Tiffany Raynaud

Manuel Blouin

Marion Devers-Lamrani

Dominique Garmyn

Aymé Spor
email: ayme.spor@inrae.fr

Marion Devers

Assessing the importance of interspecific interactions in the evolution of microbial communities

Keywords: abiotic environment, experimental evolution, interspecific interactions, productivity, synthetic bacterial communities

. Thus, it can be relevant to consider the phenotype of a community as a whole. Moreover, it is increasingly recognized that community phenotype

can respond to evolution [START_REF] Whitham | A framework for community and ecosystem genetics: from genes to ecosystems[END_REF]. From a theoretical standpoint, it is accepted that microbial community evolution can occur through genetic changes in the community members (e.g., through mutations, horizontal gene transfer, and gene loss; [START_REF] Barraclough | How do species interactions affect evolutionary dynamics across whole communities? Annual Review of Ecology[END_REF][START_REF] Gorter | Understanding the evolution of interspecies interactions in microbial communities[END_REF]. Furthermore, as well as the interspecific interactions are involved in the establishment of community phenotype, there is also evidence that they can contribute to the evolution of this phenotype. This has been investigated in the field of artificial selection at the community level through modeling [START_REF] Williams | Artificial selection of simulated microbial ecosystems[END_REF] and experimental approaches on communities made of two beetle species [START_REF] Goodnight | Experimental studies of community evolution II: The ecological basis of the response to community selection[END_REF]. Both approaches highlighted that independent genetic changes in the species within a community are not always sufficient to explain the observed response of the community to evolution by selection. It suggested that the interspecific interactions, whether they be under the genetic or epigenetic influence, can be involved in community evolution.

In parallel, other studies provided detailed assessments of the evolution of interspecific interactions in synthetic microbial communities. It has been shown that, in a two-species bacterial community, a mutation in one of the two strains induced a shift from a commensal interaction to a more exploitative one [START_REF] Hansen | Evolution of species interactions in a biofilm community[END_REF]. This shift in the interaction occurred after 5 days of experimental evolution and gave rise to enhanced productivity at the community level. Thus, the interspecific interactions can evolve through the evolution of one of the community members (e.g., a genetic change in one of the species that induces a change in the interaction with the other species). Another way for the interactions to evolve is through the evolution of multiple species in a community. As an example, an experimental study showed that, in a four-species bacterial community, changes in resource use in the four species when experimentally evolved together reduced the occurrence of negative interspecific interactions [START_REF] Lawrence | Species interactions alter evolutionary responses to a novel environment[END_REF]. It was associated with higher productivity at the community level than that of a community that was built from the four species evolved in isolation. Finally, interspecific interactions can also evolve through the evolution of several species in a community as a result of coevolution, i.e., reciprocal adaptive changes in two populations or species [START_REF] Brockhurst | Experimental coevolution of species interactions[END_REF][START_REF] Janzen | When is it coevolution?[END_REF]).

An additional level of complexity emerges from a possible influence of the abiotic environment on the evolution of interspecific interactions in a community. For example, in bacterial communities, the evolution of interspecific interactions can be promoted by a structured environment, allowing the formation of biofilm, as compared to a homogeneous environment [START_REF] Hansen | Evolution of species interactions in a biofilm community[END_REF]. It has also been shown that whether or not interactions are involved in a bacterial community evolutionary response can depend on the resources or on the pH of the culture medium [START_REF] Fiegna | Evolution of species interactions determines microbial community productivity in new environments[END_REF].

Interestingly, in the study of [START_REF] Fiegna | Evolution of species interactions determines microbial community productivity in new environments[END_REF], community productivity increased as compared to the ancestral community only when the interactions were involved in the community response to evolution. To go further, the influence of the abiotic environment on the evolution of interspecific interactions can occur through niche construction [START_REF] Matthews | Under niche construction: an operational bridge between ecology, evolution, and ecosystem science[END_REF]. This occurs when the abiotic environment is modified by a species, which in turn influences the evolution of other species in the community. For example, it has been shown that the pairwise interaction between a bacterial population and a yeast shifted from commensalism to amensalism and then to antagonism when the environment started to be changed by the yeast. Indeed, the excretion of a bacterial growth inhibitor promoted the evolution of resistance in the bacterial population, which lowered the fitness of the yeast [START_REF] Andrade-Domínguez | Eco-evolutionary feedbacks drive species interactions[END_REF]. Thus, eco-evolutionary feedbacks are also involved in the evolution of interspecific interactions and of the communities.

There are many studies that illustrate well the evolution of interspecific interactions, the question is not whether the interactions can evolve but how important is the evolution of the interactions in the communities [START_REF] Gorter | Understanding the evolution of interspecies interactions in microbial communities[END_REF]. In this study, we aimed at providing an insight into how frequently the evolution of interspecific interactions was involved in the evolution of community phenotype.

Following a five-month experimental evolution of synthetic bacterial communities [START_REF] Raynaud | Community diversity determines the evolution of synthetic bacterial communities under artificial selection[END_REF], we re-isolated eight pairs of strains that evolved within different communities. We assessed the bacterial strain and community (i.e., co-cultures) phenotypes by measuring the optical density as a proxy of productivity. We compared the phenotypes after the experimental evolution to the ancestral phenotypes (i.e., before experimental evolution) and to the phenotypes obtained by assembling the same strains evolved in isolation to discuss the evolution of interactions. We hypothesized that: (i) the interspecific interactions played a role in the evolution of community phenotype (i.e., the phenotype of the evolved community would be different from this of a community reconstructed from strains that evolved in isolation); (ii) this role occurred through an evolution of the interactions themselves (i.e., the evolutionary response of the community would not be predictable from the separate evolutionary responses of the strains composing the community); (iii) the evolution of community phenotype depended on the abiotic environment. To verify this third hypothesis, we assessed the phenotype of the strains and communities after experimental evolution in a second abiotic environment in order to discuss the adaptation to the abiotic conditions of the experimental evolution.

This study evidenced that the interspecific interactions were often involved in the evolution of the bacterial communities and that their evolution contributed to changes in community productivity.

| MATERIAL S AND ME THODS

| Origin of the studied communities

The eight two-strain communities studied in this experiment stemmed from an experimental evolution procedure in which bacterial strains (= monocultures) and communities (= co-cultures) were grown for 5 months with a serial transfer every 3.5 days. This experiment involved 18 laboratory strains that were used to create communities differing in their initial richness levels (see [START_REF] Raynaud | Community diversity determines the evolution of synthetic bacterial communities under artificial selection[END_REF] experimental evolution, the strains and communities were grown in sterile 2 ml deep-well plates (Porvair Sciences, Wrexham, UK) filled with 1 ml of a mix of 1:5 lysogeny broth (LB) and 1:5 tryptic soy broth (TSB), hereafter called EE medium for Experimental Evolution, and placed at 28°C without shaking. An optical density (OD) measurement (600 nm) was performed at each serial transfer (i.e., measurement of the light scattered by the bacterial cells in suspension as a proxy of productivity) and the transfer occurred following two treatments: artificial selection (where the transferred culture was the one with the highest OD among 10) and no artificial selection (where the culture was transferred whatever its OD). The strains and communities were stored at -80°C in 30% glycerol before the experimental evolution (ancestors) and after the experimental evolution (evolved strains and communities). In the first step of isolation, all of the 2-species communities (six), both under artificial selection and no artificial selection (see [START_REF] Raynaud | Community diversity determines the evolution of synthetic bacterial communities under artificial selection[END_REF], were considered for being analyzed in the present study. In the second step, all of the 4-species communities (six) either under artificial selection or under no artificial selection were also considered to complete the experimental design. The pairs of strains that were finally included in the experiment are presented in Table 1 and responded to the following criteria: successful isolation of the strains from the evolved community and availability of the corresponding strains evolved in isolation. The resulting experimental design was not suitable to test neither for an effect of the initial richness level of the native community (two or four strains) nor for an effect of the selection regime applied to the native community (artificial selection or no artificial selection).

| Isolation of the strains from the evolved communities

To isolate the strains that evolved in communities, we revived the evolved communities from glycerol stocks by growing them on agar plates (EE medium) by streaking. After 72 h of growth at 28°C, we picked the colonies of differing morphologies and placed them on new separated agar plates by streaking. After a new cycle of growth, one colony per plate was picked and placed in 200 μl of 0.9% NaCl, and 100 μl of this suspension was plated on an agar plate with glass beads. At this step, 2 μl of suspension was used to perform a PCR for the identification of the strains (see below). After a new cycle of growth, several colonies were picked on each plate and put in 20 ml of EE medium in a flask (48 h, 120 rpm). 800 μl of suspension were then stored at -80°C in 800 μl of 60% glycerol. As these isolation steps required four growth cycles during which evolution could act, we also performed these four growth cycles in the same conditions for the corresponding ancestral and evolved in isolation strains.

| Identification of the strains

A PCR targeting 16 S rRNA gene with the primers 27F/1492R [START_REF] Miller | Short-Read Assembly of Full-Length 16 S Amplicons Reveals Bacterial Diversity in Subsurface Sediments[END_REF] was performed for each strain isolated from the evolved communities. Digestion of the PCR products was then performed with the AluI restriction enzyme and followed by electrophoresis for the identification of the strains at the genus level. For the Note: Some of the pairs of strains evolved in the absence of other strains (i.e., in two-strain native communities), whereas other pairs evolved in the presence of other strains (i.e., in four-strain native communities), this is specified in the column "Initial richness level of the native community". Some of the native communities evolved under artificial selection whereas others evolved under "no artificial selection" (i.e., natural selection only), this is specified in the column "Selection regime applied to the native community". In each community, strain 1 is the most productive of the two strains (highest OD) and strain 2 is the least productive one (lowest OD).

TA B L E 1 Two-strain communities studied in the experiment

genera that were represented by several strains in our experiment (i.e., Pseudomonas and Escherichia), we performed further analyses for identification at the strain level. We used data from gyrB sequencing at the community level [START_REF] Raynaud | Community diversity determines the evolution of synthetic bacterial communities under artificial selection[END_REF] to determine which Pseudomonas strain was present in the community and coupled it with analyses at the strain level for formal identification.

The different strains were identified based on the presence or not of atzD gene (assessed by PCR) and the resistance or not to nalidixic acid and amoxicillin (assessed by growing the strains on agar plates containing a mix of the two antibiotics at a final concentration of 100 μg ml -1 ). Escherichia coli K12 and Escherichia coli WA803

were identified based on their ability to do or not lactose fermentation (which was assessed by growing the strains on agar plates on Drigalski agar medium).

| Evolutionary history treatments

Each of the two strains of a community (eight in total, hereafter identified as communities A to H; Table 1) was grown in its ancestral version (i.e., before experimental evolution), in its "evolved in isolation" version (i.e., after experimental evolution as an isolated strain), and in its "evolved in community" version (i.e., after experimental evolution within a community). It resulted in six treatments (two strains and three evolutionary histories per strain; Figure 1a).

Within each community (i.e., co-culture of two strains), the most productive (highest OD 600nm at 3.5 days) of the two ancestral strains were referred to as "strain 1" and the least productive was referred to as "strain 2". In addition, each community was grown in its ancestral version (i.e., co-culture of the two ancestral strains), in its "evolved in isolation" version (i.e., co-culture of the two strains that evolved in isolation), and in its "evolved in community" version (i.e., co-culture of the two strains that evolved together within a community). Two treatments mixing ancestral strains and strains evolved in community were also included: mixed community 1 (i.e., co-culture of strain 1 evolved in community and ancestral strain 2) and mixed community 2 (i.e., co-culture of strain 2 evolved in community and ancestral strain 1). It resulted in five

treatments at the community level (Figure 1b) plus the six treatments at the strain level (Figure 1a).

| Community construction, growth conditions and phenotype assessment

Before the start of the experiment, each strain was revived from the and transferring it into a new plate for OD measurement at 600 nm (Infinite M200 PRO). These growth conditions were identical to the growth conditions of the experimental evolution, hereafter we refer to these conditions as "environment 1". We wanted to test whether the evolution of the community phenotype depended on the abiotic environment. We therefore chose a second environment, hereafter referred to as "environment 2". This consisted of growth in honeycomb plates at 28°C, 15 s of shaking 5 s before each OD measurement (600 nm, 400 μl of suspension per well, Bioscreen, Oy Growth F I G U R E 1 Experimental design. (a) Each bacterial strain was previously experimentally evolved in isolation and as a member of a community [START_REF] Raynaud | Community diversity determines the evolution of synthetic bacterial communities under artificial selection[END_REF]. At the end of this experimental evolution, the strains were isolated from the community in which they evolved. (b) From the strains, different communities were built: ancestor (co-culture of two ancestral strains), evolved in community (co-culture of two strains that evolved together), evolved in isolation (co-culture of two strains that evolved in isolation), mixed 1 (co-culture of one ancestral strain and one strain evolved in community), and mixed 2 (co-culture of one ancestral strain and one strain evolved in community conversely to mixed 1).

Curves Ab Ltd, Helsinki, Finland), one measurement every 30 min for 3.5 days.

| Statistical analyses

The OD after 3.5 days of growth was analyzed in two steps with two linear mixed models. The following model was used to analyze the effect of the evolution on strain and community phenotypes:

Y ijkl is the OD of the biological entity i (three levels: strain 1, strain 2, community), of identity l (24 levels: strain or community identity), of evolutionary history j (three levels: ancestor, evolved in isolation, evolved in community), and in environment k (two levels:

environment 1, environment 2). μ is the intercept, α i is the effect of the biological entity, β j is the effect of the evolutionary history, γ k is the effect of the environment. The interaction effects between (i) the biological entity and the evolutionary history (αβ) ij ; (ii) the biological entity and the environment (αγ) ik ; (iii) the evolutionary history and the environment (βγ) jk ; (iv) the biological entity, the evolutionary history and the environment (αβγ) ijk were also included in the model.

I l is the random effect of the strain or community identity, E ijkl is the residual error.

A second linear mixed model was built to analyze the effect of the evolutionary history of the community members on the community phenotype:

Y jkl is the OD of the community of identity l (8 levels: A to H), of evolutionary history j (five levels: ancestor, evolved in isolation, evolved in community, mixed 1, mixed 2), in environment k (two levels: environment 1, environment 2). μ is the intercept, β j is the effect of the evolutionary history, γ k is the effect of the environment, (βγ) jk is the effect of the interaction between the evolutionary history and the environment. I l is the random effect of the community identity, E jkl is the residual error.

To go into the details of the responses for each community, the OD after 3.5 days was then analyzed with a linear model that included the identity of the individual as a fixed-effect factor, as well as the evolutionary history and the interaction between the identity and the evolutionary history. One model was built for the strains and one for the communities in both environments. Then, the predictability of the community evolutionary response was analyzed by comparing the response of the community (i.e., change in OD during experimental evolution) to (i) the response of strain 1 evolved in community, (ii) the response of strain 2 evolved in community, (iii) the sum of the responses of strains 1 and 2 (which corresponds to the expected response under the hypothesis of an additivity of the individual responses, i.e., the absence of evolution of interspecific interactions). The mean responses and the corresponding 95% confidence intervals were obtained by bootstrapping (1000 iterations of the calculation of the response from randomly sampled values with replacement).

All the analyses were performed with R software version 3.6.3 with lmerTest package for linear mixed models [START_REF] Kuznetsova | lmerTest package: tests in linear mixed effects models[END_REF], car package for type II analyses of variance [START_REF] Fox | An {R} companion to applied regression[END_REF], and emmeans package for pairwise comparisons [START_REF] Lenth | emmeans: Estimated Marginal Means, aka Least-Squares Means[END_REF].

| RE SULTS

| The strains' responses are driven by their initial productivity in monoculture

The effect of the evolutionary history on optical density (OD)

depended on the biological entity, i.e., whether the considered phenotype was this of the community or of the community members, and it also depended on the abiotic environment (biological entity*history*environment: 𝜒 2 = 48; p df = 4 = 1.0 × 10 -9 ; Table 2).

Strain 1 and strain 2, the initially most and least productive strain, R 2 = 0.85

Y ijkl = + i + j + k + ( ) ij + ( ) ik + ( ) jk + ( ) ijk + I l + E ijkl Y jkl = + j + k + ( ) jk + I l + E jkl Df Chi-

Note:

The effect of the biological entity (strain 1, strain 2, community), the history (ancestors, evolved in community, evolved in isolation), the environment (1, 2), and their interactions on OD were estimated with a linear mixed model including the identity of the strain or community as a random effect factor. The conditional R 2 is presented (i.e., variance explained by both fixed and random effect factors; the marginal R 2 -fixed-effect factors only-was 0.63).

TA B L E 2 Analysis of variance (ANOVA) of the optical density (OD) of the communities and community members.

respectively, responded differently to the evolution in environment 1. The OD of strain 1 when evolved in community tended to be higher than that of strain 1 as an ancestor and was higher than strain 1 evolved in isolation (respectively, 0.68 ± 0.18, 0.62 ± 0.12, 0.55 ± 0.22; Figure 2a). On the contrary, strain 2 showed a lower OD when evolved in community as compared to evolved in isolation (0.30 ± 0.12 and 0.37 ± 0.18, respectively). The OD of the ancestral strain 2 was 0.34 ± 0.15, which was not significantly different from the two other treatments.

| Community response is driven by the most productive strain in monoculture

In environment 1, the OD of the communities composed of strains that evolved together was not significantly different from the OD of the ancestral communities (respectively, 0.65 ± 0.18 and 0.63 ± 0.13; Figure 2a). But, it was higher than the OD of the communities in which the members evolved in isolation (0.47 ± 0.15) suggesting that the evolution in community (i.e., co-culture) did not produce the same outcome than evolution in isolation. However, the communities composed of strains that evolved together produced the same phenotype as mixed communities (one ancestral and one evolved strain, Figure 2b).

The OD of the community was not different from the OD of strain 1 whatever the evolutionary history (respectively, 0.61 ± 0.16 and 0.62 ± 0.18 on average; Figure 2a). Also, the response of the community to the evolutionary history was similar to this of strain 1 (i.e., trend to increase in OD with evolution in community as compared to the ancestor and trend to decrease in OD with evolution in isolation; Figure 2a, environment 1). Thus, community phenotype seemed to be driven by strain 1.

| Community response involves an evolution of the interactions in half of the cases

For all of the studied communities, there was a significant difference in OD between the evolved in community and evolved in isolation treatments (Figure 3), which highlighted the importance F I G U R E 2 Optical density depending on the evolutionary history and the environment. (a) Ancestral and evolved strains and communities. (b) Communities depending on the evolutionary history of their members. Environment 1: identical growth conditions to the experimental evolution; Environment 2: different growth conditions from the experimental evolution. Strain 1 is the most productive of the two strains in a given community (highest OD) and strain 2 is the least productive of the two strains (lowest OD).

Community refers to the co-culture of strain 1 and strain 2. In mixed community 1, the strain 1 evolved in community was grown with the ancestral strain 2. In mixed community 2, the ancestral strain 1 was grown with the strain 2 evolved in community. Different letters represent significant differences in OD within a given environment (α = 0.05). Mean values are given ± SD. Sample sizes are given at the bottom of the graphs.

of the interactions in the evolution of community phenotype. This difference was in favor of the evolved in community treatment in seven of the eight communities (higher OD than evolved in isolation, Figure 3). One evolved community showed no difference in OD as compared to the ancestral community (community G; Figure 3).

Three communities showed differences in OD with the communities of all other evolutionary histories (communities A, C, and F). It indicated that, in these cases, the only way to obtain the evolved community phenotype was through the presence of the two strains in their evolved community version. The four remaining communities (B, D, E, and H) showed no difference in OD as compared to at least the mixed community 1 (Figure 3) highlighting the role of strain 1 in the expression of the evolved community phenotype in these cases.

The evolutionary response of four of the communities was predictable neither from the responses of the community members nor from the expected response under the hypothesis of additivity of the individual responses, i.e., an absence of evolution of the interspecific interactions (communities A, C, F, and H; Figure 4). It suggested that the evolutionary response involved an evolution of the interactions. In communities D and E, the community response was predictable from the response of strain 1, and in community B, it was predictable from the sum of the responses of the two strains (Figure 4). Thus, we did not evidence an evolution of the interspecific interactions in these communities.

| The abiotic environment influences the evolutionary responses

In environment 2, where the conditions differed from these of the experimental evolution, strain 2 showed a similar response to the evolutionary history than in environment 1 (Figure 2a). On the F I G U R E 3 Effect of an evolution in community on optical density in environment 1 depending on the community. The OD of a community composed of strains that evolved together (in columns) is compared with the OD of a community including ancestral strains, strains evolved in isolation, or one ancestral strain and one strain evolved in community (mixed 1 and mixed 2) (in rows). The OD of strains 1 and 2 evolved in community (in columns) is compared with the OD of the corresponding strain as an ancestor or evolved in isolation (in rows). Blue: significantly higher. Red: significantly lower. Light gray: no significant difference (α = 0.05). Black: not applicable.

contrary, the responses of strain 1 and of the community changed:

the highest OD was observed for the ancestors followed by evolved in community and by evolved in isolation treatments. The expression of the "evolved phenotype" thus depended on the abiotic environment. As in environment 1, community phenotype and community response to the evolutionary history were similar to strain 1 (Figure 2a). The OD of the mixed community 1 was similar to this of the community in which the strains evolved together (respectively, 0.97 ± 0.20 and 0.97 ± 0.13) whereas mixed community 2 showed a higher OD that did not differ from that of the ancestral community (Figure 2b). This clearly highlighted the influence of strain 1 on community phenotype.

Whether the detected response to evolution (i.e., positive or negative change in OD) in environment 1 was also observed in environment 2 depended on the considered strain or community (Figure 5a).

The phenotypic change in response to evolution in the evolved community (i.e., change in OD as compared to the ancestral community or to the community with evolved in isolation members) was maintained in environment 2 for three communities over eight (A, C, and F; Figure 5b). When a strain that evolved in community showed a significant increase in OD as compared to the ancestor, this pattern was always lost when the environment changed (Figure 5c,d). On the contrary, when a strain that evolved in community showed a significant decrease in OD as compared to the ancestor, this pattern was maintained in environment 2 in three cases over four. The changes in OD in a strain that evolved in community as compared to the corresponding strain that evolved in isolation were maintained in environment 2 in nine cases over 13 (Figure 5c,d).

| DISCUSS ION

We showed that the evolution of the strains in a community was influenced by interspecific interactions. Indeed, evolution in isolation did not produce the same phenotype as evolution in community (Figure 2a). These results are in accordance with an increasing body of literature that highlights the effect of the biotic context, i.e., of the evolution within a community, on the evolutionary response, and on the fitness of the community members [START_REF] Fiegna | Evolution of species interactions determines microbial community productivity in new environments[END_REF][START_REF] Hansen | Evolution of species interactions in a biofilm community[END_REF][START_REF] Jousset | High functional diversity stimulates diversification in experimental microbial communities[END_REF][START_REF] Scheuerl | Bacterial adaptation is constrained in complex communities[END_REF]. The characterization of the community members on the basis of their productivity before experimental evolution allowed a good explanation of their responses to evolution despite the fact that we grouped species from different genera under the entities "strain 1"

and "strain 2" (model R 2 = 0.85; Table 2). To go further, we found that the most productive strain had a dominant role in explaining community phenotype and community response to evolution (Figure 2a).

It was probably highly linked to the fact that the studied community phenotype was productivity but, it also suggested that the most productive strain in monoculture was also the dominant strain in the community as previously observed in two-species communities [START_REF] Meroz | Community composition of microbial microcosms follows simple assembly rules at evolutionary timescales[END_REF].

F I G U R E 4 Predictability of the evolutionary response of the community.

The observed evolutionary responses of the community, strain 1, and strain 2 in environment 1 were expressed as the difference in optical density between the treatment evolved in community and the corresponding ancestor (i.e., ancestral community or ancestral strain 1 or ancestral strain 2 in environment 1). "Strain 1 + Strain 2" refers to the expected response to evolution under the hypothesis of additivity of the individual responses, it was obtained by summing the observed responses of strain 1 and strain 2. Bars represent 95% CI. On each graph, the black dashed line represents the mean value of the response of the community. Mean values and 95% CI were obtained by bootstrapping.

Beyond an effect at the individual level, our results indicated that the evolution of community phenotype, i.e., productivity, was influenced by evolutionary changes in interspecific interactions.

Indeed, as in a previous study [START_REF] Lawrence | Species interactions alter evolutionary responses to a novel environment[END_REF], the phenotype of the evolved community could not be obtained by reconstructing a community from strains that evolved in isolation (Figures 2 and3).

We observed an effect of the interactions on community evolutionary response in all of the communities that showed an evolution in their phenotypes, i.e., seven among the eight (Figure 3, except G).

However, this effect of the interactions depended on the studied community and occurred in three different ways. Community phenotype evolved through (i) an evolutionary response of one strain conditionally to the presence of the second strain without the evolution of the interaction (communities D and E), (ii) an evolutionary response of the two strains conditionally to their respective presence without the evolution of the interaction (B), (iii) an evolution of the interaction itself under the influence of one (H) or of the two strains (A, C, and F; Figures 3 and4). Thus, the evolution of the community phenotype involved an evolution of the interactions in more than half of the cases. It suggested that the implication of the evolution of the interactions in the evolution of community phenotype is not rare in the experimental evolution of microbial communities. In another study [START_REF] Williams | Artificial selection of simulated microbial ecosystems[END_REF], a modeling approach showed that the responses of ecosystems to evolution under artificial selection would involve an evolution of the interspecific interactions in 4% of the cases when targeting an increase in a property and in 38% of the cases when targeting a decrease in a property (this could be modulated by specific experimental choices). More recently, it has been estimated that the evolution of the productivity of beech tree bacterial communities was explained by ecological sorting at 0.35%, by additive evolution at 17.7%, and by the evolution of the interspecific interactions at 14.3% [START_REF] Fiegna | Evolution of species interactions determines microbial community productivity in new environments[END_REF]. It is not straightforward to estimate the importance of the interspecific interactions in community evolutionary dynamics as their role seems to be highly dependent on the studied community but, together, these results suggest that it is relevant to consider the evolution of the interactions when studying community dynamics, at least in laboratory experiments.

In the communities in which an evolution of the interspecific interactions was detected, the change in community productivity was higher than expected, but the direction of this change was community-dependent. The response to evolution when the interactions evolved (i.e., in communities A, C, F, and H) gave rise to a mean change in the productivity of 35 ± 13%, i.e., +15 ± 7% as compared to what was expected from the individual responses. However, in two communities over four (C and F) this change was negative (i.e., the productivity of the evolved community was lower than this of the ancestral community), and in one case, it occurred whereas the sum of the individual responses was positive (C; Figure 4). In the other studies that reported an evolution of interactions, the effect was to enhance community productivity [START_REF] Fiegna | Evolution of species interactions determines microbial community productivity in new environments[END_REF][START_REF] Hansen | Evolution of species interactions in a biofilm community[END_REF][START_REF] Lawrence | Species interactions alter evolutionary responses to a novel environment[END_REF]. Furthermore, some authors showed a reduction in the negative interactions and the evolution F I G U R E 5 Effect of the environment on the expression of the evolved phenotype. The OD of a community composed of strains that evolved together (in columns) is compared with the OD of a community including ancestral strains or strains that evolved in isolation (in rows). Moreover, the OD of strains 1 and 2 evolved in community (in columns) is compared with the OD of the corresponding strains as ancestors or evolved in isolation (in rows). The results are presented for both environments. Environment 1: identical growth conditions to the experimental evolution; Environment 2: different growth conditions from the experimental evolution. Blue: significantly higher. Red: significantly lower. Light gray: no significant difference (α = 0.05). The overall results are shown on panel a, and panels b, c, and d show the results of the community, strain 1, and strain 2, respectively. On those panels, only the comparisons of interest are shown, and the others are shaded in dark gray for readability.

towards positive ones [START_REF] Lawrence | Species interactions alter evolutionary responses to a novel environment[END_REF]. In our study, we did not characterize the interactions, but we can hypothesize that different types of interactions (i.e., positive or negative) led to different responses of the community phenotype to the evolution of the interactions.

The influence of the abiotic environment on the evolutionary responses of the communities and community members was community-dependent. For three of the four communities in which an evolution of the interactions was detected, the response to evolution was consistently observed in the two environments (communities A, C, and F; Figure 5b) contrary to what was observed for the strains composing these communities (Figure 5c,d). It suggested that the evolutionary responses of the strains involved an adaptation to the abiotic component (so that the response is not consistently observed when changing the environment), but that the expression of the "evolved" interaction did not rely on an adaptation to the abiotic component or relied on an adaptation to a condition that is found in the two environments [START_REF] Hillesland | Rapid evolution of stability and productivity at the origin of a microbial mutualism[END_REF]. Previous studies have shown the importance of resources on the outcome of the evolution of interactions [START_REF] Lawrence | Species interactions alter evolutionary responses to a novel environment[END_REF][START_REF] Rivett | Resource-dependent attenuation of species interactions during bacterial succession[END_REF]. As the same culture medium was used in the two environments in our experiment, it could suggest that the evolution of the interactions implied modifications in resource sharing.

Our results also suggested that the evolution in community often promoted an adaptation of the strains to the abiotic component, especially in strain 1 (Figure 5c,d). This is not expected since the theory predicts that there are trade-offs between the adaptation to the abiotic and to the biotic components [START_REF] Barraclough | How do species interactions affect evolutionary dynamics across whole communities? Annual Review of Ecology[END_REF][START_REF] Lawrence | Species interactions alter evolutionary responses to a novel environment[END_REF] and, that biotic forces are dominant over abiotic forces in driving species evolution (Red Queen hypothesis; [START_REF] Brockhurst | Running with the Red Queen: The role of biotic conflicts in evolution[END_REF]. Thus, it is expected that strains that evolved in isolation would show a better adaptation to the abiotic environment than strains that evolved in community. It has been observed

experimentally [START_REF] Castledine | Experimental (co)evolution in a multi-species microbial community results in local maladaptation[END_REF][START_REF] Lawrence | Species interactions alter evolutionary responses to a novel environment[END_REF] but seemed to be strain-dependent. Our results suggested that the interspecific interactions could have promoted evolutionary responses to the abiotic conditions, which can occur through competition for example [START_REF] Barraclough | How do species interactions affect evolutionary dynamics across whole communities? Annual Review of Ecology[END_REF]. These results may be linked to the structure of the environment. Indeed, it has been suggested that in homogeneous environments, the evolution would act through the selection of traits that are directly beneficial for the carrier species [START_REF] Gorter | Understanding the evolution of interspecies interactions in microbial communities[END_REF]. Thus, the evolutionary response of a strain to the presence of another strain could be an adaptation to the abiotic conditions, which could have a direct and positive effect on the strain fitness.

In this study, we aimed at investigating the importance of the evolution of the interactions in community evolution. There was evidence for an evolution of the interactions in half of the studied communities. Moreover, even when they did not evolve themselves, the interactions influenced the evolution of both community phenotype and community members' phenotype. To go further, our results suggested that the communities in which an evolution of the interspecific interactions was detected were also the most robust to environmental change regarding the expression of community phenotype. This is of particular interest in the field of the artificial selection at the community level and its possible applications.

  glycerol stock and grown in 20 ml of EE medium in a flask (48 h, 28°C, 110 rpm). The OD (600 nm) of the suspensions was measured (200 μl per well in a microplate, Infinite M200 PRO, Tecan, Männedorf, Switzerland) and the suspensions were diluted to a final OD of 0.002 in EE. The eight two-strain communities were built by mixing an equivalent volume of each of the suspensions of the required strains. Then, two plates per community were inoculated with the suspensions at OD 0.002: a 2 ml deep-well plate (Porvair Sciences, Wrexham, UK, 1 ml of suspension per well, eight replicates per treatment) and a honeycomb plate (Thermo Fisher Scientific, Waltham, Massachusetts, USA; 400 μl of suspension per well, eight replicates per treatment). The growth conditions in deep-well plates were: 28°C, no shaking; the OD was measured after 3.5 days of growth by homogenizing the well content, pipetting 200 μl of suspension,

  . During the 20457758, 2022, 11, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.9494 by Cochrane France, Wiley Online Library on [01/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

Community identifier Strains Initial richness level of the native community Selection regime applied to the native community

	A	1	Variovorax sp. 38R	2 strains	No artificial selection
		2	Pseudopedobacter saltens DSM12145
	B	1	Variovorax sp. 38R	4 strains	No artificial selection
		2	Pseudopedobacter saltens DSM12145
	C	1	Pseudomonas knackmussii DSM6978	4 strains	No artificial selection
		2	Variovorax sp. 38R
	D	1	Pseudomonas sp. ADPe	2 strains	No artificial selection
		2	Escherichia coli WA803
	E	1	Pseudomonas knackmussii DSM6978	4 strains	No artificial selection
		2	Pseudopedobacter saltens DSM12145
	F	1	Pseudomonas sp. ADP3	4 strains	No artificial selection
		2	Escherichia coli K12
	G	1	Escherichia coli WA803	4 strains	Artificial selection
		2	Agrobacterium sp. 9023
	H	1	Pseudomonas sp. ADPe	2 strains	Artificial selection
		2	Escherichia coli WA803

squared p

	Biological entity	2	19.3	6.38 × 10 -5
	History	2	104	<2.2 × 10 -16
	Environment	1	2817	<2.2 × 10 -16
	Biological entity * History	4	193	<2.2 × 10 -16
	Biological entity * Environment	2	19.3	6.32 × 10 -5
	History * Environment	2	46.5	7.95 × 10 -11
	Biological entity * History * Environment	4	47.9	1.01 × 10 -9

20457758, 2022, 11, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/ece3.9494 by Cochrane France, Wiley Online Library on [01/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

INRAE Agroecosystem department, "Pari scientifique" program, Grant/Award Number: 6503

DATA AVA I L A B I L I T Y S TAT E M E N T

All the data and codes that support the results of this study are available in Zenodo (https://doi.org/10.5281/zenodo.7189920).

O RCI D

Tiffany Raynaud

https://orcid.org/0000-0002-1962-5233

R E FE R E N C E S

ACK N OWLED G M ENTS

We thank Alain Hartmann and Baptiste Serbource, MERS team, and INRAE Dijon, for their advice and the provision of the equipment allowing for the assessment of the antibiotic resistance of the strains.

CO N FLI C T O F I NTE R E S T

The authors have no conflict of interest to declare.

| 11 of 11


Baptiste Chide
email: baptiste.chide@irap.omp.eu

N L Lanza

C Alvarez

Stanley M Angel

Pernelle Bernardi

Olivier Beyssac

Bruno Bousquet

A Cadu

E Clavé

Olivier Forni

T Fouchet

O Gasnault

X Jacob

G Lacombes

J Laserna

J Lasue

R D Lorenz

P.-Y Meslin

F Montmessin

J Moros

N Murdoch

A M Ollila

P Pilleri

P Purohit

A L Reyes-Newell

S Schröder

A Stott

D Vogt

S Maurice

R C Wiens

D Mimoun

The SuperCam Microphone and Expected First Sounds at Jezero Crater, Mars

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

Introduction:

All the spectacular views of the surface of Mars returned since the first in situ missions are silent to a human-ear. Indeed, no microphone has ever been able to record the acoustic environment associated with these landscapes. The Mars atmosphere is the least favorable to sound propagation among all planetary atmospheres (Venus, Earth, Mars and Titan) due to the low acoustic impedance and the drastic attenuation of carbon dioxide [START_REF] Petculescu | Icarus[END_REF]. Although the sound sources are probably scarce, acoustic waves do propagate on Mars. It is rather because of a lack of science objectives that the first audible sounds from Mars are still pending. The NASA Mars2020 Perseverance rover that is planned to land in Jezero crater on the 18 th of February 2021 will begin the acoustic exploration of the surface of Mars thanks to two microphones, one activated during the landing phase [START_REF] Maki | [END_REF] and the other one which is part of the SuperCam instrument suite [START_REF][END_REF]4]. The latter is designed to record sounds in the audible range, from 100 Hz to 10 kHz during the surface mission. The microphone will open a new field of investigation on Mars by complementing the Laser-Induced Breakdown Spectroscopy (LIBS) investigation of the Mars surface by SuperCam and contributing to atmospheric science.

The SuperCam microphone:

The microphone is a commercial off-the-shelf electret (based on Knowles EK-23132). It is positioned outside the Remote Warm Electronic Box (see Fig. 1) and therefore it benefits from the pointing capabilities of the mast. The microphone sensitivity at 1 kHz is 29.6 mV/ Pa. Its front-end electronics filters and amplifies the signal with four switchable gains, from a factor of x29 to x972. Data can be acquired either at 25 kHz or 100 kHz. Depending on the sound sources of interest, the microphone can be operated over three observation modes:

i.

Standalone recordings that aim to monitor natural or artificial noise, independently of any other SuperCam operations. It provides 167 s long acquisitions at 25 kHz. ii.

Continuous recordings at 100 kHz to cover a full LIBS burst from the first to the last shot. It lasts 10 s for a standard burst of 30 shots. iii.

A pulsed mode that records LIBS acoustic signals over 60 ms windows thanks to an accurate synchronization between the microphone and the laser. In addition to saving data volume, it allows the determination of the propagation time of the acoustic wave from the target to the microphone.

Pre-Launch Performance: The microphone performance has been validated on engineering models in Mars conditions (6 mbar, CO 2 atmosphere and ablation capabilities similar to SuperCam) during tests in the Aarhus Wind Tunnel [5]. It showed that LIBS acoustic signals can be retrieved with a signal-to-noise higher than 10 dB for targets ablated at 4 m from the instrument even under a wind of 4.5 m/s. Characterization of the flight-model during Perseverance's system thermal tests conducted under 10 mbar of N 2 highlighted a good synchronization and a signal-to-noise ratio higher than 16 dB for LIBS shots performed on the titanium and the ferrosilite calibration targets. Science investigations: Science objectives of the SuperCam microphone correspond to the variety of the sound sources identified: LIBS plasma expansion and the associated shock-waves, atmospheric phenomena and rover noises and vibrations.

LIBS acoustic signal:

The expansion of the laserinduced plasma in the Mars atmosphere generates a shock-wave that propagates to the microphone. Therefore it will be recorded by the microphone. It was demonstrated that listening to laser sparks on Mars [6,7] can help determine the depth of the laser-induced pits which is new information to be used with the chemistry provided by the LIBS plasma's optical spectrum. Moreover the hardness of the target can be estimated by looking at the decrease rate of the acoustic signal energy as a function of the number of shots at the same location on a target. Finally, since the acoustic signal during a LIBS raster depends on the target hardness and ablated depth, acoustics data may also be used to characterize rock coatings. In particular, it will help to determine the depth of the transition between the coating and the underlying rock [START_REF] Lanza | LPSC LI[END_REF]. LIBS acoustic data will provide new information relative to the ablation process that is complementary to the LIBS emission spectrum and that will document rocks and soils analyzed by SuperCam on Mars.

Atmospheric phenomena:

The dynamic atmospheric surface layer of Mars will be the source of phenomena that will create acoustic waves or aeroacoustic noise due to the interaction of the atmospheric flow with the structure of the rover. It was shown experimentally that the low-frequency content (resp. the high-frequency content) of the wind-induced acoustic spectrum may be used to retrieve the velocity (and the orientation) of the wind [START_REF] Chide | [END_REF]. However this calibration was performed in a closed tunnel with mechanical noise and an uncontrolled level of turbulence. Hence, it has to be checked and calibrated in situ with the help of the MEDA weather station [10]. Models also predict the acoustic signature of convective vortices due to the variation of the tangential wind speed during a vortex encounter [11]. In addition, the atmosphere will be the propagation medium of acoustic waves. Studying the propagation parameters will allow the determination of some atmospheric properties. In particular, the propagation time of the LIBS acoustic signal from the ground to the height of the microphone can be measured to quantify the atmospheric thermal gradient [START_REF] Chide | LPSC LI[END_REF], giving hints into the convective activity during the daytime. Finally, the reference models for the atmospheric attenuation models [START_REF] Williams | [END_REF]14], that exhibit obvious discrepancies in the audible range, will be constrained in situ with the evolution of the LIBS signal amplitude from targets at several distances from the rover [START_REF] Chide | AGU Fall meeting[END_REF].

Rover noises:

The microphone can also record noises generated by the operations of Perseverance such as drill and drive activities, mast rotation (see Fig. 2) and other instruments. In particular, the microphone may be used as a diagnostic tool to listen to MOXIE (Mars Oxygen ISRU Experiment [START_REF] Hecht | [END_REF]) pumps during oxygen production. It may also help to understand a potential failure of a rover subsystem.

Operating a microphone on the surface of Mars is an unprecedented experience. By the time of the conference, the SuperCam microphone should have acquired the first sounds on Mars, which will be the focus of the poster, to compare these results to our prelanding expectations.

Figure 1 -

 1 Figure 1 -The SuperCam microphone integrated on the top of the Remote Sensing Mast of Perseverance (Credits: NASA/JPL)

Figure 2 -

 2 Figure 2-Rover mast motion recorded at JPL on Optimism, the vehicle system test bed rovet, by the SuperCam microphone Test-Unit. Az: azimuth, El: Elevation


Johan Moreau

Floriane Socchi

Marie Catherine Renoux

Anne Requirand

Hamouda Abassi

Sophie Guillaumont

Stefan Matecki

Helena Huguet

Martina Avesanni

Marie-Christine Picot

Professor Pascal Amedro
email: pascal.amedro@chu-bordeaux.fr

Cardiopulmonary fitness in children with asthma versus healthy children

INTRODUCTION

Asthma is the most common paediatric chronic disease worldwide, leading to frequent hospital admissions and school absenteeism.1 Paediatric asthma has become a serious public health issue and current management mainly relies on symptom control in order to ensure to children with asthma a normal quality of life.2 Asthma is characterised by chronic inflammation of lower airways, marked by expiratory airflow limitation and repeated respiratory symptoms, including wheezing, shortness of breath, chest tightness and cough.3 Asthma symptoms, associated with patient's fear of exertional dyspnoea, may limit the ability to be physically active, leading to a sedentary lifestyle, and ultimately affect child's quality of life. [START_REF] Winn | Perceptions of asthma and exercise in adolescents with and without asthma[END_REF] In 1980, the WHO stated that functional capacity explorations best reflected the impact of chronic disease on health-related quality of life.5 Therefore, cardiopulmonary exercise test (CPET) with maximum oxygen uptake (VO2max) assessment has become the reference functional examination in cardiorespiratory diseases,6 first in adult population, and more recently in paediatric population. Indeed, in children, VO2max is associated with patient-reported outcomes, such as health-related quality of life. [START_REF] Amedro | Correlation between cardio-pulmonary exercise test variables and health-related quality of life among children with congenital heart diseases[END_REF] The existence of an impaired cardiopulmonary fitness in children may be related to various limiting factors, such as cardiac limitation, ventilatory limitation, physical deconditioning or even psychological barriers to physical activity.8

Most paediatric studies on cardiopulmonary fitness have enrolled healthy children, children with cardiac conditions9 or children with cystic fibrosis.10 By contrast, the level of VO2max in children with asthma from large paediatric CPET cohorts has been scarcely reported, especially in comparison with healthy children.11-14 Previous reports suggested that ventilatory exercise limitations could be related to the severity of bronchial obstruction,15 [START_REF] Westergren | Perceived exercise limitation in asthma: the role of disease severity, overweight, and physical activity in children[END_REF] but the predictors of VO2max and the level of physical deconditioning in children with asthma remain unclear.17 [START_REF] Pianosi | Determinants of physical fitness in children with asthma[END_REF] In this study, we aimed to evaluate the cardiopulmonary fitness, assessed by a CPET, in a large cohort of children with asthma, and to compare the results to healthy controls. We also intended to identify the predictors of VO2max in children with asthma.

METHODS

Study design and population

This cross-sectional controlled study was carried out from January 2015 to December 2019 in two tertiary care paediatric centres (Montpellier University Hospital and Saint-Pierre-Institute, France). Children aged 5-18 years old, and measuring at least 120 cm, were recruited in two paediatric CPET laboratories. Children with absolute contraindications for CPET were not eligible.9

Two groups were identified: children with asthma and healthy control children.

The asthma group consisted of children followed up for asthma and referred to one of the two centres by paediatricians, pulmonologist or general practitioners. The level of asthma severity was assessed by the therapeutic burden from the Global Initiative for Asthma (GINA) classification.3 From patients' interviews, the absence of any physical activity, apart from physical education at school, was indicative of sedentary lifestyle. Also, exercise-related symptoms (chest pain or excessive dyspnoea) were reported.

As in our previous similar CPET controlled paediatric studies, the control group consisted of children referred for non-severe functional symptoms linked to exercise (murmur, palpitation or dyspnoea) or for medical sports certificate.9 19 20 These children were classified as controls after a completely normal check-up, including physical examination, ECG, echocardiography and spirometry. Children with any chronic disease, medical condition (cardiac, neurologic, respiratory, muscular or renal), or medical treatment, and those requiring any further specialized medical consultation were not eligible.

Paediatric laboratory functional tests: spirometry and CPET

Functional tests procedures in both centres were harmonized before the study started. We used the Quark CPET calibrated gas analyser (Cosmed Srl., Pavonna di Albano, Italy).

Spirometry tests were repeated at least three times to ensure reproducibility,21 and the results of FEV1, FVC and FEV1/FVC ratio (FEV1/FVC%) were expressed in Z-scores. [START_REF] Quanjer | Multi-Ethnic reference values for spirometry for the 3-95-yr age range: the global lung function 2012 equations[END_REF] Both centres used the same CPET paediatric cycle ergometer protocol to obtain a homogeneous incremental overall duration between 8 and 12 min. Whenever the VO2max did not reach a plateau, which is common in children, the peak VO2 was collected.8 VO2max values were normalised as percentage of the predicted VO2max.23 A VO2max<80% of predicted values indicated impaired cardiopulmonary fitness. [START_REF] Blais | A systematic review of reference values in pediatric cardiopulmonary exercise testing[END_REF] The ventilatory anaerobic threshold (VAT) was measured using the V-slope method.25 A decreased VAT <55% of predicted VO2max values was in favour of physical deconditioning.9 [START_REF] Stephens | Exercise testing in pediatrics[END_REF] The ventilatory efficiency (VE/VCO2 slope) was measured from the maximum exercise.27 A VE/VCO2 slope >30 indicated impaired ventilatory efficiency. [START_REF] Forman | Cardiopulmonary exercise testing: relevant but underused[END_REF] The maximal voluntary ventilation, the breathing reserve (BR), the oxygen pulse, the oxygen uptake efficiency slope (OUES)28 and the main respiratory variables at maximal exercise were collected (detailed CPET protocol and parameters available in online supplemental material).

Statistical analysis

The study population was described using means and SD or median and interquartiles (Q1-Q3) for quantitative variables, and with frequencies for categorical variables. The continuous variable distributions were tested using the Shapiro-Wilk test. Quantitative variables were compared using Student's t-test when the distribution was Gaussian and using the Mann-Whitney test otherwise. For qualitative variables, groups were compared using the 2 test or Fisher's exact test.

Depending on the distribution of variables, correlations were performed using Pearson's or Spearman's coefficients.

We compared CPET parameters between asthma and control groups with generalised linear models adjusted on gender, age and body mass index (BMI).

A linear model was used to compare the variation of VO2max per year between asthma and control groups. This analysis was stratified on gender because the interaction between age and gender was significant (p<0.01). The results were illustrated with a scatter plot and their regression line for each group. On these figures, the p value represented the slope comparison between asthma and control groups.

A multiple linear regression was used to identify the explanatory factors for VO2max in the asthma group. The clinically relevant variables (such as the BR to explore ventilatory limitation, the VAT to reflect muscular deconditioning and usual spirometric parameters), some clinically relevant interactions (such FEV1*BMI or VAT*BMI and BR*BMI) and the variables with a p value ≤0.2 in the univariate analysis were included in the model. The final model was obtained using an upward selection based on the corrected Akaïke information criterion and with an exit threshold of 0.10. The normality of residuals in the final model was tested using the Shapiro-Wilk test.

The statistical significance was set at 0.05 and analyses were performed using Statistical Analysis Systems Enterprise Guide V.4.3 (SAS Institute).

Ethics

The study was conducted in compliance with the Good Clinical Practices protocol and Declaration of Helsinki principles. Informed consent was obtained from all parents or legal guardians.

RESULTS

Population

A total of 446 children were included in the study (centre 1, n=236; centre 2, n=210): 144 in the asthma group and 302 in the control group. No family refused to participate.

There was no difference between the two centres in terms of demographic data and asthma severity assessed by the therapeutic burden from the GINA classification (p=0.35). Moreover, asthmatic and control children were similar, except for anthropometric and spirometry data (table 1). No adverse events were reported during the exercise tests.

In the asthma group, 70% of patients had a specific asthma treatment and one-third was classified in the step 4 or 5 of GIN classification. FEV1, FVC and ratio FEV1/FVC were significantly lower in the asthma group. Most patients with asthma felt chest pain or excessive dyspnoea during daily exercise, and one-third had a sedentary lifestyle.

CPET results

All enrolled children performed a maximal exercise test and both groups were similar in terms of maximum load. In the control group, the mean VO2max per body weight was normal (43.5±7.5 mL/kg/min), representing 107%±17% of the predicted values. In the asthma group, the mean VO2max per body weight was good, overall (38.6±8.6 mL/kg/min), representing 94%±19% of the predicted values, but was significantly lower than in healthy controls, with an absolute difference of -4.9 mL/ kg/min and a 95% CI of (-6.5 to -3.3) mL/kg/min (p<0.01).

Expressed in VO2max percent-predicts, this represented an absolute difference of -13% (95% CI (-17 to -9)%; p<0.01). We found similar results after adjustment on age, gender and BMI. The proportion of children with an impaired VO2max was four times higher in the asthma group than in the control group (24% vs 6%, p<0.01, respectively). The VAT was significantly lower in the asthma group (26.7±6.9 vs 29.1±6.4 mL/kkg/min), with an absolute difference of -2.4 mL/kg/min (95% CI (-3.7 to -1.1) mL/kg/min; p<0.01).

Expressed in VO2max percent-predicts,this represented 65%±16% of predicted VO2max and an absolute difference of -6% (95% CI (-9 to -3)%; p<0.01). The proportion of children with a decreased VAT <55% was three times higher in the asthma group (31% vs 11%, p<0.01).

In the asthma group, the VE/VCO2 slope, the proportion of children with an impaired ventilatory efficiency and the PetCO2 were higher. The BR, Vt and oxygen pulse values were significantly lower in the asthma group. No significant differences were observed for respiratory rate, ventilatory equivalents and OUES values.

After adjustment of CPET parameters on age, gender and BMI, similar differences between asthma and control groups were observed for VO2max, VAT, VE/VCO2 slope and BR (table 2). In female children, VO2max decreased significantly faster with age in the asthma group than in controls (-1.36 mL/kg/min per year, 95% CI (-1.87 to -0.84) vs -0.55 mL/kg/min per year, 95% CI (-0.96 to -0.15), p=0.02, respectively) (figure 1). We observed a decrease of 1.95% of predicted VO2max per year of age in girls with asthma. No difference was observed in male subjects (-0.36 mL/kg/min per year, 95% CI (-1.04 to 0.33) vs 0.10 mL/kg/min per year, 95% CI (-0.27 to 0.48), p=0.20, respectively, for asthma and control groups) (figure 2).

Predictors of VO2max in the asthma group

A lower VO2max was associated with female gender, high BMI, low FEV1, low VAT and high BR, in both univariate and multivariable analyses (table 3). However, no association was found between VO2max and the level of asthma severity from the GINA classification. There was no significant interaction between BMI with FEV1, VAT and BR. The final multivariate model explained 82% of the variability of VO2max in the asthma group.

DISCUSSION

From a paediatric cohort of 446 subjects, this multicentre controlled study provided values and predictors of cardiopulmonary fitness in 144 children with asthma compared with 302 healthy controls.

Overall, the cardiopulmonary fitness of children with asthma was good (VO2max >90%). However, their VO2 max was lower than that of healthy subjects, with a mean difference of 13% between their respective percent-predict values. Moreover, the VO2max was impaired in nearly one-fourth of children with asthma, which was four times higher than in healthy subjects. The VO2max decrease was associated with lower FEV1.

Previous reports on cardiopulmonary fitness in children with asthma are controversial,29 as some studies found VO2max values similar to healthy controls,13 14 whereas others found lower VO2max values in children with asthm.12 30 However, those studies enrolled small numbers of subjects and used heterogeneous CPET protocols. Unsurprisingly, the VO2max was lower in children with high BMI, as adipose tissue does not consume oxygen, as well as in girls, reflecting the well-known gender differences in muscle mass and adipose tissue.9 31 Exercise-induced symptoms may be associated with misdiagnosis of asthma in overweight subjects. [START_REF] Malmberg | Overweight and exercise-induced bronchoconstriction -Is there a link?[END_REF] Therefore, in this study, CPET parameters' comparisons used generalised linear models adjusted not only on gender and age, but also on BMI, in order to determine the impact of paediatric asthma on aerobic fitness. Interestingly, the results of this study suggest the existence of an early onset of physical deconditioning in childhood asthma. Indeed, the VAT was a predictor of VO2max and nearly onethird of children with asthma had low VAT values, which was three times higher than in healthy controls. Moreover, asthma severity, as assessed by the GINA classification, was not associated with VO2max.

Nevertheless, few subjects with severe asthmatic forms were included in this study. In this cohort, most children with asthma felt chest pain or excessive dyspnoea during daily exercise, and one-third had a sedentary lifestyle. In the literature, barriers to physical activity in children with asthma have been mainly related to an altered self-perception of physical capacity16 18 and, to a lesser extent, to insufficient asthma control, dynamic hyperinflation and air trapping during effort, or exercise-induced bronchoconstriction.33 34 Indeed, some children may limit their activities for fear of experiencing symptoms during exercise. [START_REF] Winn | Effect of high-intensity Interval training in adolescents with asthma: the eXercise for asthma with Commando Joe's® (X4ACJ) trial[END_REF] They gradually adopt a sedentary lifestyle, responsible for increased symptoms during exercise, which will ultimately aggravate the vicious circle of physical deconditioning. This is supported in this study by the VO2max decrease over time in the asthma group (1.36 mL/kg/min/ year in girls and 0.36 mL/kg/min/year in boys).

Furthermore, this study provided original data on ventilatory parameters during exercise in childhood asthma. Previous studies failed to identify any significant differences in exercise ventilatory parameters between children with and without asthma.15 [START_REF] Clark | Assessment of work performance in asthma for determination of cardiorespiratory fitness and training capacity[END_REF] Globally, children with asthma were prone to some degree of ventilatory inefficiency, as reflected by the VE/VCO2 slope. Previous reports have described the hyperventilation syndrome37 as indicative of ventilatory limitation in children with asthma,38 but this has not been observed in our study, where higher PetCO2 values at the end of exercise have been observed. Interestingly, in children with asthma from our cohort, a higher BR was a predictor of a lower VO2max, suggesting that exercise limitation in childhood asthma does not only rely on the respiratory impairment but also includes a certain degree of physical deconditioning.

As a result, the use of CPET in childhood asthma follow-up provides a comprehensive assessment of cardiopulmonary fitness and evaluates the impact of the obstructive lung disease on physical capacity. Ultimately, the CPET identifies the patients most at risk of physical deconditioning in this population, and therefore could be the first step of exercise rehabilitation in childhood asthma. Unfortunately, cardiopulmonary rehabilitation programmes in asthma have been scarcely developed, despite preliminary positive results in small studies focused on physical training.39 Nevertheless, previous studies have highlighted the benefits of regular physical activity in asthma in terms of disease control, lung function and mental health.30 Moreover, regular physical activity is highly recommended by GINA in the global management of asthma.3 In a near future, evaluation and promotion of physical activity in children with asthma and impaired physical fitness could therefore be supported by structured rehabilitation programmes, including both exercise training and patient education, as recently reported in other paediatric conditions.40

Study limitations

This study was performed in tertiary care centres, therefore, children with more severe asthma status may have been enrolled, which may not represent asthmatic children in the general population.

Data on asthma control were not available but would have been of great interest in the perspective of further patients' education. Moreover, further work should be carried out to evaluate the impact of exercise-induced bronchospasm and dynamic hyperinflation on cardiopulmonary fitness in paediatric asthma.

CONCLUSION

Cardiopulmonary fitness in children with asthma was moderately but significantly altered, in comparison with healthy children, and VO2max was impaired in one-fourth of children with asthma. A decreased VO2max was associated with female gender, high BMI, low VAT and low FEV1. Nearly one-third f children with asthma had low VAT values, suggesting an early onset of physical deconditioning. Children with asthma were prone to ventilatory inefficiency during exercise. The use of CPET in the follow-up of paediatric asthma evaluates the impact of the obstructive lung disease on

cardiopulmonary fitness and identifies the children most at risk of physical deconditioning. Further work will have to determine the interest of CPET in promotion of physical activity and cardiopulmonary rehabilitation for childhood asthma.

Acknowledgements We thank Annie Auer and Christelle Sarran (CPET laboratory echnicians).

Contributors Study concept and design: JM, PA. Drafting of the manuscript: FS, M, PA. Critical revision of the manuscript for important intellectual content: All. tatistical analysis: HH, M-CP. btained funding: PA. Guarantor: PA. All authors pproved the final manuscript as submitted and agree to be accountable for all spects of the work.

Funding The authors have not declared a specific grant for this research from any unding agency in the public, commercial or not-for-profil sector

Data availability statement

All data relevant to the study are included in the article or uploaded as supplementary information.

Author affiliations

Ethics approval This study involves human participants and was approved by IRB MTP 2020-02-202000335 Participants gave informed consent to participate inthe study before taking part.

Provenance and peer review Not commissioned; externally peer reviewed.