Metabolic complementarity between a brown alga and associated 1 cultivable bacteria provide indications of beneficial interactions

: Brown algae are key components of marine ecosystems and live in association with bacteria that are 19 essential for their growth and development. Ectocarpus siliculosus is a genetic and genomic model 20 for brown algae. Here we use this model to start disentangling the complex interactions that may 21 occur between the algal host and its associated bacteria. We report the genome-sequencing of 10 22 alga-associated bacteria and the genome-based reconstruction of their metabolic networks. The 23 predicted metabolic capacities were then used to identify metabolic complementarities between the 24 algal host and the bacteria, highlighting a range of potentially beneficial metabolite exchanges 25 between them. These putative exchanges allowed us to predict consortia consisting of a subset of 26 these ten bacteria that would best complement the algal metabolism. Finally, co-culture experiments were set up with a subset of these consortia to monitor algal growth as well as the presence of key algal metabolites. Although we did not fully control but only modify bacterial communities in our experiments, our data demonstrated a significant increase in algal growth in cultures inoculated with the selected consortia. In several cases, we also detected, in algal extracts, the presence of key metabolites predicted to become producible via an exchange of metabolites between the alga and the microbiome. Thus, although further methodological developments will be necessary to better control and understand microbial interactions in Ectocarpus , our data suggest that metabolic complementarity is a good indicator of beneficial metabolite exchanges in holobiont.

Brown algae are key components of marine ecosystems and live in association with bacteria that are 19 essential for their growth and development. Ectocarpus siliculosus is a genetic and genomic model 20 for brown algae. Here we use this model to start disentangling the complex interactions that may 21 occur between the algal host and its associated bacteria. We report the genome-sequencing of 10 22 alga-associated bacteria and the genome-based reconstruction of their metabolic networks. The 23 predicted metabolic capacities were then used to identify metabolic complementarities between the 24 algal host and the bacteria, highlighting a range of potentially beneficial metabolite exchanges 25 between them. These putative exchanges allowed us to predict consortia consisting of a subset of 26 these ten bacteria that would best complement the algal metabolism. Finally, co-culture experiments 27 were set up with a subset of these consortia to monitor algal growth as well as the presence of key 28 algal metabolites. Although we did not fully control but only modify bacterial communities in our 29 experiments, our data demonstrated a significant increase in algal growth in cultures inoculated with 30 the selected consortia. In several cases, we also detected, in algal extracts, the presence of key 31 metabolites predicted to become producible via an exchange of metabolites between the alga and the 32 microbiome. Thus, although further methodological developments will be necessary to better control 33 and understand microbial interactions in Ectocarpus, our data suggest that metabolic 34 complementarity is a good indicator of beneficial metabolite exchanges in holobiont. 35

1
Introduction 36 Microbial symbionts are omnipresent and important for the development and functioning of 37 multicellular eukaryotes. Together the eukaryote hosts and their microbiota form meta-organisms 38 also called holobionts. Elucidating the interactions within microbial communities and how they affect 39 host physiology is a complex task and requires an understanding of the dynamics within the 40 microbiome and the host, as well as of possible inter-species interactions and/or metabolic exchanges 41 that could occur between the partners. One way to dissect those interactions is via targeted co-culture 42 experiments using culturable bacteria. This approach works particularly well for 1:1 or 1:2 43 interactions, but as the number of potentially interacting organisms increases, selecting the "right" 44 bacterial consortia becomes a major bottleneck (Lindemann et al. 2016). 45 Metabolic complementarity has previously been proposed as an indicator for potentially beneficial 46 host-symbiont interactions and can be assessed in silico using the metabolic networks of the host and 47 the microbiota (Dittami, Eveillard, et al. 2014;Levy et al. 2015). Common examples of metabolic 48 complementarity are associations of autotrophic and heterotrophic organisms such as corals and their 49 photosynthetic symbionts (Rohwer et al. 2002), or algae, and their heterotrophic bacterial biofilm 50 (Wahl et al. 2012). In this case, the autotrophic partner has a metabolic capacity (photosynthesis) that 51 allows for the production of metabolic intermediates (organic carbon), which can be further 52 metabolized by the heterotrophic partners. However, especially in systems with long-lasting 53 interactions more complex metabolic interdependencies are likely to evolve (e.g. Amin et al. 2015). 54 As a tool to further explore such interactions, Frioux et al. (Frioux et al. 2018) have proposed the 55 pipeline MiSCoTo. Given the metabolic networks of a host and several symbionts, this tool predicts 56 potential metabolic capacities of one partner that could be unlocked by a contribution of a metabolite 57 from another (e.g. the provision of carbohydrates by a photosynthetic organism unlocking the 58 biochemical processes related to primary metabolism in heterotrophs). Furthermore, this 59 computational approach uses these complementarities to define minimal consortia (i.e. with the 60 lowest possible number of exchanges/contributors) allowing the host to reach its maximum metabolic 61 potential. However, the actual predictive value of these models, both in terms of the effect on host 62 growth and fitness, and in terms of the metabolic scope (i.e. the metabolites producible by the 63 holobiont system), remains to be assessed. 64 Here exchanges with other partners in the environment, e.g. bacteria. The more such gaps can be filled by 100 exchanging compounds between two metabolic networks, the higher we consider the degree of 101 metabolic complementarity between the corresponding organisms. 102 Here we used the MiSCoTo tool (Frioux et al. 2018) to compute such potential metabolic exchanges 103 between Ectocarpus and any of the ten targeted bacteria. The underlying model of MiSCoTo 104 assumes that a compound is producible by a host-symbiont community if there is a chain of 105 metabolic reactions which transforms the culture medium into the expected compound without taking 106 into consideration flux accumulations or competition for resources, and allowing for the exchange of 107 compounds across cell boundaries. These simplifications imply that compounds predicted to be 108 producible in silico may, in some cases, remain unproducible in vivo, although the consortium has all 109 the genes to activate the pathways. 110 In this study MiSCoTo was run twice, first to determine the scope of all algal compounds that 111 become producible via exchanges with all 10 bacterial genomes together, and as second time to select 112 minimal bacterial consortia for the production of these compounds. (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. . Filaments were exposed 124 to 25 ml of this solution for 3 days and then placed in Provasoli-enriched artificial seawater for 3 125 days to recover. The absence of bacteria on the algal surface was verified by microscopy using phase-126 contrast (Olympus BX60, 1.3-PH3 immersion objective, 800x magnification) and by plating of algal 127 filaments on Petri dishes with Zobell medium followed by three weeks of incubation at room 128 temperature. 129

Co-culture experiments 130
For co-culture experiments, cell densities of bacterial cultures were determined using a BD FACS 131 CantoTM II flow cytometer (BD Bioscience, San Jose, CA) using samples fixed in Tris-EDTA. 132 Before the start of the experiment, antibiotic-treated algae (three replicate cultures per condition) 133 were inoculated with 2.3*10 5 bacterial cells per strain and ml medium. Each co-culture was then 134 incubated for 4 weeks under standard algal growth conditions (see above). During this time, algal 135 growth was quantified by measuring the filament length of the algae each week using the binocular 136 microscope (3 measurements per replicate). Furthermore, bacterial abundance in the algal growth 137 medium was estimated using flow cytometry (described above) and bacteria attached to algal cell 138 walls were counted by microscopy (5x 10 μ m long filaments observed per biological replicate, 800x 139 magnification in phase contrast). At the end of the experiment, general algal morphology was 140 observed using a LEICA DMi8 microscope and in parallel, remaining algal tissues were frozen in 141 liquid nitrogen and freeze-dried for downstream analyses. Two controls (three replicates each) were 142 run in parallel: a non-antibiotic treated positive control (CTRL w/o. ATB), and an antibiotic-treated 143 non-inoculated alga as a negative control (CTRL w. ATB). 144 (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

Bacterial community composition after co-culture experiments
The copyright holder for this preprint . http://dx.doi.org/10.1101/813683 doi: bioRxiv preprint first posted online Oct. 22, 2019; 5 2.6 Targeted metabolomics 167 Seven metabolites predicted to be producible by the algae only in presence of metabolic exchanges 168 with specific bacteria were selected for targeted metabolite profiling after manual verification of 169 automatic predictions of corresponding pathways in the algal and bacterial networks and based on 170 their biological importance for the alga: L-histidine, putrescine, beta-alanine, nicotinic acid, folic 171 acid, auxin, and spermidine. Metabolites were extracted from 10 mg of ground, freeze-dried tissue 172 using a triple extraction protocol based on the method of Bligh and Dyer (1959): two ml of 173 methanol:chloroform:water (6:4:1) were used as first extraction solvent, then the remaining pellet 174 was extracted with 1 ml of chloroform:methanol (1:1), and finally, a 3rd extraction was performed 175 using 1ml

Predicted metabolic interactions and selection of beneficial bacterial consortia 196
Genome sequencing and subsequent bioinformatics analyses yielded bacterial genome assemblies 197 with sufficient coverage and 11-72 scaffolds per genome Table 1). Metabolic networks were then 198 reconstructed for these ten genomes. On average, 1,714 reactions, 111 transport reactions, and 1,405 199 metabolites (Table 2) were predicted per bacterium. These reactions belonged, again on average, to 200 261 pathways, 137 of which were complete and 124 were incomplete (i.e. missing one or more 201 reactions). Based on metabolic complementarity analysis carried out using MiSCoTo, these bacterial 202 networks were predicted to enable the production of 160 additional compounds with the algal 203 networks, including several polyamines (Cadaverine, Spermidine, Agmatine), amino acids (Histidine, 204 Tyrosine, beta-alanine), vitamins B3, B9, and E, several lipids and lipid derivatives, and nucleic 205 acids. Please refer to Supplementary Table S1 for a complete list of compounds. Many of these 206 compounds were also previously predicted via the metabolic interaction between the same strain of 207 E. siliculosus and the associated bacterium Candidatus Phaeomarinobacter ectocarpi (Dittami, 208 Barbeyron, et al. 2014;Prigent et al. 2017). A total of six bacterial consortia comprising three 209 bacterial strains each (Table 3) were predicted to be sufficient to enable the production of all of these 210 . CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

Growth rates in co-culture experiments 214
The inoculation with one or several bacterial strains significantly enhanced algal growth by a factor 215 of 2 compared to controls ( Figure 1A). This positive effect was observed both for the predicted 216 bacterial consortia and for all the individual strains tested. At the same time, the abundance of 217 bacteria on algal filaments after four weeks of cultivation was significantly lower in cultures initially 218 inoculated with bacteria compared to both controls with and without initial antibiotic treatment 219 ( Figure 1B), although bacterial cell counts in the medium were similar between co-culture 220 experiments and the non-inoculated control after 28 days (Supplementary Figure S1). 221

Bacterial impact on morphology 222
Compared to the negative control, which exhibited a ball-like morphology typical for "axenic" 223 cultures (Tapia et al. 2016), all bacterial inocula tested resulted in filamentous thalli with clear 224 branching patterns (Figure 2). We furthermore observed differences in the branching patterns 225 depending on the bacterial inocula. For example, Sphingomonas-inoculated cultures produced 226 relatively long filaments with few branching sites ( Figure 2H), whereas Hoeflea-inoculated cultures 227 produced filaments with frequent branching ( Figure 2E). Imperialibacter induced aggregation of 228 individual filaments ( Figure 2F), while in all other co-cultures, filaments remained more or less 229 separated. These differences were, however, difficult to quantify given complexity of their 230 morphology. 231

(Algal) metabolome in co-culture conditions 232
Seven putatively key metabolites (l-histidine, putrescine, beta-alanine, nicotinic acid, folic acid, 233 auxin, and spermidine) predicted to be non-producible by the alga alone but producible via exchanges 234 with some bacterial consortia, were quantified in algal tissues by UPC 2 -MS after four weeks of co-235 culture. The presence/absence of these metabolites is shown in Figure 3, comparing both the 236 predicted producibility by metabolic network analysis and the experimental UPC 2 -MS results. In the 237 negative control, i.e. antibiotic-treated algae that were not inoculated with bacteria, none of the 238 compounds could be identified by UPC 2 -MS confirming the computational predictions. In contrast,239 in all co-cultures, at least one target compound was experimentally detected. Furthermore, each 240 compound became producible in at least one of the co-cultures. Overall, across the 56 predictions 241 made based on the metabolic networks (7 metabolites x 8 consortia including the individual bacteria 242 and the negative control) in silico and in vivo data agreed in 28 cases (Figure 3). Only in four cases 243 did we observe the presence of a metabolite although it was not predicted by the networks. Finally, in 244 24 cases we did not detect the presence of a metabolite predicted to be producible in the co-cultures. 245

Bacterial community composition after co-culture experiments 246
The bacterial community composition of each sample was analyzed by 16S rDNA metabarcoding at 247 the end of the co-culture experiments. This was done to verify if the bacteria inoculated had grown in 248 the co-cultures and to determine to what extent other bacteria were present and affected by the 249 inoculations. The results (Table 4) show that, except for Imperialibacter, all of the bacterial strains 250 inoculated were detected in the corresponding co-cultures 28 days after inoculation. However, except 251 for Marinobacter and Hoeflea, read abundances of these strains were low compared to the total 252 . CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. simplifications, our results discussed below provide a strong indication that, genome-based 276 predictions of metabolic complementarity is a powerful tool to handle the complexity of host microbe 277 systems and to generate hypotheses on their interactions. 278

Similar complementarities found across studies and Ectocarpus symbionts. 279
Compared to a previous analysis of metabolic complementarity between Ectocarpus and another 280 associated bacterium, Candidatus Phaeomarinobacter ectocarpii, (Dittami, Barbeyron, et al. 2014;281 Prigent et al. 2017), newly producible compounds predicted in this study were largely similar, 282 notably regarding polyamines, histidine, beta-alanine, and auxin. This similarity persists even though 283 metabolic complementarity analyses were performed using MiSCoTo, which incorporates the notion 284 of different compartments minimizing the number metabolite exchanges (Frioux et al. 2018) and 285 despite the fact that different bacteria were examined. The main difference compared to the previous 286 study is that numerous additional compounds were predicted to be exchanged, which can be 287 explained by the fact that ten rather than one bacterial network were available to complete the algal 288 network. 289 Inoculation with metabolically complementary bacteria enhances growth rate and impacts 290 morphology and metabolism 291 As described above, both the bacterial consortia tested, as well as all of the bacteria inoculated 292 individually had clear positive effects on algal growth and impacted algal morphology and metabolite 293 profiles, even though, by the time the co-cultures were harvested, some of the inoculated bacteria 294 were present only in very low abundance or even below the detection limit. These positive effects 295 . CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint . http://dx.doi.org/10.1101/813683 doi: bioRxiv preprint first posted online Oct. 22, 2019; 8 could be due either to interactions early in the co-culture experiments followed by a decline in 296 bacterial abundance, or due to the capacity of bacteria to impact and interact with their algal hosts 297 even at very low cell concentrations. The latter would support the hypothesis that part of the 298 observed effects may not be due to the exchanges of (abundant) primary metabolites, such as the 299 predicted histidine/histidinol, but due to lowly concentrated signaling molecules or growth hormones. 300 One such compound could be the examined auxin, which was detected in 5 of the 7 tested co-301 cultures, and which has previously been shown to modify the developmental patterns and 302 morphology of Ectocarpus cultures (Le Bail et al. 2010) in a similar way as bacterial inoculations. 303 Another observation was that the abundance of bacteria on algal filaments but not in the medium was 304 significantly lower in co-culture conditions compared to the controls. This suggests that the 305 inoculated bacteria, either directly, or indirectly, by stimulating algal growth or defense, can also 306 regulate biofilm formation (see Goecke et al. 2010 for a review). 307 Interestingly, although differences in the effects of individual bacteria and bacterial consortia were 308 observed on metabolite profiles and morphology, all consortia had similar effects on algal growth. 309 Indeed, all of the tested bacteria, including Sphingomonas, which was not part of the minimal 310 solutions proposed by MiSCoTo, were to a great extent complementary to the alga, already covering 311 a large part of the metabolic gaps. In future studies, it may be particularly useful to incorporate a 312 larger range of bacteria, possibly from other sources so that they are not expected to have evolved 313 mutualistic interactions with brown algae. These negative controls could then be used to correlate 314 growth rates with the presence or absence of specific metabolic capacities in the network. Once the 315 list of candidate metabolite exchanges has been narrowed down by such comparisons, supplying 316 these metabolites from artificial sources but also testing for their excretion into the medium by 317 bacteria can be used to corroborate their role. 318

Predicted metabolic exchanges likely to occur in part 319
With respect to the predictions of target metabolites, we observed that for a large number of cases, 320 predictions from the metabolic networks corresponded to the observations made by experimental 321 metabolic profiling: none of the target metabolites were detected in the negative control, and only in 322 four cases (Figure 3), did we detect compounds in co-cultures that were not predicted to be there. 323 This could either be attributed to undetected metabolic pathways in the examined/added bacteria (e.g. 324 due to missing annotations) or, more likely, to the activity of other bacteria present in our co-culture 325 experiments (see below). Furthermore, there were several cases in which a potentially co-producible 326 metabolite was not detected in our co-cultures. Here two explanations appear particularly likely: first, 327 the metabolites in question may be produced but quickly metabolized in certain consortia, so that 328 they do not accumulate sufficiently to be detectable in our cultures; secondly, it is possible that the 329 corresponding biosynthetic pathway of the metabolite was not active or that the necessary exchange 330 of metabolites was not taking place. To resolve this point in future experiments, the addition of gene 331 expression data may help to establish whether or not biosynthetic or degradation pathways are active. 332 From a global perspective, however, the fact that none of the compounds in question were detected in 333 negative controls, but all of them it at least one co-culture condition, constitutes a highly promising 334 result. 335 Outlook 336 In our opinion, the main challenge for future in vivo studies of metabolic complementarity will be to 337 better control the Ectocarpus-associated microbiome in co-culture experiments, and thus to avoid any 338 impact of non-inoculated microbes. The currently applied antibiotic treatments are successful in 339 . CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint . http://dx.doi.org/10.1101/813683 doi: bioRxiv preprint first posted online Oct. 22, 2019; 9 removing bacteria from the algal surface to a level where they are no longer detectable by 340 microscopy and spreading on culture medium, but once the treatment is stopped and algae are left to 341 recover, so do parts of the microbiome, possibly from spores that were inactive or embedded in the 342 algal cell wall and thus less susceptible to our treatments (Tetz and Tetz 2017). In the light of these 343 results, we strongly recommend routine metabarcoding analysis for any type of coculture experiment, 344 also in other model systems. One possibility in the future would be to use axenization protocols 345 based on the movement of gametes, as has been done for Ulva mutablilis (Spoerner et al. 2012); at 346 least some strains of Ectocarpus have previously been shown to produce phototactic gametes (Kawai 347 et al. 1990). A second alternative is the continuous use of antibiotics throughout the experiment, and 348 working with antibiotic-resistant bacterial strains. In this context a better understanding of the 349 metabolic requirements of the algae will help to durably maintain axenic cultures. 350 Despite these challenges, the present study constitutes an important proof of concept for the use of 351 metabolic complementarity to study simplified system of mutualistic host-symbiont interactions. We 352 anticipate that, in the long run, this concept can be applied not only to controlled co-culture 353 experiments, but that it will also prove useful for the interpretation of more complex datasets such as 354 metatranscriptomic or metagenomic data. 355

Conflict of Interest 356
The authors declare that the research was conducted in the absence of any commercial or financial 357 relationships that could be construed as a potential conflict of interest.  (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. Marinobacter-Roseovarius-Hoeflea; RIH = Roseovarius-Imperialibacter-Hoeflea. 389 . CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. . CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.  ys of nt . CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. 1% of the total number of reads and that were not inoculated (See Table 4 for the latter). This 434 heatmap as generated using the ClustVis service (Metsalu and Vilo 2015) using "correlation" as a 435 distance measure and "average linkage" as clustering method. The color code corresponds to the 436 mean sequence abundance for each OTU in the three replicates a percentage of total reads; uc. = 437 unclassified 438 439 tions 15 over . CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.