protocols. In supraspinal structures, the expression of STDP has been shown to rely on the precise temporal interval between pre-and postsynaptic neuronal activities (for reviews and details on STDP protocols see [START_REF] Suvrathan | Beyond STDP -towards diverse and functionally relevant plasticity rules[END_REF][START_REF] Frémaux | Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules[END_REF][START_REF] Shulz | Spike-timing-dependent plasticity in the intact brain: counteracting spurious spike coincidences[END_REF][START_REF] Froemke | Temporal modulation of spike-timing-dependent plasticity[END_REF]). Indeed, when the presynaptic neuron is repetitively activated (by hundreds of stimulations) before its postsynaptic partner, a LTP is evoked while LTD is expressed when the postsynaptic neuron is repetitively activated before its presynaptic partner (Fig. 1B). We have so far no evidence for STDP expression occurring in in vitro spinal motor circuits. However, several recent studies have emphasized the fact that STDP-like protocols succeeded in changing synaptic efficacy in spinal motor networks in vivo.

Paired associative stimulations consisting of repeated pairs of single electrical stimulation of peripheral nerve followed by a transcranial magnetic stimulation (TMS) pulse delivered to the motor cortex (Fig. 1B) have been shown to increase motor evoked potentials in the human spinal cord depending on the delay between the two paired stimuli [START_REF] Shulga | The use of Fresponse in defining interstimulus intervals appropriate for LTP-like plasticity induction in lower limb spinal paired associative stimulation[END_REF][START_REF] Bunday | Motor Recovery after Spinal Cord Injury Enhanced by Strengthening Corticospinal Synaptic Transmission[END_REF][START_REF] Urbin | Spike-timing-dependent plasticity in lower-limb motoneurons after human spinal cord injury[END_REF][START_REF] Fitzpatrick | More conditioning stimuli enhance synaptic plasticity in the human spinal cord[END_REF][START_REF] Mrachacz-Kersting | Changes in Excitability of the Cortical Projections to the Human Tibialis Anterior After Paired Associative Stimulation[END_REF][START_REF] Kraus | Recruitment of Additional Corticospinal Pathways in the Human Brain with State-Dependent Paired Associative Stimulation[END_REF][START_REF] Ozdemir | Afferent input and sensory function after human spinal cord injury[END_REF].

Nishimura and collaborators, for example, tested the synaptic efficacy of corticomotoneuronal (CM) connections after pairing CM cell spiking activity to intraspinal stimulation (ISS) in behaving monkeys [START_REF] Nishimura | Spike-timing-dependent plasticity in primate corticospinal connections induced during free behavior[END_REF]. They reported increases in EMG activity amplitude when the ISS was applied 12-25 ms after the CM cell action potential.

Moreover, STDP-inspired protocols have been shown to improve motor recovery after SCI or stroke in rats [START_REF] Mcpherson | Targeted, activity-dependent spinal stimulation produces long-lasting motor recovery in chronic cervical spinal cord injury[END_REF] and humans [START_REF] Bunday | Motor Recovery after Spinal Cord Injury Enhanced by Strengthening Corticospinal Synaptic Transmission[END_REF][START_REF] Urbin | Spike-timing-dependent plasticity in lower-limb motoneurons after human spinal cord injury[END_REF][START_REF] Tolmacheva | Long-Term Paired Associative Stimulation Enhances Motor Output of the Tetraplegic Hand[END_REF][START_REF] Palmer | Paired associative stimulation modulates corticomotor excitability in chronic stroke: A preliminary investigation[END_REF]. In their study, McPherson and collaborators attempted to strengthen synaptic transmission in spared motor circuits by associating an intraspinal microstimulation applied below the lesion site around 10 ms after the detection of an increased in forelimb EMG activity. Animals submitted to this activitydependent spinal stimulation (TADSS) paradigm exhibited an enhanced functional recovery of a reaching task compared to rats submitted to open-loop intraspinal microstimulation (stimulation unassociated with EMG activity). This functional recovery was maintained over 3 weeks after the cessation of the simulation, indicating long lasting-induced changes by the TADSS protocol in motor spinal circuits [START_REF] Mcpherson | Targeted, activity-dependent spinal stimulation produces long-lasting motor recovery in chronic cervical spinal cord injury[END_REF]. These different studies reveal that the rules of temporal relationship between pre-and postsynaptic neuron activities described in various brain structures to induce STDP seem valid and applicable to the motor spinal cord circuits.

Cellular basis of spinal motor ADSP.

In a previous review, we discussed the important role that ADSP should play in normal information processing in spinal motor circuits as well as in pathophysiological conditions [START_REF] Bertrand | Activity-dependent Synaptic Plasticity and Metaplasticity in Spinal Motor Networks[END_REF]. The aforementioned recent advances in the field further underline the salient role of ADSP in motor functional recovery.

Paradoxically, little effort has been made in exploring the cellular basis of ADSP in spinal motor networks. As previously mentioned, to the best of our knowledge, we still lack cellular evidence for STDP expression in the motor spinal cord. Similarly, we have no idea of the impact at the synaptic level of a TBS stimulation applied in spinal motor networks. It could be argued that both TBS and STDP protocols established on the basis of studies performed on the hippocampus or cortex seems to work perfectly well in the spinal cord in vivo and contribute to alleviate motor deficits after injury. However, the uniqueness of spinal neuronal circuits should be taken into account. For example, spinal MNs do not constitute a homogenous population, and we have previously shown that the same tetanic stimulation induces different types of ADSP depending on the flexor or extensor phenotype of the MNs [START_REF] Lenschow | Distinct and developmentally regulated activity-dependent plasticity at descending glutamatergic synapses on flexor and extensor motoneurons[END_REF]. The function and neurotransmitter release properties of MN presynaptic partners together with the associated network excitability have been shown to determine the ADSP expressed in spinal motor synapses [START_REF] Jiang | The transformation of synaptic to system plasticity in motor output from the sacral cord of the adult mouse[END_REF][START_REF] Parker | Synaptic Variability Introduces State-Dependent Modulation of Excitatory Spinal Cord Synapses[END_REF][START_REF] Jia | Short-Term Synaptic Plasticity at Interneuronal Synapses Could Sculpt Rhythmic Motor Patterns[END_REF]. The various neuromodulatory inputs that shape the spinally-generated locomotor command could also dramatically condition the expression of synaptic plasticity in spinal circuits [START_REF] Parker | Synaptic Variability Introduces State-Dependent Modulation of Excitatory Spinal Cord Synapses[END_REF]. Finally, it seems highly likely that after impairments of descending pathways and the massive remodeling of sub-lesion networks following stroke or SCI, the plastic capabilities of spinal synapses differs completely from those observed in pre-traumatic conditions. We currently have no data on this latter point.

Conclusions. This review highlights the interest of the field of spinal disorders in stimulation protocols that induce ADSP in order to enhance spinal plasticity as well as behavioral recovery after trauma. In this sense, TBS and STDP-like protocols appear to be highly valuable tools towards obtaining therapeutic benefits. In this context, it is noteworthy, that the data gathered on the PAD of H-reflex show that functional motor recovery is always associated with the restoration of a capability for synaptic plasticity (or vice versa) [START_REF] Kawaishi | Postactivation depression of soleus H-reflex increase with recovery of lower extremities motor functions in patients with subacute stroke[END_REF][START_REF] Trompetto | Postactivation depression changes after robotic-assisted gait training in hemiplegic stroke patients[END_REF][START_REF] Yen | Vibration training after chronic spinal cord injury: Evidence for persistent segmental plasticity[END_REF][START_REF] Bose | Altered Patterns of Reflex Excitability, Balance, and Locomotion Following Spinal Cord Injury and Locomotor Training[END_REF]. Such findings lead to the chicken and egg dilemma. Is a functional recovery due to the restoration of synaptic dynamics in spinal circuits or is it the contrary? In any case, the recuperation and/or re-emergence of synaptic dynamics (the same or those adapted to the new functioning of the network after insults) seems to be mandatory for reaching a certain degree of functional motor recovery.

It is now well accepted that combinatorial therapeutic approaches with synergistic effects are required to improve functional motor restoration (see for examples: [START_REF] Wenger | Spatiotemporal neuromodulation therapies engaging muscle synergies improve motor control after spinal cord injury[END_REF]. To accurately induce synaptic plasticity in motor circuits of the spinal cord, a precise knowledge of the cellular aspects of ADSP is now required, in order (1) to refine the stimulation protocols or motor training strategies used to promote synaptic dynamics in spinal motor circuits, and (2) to identify appropriated pharmacological agents that will cooperatively favor the expression of adapted activity-dependent synaptic dynamics in spinal motor networks after insults or diseases. We therefore believe that establishing the most appropriate protocols to induce/restore the synaptic dynamics is a major goal towards improving motor recovery after injury. After the pioneering studies performed on ADSP in the spinal motor cord back in the early nineties (for review see [START_REF] Bertrand | Activity-dependent Synaptic Plasticity and Metaplasticity in Spinal Motor Networks[END_REF]), data obtained recently should rekindle our interest in the cellular basis of synaptic dynamics in motor systems. It is time to re-open the game and to push forward the cellular studies of ADSP in motor circuits of the spinal cord: let's kick the ball off the 35-yard line! (presynaptic partners, green traces). This pairing is repeated several hundred times (usually 200-300 times). If we hypothesize that the STDP rules are similar in supraspinal neurons and in the spinal cord, the STDP expressed in motoneurons should be function of the time difference (t= t post -t pre ) between the pre-and postsynaptic activation: if the excitatory presynaptic potential precedes the postsynaptic spike, the synapse is potentiated (purple curve), on the contrary if the presynaptic potential follows the postsynaptic spike, the synapse is depressed (blue curve). Relative changes: relative changes in the amplitude of postsynaptic potentials recorded after plasticity induction. Figure adapted from [START_REF] Lenschow | Distinct and developmentally regulated activity-dependent plasticity at descending glutamatergic synapses on flexor and extensor motoneurons[END_REF] and [START_REF] Lenschow | Distinct and developmentally regulated activity-dependent plasticity at descending glutamatergic synapses on flexor and extensor motoneurons[END_REF].

Figure 1 .

 1 Figure 1. Post-activation of the H-reflex and STDP-like protocols. A. Schematic representation of the H-reflex induction and post-activation depression. The electrical stimulation of a motor nerve triggers a direct activation of motoneuron axons that produces the first response observed in the electromyogram (EMG) know as the muscle response (the M-wave). In a second time, the synaptic activation of motoneurons by the sensory afferents causes the second potential of the EMG recording, the H-reflex. When a train of 10 stimulations (for example) is applied at 1 Hz on the motor nerve, the normalized amplitude of the H-reflex (norm H-reflex amplitude) exhibits a progressive decrease: a post-activation depression (blue trace and dots).Figure adapted from [53] and [53] B. Example of spike

  Figure adapted from [53] and [53] B. Example of spike timing dependent plasticity (STDP)-like protocols used to induce synaptic plasticity in the spinal cord. The antidromic activation (AD) of motoneurons (postsynaptic partners, red traces) is paired with transmagnetic stimulations (TMS) of corticospinal motor neurons

Acknowledgment. SSB is supported by the Association pour la Recherche sur la Sclérose Latérale Amyotrophique (ARSLA). The authors wish to thank Dr. Jean-René Cazalets for discussion and critical reading of the manuscript and Dr. John Simmers for correcting the English