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1. Enhancing Sewage Sludge Stabilization, Pathogen Removal, and Biomass Production through Indigenous Microalgae Promoting Growth: A Sustainable Approach for Sewage Sludge Treatment

Author

Hajer Ben Hamed, Antoine Debuigne, Hetty Kleinjan, Dominique Toye, and Angélique Léonard

Subjects
sewage sludge, stabilization, pathogen removal, microalgae–bacteria biomass, sludge properties, extracellular polymeric substances, Environmental sciences, GE1-350
Abstract

Sewage sludge (SS), a byproduct of wastewater treatment plants, poses significant environmental and health risks if not properly handled. Conventional approaches for SS stabilization often involve costly and energy-consuming processes. This study investigated the effect of promoting native microalgae growth in SS on its stabilization, pathogen bacteria removal, and valuable biomass production. The effect on settleability, filterability, and extracellular polymeric substances (EPSs) was examined as well. Experiments were conducted in photobioreactors (PBRs) without O2 supply and CO2 release under controlled parameters. The results show a significant improvement in SS stabilization, with a reduction of volatile solids (VSs) by 47.55%. Additionally, fecal coliforms and E. coli were efficiently removed by 2.25 log and 6.72 log, respectively. Moreover, Salmonella spp. was not detected after 15 days of treatment. The settleability was improved by 71.42%. However, a worsening of the sludge filterability properties was observed, likely due to a decrease in floc size following the reduction of protein content in the tightly bound EPS fraction. Microalgae biomass production was 16.56 mg/L/day, with a mean biomass of 0.35 g/L at the end of the batch treatment, representing 10.35% of the total final biomass. These findings suggest that promoting native microalgal growth in SS could be sustainable and cost-effective for SS stabilization, microalgal biomass production, and the enhancement of sludge-settling characteristics, notwithstanding potential filtration-related considerations.

Published
2024
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2. Metabolic Complementarity Between a Brown Alga and Associated Cultivable Bacteria Provide Indications of Beneficial Interactions

Author

Bertille Burgunter-Delamare, Hetty KleinJan, Clémence Frioux, Enora Fremy, Margot Wagner, Erwan Corre, Alicia Le Salver, Cédric Leroux, Catherine Leblanc, Catherine Boyen, Anne Siegel, and Simon M. Dittami

Subjects
Ectocarpus siliculosus, symbiotic/mutualistic bacteria, genome-scale metabolic networks, metabolic complementarity, holobiont, Science, General. Including nature conservation, geographical distribution, QH1-199.5
Abstract

Brown algae are key components of marine ecosystems and live in association with bacteria that are essential for their growth and development. Ectocarpus siliculosus is a genetic and genomic model for brown algae. Here we use this model to start disentangling the complex interactions that may occur between the algal host and its associated bacteria. We report the genome-sequencing of 10 alga-associated bacteria and the genome-based reconstruction of their metabolic networks. The predicted metabolic capacities were then used to identify metabolic complementarities between the algal host and the bacteria, highlighting a range of potentially beneficial metabolite exchanges between them. These putative exchanges allowed us to predict consortia consisting of a subset of 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 modified 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 the holobiont.

Published
2020
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3. Exploring the Cultivable Ectocarpus Microbiome

Author

Hetty KleinJan, Christian Jeanthon, Catherine Boyen, and Simon M. Dittami

Subjects
Ectocarpus, holobiont, bacterial cultivation, brown macroalgae, dilution-to-extinction, metabarcoding, Microbiology, QR1-502
Abstract

Coastal areas form the major habitat of brown macroalgae, photosynthetic multicellular eukaryotes that have great ecological value and industrial potential. Macroalgal growth, development, and physiology are influenced by the microbial community they accommodate. Studying the algal microbiome should thus increase our fundamental understanding of algal biology and may help to improve culturing efforts. Currently, a freshwater strain of the brown macroalga Ectocarpus subulatus is being developed as a model organism for brown macroalgal physiology and algal microbiome studies. It can grow in high and low salinities depending on which microbes it hosts. However, the molecular mechanisms involved in this process are still unclear. Cultivation of Ectocarpus-associated bacteria is the first step toward the development of a model system for in vitro functional studies of brown macroalgal–bacterial interactions during abiotic stress. The main aim of the present study is thus to provide an extensive collection of cultivable E. subulatus-associated bacteria. To meet the variety of metabolic demands of Ectocarpus-associated bacteria, several isolation techniques were applied, i.e., direct plating and dilution-to-extinction cultivation techniques, each with chemically defined and undefined bacterial growth media. Algal tissue and algal growth media were directly used as inoculum, or they were pretreated with antibiotics, by filtration, or by digestion of algal cell walls. In total, 388 isolates were identified falling into 33 genera (46 distinct strains), of which Halomonas (Gammaproteobacteria), Bosea (Alphaproteobacteria), and Limnobacter (Betaproteobacteria) were the most abundant. Comparisons with 16S rRNA gene metabarcoding data showed that culturability in this study was remarkably high (∼50%), although several cultivable strains were not detected or only present in extremely low abundance in the libraries. These undetected bacteria could be considered as part of the rare biosphere and they may form the basis for the temporal changes in the Ectocarpus microbiome.

Published
2017
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4. Insights into the potential for mutualistic and harmful host‐microbe interactions affecting brown alga freshwater acclimation

Author

Hetty KleinJan, Clémence Frioux, Gianmaria Califano, Méziane Aite, Enora Fremy, Elham Karimi, Erwan Corre, Thomas Wichard, Anne Siegel, Catherine Boyen, Simon M. Dittami, Laboratoire de Biologie Intégrative des Modèles Marins (LBI2M), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), from patterns to models in computational biodiversity and biotechnology (PLEIADE), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Biodiversité, Gènes & Communautés (BioGeCo), Université de Bordeaux (UB)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Dynamics, Logics and Inference for biological Systems and Sequences (Dyliss), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Friedrich-Schiller-Universität = Friedrich Schiller University Jena [Jena, Germany], ABiMS - Informatique et bioinformatique = Analysis and Bioinformatics for Marine Science (ABIMS), Fédération de recherche de Roscoff (FR2424), Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), ANR-10-BTBR-0004,IDEALG,Biotechnologies pour la valorisation des macroalgues(2010), and European Project: 624575,EC:FP7:PEOPLE,FP7-PEOPLE-2013-IEF,MORPHIC(2014)

Subjects
meta-transcriptomics, virome, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Genetics, metabolic networks, low salinity acclimation, metabolite profiling, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, host–microbiome interaction, brown algae, Ecology, Evolution, Behavior and Systematics, holobiont, [SDV.EE.IEO]Life Sciences [q-bio]/Ecology, environment/Symbiosis
Abstract

International audience; Microbes can modify their hosts' stress tolerance, thus potentially enhancing their ecological range. An example of such interactions is Ectocarpus subulatus, one of the few freshwater-tolerant brown algae. This tolerance is partially due to its (un)cultivated microbiome. We investigated this phenomenon by modifying the microbiome of laboratory-grown E. subulatus using mild antibiotic treatments, which affected its ability to grow in low salinity. Low salinity acclimation of these algal-bacterial associations was then compared. Salinity significantly impacted bacterial and viral gene expression, albeit in different ways across algal-bacterial communities. In contrast, gene expression of the host and metabolite profiles were affected almost exclusively in the freshwater-intolerant algal-bacterial communities. We found no evidence of bacterial protein production that would directly improve algal stress tolerance. However, vitamin K synthesis is one possible bacterial service missing specifically in freshwater-intolerant cultures in low salinity. In this condition, we also observed a relative increase in bacterial transcriptomic activity and the induction of microbial genes involved in the biosynthesis of the autoinducer AI-1, a quorum-sensing regulator. This could have resulted in dysbiosis by causing a shift in bacterial behavior in the intolerant algal-bacterial community. Together, these results provide two promising hypotheses to be examined by future targeted experiments. Although they apply only to the specific study system, they offer an example of how bacteria may impact their host's stress response.

Published
2022
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5. Changing microbial activities during low salinity acclimation in the brown alga Ectocarpus subulatus

Author

Gianmaria Caliafano, Hetty KleinJan, Clémence Frioux, Enora Fremy, Simon M. Dittami, Anne Siegel, Méziane Aite, Catherine Boyen, Erwan Corre, Elham Karimi, Thomas Wichard, Laboratoire de Biologie Intégrative des Modèles Marins (LBI2M), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff (SBR), and Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
0106 biological sciences, [SDE.MCG]Environmental Sciences/Global Changes, Biology, brown algae, 01 natural sciences, Ectocarpus microbiome impacts stress tolerance Low salinity acclimation, 03 medical and health sciences, Algae, Botany, Microbiome, 030304 developmental biology, holobiont, 0303 health sciences, host-microbiome interaction, virome, Ectocarpus, biology.organism_classification, 6. Clean water, Holobiont, Brown algae, meta-transcriptomics, Quorum sensing, 13. Climate action, metabolic networks, metabolite profiling, Autoinducer, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Bacteria, 010606 plant biology & botany
Abstract

SummaryEctocarpus subulatus is one of the few brown algae found in river habitats. Its ability to tolerate freshwater is due, in part, to its uncultivated microbiome. We investigated this phenomenon by modifying the microbiome of laboratory-grown E. subulatus using mild antibiotic treatments, which affected its ability to grow in low salinity. The acclimation to low salinity of fresh water-tolerant and intolerant holobionts was then compared. Salinity had a significant impact on bacterial gene expression as well as the expression of algae- and bacteria-associated viruses in all holobionts, albeit in different ways for each holobiont. On the other hand, gene expression of the algal host and metabolite profiles were affected almost exclusively in the fresh water intolerant holobiont. We found no evidence of bacterial protein production that would directly improve algal stress tolerance. However, we identified vitamin K synthesis as one possible bacterial service missing specifically in the fresh water-intolerant holobiont in low salinity.We also noticed an increase in bacterial transcriptomic activity and the induction of microbial genes involved in the biosynthesis of the autoinducer AI-1, a compound that regulates quorum sensing. This could have caused a shift in bacterial behavior in the intolerant holobiont, resulting in virulence or dysbiosis.Originality-Significance StatementThe importance of symbiotic microbes for the health and stress resistance of multicellular eukaryotes is widely acknowledged, but understanding the mechanisms underlying these interactions is challenging. They are especially difficult to separate in systems with one or more uncultivable components. We bridge the gap between fully controlled, cultivable model systems and purely environmental studies through the use of a multi-omics approach and metabolic models on experimentally modified “holobiont” systems. This allows us to generate two promising working hypotheses on the mechanisms by which uncultivated bacteria influence their brown algal host’s fresh water tolerance.

Published
2021

6. Talk2clean: application of probiotics to control biofilm in industrial water circuits – a innovative application

Author

Christophe F. Meunier, Stéphane Nonet, Hetty KleinJan, and Mickael Michel

Subjects
Biofilm, Environmental science, Biochemical engineering, Industrial water, Electronic circuit
Abstract

Biofilms are omnipresent in industrial cooling water circuits and frequently lead to technical and economic problems. Yet, methods to remove biofilms may be inefficient, due to the EPS layer, which provides a protective layer against penetration of disinfectants. In combination with stricter legislation and increased environmental awareness, this has opened the search for alternative strategies to control biofilms in cooling water systems. In this study we implement a novel biofilm removal strategy, in which we target the intercellular language involved in the formation of biofilm structures. This language consists of signalling molecules (autoinducers) excreted by biofilm forming bacteria, a process referred to as quorum sensing (QS). We aim to alter QS via the activation of an antagonistic process called quorum quenching (QQ). Quorum quenching is a process that naturally occurs in bacterial communities; hence the final product is environment, - and user-friendly and thus a valuable alternative to the oxidising chemical products that are often used to clean cooling water circuits. Here we present the first application of a QQ-product tested on pilot scale using parameters that resemble industrial evaporative cooling towers. This setting is particularly interesting as it an open system, fed with various types of make-up water, and comprised of biofilms adjusted to high operating temperatures. The QQ product was tested using a closed, tube-like system, under continuous flow, fed with a propionic, - and acetic acid rich synthetic medium. Hydraulic retention time (HRT) was gradually shifted over a time span of 6 weeks. Heterotrophic plate counts were acquired once per week from the planktonic and biofilm phase. A pilot without the addition of QQ was ran in parallel as a control. Our results show that the QQ product reduced and delayed the formation of biofilm compared to the control. Interestingly, this difference diminished when the HRT was modified. Metagenomic analysis of the biofilm phase, revealed that 16S rRNA sequences corresponding to the QQ were also strongly reduced during this shift, indicating a wash-out of the QQ product. The exact interaction of HRT and presence of QQ will be analysed in more depth using QQ specific qPCR primers.

Published
2020
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7. Metabolic Complementarity Between a Brown Alga and Associated Cultivable Bacteria Provide Indications of Beneficial Interactions

Author

Alicia Le Salver, Hetty KleinJan, Anne Siegel, Clémence Frioux, Margot Wagner, Erwan Corre, Catherine Leblanc, Simon M. Dittami, Bertille Burgunter-Delamare, Catherine Boyen, Cédric Leroux, Enora Fremy, Laboratoire de Biologie Intégrative des Modèles Marins (LBI2M), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Dynamics, Logics and Inference for biological Systems and Sequences (Dyliss), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), from patterns to models in computational biodiversity and biotechnology (PLEIADE), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Biodiversité, Gènes & Communautés (BioGeCo), Université de Bordeaux (UB)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), ABiMS - Informatique et bioinformatique = Analysis and Bioinformatics for Marine Science (FR2424), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), This work was funded partially by the CNRS Momentum call, the ANR project IDEALG (ANR-10-BTBR-04) 'Investissements d’Avenir, Biotechnologies-Bioressources,' the European Union’s Horizon 2020 research and innovation Programme under the Marie Sklodowska-Curie grant agreement number 624575 (ALFF), and joint Ph.D. scholarship from the Brittany region (Project HOSALA) and the Sorbonne University (ED227)., Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, ABiMS - Informatique et bioinformatique = Analysis and Bioinformatics for Marine Science (ABIMS), Fédération de recherche de Roscoff (FR2424), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff (SBR), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1)

Subjects
0106 biological sciences, 010504 meteorology & atmospheric sciences, lcsh:QH1-199.5, Metabolite, genome-scale metabolic networks, Ocean Engineering, Aquatic Science, Biology, lcsh:General. Including nature conservation, geographical distribution, Oceanography, 01 natural sciences, Genome, 03 medical and health sciences, chemistry.chemical_compound, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, 14. Life underwater, Microbiome, lcsh:Science, symbiotic/mutualistic bacteria, 0105 earth and related environmental sciences, Water Science and Technology, 030304 developmental biology, holobiont, Genetics, Global and Planetary Change, 0303 health sciences, 010604 marine biology & hydrobiology, Ectocarpus siliculosus, fungi, food and beverages, Ectocarpus, biology.organism_classification, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Holobiont, Brown algae, chemistry, metabolic complementarity, lcsh:Q, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Bacteria
Abstract

Brown algae are key components of marine ecosystems and live in association with bacteria that are essential for their growth and development. Ectocarpus siliculosus is a genetic and genomic model for brown algae. Here we use this model to start disentangling the complex interactions that may occur between the algal host and its associated bacteria. We report the genome-sequencing of 10 alga-associated bacteria and the genome-based reconstruction of their metabolic networks. The predicted metabolic capacities were then used to identify metabolic complementarities between the algal host and the bacteria, highlighting a range of potentially beneficial metabolite exchanges between them. These putative exchanges allowed us to predict consortia consisting of a subset of 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.

Published
2020
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8. The genome of Ectocarpus subulatus – A highly stress-tolerant brown alga

Author

Erwan Corre, Didier Jollivet, Xi Liu, Jonas Collén, Noé Pontoizeau, Thierry Tonon, Simon M. Dittami, Gabriel V. Markov, Alexandre Cormier, Dominique Marie, Hwan Su Yoon, Catherine Leblanc, Loraine Brillet-Guéguen, Camille Trottier, Akira F. Peters, André E. Minoche, Sylvie Doubleau, Komlan Avia, Misharl Monsoor, Amandine Siméon, Angélique Gobet, Hetty KleinJan, Catherine Boyen, Christophe Caron, Agnieszka P. Lipinska, Chung Hyun Cho, Clémence Frioux, Agnès Groisillier, Marie-Mathilde Perrineau, Pierre Pericard, Irene González-Navarrete, Heinz Himmelbauer, Cécile Hervé, Méziane Aite, Anne Siegel, Ludovic Delage, Laboratoire de Biologie Intégrative des Modèles Marins (LBI2M), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Végétaux marins et biomolécules, Station biologique de Roscoff [Roscoff] (SBR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-GOEMAR-Centre National de la Recherche Scientifique (CNRS), ABiMS - Informatique et bioinformatique = Analysis and Bioinformatics for Marine Science (FR2424), Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Dynamics, Logics and Inference for biological Systems and Sequences (Dyliss), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Université de Rennes (UNIV-RENNES)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Université de Rennes (UNIV-RENNES), Santé de la vigne et qualité du vin (SVQV), Institut National de la Recherche Agronomique (INRA)-Université de Strasbourg (UNISTRA), Unité Mathématique Informatique et Génome (MIG), Institut National de la Recherche Agronomique (INRA), Diversité, adaptation, développement des plantes (UMR DIADE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), from patterns to models in computational biodiversity and biotechnology (PLEIADE), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université Sciences et Technologies - Bordeaux 1-Université Bordeaux Segalen - Bordeaux 2-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université Sciences et Technologies - Bordeaux 1-Université Bordeaux Segalen - Bordeaux 2-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Biodiversité, Gènes & Communautés (BioGeCo), Université de Bordeaux (UB)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université de Bordeaux (UB)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Evolution des Protistes et Ecosystèmes Pélagiques (EPEP), Adaptation et diversité en milieu marin (AD2M), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff [Roscoff] (SBR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Centre for Genomic Regulation [Barcelona] (CRG), Universitat Pompeu Fabra [Barcelona]-Centro Nacional de Analisis Genomico [Barcelona] (CNAG), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Tereos, ECOlogy of MArine Plankton (ECOMAP), Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff (SBR), Institut de Génomique Fonctionnelle de Lyon (IGFL), École normale supérieure - Lyon (ENS Lyon)-Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189 (CRIStAL), Ecole Centrale de Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Laboratoire des symbioses tropicales et méditerranéennes (UMR LSTM), Université Montpellier 1 (UM1)-Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Université de Montpellier (UM)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université Montpellier 2 - Sciences et Techniques (UM2), University of Aberdeen, Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Station biologique de Roscoff [Roscoff] (SBR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), DMAS, ONERA, Université Paris Saclay [Châtillon], ONERA-Université Paris-Saclay, Sung Kyun Kwan University, Max-Planck-Institut für Molekulare Genetik (MPIMG), Max-Planck-Gesellschaft, ABiMS - Informatique et bioinformatique = Analysis and Bioinformatics for Marine Science (ABIMS), Fédération de recherche de Roscoff (FR2424), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff (SBR), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Sungkyunkwan University [Suwon] (SKKU), Universitat Pompeu Fabra [Barcelona] (UPF)-Centro Nacional de Analisis Genomico [Barcelona] (CNAG), Key Laboratory of Intelligent Information Processing, Institute of Computing Technology [Beijing], Chinese Academy of Sciences [Changchun Branch] (CAS), Bezhin Rosko, Université de Rennes (UR), Université de Strasbourg (UNISTRA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Université de Bordeaux (UB)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Adaptation et diversité en milieu marin (ADMM), Institut national des sciences de l'Univers (INSU - CNRS)-Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Station biologique de Roscoff (SBR), Max Planck Institute for Molecular Genetics (MPIMG), University of York [York, UK], This work was funded partially by ANR project IDEALG (ANR-10-BTBR-04) 14'Investissements d’Avenir, Biotechnologies-Bioressources', the European Union’s Horizon 2020 research and innovation Programme under the Marie Sklodowska-Curie grant agreement number 624575 (ALFF), and the CNRS Momentum call. Sequencing was performed at the Genomics Unit of the Centre for Genomic Regulation (CRG), Barcelona, Spain, Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Laboratoire de Biologie Intégrative des Modèles Marins [LBI2M], ABiMS - Informatique et bioinformatique = Analysis and Bioinformatics for Marine Science [ABIMS], Dynamics, Logics and Inference for biological Systems and Sequences [Dyliss], Santé de la vigne et qualité du vin [SVQV], Unité Mathématique Informatique et Génome [MIG], Diversité, adaptation, développement des plantes [UMR DIADE], from patterns to models in computational biodiversity and biotechnology [PLEIADE], Evolution des Protistes et Ecosystèmes Pélagiques [EPEP], Centre for Genomic Regulation [Barcelona] [CRG], ECOlogy of MArine Plankton [ECOMAP], Institut de Génomique Fonctionnelle de Lyon [IGFL], Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL], Laboratoire des symbioses tropicales et méditerranéennes [UMR LSTM], DMAS, ONERA, Université Paris Saclay (COmUE) [Châtillon], and Max-Planck-Institut für Molekulare Genetik [MPIMG]

Subjects
0106 biological sciences, Victoria, Retrotransposon, Aquatic Science, Biology, Phaeophyta, 010603 evolutionary biology, 01 natural sciences, Genome, [SDV.GEN.GPL]Life Sciences [q-bio]/Genetics/Plants genetics, 03 medical and health sciences, Stress, Physiological, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Genetics, Gene family, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Gene, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, 0303 health sciences, Abiotic stress, Algal Proteins, Ectocarpus, biology.organism_classification, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Brown algae, Multicellular organism, Multigene Family, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Metabolic Networks and Pathways, 010606 plant biology & botany
Abstract

Brown algae are multicellular photosynthetic stramenopiles that colonize marine rocky shores worldwide. Ectocarpus sp. Ec32 has been established as a genomic model for brown algae. Here we present the genome and metabolic network of the closely related species, Ectocarpus subulatus Kützing, which is characterized by high abiotic stress tolerance. Since their separation, both strains show new traces of viral sequences and the activity of large retrotransposons, which may also be related to the expansion of a family of chlorophyll-binding proteins. Further features suspected to contribute to stress tolerance include an expanded family of heat shock proteins, the reduction of genes involved in the production of halogenated defence compounds, and the presence of fewer cell wall polysaccharide-modifying enzymes. Overall, E. subulatus has mainly lost members of gene families down-regulated in low salinities, and conserved those that were up-regulated in the same condition. However, 96% of genes that differed between the two examined Ectocarpus species, as well as all genes under positive selection, were found to encode proteins of unknown function. This underlines the uniqueness of brown algal stress tolerance mechanisms as well as the significance of establishing E. subulatus as a comparative model for future functional studies.

Published
2020
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9. Genome Sequences of 72 Bacterial Strains Isolated from Ectocarpus subulatus: A Resource for Algal Microbiology

Author

Elham Karimi, Erwan Legeay, Gwenn Tanguy, Enora Geslain, Hetty KleinJan, Simon M. Dittami, Erwan Corre, Laboratoire de Biologie Intégrative des Modèles Marins (LBI2M), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff (SBR), and Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Lineage (evolution), Microorganism, [SDV]Life Sciences [q-bio], Secondary Metabolism, Phaeophyta, brown algae, Genome, 03 medical and health sciences, alga-associated bacteria, Genetics, 14. Life underwater, Microbiome, detoxification, Symbiosis, Gene, Ecology, Evolution, Behavior and Systematics, Phylogeny, 030304 developmental biology, holobiont, 0303 health sciences, biology, Bacteria, 030306 microbiology, Ectocarpus, Vitamins, biology.organism_classification, biosynthetic gene clusters, Genome Report, Holobiont, Multicellular organism, metabolic networks, Genome, Bacterial
Abstract

Brown algae are important primary producers and ecosystem engineers in the ocean, and Ectocarpus has been established as a laboratory model for this lineage. Like most multicellular organisms, Ectocarpus is associated with a community of microorganisms, a partnership frequently referred to as holobiont due to the tight interconnections between the components. Although genomic resources for the algal host are well established, its associated microbiome is poorly characterized from a genomic point of view, limiting the possibilities of using these types of data to study host–microbe interactions. To address this gap in knowledge, we present the annotated draft genome sequences of seventy-two cultivable Ectocarpus-associated bacteria. A screening of gene clusters related to the production of secondary metabolites revealed terpene, bacteriocin, NRPS, PKS-t3, siderophore, PKS-t1, and homoserine lactone clusters to be abundant among the sequenced genomes. These compounds may be used by the bacteria to communicate with the host and other microbes. Moreover, detoxification and provision of vitamin B pathways have been observed in most sequenced genomes, highlighting potential contributions of the bacterial metabolism toward host fitness and survival. The genomes sequenced in this study form a valuable resource for comparative genomic analyses and evolutionary surveys of alga-associated bacteria. They help establish Ectocarpus as a model for brown algal holobionts and will enable the research community to produce testable hypotheses about the molecular interactions within this complex system.

Published
2019
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10. Revisiting Australian Ectocarpus subulatus (Phaeophyceae) from the Hopkins River: distribution, abiotic environment, and associated microbiota

Author

Aurélie Préchoux, Hetty KleinJan, Suhelen Egan, John A. West, Thierry Cariou, Simon M. Dittami, Bertille Burgunter-Delamare, Akira F. Peters, Catherine Boyen, Laboratoire de Biologie Intégrative des Modèles Marins (LBI2M), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut de biologie structurale (IBS - UMR 5075 ), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Station biologique de Roscoff [Roscoff] (SBR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Station biologique de Roscoff [Roscoff] (SBR), and Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
0106 biological sciences, Salinity, Victoria, Population, Zoology, Plant Science, Aquatic Science, Phaeophyta, 010603 evolutionary biology, Acclimatization, 01 natural sciences, 03 medical and health sciences, Rivers, Algae, Genus, Microbiome, education, 030304 developmental biology, Abiotic component, 0303 health sciences, education.field_of_study, biology, Ecology, Microbiota, 010604 marine biology & hydrobiology, Planctomycetes, Ectocarpus, biology.organism_classification, 6. Clean water, Brown algae, Adaptation, [SDE.BE]Environmental Sciences/Biodiversity and Ecology
Abstract

Ectocarpus is a genus of common marine brown algae. In 1995 a strain of Ectocarpus was isolated from Hopkins River Falls, Victoria, Australia, constituting one of few available freshwater or nearly freshwater brown algae, and the only one belonging to Ectocarpus. It has since been used as a model to study acclimation and adaptation to low salinities and the role of its microbiota in these processes. However, little is known about the distribution of this strain or whether it represents a stable population. Furthermore, its microbiota may have been impacted by the long period of cultivation.Twenty-two years after the original finding we searched for Ectocarpus in the Hopkins River and surrounding areas. We found individuals with ITS and cox1 sequences identical to the original isolate at three sites upstream of Hopkins River Falls, but none at the original isolation site. The osmolarity of the water at these sites ranged from 74-170 mOsmol, and it was rich in sulfate. The diversity of bacteria associated with the algae in situ was approximately one order of magnitude higher than in previous studies of the original laboratory culture, and 95 alga-associated bacterial strains were isolated from E. subulatus filaments on site. In particular, Planctomycetes were abundant in situ but rare in the laboratory-cultured strain.Our results confirm that E. subulatus has stably colonized the Hopkins River, and the newly isolated algal and bacterial strains offer new possibilities to study the adaptation of Ectocarpus to low salinity and its interactions with its microbiome.

Published
2019

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