Defence on Surface of Rhodophyta Halymenia Floresii

THESE DE DOCTORAT DE UNIVERSITE BRETAGNE SUD

ECOLE DOCTORALE N° 598 Sciences de la Mer et du littoral Spécialité : Biotechnologie Marine

Par Shareen A ABDUL MALIK

Defence on surface of Rhodophyta Halymenia floresii: metabolomic fingerprint and interactions with the surface-associated bacteria Thèse présentée et soutenue à « Vannes », le « 7 July 2020 » Unité de recherche : Laboratoire de Biotechnologie et Chimie Marines Thèse N°: Rapporteurs avant soutenance : Composition du Jury :

Prof. Gérald Culioli Associate Professor Président : Université de Toulon (La Garde) Prof. Claire Gachon Professor Dr. Leila Tirichine Research Director (CNRS) Museum National d’Histoire Naturelle, Paris Université de Nantes Examinateur(s) : Prof. Gwenaëlle Le Blay Professor Université Bretagne Occidentale (UBO), Brest Dir. de thèse : Prof. Nathalie Bourgougnon Professor Université Bretagne Sud (UBS), Vannes Co-dir. de thèse : Dr. Daniel Robledo Director CINVESTAV, Mexico i Invité(s) Dr. Gilles Bedoux Maître de conferences Université Bretagne Sud (UBS), Vannes

Title: Systèmes de défence de surface de la Rhodophycée Halymenia floresii : Analyse metabolomique et interactions avec les bactéries épiphytes Mots clés: Halymenia floresii, antibiofilm, antifouling, métabolomique, bactéries associées à la surface, quorum sensing, molecules de défense

Abstract : Halymenia floresii, une Rhodophycée présente Vibrio owensii, ainsi que son signal C4-HSL QS, a été une surface remarquablement exempte d'épiphytes dans les identifié comme pathogène opportuniste induisant un conditions de l'Aquaculture MultiTrophique Intégrée (AMTI). blanchiment. Les métabolites extraits de la surface et Ce phénomène la présence en surface de composés actifs de cellules entières de H. floresii ont été analysés par allélopathiques. L'objectif de ce travail a été d'explorer les LC-MS. Une base de données a été constituée à partir mécanismes de défense développés par H. floresii contre d’une analyse métabolomique non ciblée. Quarante et l'épibiose, de détecter et d'identifier les métabolites un putatives actifs ont été identifiés, parmi lesquels les secondaires produits à la surface de l’algue et d'étudier les composés halogénés, des furanones et divers relations avec les bactéries épiphytes. Nous avons ainsi pu inhibiteurs étaient surreprésentés. Fait intéressant, isoler la communauté épibactérienne de H. floresii cultivée les deux premières classes sont connues comme de dans des conditions contrôlées (AMTI) et non contrôlées puissants composés interférant avec le QS. La (échantillons collectés in situ). Les épibactéries isolées ont présence relativement élevée de putatives été criblées in vitro pour analyser les signaux de détection allélopathiques à la surface de H. floresii soutient de Quorum Sensing (QS). Lesextraits produits en surface fortement l'hypothèse selon laquelle ils doivent être ont été analysés pour détecter toute interférence avec le impliqués dans la protection de l'hôte. Des recherches Quorum Sensing. Les épibactéries pathogènes et non- supplémentaires seront nécessaires pour explorer pathogènes ont été différenciées par leur capacité à induire l’ensemble des métabolites secondaires produits par une maladie algale, le blanchiment. H. floresii et leurs rôles chez l’algue.

Title: Defence on surface of Rhodophyta Halymenia floresii: metabolomic fingerprint and interactions with the surface-associated bacteria Keywords: Halymenia floresii, antibiofilm, antifouling, metabolomics, surface-associated bacteria, quorum sensing, defence molecules

Abstract: The surface of Halymenia floresii, a Mexican Vibrio owensii was identified as an opportunistic Rhodophyta, was observed to be remarkably free of pathogen inducing bleaching in H. floresii which was epiphytes under Integrated MultiTrophic Aquaculture also associated to the presence of its C4-HSL QS (IMTA) conditions. This suggests the presence of signal. The surface and whole cell metabolites allelopathic active compounds released by this extracts from H. floresii specimens cultivated under macroalgae. The aim of this work was to explore the controlled conditions were analysed by means of defence mechanisms developed by H. floresii against LC/MS. An untargeted metabolomic analysis of H. surface epibiosis, to detect and identify the secondary floresii was performed to provide a global metabolic metabolites produced at the surface of the alga, and to fingerprint as a first baseline. We identified ‘41’ active study its relation with surface associated bacteria. For the putatives in H. floresii, among which halogenated first time, we isolated the epibacterial community of H. compounds, furanones and various inhibitors were floresii cultivated under controlled conditions (IMTA) and overrepresented. Interestingly, the first two classes uncontrolled ones (beach-cast material collected in the are well known potent QS interfering compounds. area). The isolated epibacteria were screened in vitro to The relatively higher occurrences of allelopathic analyse Quorum Sensing (QS) signals, and others H. putatives at the surface of H. floresii strongly floresii surface extracts were assayed for any QS supports the hypothesis that they must be involved in interference with them. We differentiated the epibacteria the host protection. Further investigations are significant pathogens from the non-pathogens ones by their needed to explore the secondary metabolites of H. ability to induce bleaching, a well-known algal disease. floresii and their role in the seaweed.

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

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Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

Empty your mind, be formless, shapeless — like water. Now you put water in a cup, it becomes the cup; You put water into a bottle it becomes the bottle; You put it in a teapot it becomes the teapot. Now water can flow or it can crash. Be water, my friend.

- Bruce Lee

Be careful what you water your dreams with. Water them with worry and fear and you will produce weeds that choke the life from your dream. Water them with optimism and solutions and you will cultivate success. Always be on the lookout for ways to turn a problem into an opportunity for success. Always be on the lookout for ways to nurture your dream

- Lao Tzu

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 Acknowledgements

Wow... Expressing gratitude is such a greatest human emotion and it’s very exciting to start acknowledging the BEings who have been and/or being with me throughout my PhD. This period of ‘3’ years in my life gave me a lot of people and lots and lots of wonderful experiences. And I am very grateful for it.

Firstly, I sincerely thank Prof. Gérald Culioli, Associate Professor of the Laboratory of Matériaux Polymères Interfaces Environnement Marin (MAPIEM), Université de Toulon (La Garde) and Dr. Leila Tirichine, Research Director of the Epigénomique des microalgues et interactions avec l’environment, Université de Nantes, for being rapporteurs and examining the work. I also thank other members of jury Prof. Claire Gachon, Professor, Museum National d’Histoire Naturelle and Prof. Gwenaëlle Le Blay, Professor, Université Bretagne Occidentale (UBO) for equally participating in examining my work.

I profoundly thank my doctoral school, Ecole Doctorale Sciences de la Mer et du Littoral (EDSML) for constantly supporting throughout my thesis and granting me scholarship for my international mobility. I also thank ECOS-Nord CONACYT for their financial support towards my research stay in Mexico. I sincerely thank GlobalSeaweedSTAR for their financial help towards attending the conference of ISS, 2019, Korea.

Well, my Directors of the thesis... Words are not enough to express my deep gratitude to Prof. Nathalie Bourgougnon, firstly for the offering me this wonderful opportunity to pursue my PhD under her wonderful supervision. Her immense knowledge always influenced me to think critically and develop my ideas. I always admired her way of valuing others TIME equally as she values her own. I could not have imagined having a better supervisor for my PhD study. I equally thank my Co-director Dr. Daniel Robledo for sharing his knowledge on his all-time favourite, macroalgae. I really appreciate his guidance in writing the publications for his insightful comments which incented me to widen my perspectives of the subject. Nothing would have been possible without their continuous support to my PhD.

My sincere thanks also go to Dr. Gilles Bedoux for his valuable comments and guidance in the chemistry part of the research and his advice on chromatographic analyses is highly valuable. I greatly appreciate his deep knowledge on the subject which really ignited my interest in the field of chemistry. I extend my gratitude to Dr. José Q. Garcia Maldonado for his guidance and comments in the molecular biology part of the research. His direction really helped me a lot and earned me a confidence in the field of molecular biology. I also gratefully acknowledge Prof. Alain Dufour and Dr. Alexis Bazire for their guidance and views in the quorum sensing related work of the research. I also sincerely thank Dr. Bazire for offering me

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 the reporter strains that really helped me carry out the QS related works. I also deeply thank Dr. Yolanda Freile-Pelegrín for her care and concern during my research stay in Mexico and also for the GC-MS instrument in her lab for analysing my extracts. Furthermore, I extend my deepest gratitude to Dr. Mahasweta Saha (Plymouth Marine Laboratory, UK) for her very detailed description and guidance through the disease part of the work. I also thank Mme. Florence Le Sann, Mme. Noluenn Chauvin and Mme. Axelle Guitton for their administrative help accompanying throughout the thesis, regarding the mobility’s and conferences. I also thank Yohan Duhautois for his administrative assistance regarding the formations.

Technicians are the real heroes of any lab. As my research includes different laboratories, I had an opportunity to meet many heroes during my thesis. First of all, Christel Marty (LBCM, France), I truly thank her for teaching and explaining me the preventive measures of the laboratory and being very supportive all through my research in LBCM. My heartfelt thanks to Crescencia and Edgar Caamal-Fuentes (Phycology, Mexico) for all their support and help, both professionally and morally throughout my stay in Mexico. I will never forget Cres’s help in finding me accommodation for my relaxed stay in Merida. Edgar’s knowledge on chemistry, particularly on GC-MS, always astonished me and I am very grateful for sharing it with me. I also thank V. Avila-Velazquez for his skillful technical assistance for the algal cultivation.

My earnest thanks to Philippe Douzenel (UBS, France) for his great help and support in carrying out different experimentations and especially for his support in HPLC related works. My heartfelt thanks extend to Laure Taupin (LBCM, France) for her tremendous and wonderful help and support in LC-MS related works. I should definitely mention her fastest reply to my doubts and her availability in sending me the results. I wholeheartedly thank Bernard Lassard (UBS, France) for all his help and support and very specially for the Luminometer. Being a very happy person, Bernard always gave me answers for my questions and never judged me for my silly doubts. And Abril Gamboa and Roscita (Parasitology and Molecular Biology, Mexico), truly I thank both of them for their immense support in molecular works and maintaining my bacteria. I deeply appreciate their efforts in understanding my needs though I could able to communicate only in English with them. I also truly thank Ana and Dora (Microscopy, Mexico) for their patience and assistance in travelling with me over the surface of my alga and finally getting good pictures of the microbiome.

And my team….LBCM… I should obviously start with Maya with whom I started my PhD days. Thank you, Maya for teaching and assisting me to learn basic experimentations in my early days. I sincerely thank Anne-Sophie getting me the algal samples from Mexico. I wholeheartedly thank Florence for BEing with

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 me during the very first isolation of my bacteria. I am also happy that I had a chance to meet Romain who also helped me learning basic manipulations. I happily thank Nolwenn, Marie and Kevin for having been there for me and I shall definitely mention about my coffee time with Nolwenn and chatting about the whole world. I have only few people in UBS to talk with and Hugo, you are first among them. Thank you very much, my friend for everything! For sharing your experiences and stories and moreover your family with me. I think of Paola, Emiliano, Rebecca at this time and of course, Gael. And IRDL… I sincerely owe my gratitude to the whole team for the beautiful smiling morning wishes.

And my team…. Phycology…. I dearly thank Ana for the beautiful friendship and all those big laughs we shared and of course Adrian too, though he did not have a clue of what we were talking he laughed with us. Well…now, Parasitology, I thank each and every one of the team for the wonderful everlasting experiences, in and out of the lab! I sincerely extend my gratitude to Dr. Victor and Dr. Leo for their very warm love and affection and especially for the Piñata during Christmas! I also thank them for allowing me to use the Epifluorescence microscope. I am very grateful to the whole team. Thanking the friends is not my way, I extend all my love and care to Karen, Eric, Roman, Fer, Sonia, Regina, Celina, Danilu, Catherine and I promise to stand by them as far as I can. I thank Danilu and Catherine, together with their family, for giving me most memorable days in my life by incorporating me in their life.

I also thank Mr. Jean-Roch Sauvé for the pleasant ride to Lorient for few of my manipulations there. Your help really matters a lot for me and moreover I cannot forget the joke you prepared for your daughter on 1st of April. And finally, Olivier Sire… Thank you for everything! I deeply extend my gratitude and love for BEing with me through my hard times, Just BE, my friend. Thank you very much, Olivier, particularly for clearing the fog on my way and walking me through it.

And my Family… it is hard to find words to thank them. Thank you Mummy…Machi…Thashu…Sunil ma…Kuttima…Mithu… and my dear Don. Thank you, Mummy for believing in me even at times when even I didn’t believe me. I promise you all that my every single action will make you all proud. And Raphael… Thank you da for being such a wonderful companion, for being my soul mate, roommate and my everything. Love you all forever and ever.

I should definitely mention few of several invisible people and friends who were with me in this journey. I wholeheartedly thank Ambika, Navashree annae and Sahana who supported me during all my visas processes. And I also thank Vimal annae, Baba annae, Sangeeth annae and all friends in Divonne-les bains who helped us commencing our journey to Bretagne and for all their moral support.

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Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

And To My DADDY… I made of one of your several DREAMS for me to come alive now…! Thank you for protecting me from Ignorance and enlightening me with the Wisdom, Daddy! JUST BE, Daddy! LOVE YOU FOREVER!

Shareen

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Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 List of Publications

The work of this thesis was published by means of the following publications.

Chapter I

A Abdul Malik S., Bedoux G., Garcia Maldonado J. Q., Freile-Pelegrín Y., Robledo D., & Bourgougnon N. (2020) Chapter 10. Defense on surface: macroalgae and their surface-associated microbiome. Seaweeds Around the World: State of Art and Perspectives. Advances Botanical research, Volume 95. ISBN: 978- 0-08-102710-3.

Chapter II

A Abdul Malik S., Bazire A., Gamboa A., Bedoux G., Robledo D., Garcia Maldonado J. Q., & Bourgougnon N. (2020) Quorum Sensing screening in the surface-associated bacteria of the Mexican red alga, Halymenia floresii. Microbiology, accepted (In press) (Manuscript ID: MICBIO2004A005AABDULM)

Chapter III

A Abdul Malik S., Bedoux G., Robledo D., Garcia Maldonado J. Q., Freile-Pelegrín Y., & Bourgougnon N. (2020) Chemical defense against microfouling by allelopathic active metabolites of Halymenia floresii (Rhodophyta). Journal of Applied Phycology 10.1007/s10811-020-02094-4.

Chapter IV

A Abdul Malik S., Saha M., Bedoux G., Robledo D., & Bourgougnon N. Identification and characterisation of the quorum sensing signal of the opportunistic pathogen causing bleaching disease in Halymenia floresii by HPLC/MS method (under preparation).

Chapter V

A Abdul Malik S., Bedoux G., Freile-Pelegrín Y., Bourgougnon N., & Robledo D. (2020) Screening Halymenia floresii secondary metabolites which underly its surface defence mechanisms. Metabolites, submitted (Manuscript ID: metabolites-956819).

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 Table of Contents

1. General Introduction ...... 2 1.1 Part I – Introduction to Macroalgae ...... 2 1.1.1 Macroalgae ...... 2 1.1.2 History and Classification ...... 2 1.1.3 Ecology ...... 3 1.1.4 Valorization of Macroalgae ...... 4 1.1.5 Macroalgae in Mexico ...... 6 1.2 Part – II– Red Macroalgae ...... 8 1.2.1 Introduction ...... 8 1.2.2 Biology of Red Macroalgae ...... 10 i. Cell wall ...... 10 ii. Polysaccharides: Structural and Storage ...... 11 a. Carrageenans ...... 11 b. Agar ...... 13 c. Starch ...... 13 iii. Pigments and Proteins ...... 16 iv. Lipids ...... 18 1.2.3 Species under Study - Halymenia floresii ...... 18 1.3 Part – III- Abiotic and Biotic parameters ...... 23 1.3.1 Introduction ...... 23 1.3.2 Definition and Types - Abiotic parameters ...... 24 i. Hydrodynamism ...... 24 ii. Nutrients ...... 25 iii. Desiccation ...... 25 iv. Temperature ...... 26 v. Light ...... 26 vi. Salinity ...... 26 1.3.3 Definition and Types of Biotic parameters ...... 27 i. Herbivory ...... 27 ii. Biofilm ...... 27 iii. Epiphytism ...... 28 1.4 Part – IV - Influence of the stressors (abiotic and biotic parameters) ...... 28

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 1.4.1 Introduction ...... 28 1.4.2 Chemical Defence ...... 29 i. Oxidative bursts ...... 30 ii. Halogenation of volatile and non-volatile compounds ...... 30 iii. Distance signaling...... 30 1.4.3 Microbial Defence...... 31 1.5 Part – V – Consequence of Holobiont break-up - Macroalgal Disease ...... 32 1.5.1 Introduction ...... 32 1.5.2 Definition ...... 33 1.5.3 Algal diseases ...... 33 1.5.4 Nature of pathogens ...... 34 1.5.5 Mechanisms of action of pathogen ...... 35 1.5.6 Impact of disease on the aquaculture sector ...... 36 1.6 Context and Aims of the Thesis ...... 38 2. Chapter I - Defence on Surface: Macroalgae and their associated-microbiome ...... 42 2.1 Abstract ...... 44 2.2 Introduction ...... 45 2.3 Seaweed surface-microbe interactions – biofilm development...... 46 2.3.1 Seaweed Surface: as a substratum...... 46 i. Cell wall structure ...... 47 ii. Surface topographical features...... 47 2.4 Macroalgae defence ...... 47 2.4.1 Removal of surface layers ...... 47 2.4.2 Production of Reactive Oxygen Species ...... 48 2.4.3 Antimicrobial compounds ...... 48 2.5 Microbiome on the surface ...... 50 2.5.1 Bacteria ...... 50 2.5.2 Fungi ...... 57 2.5.3 Microalgae ...... 60 2.5.4 Virus ...... 61 2.6 Role of Microbiome: Microbial Interactions and their Hosts Response ...... 62 2.6.1 Quorum Sensing ...... 63 2.6.2 Quorum Quenching ...... 65 2.6.3 Positive Effects: Symbiotism ...... 66

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Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 2.6.4 Negative Effects: Pathogenism ...... 68 i. Spatial effects ...... 69 ii. Temporal Effects ...... 70 2.7 Future Perspectives ...... 70 2.8 References ...... 72 3. Chapter II - Screening of surface-associated bacteria from the Mexican red alga Halymenia floresii for Quorum Sensing activity ...... 74 3.1 Abstract ...... 78 3.2 Introduction ...... 80 3.3 Materials and methods ...... 81 3.3.1 Algal material ...... 81 3.3.2 Isolation of surface-associated bacterial strains ...... 82 3.3.3 Characterization of selected bacterial strains ...... 82 i. Morphological and biochemical characterization ...... 82 ii. Molecular characterization ...... 82 iii. Phylogenetic analyses ...... 83 3.4 Screening of the selected bacterial for QS signals - Reporter assay ...... 83 3.4.1 Reporter strains ...... 83 3.4.2 Cross-feeding assay...... 83 3.4.3 Bioluminescent assay ...... 84 3.5 Statistical analysis ...... 84 3.6 Results ...... 84 3.6.1 Isolation and identification of surface-associated bacteria ...... 85 i. Morphological and biochemical characterization ...... 85 ii. Molecular characterization ...... 88 a. Alphaproteobacteria ...... 88 b. Gammaproteobacteria ...... 88 c. Bacteroidetes ...... 88 d. Firmicutes ...... 89 3.6.2 Detection and screening of QS signal production ...... 90 3.7 Discussion ...... 96 3.7.1 Isolation and identification of selected surface-associated bacteria of H. floresii ...... 96 3.7.2 Detection and screening of QS signals...... 98 i. Cross-feeding assay...... 98 ii. Bioluminescence assay ...... 99

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Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 3.8 Conclusions ...... 100 3.9 Future perspectives ...... 100 3.10 Acknowledgements ...... 101 3.11 References ...... 101 4. Chapter III - Chemical defense against microfouling by allelopathic active metabolites of Halymenia floresii (Rhodophyta) ...... 103 4.1 Abstract ...... 107 4.2 Introduction ...... 109 4.3 Materials & Methods ...... 111 4.3.1 Algal material ...... 111 4.3.2 Optimization of Selective Extraction of surface-associated metabolites ...... 111 i. Surface-associated metabolites (DIP) extraction – Determination of Immersion solvent and Immersion period ...... 111 ii. Whole-Cell Metabolites (WCM) extraction ...... 112 4.3.3 Quorum Quenching activity by Bioluminescent reporter assay...... 113 i. Bacterial strains – Isolation from the surface of H. floresii ...... 113 ii. Identification – Taxonomic affiliation of the surface-associated bacteria ...... 113 iii. Bioluminescence – Reporter Assay ...... 113 a. Preparation of supernatants ...... 113 b. Reporter strain ...... 114 c. Bioluminescent assay ...... 114 4.3.4 Untargeted Metabolomic profiling - Identification of the compounds by LC-MS ...... 114 i. LC-MS conditions ...... 114 ii. Pre-processing and processing of data: XCMS Online and the Madison Metabolite Consortium Database ...... 115 4.4 Statistical Analysis ...... 115 4.5 Results ...... 115 4.5.1 Algal material - Culture of H. floresii ...... 115 4.5.2. Optimization of Selective Extraction of surface-associated metabolites ...... 116 i. DIP Extraction...... 116 ii. WCM Extraction ...... 119 4.5.3. Quorum Quenching activity of DIP extracts by Bioluminescence – Reporter Assay ...... 119 i. Identification – Taxonomic affiliation of bacterial strains ...... 119 ii. Bioluminescence – Reporter Assay ...... 120 4.5.4. Untargeted metabolomic profiling ...... 121

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Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 4.6 Discussion ...... 124 4.6.1 Selective extraction of surface-associated metabolites ...... 124 4.6.2 Influence of DIP n-hexane extract on bacterial communication ...... 126 4.6.3 Untargeted Metabolomic Profiling ...... 127 4.7 Acknowledgements ...... 129 4.8 References ...... 129 5. Chapter IV - Identification and characterisation of the quorum sensing signal of the opportunistic pathogen causing bleaching disease in Halymenia floresii by HPLC/MS method ...... 132 5.1 Materials and Methods ...... 134 5.1.1 Algal material ...... 134 5.1.2 Bacterial isolates ...... 134 5.1.3 Tip bleaching assay with single epibacterial strains ...... 134 5.1.4 Extraction of HomoSerine Lactones (HSLs) from the ‘significant pathogen’ ...... 135 5.1.5 Chromatographic conditions – LC-MS QTOF analysis ...... 136 5.2 Results ...... 137 5.3.1 Tip bleaching assay with single epibacterial strains ...... 137 8.3.2 Identification of homoserine lactones (HSLs) from the ‘significant pathogen’ ...... 140 5.3 Discussion ...... 143 5.5 Conclusion and perspectives ...... 145 6. Chapter V - Screening Halymenia floresii secondary metabolites which underly its surface defence mechanisms ...... 147 6.1 Abstract ...... 153 6.2 Introduction ...... 155 6.3 Materials and Methods ...... 157 6.3.1 Algal material and extractions ...... 157 6.3.2 LC-MS conditions and data pre-processing ...... 158 6.3.3 LC-MS data processing – Madison Metabolomics Consortium Database...... 159 6.3.4 Data post-processing - SMILES ...... 160 6.3.5 Data Validation & Interpretation - Statistical Analysis ...... 161 6.4 Results ...... 163 6.4.1 LC-MS analyses ...... 163 6.4.2 Identifying the H. floresii secondary metbolites ...... 163 6.4.3 H. floresii secondary metabolites ...... 172 6.4.4 Hydrophobicity of the secondary metabolites ...... 176 6.5 Discussion ...... 177

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 6.6 Conclusion ...... 183 6.7 Acknowledgements ...... 184 6.8 References ...... 184 7. General Discussion ...... 186 7.1 Origin of the Thesis ...... 187 7.2 Holobiont: Halymenia floresii and the associated bacterial community ...... 187 7.3 The partner: associated bacteria ...... 188 7.3.1 Characterisation of bacterial community ...... 188 7.3.2 Influence of the epibacterial community...... 190 7.4 The Host - H. floresii ...... 191 7.5 Interactions among bacteria and the Host’s Interference ...... 193 7.5.1 Analysis of positive relation– symbiotism ...... 194 7.5.2 Putative opportunistic pathogenicity ...... 194 7.6 Aquaculture/Disease/Global Change ...... 195 7.7 Future perspectives ...... 196 7.8 Future possible experimentations ...... 200 8. Experimentation Setup ...... 203 8.1 Media composition ...... 203 8.2 Chemicals ...... 203 8.3 Antibiotics ...... 204 8.4 DNA Extraction Kits ...... 204 8.5 PCR Amplification – 16S ...... 205 9. References ...... 206

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Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

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Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 List of communications

Oral International

A Abdul Malik S., Gamboa A., Bedoux G., Garcia Maldonado J. Q., Bourgougnon N., & Robledo D. Composition, symbiosis and pathogenesis, an equilateral relationship of the surface-associated microbiome of the cultivated Mexican Rhodophyta, Halymenia floresii, 67th Annual Meeting of The British Phycological Society (07 January – 10 January, 2019) Scottish Association of Marine Sciences, Oban, Scotland.

A Abdul Malik S., Bedoux G., Robledo D., Garcia Maldonado J. Q., Freile-Pelegrín Y., & Bourgougnon N. Chemical defence by allelopathic active metabolites on the surface of cultivated Mexican Rhodophyta Halymenia floresii against biofouling, 23rd International Seaweed Symposium (28 April – 03 May, 2019), International Seaweed Association, Jeju, Korea.

Oral National

A Abdul Malik S., Caamal-Fuentes E., Ávila-Velázquez V., Bedoux G., Bourgougnon N., Robledo D. Involvement of allelopathic active metabolites on the surface of the cultivated Halymenia floresii in the biofouling phenomenon, Société Phycologique de France, Paris.

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Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 List of Figures (outside the chapters)

Fig. 1 Worldwide macroalgal production (%) under aquaculture per species and countries-year 2014 (Source: FAO, 2016) (Buschmann et al. 2017)...... 5 Fig. 2 Coastline of the Yucatan Peninsula including the two outstanding marine ecosystems, Gulf of Mexico and Caribbean Sea ...... 7 Fig. 3 Spatial distribution and Genus richness of Rhodophyta clade worldwide as depicted in (Keith et al. 2014)...... 9 Fig. 4 Schematic cell wall of red algae with characteristic polysaccharides (Stiger-Pouvreau et al. 2016)...... 11 Fig. 5 Types of carrageenans (Jiao et al. 2011)...... 13 Fig. 6 Floridean Starch ...... 14 Fig. 7 Floridoside ...... 15 Fig. 8 Isofloridoside ...... 15 Fig. 9 Digeneaside ...... 15 Fig. 10 R-Phycoerythrin ...... 17 Fig. 11 R-Phycocyanin ...... 17 Fig. 12 Allophycocyanin ...... 17 Fig. 13 Halymenia floresii, (Clemente & Rubio) C.A. Agardh (Rhodophyta, Halymeniales), from the Yucatan peninsula, Mexico ...... 19 Fig. 14 Observation of the surface of H. floresii under different microscopes; (a, b - cross and longitudinal section view of H. floresii under ‘Phase Contrast’ microscope; c, d - the surface of H. floresii under ‘Epifluorescence microscope’ (40x and 400x); e, f – the surface of H. floresii under ‘Scanning Electron Microscope’ (10 µm and 1 µm) ...... 21 Fig. 15 Main abiotic and biotic factors influencing the ecology and physiology of macroalgae (Lalegerie et al. 2020) ...... 24 Fig. 16 Defence mechanism of marine macroalgae against epibionts (da Gama et al. 2014) ...... 29 Fig. 17 Macroalgal diseases in different algal species. a – Red-rot in Pyropia (Kim et al. 2014); b – Red- rot in Pythium porphyrae mycelium (Kim et al. 2014); c – bleached D. pulchra (Zozaya-Valdés et al. 2016); d – bleached K. alvarezii green (Arasamuthu and Patterson Edward 2018); e – bleached coralline algae (downloaded from http://coralreefdiagnostics.com/); f – bleaching in H. floresii ...... 34 Fig.18 Isolation of the epibacterial strains by swabbing, streaking, and sub-culturing ...... 76 Fig. 19 Identification of the isolates by biochemical and molecular characterization ...... 76 Fig. 20 Reporter assays to screen the isolates for QS activity ...... 77 Fig. 21 Extraction of surface-associated metabolites by the method of DIP ...... 105 Fig. 22 Evaluation of Quorum Quenching activity by bioluminescent assay using E. coli pSB406 ...... 105 Fig. 23 Identification of the surface-metabolites by untargeted metabolomic approach ...... 106 Fig. 24 Extraction and analysis of surface (DIP) and whole-cell metabolites of H. floresii ...... 149

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Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 Fig. 25 Pre-processing of the raw data (.mzXML) in XCMS Online platform ...... 150 Fig. 26 Processing of the data in Madison Metabolomics Consortium Database (MMCD) ...... 151 Fig. 27 Metabolite organization to yield the corresponding structural and functional descriptors of the predicted metabolites ...... 151 Fig. 28 Statistical analysis to validate the data performed in ‘R Studio’ ...... 152 Fig. 29 Different classes of bacteria (%) isolated from the surface of H. floresii ...... 189 Fig. 30 Scanning electron micrographs of the surface of H. floresii; a – apical fronds; b – mid-thallus; c – stalk...... 190 Fig. 31 Count of Metabolites (‘41’) grouped based on their Activity/Biochemical classes (ATB – Antibiotics; ATC - Anti-cancerous; ATF – Antifungal; ATI - Anti-inflammatory; ATO - Anti-oxidant; ATP – Antiproliferative; ATV – Antiviral; INH – Inhibitors; DIV – Various; FUR – Furans; HAL – Halogenated; HOR – Hormones; IME - Intermediary Metabolites; KET – Ketones; LIP – Lipids; NUC – Nucleotides; PIG – Pigments; SAC - (Poly)-Saccharides; VIT - Vitamins) ...... 191 Fig. 32 Bioactive potential of H. floresii identified by untargeted metabolomics study ...... 198

List of Figures (in the chapters)

Chapter I

Fig. 1 Bioactive compounds produced by the red, green and brown macroalgae ...... 49 Fig. 2 Bioactive metabolites from the macroalgal associated bacteria ...... 56 Fig. 3 Bioactive metabolites from the macroalgal associated fungi ...... 59 Fig. 4 Diatoms embedded on the extracellular matrix of H. floresii (left) and associated with the macroalgal surface bacteria (right) ...... 61 Fig. 5 Different homoserine lactones with differing chain length produced by the bacteria ...... 64

Chapter II

Fig. 1 Induction of bioluminescence in the reporter E.coli pSB406 by Vibrio owensii (B3IM); Maribacter sp., (B9IM); Pseudoalteromonas arabiensis (B4BC); P. tetraodonis (B6BC) and V. owensii (B7BC) No. of cycles (30 minutes per cycle) plotted against relative light units/optical density at 600 nm ...... 92 Fig. 2 Induction of bioluminescence in the reporter E.coli pSB406 by Tenacibaculum sp., (B9BC); Spongiimicrobium sails (B9CC); Aquimarina sp., (B9.1CC); Alteromonas sp., (B12CC) and Pseudomonas aeruginosa PA01 (positive control). No. of cycles (30 minutes per cycle) plotted against relative light units/optical density at 600 nm ...... 93 Fig. 3 Induction of bioluminescence in the reporter E.coli pSB406 by Pseudoalteromonas sp., (B5BC); North sea bacterium (B6.1BC) and Alteromonas marina (B16CC). No. of cycles (30 minutes per cycle) plotted against relative light units/optical density at 600 nm. Culture period, in hours, of the isolates were denoted as ‘h’...... 94

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 Fig. 4 Induction of bioluminescence in the reporter E.coli pSB406 by Ruegeria lacuscaerulensis (B9CC); Uncultured bacterium clone (B9.2CC) and Erythrobacter sp., (B14CC). No. of cycles (30 minutes per cycle) plotted against relative light units/optical density at 600 nm. Culture period, in hours, of the isolates were denoted as ‘h’...... 95 Fig. 5 Induction of bioluminescence in the reporter E.coli pSB406 by Roseobacter sp., (B13CC) and Thalassococcus sp., (B20CC). No. of cycles (30 minutes per cycle) plotted against relative light units/optical density at 600 nm. Culture period, in hours, of the isolates were denoted as ‘h’...... 96

Chapter III

Fig. 1 Selective extraction of surface-associated and whole-cell metabolites. Diagram of extracts analyses and data processing of cultivated H. floresii (CC)...... 112 Fig. 2 Epifluorescence micrographs showing the cortical cells of H. floresii after being dipped in n- hexane for 10s; 20s; 30s; 40s; 50s and 60s and positive control treated in exactly the same way with sterile seawater for 30s...... 117 Fig. 3 Number of intact and lysed cells vs. immersion period (in seconds) after dipping in n-hexane counted using ImageJ software. The y-axis represents the Means ± SE of epifluorescence micrographs of H. floresii at ‘5’ different angles (n=3)...... 118 Fig. 4 (a-d) Quorum quenching activity of the DIP extract on the chosen phylotypes. a –Vibrio owensii; b – Tenacibaculum sp.; c – Maribacter sp. and d – Aquimarina sp. No. of cycles (30 minutes per cycle) plotted against relative light units/optical density at 600 nm...... 121 Fig. 5 TIC of DIP_n-hexane (red) and WCM_n-hexane (blue) crude extract after retention time correction by XCMS Online platform...... 122 Fig. 6 LC-MS spectrum of surface extract showing bromine isotopic pattern (red arrow marks) a characteristic of halogenated metabolite...... 128

Chapter IV

Fig. 1 Schematic illustration of HSLs extraction process………………………………………………..136 Fig. 2 Risk of thallus tip bleaching in Halymenia floresii after inoculation of ‘25’ bacterial strains relative to control thalli without such inoculation. No. of replicates (n=6) were assigned for each isolate. Error bars ± in % ...... 138 Fig. 3 Chromatogram of V. owensii HSL extract pre-processed in XCMS Online platform ...... 141 Fig. 4 Chromatogram showing the presences of C4 HSL in the standard and V. owensii extract at a retention time of 6.2 to 7.0 minutes ...... 141 Fig, 5 Mass Spectrum showing the fragments of C4 HSL in the standard and V. owensii extract as 102.05 (lactone ring); 172.09 (M+H)+ and 194.08 (M+Na)+...... 142 Fig. 6 Chemical structures corresponding to the mass spectrum at 102.05 (lactone ring); 172.09 (M+H)+ and 194.08 (M+Na)+...... 142

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Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

Chapter V

Scheme I Schematic workflow of untargeted metabolomic approach on Halymenia floresii ...... 162 Fig. 1 Metabolite counts of the Whole-cell metabolites (WCM); surface-associated metabolites (DIP) and common metabolites (CMN) plotted against Activity/Biochemical classes (symbols are from Table 1).164 Fig. 2 Left: Relative class occurrence (%) of the Whole-cell metabolites (WCM); surface metabolites (DIP) and common metabolites (CMN) plotted against Activity/Biochemical classes (symbols as in Fig. 1). Right: Metabolic count (A) and Relative Class occurrence (B) in % of the Whole-cell metabolites (WCM=416) and common metabolites (CMN=41) plotted against Activity/Biochemical classes ...... 165 Fig. 3 Dendrogram resulting from the hierarchical cluster analysis (HCA) of ’41’ metabolites ordered into ‘8’ clusters (I -VIII) at a Ward distance of 1.0. The ‘26’ compounds for which a bioactivity has been reported are green labelled ...... 172 Fig. 4 Clustering of the H. floresii ‘41’ common metabolites (CMN) according to chemical structures (retrieved from Data Warrior) calculated from their SMILES strings. Numbers correspond to Table 2 ordering...... 174 Fig. 5 Entanglement of chemical (left) and lipophilicity (right) dendrograms. The level of entanglement is 0.535 (intermediate between 0 and 1) ...... 176 Fig. 6 Lipophilicity of the H. floresii secondary metabolites as a function of the compound's bioactivity. Octanol-water partition coefficients are calculated from the XLOGP3 method ...... 177

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Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 List of Tables

Chapter I

Table 1 List of bacteria isolated from the ‘3’major phyla of macroalgae ...... 51 Table 2 List of fungi isolated from the ‘3’major phyla of macroalgae ...... 57 Table 3 List of microalgae isolated from the macroalgae ...... 60 Table 4 List of viruses isolated from the macroalgae ...... 62

Chapter II Table 1 Motility, Gram stain and Biochemical characteristics of the isolates ...... 86 Table 2 Halymenia floresii surface-associated bacteria; * - Strain with <97% similarity...... 89 Table 3 Screening of the isolates based on QS signals production; * - Strain with <97% similarity...... 91

Chapter III

Table 1. Concentration of the H. floresii, DIP extracts with different solvents and WCM extracts with n- hexane ...... 118 Table 2. Taxonomic affiliation of the isolated phylotypes...... 119 Table 3. List of common metabolites in DIP and WCM n-hexane extracts proposed by the MMCD mass bank ...... 123

Chapter IV

Table I Epibacterial strains isolated from Halymenia floresii tested for their pathogenicity and/or non- pathogenicity in tip bleaching assay ...... 139

Chapter V

Table 1 Codes assigned, based on the bioactivity (functional descriptors) and/or chemical class (chemical descriptors), to the metabolites (DIP and WCM) proposed by the massbank ...... 159 Table 2 Codes assigned to the ‘41’ CMN metabolites, names of the putatives, raw/chemical formula, confidence level (∆), and actual mass as proposed by the database MMCD, experimental m/z features (Exp m/z), retention time (RT in minutes) and maximum intensity resulted from the XCMS Online platform, the corresponding Bioactivity /Biochemical classes of putatives (BIO) and their LOGP3 octanol water partition coefficients (XLogP3-AA). ND – Not Detected...... 166

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Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 List of Abbreviations

AHLs – Acylated Homoserine Lactones AHPND – Acute Hepato Pancreatic Necrosis Disease AIs - AutoInducers AIPs – Auto Inducing Peptides ANOVA – Analysis of Variance BC – Beach-cast BHL - N-Butryl-DL-Homoserine Lactone BLAST – Basic Local Alignment Search Tool CC – Cultivar Chamber CFB – Cytophaga-Flavobacterium-Bacteroides CMN - Common DCM - Dichloromethane DIN – Dissolved Inorganic Nitrogen EDTA – Ethylene Diamine Tetraacetic acid HHL - N-Hexanoyl-DL-Homoserine Lactone IMTA – Integrated Multi Trophic Aquaculture LB – Luria Broth MMCD – Madison Metabolomic Consortium Database MA – Marine Agar MB – Marine Broth MeOH – Methanol NCBI – National Center for Biotechnology Information OOHL - N-(3-Oxooctanoyl)-L-homoserine lactone PCR – Polymerase Chain Reaction rRNA – ribosomal Ribonucleic acid RLU – Relative Light Unit SMILES – Simplified .sdf - Structure-Data File TAE – Tris base, Acetic acid, EDTA QS – Quorum Sensing QSI – Quorum Sensing Interference QQ – Quorum Quenching

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Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 UV – Ultra-Violet WCM – Whole-Cell Metabolites XCMS – ‘X’ (Any) Chromatography Mass Spectrometry

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Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 Genesis of the project

In macroalgal cultivation, epiphytism and fouling are the major problems, severely reducing the productivity and cost efficiency of a diverse range of open-water and on-shore farms. Antifouling practices predominantly include the use of copper-based antifoulant coating, in combination with practical fish husbandry and site management practices. With rapid expansion of the aquaculture industry, coupled with the tightening of legislation on the use of antifouling biocides, the problems of aquaculture biofouling are increasing. The development of environmentally safe antifouling substances is urgently needed. In such a circumstance, during the experimental cycle of culture of Mexican Rhodophyta, Halymenia floresii (Clemente) C. Agardh cultivated in experimental Integrated Multi-trophic Aquaculture systems (IMTA) at onshore tanks, it was observed that, contrary to the tank cultures with Eucheuma, the walls and the propellers remained clean of any exogenic algae colonisation. The presence of H. floresii appeared to limit the establishment of opportunist green algae (Cladophora, Chaetomorpha or Ulva) and the colonisation of barnacles usually disturbing the cultures. Furthermore, the surface of the alga was remarkably free from settlement by fouling organisms. This ecological phenomenon could reveal the release of allelopathically active compounds interfering with the settlement and growth of competitors.

The ECOS Nord project is an international scientific cooperation project between FRANCE and MEXICO with the aim of upgrading the macroalgae by combining innovative biotechnologies and process for human health. This project brings together the Laboratory of Marine Biotechnology and Biochemistry (LBCM), University of South Brittany (UBS), FRANCE and the research team of the Department of Marine Resources integrated into The Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV), MEXICO. With this perspective of ECOS Nord, the objective of this project was proposed to better understand the ecological phenomenon behind the exceptional observation in onshore tanks of H. floresii culture.

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Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

1. General Introduction

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 1. General Introduction

1.1 Part I – Introduction to Macroalgae

1.1.1 Macroalgae

Approximately 70% of Earth’s surface is occupied by a continuous stretch of seawater with an average depth of 5 km (Baweja et al. 2016) with a rich source of both biological and chemical diversity. Oceans support floating forests with variable sea plants and animals, where marine vegetation is considered to be more primitive and richer than land-based plants. Among them, macroalgae (seaweeds) are important marine resources playing a major role in supporting the rich biodiversity of the sea. Macroalgae constitute a diverse polyphyletic group of predominantly marine, multicellular, photosynthetic, chlorophyll “a” containing, eukaryotic organisms with simple reproductive structures and found from the intertidal zone to 300 m deep (Levine 2016).

1.1.2 History and Classification

Most algae are photosynthetic organisms that acquired the trait some 1.8 billion years ago after an unknown non-photosynthetic unicellular eukaryote engulfed or was invaded by a photosynthetic cyanobacterium similar to Gloeomargarita. This trait ultimately resulted in a cell containing a photosynthetic plastid surrounded by two membranes with highly reduced cyanobacteria derived genome. This event, known as the primary endosymbiosis, gave rise to the extant Archaeplastida, which includes the Glaucophyta, Rhodophyta (red algae), and Chloroplastida (green algae and land plants) (Tirichine and Bowler 2011; Leliaert et al. 2012; Adl et al. 2019). Brown algae have a very different evolutionary history, as they have acquired the ability to achieve photosynthesis through a secondary endosymbiosis event. The other lines such as Cryptophytes, Haptophytes, Golden brown algae and Dinophytes, are therefore eukaryotes with no direct relationship with terrestrial plants. The lines resulting from secondary or even tertiary endosymbioses between a primitive non-photosynthetic eukaryote and a unicellular red alga include the Ochrophytes within Stramenopiles, Cryptophytes and Haptophytes lines. Derived from endosymbiosis with a green unicellular alga, the lines are grouped within Euglenophyceae and Rhizaria (Niklas and Kutschera 2010). Such events involved a second heterotrophic eukaryote that engulfed not a cyanobacterium but a green or red photosynthetic eukaryote and resulted in plastids with three or four membranes around them.

The customary notion of macroalgae actually encompasses a polyphyletic complex within eukaryotes (Boudouresque 2015). The evolutionarily diverse macroalgae are found in two kingdoms,

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 Plantae and Chromista, and four phyla, Charophyta (Chara), Chlorophyta (green), Rhodophyta, and Ochrophyta (brown). Most marine macroalgae belong to phylum Ochrophyta, class Phaeophyceae, and to phylum Rhodophyta. A few marine macroalgae occur in the phylum Charophyta and about 600 large green algal taxa occur in the phylum Chlorophyta. Macroalgae in these four clades have more fundamental (e.g. cytological, chemical, life histories) differences between one another than with the vascular plants (Dawes 1998). Dring (1982) listed 900 species of green algae, 997 brown algae, and 2540 red algae worldwide. These advancements in phycology have resulted in major changes in the classification of algae (Schneider and Wynne 2007, 2013; Wynne and Schneider 2010). Thus, the number of macroalgal species has increased. Guiry and Guiry (2020) listed 4885 species in the Charophyta and 6759 in the Chlorophyta, 2057 species in the class Phaeophyceae, and 7303 species in the Rhodophyta. In W.R. Taylor’s 1957 flora Seaweeds of the Northeastern Coast of North America, he listed 170 genera (37 green, 57 brown, 76 red algae) and 401 species (95 green, 142 brown, and 164 red algae), while Mathieson and Dawes (2016) have described 248 genera (57 green, 89 brown, 102 red) and 535 species (146 green, 180 brown, and 209 red taxa) for the same geography (Dawes 2016). As multicellular eukaryotes, the classification of marine macroalgae are made according to their photosynthetic pigments like green (chlorophyll α and β), red (phycoerythrin) and brown (xanthophyll/fucoxanthin) (Sudhakar et al. 2018).

1.1.3 Ecology

Macroalgae are ecologically important primary producers, competitors, and ecosystem engineers. They play a vital role in ecosystems as foundation species by providing habitat for higher trophic levels (Fulton et al. 2019). They constitute one of the important biotic components and play a central role in the different ecosystem in coastal habitats ranging from kelp forests to coral reefs (Gattuso et al. 2006; Gachon et al. 2010; Keith et al. 2014; Singh and Reddy 2016; Krause-Jensen et al. 2018). Seaweeds serve as the base of the marine food webs and are a direct food source for sea urchins and fish. In addition, they provide shelter and reproductive grounds for fish, invertebrates, birds, and mammals. Seaweeds along with coral animals are the dominant benthic organisms whose relative abundance is often used as an indicator of ecosystem health (Baweja et al., 2016).

The distribution of seaweeds depends upon many factors such as physical (substrate, temperature, light quality and quantity, dynamic tidal activity, winds, and storms), chemical (salinity, pH, nutrients, gases, and pollution level), and biological (herbivores, microbes, epiphytes, endophytes, symbionts, parasites, and diseases). Indeed, as sessile organisms, macroalgae have developed effective mechanisms to survive many biotic and live in a variable, extreme, and hostile abiotic environmental and stress conditions. As a result, these beings are able to produce a wide range of compounds called "secondary metabolites", like pigments, vitamins, phenolic compounds, sterols, other bioactive agents and some extremely relevant

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 biochemical mechanisms with biomedical and agricultural applications (Ioannou and Roussis 2009; Stengel et al. 2011; Lalegerie et al. 2020).

1.1.4 Valorization of Macroalgae

Marine algae contain significant quantities of vitamins, minerals, dietary ﬁbers, proteins, polysaccharides, and various functional polyphenols while nutrient contents can vary with species and, geographical location, season, and temperature. With such potential, macroalgae are subjected to numerous valorizations, particularly in the fields of human and animal nutrition. As human food, the valorization is direct. More than 600 species of seaweeds are edibles and used as vegetables in the world. Their nutritive value may vary depending on the geographic location, the season of the year, the growth stage, part of the seaweed harvested, etc. (Leandro et al., 2019). Thus, to assure the nutritional value of seaweeds, they need to be evaluated before being used as supplements. Seaweeds are industrially processed to extract thickening agents such as alginate, agar, and carrageenan cell wall polysaccharides or used, generally in dried powder form, as an animal-feed additive (Delaney et al. 2016). According to FAO (2018), some 221 species of seaweed are of commercial value and about ten species, such as brown seaweed (Saccharina japonica, Undaria pinnatifida, and Sargassum fusiforme); red seaweed (Porphyra spp. Eucheuma spp. Kappaphycus alvarezii and Gracilaria spp.); and green seaweed (Enteromorpha clathrata, Monostroma nitidum and Caulerpa spp.). Red macroalgae, such as Gelidium, Gracilaria and Pterocladia, are important for human consumption and other uses, mainly as a binder in food products as well as the bacterial substrate in laboratories. Eucheuma and Kappaphycus are essential for the manufacture of carrageenan, used in cosmetics, food processing and industrial usage (FAO, 2018).

Macroalgae and their uses have a long history. Human and macroalgal interactions seem to date back to the Neolithic period and over the years the industrial use of macroalgae has shifted as the potential of the future industry has been viewed as being larger than its actual scale. From exploiting beach-cast macroalgae as fertilizer to their higher value as a source of polysaccharides and their technical applications in agronomics, cosmeceuticals, nutraceuticals and pharmaceuticals (Galland-Irmouli et al. 1999; Smit 2004; Fleurence et al. 2012; Buschmann et al. 2017), the potential of macroalgae has raised the need of their extensive cultivation. By far the largest producers are China and Indonesia followed by the Philippines, Korea, Japan, Malaysia, and Zanzibar in Asia whereas in the Americas only Chile has appeared in the farming statistics tables (Fig. 1). In contrast, Europe, Canada, and Latin America, the macroalgal industries stilly rely on natural harvest (Rebours et al. 2014).

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

Fig. 1 Worldwide macroalgal production (%) under aquaculture per species and countries-year 2014 (Source: FAO, 2016) (Buschmann et al. 2017)

According to FAO statistics (FAO, 2014, 2016), of the top seven most cultivated seaweed taxa, three were used mainly for hydrocolloid extraction: Eucheuma spp. and Kappaphycus alvarezii for carrageenans, and Gracilaria spp. for agar; Saccharina japonica (formerly Laminaria japonica), Undaria pinnatifida, Porphyra/Pyropia spp. and Sargassum fusiforme were most important in human food usage (Fig. 1). The main producing countries were China, Indonesia, and the Philippines, which were also those that cultivated the greatest diversity of seaweed species. While seaweed consumption in South-east Asia has been common and traditional and has depended on taste and price, seaweed use as food in non-Asian European and USA markets has considered additional parameters such as nutritional value and ‘food for health’, with a strong consumer preference towards organic, sustainable and fair trade products (representing low impacts both on the environment and biodiversity) (Chapman et al. 2015; Gomez Pinchetti and Quintana 2016). However, the global market share of seaweed farming production used for food and ‘other uses’, i.e. other than for hydrocolloids, is still below 1% of the total biomass production (Buschmann et al. 2017). In 2014, about 28.5 million tonnes of seaweeds and other algae were harvested for direct consumption or further processing for food (traditionally in Japan, the Republic of Korea, and China) or for use as fertilizer and in pharmaceuticals, cosmetics and other purposes (FAO, 2016). In 2015 the total production of seaweed was 30.4 million tonnes, with the culture and capture sectors responsible for 29.4 million tonnes and 1.1 million tonnes, respectively (FAO, 2018).

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 The global seaweed industry is worth more than USD 6 billion per annum (approximately 12 million tonnes per annum in volume) of which 85% comprises food products for human consumption. The seaweed-derived extracts make up almost 40% of the world’s hydrocolloid market in terms of foods (FAO, 2018). The increasing worldwide demand for raw materials raises the issues of sustainability, access, or availability. Aquaculture presents an alternative to ensure the availability of this resource and improve its homogeneous quality. In addition, advances in the development of seaweed cultivation can compensate the wild production and collection, and thus improving productivity and quality of seaweed biomass. Fulfilling aquaculture's growth potential requires responsible technologies and practices. Sustainable aquaculture should be ecologically efficient, environmentally benign, product-diversified, profitable, and societally beneficial. This ultimately paved a way for the emergence of innovative aquaculture practices and one such method is Integrated Multitrophic Aquaculture (IMTA), which can help address many of the environmental impacts of aquaculture. It also aims at a balanced ecosystem-based management approach to aquaculture (Chopin 2013, 2017).

IMTA aims to be an ecologically balanced aquaculture practice that co-cultures species from multiple trophic levels to optimize the recycling of farm waste as a food resource (Troell et al. 2009). By integrating fed aquaculture (finfish or shrimp) with inorganic and organic extractive aquaculture (macroalgae and invertebrates), IMTA can significantly increase the sustainability of aquaculture. The IMTA system is driven only by sunlight, natural inorganic nutrients, as well as carbon dioxide and provides efficient food provisions, nutrient extraction, and climate regulating services to the marine ecosystem (Cottier-Cook et al. 2016). The land-based IMTA operations have also been advocated as a way of controlling diseases and their transmission, though some concerns have been expressed that multiple species on the site might increase the risk of disease transmission (Chopin 2013). Still, this raises a question on density rather than the diversity of the species.

1.1.5 Macroalgae in Mexico

The coastline of Mexico extends for 11 769 km of which 72% is located at the Pacific Coast and 28% at the Gulf of Mexico and the Caribbean coast (Robledo 2006). They include five geographic regions in temperate to tropical latitudes, Baja California, Gulf of California, Tropical Pacific, Gulf of Mexico, and the Mexican Caribbean with distinctive physiographic, geological, and climatic conditions make possible the existence of a very diverse algal flora. The Gulf of Mexico and the Caribbean Sea (Fig. 2) are two outstanding marine ecosystems joining in the Yucatan channel. Even though the Yucatan Peninsula (known as a biotic province) occupies 17.4% of the total of the Mexican coast, the investigation of marine resources has not been intensively investigated (Pech-Puch et al. 2020). This particular geographical situation promotes the existence of high diversity and abundance of different marine algal species, which represent

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 a potential source of bioactive compounds and food (Morales et al. 2006; Vázquez-Delfín et al. 2019; Alemañ et al. 2019).

Fig. 2 Coastline of the Yucatan Peninsula including the two outstanding marine ecosystems, Gulf of Mexico and the Caribbean Sea

Macroalgae of Mexico are very diverse. All three major types, green, brown, and red macroalgae show a greater species richness. In Mexico, 2,702 macroalgal species were described including freshwater (1,102 species) and marine (1,600 species) and nearly 800 species were reported in the Yucatan Peninsula. The diversity of habitats in the Yucatan Peninsula favours the most diverse red algae group of the region, with approximately 350 recorded species, distributed in about 15 taxonomic families (Dreckmann 1998; Ortega et al. 2001; Mendoza-González et al. 2007, 2016; Wynne 2017). In spite of their high diversity algal drifts, however, in this region of Mexico pose a challenge rather than their economic importance. In Yucatan, the most abundant families that drift for both the number of species and the amount of biomass were Gracilariaceae (10 species), Rhodomelaceae (6 species) and Solieriaceae (3 species) (Nuñez Resendiz et al. 2019).

In Mexico, for about 50 years, the commercial exploitation of phycocolloid-producing algae has occurred continuously and they exported 439 tonnes of macroalgae in 2009 (Robledo et al. 2013). The commercial harvesting of natural populations of macroalgae includes three species of Rhodophyta,

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 Gelidium robustum, Chondracanthus canaliculatus, and Gracilariopsis lemaneiformis (Robledo 2006) and one species of Phaeophyta, Macrocystis pyrifera (Robledo et al. 2013).

The Peninsula of Yucatan has 1,940 km of coastline, reportedly with the rich diversity of macroalgae. Rhodophyta has the greatest diversity on the coasts of Yucatan. A large variation in the chemical composition of the macroalgae from Yucatan was observed, particularly in soluble carbohydrates, ash, and protein (Robledo and Freile-Pelegrín 1997) which encouraged the experimental cultivation of native and exotic species during the years of 2000-2010. The lower seawater temperature may explain the abundance and diversity of macroalgal species as well the cold and warm water currents influence the distribution patterns of marine macroalgae (Robledo 2006). The presence of structurally diverse secondary metabolites is invaluable due to its potential application in drug discovery (Stengel et al. 2011; Goulitquer et al. 2012; Altmann 2017; Wells et al. 2017; Hernández-Bolio et al. 2019; Pech-Puch et al. 2020). Being a most conspicuous component on the Yucatan coasts, red algal species are highly valued in the international market for their secondary metabolites (Nuñez Resendiz et al. 2019).

1.2 Part – II– Red Macroalgae

1.2.1 Introduction

Rhodophyta represents an ancient photosynthetic lineage of eukaryotes that arose through primary endosymbiosis, with a fossil record dating back over 1.2 billion years (Butterfield 2000). Presently the phylum contains about 7303 species (Guiry and Guiry 2020) of which 3% is freshwater taxa (Sheath and Cole 1984). Based on molecular and morphological studies, Saunders and Hommersand (2004) identified three subphyla in the Rhodophyta: Metarhodophytina, Rhodellophytina, and Eurhodophytina. With the subphylum Eurhodophytina containing macroalgae divided into two classes: Florideophyceae (6950 species) and Bangiophyceae (182 species). Florideophyceae regroups many orders: Ahnfeltiales (11 species), Ceramiales (2690 species), Corallinales (603 species), Gelidiales (235 species), Gigartinales (951 species), Gracilariales (238 species), Halymeniales (357 species), Nemaliales (281 species), Palmariales (45 species), and Rhodymeniales (410 species). Bangiophyceae is a taxonomic unit less diversified. Among the two orders constitutive of the class, the order Bangiales regroups 182 recognized species with specimens of the genera Bangia and Porphyra/Pyropia (Schneider and Wynne 2007; Verbruggen et al. 2010; Yoon et al. 2016; Adl et al. 2019).

Many red algae are found in the intertidal and subtidal zones to depths of up to 40, or occasionally, 250 m, and of course, they have the ability to live at greater depths in the ocean than the members of other algal groups. They probably represent one of the oldest groups of eukaryotes and most diversified groups.

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 From a morphological point of view, the majority of Rhodophyta is filamentous (Antithamnion), pseudoparenchymatous (Palmaria), or parenchymatous (only in Bangiales, Porphyra/Pyropia). Ceramium and Polysiphonia present a vegetative and siphoned filamentous. Some species are calcified. Corallinales contain a form of calcite (calcium carbonate) and are important contributors to the formation of tropical reefs. Red macroalgae are found in tropical, temperate and arctic regions, but are generally the most abundant in temperate regions and in tropical regions on sandy or rocky substrates (Keith et al. 2014). The overall distribution of genus richness for Rhodophyta had a hotspot on the west coast of North America and also, they had the lowest genus richness in the tropics compared with higher altitudes (Fig. 3).

Red algae are considered as the most important source of many biologically active metabolites in comparison to the other algal class. The majority of macroalgal secondary metabolites has discovered in red macroalgae accounting for >1500 bioactives (Amsler et al. 2005; Maschek and Baker 2008; Wijesekara et al. 2011; Stengel et al. 2011). Meanwhile, the red macroalgae are wild-harvested from 32 countries, indicating their widespread popularity, with a total of 216 456 t harvested worldwide (FAO, 2014).

Fig. 3 Spatial distribution and Genus richness of Rhodophyta clade worldwide as depicted in (Keith et al. 2014).

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 1.2.2 Biology of Red Macroalgae

One of the oldest groups of eukaryotic algae, red algae are an ancient group of eukaryotic plants and the majority of them are filamentous (Polysiphonia), pseudoparenchymatous (Ceramium) or parenchymatous (Porphyra/Pyropia, Halymenia). Rhodophyceae vary considerably in their response to different environmental factors, having adapted to living in nearly freshwater to brackish and marine conditions. Members of red algae are found in the tropical, temperate, and arctic waters, but are most abundant in the temperate and tropical regions. The common features of red algae include eukaryotic cells, a complete lack of flagellar structure, food reserves of floridean starch, the presence of phycobiliproteins (details in “Pigments and Proteins”) chloroplasts without stacked thylakoids and no external endoplasmic reticulum (Baweja et al. 2016). The members of Rhodophyta can tolerate a wider range of light levels than any other groups of photosynthetic organisms and this is supported by the presence of additional accessory pigments together with chlorophyll a. These accessory pigments generally known as phycobiliproteins allow them to live in deep waters. The major product of photosynthesis in red algae is floridoside (O-α-d- galactopyranosyl-(1,2)-glycerol), isofloridoside digeneaside or floridean starch (details in ‘Starch’). Another very common feature of red algae is pit connections providing symplastic communication between cells (Lee 2008). Marine red algae contain significant quantities of vitamins, dietary fibers, proteins, polysaccharides, and various micro- and macro elements (Baweja et al. 2016).

i. Cell wall

The cell wall organization and composition of the Rhodophyta differ significantly from plants and other macroalgae. The cell wall is a rather well-ordered structure as observed by optical and electronic microscopy (Gordon-Mills et al. 1978; Craigie 1990). The cell wall is considered here as including polymeric materials originating from the metabolic activities of the algae. Most investigators recognize polysaccharides, proteoglycans, peptides, proteins, lipids, and associated inorganic constituents as components of the native cell wall of red algae. According to previous studies on biological function, the polysaccharides may be grouped with the more rigid structural (β-linked) glycans such as cellulose, mannans, and xylans, as well as with the more flexible and frequently sulfated glycans that comprise the matrix in which the skeletal fibers are embedded (Rees 1981; Baweja et al. 2016).

The wall of red algae is classically illustrated by two phases or matrices:

1) neutral lower crystalline form with cellulose that forms a weft-like mat (Dawes et al. 1961). Some xylan and mannan have been also characterized in the amorphous embedding matrix.

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 2) charged phase that contains sulphated galactan polymers, some of which are economically important phycocolloids (carrageenan or agar). The cell wall consists of rigid components such as microfibrils and a mucilaginous matrix. (Fig. 4) (Stiger-Pouvreau et al. 2016).

Fig. 4. The schematic cell wall of red algae with characteristic polysaccharides (Stiger-Pouvreau et al. 2016).

Under the optical microscope and in electron microscopic studies Gracilaria corticata reveals cells having thick cell walls that have microfibrils arranged in three distinct layers: (i) the inner-most electron- dense (glycoprotein domain), (ii) middle electron-translucent (amorphous matrix) and (iii) outermost electron-dense regions (fibrillar wall). In all three regions, microfibrils are arranged in parallel. The extracellular matrix consists of a cellulose microfibrillar network and an amorphous matrix of cellulose, sulphated galactans, and mucilage.

ii. Polysaccharides: Structural and Storage

a. Carrageenans

Red seaweeds contain large amounts of cell-wall polysaccharides, most of which are sulphated galactans. These galactans are generally built on repeated alternating l,3-linked α-galactopyranose and l,4- linked β-galactopyranose units and differ in the level and pattern of sulphation, in the substitution of

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 methoxyl and/or pyruvate groups and in other sugar residues (galactose, xylose). They also differ in 3,6- anhydrogalactose content and the configuration of the 1-3-linked α-galactopyranose residue (Percival and Mc Dowell 1967; Craigie 1990; Usov 1998). Among these galactans, carrageenans and agars are widely used as gelling or thickening additives by the food industry and in biotechnologies.

The main carrageenans on the market belong to kappa (κ-; G4S-DA), iota (ι-; G4S-DA2S), and lambda (λ-; G2S-D2S,6S) (Fig. 5). Minor types are mu (µ-; G4S-D6S) and nu (ʋ-; G4S-D2S6S), which are κ- and ι-precursors, respectively (Fig. 5). ‘G’ defines the units linked in α (1-3) and ‘D’ for the units linked in β (1-4) and the substituents linked to the units were named as ‘M’ for methyls and ‘S’ for sulphates and they can be linked to the carbons from 1-6. The 3, 6 anhydrogalactose units (3,6 AG) are differentiated by the letters DA (Knutsen et al. 1994). Natural carrageenans usually occur as mixtures of different hybrid types, such as κ/ι-hybrids, κ/μ-hybrids or μ/ι-hybrids, which often form cyclized derivatives. Especially, the repeating unit of κ-carrageenan is composed of a D-galactose with a sulphated group at C4 linked to a hydrogalactose, the repeating unit of λ-carrageenan is constructed by a D-galactose with a sulphated group at C2 linked to a D- galactose sulphated at C2 and C6, and the repeating unit of ι- carrageenan consists of galactose with a sulphated group at C4 linked to an anhydrogalactose sulphated at C2 (Vera et al. 2011).

Carrageenans constitute 30%-75% of the red algal cell wall by their dry weight and are extensively used for stabilizing and texturing products in the food industry, which accounts for 70–80% of the total world carrageenan production estimated at about 60,000 tonnes/year with a value of US$629 million (McHugh 2003; Bixler and Porse 2011; Naseri et al. 2019). Based on this, and on assuming a yield of carrageenan extraction in the industrial scale of 20%, it can be estimated that the total dried macroalgal consumption was at least 300,000 tonnes/year (Naseri et al. 2019).

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

Fig. 5 Types of carrageenans (Jiao et al. 2011)

b. Agar

Agar consists of a family of compounds mainly constituted by agarose and agaropectin. Agarose is a polymer of repeating residues of 3-β-D-galactopyranose and 3,6-anhydro-α-L-galactopyranose. Agar possesses the property of being insoluble in cold water. The principal distinguishing feature of the less sulfated agars is the presence of D-galactose, L-galactose, or anhydro L- galactose. The seminal concept of the masked repeating structure first reported for the agar-like porphyran and the repeating units may be substituted or modified in a number of ways to mask the underlying pattern. In that case, the agarobiose repeating structure of agars may be masked by replacing 3,6-anhydro-L- galactose with L-galactose, and/or adding methyl ethers, sulfate hemiesters, and, exceptionally, pyruvic acid ketal at specific sites on either glycosyl unit (Stiger-Pouvreau et al. 2016). Agar is an important phycocolloid, occurs in members of the orders Gelidiales with over 200 species and Gracilariales with 250 taxa (Dawes 2016).

c. Starch

The main characteristic reserve storage polysaccharide of red macroalgae is floridean starch, first described by Kutzing in 1843 (Stiger-Pouvreau et al. 2016). Floridean starch represents the major sink for photosynthetically fixed carbon and, under certain growth conditions, floridean starch granules can amount

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 to as much as 80% of the total cell volume (Ekman et al. 1991). The starch from red algae also differs from higher plant starches in its apparent lack of amylose (except in some unicellular species) (McCracken and Cain 1981). The floridean starch a branched α-1,4-glucosidic linked glucose homopolymer with α-1,6- branches (Fig. 6) functioning as carbon and energy reserve in the cells (Yu 1992). They were stored in the cytoplasm as a major reserve food and also has an osmoregulatory function (Dawes 2016).

Fig. 6 Floridean starch

Red algae constitute an exception to this rule, unlike chlorophytes, as they synthesize and store starch as granules outside their plastids in the cytosol (Pueschel 1990). Compositionally, floridean starch granules are constructed from a polymer more similar to amylopectin than glycogen (Peat et al. 1961; Manners and Wright 1962; Craigie 1974) and with similar structural features to higher plant starches, e. g. a radially arranged fibrillar-like pattern and concentric layers (Meeuse et al. 1960; Sheath et al. 1981).

The soluble organic carbon compounds are fairly diverse: floridoside and related compounds. Floridoside (Fig. 7) is the principal low-molecular-weight carbohydrate present in all orders of the Rhodophyta except the Ceramiales, while the isofloridoside (Fig. 8) only occurs at significant concentrations in members of the Bangiales where it may exceed the levels of floridoside. Isofloridoside could be considered as resulting from the isomerization of floridoside rather than as a direct product of photosynthesis. In most members of the Ceramiales, digeneaside (O-α-D-mannopyranosyl-(1-2)- glyceric) (Fig. 9) is the dominant low-molecular carbohydrate (Kremer 1981).

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

Fig. 7 Floridoside

Fig. 8 Isofloridoside

Fig. 9 Digeneaside

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 iii. Pigments and Proteins

It is well known that red algae have high protein levels (Galland-Irmouli et al. 2000; Wells et al. 2017). Reports have shown that they have almost 47% w/w of dry matter (Sánchez-Machado et al. 2004). It is noteworthy that the protein content of macroalgae varies not only between species (Fleurence et al. 2018) but also among seasonal periods, geographic location, culture conditions, and processes, biomass storage, and analytical methods used (Mishra et al. 1993; Wells et al. 2017). Although the structure and biological properties of algal proteins are still relatively poorly documented, the amino acid composition among several species of algae is known (Harnedy and FitzGerald 2011). Most red algae contain all the essential amino acids and are a rich source of the acidic residues asparatic and glutamic acid (Fleurence et al. 2018). The predominance of acidic over basic amino acids is typical of red algae (Galland-Irmouli et al. 2000), their high levels being responsible for the algal flavor and taste (Mabeau et al. 1992). Threonine, lysine, tryptophan, cysteine, methionine, and histidine have been shown to be present at low levels in macroalgal proteins. Phycobiliproteins (PBS), the water-soluble proteins (MacColl 1998) are the main proteins of the red algae, representing up to 50% of the total protein content. They are a family of fluorescent proteins covalently linked to tetrapyrrole groups, known as bilins, a prosthetic group (Niu et al. 2007). These proteins act as antennae, absorbing energy in the portions of the visible spectrum where chlorophyll barely does (Sekar and Chandramohan 2008). Unlike carotenoids and chlorophylls, phycobiliproteins are not part of the photosystems located in the lipid bilayer but constitute a structure attached to the cytoplasmic surface of thylakoid membranes named phycobilisomes.

The red colour of the algae results from the dominance of the pigments R-phycoerythrin and R- phycocyanin and these masks the other pigments, chlorophyll a (no chlorophyll b), beta carotene, and a number of unique xanthophylls. Chlorophyll-b is replaced by the chlorophyll-d. The chief xanthophyll is taraxanthin (Bold and Wynne 1985). R-phycoerythrin (phycoerythrin from Rhodophyta) (Fig. 10) is an oligomeric water-soluble chromoprotein characterized by an absorption spectrum with three peaks at 499, 545, and 565 nm (Denis et al. 2009). The apparent molecular weight of R-phycoerythrin is approximately

240 - 260 kDa. It has three protein (αβ)6γ subunits: α, β and γ, whose apparent molecular weights are 18,

20 and 30-33 kDa, respectively. R-phycocyanin (Fig. 11) is a minor pigment with an (αβ)3 trimeric structure and is the sole algal biliprotein possessing both phycoerythrobilin (PEB) and phycocyanobilin (PCB) prosthetic groups (Fan-Jie et al. 1984; Dumay and Morançais 2016). The maximum absorption is observed at 533 and 544 nm and the molecular weights of R-phycocyanin subunits, α and β are 18 and 20 kDa (Glazer

and Hixson 1975). Allophycocyanin (Fig. 12) is composed of a trimer (αβ)3 with a molecular weight of 110 kDa with maximum absorption at 650 nm. Each subunit of R-phycoerythrin has two PEB chromophores

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 and the R-phycocyanin has one PEB and one PCB chromophores whereas both the subunits of allophycocyanin possess one PCB chromophore (Dumay and Morançais 2016).

Fig. 10 R-Phycoerythrin

Fig. 11 R-Phycocyanin

Fig. 12 Allophycocyanin

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 iv. Lipids

Despite the lower amount of lipids and fatty acids in red macroalgae, there is a significantly higher level of polyunsaturated fatty acids that act as strong antioxidants, such as omega-3 (n=3) and omega-6 (n=6) (Mendis and Kim 2011). They are known to contain around twenty carbon-chain fatty acids: eicosapentaenoic acid (EPA, ω-3 C20:5) and arachidonic acid (AA, ω-6 C20:4). Red algae contain a proportion of terpene compounds, tocopherols, and sterols and they differ from others by the presence of oxylipins, oxygenated fatty acids, known to intervene in cell differentiation, immune responses and homeostasis (Gerwick et al. 1999). Glycolipids are glycosylated derivatives of glycerols and ceramides, known as glycoglycerolipids and glycosphingolipids, respectively and they play an important role in energy transfer during photosynthesis. Their localization within the membrane contributes to the structural stability of bilayer, to the protection and regulation of any chemical stress and they also act as markers in cell communication (Boudière et al. 2014). Similarly, the phospholipids are represented as glycophospholipids and sphingophospholipids and they are formed of two fatty acids and a phosphate group binding to different polar compounds such as glycerol, serine, choline, ethanolamine, and inositol. In glycophospholipids, the fatty acids are linked to glycerol via an ester bond to the phosphate group whereas in sphingophospholipids it is linked by an amino alcohol structure of sphingosine type (Terme et al. 2017).

1.2.3 Species under Study - Halymenia floresii

The genus Halymenia (Halymeniaceae, Rhodophyta) currently includes 76 taxonomically accepted species (Guiry and Guiry, 2020) out of the 178 species names and 44 intraspecific in the database (www.algaebase.org). Recent studies on phylogenetic analyses revealed that Halymenia is a polyphyletic genus, which requires further taxonomic studies (Tan et al. 2015, Araujo et al. 2016). The genus is mainly characterized by gelatinous thalli, the presence of anticlinal filaments and refractive ganglionic cells in the medulla, stellate cells in the inner cortex, and auxiliary cell ampullae with branched secondary filaments (Balakrishnan 1961; Abbott 1967; Chiang 1970; De Smedt et al. 2001; Araújo De Azevedo et al. 2016).

Classification

Empire: Eukaryota Kingdom: Plantae Subkingdom: Biliphyta Phylum: Rhodophyta Subphylum: Eurhodophytina Class: Florideophyceae Subclass: Rhodymeniophycidae

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 Order: Halymeniales Family: Halymeniaceae Genus: Halymenia Species: H. floresii

Fig. 13 Halymenia floresii (Clemente & Rubio) C.A. Agardh (Rhodophyta, Halymeniales), from the Yucatan peninsula, Mexico

Halymenia was established by C. Agardh (1817) with Halymenia floresii (Clemente) C. Agardh collected from Cádiz, Spain as the generitype. Chiang (1970) used the architecture of auxiliary cell ampullae as a primary feature to group species at the generic level in the Halymeniaceae (Tan et al. 2015). Halymenia is a widespread genus distributed throughout the world’s oceans (Rao and Gupta 2015), but mainly in tropical and subtropical regions (Gargiulo et al. 1986; Kawaguchi and Lewmanomont 1999; Hernández-Kantun et al. 2009). This genus presents mucilaginous to softly cartilaginous thalli, cylindrical

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 or foliose, narrowed or wide fronds or entire, cleft, or branched. The thallus is described as an erected blade- like, with a discoid holdfast with or without a short stipe (Guiry and Guiry 2020).

The species, H. floresii (Fig. 13), is named to honour D. Francisco Flores and the epithet should accordingly be “floresii” not “floresia” as commonly named in the literature. Halymenia floresii (Clemente & Rubio) C. A. Agardh is one of the most characteristic representatives from the Gulf of Mexico. In the Yucatan peninsula coast of Mexico, H. floresii dominates rocky substrates between 3 and 40 m where it grows up to 50 cm high and has been considered of economic interest because of its human consumption in Asian countries and its λ-carrageenan content (Godínez-Ortega et al. 2008; Freile-Pelegrín et al. 2011). Two of the 15 species of Halymenia distributed in tropical and subtropical western Atlantic coasts have been described for the Yucatan Peninsula. Halymenia floresii from Progresso, Yucatan and Isla Mujeres, Quintana Roo, and H. duchassaingii from Quintana Roo. For the Mexican Caribbean, its presence is rare and has been found in the dry season (March) growing on solid rocks or coral debris at depths of 5-40 m, whereas in Yucatan it grows on rocky substrates at a depth of 8-15 m and is abundant as beach cast material during north wind season (October-February) (Robledo and Freile-Pelegrín 2011).

Halymenia floresii (Clemente) C. Agardh, is widely distributed in warm temperate to tropical seas. Despite their widespread availability the studies on this species, H. floresii, are sparingly made to date. Until now Halymenia has not been exploited and cultivated commercially although it is an interesting source of raw material for carrageenan and bioactive studies (Robledo and Freile-Pelegrín 2011; Freile- Pelegrín and Robledo 2016). The microtopography of H. floresii was observed under the different microscopes is shown in Fig. 14. The epifluorescence images depict the intact cortical cells of the alga (Fig. 14 (d)) and the electron micrographs showed a thick matrix of polysaccharide over the cortex (Fig. 14 (f)).

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

Fig. 14 Observation of the surface of H. floresii under different microscopes; (a, b - cross and longitudinal section view of H. floresii under ‘Phase Contrast’ microscope; c, d - the surface of H. floresii under ‘Epifluorescence microscope’ (40x and 400x); e, f – the surface of H. floresii under ‘Scanning Electron Microscope’ (10 µm and 1 µm)

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 An ecophysiological study on H. floresii from the Mexican coast related to growth and chromatic adaptation evidenced the presence of highly sulphated carrageenan, with minor amounts of 3,6– anhydrogalactose of lambda-family carrageenan (Godínez-Ortega et al. 2008; Freile-Pelegrín et al. 2011). The crude sulphated polysaccharide of H. floresii has been reported to exhibit anticoagulant and antibacterial activity (Rodrigues et al. 2011; Meesala et al. 2018). Halymeniaol isolated from H. floresii off the west coast of India showed significant antimalarial activity (Meesala et al. 2018). The methanolic extract of H. floresii from Yucatan coasts has selective antibacterial activity against Bacillus subtilis (Morales et al. 2006). Berti et al. in 1962 observed the antiviral activity of H. floresii against the A-PR8 influenza virus. The dichloromethane:methanol extract of H. floresii showed cytotoxic activity and antiproliferative activity against human laryngeal carcinoma (Hep-2) cells, human cervical adenocarcinoma (HeLa) cells, and human nasopharyngeal carcinoma (KB) cells (Moo-Puc et al. 2009).

H. floresii has a good supply of fatty acids including three saturated fatty acids (SAFA – C14:0, C16:0, and C18:0) and a monosaturated fatty acid (MUFA – C18:0∆9) (Ortega et al. 2012). In relation to the light-quality treatments the H. floresii thalli exhibited different response patterns regarding their pigment concentrations (Godínez-Ortega et al. 2008). Pliego-Cortés et al. (2017) reported that H. floresii grew rapidly during experimental trials under IMTA with a three-fold increase in phycoerythrin content and also reported an increase in chlorophyll-a and total carotenoids. It was also suggested that H. floresii is a good candidate to recycle inorganic nutrients in a land-based IMTA system (Pliego-Cortés et al. 2017). In addition, Polat et al. (2008) observed a high protein content in H. floresii of 3.05% of its dry weight and 2.46% lipids.

In one of the study areas of this work, H. floresii is cultivated in a IMTA system, a land-based outdoor culture system located at the Cinvestav Coastal Marine Station at Telchac, Yucatan, Mexico (21°20´28´ N, 89°18´25´ W) (Peñuela et al. 2018). The IMTA system designed for the present study is described as follows: a circular 2000 L tank was stocked with 19 adult snooks (Centropomus undecimalis). The effluent from this tank was connected to a circular 700 L tank used as sedimentation tank to trap particulate organic matter. In the sedimentation tank, 12 chocolate chip sea cucumbers (Isostichopus badionotus) were placed to feed with the excess organic matter generated. Finally, the effluent from these tanks were used to cultivate H. floresii at a density of 3 g fresh weight L-1. H. floresii vegetative fragments were placed into 10 replicate 50 L containers supplied with a continuous flow of wastewater from the sedimentation tank (Figure 3). All tanks and containers were continuously aerated.

Other species of Halymenia have also been studied around the world. The alcoholic extract of Halymenia porphyroides, collected from the Gujarat coast, exhibited moderate hypotensive and diuretic activities (Lakshmi et al. 2006). Carrageenan, a sulphated polysaccharide, has been isolated from H. 22

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 porphyroides, while a large number of saturated and unsaturated fatty acids have been isolated and characterized from it by Shameel. Lai et al. (1994) carried out detailed investigations on sulphated polysaccharides obtained from the genus, Halymenia, and reported that due to high heterogeneity, these polysaccharides did not resolve well. H. durvilliae contains a high level of iodine (0.25% dry weight), magnesium (1.65%) and zinc (25.69 ppm) (Lakshmi et al. 2006).

To the best of our knowledge, this is the first of this kind on this species, H. floresii, regarding surface-associated metabolites, which ultimately led to metabolite fingerprinting of the species, and its surface-associated microbiome.

1.3 Part – III- Abiotic and Biotic parameters

1.3.1 Introduction

The external parameters such as the geographic location, environmental, season, sampling conditions, ocean warming, and acidification have direct physiological effects on habitat-forming species and thus directly affecting their secondary and primary metabolites (Stengel et al. 2011; Lalegerie et al. 2020). Thus, to protect and adapt themselves against these abiotic (temperature, hydrodynamism, salinity, etc.) and biotic (epiphytism, herbivory, etc.) (Fig. 15) stresses, macroalgae produce a wide range of metabolites in order to ensure effective photosynthesis, respiration, growth, and reproduction (Stengel et al., 2011). Indirectly, these stressors change the species’ interactions among themselves as well as between other organisms. They especially alter the microbial communities and disturb the symbiotic relationships (Qiu et al. 2019; van der Loos et al. 2019). As a consequence, there is a crucial shift from complex and productive forests to simpler, less productive habitats, with significant impacts on ecosystem services (Connell et al. 2008; Wahl et al. 2015; Qiu et al. 2019).

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

Fig. 15 Main abiotic and biotic factors influencing the ecology and physiology of macroalgae (Lalegerie et al. 2020)

1.3.2 Definition and Types - Abiotic parameters

i. Hydrodynamism

Hydrodynamism can be referred to as water motions, due to the tidal cycle, and to waves or ocean currents resulting from winds and differences in the densities of water masses (Hurd et al. 2014; Lalegerie et al. 2020). Hydrodynamism influences the distribution and the vertical zonation of algae on the shore. This is mainly due to currents and waves which affect other environmental factors such as light penetration, temperature, and the availability of nutrients and all elements necessary for algal development; it also influences the distribution of fauna, indirectly affecting algae through spatial competition and potential impact of herbivores. Moreover, to survive on exposed sites, macroalgae must be resistant enough to remain attached to their substrate and withstand wave action (Lüning et al. 1990; Hurd 2000). Hydrodynamism can also affect the growth, the morphology or the phenology of macroalgae (Kraemer and Chapman 1991) and the reproduction by affecting gametes and spore releases, and dispersal (Gordon and Brawley 2004; Lalegerie et al. 2020). Moreover, water motion affects the algal nutrient uptake, impacting

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 growth, and photosynthesis (Gerard 1982; Hurd 2000; Lalegerie et al. 2020). Thus, hydrodynamism often makes the macroalgae acclimatize to a type of wave exposure, either sheltered (low hydrodynamism), exposed (high hydrodynamism), or the intermediate, semi-exposed environment. In addition, macroalgae can adapt their size, shape and direction to thrive/disperse with the flow and increase their resistance to currents (Boller and Carrington 2006). They also cope with this stress by having a strong holdfast, a flexible stipe, and blades. Its flexibility allows for bending toward the substratum as wave energy envelops them. A stable substratum is found to be most important for algal growth in the intertidal area and attachment to rough substratum enhances settlement (Baweja et al. 2016).

ii. Nutrients

To achieve their photosynthesis and growth, algae need nutrients that are essential to their development (Lalegerie et al. 2020). Nutrient dynamics in the sea are inextricably linked to variations in physical processes. Either enhanced nutrient delivery from turbulent mixing or upwelling or enhanced stratification can lead to shifts in microbial assemblages, with significant consequences for nutrient cycling (Cullen et al. 2002). The concentration of the nutrients is highly variable in the marine environment and availability greatly depends on the time of day or season. Moreover, nutrient concentrations depend on several abiotic factors, for instance, on hydrodynamism, since the water motion decreases the size of the water boundary layer of the macroalgae (Hurd 2000). To cope with the spatial and temporal variations of nutrients, different species have adapted different strategies, like increased uptake during optimal seasons (Hurd et al. 1995) and incorporating and storing more or less important nutrients in their cells for a shorter or longer time (Gagné et al. 1982).

iii. Desiccation

Being sessile organisms, macroalgae live attached to the substrate where they are faced with desiccation for a short or longer period depending on the location, tidal action, and the alternation of immersion and emersion (Lalegerie et al. 2020). The loss of water induced by desiccation causes morphological changes and to avoid this different algal species display variable desiccation tolerance, for instance, some algae regularly subjected to emersion present a small specific surface to reduce desiccation, as species with a lower surface/volume ratio lose their water content less quickly (Lüning et al. 1990; Flores-Molina et al. 2014; Lalegerie et al. 2020). The ability of algae to withstand significant water loss and mostly to rehydrate quickly define their tolerance to drought, and not their ability to retain water i.e. slower water loss, faster rehydration, and greater resistance to high water loss (Contreras-Porcia et al. 2017; Lalegerie et al. 2020).

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 iv. Temperature

Temperature is the major factor controlling survival, growth, reproduction and geographical distribution of macroalgae (Yarish et al. 1986) and the macroalgae respond to it in three different ways: genetic adaptation to the constant temperature of the living environment; phenotypic acclimatization; or short-term physiological regulation in response to the temporal temperature shocks (Eggert 2012). Moreover, the temperature tolerance of macroalgae depends on latitude (Einav et al. 1995). Elevated seawater temperatures resulting from climate change impact metabolic activity as well as growth rates and reproduction. Apart from this temperature also affects the biochemical composition (e.g., lipids, proteins, and pigments) of the macroalgae in various ways (Schram et al. 2017; van der Loos et al. 2019). The effect of temperature changes perhaps more importantly concerns changing interactions between species across trophic levels. Alteration of the community structure and regime shifts ultimately result in the “dysbiosis”, a microbial imbalance (van der Loos et al. 2019).

v. Light

As macroalgae are photosynthetic organisms, light is a key factor for their growth and distribution (Lalegerie et al. 2020) and its intensity plays an important role in the photosynthetic reactions. However, the requirements greatly vary depending on the depth and the algae density. Intense sunlight triggers multiple physiological responses and may also cause variations in their defensive capacity (Saha et al. 2014). For their protection, algae are able to produce photoprotective compounds that will dissipate excessive energy (Rastogi et al. 2010; Bhatia et al. 2011). Whereas under low light intensity algae, particularly red algae, possess extra pigments called phycobiliproteins (Dumay et al. 2014). Algal growth and metabolism are profoundly influenced by the light characteristics (Schramm 1999; Wu 2016), especially in the UV-A and UV-B range can be harmful, mainly for the early development stages (Wiencke et al. 2000; Karentz 2001; Lalegerie et al. 2020). Rather than pigments mycosporine-like amino acids are second group compounds that are reported to be photoprotective (Rastogi et al. 2010; Sonani et al. 2016).

vi. Salinity

Salinity is an important abiotic factor since it is responsible for the local and/or regional distribution of algae. Extreme salinities decrease the growth rate and concentrations of secondary metabolites in the red alga, Acanthophora spicifera (Pereira et al. 2017). It has been identified as a major environmental determinant in structuring the microbial communities, showing a high species-level diversity and below- average phylogenetic diversity (Lozupone and Knight 2007). Thus, salinity influences the complex epibacterial community associated with the macroalgal host (Lachnit et al. 2009, 2011). This can be affected directly by environmentally induced physiological changes and/or indirectly, via altered biotic

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 interactions. Such a complex shift in the epibacterial community may change the interactions between the host and the environment (Stratil et al. 2014).

1.3.3 Definition and Types of Biotic parameters

i. Herbivory

Herbivory is a key biotic influence on macroalgae. Its effects may be sustained over long periods, often in the face of physiological stress, because grazed individuals are rarely killed. Herbivores affect algal biomass, distribution, abundance, and deterrent chemistry (Dethier et al. 2005; Cardoso et al. 2020) and they also exert a differential grazing pressure on the native and non-native species of macroalgae where the generalist herbivores were also observed (Cardoso et al. 2020). In the coastal communities, grazing is a structuring element because herbivores are able to significantly affect the amount of seaweed biomass available (Lubchenco and Gaines 1981; Lalegerie et al. 2020). Herbivore pressures fluctuate as a function of available habitats and seasonal patterns. And the different stages of the macroalgal life cycle, make it difficult to escape herbivory. Whereas algae can avoid herbivores by growing in habitats where densities of herbivores are reduced, or attempt to escape in space or time by growing at such times when herbivore densities are low. Many algae may grow in crevices or holes where there is no herbivore accessibility. In addition, the strategies of coexistence of palatable and unpalatable algae and, the production of their most palatable tissues during periods of reduced herbivory are also adapted by the macroalgae (Baweja et al. 2016).

ii. Biofilm

Biofilm formation is an interactive process affected by local hydrodynamics of the fluid environment, physicochemical properties of the surface, and behavioral response of bacterial colonisers (Geesey 2001). Biofilms are composed of bacteria, diatoms, fungi, and other microorganisms within a self- synthesized polymer matrix (e.g., amino acids, glycoproteins, humic materials) (Fig. 12) (Schafer et al. 2002; Garg et al. 2009). Although biofilms are important for macroalgae biology, they can also be detrimental to the host by competing for nutrients, interfering with the gaseous exchange, and forming a barrier to light, which ultimately reduces the photosynthesis rate. According to Wahl et al. (1989), basibiont can be defined as any substrate organism, which is host to the epibiont. Epibionts are organisms growing attached to a living surface (here, the macroalgal host). The terms basibiont and epibiont describe ecological roles. Numerous sessile organisms may live either as basibiont or as epibiont, or both simultaneously (in an epibiosis of the second or third degree) according to circumstances. Epibiosis can be referred to as a non-symbiotic, facultative association between epibionts and basibionts. Rather there are two terms such

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 as Epiphytes i.e. epibiotic plants (mostly algae) and Epizoans i.e. sessile epibiotic animals (Wahl 1989). Biofouling on the surface of macroalgae (called epibiosis) leads to the reduction of access to light, gases and nutrients, and probably increases grazing and infections by pathogens (Wahl et al. 2012). This can lead to the degradation of macroalgal tissue and cause disease outbreak. The epiphytic microbial interactions and their host responses are discussed in detail in Chapter I.

iii. Epiphytism

In the marine environment, epiphytism is a widespread phenomenon that originates due to limited space availability and subsequently competition for a substrate (Kersen et al. 2011). Following the establishment of primary biofilm, secondary colonisation occurs with the epiphytes and epizoans (Lalegerie et al. 2020). From an anatomical perspective, the wide variety of algae that grows as epibionts on other algae represent a continuum between epiphytes and endophytes (Leonardi et al. 2006; da Gama et al. 2014). The abundance of these epiphytic organisms, either prokaryotes or eukaryotes, is determined by abiotic factors such as wave motion, nutrient availability, and tides. The occurrence of epiphytes is specific to different algal frond segments and range of exposure during tides (Kersen et al. 2011). Epiphytes play a major role in the growth and development of the host macroalgae as the epiphytes secrete metabolites that are important for their survival but may be harmful to their hosts. Five anatomical relationships between epiphytes and their host have been detected from the weak attachment to the host surface with no tissue damage (Type I), up to the deep penetration of the epiphyte into the host cortex, reaching the medullary tissue, and causing the destruction of the host’s cells in the area around the infection (Type V) (Potin 2012; Lalegerie et al. 2020). Negative effects also include the shading of light and disruption of nutrient uptake by the host (Baweja et al. 2016).

1.4 Part – IV - Influence of the stressors (abiotic and biotic parameters)

1.4.1 Introduction

Macroalgae have evolved a number of long- and short-term acclimation strategies to survive under constant changing and challenging environmental conditions. These involve changes in the thallus morphology (Monro and Poore 2005), cell wall composition at the individual level (Carmona et al. 1998), differences in chloroplast morphology and thylakoid organization at the cellular level (long-term adaptation), and alterations in pigmentation, photosynthetic membrane composition, and functionality at the molecular level (short-term acclimation) (Talarico and Maranzana 2000).

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

Fig. 16 The defence mechanism of marine macroalgae against epibionts (es – epithallus sloughing; ep – epibionts; ob – oxidative bursts; qs – quorum sensing; ph – physodes; cd – chemical defence; cc – corps en cerise; gc – gland cells mt – microtopography) (da Gama et al. 2014)

The multifaceted defence system of marine macroalgae against epibionts (ep) is depicted in Fig. 16 (da Gama et al. 2014). Physical defences such as epithallus sloughing (es) of colonised surfaces (a settling Ulva spore is represented) have been demonstrated in red, brown, and green seaweeds, while thallus microtopography (mt) has only recently been investigated. Cellular structures related to antifouling chemical defence (cd), in particular halogenated natural products, were identified in the thalli cortex from the Laurencia spp. (corps en cerise – cc), Delisea pulchra, Asparagopsis armata (gland cells – gc), and Plocamium brasiliense and are possibly yet to be revealed in other red algae of the class Florideophyceae. Physodes (ph) are known intracellular structures related to polyphenolic chemical defences in brown algae and are often suggested as antifoulants. Oxidative bursts (ob) and bacterial quorum sensing (qs) inhibition are antifouling mechanisms recently demonstrated in a number of algae (da Gama et al. 2014).

1.4.2 Chemical Defence

Chemical defence response plays a critical role in the overall defence mechanism of the macroalgae. The chemical defences of macroalgae such as natural products and oxidative bursts are among the diverse strategies, they employ to remain devoid of harmful epibionts, herbivores, foulers, etc. From the earliest defence response, the oxidative burst, it includes halogenation of the volatile and non-volatile compounds, wound-activated defences by converting the defensive precursors into toxins, production of endogenous chemical signals, oxylipins, and distance signaling by giving water-borne cues (Potin et al. 2002). The chemical defence of Bonnemaisonia hamifera was proven to be metabolically costly, where the algae with

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 impaired defence metabolite production showed an average growth of 20% more than that of the normal metabolite production (Nylund et al. 2013). These defence metabolites are localized in specific structures of the red algae i.e. gland cells (Bonnemaisoniales) and/or at the surface and within the thallus (Delisea pulchra). These unique compartments pave the way for the metabolites to be delivered to the surface of the macroalgae (Steinberg and de Nys 2002). Since Laurencia (Ceramiales, Rhodomelaceae) is one of the most prolific red algae, it has been widely studied in the production of secondary metabolites derived from the sea (Bawakid et al. 2017). Sesquiterpenes, diterpenes, triterpenes, and acetogenins (characterized by the presence of halogen atoms in their chemical structures) have been found present in this alga (Davis and Vasanthi 2011).

Allelopathy, one of the direct competitive defence mechanisms, can be a potent way through which macroalgal communities are structured (Xu et al. 2012). The pioneering marine experiments on allelopathy concerned competition among sessile invertebrates (Goodbody 1961; Jackson and Buss 1975; Porter and Targett 1988; Gross 2003; Harlin 1996), but later it was also demonstrated that macroalgae have the ability to suppress natural competitors through allelopathic agents (de Nys, Coll and Price 1991; Paul et al. 2011; Rasher et al. 2011; Svensson et al. 2013). Thus, it can also be defined as a biological phenomenon by which an organism produces one or more biomolecules that affect the growth, survival, and/or reproduction of other organisms. These biomolecules, which are mostly secondary metabolites, are known as allelochemicals and are produced by certain plants, algae, bacteria, coral, and fungi. These compounds can have beneficial (positive allelopathy) or detrimental (negative allelopathy) effects on the target organism. Allelopathic interactions are an important factor in determining species distribution and abundance within plant and plankton communities (Bacellar Mendes and Vermelho 2013). Allelochemicals produced by the macroalgae are a potential source of defensive strategy. Allelopathy can be stimulated or minimized by a number of biotic and abiotic factors (discussed in the previous section “Abiotic and Biotic parameters’). These factors determine the strength of allelopathic interactions (Gross 2003). Frequently, the epiphyte growth on macroalgal tissue was prevented by allelopathic mechanisms (Harlin et al.1987).

i. Oxidative bursts

The accumulation of reactive oxygen species, known as an oxidative burst, either kills the attacking agent or signals to induce other defensive responses (Potin et al. 2002). The red alga Gracilaria conferta decreases the agarolytic bacterial pathogens by up-regulating bursts of hydrogen peroxide (Weinberger and Friedlander 2000). More details are provided in Chapter I.

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 ii. Halogenation of volatile and non-volatile compounds

The macroalgal haloperoxidases are responsible for the formation of halogenated (iodinated or brominated) compounds. These haloperoxidases fall into two classes: vanadium-dependent haloperoxidases (VHPOs) and chloro- (CPOs), bromo- (BPOs), and iodoperoxidases (IPOs) (Wever et al. 2018). Oxidation of halides (X-) into hypohalous acids (HXO) by vanadium-haloperoxidases results in the formation of volatile halogenated organic compounds that are often regarded as defensive compounds. The haloperoxidase system can deactivate the bacterial communication signals, AHLs (Acyl Homoserine Lactones) and thus prevent the biofilm formation especially in Gram-negative bacteria, a predominant bacterial group in the marine environment (Potin et al. 2002).

Monoterpenes (two isoprene units), the most representative volatile compounds, are used by the macroalgae to defend against the epiphytes or grazers by releasing them into the surrounding seawater after halogenating them. Red algae are considered to be the main source of halogenated monoterpenes (linear or cyclic) with multiple halogen substitutions. Sesquiterpenes (three isoprenoid units), the primary components of marine biologically active substances, play an important role in reproduction and UV protection rather than defending against grazers and fouling organisms (Peng et al. 2015). Diterpenes, derived from geranylgeranyl pyrophosphate, show various types of bioactivity such as antimicrobial, antifungal, antifouling, cytotoxic, etc., (Gross and Königkönig 2006). Meroterpenoids, the prenylated aromatic compounds, may inhibit both the soft and hard fouling organisms (Peng et al. 2015). Three substituted aryl meroterpenoids isolated from Hypnea musciformis have proven to show potential antioxidative activities (Chakraborty et al. 2016).

iii. Distance signaling

Since the macroalgae are non-vascular plants (not evolved) they do not possess a mechanism to transfer the signals longitudinally. The red algae Chondrus crispus sporophytes bathed in the elicitor, oligosaccharides, show an induced transient resistance against Acrochaete operculata only in the bathed parts of the thallus (Potin et al. 2002). But still, macroalgae can be warned of aggressors by the conspecific neighbours. Recent advanced technologies (such as proteome, transcriptome, metabolome, and other biochemical analyses) have confirmed the role of various macroalgae constituents such as mannitol, proline, abscisic acid, polyamines, polyunsaturated fatty acids, oxylipins, and fatty acid desaturases among others that defend them from diverse environmental stress (Egan et al. 2014).

1.4.3 Microbial Defence

This part is well detailed and reviewed in Chapter I.

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 1.5 Part – V – Consequence of Holobiont break-up - Macroalgal Disease

1.5.1 Introduction

As dominant habitat-formers and primary producers in the marine ecosystem (Longford et al. 2019), there is increasing evidence to indicate that they are highly susceptible to infectious diseases (Gardiner et al. 2015). In their natural habitat, several biotic and abiotic parameters affect the macroalgal health but under cultivation, the overall unfavourable environmental conditions result in disease outbreaks (Campbell et al. 2011; Gardiner et al. 2015). During the last 10 years, seaweed aquaculture has expanded rapidly due to increasing demand for edible seaweed, nutraceuticals, pharmaceuticals, antimicrobials and other compounds with biotechnological uses (Cottier-Cook et al. 2016; Alemañ et al. 2019; Shannon and Abu-Ghannam 2019). Despite its niche market, intensive algal aquaculture might favour disease outbreaks (Gachon et al. 2010). As the macroalgal aquaculture industry grows and diversifies into new species and geographical areas, new diseases are likely to emerge and the risk will intensify introducing non-indigenous pathogens to the new areas (Cottier-Cook et al. 2016).

Bacterial diseases are one of the most crucial problems in aquaculture (Natrah et al. 2011a). These microbial infections are observed to be opportunistic in nature. Opportunistic pathogens are defined here as those that are present on both healthy and diseased hosts but only become harmful following a disturbance of their host (Brown et al., 2012). Thus, identifying the host-associated bacteria is critical in disease management in aquaculture. While macroalgae are increasingly recognized to suffer from the disease, the causative agents are mostly unknown (Kumar et al. 2016). So, the identified microbial community must necessarily be differentiated for their beneficial and/or pathogenic interactions with the host.

Interactions between the hosts and their microbiomes are fundamental for host functioning and resilience (Qiu et al. 2019) but such interactions can either be beneficial or detrimental. The disturbance and disruption of the mutualistic association between host and microbiota can be regarded as Holobiont break-up. This can be caused by environmental stress (van der Loos et al. 2019). For example, high organic matter content and high density of the cultured organisms increase the proliferation of opportunistic bacteria and this further induces stress making them more susceptible to diseases (Bachère 2003; Natrah et al. 2011b). The holobiont breakup resulting from the dynamic shift from symbiotism to pathogenism is well discussed in Chapter I. Thus, we directly focus on the consequence of the Holobiont break up, that is, macroalgal disease.

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 1.5.2 Definition

The macroalgal disease was defined as “a continuing disturbance to the plant’s normal structure and function such that it is altered in growth rate, appearance, or economic importance” (Andrews 1976). They are emerging as an important impact of global climate change, due to the effects of environmental change on host organisms and their pathogens (Campbell et al. 2011). A rapid shift in environmental parameters, followed by fungal, bacterial, and algal infestations, cause the macroalgal disease outbreak (Loureiro et al. 2017). The recent development of intensive and dense aquaculture practices has worsened the situation (Gachon et al. 2010) which ultimately results in an increase in the prevalence of diseases and thus a decline in the yields (Ward et al. 2019).

1.5.3 Algal diseases

The study of macroalgal diseases is generally made on the red algae, Eucheuma, and Kappaphycus, particularly on the ‘ice-ice’ disease phenomenon (Largo 2002). Diseases reported so far in red algae include, ‘rotten thallus syndrome’ in Gracilariopsis heteroclada; ‘epiphytic filamentous algae’ in K. alvarezii; ‘ice- ice’ in K. alvarezii and K. striatus; ‘red rot, Olpidiopsis and white spot disease in Pyropia (Prophyra) yezoensis; ‘red rot and green-spot diseases’ in P. tenera; and ‘green-spot disease’ in P. dentata, particularly in aquaculture (Ward et al. 2019). A commercially important carrageenophyte, K. alvarezii, is often plagued with ‘ice-ice’ disease due to pathogenic bacterial infections. Generally, ice-ice leads to whitening of the branches initiated with colour changes of the thalli, which become transparent in the end (Mohammed Riyaz et al. 2019). It is then followed by epiphyte infection which ultimately leads to reduced production in high- density commercial farms (Vairappan et al. 2008). Ice-ice is recently regarded as thallus whitening or Bleaching (Cottier-Cook et al. 2016; Saha and Weinberger 2019). Different and multiple opportunistic pathogens are repeatedly involved in the thalli whitening by depigmentation in the red macroalgae. Ice-ice in K. alvarezii was caused by Bacillus sp. and Vibrio sp. (Mohammed Riyaz et al. 2019), Shewanella haliotis, Vibrio alginolyticus, Stenotrophomonas maltophilia, Arthrobacter nicotiannae, Pseudomonas aeruginosa, Ochrobactrum anthropic, Catenococcus thiocycli and Bacillus subtilis subsp. spizizenii (Achmad et al. 2016) and in Gracilaria verrucosa it was caused by Acinetobacter sp., Pseudomonas sp., Bacillus sp., Cytophaga sp., and Vibrio sp. (Nasmia et al. 2014). In a temperate red alga, Delisea pulchra, bleaching was caused by phylogenetically diverse pathogens, Alteromonas sp., Aquimarina sp., and Agarivorans sp. (Kumar et al. 2016). Fig. 17 depicts algal diseases in different macroalgal species.

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

Fig. 17 Macroalgal diseases in different algal species. a – Red-rot in Pyropia (Kim et al. 2014); b – Red- rot in Pythium porphyrae mycelium (Kim et al. 2014); c – bleached D. pulchra (Zozaya-Valdés et al. 2016); d – bleached K. alvarezii green (Arasamuthu and Patterson Edward 2018); e – bleached coralline algae (downloaded from http://coralreefdiagnostics.com/); f – bleaching in H. floresii

1.5.4 Nature of pathogens Pathogenic bacteria and fungi may cause rot symptoms and diseases such as bleaching. However, due to the fact that pathogenicity is often associated with the degradation of the cell wall, it is hard to distinguish true pathogens from saprophytic epiphytes (Weinberger and Friedlander 2000). As many

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 bacteria and fungi exhibit the potential to degrade algal cell walls, it should be noted that bacteria at first might be commensal and only later become harmful as a result of changes in the environment. The pathogen causing “chytrid blight” of Pyropia in Japan was recently isolated and named Olpidiopsis porphyrae Sekimoto, Yokoo, Kawamura et Honda (Sekimoto et al. 2008). As the disease-causing agent was not a chytrid, the name of the disease was changed to Olpidiopsis disease. The causative agent of green-spot disease of Pyropia is still unknown. “Diatom felt” (i.e., large concentration of epiphytic diatoms) is produced by many different species of diatoms, but the symptoms and the extent of the damage to Pyropia have not been evaluated (Kim et al. 2014). The agarolytic Pseudomonas sp. isolated from Pyropia dentata had carboxymethyl cellulase, xylanase, protease, and agarose activities, and was able to macerate algal tissue one week after inoculation at pH 7 and 30° C (Park et al. 2001). Kordia algicida was identified as a ‘significant pathogen’ inducing bleaching in Agarophyton under laboratory conditions (16°C and a photon flux of density of 75 µmol m-2 s-1) when their natural microbiome was removed (Saha and Weinberger 2019)

1.5.5 Mechanisms of action of pathogen

Bleaching in G. verrucosa is characterized by whitening of the thalli at the base, middle and end of the young thalli (Nasmia et al. 2014) whereas in D. pulchra bleaching is distinctive to the mid-thallus (Fernandes et al. 2012). Bleaching in Halymenia floresii, the species for this study, is characterized by the localized loss of pigment at the apical ends similar to the red alga, Agarophyton (Saha and Weinberger 2019). Largo et al. (1999) explained the mechanism of the Vibrio infection in seaweed thallus when the algae are under stress. The Vibrio would colonise and reproduce in the cell wall by using polysaccharide as its carbon source. Furthermore, Lin in Yulianto et al. (2009) explained that after 2-3 days, Vibrio entered the tissue up to the medullary seam by pumping the carrageenase enzyme which caused the thallus to become pale/white and its tissue to be soft and easily broken (Nasmia et al. 2014).

The red rot disease of Pyropia begins with the appearance of distinct, small, red patches on the blades in areas where the zoospores of Pythium porphyrae germinated. The mycelium grows through the host cells killing them. The dead cells change colours, degenerate, and deteriorate forming small holes. The small holes merge into bigger holes, ultimately disintegrating the entire blade (Kim et al. 2014). To date, several different species of bacteria are thought to be potential causative agents of green-spot disease in Pyropia. The infection process of green-spot disease in Pyropia starts when seawater temperature is below 10° C. The green-spot disease can degenerate a whole blade within a day or two. The blade does not undergo typical maceration process; rather, infected Pyropia cells burst successively from the initial infection site, forming a row of dead cells (Kim et al. 2014)

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 Shifts in the microbial community in response to high temperatures have also been observed on crustose coralline algae (reef-building algae that fulfill an essential role in coral health). Webster et al. (2011) showed that, when seawater temperatures increased to 32°C, these calcified algae experienced bleaching (loss of pigmentation) and reduced maximum quantum yields (an indicator of the performance of photosystem II), together with a shift in the microbial community structure. Temperatures lower than 32°C also led to bleaching and reduced maximum quantum yields but no shifts in microbial community structure. Interestingly, algae cultured at 32°C recovered when temperatures were reduced to the ambient level (27°C), but this was not the case for algae maintained at 32°C. Controversially, a study on the effect of ocean acidification conditions on rhodoliths showed that the associated microbiota remained stable (whereas the communities in both the seawater and on dead rhodoliths shifted). The algae showed no sign of calcium carbonate biomass loss and even increased photosynthetic activity. This resilience to environmental stress was likely provided by the stability of the microbiome (Cavalcanti et al. 2018).

1.5.6 Impact of disease on the aquaculture sector

Macroalgal diseases are the biggest constraints experienced in the process of their cultivation (Nasmia et al. 2014). The commercial production of macroalgae has expanded greatly over the past century and much of this production is centered in Asia (Kim et al. 2017; Ward et al. 2019). They have been used as food for centuries in Asia including China, Japan, and Korea. Though Asian countries remain the largest consumers these products are increasingly exported to Europe, North America, and Africa (McHugh 2003; Chen and Xu 2005; Yang et al. 2017; Ward et al. 2019). The occurrence of algal diseases results in dramatic economic losses of 25-30% of harvested volumes of Saccharina japonica in China (Wang et al. 2014), 20% of the output of Porphyra/Pyropia in Korea (Kim et al. 2014), and 15% reduction in eucheumatoid stocks in the Philippines (Cottier-Cook et al. 2016). In order to attempt to eliminate or mitigate the impact and spread of disease on aquaculture, several treatments or mitigation strategies have been used (Ward et al. 2019). The washing of Porphyra/Pyropia blades in acid solutions is a widespread practice and is often used in an attempt to control all diseases (Kim et al. 2014). Changing cultivation conditions, particularly the repositioning of cultivation ropes to modify exposure to light and more favourable salinities, are effective measures that can be taken to reduce the severity of diseases caused by exposure of S. japonica to unfavourable abiotic conditions (Wang et al. 2014). The development of ice–ice symptoms in Kappaphycus and Eucheuma is thought to be the result of stress to the host from abiotic conditions, such as temperature, salinity (Vairappan et al. 2008), light intensity and water movement (Hurtado et al. 2006) in combination with the action of opportunistic bacteria (Uyengco et al. 1981; Largo et al. 1995). The triggers behind disease onset and progression are not well understood, and as a result, no effective management protocols that are cost-effective have been developed to date (Ward et al. 2019).

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 The improvement of disease mitigation strategies in macroalgal aquaculture is reliant on increasing our understanding of the agents and abiotic factors influencing disease onset and progression and the subsequent development of biosecurity programs, including the use of quarantine procedures and improved farm management practices through capacity building (Campbell et al. 2019; Ward et al. 2019).

With increasing knowledge of the macroalgal diseases and the range of micro-organisms associated with them, the paradigm of one-disease-one-pathogen is moving towards the pathobiome concept (Bass et al. 2019). As a result of interactions between the host and environmental factors, multiple host-associated organisms are linked with reduced host health status, which is regarded as a pathobiotic system (Ward et al. 2019). Despite serious issues and losses, macroalgal diseases have not drawn more attention. In their natural habitats, macroalgal diseases directly impact the infected organism and population, but may additionally have cascading effects on the ecosystem if keystone species are infected (Gachon et al. 2010; Egan and Gardiner 2016). A greater understanding of the genetic diversity of cultured seaweeds, appropriate breeding strategies, and crop selection are required to retain this genetic diversity and safeguard the disease and pest resistant cultivars for future use by the industry (Ward et al. 2019).

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 1.6 Context and Aims of the Thesis

Halymenia floresii has been identified as a carrageenophyte with high potential for a land-based IMTA (Integrated MultiTrophic Aquaculture) system. By recycling inorganic nutrients, the IMTA system produces biomass while reducing the environmental impact on coastal ecosystems through responsible aquaculture practices (Godínez-Ortega et al. 2008; Robledo and Freile-Pelegrín 2011). During the experimental culture of Halymenia floresii, we observed that the surface of this macroalgae was exceptionally free of any fouling organisms compared to any other algae under the same culture conditions. Even at high concentrations of Dissolved Inorganic Nitrogen (DIN ~ 150 µM) in IMTA system, the epiphytic macrofoulers (such as opportunistic green algae and/or any sessile invertebrates), which usually disturb the culture, were not observed in Halymenia floresii culture tanks (Pliego-Cortés et al. 2017).

This ecological phenomenon could reveal that (i) the presence of H. floresii may protect the surface by releasing allelopathic active compounds that ultimately interfere with the settlement and growth of competitors (Le Gal, 1988; Harlin, 1996; Gross, 2003) and (ii) the epibacterial community on the surface of H. floresii may prevent the settlement by their symbiotic relationship with the host. With these proposed hypotheses, we framed the objectives of this thesis as follows:

1. Selective extraction of the surface metabolites from H. floresii

2. Identification of the epibacterial community of H. floresii

3. Screening the epibacteria for their quorum sensing activity

4. Evaluation of H. floresii secondary metabolites for their corresponding interference in the QS behaviour

5. Assessment of the H. floresii secondary metabolites by Untargeted Metabolomic approach

6. Differentiating the H. floresii epibacteria for their significant pathogenic and nonpathogenic/protective interactions.

These objectives were studied and the outcomes were detailed in the following chapters:

Chapter one reviews the biological defence systems of macroalgae, by means of their associated- microbionts, as the microbiome confers a protective shield on the surface of the host (Title: Defence on Surface: Macroalgae and their surface-associated microbiome; A Abdul Malik S., Bedoux G., Garcia Maldonado J. Q., Freile-Pelegrín Y., Robledo D., & Bourgougnon N.; Book: Advances in Botanical research (2020)).

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

Chapter two focuses on the isolation and identification of the cultivable epibacterial community of wild and cultivated Halymenia floresii from the Yucatan peninsula, Mexico. Consecutively, the screening and detection of the QS activity were conducted among the total isolates (Title: Screening of surface-associated bacteria from the Mexican red alga Halymenia floresii for Quorum Sensing activity; A Abdul Malik S., Bazire A., Gamboa A., Bedoux G., Robledo D., Garcia Maldonado J. Q., & Bourgougnon N. Submitted to Microbiology on 26 March 2020).

Chapter three studies the surface-associated allelopathic metabolites of H. floresii and their interference in the QS activity of the chosen isolates (Title: Chemical defence against microfouling by allelopathic active metabolites of Halymenia floresii (Rhodophyta). A Abdul Malik S., Bedoux G., Robledo D., Garcia Maldonado J. Q., Freile-Pelegrín Y., & Bourgougnon N. Journal of Applied Phycology (2020))

Chapter four concerns identification of defence molecules from H. floresii by untargeted metabolomic analysis (Title: Investigating the Potential of Halymenia floresii to Produce Bioactive Compounds through Untargeted Metabolomic Profiling; A Abdul Malik S., Bedoux G., Freile-Pelegrín Y., Bourgougnon N & Robledo D., Submitted to Marine Drugs on 27 April 2020)

Chapter five determines the significant pathogen, from the epibacterial strains previously isolated from H. floresii, inducing Bleaching in H. floresii (under preparation).

Chapter six is a general discussion that provides an overview of the major findings of this thesis.

In this context, I mobilized twice to Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV), Mexcio.

Mobility: I

For a period of ‘4’ months (March, 2018 – July, 2018) to CINVESTAV, Mexico. Funds provided by ECOS-Nord CONACYT for the collaboration project M14A03 and PN-CONACYT 2015-01-118.

Mobility: II

For a period of ‘2’ months (November, 2019 – January, 2020) to CINVESTAV, Mexico. Financial support provided by Ecole Doctorale Science et Mer Littoral (EDSML).

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

2. Chapter I

Defence on Surface: Macroalgae and their associated-microbiome

Shareen A Abdul Malik, Gilles Bedoux, Jose Q. Garcia Maldonado, Yolanda Freile-Pelegrín, Daniel Robledo, Nathalie Bourgougnon

Published as a Book Chapter to “Seaweeds Around the World: State of Art and Perspectives. Advances Botanical Research.

Shareen at al.

January, 2020

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 Defence on Surface: Macroalgae and their surface-associated microbiome

Shareen A Abdul Malik1, Gilles Bedoux1, Jose Q. Garcia Maldonado2, Yolanda Freile-Pelegrín2, Daniel Robledo2, Nathalie Bourgougnon1

1 Laboratoire de Biotechnologie et Chimie Marines, Université Bretagne Sud, France

2 CINVESTAV Marine Resources Department, Mexico

2.1 Abstract

Macroalgae surfaces are considered as a potentially suitable substratum present underwater for the dynamic epibiotic microbiome. They secrete various organic substances as nutrients for the multiplication of bacteria and the formation of microbial biofilms. The microbial biofilm itself acts as a biological defence mechanism to prevent the establishment of pathogenic microbes as well as to protect the surface of the seaweed from the macrofoulers. In most cases, the presence and diversity of these organisms are host-specific. The macroalgal host and their associated microbiont are often referred to as a ‘Holobiont’.

The surface-associated microbiome of macroalgae plays a crucial role in the growth, morphogenesis and defence in the normal favourable environmental conditions of the macroalgae (Symbiotism). Whereas, when there is a change i.e. reversed, unfavourable conditions, the microbiome reverses its symbiotic nature and affects growth and degrades the macroalgae by causing different diseases in different species (Pathogenism). In parallel, marine macroalgae employ different defence mechanisms, a physical defence such as continuous shedding of the outer layer of cells, and the mucilaginous covering and chemical defence such as the production of secondary metabolites.

The following chapter focuses on the macroalgal surface-associated microbiome and its defence system with a certain number of relevant examples from related literature on this subject.

Keywords: Surface-associated microbiome; microbionts; holobionts; symbiotism; secondary metabolites; microbial colonization; biofilm;

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 2.2 Introduction

Macroalgae, being important habitat formers and primary producers in the marine ecosystem, provide potential settling substrate and protective environments for the early life and development of many invertebrates. Attached to their substrate, marine seaweeds are linked to their environment and confronted with various abiotic and biotic stresses. They are affected by wave action, light radiation, desiccation, and variations in salinity, temperature, nutrient supply, herbivore predation, epibiosis, microfouling and human activity. In the marine environment, competition for space and nutrients is intense. Each species lives in this complex environment according to its own physiological parameters, and its ability to resist changes in the state of the environment, particularly, during low tides or in reduced space. Due to the extraordinary complexity of the marine environment, seaweeds establish close associations with other species to ensure their survival and competitiveness for space.

Macroalgal surfaces represent a major physiological and highly functional interface with the environment. Such an interface is always threatened by fouling, and thus macroalgae need to develop many defence strategies to protect their surface from a vast number of consequences (Wahl et al. 2012). Biofilms on solid surfaces are omnipresent and therefore macroalgae are of no exception to this. Biofilm formation is an interactive process affected by local hydrodynamics of the fluid environment, physicochemical properties of the surface and behavioural response of bacterial colonizers (Geesey 2001). In biofilms, the self-produced matrix is mainly composed of polysaccharides, proteins, and eDNA synthesized by the associated bacteria, diatoms, fungi, and other microorganisms (Garg et al. 2009).

Host-microbe interactions play a crucial role in marine ecosystems, in a symbiotic community where the largest partner, based on its size, is considered as a host, e.g. macroalgae (Dittami et al. 2019). The host and all of its associated microbiota, which can be acknowledged as all the microorganisms which are present or associated in a particular environment are now considered as a Holobiont (Wahl et al. 2012; Hollants et al. 2013; Egan et al. 2013; da Gama et al. 2014; Duarte et al. 2018). The term ‘holobiont’ was coined by Lynn Margulis which referred to symbiotic associations that last for a significant part of an organism’s lifetime (Cavalier-Smith, 1992). As it includes all the associated microbiota, their interactions may be harmful, beneficial or of no consequence, which may be coevolved or opportunistic, competitive, or cooperative (Skillings 2016). The combined genetic information of the associated microbiota is known as a microbiome (Skillings 2016; Dittami et al. 2019) These microbiomes are functionally connected with their eukaryotic hosts and they are highly diverse. Specific functional genes of the associated microbiome are now regarded as secondary genomes (Singh and Reddy 2016). Any disturbances to the microbiome or even uncontrolled microbial colonization could be associated with stress and disease of the host organism (Rohde et al. 2008; Lachnit et al. 2009; Morris et al. 2016; Beattie et al. 2018).

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 Lacking a cell-based adaptive immune response, macroalgae have two different defence capabilities: constitutive defence under normal circumstances, such as the production of metabolites by macroalgae or its bacterial symbionts and induced defence activated by any tissue damage (Weinberger 2007; Egan et al. 2014). Surface- associated microbiome proved a wide range of bioactive compounds with a broad spectrum of activities, such as antibacterial, antifungal, antiviral, antiplasmodial, nematicidal, anti- inflammatory, anti-cancer and antiangiogenic activities (Singh et al. 2015). Here we consider the defence provided by the macroalgae themselves through the production of secondary metabolites as ‘Chemical Defence’ and the defence provided by the associated microbiota as ‘Microbial/Biological Defence’, however, it is now known that many of the bioactive compounds previously attributed to seaweeds were in fact produced or metabolized by their associated microbiota, the microbiome (Wahl et al. 2012; Hollants et al. 2013; Egan et al. 2013; da Gama et al. 2014). In this association, a highly and dynamic complex set of chemical, physical and biological interactions regulates the relationships between the basibiont and its epibionts (Singh et al. 2014).

2.3 Seaweed surface-microbe interactions – biofilm development

Biofilms, a consortia of surface-attached microbial cells dispersed in a self-secreted extracellular polymeric matrix, constitute the principal form of microbial growth and a widespread survival strategy amongst microorganisms (Costerton et al. 1978; Donlan 2002). In the marine environment, surfaces are rapidly colonized by marine organisms in a process known as biofouling (Callow and Callow 2002). The adsorption of dissolved macromolecules by the substratum is a preliminary step before bacterial colonization. The algal cell wall structure is a selective factor in the settlement of macromolecules and microorganisms. Bacteria are typically the primary colonizers of submerged surfaces. Marine macroalgae are known to produce secondary metabolites to defend themselves from the colonization of their exposed surfaces. In fact, these metabolites and pioneering bacterial species contribute to the chemical and physicochemical conditions responsible for a secondary recruitment of other microorganisms, but also sponges, macroalgae and invertebrates (Dang and Lovell 2000). In addition, multi-layered Extracellular Polymeric Substances (EPS), which include polysaccharides and also proteins, nucleic acids and lipids, form a base of biofilm structural organization (Brian-Jaisson et al. 2016; Lage and Graça 2016).

2.3.1 Seaweed Surface: as a substratum

A highly specific association exists between bacterial communities and marine macroalgae. The physiological and biochemical properties of the macroalgae surface predetermine the composition of the adhering microbial communities (Goecke et al. 2010). Macroalgae surfaces provide a rich habitat and release large amounts of organic carbon and oxygen into the surrounding environment, providing nutrients

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 for microorganisms, triggering chemotactic behaviour of bacteria or inducing microbial colonization and reproduction (Steinberg et al. 2002; Goecke et al. 2010).

i. Cell wall structure

The cell wall structure of macroalgae is mainly composed of mannans, xylans, cellulose, hemicellulose and sulfated or/and anionic polysaccharides. The cell wall polysaccharides are specific: Chlorophyta (green seaweeds) contain sulphated xyloarabinogalactans, glucuronoxylorhamnans, glucuronoxylorhamnogalactans and ulvans. Phaeophyceae (brown seaweeds) produce alginate and fucose- containing sulfated polysaccharides, whereas Rhodophyta (red seaweeds) contain neutral and sulfated polysaccharides such as agars and carrageenans (Percival 1979; Guedes et al. 2019). Within the algal lineages, these polysaccharides are highly diversified in terms of their degree of sulfation, esterification, molecular weight and conformation of sugar residue (Mabeau and Kloareg 1987; Kloareg and Quatrano 1988; Popper et al. 2011). Apart from the above mentioned structural polysaccharides, the red, green and, brown macroalgae also contain storage polysaccharides such as floridean starch, and, laminarin and mannitol, respectively (Stiger-Pouvreau et al. 2016).

ii. Surface topographical features

The composition and function of surface consortia are significantly influenced by the chemically mediated interactions and communication between the associated microorganisms and the hosts (Egan et al. 2008). The surface topographical features include a mucilage and sheath demonstrating a gelatinous covering of the macroalgae (Chapman et al. 2014) and it influences the overall epibiosis (Sullivan and Regan 2011). The microtopography of the interface is a selective factor in the settlement of macromolecules and microorganisms. (Chapman et al., 2014; Sullivan & Regan, 2011). Therefore, the physical and chemical properties of the substratum play a key role in the attachment and establishment of the initial bacterial colonization. Surface roughness is often speculated to have a deterring role either in attaching or promoting the easy release of fouling organisms (Callow and Callow 2011). In a study on ‘bioinspired synthetic macroalgae’ the surface topography of the brown algae, Saccharina latissima and Fucus guiryi, was replicated on synthetic elastomer and evaluated for its antifouling nature. Together with the chemical defence, the surface topography efficiently reduced the fouling by 40%, where it has been proved that the topography and chemistry work synergistically (Chapman et al. 2014).

2.4 Macroalgae defence

Macroalgae control epiphytic microbial colonization by several processes.

2.4.1 Removal of surface layers

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 Under pressure from epibionts, some algal thalli can decompose, generating a kind of desquamation of tissues on the surface. This phenomenon has been observed in several species of brown algae Ascophyllum nodosum, Sargassum muticum, some Corallinaceae and Dilsea carnosa. In the summer where light intensity and temperature are at extremes, physical processes involve regular shedding of biofilm surface cell layers. For example, the Gigartinales Chondracanthus chamissoi whitens and decomposes. During this period, many epiphytes and herbivores were observed. Over time, the alga, which is more vulnerable, produces defence molecules. 2014).)

2.4.2 Production of Reactive Oxygen Species

Many seaweed species are known to produce bioactive compounds through their specialized metabolism. The production of ROS or oxidative burst forms an important component of the stress produced in marine organisms. This production is triggered by the contact of the cells with products resulting from the degradation of the algae wall - oligosaccharides (for example oligoguluronates in Laminaria; oligo-agars in Gracilaria) (da Gama et al., 2014). When a macroalga is colonized by bacteria or herbivores, it receives a signal that will enable it to activate its defence systems. These signals of aggression or Elicitors are defined as chemical compounds that can induce physiological changes in the target organism. In marine macroalgae, they are most often oligosaccharides resulting from the degradation of the algae walls. The presence of Polysaccharides (xyloglucan, laminarin), peptides or proteins or lipid derivatives have also been reported (Potin et al. 2002)

2.4.3 Antimicrobial compounds

Exuded algal bioactive compounds on the surface have antimicrobial properties, which protect the seaweed’s surface from grazers, fouling organisms and bacterial pathogens. In different macroalgae species, the defence molecules rise to the surface as an antifouling. They can also be stored in different types of specialized structures, for example, gland cells of Delisea pulchra, ‘corps en cerise’ in Laurencia species. Physodes in brown seaweeds present in the thallus cortex are known for their antifouling compound delivery systems (Dworjanyn et al. 2006; Harizani et al. 2016; Dahms and Dobretsov 2017). In green algae, the identified compounds mainly belong to di- and sesquiterpenes. They have been relatively little studied (Fig. 1). The diversity and richness of Rhodophyte in terms of secondary metabolites are the most important of all macroalgae with more than 1600 compounds identified. Red macroalgae mainly produce terpenoids and nonterpenoids such as acetogenins. Acetogenins are fatty acid derivatives and are usually halogenated, particularly C-15 acetogenins (Bawakid et al. 2017). Red algae are also characterized by the production of halogenated compounds containing bromine and chlorine. For example, the red alga Delisea pulchra produces an analogous molecule of bacterial N-acyl-homoserine lactones (AHLs) known to inhibit the

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 signal pathways in the Gram-positive and Gram-negative bacteria Bacillus subtilis (Ren et al. 2002), Escherichia coli (Ren et al. 2001) and Pseudomonas aeruginosa (Hentzer et al. 2002) these lead to selective colonization of bacteria on thalli (Steinberg and de Nys 2002).

Fig. 1 Bioactive compounds produced by the red, green and brown macroalgae.

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 Another mechanism involved in red algae defence reactions against pathogen attack is the oxidation (enzymatic or non-enzymatic) of fatty acids that results in the production of oxylipins. Sugars (floridoside) from photosynthesis are involved in the defence mechanisms. In brown algae, more than 1200 molecules have been characterized, including terpenes, galactoglycerolipids, fatty acids, phlorotannins and small C11 acetogenins. For example, in Dictyota ciliolata, pachydictyol A, dictyol B acetate, dictyodial, and sterols were shown to be present in higher concentrations in older, less palatable tissues than in apical meristem (Cronin and Hay 1996). Therefore, the different allocation and concentration of the chemical compounds may lead to different microbial communities at the different parts of macroalgae. There is little data about the authenticity and ecological significance of algal chemical defence compounds having verified their production, presence and/or release at ecologically realistic concentrations (Maximilien et al. 1998; Kubanek et al. 2003; Lane et al. 2009; Nyadong et al. 2009; Nylund et al. 2010). In 2010 Lachnit et al. demonstrated that available surface chemical compounds on Fucus vesiculosus have different main effects on bacterial colonizers; one group of compounds reduced the overall abundance of epiphytic bacteria non- discriminatively, whereas the other displayed selective and attractant features for bacteria. Consequently, the community patterns and overall densities of epiphytic bacteria are likely to have resulted from some sort of equilibrium between these opposite effects. These authors propose that the effect size will vary over the course of the year due to environmental changes, thus resulting in algal-specific but temporally variable patterns of epibiosis in accordance with previous observations. The results of this study suggest that unidentified compounds and fucoxanthin may be significant drivers of the interaction between the alga and colonizing bacteria. Dimethylsulphopropionate (DMSP) and proline from the surface of F. vesiculosus are active against the attachment of five different bacterial strains isolated from the algae (Saha et al. 2012). Further dynamics downstream of the initial colonization by bacteria, such as microorganism– microorganism interactions are expected to add another effective layer that results in spatial heterogeneity of epiphytic bacterial biofilms.

2.5 Microbiome on the surface

2.5.1 Bacteria

Descriptive studies of bacteria isolated from the surface of macroalgae were reported as early as 1875 (Johansen et al. 1999). The interest in bacterial populations living in association with macroalgae has increased during recent decades (Goecke et al. 2010). In 2010, Goecke et al. found 107 studies on bacterial communities associated to a total of 148 macroalgae (36 Chlorophyta, 46 Phaeophyceae, 55 Rhodophyta, 12 undetermined algae) within the last 40 yrs. Bacteria often have a protective role through the production of compounds into the water that prevents the installation of biofilm on the surface. The primary biofilm of

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 bacteria serves as cues for the further settlement of unicellular and multicellular eukaryotes. These cues can be either positive or negative (Rao et al. 2007). The presence of both attractants and antibacterial molecules will determine the promoter or inhibitory effect on biofilm establishment, predation deterrence, and germination of an epiphyte spore or metamorphosis of an epibiont invertebrate.

Earlier studies have mainly focused on elucidation of the roles of the epiphytic bacterial communities in the ecophysiology of the host macroalga. However, mutualistic interactions have become a topic of current interest. It is evident from recent studies that a fraction of epiphytic bacterial communities can be categorized as “core microbial species”, suggesting an obligate association. However, the precise composition of microbiomes and their functional partnership with hosts are not fully understood (Singh and Reddy 2016). Bacterial communities associated with marine macroalgae differ strongly from those of their surrounding seawater, sediment or substrate (Bengtsson et al. 2010; Bengtsson and Øvreås 2010; Aires et al. 2016). For example, the surface microbiome of Fucus vesiculosus was analysed comparatively by amplicon sequencing, using seawater as reference, and it was found that the majority of microbial OTUs (relative abundances) were assigned to Alphaproteobacteria (64% to 68%) and their share in the seawater bacterial community was even higher (84%) (Parrot et al., 2019). Bacterial communities associated to marine macroalgae are not fixed and can change temporally and spatially across seasons, lifespans, life stages and tissue types by biotic and abiotic factors (Staufenberger et al. 2008; Michelou et al. 2013; Mancuso et al. 2016; Aires et al. 2016). For example, the epibacterial diversity of Laminaria hyperborea (Phaeophyta) increases with the age/successive colonization of the kelp surface (Bengtsson et al. 2012). Bacterial density and community composition follow the brown seaweed seasonal growth cycle. As most of the biofilm seems to consist of bacteria utilizing carbon produced by the host (Bengtsson and Øvreås 2010), microbiome dynamics are probably strongly linked to seasonal changes in the kelp metabolome and seawater temperature (Bengtsson et al. 2010). Table 1 illustrates the list of bacteria isolated from the major ‘3’ phyla, red, green and brown, of macroalgae.

Table 1 List of bacteria isolated from the ‘3’major phyla of macroalgae

Macroalgae Bacteria Isolated References Red Polysiphonia stricta Pseudoalteromonas arctica (Nasrolahi et al. 2012) Shewanella basaltis Gracilariopsis heteroclada Vibrio parahaemolyticus; V. alginolyticus (Martinez and Padilla 2016) Gracilaria blodgetti Agarilytica rhodophyticola (Ling et al. 2017) Laurencia papillosa Bacillus amyloliquefaciens (Chakraborty et al. 2017b) Ahnfeltia tobuchiensis Polaribacter staleyi (Nedashkovskaya et al. 2018a)

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 Tichocarpus crinitus Aquimarina algiphila (Nedashkovskaya et al. 2018b) Iridaea cordata Streptomyces pratensis (Alvarado et al. 2018) Gracilaria corticata Bacillus sp.; Pseudomonas stutzeri; Vibrio (Karthick and Mohanraju owensii 2018) Mastophora rosea Vibrio sp. (Karthick and Mohanraju 2018) Gracilaria edulis Corynebacterium sp. (Suvega and Arunkumar Bacillus megaterium 2019) Klebsiella oxytoca Bacillus pasteurii Bacillus cereus Aeromonas sp. Lysinibacillus xylanilyticus Lactobacillus casei Aeromonas hydrophila Undetermined Alteromonas macleodii (Naval and Chandra 2019) Kappaphycus alvarezii Bacillus sp., Vibrio sp. (Mohammed Riyaz et al. 2019) Green Ulva australis Pseudoalteromonas tunicate (Rao et al. 2007) Phaeobacter sp. Ulva sp. Kordia ulvae (Qi et al. 2016) Ulva fenestrata Olleya algicola (Nedashkovskaya et al. 2017) Ulva lactuca Bacillus sp. (Ramya R et al. 2017) Shewanella oneidensis (Karthick and Mohanraju Shewanella sp. 2018) Pseudoalteromonas sp. Pseudoalteromonas rubra Vibrio sp. Bacillus cereus Pseudomonas pseudoalcaligenes Alcanivorax dieselolei Monostroma hariotii S. pratensis; S. brevispora; Pseudonocardia (Alvarado et al. 2018) adelaidensis; Agrococcus baldri Brown Fucus vesiculosus Pseudoalteromonas mariniglutinosa (Nasrolahi et al. 2012) Pseudoalteromonas tunicata Shewanella baltica Bacillus foraminis Fucus serratus Ulvibacter litoralis, Photobacterium (Nasrolahi et al. 2012) halotolerans

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 Ascophyllum nodosum Zobellia sp.; Maribacter thermophilus; (Martin et al. 2015) Maribacter forsetii; Cellulophaga baltica; Cellulophaga geojensis; Algibacter miyuki; Vibrio sp.; Shewanella sp.; Paraglaciecola sp.; Colwellia sp.; Pseudoalteromonas sp.; Marinomonas sp.; Cobetia sp.; Padina tetrastromatica Halolactibacillus alkaliphilus (Suresh et al. 2015) Splachnidium rugosum Vibrio cyclitrophicus; Vibrio splendidus; (Albakosh et al. 2016) Vibrio comitans; Vibrio celticus; Pseudoalteromonas carrageenovora; Pseudoalteromonas espejiana; Alteromonas stellipolaris; Psychromonas arctica; Pseudomonas poae; Pseudomonas fluorescens; Neptumonas naphthovorans; Cobetia amphilecti; Shewanella sp.; Sphingomonas sp.; Sulfitobacter sp.; Polaribacter sp.; Bacillus sp.; Staphylococcus saprophyticus. Padina pavonica Paracoccus sp.; Devosia sp.; Vibrio sp.; (Ismail et al. 2016) Pseudomonas sp.; Pseudomonas putida; Brevibacterium iodinum; Staphylococcus sp.; Bacillus pumilus; Pseudoalteromonas sp.; Acinetobacter sp.; Planomicrobium glaciei; Erwinia bilingiae. Anthophycus longifolius Bacillus subtilis (Chakraborty et al. 2017a) Sargassum myriocystum Bacillus subtilis (Chakraborty et al. 2017a) S. polycystum (Susilowati et al. 2015) S. duplicatum S. echinocarpum Ecklonia cava Flavivirga eckloniae; F. aquimarina (Lee et al. 2017a) Sargassum fulvellum Winogradskyella flava (Lee et al. 2017b) Dokdonia lutea (Choi et al. 2017) Adenocystis utricularis Arthrobacter flavus; Staphylococcus (Alvarado et al. 2018) haemolyticus; Micrococcus luteus; Tessaracoccus flavescens ; Pseudarthrobacter oxydans Fucus serratus L. Cellulophaga baltica sp. (Johansen et al. 2018) Cellulophaga fucicola sp. Turbinaria ornate Exiguobacterium profundum (Karthick and Mohanraju 2018) Pelvitia canaliculata Kocuria marina (Uzair et al. 2018) Laminaria japonica Algibacter alginolytica ; Alginococcus Sun et al. 2016 marinus (Ying et al. 2019)

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

Undertermined Gilvimarinus polysaccharolyticus (Cheng et al. 2015) Undertermined Marinirhabdus citrea (Yang et al. 2018)

Macroalgal growth and morphogenesis are particularly shown to depend on the associated bacterial communities which produce plant growth-promoting substances, quorum sensing signal molecules and several bioactive compounds (Singh and Reddy 2014). Bacterial biofilms can have a repellent effect, for example, nonviable bacteria tended to repel cyprids when compared to the surfaces without a biofilm, which ultimately prevent heavy fouling on the surface (Nasrolahi et al. 2012). Bacterial communities belonging to the phyla Proteobacteria and Firmicutes are generally the most abundant epiphytic bacteria associated with macroalgal host (Singh and Reddy 2016). The ‘71’ heterotrophic bacteria isolated from the green alga Ulva rigida belong to the four phyla Proteobacteria, Bacteroidetes, Firmicutes and Actinobacteria (Ismail et al. 2018). Marinomonas sp. and Bacillus spp. were observed to have the morphogenesis-inducing ability towards its host macroalgae U. fasciata (Singh et al. 2011). An in-depth study on the 16s rRNA gene libraries of the bacterial community on the surface of U. australis and the surrounding water determines the uniqueness of the association. Both the surface and seawater libraries were dominated by Alphaproteobacteria and Bacteroidetes families, but they were observed to be clearly distinct from each other through clustering into operational taxonomical units (OTUs) (Burke et al. 2011). Pseudoalteromonas tunicata and Phaeobacter sp. in association with the green alga Ulva australis inhibits the common fouling organisms by producing extracellular compounds even at low densities, 102 to 103 cells cm-2 and 103 to 104 cells cm-2, respectively (Rao et al. 2007).

The Roseobacter species from the surface of a green alga Ulva mutabilis exhibits a specific chemotactic affinity to the alga and chemically communicates with the alga as well as with another associated bacterium Cytophaga sp. forming a symbiotic tripartite community (Spoerner et al. 2012). Additionally, some bacterial species produce regulatory compounds resembling cytokinin (from Roseobacter, Sulfitobacter, and Halomonas) and auxin (from Cytophaga) that assist in the differentiation of U. mutabilis (Spoerner et al. 2012). The epibiotic bacteria associated with the brown alga Fucus vesiculosus tended to repel the cyprids benefitting the host by preventing heavy fouling on its surface (Nasrolahi et al. 2012). Specific functionally active bacterial species (FABS) will serve to improve the health and performance of the associated host by suppressing the growth of pathogens (Singh and Reddy 2016). Bondoso et al. (2017) hypothesized that specific association of Planctomycetes and their macroalgal hosts, such as the brown alga Fucus spiralis, the green alga Ulva sp. and the red alga Chondrus crispus, is likely to be determined by the nutrients provided by the host and the set of sulfatases inherent to the associated Planctomycetes species.

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 In red alga, Asparagopsis armata-associated bacteria exhibited a potential antitumour and antibacterial activity. The extract of Shewanella sp. ASP 26 showed increased antimicrobial activity against Bacillus subtilis and Staphylococcus aureus (Horta et al. 2019). Ma et al. (2009) isolated 192 bacterial strains from the surfaces of seaweeds, of which ‘63’ isolates were shown to inhibit the settlement of algal spores and ’62’ isolates inhibited larval settlement under laboratory conditions. About ‘25’ heterotrophic algae-associated bacteria from the surfaces of U. rigida show antibacterial activity whereas the free-living bacteria from the surrounding seawater did not show such activity (Ismail et al. 2018). Sixty of the ‘280’ strains isolated from different marine macroalgae exhibited antibiotic activity contrary to a series of fouling bacteria and therefore have the potential to control the microbial population on the seaweed’s surface either by inhibiting their growth or by influencing the tactic behaviour of potentially competing bacteria (Boyd et al. 1999).

A broad range of antibacterial activity contra important food pathogens such as Vibrio parahaemolyticus, V. vulnificus and Aeromonas hydrophilia was observed in Bacillus subtilis associated with the brown alga Anthophycus longifolius (Chakraborty et al. 2017a). Two polyketides from Bacillus amyloliquefaciens, a heterotrophic bacteria associated with the edible red alga, Laurencia papillosa, 3- (octahydro-9-isopropyl-2H-benzo[h]chromen-4-yl)-2-methylpropyl benzoate and methyl 8-(2- (benzoyloxy)-ethyl)-hexahydro-4-((E)-pent-2-enyl)-2H-chromene-6-carboxylate exhibits an activity contra to human opportunistic food pathogens (Chakraborty et al. 2017b). Members of the genus Pseudoalteromonas, Bacillus, Vibrio and Shewanella isolated from the surface of the green alga, Ulva lactuca, showed an anti-diatom activity on the pennate diatom, Cylindrotheca fusiformis (Kumar et al. 2011). Figure 2 illustrates bioactive metabolites isolated from the macroalgal associated bacteria.

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

Fig. 2 Bioactive metabolites from the macroalgal associated bacteria.

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 2.5.2 Fungi

The fungal population on the surface of seaweed mainly consists of parasites, saprobes or asymptomatic fungal endosymbionts where the ascomycetes and anamorphic fungi were dominant. Suryanarayanan et al. (2010) observed that among the different red, green and brown macroalgae studied, the brown algae supported a higher diversity of fungal endophytes whereas the green algae had a low diversity with dense colonization. In different seaweeds, members of genera Acremonium, Alternaria, Arthrinium, Aspergillus, Cladosporium, Fusarium, Geomyces, Penicillium and Phoma were the most common fungal endosymbionts (reviewed by Singh, Kumari, & Reddy, 2014). More recently, fungi of the Eurotiomycetes class were visualised for the first time on the surface of the brown alga Fucus vesiculosus, by CARD-FISH imaging (Parrot et al. 2019).

Aspergillus terrreus isolated from Halimeda macroloba, Gracilaria edulis and Ulva lactuca produced metabolites which were active against bacteria, algae and fungi (Suryanarayanan et al. 2010). However, in contrast to the well-studied bacteria-algae surface interactions, there is only a little evidence on fungi and their role on macroalgal surfaces (Egan et al. 2013). Most of the studies on macroalgal associated fungi dealt with the endophytic fungi and they are well detailed by Habbu et al. (2016) together with their antimicrobial activities. The filamentous fungal community associated with the healthy and the decaying brown alga, Fucus serratus, is observed to be different. Such a change in the community structure could be related to the release of nutrients resulting from tissue breakdown (Zuccaro et al. 2008). A total of ‘75’ fungi were isolated from marine macroalgae of Antarctica and despite the fact that these fungi were isolated from living surfaces most of the isolates are probably saprobic decomposers, which may have an important role in the ecosystem for organic matter recycling (Loque et al. 2010). Table 2 illustrates the list of fungi isolated from the ‘3’ major phyla of macroalgae, such as red, green and brown.

Table 2 List of fungi isolated from the ‘3’major phyla of macroalgae

Macroalgae Fungi Isolated References

Red

Eucheuma sp. Aspergillus flavus, A. niger (Vala et al., 2004)

Palmaria decipiens Metschnikowia australis, Cryptococcus (Loque et al. 2010) carnescens

Grateloupia lithophila Cladosporium sp., Chaetomium sp., (Murali 2011) Nigrospora sp.,

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 Halymenia sp. Emericella nidulans, Chaetomium sp., (Murali 2011) Emericella nidulans,

Portieria hornemonii Cladosporium sp., Emericella nidulans, (Murali 2011) Phomopsis sp.

Jania adherens Nigrospora sp. (Murali 2011)

Bostrychia radicans Phomopsis longicolla (Erbert et al. 2012)

Green

Halimeda macroloba Aspergillus sp., Aspergillus terreus, (Murali 2011) Penicillium sp.

Caulerpa racemosa Aspergillus niger (Murali 2011)

Caulerpa scalpelliformis Aspergillus niger, Aspergillus terreus, (Murali 2011) Paecilomyces sp.

Ulva lactuca Aspergillus terreus, Chaetomium sp., (Murali 2011) Cladosporium sp.,

Chondrus ocellatus Paraconiothyrium sp. (Suzuki et al. 2019)

Brown

Fucus serratus Lindra, Lulworthia, Engyodontium, Sigmoidea (Zuccaro et al. 2008) marina, Acremonium fuci.

Desmarestia anceps Geomyces pannorum, M. australis (Loque et al. 2010)

Adenocystis utricularis Antarctomyces psychrotrophicus, G. (Loque et al. 2010) pannorum, Oidiodendron sp, Penicillium sp, Phaeosphaeria herpotrichoides, M. australis, Rhodotorula mucilaginosa, Rhodotorula mucilaginosa

Sargassum ilicifolium Aspergillus terreus (Murali 2011)

Turbinaria sp. Alternaria sp., Aspergillus sp., Colletotrichum (Murali 2011) sp,, Curvularia tuberculate, Drechslera sp., Fusarium sp., Phaeotrichoconis sp., Paecilomyces sp., Nigrospora sp.,

Stoechospermum Alternaria sp. (Murali 2011) marginatum

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 Sargassum wightii Alternaria sp., Aspergillus sp., Cladosporium (Murali 2011) sp., Curvularia sp., Emericella nidulans, (M Hulikere et al. 2017) Trichoderma sp., Pestalotiopsis sp.

Cladosporium cladosporioides

Enteromorpha flexuosa Penicillium daleae (Iswarya and Ramesh 2019)

Seaweed associated-fungi, Aspergillus spp. and Penicillium were common colonizers and also serve as a potential source of pharmacologically active compounds (Singh et al. 2015). A. terreus from the tissues of Caulerpa scalpelliformis, C. sertularioides, G. edulis, Sargassum ilicifolium and U. lactuca produced metabolites which have insecticidal activity (Suryanarayanan et al. 2010). Xylaria sp. isolated from red alga Bostrychia tenella synthesize cytochalasin D, a known antibiotic and antitumour compound (de Felício et al. 2015) whereas A. niger EN-13, isolated from brown alga, Colpomenia sinuosa, produces a sphingolipid and their corresponding glycosphingolipid named Asperamides A and B, where compound A exhibits antifungal activity against Candida albicans (Zhang et al. 2007) (Fig. 3).

Fig. 3 Bioactive metabolites from the macroalgal associated fungi.

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 2.5.3 Microalgae

Table 3 illustrates the list of microalgae isolated from different macroalgal species. The epiphytic microalgal community is mostly dominated by benthic diatoms and a few centric species possessing an attached mode of life (Lage and Graça 2016). As photosynthetic eukaryotes, diatoms can also serve as food for heterotrophic bacteria through specific interactions with growing diatoms as well as colonizing the sinking diatom and

Table 3 List of microalgae isolated from the macroalgae

Macroalgae Microalgae Isolated References

Chaetomorpha linum Leptocylindrus danicus; Navicula distans; (Al-Harbi 2017) Nitzschia hungarica; Thalassionema frauenfeldi

Enteromorpha intestinalis Chaetoceros fragile ; Gyrosigma fasciola ; L. (Al-Harbi 2017) danicus ; N. distans ; N. transitans ; Pleurosigma angulatum ; P. normanii

Ulva lactuca Licmophora flabellata ; L. abbreviata ; P. (Al-Harbi 2017) normanii ; T. frauenfeldii

Laminaria sp. L. danicus; L. flabellata; Navicula (Marzoog Al-Harbi 2017) ramosissma;

Padina fraseri Cylindrotheca closterium; Navicula sp.; (Marzoog Al-Harbi 2017) Nitzschia sp.; Prorocentrum lima; Cocconeis lineatus

Sargassum muticum N. distans ; N. socialis ; N. vanhoeffeni (Marzoog Al-Harbi 2017)

Turbinaria ornata Leptocylindrus minimus; L. danicus; (Marzoog Al-Harbi 2017) Bacillaria paxillifer ; N. hungarica; T. frauenfeldii

Ecklonia maxima Amphora; Gomphoseptatum; Navicula, (Mayombo et al. 2019) Rhoicosphenia

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 Laminaria pallida Amphora; Gomphoseptatum; Navicula, (Mayombo et al. 2019) Rhoicosphenia

decomposing their organic matter. The interactions between diatoms and bacteria are generally confined and invariably, it is observed with Proteobacteria and Bacteroidetes phyla (Amin et al. 2012). Diatoms release transparent exopolymer particles (TEP) which are often colonized by bacteria and in response, bacteria produce exopolysaccharides probably to initiate attachment (SchÃ¤fer et al. 2002; Fukao et al. 2010; Amin et al. 2012). The scanning electron micrographs of the red alga Halymenia floresii depict the association of different bacterial phylotypes with the surface attached diatoms (Figure 4). The abundance and taxonomic composition of the diatoms on the macroalgae are highly affected by the architecture of the macroalgal thallus and its surface characteristics (Totti et al. 2009).

Fig. 4 Diatoms embedded on the extracellular matrix of H. floresii (left) and associated with the macroalgal surface bacteria (right) (unpublished work of the author).

2.5.4 Virus

As ubiquitous components of marine holobionts, viruses are now emerging as pathogens or potential pathogens in various marine organisms including macroalgae (Lachnit et al. 2016; Beattie et al. 2018). Viruses can have both positive and negative impacts on the holobiont which either may spread diseases among hosts or may be a part of the holobiont immune system (Ying et al. 2018). Lachnit et al. (2016) reported the viruses associated with a red macroalga, Delisea pulchra, identifying diverse morphotypes of virus-like particles from icosahedral to bacilliform to coiled pleomorphic as well as bacteriophages. The virome sequencing revealed the presence of a diverse group of dsRNA viruses

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 affiliated to the genus Totivirus, known to infect plant pathogenic fungi. Furthermore, an ssRNA virus belonging to the Picornavirales order with a close phylogenetic relationship to a pathogenic virus infecting marine diatoms was also identified in association with D. pulchra. Viruses have a large potential effect by not only infecting the host but also other microorganisms, like bacteriophages which regulate the bacterial community composition, thus adding a complexity to the holobiont structure (Gachon et al. 2010; Ying et al. 2018). Table 4 includes the viruses isolated from different macroalgae.

Table 4 List of viruses isolated from the macroalgae

Macroalgae Viruses References

Delisea pulchra Totivirus; Picornavirales (Lachnit et al. 2016)

Chondrus crispus Totivirus-like entities (Rousvoal et al. 2016)

Ectocarpus siliculosus Ectocarpus DNA virus (Lanka et al. 1993)

Heterosigma akashiwo Phycodnaviridae (Nagasaki et al. 2005)

Ecklonia radiata Siphoviridae; Myoviridae; Inoviridae; (Beattie et al. 2018) Podoviridae

Laminaria sp. and Phaeovirus (McKeown et al. 2017) Saccharina sp.

2.6 Role of Microbiome: Microbial Interactions and their Hosts Response

The microbiome performs a ‘protective’ role with epibiotic bacteria present on surfaces that release chemicals that prevent biofouling by other organisms (Armstrong et al. 2001). Similarly, macroalgal growth and development are shown to depend on associated microorganisms, particularly on bacterial communities (Singh and Reddy 2014; Wichard et al. 2015). With such an obligate association, a fraction of epiphytic bacterial communities can be regarded as ‘core microbial species’ (Singh and Reddy 2016). The group of a microbial community which are consistently present on the surface of different individuals of the macroalgae is known as a ‘core community’(Burke et al. 2011). The interactions between the macroalgae and the microbiota are extremely diverse (van der Loos et al. 2019). As the microorganisms in the diverse marine environment are concentrated on microscale patches, their close proximity suggests many potential cell-cell communications (Azam and Malfatti 2007; Amin et al. 2012). The interactions of macroalgae and

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 bacteria are mainly for nutrient exchange, signal transduction and gene transfer (Kouzuma and Watanabe 2015).

2.6.1 Quorum Sensing

Quorum Sensing (QS), an essential language of bacteria, is a cell density-dependent communication system that regulates a diverse array of physiological activities such as symbiosis, virulence, competence, conjugation, antibiotic production, motility, sporulation, and biofilm formation. This phenomenon relies on the accumulation of signal molecules in the surrounding environment up to threshold concentrations, at which the target genes are activated (Williams et al. 2007; Romero et al. 2011). These QS signals, otherwise known as Auto Inducers (AI), broadly fall into three categories. Acyl Homoserine Lactones (AHLs) are the major group of auto inducer signals in gram-negative bacteria (Di Cagno et al. 2011) (Figure 5 ); Auto Inducer Peptides (AIPs) are the major auto inducers in gram-positive bacteria (Thoendel et al. 2011; Zhang and Li 2016), whereas, regardless of gram-positive or gram-negative bacteria, Auto inducer 2 (AI 2) known as universal language, is used for intra- and inter-species communication (Lowery et al. 2008). Besides, there have been cyclic dipeptides, bradyoxetin, AI -3 in Escherichia coli and diffusible signal factors, documented in different QS systems (Gonzalez and Keshavan 2006).

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

Fig 5 Different homoserine lactones with differing chain lengths produced by the bacteria.

Apart from defending the attached basibiont the surface-associated bacteria also assist the host in growth and morphogenesis. AHLs, N-acyl homoserine lactones, produced by the macroalgae-associated bacteria facilitates the settlement of zoospores in Ulva spp. (Joint et al. 2002; Williams 2007; Singh et al. 2015). The bacterial isolates, Shewanella algae, Pseudomonas aeruginosa, Photobacterium sp., P. lutimaris, Vibrio gallicus, V. fluvialis, V. parahaemolyticus, associated with green macroalgae Ulva (U.

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 fasciata and U. lactuca) and red macroalgae Gracilaria (G. corticata and G. dura), induced carpospore liberation from cyctocarps of G. dura by producing different AHLs (Singh et al. 2015). The AHLs produced by Acrochaetium sp. control spore release in the red alga, G. chilensis whereas its production was inhibited in the brown alga, Colpomenia sinuosa (Weinberger et al. 2007; Kanagasabhapathy et al. 2009). The motile zoospores of Enteromorpha exploit the diffusible signal molecules produced by bacterial biofilms for temporary settlement on the surface (Joint et al. 2002).

QS-mediated interactions widely influence algal-bacterial symbiotic relationships, which in turn determine community organization, population structure, and ecosystem functioning (Zhou et al. 2016). In U. mutabilis the chemical communication signals between the associated bacteria, Roseobacter and Cytophaga, up-regulate the growth and thallus morphogenesis (Spoerner et al. 2012). A chemical elicitor protein, probably a quorum signal N-acyl homoserine lactone produced by a gram-positive probiotic Lysinibacillus xylanilyticus associated with a red alga G. edulis promotes the growth of the host (Suvega and Arunkumar 2019).

2.6.2 Quorum Quenching

Quorum Quenching (QQ), is defined as any interference on the QS communication system either by enzymatic degradation of the AHLs, or by production of inhibitors or antagonists of signal reception. QQ strategy plays an important role in preventing microbial disease and affecting beneficial bacteria (Chen et al. 2013). Many algae use defence strategies to protect themselves from the potentially opportunistic pathogens associated with them by altering their QS communication systems i.e. quenching their signal molecules.

The first QS inhibitory activity was reported by the halogenated furanones from the red alga Delisea pulchra which exhibited antibiofilm activity against Bacillus subtilis, Pseudomonas aeruginosa and Escherichia coli, and these compounds were released at the surface at concentrations capable of inhibition (Givskov et al. 1996; Ren et al. 2001, 2002; Steinberg and de Nys 2002) . In particular the brominated furanones significantly inhibited the settlement of cyprid larvae of Balanus amphitrite (de Nys et al. 1995). A number of halogenated furanones from this alga antagonize the bacterial AHLs and interfere with their associated biological activity, such as competitively inhibiting swarming motility (Givskov et al. 1996). A mixture of relatively less lipophilic compounds, floridoside, betonicine and isethionic acid, isolated from the red alga Ahnfeltiopsis flabelliformis was observed to inhibit the exogenous QS signalling molecule, N- octanoyl-DL-homoserine lactone (OHL), in the quorum-sensing inhibition assay (Kim et al. 2007). The methanolic extract of the red alga, Asparagopsis taxiformis, showed quorum sensing inhibitory activity against the reporter strain, Chromobacterium violaceum CV026 (Jha et al. 2013) whereas the polar extracts

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 of the green alga Ulva fasciata and Codium sp. exhibited QS inhibitory activity on C. violaceum CV017 (Batista et al. 2014). In the brown alga Laminaria digitata, QQ activity was demonstrated by the inactivation of AHLs based on the enzymatic production of the oxidized halogen HOBr which reacts specifically with 3-oxo-acyl HSLs (Borchardt et al. 2001; Romero et al. 2011). The polar and non-polar extracts of the different brown algae, Sargassum spp. S. vulgare, S. furcatum, showed anti-quorum sensing activity on the reporter strain, C. violaceum CV017 (Batista et al. 2014; Schwartz et al. 2017; Carvalho et al. 2017).

By interfering with the AHL signalling of Vibrio anguillarum, the settlement of Enteromorpha, an important fouling organism, can be prevented (Joint et al. 2002). In aquaculture systems, several algal species were used for controlling pathogenic bacteria by disrupting the quorum sensing communication between them, for example, Vibrio harveyi (Natrah et al. 2014; Ramanan et al. 2016). The halogenated furanones of D. pulchra protect both shrimp and fish from vibriosis by interfering with their AHL signalling system (Rasch et al. 2004; Torres et al. 2019). A QS antagonist, (5Z)-4-bromo-5-(bromomethylene)-3- butyl-2(5H)-furanone (furanone) from the D. pulchra was found to inhibit the formation of the siderophore produced by Pseudomonas putida and P. aeruginosa (Ren et al. 2005). QQ is a promising approach for very effective next-generation antibacterial drugs which block the QS-mediated pathogenic infection by interfering with the bacterial communication (Chen et al. 2013).

2.6.3 Positive Effects: Symbiotism

There are many beneficial functions for the macroalgal host, for example, normal growth, morphological development, spore germination and nitrogen fixation activity. This is despite the low average microbial density (105 to 106 cells per gram of seawater), the close proximity and the heterogeneous distribution that suggest their interaction is across multiple spatial scales (Amin et al. 2012). Generally, the associated microorganisms were assumed to use the readily available range of organic carbon sources produced by the macroalgae in the form of complex diversified polysaccharides (Martin et al. 2014). The microbial metabolites produced by these symbiotic microorganisms help in chemical defences of the macroalgae and its biological interactions with foulers and pathogens (Wahl et al. 2012a; Chakraborty et al. 2017a). In such a microhabitat, the macroalgae supply bacteria with oxygen and fixed carbon, and the

bacteria supply CO2, minerals, vitamins and growth factors. The microbial community on the surface of the macroalgae was mainly formed by bacteria but also included different eukaryotes in a ratio of 640:4:1 of bacteria:diatoms: flagellates (Railkin 2004; Lage and Graça 2016).

Epiphytic bacteria are known to maintain the health of the host. Several algal species need specific vitamins for their growth and bacteria may be partly responsible for the production of these substances

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 (Villarreal-Gómez et al. 2010). Conversely, bacterial colonization could be especially rapid if the surface is a potential source of nutrients, such as polysaccharides in exudates of seaweeds that may serve as a source of carbon for heterotrophic bacteria living on the surface. For example, Kelp exudates could shape bacterial community composition, and create communities that are kelp-specific rather than randomly assembled from the surrounding seawater (Collen and Davison 2001; Michelou et al. 2013). Bacteria may degrade algal polysaccharides such as for fucoidan (Nedashkovskaia et al. 2002) or alginates (Preston et al. 1986; Brown et al. 1991).

An overarching clade of bacteria such as Proteobacteria and Bacteroidetes are more likely to be associated with the green alga and furthermore, they are functionally equipped to be associated with them (Ramanan et al. 2015, 2016). The temperate green alga Ulva lactuca relies on the defence provided by the microbial community on its surface to regulate the fouling by colonizers (Kumar et al., 2011) and ‘73’ different bacterial strains were isolated from the surfaces of U. lactuca, U. pertusa and Enteromorpha sp. (Ma et al. 2009). In the green alga, Ulva rigida ‘71’ bacterial phylotypes were isolated from its surface which belongs to four phyla: Proteobacteria, Bacteroidetes, Firmicutes and Actinobacteria (Ismail et al. 2018). Different studies showed that induction of cell divisions, differentiation, wall formation, and normal morphogenesis in green algae belonging to Ulvophyceae depend on bacterial compounds (Matsuo et al. 2005; Wichard 2015; Grueneberg et al. 2016). Pseudoalteromonas ulvae and Pseudoalteromonas tunicata isolated from the surface of the green seaweed U. lactuca have shown the ability to reduce the adhesion of colonizing organisms and inhibit the germination of spores of this alga. This result was also observed in the red algae Polysiphonia sp. in the presence of P. tunicata. Ulva spores can be attracted by the emission of QS molecules from biofilms dominated by Pseudoalteromonas. In other species, spore germination may be inhibited by the presence of P. tunicata. Ulva australis is colonized by a very poorly diversified microflora, dominated by Roseobacter gallaeciensis, which facilitates the selective establishment of the bacterium Pseudoalteromonas tunicata. The two bacteria use different strategies. R. gallaeciensis invades and disintegrates the biofilms of competing bacteria, with the exception of P. tunicata. P. tunicata takes advantage of its "immunity" to proliferate in the face of competition that it cannot manage alone and then forms spatially separated microcolonies. These bacterial behaviours on the surface of U. australis allow the algae to have an inexpensive metabolic control of the bacterial biofilm.

The red alga Agarophyton vermiculophyllum chemically ‘gardens’ and recruits protective epibacterial strains in order to strengthen its disease resistance against potentially pathogenic microbes (Saha and Weinberger 2019). Twenty one phenotypically different bacterial strains were isolated from the red alga Asparagopsis armata which belong to seven different genera: Vibrio, Staphylococcus, Bacillus, Cobetia, Photobacterium, Shewanella and Alteromonadaceae (Horta et al. 2019). Ma et al. (2009) isolated

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 ‘88’ bacterial strains from the surfaces of the red algae: Chondrus ocellatus, Dumontia simplex, Grateloupia filicina, Halymenia sinensis and Porphyra sp.

A study by Ji et al. (2017) suggested a complementary and mutualistic relationship between the female gametophyte of the Brown seaweed Saccharina japonica and its microbiome, in which bacteria seem to benefit from seaweed polysaccharides, and the kelp profits from enhanced growth and nutrient uptake by bacterial bioactive compounds such as vitamins and hormones. Analysis of gene functions within this kelp epimicrobiome was mainly symbiosis-associated, indicating that selective pressures shape these microbiomes to sustain a mutual benefit for both kelp and bacteria (Ji et al. 2017). Fucus vesiculosus L. is recognized as an important habitat-forming macroalga and could be able to produce secondary metabolites that directly act on the invertebrate larvae to prevent fouling (Lachnit et al. 2010; Kersen et al. 2011). Several species of marine bacteria have been isolated from brown algae such as the spot-wounded fronds of Laminaria japonica (Sawabe et al. 2000), the surface of Fucus serratus (Johansen et al. 1999; Lachnit et al. 2009) and from the rotten thallus of Fucus evanescens (Ivanova et al. 2004), Undaria pinnatifida (Lee et al. 2006), Padina arborescens, P. pavonica, P.tetrastromatica (Ismail et al. 2016), Macrocystis pyrifera (Michelou et al. 2013), Ecklonia radiata (Qiu et al. 2019), Taonia otamaria (Othmani et al. 2016). Around ‘18’ different bacteria were isolated from the surfaces of two different brown algae, Sargassum thunbergii and Sargassum sp. (Ma et al. 2009). Such a complex microbiome on the surface of macroalgae is regarded as an indirect defence mechanism which may strengthen the direct antifouling defence mechanism of the host against any macrofoulers (Nasrolahi et al. 2012).

2.6.4 Negative Effects: Pathogenism

Any disturbance and disruption of the mutualistic association between host and microbiota are regarded as an ‘Holobiont break-up’ which can be caused by environmental stress (Ying et al. 2018). Such disturbances are widely caused by spatial and temporal effects. The spatial and temporal shifts of seaweed associated microbiota are induced by the changing environmental conditions and seaweed physiology. Such shifts may result in potential consequences provoked by the structural, functional and behavioural changes in the host posing a challenge for its fitness (Serebryakova et al. 2018). This shifts the symbiotic relationship of the microbiota to a potential pathogenic relationship with its associated hosts. The interplay between temperature, the algal chemical defence mechanism and bacterial virulence demonstrates a complex environmental change impact on an ecosystem (Case et al. 2011). Though the epibacterial community structures on the surface of macroalgae are host-specific, they are highly prone to spatial and temporal variation (Wu et al. 2014). A shift in the microbiome from a stable state to a disturbed state where the colonizers may become opportunist pathogens when the host system is stressed by abiotic or biotic challenges is referred to as ‘dysbiosis’ (Minich et al. 2018).

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 Pathogenic bacteria can cause severe degradation of algal host cells or even lead to seaweed mortality, causing major financial losses for seaweed mariculture every year (Correa et al. 1993; Vairappan et al. 2008; Goecke et al. 2010). Also, biofouling forms a permanent threat to macroalgae as bacterial biofilms increase the hydrodynamic drag on their host and enhance the attachment of other fouling organisms and grazers. Biofilms may also compete for nutrients, inhibit gaseous exchange, or block light essential for photosynthesis.

Microbial colonization can lead to the degradation of algal tissue, decreased photosynthesis and gas and nutrient exchange and ultimately disease. Marine macroalgal diseases have been recognised for a long time, but identifying the causative agents and distinguishing them from other opportunistic bacteria is still an obstacle for describing the virulence mechanism (Egan et al. 2013). Macroalgae are under constant colonization pressure from the millions of microorganisms within the surrounding seawater, some of which are potential pathogens. The primary biofilm undergoes a transition to a much more complex community which eventually ends in fouling on the surface (Bhadury and Wright 2004). All such biofouling communities vary spatially and temporally (Fitridge et al. 2012). Due to the effects of environmental change on host organisms and their pathogens, temporal-mediated diseases can have severe consequences for marine macroalgal ecosystems (Campbell et al. 2011).

i. Spatial effects

The close spatial proximity of surface-associated microorganisms on macroalgae operates and controls specific intercellular interactions (Egan et al. 2008). The spatial distribution and the abundance of the epiphytes are largely determined by several abiotic and biotic factors which ultimately leads to uncontrolled growth (Nylund and Pavia 2005; Kotta et al. 2008; Kotta and Witman 2009; Kersen et al. 2011). Spatial variability of the epiphytic communities varies both on the large and small scale (Fitridge et al. 2012). The protective effects of the microbiome on the surface were affected by severe settlement pressure and stressful temperatures as observed in Fucus vesiculosus (Nasrolahi et al. 2012), in which the composition and dominance were significantly influenced by wave exposure and frond segment (Kersen et al. 2011). Also, reduced light conditions due to the uncontrolled colonization of the surface have detrimental effects on the growth of the macroalgae (Rohde et al. 2008). The occurrence and richness of the epiphytes were observed to be specific and different, according to the different parts of the host macroalgae (Kersen et al. 2011). The spatial distribution of epibiotic organisms is determined by their ability to tolerate desiccation during low tides (Molina-Montenegro et al. 2005). Multiple opportunistic pathogens, Alteromonas sp. BL110, Aquimarina sp. AD1 and BL5 and Agarivorans sp BL7 rather than the previously identified Nautella R11, cause bleaching disease in a temperate red alga Delisea pulchra with loss of its photosynthetic pigments followed by tissue necrosis and death. Multiple opportunistic pathogens are

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 involved in bleaching disease in D. pulchra (Gonzalez and Keshavan 2006; Case et al. 2011; Campbell et al. 2011; Fernandes et al. 2011; Kumar et al. 2016).

ii. Temporal Effects

The temporal variation of the macroalgal associated microbiome may be affected by biotic factors such as algal growth, exudation of macroalgal metabolites, the composition of the microbial community in the surrounding seawater and abiotic factors like temperature, wave action, light conditions and nutrient levels in seawater (Bengtsson et al. 2010). Different seasons affect the temporal variations of macroalgal surface-associated bacterial communities (Singh et al. 2015). As a result of climate change, environmental stress can render macroalgae susceptible to the presence of opportunistic pathogens which are otherwise commensal to them, but can still take advantage of the weakened host and this results in disease outbreak (Egan et al. 2014). As most of the biofilm seems to consist of bacteria utilizing carbon produced by the host (Bengtsson and Øvreås 2010), microbiome dynamics are probably strongly linked to seasonal changes in the kelp metabolome and seawater temperature (Bengtsson et al. 2010).

The temporal shifts of bacterial communities associated with the brown alga Sargassum muticum were reflected in a significant abundance of unidentified Rhodobacterales and Laktonella in September- March and the substantial prevalence of unidentified Pirellulales in the summer. Such changes were related to the temporally changing algal exudates (Serebryakova et al. 2018). In another brown alga Laminaria hyperborea, the substratum and seawater temperature manifested a variation in their associated microbiome where Planctomycetes and Alphaproteobacteria were frequently detected throughout the year and Verrucomicrobia, Cyanobacteria, Gammaproteobacteria, Betaproteobacteria, and Bacteroidetes were more sporadically detected (Bengtsson et al. 2010). The kelp microbiome was most influenced by an elevated temperature exhibiting a reduction in the dominant kelp-associated Alteromondales, which negatively affects its growth (Minich et al. 2018). Any temporal variations in the primary biofilm will cause major temporal changes in the marine invertebrate populations, particularly, seasonality (Fitridge et al. 2012).

2.7 Future Perspectives

Increased physical disturbances and stress, resulting from changing environmental conditions, are known to affect the macroalgae-associated microbial composition (directly or via its physiological responses) and cause its structural, functional or behavioural changes. In the marine environment, changing climatic conditions such as ocean warming and acidification can have direct physiological effects on the marine-habitat forming macroalgal species, which sequentially, can have negative impacts on the associated microbial communities. The microbiome of the brown alga, Ecklonia radiata, was affected by warming,

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 followed by acidification which resulted in disease-like symptoms in the host (Qiu et al. 2019). The characteristic ‘core’ microbial community was replaced by taxonomically diverse microbial communities following the host’s stress (Marzinelli et al. 2015). Case et al. (2011) showed that temperate marine macroalga, D. pulchra, has become more susceptible to the temperature-enhanced bacterial virulence of Ruegeria sp. R11 as a result of high-water temperatures or increased solar radiation. This also affects the production or delivery of furanones potentially leading to in situ bleaching (Case et al. 2011). Ice-ice disease in the red alga Eucheuma/Kappaphycus is still a major problem in this seaweed cultivation where the changes in extreme environmental conditions make the alga susceptible to the infection of Vibrio sp. (Irmawati and Sudirjo 2017).

Bioactive metabolites of macroalgae surface-associated bacteria open up a new approach as a novel source of therapeutic molecules. For instance, Asparagopsis armata-associated bacteria revealed to be a potential source of compounds with antitumor and antibacterial activity (Horta et al. 2019). Marine bacteria can be a vast source of new antimicrobial molecules and antibiofilm molecules with a wide range of applications in human health and Industry (Desriac et al. 2018). In the future, beneficial bacteria identified from healthy seaweed holobionts could be applied to diseased plantlets in order to suppress the growth of detrimental ones and/or to prevent disease outbreaks in aquaculture settings. In addition to bacteria, these macroalgae frequently host endophytic fungi that may have protective functions for the algae (Dittami et al. 2019). Thus, metabolites of these bacteria will provide a new possibility of disease management in aquaculture systems.

The surface-associated microbiome of macroalgae is phylogenetically diverse and ecologically important. Thus, assessing the microbial functions and interactions has emerged as a critical need in macroalgal research. Massive sequencing of the 16S rRNA gene and using a metagenomic approach have been the main tools to explore these aspects. However, the complete understanding of the relationships between host and microorganisms, as well as the dynamics of the symbiotic community, probably requires the generation of comprehensive insights with another novel integrative “omics” approach, such as single- cell sequencing, metagenome binning, GeoChip, RNA-Seq, etc. To characterise the interactions of organisms with their environment, environmental metabolomics shall be further explored in this area (Bundy et al. 2009). Bioactive compounds from macroalgae and its associated microbiome may provide candidates with a high-efficiency and have broad implications in disease control. Concentration and quantity are the two main challenges to be addressed here. Different biotechnological tools should be developed for the isolation and identification of these compounds even at lesser concentrations, which again are the biggest challenges in this field.

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 2.8 References

The references of this article are available at the end of the manuscript (‘References’, page no. 206)

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

3. Chapter II

Screening of surface-associated bacteria from the Mexican red alga Halymenia floresii for Quorum Sensing activity Shareen A Abdul Malik, Alexis Bazire, Abril Gamboa-Muñoz, Gilles Bedoux, Daniel Robledo, José Q. García-Maldonado, Nathalie Bourgougnon

Published as a Research Article to “Microbiology” (In press) Shareen at al. Submitted on 26 March 2020

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

Isolation and Identification of the surface-associated bacteria of Halymenia floresii and Screening for their Quorum Sensing activity

Macroalgae host a dense bacterial epibiome forming surface biofilms, and those microbial biofilms act as a biological defense by protecting the surface from macrofoulers. The surface of Halymenia floresii was observed to be free from any fouling organisms when cultivated under Integrated MultiTrophic Aquaculture (IMTA). Moreover, the culture tanks remained clean of any exogenic algae colonization. Thus, we hypothesize that the epibacterial community on the surface of the alga may protect the surface by forming a ‘protective coat’ (Wahl et al. 2012). In order to explore the ecological phenomenon behind this, we started the study by isolating the epibacterial community of H. floresii from different habitats. Following isolation, they have screened for any production of quorum sensing (QS) signals as these communication signals play a vital role in biofilm formation.

In this chapter, we dealt with the isolation and identification of those epibacterial community and then we further proceeded to detect any QS bacteria among them. Here we outline the work of the following Chapter II. The objectives of this chapter are as follows:

STEP I

The cultivable epibacterial of H. floresii was isolated swabbing the surface of the algal specimens and streaking on the Marine Agar (MA) after serially diluting. The plates were incubated at 25° C for up to 7 days and the bacterial colonies were selected based on their unique morphological features, such as size, shape, surface, chromogenesis. The selected colonies were carried over for further sub-culturing to obtain axenic cultures (Fig. 18).

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

Fig.18 Isolation of the epibacterial strains by swabbing, streaking, and sub-culturing

STEP II

The isolated bacterial strains were characterized morphologically and biochemically. The phenotypic characteristics were determined by using API 20 NE and APIZYM galleries (bioMérieux) (Fig. 19) and the results were listed in the Table 1 (Chapter II). The molecular identification of the isolates was performed using DNeasy Blood and Tissue Kit (Van der Zee et al. 2002) and ZymoBIOMICS DNA Miniprep Kit (Al-Hebshi et al. 2019) for DNA extraction and the 16S rRNA (Fig. 19) was amplified and the bacterial isolates were commercially sequenced by GENEWIZ Laboratories. The protocol is detailed in Section 3.3.3 and the identified isolates were listed in the Table 2.

Fig. 19 Identification of the isolates by biochemical and molecular characterisation

STEP III

The ability of the isolated to produce QS signals was screened using two reporters, Chromobacterium violaceum CV026 by cross-feeding assay and Escherichia coli pSB406 by luminescent assay (Fig. 20). In both the assays Pseudomonas aeruginosa PA01 was used as a positive control. The positively responded isolates were listed in the Table 3.

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

Fig. 20 Reporter assays to screen the isolates for QS activity

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 Screening of surface-associated bacteria from the Mexican red alga Halymenia floresii for Quorum Sensing activity

Shareen A Abdul Malika, 1, Alexis Bazirea, Abril Gamboa-Muñozc, Gilles Bedouxa, Daniel Robledob, José Q. García-Maldonado d, Nathalie Bourgougnona

aUniversité Bretagne Sud, EA 3884, LBCM, IUEM, F-56000 Vannes, France bMarine Resources Department, CINVESTAV-Unidad Merida, Mexico cCentro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Unidad Mérida, Departamento de Recursos del Mar (CINVESTAV-Unidad) Merida, Mexico dCONACYT - CINVESTAV Merida, Mexico. Marine Resources Department 1Author for correspondence: [email protected]

3.1 Abstract

Macroalgae host a dense bacterial epibiome forming surface biofilms, and those microbial biofilms act as a biological defense by protecting the surface from macrofoulers. During the experimental cultivation of Halymenia floresii (Rhodophyta, Halymeniales) under Integrated MultiTrophic Aquaculture (IMTA), the culture tanks remained clean of any exogenic algae colonization, and the surface of the H. floresii was remarkably free from any fouling organisms. The presence of Halymenia also appeared to restrict the establishment of opportunist green algae and the colonization of barnacles usually disturbing the cultures. To date, nothing is known about the diversity and biological potential of H. floresii epibionts. Hence, to better understand their epiphytic bacterial community, the surface-associated bacteria from different H. floresii samples, Beach-cast (BC), Integrated Multi Trophic Aquaculture cultivar (IMTA), and Cultivar Cylinders (CC), were isolated. Thirty-one axenic bacterial strains were identified by 16S rRNA sequencing, and they belonged to ‘4’ phyla, ‘20’ genera, and ‘25’ species. Following isolation, they were screened for any production of quorum sensing (QS) signals as these communication signals play a vital role in biofilm formation. Almost all the isolates (except one) were identified as Gram-negative; hence, Acylated Homoserine Lactones (AHLs) were focused upon. Using the reporter strains Chromobacterium violaceum and Escherichia coli pSB406, the isolates were screened by their violet pigmentation and luminescence respectively. Of ‘31’ isolates screened, ‘17’ isolates, such as Pseudoalteromonas arabiensis, Pseudoalteromonas sp. (B5BC and B6.1BC), Pseudoalteromonas mariniglutinosa, Vibrio owensii, Tenacibaculum sp., Maribacter sp., Spongiimicrobium salis, Aquimarina sp., Uncultured Kordiimonas sp., Alteromonas sp. (B12CC and B16CC), Roseobacter sp., Erythrobacter sp., Ruegeria lacuscaerulensis, and Epibacterium sp., showed the presence of extracellular QS signals. The epibacterial community on the

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 surface of H. floresii in culture conditions and the presence of QS bacteria among them are demonstrated for the first time. Our results may open a new direction to explore the host interference in these QS bacteria.

Keywords: Halymenia floresii, quorum sensing, AHLs, 16S rRNA.

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 3.2 Introduction

Macroalgae and their associated microbiota form a functional unit termed 'holobiont', characterized by its complex mutualistic relations. In a holobiont, the microbial communities contain a diverse assembly of organisms that include bacteria, fungi, microalgae, viruses, protozoa, and archaea (Ying et al. 2018). The community is mostly dominated by bacteria (in a ratio of 640:4:1 of bacteria:diatoms:flagellates) as stipulated by metagenomic and transcriptomic research. However, other members of the microbial community may also play a significant role in the functioning of the holobiont (Railkin 2003; de Oliveira et al. 2012; Thajuddin et al. 2016; Ying et al. 2018). A broad range of beneficial or detrimental interactions between macroalgae and epi-and endosymbiotic bacteria have been found. Microbes often play a crucial part in macroalgal health, functioning, and development through mutualistic interactions during the various life cycle stages of the host. The interplay of macroalgae with their microbial component affects – among other things – nutrient exchange, defence chemical mechanisms, morphology, reproduction, and settlement. Without their associated microbiota, macroalgae cannot function optimally, indicating that the association influences the provision of ecosystem services (Barott et al. 2011; Busetti et al. 2017; Ying et al. 2018; van der Loos et al. 2019).

A definite core bacterial community, consisting of Gammaproteobacteria, CFB group, Alphaproteobacteria, Firmicutes, and Actinobacteria species, seems to specifically adapt to an algal host- associated lifestyle (Hollants et al. 2013). Among these bacteria, the Gram-negative Proteobacteria and Bacteroidetes phyla are the most abundant bacterial group associated with the macroalgae. These structured bacterial communities form a protective layer on the host surface acting as a physical and physiological barrier between the host and the environment and thus providing an insulating effect (van der Loos et al. 2019). On such layers, they functionally interact and communicate through quorum sensing, although the cells are genetically different ( Thajuddin et al. 2016).

Discovered in the 1990s, the Quorum Sensing (QS) bacterial communication is a system of stimuli and response correlated to population density (Waters and Bassler 2005; Zhou et al. 2016). The epiphytic bacterial community of macroalgae communicate and cooperate with each other (within a species or between species) (Whitehead et al. 2001; Waters and Bassler 2005; Abisado et al. 2018; Borges and Simões 2019). They also interact with the host, using chemical signal molecules through the QS (inter-kingdom) phenomenon. During QS, bacteria produce and excrete signals that accumulate to a threshold level within a diffusion-limited environment (Dobretsov et al. 2009). QS regulates several bacterial activities such as bacterial virulence and resistance, bioluminescence, and biofilm formation. Such coordinated activities are achieved only when the bacterial population reaches a high density (Turan et al. 2017). QS operates through the synthesis of small molecules called Autoinducers (AIs) whereas AutoInducing Peptides (AIPs) and

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 AcylHomoserine Lactones (AHLs) have been shown to regulate QS in Gram-positive and Gram-negative bacteria, respectively (Schuster et al. 2013; Monnet et al. 2016; Papenfort and Bassler 2016; Kalia et al. 2019).

Halymenia floresii (Clemente & Rubio) C.A. Agardh (Rhodophyta, Halymeniales) dominates in sublittoral rocky substrates from the Yucatan Peninsula (Mexico) between 3m and 40m (Godínez-Ortega et al. 2008). Halymenia floresii has been also identified as a carrageenophyte with high potential for land- based IMTA (Integrated MultiTrophic Aquaculture) system. By recycling inorganic nutrients, the IMTA system produces biomass while reducing the environmental impact of coastal ecosystems through responsible aquaculture practices (Godínez-Ortega et al. 2008; Robledo and Freile-Pelegrín 2011). During the experimental culture of Halymenia floresii, we observed that the surface of this macroalgae was exceptionally free of any fouling organisms compared to any other algae under the same culture conditions. Even at high concentrations of Dissolved Inorganic Nitrogen (DIN ~ 150 µM) in IMTA system, the epiphytic macrofoulers (such as opportunistic green algae and/or any sessile invertebrates), which usually disturb the culture, were not observed over Halymenia floresii culture tanks (Pliego-Cortés et al. 2017).

Thus, we hypothesized that the epibacterial community on the surface of Halymenia floresii may prevent the settlement by forming a primary insulating coat possibly by their QS communication system that interfere with the growth of competitors. In this study, we propose to isolate and identify the cultivable epibacterial community from the surface of Halymenia floresii from three different thalli: Beach-Cast (BC), Halymenia floresii collected from the Yucatan beach, and the collected BC samples cultivated under two different conditions: Cultivar Chambers (CC) and Integrated MultiTrophic Aquaculture (IMTA). After identification of the epibacterial community, the objective is to screen the identified surface-associated bacteria for Quorum Sensing activity using bioreporters.

3.3 Materials and methods

3.3.1 Algal material

H. floresii, Beach Cast (BC) was initially collected from the shores near the CINVESTAV Coastal Marine Station at Telchac, Yucatan, Mexico (21.3419° N, 89.2636° W) during autumn 2017 (October- November). The BC collected samples were used for cultivation by IMTA and by Cultivar Chambers (CC), under uncontrolled and controlled environmental conditions, respectively. For IMTA cultivation, land- based aquaculture ponds were set to receive an independent flow of water (mix of shrimp pond effluent with clean, filtered seawater) and constant aeration by air diffusers placed in the bottom of the tanks. For CC cultivation, the macroalgae were grown in transparent polyacrylic cultivation cylinders with sterilized natural seawater, which was further filtered (normal filter 0.5 microns) and UV-sterilized (UV-filter 0.4

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 microns) at 30 psu, 22 ± 2° C and pH around 8.0 ± 0.3 under continuous aeration at a 12:12 light/dark cycle with an intensity of 110 ± 7.2 µmol photons m-2 s-1 inside a cultivation chamber. Every week, the CC samples were pulse-fed with Provasoli (0.3%) media for 24 h. The cultivation was carried for ‘2’ weeks and ‘4’ weeks in IMTA and CC, respectively, after several weeks of acclimatization.

3.3.2 Isolation of surface-associated bacterial strains

After collection and/or algae cultivation for a given period of time (see section 3.3.1), the samples were immediately brought to the laboratory under cool conditions. Specimens were washed once with sterile filtered seawater and rinsed twice with Phosphate Buffered Saline (PBS) to remove the loosely attached bacteria or any other debris from the surface. The algal samples were swabbed with sterile cotton swabs and were serially diluted in sterile seawater or in distilled water with sea salts. A 0.1 mL of each dilution (100; 10-1; 10-2) was spread on marine agar plates and immediately incubated at 25° C for up to 7 days. Bacterial colonies were selected based on their unique morphological features, such as size, shape, surface, chromogenesis. The selected colonies were carried over for further sub-culturing to obtain axenic cultures. The sub-culturing was continued until single pure morphotype colonies were obtained. The isolates were cryopreserved at -80° C in 20% glycerol.

3.3.3 Characterization of selected bacterial strains

i. Morphological and biochemical characterization

A single drop from the 24h broth culture of the bacterial isolates was observed under a phase- contrast microscope for swimming motility. To determine the twitching motility, a loopful of 24h broth culture was stabbed onto the semisolid agar (0.5 %) and incubated at 25° C for 24h. The motility was determined by observing the growth of the bacteria away from the stabbed line. The Gram type of each strain was determined by both a rapid non-staining procedure, KOH (Buck 1982), and the classical Gram staining. Moreover, phenotypic characteristics were determined by using API 20 NE and APIZYM galleries (bioMérieux) as recommended by the manufacturers. In brief, 65 µL of culture were dispensed into each cupule and inoculated at 30°C for 4 hours. After incubation, one drop of ZYM A and ZYM B was added. After 5 minutes, a value ranging from 0 to5 was assigned, corresponding to the color reaction, according to the table given by the manufacturer.

ii. Molecular characterization

DNA was extracted from the 24h broth culture of the bacterial isolates using two different kits: DNeasy Blood and Tissue Kit (Van der Zee et al. 2002) and ZymoBIOMICS DNA Miniprep Kit (Al- Hebshi et al. 2019). Extracted DNA was observed on 1% agarose gel electrophoresis in 1X TAE buffer and

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 quantified by Qubit Fluorometer. The 16S rRNA gene was amplified by PCR using the primers 16sF (5’- AGAGTTTGATCCTGGCTC-3’) and 16sR (5’-CGGGAACGTATTCACCG- 3’) with the following protocol: 12.5 µL of Promega GoTaq@Green Master Mix 2X (2X Green GoTaq reaction buffer_pH 8.5;

400 µM of dNTPs; 3mM MgCl2), 010µM of each primer, 11 µL DNase free H2O and 10 ng of DNA template. The PCR reaction was performed in the Axygene Thermal Cycler. The PCR amplifications were visualized by agarose gel and documented under BioDoc-It Imager. The amplified 16S rRNA gene of all the bacterial isolates were commercially sequenced by GENEWIZ Laboratories (New Jersey, USA).

iii. Phylogenetic analyses

To obtain the phylogenetic affiliations from the 16S rRNA amplicons, each sequence was preliminarily aligned with ClustalW Multiple Alignment (Susilowati et al. 2015), and the phylogenetic analyses were performed using MEGA 7. The sequences were compared against the GenBank database using BLAST (www.ncbi.nlm.nih.gov/BLAST/). The sequence similarity of greater than 97% was considered as the closest neighbor. All the 16S rRNA gene sequences retrieved from this study were deposited to NCBI under accession number MT176133 – MT176163.

3.4 Screening of the selected bacterial for QS signals - Reporter assay

3.4.1 Reporter strains

Two reporters, Chromobacterium violaceum CV026 and Escherichia coli pSB406, were used to screen the ability of the isolates to produce QS signals. The reporters are routinely maintained in Luria Bertani (LB) media with 100 µg mL-1 of Ampicillin for E. coli pSB 406.

3.4.2 Cross-feeding assay

The cross-feeding assay was performed with the bioreporter C. violaceum CV026. C. violaceum, a Gram-negative bacterium, produces a characteristic purple pigment violacein whereas its double mini- Tn5 mutant C. violaceum CV026 is derived from C. violaceum ATCC31532, Hgr, cviI::Tn5 xylE, Kmr, plus spontaneous Smr (McClean et al. 1997), uses a class of signal molecules, acyl-homoserine lactones, and exhibits violet pigmentation only in the presence of exogenous short-chain, C-4 and/or C-6 HSLs (González et al. 2001).

Both the isolates and the reporter were revived from -80°C by culturing on Marine Agar (MA) and LB agar respectively and incubated at 30°C for 24h. After incubation, a loopful of isolate and the reporter were cross-streaked parallel to each other, and the streaked plates were incubated at 30°C. After 24h, the results were observed for any purple colour production by the reporter against each isolate. An overnight culture of Pseudomonas aeruginosa PA01 was used as a positive control, and the reporter strain itself was served

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 as a negative control. The production of violacein by C. violaceum CV026 indicated the presence of QS signals (McClean et al. 1997).

3.4.3 Bioluminescent assay

The bioluminescent assay was performed with the bioreporter E. coli pSB406. pSB406 contains rhlRI’::luxCDABE with EcoRI-flanked luxCDABE cassette from pSB390 (Winson et al. 1998) with an ampicillin-resistant gene. With this plasmid, E. coli pSB406 can sense and detect the QS signals ranging from short- to long-chain (C-4 to C-12) (Winson et al. 1998). For the assay, the E. coli pSB406 was grown

-1 in the LB broth with the ampicillin (100 mg mL ) at 37°C to reach OD600 = 0.5.

The isolates were cultured in 25 mL of Marine Broth (MB) and incubated at 30°C with stirring, 120 rpm, for a previously determined period of time (16 h – 36 h). After incubation, 10 mL of culture was harvested, centrifuged at 5,000 g for 10 min at 4 °C, and the supernatants were collected through a 0.2 µm (Sartorius) sterile filter. The supernatant extracts were stored at -20°C for 1-2 days. The supernatant of P. aeruginosa PA01 was used as a positive control and that of the reporter strain itself as a negative control.

The luminescent assay was carried out in a 96-well sterile white microplate (Class et al. 2015) .200 µL of the reporter strain E.coli pSB406 and 100 µL of the supernatant were added to each well. After incubation, the luminescence was measured by a luminometer (Fletcher et al. 2007) every 30 min for 24 h – 48 h at 37 °C with a 5s shaking period prior to each measurement. All measurements were performed in triplicates. The production of luminescence of E. coli pSB406 is the reporting character of this strain for the presence of QS signals. The RLU of the negative control, i.e. E. coli pSB406, was deducted from the tests as the reporter strain itself produces luminescence.

3.5 Statistical analysis

The data were analyzed by one-way analysis of variance (ANOVA) to determine the significance of the results obtained. The normality of the QS data was tested using the Normal Distribution Comparison of SigmaPlot Version 14.0. Out of the total ‘96’ dataset for each bacterial strain, only the normally distributed datasets were taken for the one-way ANOVA. A post-hoc test was performed following the one- way ANOVA to compare the significant differences between the groups.

3.6 Results

In this study, for the first time, we identified the cultivable epibacterial community on the surface of H. floresii under different environmental conditions and the presence of QS bacteria among them.

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 3.6.1 Isolation and identification of surface-associated bacteria

Before molecular identification of the isolated strains, these were preliminarily identified and grouped based on their motility, Gram staining, and the biochemical characteristics determined by APIZYM (Table 1).

i. Morphological and biochemical characterization

Except for the strain B14CC, all strains were observed to be motile in nature (either swimming or twitching, see Table 1). The strains B9BC, B9IM, B9CC, and B9.1CC did not show any motility in agar- stabbing, and it is worth noting that they glide when observed by hanging-drop. Using our methodology, all the isolated bacteria were identified as Gram negative, and the B10CC strain was the only Gram-positive bacteria.

Biochemical characterization of the isolates performed using APIZYM (Biomerieux) consistently tested ‘20’ different characteristics. Among the ‘31’ isolated strains, almost all of the strains reacted negatively to the following enzymatic evaluations: β-glucuronidase; α-mannosidase; α-fucosidase. Similarly, all the isolates showed a negative reaction to α-glucosidase, except for B4CC. An intermediate reaction was observed in B9.1CC towards the β-glucosidase test and in B10CC towards the N-acetyl-β- glucosaminidase test, whereas the rest of the isolates showed a negative reaction. Only B6BC reacted positively for α-galactosidase and β-galactosidase, whereas B9.1CC and B10CC were the only strains to react positively to β-galactosidase. Only B16CC showed a positive reaction towards the Lipase (C18) test. All the isolates showed a positive reaction to Naphthol-AS-BI-phosphohydrolase, though their intensity varies depending on the phylotypes.

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 Table 1 Motility, Gram stain and Biochemical characteristics of the isolates Bacterial isolates/ B1BC B4BC B5BC B6BC B6.1BC B7BC B8BC B9BC B2IM* B3IM B9IM B1CC B2CC B3NBCC B4CC B7CC Characteristics Motility Motilea Motilea Motilea Motilea Motilea Motilea Motilea Motilec Motile Motile Motilec Motileb Motilea Motilea Motilea Motilea Gram reaction ------Alkaline +++++ +++++ +++++ +++++ +++++ +++++ +++++ +++++ +++++ +++++ n.d + - - ++ +++ phosphatase Esterase (C 4) ++ +++ + +/- - - - ++++ + +++ n.d ++++ +++ - ++ ++++ Esterase Lipase +++ ++++ + ++ ++ -/+ +/- +++ + ++ n.d +++ ++ - ++ +++++ (C8) Lipase (C18) ------n.d - - - - - Leucine +++++ +++++ +++++ +++++ +++++ ++ +++++ +++++ + + n.d - ++ - - +++ arylamidase Valine arylamidase + ++ ++ + + - ++ + - - n.d - - - + - Cystine + + - + - - +/- + /- - - n.d - - - - - arylamidase Trypsin ------n.d - - - - - α-chymotrypsin - + - - - +++ - - - ++++ n.d - - - + - Acid phosphatase +++++ +++++ +++++ ++++ +++++ +++++ +++++ ++ +++ +++++ n.d - - + - - Naphthol-AS-BI- ++++ +++++ +++++ ++++ ++++ ++ ++++ ++++ ++ ++++ n.d +++ +++ ++++ + +++ phosphohydrolase α-galactosidase - - - +++++ ------n.d - - - - - β-galactosidase - - - +++++ ------n.d - - - - - β-glucuronidase ------n.d - - - - - α-glucosidase ------n.d - - - ++++ - β-glucosidase ------n.d - - - - - N-acetyl-β------n.d - - - - - glucosaminidase α-mannosidase ------n.d - - - - - α-fucosidase ------n.d - - - - -

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020 Bacterial isolates/ B7.1CC B8CC B8.1CC B9CC B9.1CC B9.2CC B10CC B10.1CC B10.2CC B12CC B13CC B14CC B16CC B17CC B19CC B20CC Characteristics Motility Motilea Motilea Motilea Motilec Motilec Motilea Motilea Motilea Motilea Motilea Motilea Non- Motilea Motilea Motilea Motileb motile Gram reaction ------+ ------Alkaline phosphatase + + +++++ - - - - + +++ +++ +++++ - ++++ +++++ - -

Esterase (C 4) ++ +++++ +/- ++ +++ +++++ +++ ++++ - ++++ + / - +++++ + ++++ - +++++

Esterase Lipase (C8) +++ ++ +++ ++ + ++ + +++ - +++ + / - ++ +++ +++ - +

Lipase (C18) ------++ - - - Leucine +++ - ++ + +++ - ++ - - - + - ++++ ++ ++ - arylamidase Valine arylamidase ++ - - ++ + ------+ - - -

Cystine arylamidase - - - + / - + / - - ++ - - - - - + / - - - -

Trypsin + / ------α-chymotrypsin - - - + / - - - + - +/- - - - +++ + - - Acid phosphatase - + +++ - + ++ - ++ +++ + ++ ++ +++++ +++++ + + Naphthol-AS-BI- +++ +++ +++ ++ +++ +++ ++ ++++ ++++ ++ ++ ++++ + ++++ ++++ +++++ phosphohydrolase α-galactosidase ------β-galactosidase - - - - ++++ - ++++ ------β-glucuronidase ------α-glucosidase ------+++++ - β-glucosidase - - - - + ------++ -

N-acetyl-β------+ ------glucosaminidase α-mannosidase ------

α-fucosidase ------Biochemical characteristics were determined by APIZYM. (-) represents negative reaction; (+) to (+++++) represents level of intensity; (+++), (++++) and (+++++) considered as positive reactions. n.d – No Data; a – Swimming; b – Twitching; c – Gliding. * - Strain with <97% similarity. 87

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

ii. Molecular characterization

BLAST analyses of the 16S rRNA gene sequences revealed that all the ‘31’ bacterial strains corresponded to four phyla: Alphaproteobacteria, Gammaproteobacteria, Bacteroidetes, and Firmicutes (Table 2). The bacterial isolates from BC, IMTA, and CC H. floresii included the members of Gammaproteobacteria and Bacteroidetes, whereas the members of Alphaproteobacteria and Firmicutes were found only in the CC H. floresii.

a. Alphaproteobacteria

The genera identified within Alphaproteobacteria from CC H. floresii were: Tateyamaria omphalii (B1CC_Accession No. MT176143 and B3CC_Accession No. MT176145); Phaeobacter sp. (B2CC_Accession No. MT176144); Ruegeria sp. (B4CC_Accession No. MT176146); Ruegeria lacuscaerulensis (B8CC_Accession No. MT176149 and B19CC_ Accession No. MT176162); Uncultured Kordiimonas sp. (B9.2CC_Accession No. MT176153) Epibacterium sp. (B10.1CC_ Accession No. MT176155; B10.2CC_ Accession No. MT176156 and B20CC_ Accession No. MT176163); Roseobacter sp. (B13CC_ Accession No. MT176158); Erythrobacter sp. (B14CC_ Accession No. MT176159).

b. Gammaproteobacteria

The Gammaproteobacteria from the BC H. floresii mainly included the Pseudoalteromonas species, whereas the CC samples included the Alteromonas species. Vibrio owensii was observed in BC (B7BC_Accession No. MT176139) and IMTA (B3IM_Accession No. MT176134) samples, but one strain, Vibrio sp. (results not shown) from IMTA, showed <97% of similarity. We could not observe any Pseudoalteromonas sp. in IMTA H. floresii and Vibrio sp. from CC H. floresii.

c. Bacteroidetes

The phylotypes belonging to the class Bacteroidetes were observed in H. floresii regardless of its origins such as BC, IMTA, and CC. Bacteroidetes of BC, IMTA H. floresii were found to be affiliated to Tenacibaculum sp. (B9BC_Accession No. MT176141) and Maribacter sp. (B9IM_Accession No. MT176142) respectively and Spongiimicrobium salis (B9CC_Accession No. MT176151) and Aquimarina sp. (B9.1CC_Accession No. MT176152) from CC H. floresii. The members of this class have shown the same pattern of chromogenesis by the production of yellow pigments. Another interesting characteristic of these members was their gliding motility. Bacteroidetes isolated in this study also showed iridescence; in particular, Tenacibaculum sp. from BC H. floresii is observed to have higher iridescence (observed macroscopically) compared to the other two members. This strain showed a glitter-like iridescence macroscopically on the culture plate itself.

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

d. Firmicutes

In this study, the Firmicutes phylum was represented by only one phylotype affiliating to Bacillus circulans (B10CC Accession No. MT176154).

Of the ‘31’ isolates from three different samples of H. floresii, Gammaproteobacteria, Alphaproteobacteria, Bacteroidetes and Firmicutes comprise 47%, 37%, 13%, and 3%, respectively.

Table 2 Halymenia floresii surface-associated bacteria. Group, Code, Closest neighbor, similarity and sample origin were listed in the table Accession Group Code Closest Neighbour Similarity Sample Origin No. MT176133 Gammaproteobacteria B1BC Pseudoalteromonas arctica. 100% Beach Cast Pseudoalteromonas MT176135 Gammaproteobacteria B4BC 100% Beach Cast arabiensis MT176136 Gammaproteobacteria B5BC Pseudoalteromonas sp. 99.83% Beach Cast Pseudoalteromonas MT176138 Gammaproteobacteria B6BC 99% Beach Cast mariniglutinosa MT176137 Gammaproteobacteria B6.1BC Pseudoalteromonas sp. 100% Beach Cast MT176139 Gammaproteobacteria B7BC Vibrio owensii 100% Beach Cast MT176134 Gammaproteobacteria B3IM Vibrio owensii 100% IMTA MT176140 Gammaproteobacteria B8BC Pseudoalteromonas arctica 99.84% Beach Cast Cultivars MT176147 Gammaproteobacteria B7CC Alteromonas sp. 99% Cylinders Alteromonadaceae bacterium Cultivars MT176148 Gammaproteobacteria B7.1CC 98.68% strain Cylinders Cultivars MT176150 Gammaproteobacteria B8.1CC Alteromonas sp. 99.37% Cylinders Cultivars MT176157 Gammaproteobacteria B12CC Alteromonas sp. 99.53% Cylinders Cultivars MT176160 Gammaproteobacteria B16CC Alteromonas sp. 99.68% Cylinders Cultivars MT176161 Gammaproteobacteria B17CC Alteromonadaceae bacterium 98.74% Cylinders Cultivars MT176143 Alphaproteobacteria B1CC Tateyamaria omphalii 100% Cylinders Cultivars MT176144 Alphaproteobacteria B2CC Phaeobacter sp. 100% Cylinders Cultivars MT176145 Alphaproteobacteria B3CC Tateyamaria omphalii 99% Cylinders Cultivars MT176146 Alphaproteobacteria B4CC Ruegeria sp. 100% Cylinders Cultivars MT176149 Alphaproteobacteria B8CC Ruegeria lacuscaerulensis 99.83% Cylinders

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

Cultivars MT176153 Alphaproteobacteria B9.2CC Uncultured Kordiimonas sp. 97.39% Cylinders Cultivars MT176155 Alphaproteobacteria B10.1CC Epibacterium sp. 99.75% Cylinders Cultivars MT176156 Alphaproteobacteria B10.2CC Epibacterium sp. 99.26% Cylinders Cultivars MT176158 Alphaproteobacteria B13CC Roseobacter sp. 99% Cylinders Cultivars MT176159 Alphaproteobacteria B14CC Erythrobacter sp. 99.43% Cylinders Cultivars MT176162 Alphaproteobacteria B19CC Ruegeria lacuscaerulensis 100% Cylinders Cultivars MT176163 Alphaproteobacteria B20CC Epibacteirum sp. 97.10% Cylinders MT176141 Bacteroidetes B9BC Tenacibaculum sp. 100% Beach Cast MT176142 Bacteroidetes B9IM Maribacter sp. 97% IMTA Cultivars MT176151 Bacteroidetes B9CC Spongiimicrobium salis 99% Cylinders Cultivars MT176152 Bacteroidetes B9.1CC Aquimarina sp. 100% Cylinders Cultivars MT176154 Firmicutes B10CC Bacillus circulans 99% Cylinders

3.6.2 Detection and screening of QS signal production

In this study, we used ‘2’ different reporter strains to detect the presence of extracellular QS signal molecules and screen all the isolates for the production of QS signals (Table 3). First, all the isolates were screened against the Gram negative AHL-based reporter strain C. violaceum CV026 by cross-feeding each other. This assay did not show a positive response for any of the isolates as none of the isolates induced the purple color formation of the C. violaceum CV026.

Second, all the isolates were screened against E. coli pSB406 in a bioluminescent assay. Out of the ‘31’ isolates, ‘14’ strains did not induce any luminescence in E. coli pSB406, and others induced a light production. The phylotypes that did not induce the light production are as follows: in the class of Gammaproteobacteria, Pseudoalteromonas arctica (B1BC and B8BC); Alteromonas sp. (B7CC and B8.1CC); Alteromonadaceae bacterium strain (B7.1CC and B17CC); in the class of Alphaproteobacteria, Tateyamaria omphalii (B1CC and B3CC), Phaeobacter sp., Ruegaria sp., Ruegeria lacuscaerulensis (B8CC), and Epibacterium sp. (B10.1CC and B10.2CC); and also in the only Firmicutes, B. circulans. We also observed that the induction of bioluminescence in E. coli pSB406 was highly influenced by the culture period of the isolate supernatant. The phylotypes that induced luminescence are grouped according to the culture period, and they are as follows: (i) the isolates of 16H culture period include the Gammaproteobacteria, Pseudoalteromonas arabiensis, Pseudoalteromonas sp., Pseudoalteromonas 90

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

mariniglutinosa, Vibrio owensii (B3BC and B3IM), Pseudoalteromonas sp., and the Bacteroidetes, Maribacter sp.; (ii) the isolates of 36H culture period include the Gammaproteobacteria, Alteromonas sp.(B12CC); the Bacteroidetes, Tenacibaculum sp., Spongiimicrobium salis, Aquimarina sp. Apart from these ‘2’ major culture periods, the following strains, such as Epibacterium sp., Roseobacter sp. and Alteromonas sp. (B16CC), induce the luminescence after 12h, 24h, and 48h of the culture period, respectively.

Table 3 Screening of the isolates based on QS signals production; * - Strain with <97% similarity. S.No Bacterial Closest neighbour Chromobacterium violaceum Escherichia coli pSB isolates CV026 406 1 B1BC Pseudoalteromonas arctica - - 2 B4BC Pseudoalteromonas arabiensis - + 3 B5BC Pseudoalteromonas sp. - + 4 Pseudoalteromonas - + B6BC mariniglutinosa 5 B6.1BC Pseudoalteromonas sp. - + 6 B7BC Vibrio owensii - + 7 B8BC Pseudoalteromonas arctica - - 8 B9BC Tenacibaculum sp. - + 9 B3IM Vibrio owensii - + 10 B9IM Maribacter sp. - + 11 B1CC Tateyamaria omphalii - - 12 B2CC Phaeobacter sp. - - 13 B3CC Tateyamaria omphalii - - 14 B4CC Ruegeria sp. - - 15 B7CC Alteromonas sp. - - 16 Alteromonadaceae bacterium - - B7.1CC strain 17 B8CC Ruegeria lacuscaerulensis - - 18 B8.1CC Alteromonas sp. - - 19 B9CC Spongiimicrobium salis - + 20 B9.1CC Aquimarina sp. - + 21 B9.2CC Uncultured Kordiimonas sp. - + 22 B10CC Bacillus circulans - - 23 B10.1CC Epibacterium sp. - - 24 B10.2CC Epibacterium sp. - - 25 B12CC Alteromonas sp. - + 26 B13CC Roseobacter sp. - + 27 B14CC Erythrobacter sp. - + 28 B16CC Alteromonas sp. - + 29 B17CC Alteromonadaceae bacterium - -

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

30 B19CC Ruegeria lacuscaerulensis - + 31 B20CC Epibacterium sp. - +

The luminescent curve of the isolates plotted by number of cycles (30 min interval per cycle) against the relative light units by optical density of the reporter, E. coli pSB406 (Fig. 1 - 4). The light production of the ‘36h’ culture isolates seemed quite similar to the positive control, P. aeruginosa PA01 (1-factor ANOVA: F (1, 68) = 414.45, p < 0.0001). The light production was observed to increase after 10 cycles of interval. Among the three strains of the Bacteroidetes group, Tenacibaculum sp. (1-factor ANOVA: F (1, 68) = 65.25, p < 0.0001) induced higher light production compared to that of Spongiimicrobium salis (1- factor ANOVA: F (1, 66) = 100.61, p < 0.0001) and Aquimarina sp. (1-factor ANOVA: F (1, 66) = 70.81, p < 0.0001). Fig. 1 also shows the bioluminescence induced by Alteromonas sp. (B12CC) (1-factor ANOVA: F (1, 66) = 71.55, p < 0.0001).

Fig. 1 Induction of bioluminescence in the reporter E. coli pSB406 by Tenacibaculum sp., (B9BC); Spongiimicrobium sails (B9CC); Aquimarina sp., (B9.1CC); Alteromonas sp., (B12CC) and Pseudomonas aeruginosa PA01 (positive control). No. of cycles (30 minutes per cycle) plotted against relative light units/optical density at 600 nm

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

The induction of bioluminescence in the reporter by the isolates of culture period of 16h was shown in Fig 2. Among the five isolates described in this figure, one member of the Bacteroidetes group, Maribacter sp. (1-factor ANOVA: F (1, 96) = 167.26, p < 0.0001) induced relatively higher luminescence when compared to the members of Gammaproteobacteria such as V. owensii (B3IM & B7BC) (1-factor ANOVA: F (1, 96) = 200.62 and 126.55, p < 0.0001), P. arabiensis (1-factor ANOVA: F (1, 96) = 116.82, p < 0.0001) and P. mariniglutinosa (1-factor ANOVA: F (1, 96) = 225.84, p < 0.0001) (Fig. 2).

Fig. 2 Induction of bioluminescence in the reporter E.coli pSB406 by Vibrio owensii (B3IM); Maribacter sp., (B9IM); Pseudoalteromonas arabiensis (B4BC); P. mariniglutinosa (B6BC) and V. owensii (B7BC) No. of cycles (30 minutes per cycle) plotted against relative light units/optical density at 600 nm

The luminescence induction of E. coli pSB406 by the isolates Pseudoalteromonas sp. (B5BC and B6.1BC) (1-factor ANOVA: F (1, 190) = 116.25, p < 0.0001 and 1-factor ANOVA: F (1, 190) = 62.59, p < 0.0001) of the 16H culture period and Alteromonas sp. (B16CC) (1-factor ANOVA: F (1, 188) = 181.66, p < 0.0001) of the 48h culture period is illustrated in Fig 3, where all the isolates influenced the bioluminescence of the reporter after ‘40’ cycles of interval. Among these three isolates, the member of

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

Gammaproteobacteria, Alteromonas sp. showed a lesser induction of luminescence compared to that of the other isolates.

Fig. 3 Induction of bioluminescence in the reporter E.coli pSB406 by Pseudoalteromonas sp., (B5BC); Pseudoalteromoas (B6.1BC) and Alteromonas sp. (B16CC). No. of cycles (30 minutes per cycle) plotted against relative light units/optical density at 600 nm. Culture period, in hours, of the isolates, were denoted as ‘h’.

The positive response by the isolates, such as an Uncultured Kordiimonas sp. (B9.2 CC) (1-factor ANOVA: F (1, 160) = 7.90 p = 0.005) of the class Gammaproteobacteria and members of Alphaproteobacteria, Ruegeria lacuscaerulensis (B19CC) (1-factor ANOVA: F (1, 160) = 387.52, p < 0.0001) and Erythrobacter sp. (1-factor ANOVA: F (1, 160) = 20.76, p < 0.0001) was observed from ‘20’ cycles of interval (Fig. 4). These two isolates were cultured for a period of 24H.

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

Fig. 4 Induction of bioluminescence in the reporter E.coli pSB406 by Ruegeria lacuscaerulensis (B9CC); Uncultured bacterium clone (B9.2CC) and Erythrobacter sp., (B14CC). No. of cycles (30 minutes per cycle) plotted against relative light units/optical density at 600 nm. Culture period, in hours, of the isolates, were denoted as ‘h’.

Apart from these results, Fig. 5 displayed the light induction of two members of Alphaproteobacteria, Roseobacter sp. (1-factor ANOVA: F (1, 188) = 474.19, p < 0.0001) and Epibacterium sp. (1-factor ANOVA: F (1, 190) = 12.61, p = 0.0005) whose culture periods were 24h and 12h, respectively. Interestingly, Roseobacter sp. induced the luminescence at the beginning of incubation intervals and then reduced, whereas Epibacteirum sp. induced it at the later period of incubation intervals, after ‘60’cycles.

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

Fig. 5 Induction of bioluminescence in the reporter E.coli pSB406 by Roseobacter sp., (B13CC) and Epibacterium sp., (B20CC). No. of cycles (30 minutes per cycle) plotted against relative light units/optical density at 600 nm. Culture period, in hours, of the isolates, were denoted as ‘h’.

3.7 Discussion

In recent years, marine macroalgae have emerged as a rich source of microbial diversity and biologically active secondary metabolites (Singh et al. 2015; Leiva et al. 2015). A fraction of epiphytic bacterial communities can be categorized as “core microbial species”, suggesting an obligate association (Singh and Reddy 2016).

3.7.1 Isolation and identification of selected surface-associated bacteria of H. floresii

Motility is very important for several biofilm-forming bacteria (Fernandes et al. 2011). All the isolates in this study were motile in nature; in particular, the Bacteroidetes members were observed to have gliding motility, which is a surface-dependent mode of motility (Raina et al. 2019). A mutant Capnocytophaga ochracea lacking gliding motility showed significantly attenuated biofilm formation, compared with that of the wild-type strain (Kita et al. 2016). Thus, we assume that the gliding motility of the Bacteroidetes members may play a significant role in the initial biofilm formation on the surface of H. floresii. Gram-negative bacteria have previously been reported as the predominant group in the marine and

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

marine macrophyte environment (Cottrell and Kirchman 2000; Steinberg and de Nys 2002). This study shows no exception to this rule of thumb as all the strains isolated from H. floresii were Gram-negative, except for one phylotype (Table 1). The main groups of bacterial phyla associated with macroalgae include Alphaproteobacteria, Gammaproteobacteria, Bacteroidetes, Firmicutes and Actinobacteria (Goecke et al. 2010). Except for Actinobacteria, H. floresii associated bacteria identified in the study essentially also fall under the same groups.

Lachnit et al. (2009) demonstrated that bacterial communities derived from macroalgae belonging to the same species but originating from a different habitat were more similar (Lachnit et al. 2009; Goecke et al. 2010). The same was observed in the red macroalgal species Bonnemaisonia asparagoides, Lomentaria clavellosa and Polysiphonia stricta (Nylund et al. 2010). However, with H. floresii, the bacterial communities were quite dissimilar with different habitats, such as BC, IMTA, and CC. We assume that this result may be mainly attributed to the macroalgal cultivation system. The abiotic physiological responses such as temperature, light, water turbulence, and nutrient availability in different habitats are believed to play a major role in this dissimilarity. Understanding the temporal dynamics of epiphytic bacteria can help identify the possible modifications in the ‘protective layer’ due to external factors of stress (Mancuso et al. 2016). In the case of H. floresii, the controlled and uncontrolled culture conditions in CC and IMTA, respectively, significantly contributed to the stress, whereas in BC, it was mainly caused by the overexposure to increased sunlight (i.e. UV radiation).

The macroalgal-associated microbial communities belong to various genera such as the Pseudomonas, Pseudoalteromonas, Stenotrophomonas, Vibrio, Alteromonas, Shewanella, Streptomyces, and Bacillus species, and evolved through a highly competitive environment due to nutrient and space limitation on their host surface, subsequently producing allelochemicals capable of preventing secondary colonization (Egan et al. 2001, 2008; Wiese et al. 2009; Tujula et al. 2010; Uzair et al. 2018).

Among the 21, 25, and 27 phylotypes isolated from the red algae, Chondrus crispus, Palmaria palmata and Porphyra umbilicalis, respectively, Gammaproteobacteria was the dominant phyla followed by the Firmicutes (del Olmo et al. 2018). We observed the same result regarding the Gammaproteobacteria (48%), whereas the Firmicutes were the least dominant in H. floresii. After a review of numerous studies, Hollants et al. (2013) conclude that macroalgal-bacteria interactions primarily consist of Gammaproteobacteria (37%) followed by Bacteroidetes (20%), Alphaproteobacteria (13%), Firmicutes (10%), and lastly Actinobacteria (9%). We also perceived the same pattern in H. floresii, whereas Alphaproteobacteria (37%) was followed by Bacteroides (13%).

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

To conclude, among the ‘31’ strains identified in this study, few isolates were described as opportunistic putative pathogens in different marine macroalgae and other organisms. For instance, Nautella italica R11 and Phaeobacter gallaeciensis LSS9, which have been shown to cause bleaching symptoms in vitro, just like Alteromonas, Aquimarina, Agarivorans, are associated with the bleaching disease in a temperate red alga Delisea pulchra (Fernandes et al. 2011; Zozaya-Valdes et al. 2015; Kumar et al. 2016). Several taxa belonging to Vibrio, Alteromonas, Aquimarina, Flavobacteriaceae, and Rhodobacteriaceae were found to be associated with the bleached brown alga Ecklonia and Delisea and diseased sponges and coral. Members belonging to the same genera mentioned in these studies were also isolated in our study, but from the good healthy surface of H. floresii without any bleaching or other disease symptoms. This may open a new avenue for exploring their opportunistic pathogenicity to the host, and in our future research work, we intend to find out whether there are any opportunistic pathogens under any diseased condition (for example, bleaching) of H. floresii.

3.7.2 Detection and screening of QS signals

Among the various means of bacterial communication, cell density-dependent communication, QS, plays a major role in achieving a dynamic and equilibrated biofilm. Such a biofilm is the most evident form of biofouling. A potential approach to inhibit the process of biofouling is to interfere with bacterial QS signals. Hence, the very first step is to detect the presence of QS communication signals in the epibacterial community of the macroalgae. This study has detected and screened the surface bacteria isolated from H. floresii for the production of exogenous QS signals, and the results of the tests revealed that ‘17’ bacterial isolates produce communication signals. As mentioned earlier, ‘2’ different biosensors, Chromobacterium violaceum CV026 and Escherichia coli pSB406, were used in the study. The biomonitor strains enable sensitive, quantitative, and real-time detection of QS signals such as AHLs (Kalia 2013).

i. Cross-feeding assay

In this assay, with the C. violaceum CV026 reporter strain, we did not observe any positive reaction by the formation of purple color, even though for several reasons, it is not possible to conclude from the non- appearance of a signal that the strains do not produce AHLs. They might produce structurally different and novel AHLs that are not easily detected by the biosensors used, so it is rather unlikely to be excluded. Moreover, the most challenging issue in AHLs detection is that the bacterial strains may produce the signals in very low concentration, so that they are below the threshold of sensitivity of the biosensors used (Steindler and Venturi 2007).

Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020

ii. Bioluminescence assay

The bioluminescent assay was performed with the reporter strain E. coli pSB406. Using this reporter strain, it was possible to screen the isolates qualitatively and quantitatively for the production of exogenous AHLs by their luminescence induction.

Except for B. circulans (B10CC), all the bacterial strains isolated from the surface of H. floresii, in this study were Gram-negative; hence, the main focus of QS signals is on the AHLs, irrespective of their

classes. AHLs are classified based on the N-linked acyl chains, 4-16 carbon long and substitution on the C3 carbon of the N-linked acyl chain, usually with a hydroxy or oxo group, and they are known to modulate biofilm formation, motility, antibiotic production, and the exchange of genetic material (Chhabra et al. 1993; Singh and Reddy 2014). Of the ‘31’ epibacterial strains isolated from H. floresii, more than 50% of the strains were detected as QS bacteria. Girard et al. (2017) did not detect any QS signals produced by V. owensii using C. violaceum CV026, Pseudomonas putida F117, and E.coli MT102, but at the same time, Torres et al. (2018) found AHLs using C. violaceum CV026. Though we could not detect the QS activity of V. owensii using C. violaceum CV026, we confirmed its presence using E. coli pSB406. The detection of QS activity in V. owensii from the IMTA cultivated H. floresii may open a new way of disease control in the aquaculture system because V. owensii is the most important aquaculture-related pathogen, particularly in shrimp ponds, causing AHPND (Acute HepatoPancreatic Necrosis Disease) (Liu et al. 2018; Torres et al. 2018).

QS systems were intensively confirmed to be involved in the regulation of pathogen virulence in aquaculture. Virulence factors of the several opportunistic pathogens in the aquatic environments such as shrimp and freshwater prawn aquaculture were regulated and/or attenuated by their corresponding QS system (Zhang and Li 2016). In our study, ‘3’ isolates were found to be affiliated to Ruegaria sp., but the QS signal production was observed only in Ruegaria lacuscaerulensis (B19CC), isolated from CC H. floresii. A QS bacterium, Ruegaria sp. R11, isolated from the red alga Delisea pulchra, produces two AHLs, N-octanoyl-DL-homoserine lactone (OHL) and N-hexanoyl-DL-homoserine lactone (HHL), which play a crucial role in causing the bleaching disease.

The pathogenicity of microbes that infect aquaculture systems is closely related to the release of virulence factors, which is regulated by the QS system (Zhao et al. 2015). There seems to be a link between QS and diseases through the regulation of certain phenotypes and the induction of virulence factors responsible for the pathogen-host association. Macroalgae interfere with bacterial QS systems that regulate bacterial cell-to-cell communication (Goecke et al. 2010). Such disruption or interference of the QS mechanism of pathogens is a novel and environment-friendly way to control disease outbreaks in

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aquaculture (Natrah et al. 2011a). Thus, it is necessary to differentiate the potential pathogens and non- pathogens among the QS bacteria of H. floresii and to identify the QS interfering compounds produced by H. floresii, if any.

3.8 Conclusions

Our results provide an important knowledge base as first step to illustrate the epibacterial community of H. floresii under different environmental conditions, such as BC, CC, and IMTA. In this study, we have reported the cultivable epibacterial community isolated from the surfaces of BC, IMTA, and CC Halymenia floresii (see section 3.3.1 for details) of Yucatan, Mexico. This study is the first report about the epibacterial community of H. floresii and their QS activity, particularly in culture conditions. Thus, the results may open new avenues to explore the H. floresii holobiont and the possible interference in the QS system of its own epibiome. However, the epibacterial community identified in this study by culture-dependent techniques does not provide an entire picture of the bacteria present in H. floresii. Thus, this study must be extended by the metagenomics approach to elucidate a complete bacterial community of H. floresii for greater understanding of the benefits of the associated host and its environment. As this study has revealed the presence of QS signals in different opportunistic pathogens (as reported in several literatures), their pathogenic shift may be provided by their respective communication system. We also assume that any adverse environmental conditions may affect the bacterial-host relationship, and it is very important to identify the potential pathogens or non-pathogens and/or significant protectors in these cultivable bacterial isolates. A further in-depth exploration of the QS communication system and isolation and characterization of the AHLs in these isolates will provide a functional perspective of the signals towards their pathogenicity shift.

3.9 Future perspectives

In the Yucatan peninsula, H. floresii represent a potential raw material source for the production of carrageenan (Robledo and Freile-Pelegrín 2011). As a lamda-carrageenophyte, H. floresii was identified as a good candidate to recycle inorganic nutrients in an IMTA system (Pliego-Cortés et al. 2019), and bacteria- degrading algal polysaccharides are key players in algal biomass recycling (Martin et al. 2015). Identifying the bacterial community associated with the carrageenophyte (particularly in aquaculture) is an essential requirement to further explore the production of carrageenan-degrading enzymes, as it is a primary ‘carbon’ source for the epibiome. Using our results, it is worth conducting experiments to identify the carrageenan- degrading bacteria, and particular consideration should be given to Proteobacteria and Bacteroidetes, as they are the main producers of carrageenases (Chauhan and Saxena 2016).

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3.10 Acknowledgements

Financial support from ECOS-Nord CONACYT for the collaboration project M14A03 and PN-CONACYT 2015-01-118 is gratefully acknowledged.

3.11 References

The references of this article are available at the end of the manuscript (‘References’, page no. 206)

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4. Chapter III

Chemical defense against microfouling by allelopathic active metabolites of Halymenia floresii (Rhodophyta)

Shareen A Abdul Malik*1, Gilles Bedoux1, Daniel Robledo2, José Q. García- Maldonado2, Yolanda Freile-Pelegrín2, Nathalie Bourgougnon1

Published as a Research Article to “Journal of Applied Phycology” Shareen at al. 28 February, 2020

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Chemical defense against microfouling by allelopathic active metabolites of Halymenia floresii (Rhodophyta)

Following the identification of the surface-associated bacteria of H. floresii and detecting the Quorum Sensing (QS) strains (Chapter II), we further proceeded to evaluate the chemical defence of the alga on the surface by means of allelopathic active metabolites. The interference of the surface metabolites with the QS activity of the chosen strains was also studied and the metabolite identification was done by untargeted metabolomic analysis. Here we outline the work of the following Chapter III. Many macroalgae appear to be chemically defended at constantly high levels by producing a rich variety of secondary metabolites (Weinberger 2007; Nylund et al. 2011). As the surface of Halymenia floresii was observed to be remarkably free from any fouling organisms when cultivated under Integrated MultiTrophic Aquaculture (IMTA), we also hypothesized that the chemical defence of H. floresii may be responsible for this ecological phenomenon. The allelopathic active compounds may contribute to algal’s defence on the surface by preventing biofilm formation that ultimately interferes with the settlement and growth of competitors. In this chapter, we explored the chemical defence of H. floresii by extracting its surface metabolites and evaluating their interference in the quorum sensing (QS) communication of the surface-associated bacteria (more details inside the Chapter III). The objectives of this chapter are as follows:

Objectives 1. Extraction of the surface metabolites from H. floresii by DIP method using different solvents with increasing polarity and immersion periods 2. Evaluation of the QQ activity behaviour of the surface extracts on the H. floresii associated bacterial population by bioluminescent assay 3. Identification of compounds by LC-MS metabolomics approach.

Methodology

Step I

As represented below, the surface metabolites (DIP) of H. floresii were extracted by the method of DIP as proposed by de Nys et al (1998) (Fig. 21). The algal specimens were dipped in different organic solvents (n-hexane, dichloromethane, dichloromethane: methanol (1:1 v/v) and methanol) and assessed for their effect on the lysis of macroalgal cortical cells at different times (10, 20, 30, 40, 50 and 60 seconds) using an epifluorescence microscope. The effect of the solvent on the surface cells was determined by analyzing the microscopic images using Image J software to count the number of lysed versus intact cells in five different fields of view for each replicate. Additionally, the whole-cell metabolites (WCM) were extracted by complete maceration of the algal samples in the chosen solvent (same as determined for the DIP method). This step is well detailed in (Section 4.3.2).

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Fig. 21 Extraction of surface-associated metabolites by the method of DIP

Step II

The surface-associated bacteria of H. floresii were previously isolated and screened for their QS activity (detailed in Chapter II). In this step, we evaluated the interference of the surface-extracts with the QS activity of the chosen bacterial strains by Bioluminsecent assay using Escherichia coli pSB406 as a reporter. The luminescent assay was carried out in a 96-well sterile white microplate (Fig. 22). To each well, the reporter strain E. coli pSB406, the supernatant of the bacterial culture, and DIP n-hexane extract at ‘3’ different concentrations (250, 500 and 1000 ng cm-2) was added. The luminescence (RLU – Relative Light Units) of the reporter was measured by a luminometer at an interval of 30 min for 24 h – 48 h at 37 °C. This step is explained in detail in (Section 4.3.3).

extract STEP II

scent assay to evaluate the

Biolumine

Quorum Quenching activityof the surface

Fig. 22 Evaluation of Quorum Quenching activity by bioluminescent assay using E. coli pSB406

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Step III

Finally, the chemical profiling of the DIP and WCM extracts were analyzed by LC-MS to identify the allelopathic metabolites actively present on the surface of H. floresii. Thus, the chemical fingerprints of surface metabolites (DIP-n-hexane extract) were compared with the whole-cell extracts (WCM-n-hexane) of H. floresii. For which raw LC-MS data of both the DIP and WCM extracts were pre-processed by XCMS Online metabolomics platform and then they were searched for any possible candidates in a mass bank, Madison Metabolomics Consortium Database (Fig. 23).

STEP III Identification of the compounds by MMCD_massbank search

LC/MS metabolomics approach Analysis of the surface extracts Pre-processing the raw data by by LC/MS XCMS Online Identification of bioactive compounds by

Fig. 23 Identification of the surface-metabolites by untargeted metabolomic approach

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Chemical defense against microfouling by allelopathic active metabolites of Halymenia floresii (Rhodophyta)

Shareen A Abdul Malik*1, Gilles Bedoux1, Daniel Robledo2, José Q. García-Maldonado2, Yolanda Freile- Pelegrín2, Nathalie Bourgougnon1

1 Université Bretagne Sud, EA 3884, LBCM, IUEM, F-56000 Vannes, France 2 Marine Resources Department, CINVESTAV-Unidad Merida, Mexico

*Author for correspondence: [email protected]

4.1 Abstract

During the experimental cultivation of Halymenia floresii under Integrated MultiTrophic Aquaculture (IMTA), the establishment of opportunist green algae and the colonization of sessile invertebrates, which were usually disturbing the cultivation, were not observed. The culture tanks were clean and the surface of the H. floresii was remarkably free from any fouling organisms. This phenomenon could reveal that the presence of H. floresii may prevent biofilm formation by releasing allelopathic active compounds that ultimately interfere with the settlement and growth of competitors. In order to understand this phenomenon, H. floresii was cultivated under controlled environmental conditions and analyzed for its surface chemical defense metabolites. The surface-associated metabolites were extracted using the DIP extraction method, using different solvents with increasing polarity and immersion periods. Using epifluorescence microscopy, n-hexane was found to be the suitable immersion solvent for H. floresii for a period of 10s to 60s to extract surface metabolites. The whole cell metabolites were extracted exhaustively with the same solvent for a period of 24 h. The chemical profiling of the surface compounds was performed by liquid chromatography mass spectroscopy (LC-MS), followed by a Mass Bank search and compared with those obtained from the whole-cell extracts. The mean concentration of H. floresii surface metabolites was 600 ng cm-2 (c. 60 g of a fresh sample) whereas the whole-cell metabolites concentration was around 4.5 µg mg-1 (400 mg of the lyophilized sample). The bioactivity of the H. floresii surface extracts was studied by evaluating their quorum quenching behavior on the surface-associated bacteria. The cultivable bacteria isolated from the surface of H. floresii were identified as Vibrio owensii (B3IM), Alteromonas sp. (B7CC), Pseudoalteromonas arabiensis (B4BC), Ruegeria sp. (B4CC), Tenacibaculum sp.(B9BC), Maribacter sp. (B9IM) and Aquimarina sp. (B9.1CC). All the isolated strains belonged to Alphaproteobacteria, Gammaproteobacteria and Bacteroides. The results of this bioactivity proved that the surface-associated metabolites extract (DIP) interfere with the communication signals produced by the bacteria isolates with

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the reporter strain employed. According to the Mass Bank compound analysis, we hypothesized that flavonoids and/or halogenated compounds might have contributed to this activity. This work provides an understanding of the influence of surface-associated metabolites on the associated bacterial community and by which H. floresii manages to control the biofouling on its surface.

Keywords: Halymenia floresii, IMTA, Quorum Quenching, surface-area concentration, surface-associated metabolites, whole-cell metabolites.

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4.2 Introduction

Marine biofouling is a natural process of colonization involving a wide range of organisms on any surface in aqueous environments. This phenomenon generally takes place over a four-step sequential process (i) initial ‘conditioning’ of the surfaces by organic compounds, (ii) subsequent development of bacterial biofilm, (iii) secondary colonization by unicellular eukaryotes (maturation I), and (iv) a final step of attachment of invertebrate larvae and algal spores (maturation II) (Davis et al. 1989; Wahl 1989; Parsek and Greenberg 2005). In artificial substrates, cylinders and panels, maturation II is possible without initial conditioning of the surface (Rittschof et al. 2007). The seaweed’s surface provides a suitable substratum for the settlement of microorganisms including bacteria, diatoms, protozoa and fungi (Longford et al. 2007; Wahl et al. 2012; Hollants et al. 2013; Egan et al. 2013). The seaweed’s surface secretes various organic substances that function as nutrients for the multiplication of bacteria and the formation of microbial biofilms (Singh and Reddy 2016). In this association, known as ‘holobiont’ (Zilber-Rosenberg and Rosenberg 2008; Vandenkoornhuyse et al. 2015) a highly complex set of chemical, physical and biological interactions regulate the relationships between the basibiont and its epibionts. To avoid consequences of epibiosis, some seaweeds have developed a variety of defensive strategies against biofouling settlement.

Biofouling is a great challenge in seaweed aquaculture and in Integrated MultiTrophic Aquaculture (IMTA) as it increases the cost of cultivation (Buck et al. 2018). It also results in the potential loss of biomass by affecting their flexibility, decreasing the photosynthesis and nutrient uptake, and inhibiting reproduction (Walls et al. 2017). In addition, biofouling poses a health risk to cultured fish as it can facilitate and amplify the presence of pathogens that cause various diseases. This issue is also a concern for IMTA sites, where fish and shellfish are intentionally cultivated in close proximity (Bannister et al. 2019).

One of the vital steps in biofouling is chemically mediated and regulated by the microbial Quorum Sensing (QS) activity. QS is a cell density-dependent communication system, which regulates a diverse array of physiological activities including symbiosis, virulence, competence, conjugation, antibacterial molecule production, motility, sporulation, and biofilm formation (Miller and Bassler 2001; Wahl et al. 2012; Zhou et al. 2016). In biofilms, QS systems are expected to be active and are thought to play a key role in their formation when high cell densities are reached. The process which prevents the QS is known as Quorum Quenching (QQ) (Alagarasan et al. 2017) and it refers to all processes involved in the disturbance of QS (Dong et al. 2001). QQ can be achieved through inhibition, degradation or by producing antagonistic compounds that interfere with the QS signals (Chen et al. 2013). For example, the red alga Delisea pulchra produces an analogous molecule of bacterial N-Acyl-Homoserine Lactones (AHLs) known to inhibit the signal pathways in Gram-negative bacteria that leads to selective colonization of Gram- positive bacteria on this species thalli (Steinberg and de Nys 2002; Dahms and Dobretsov 2017; Saha et al.

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2018). QQ would thus represent an alternative strategy to control biofilm formations (Chen et al. 2013; La Barre and Bates 2018). Understanding and harnessing the natural antifouling defenses of seaweeds could be a good strategy to control the biofouling by prevention and inhibition (Bannister et al. 2019).

The genus Halymenia includes several species that are appreciated and consumed in Asian countries (H. discoidea, H. durvillei, H. venusta, H. dilatata, H. formosa). Halymenia spp. has economic importance as a lambda-carrageenophyte and R-phycoerythrin content. Halymenia floresii a native species in the Yucatan peninsula coast of Mexico dominates rocky substrates and has also been identified as a species with a high potential for cultivation. Studies related to growth under different light qualities and the characterization of its carrageenan have also been published, suggesting their use as raw material or for pharmacological utilization (Freile-Pelegrín et al. 2011; Robledo and Freile-Pelegrín 2011; Pliego-Cortés et al. 2017). For example, the use of light quality treatments in the cultivation of H. floresii can be used to manipulate lutein, antioxidants and a Mycosporine-like Amino acids composition which may be of particular interest for the market of edible seaweeds and Human Health (Godínez-Ortega et al. 2008). H. floresii was also observed as a good candidate to recycle inorganic nutrients in the IMTA system, producing biomass while reducing the environmental impact of coastal ecosystems through responsible aquaculture practices (Godínez-Ortega et al. 2008; Robledo and Freile-Pelegrín 2011). In addition, the establishment of opportunist green algae and the colonization of sessile invertebrates, which usually disturb the cultivation, were not observed during the experimental cultivation of H. floresii under IMTA. The culture tanks were clean and the surface of the H. floresii was remarkably free from any fouling organisms. In addition, even at high concentrations of Dissolved Inorganic Nitrogen (DIN ~ 150 µM), no noticeable growth of epiphytes was observed over H. floresii (Pliego-Cortés et al. 2017). This phenomenon could reveal that the presence of H. floresii may prevent biofilm formation by releasing allelopathic active compounds, which ultimately interfere with the settlement and growth of competitors.

To understand this allelopathic phenomenon and how H. floresii could chemically control bacterial epibiosis, we focused mainly on the algal surface metabolites that may affect or control the associated bacteria. For that, H. floresii was cultivated under controlled environmental conditions (CC) after previously being collected from the beach (Beach-Cast, BC). The objectives of our study include (i) the extraction of the surface metabolites from H. floresii carrying out the DIP extraction method using different solvents with increasing polarity and immersion periods; (ii) the isolation and identification of bacteria living on the surface of H. floresii; (iii) the evaluation of the QQ activity behavior of the surface extracts on the H. floresii associated bacterial population by bioluminescent assay and (iv) the identification of compounds using the LC-MS metabolomics approach.

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4.3 Materials & Methods

4.3.1 Algal material

Beach-Cast (BC) H. floresii was initially collected from the shores near Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV) Coastal Marine Station at Telchac, Yucatán, México (21.3419° N, 89.2636° W). The collected BC H. floresii samples were cultivated (CC) under controlled environmental conditions in transparent acrylic cultivation cylinders inside a cultivation chamber after several weeks of acclimatization. The cultivation was performed in sterilized natural seawater, which was further filtered (0.5 microns) and UV-sterilized (UV-filter 0.4 microns) at 30 ups, at a temperature of 22 ± 2° C and a pH of around 8.0 ± 0.3 under continuous aeration at a 12:12 light/dark cycle with an intensity of 110 ± 7.2 µmol photons m-2 s-1. Every week CC H. floresii was pulse- fed with Provasoli Enriched Seawater Media (0.3%) for 24 h. After eight weeks of cultivation, approximately 60 g was harvested and taken for extraction (~ 5 g for each time period in duplicate).

4.3.2 Optimization of Selective Extraction of surface-associated metabolites

i. Surface-associated metabolites (DIP) extraction – Determination of Immersion solvent and Immersion period

The surface-associated metabolites were extracted using the DIP extraction method proposed by de Nys et al. (1998). In brief, four different organic solvents with increasing polarity were selected: n-hexane, dichloromethane (DCM), dichloromethane: methanol (1:1 v/v) (DCM:MeOH) and methanol (MeOH) and assessed for their effect on the lysis of macroalgal cortical cells at different times using an epifluorescence microscope. Approximately 1.2 g of H. floresii tissue fragments were dipped in 15 mL of each solvent for a period of 10, 20, 30, 40, 50 and 60s under stirring. The macroalgal thalli were previously dried using cotton wipes before dipping. After each immersion, the whole axes of dipped macroalgae were rinsed and small cut sections were finely rinsed with sterile seawater. The finely rinsed algal portions were immediately observed under an epifluorescence microscope (Leica DM2500, Wetzlar, Germany) The cortical cells of H. floresii were observed for their autofluorescence in blue light 2-490 nm-450nm; magnification at 400x and qualitatively assessed for the presence of lysed cells. Intact cells have discrete chloroplasts whereas in lysed cells chloroplasts are disrupted, filling the whole cell with pigments. The effect of the solvent on the surface cells was determined by analyzing the microscopic images using Image J software, to count the number of lysed versus intact cells in five different fields of view for each replicate. A validation step was performed with the selected solvent with just one cut end of H. floresii in order to eliminate the effect of cut ends while dipping. The extracts of all the immersion periods of each solvent

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were pooled together, evaporated under pressure and quantitatively assessed for the presence of surface- associated metabolites using LC-MS. Henceforth, the surface metabolites will be referred to as DIP.

ii. Whole-Cell Metabolites (WCM) extraction

After DIP extraction protocols described above, the H. floresii samples were rinsed with sterile seawater and individually freeze-dried. The lyophilized samples were homogenized and extracted with n- hexane. Briefly, 400 mg of the lyophilized sample was exhaustively extracted by 5 – sequential extractions (i.e. initially with 8 mL of solvent for two hours, followed by 4 mL x 3 for every two hours and finally again with 8 mL up to 24 h under agitation) (de Nys et al. 1996). The extract was dried under vacuum and suspended again in the respective solvents, filtered through a 0.45 µm PTFE filter and analyzed by LC-MS. Henceforth, the whole-cell metabolites will be referred to as WCM. The workflow of both the extractions is illustrated in Fig 1.

Fig 1 Selective extraction of surface-associated and whole-cell metabolites. Diagram of extracts analyses and data processing of cultivated H. floresii (CC).

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4.3.3 Quorum Quenching activity by Bioluminescent reporter assay

i. Bacterial strains – Isolation from the surface of H. floresii

The algal sample was washed once with sterile filtered seawater and rinsed three times with Phosphate-buffered saline (pH 7.4), to remove the loosely attached bacteria or any other debris from the surface. Samples were swabbed with sterile cotton swabs and were serially diluted using sterile filtered seawater. Care was taken not to damage the algal fronds and the finest branchlets. A 0.1 mL of each dilution (up to 10-2) was spread on the marine agar plates. Plates were incubated at room temperature (25° C) from 24 h to 7 days. The plates were observed intermittently for the growth of the bacteria. Bacterial colonies with distinct morphological features were selected and carried over for further sub-culturing to obtain an axenic culture. The sub-culturing was continued until single pure morphotype colonies were obtained. The isolates were cryopreserved at -80° C in 20% glycerol. Around thirty different bacterial isolates were isolated from the surface of H. floresii.

ii. Identification – Taxonomic affiliation of the surface-associated bacteria

Bacterial strains were grown overnight in Marine broth (MB) at room temperature. The DNA was extracted using two different kits, DNeasy Blood and Tissue Kit (Qiagen, USA) and ZymoBIOMICS DNA Miniprep Kit (Zymoresearch, USA) according to the manufacturer’s instructions. The extracted DNA was amplified by PCR for their 16S rRNA gene sequences. To which 12.5 µL of Promega GoTaq@Green Master Mix 2X (2X Green GoTaq reaction buffer_pH – 8.5; 400 µM of dATP, dGTP, dTTP & dCTP; 3mM

MgCl2), 0.25 µL of each primer, 16s F - 5’ – AGAGTTTGATCCTGGCTC – 3’, 10 µM and 16s R -

CGGGAACGTATTCACCG – 3’, 10 µM, 11 µL DNase free H2O and 1 µL of the extracted DNA template was added. The PCR reaction was performed in the Axygene Thermal Cycler. The 25 µL reaction mixture was then heated to 94° C for 3 min, followed by 30 cycles of denaturation at 94° C for 30 sec and annealing at 52° C for 1 min and extension at 72° C for 1 min. A final extension was carried out at 72° C for 10 min. The PCR products (4 µL) were analyzed by 1% agarose gel in 1XTAE buffer, electrophoresed at 90V. The results were visualized under a BioDoc-It Imager. The amplified 16S rRNA genes of the bacterial isolates were sent to the GENEWIZ Laboratories, USA for sequencing.

iii. Bioluminescence – Reporter Assay

a. Preparation of supernatants

The isolates were cultured in 25 mL of Marine Broth (MB) and incubated at 30°C with stirring, at 120 rpm, for a previously determined period of time (16 h – 36 h). After incubation, a 10 mL of culture was

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harvested, centrifuged at 5,000 g for 10 min at 4 °C and the supernatants were collected by passing it through a sterile 0.2 µm (Sartorius) filter. The supernatant extract was stored at -20°C for 1-2 days.

b. Reporter strain

Escherichia coli pSB406 is the reporter strain used in this study. E. coli pSB406, a construct of rhlRI’::luxCDABE with EcoRI-flanked luxCDABE cassette from pSB390 (Winson et al. 1998) with an ampicillin-resistant gene. With this plasmid construct, E. coli pSB406 can sense and detect the QS signals ranging from short to long-chain (C-4 to C-12) (Winson et al. 1998). Thus, the reporter was routinely maintained in Luria Bretani (LB) media containing Ampicillin 100 µg mL-1. For the assay, the E. coli

-1 pSB406 was grown in the LB broth with the ampicillin (100 µg mL ) at 37°C to reach OD600 = 0.5.

c. Bioluminescent assay

The luminescent assay was carried out in a 96-well, sterile white microplate. This assay was performed only with the DIP n-hexane extract, as n-hexane was our eventual choice of immersion solvent. A 200 µL of the reporter strain E. coli pSB406, 100 µL of the supernatant and 1 µL DIP n-hexane extract at ‘3’ different concentrations (250, 500 and 1000 ng cm-2) were added to each well. The plate was covered with a sterile sealing membrane and incubated at 37 °C for 30 minutes. After incubation, the luminescence (RLU – Relative Light Units) was measured by a luminometer at an interval of 30 min for 24 h – 48 h at

37 °C, with a 5 seconds agitation period prior to each measurement. The OD600 of the reporter was measured to calculate the RLU/OD. Controls without any extract, and a blank with the solvent, n-hexane, were also included. The RLU of the control was deducted from the tests as the reporter strain itself produces luminescence. All measurements were performed in triplicate.

4.3.4 Untargeted Metabolomic profiling - Identification of the compounds by LC-MS

In this study, a straightforward approach to identify and characterize surface-associated metabolites was not possible due to the low concentration and quantity of the DIP extracts. Thus, the chemical fingerprints of surface metabolites (DIP-n-hexane extract) were compared with the whole-cell extracts (WCM-n-hexane) of H. floresii, for which raw LC-MS data of both the DIP and WCM extracts were pre- processed by the XCMS Online metabolomics platform to search for any possible candidates in a Mass Bank, the Madison Metabolomics Consortium Database.

i. LC-MS conditions

The chemical profiling of the DIP and WCM extracts were analyzed by LC-MS using an LC-ESI- Q-TOF-MS (Dionex, Ultimate 3000, Bruker, micrOTOF-QII) system (Bruker Daltonik GmbH, Bremen, Germany). Separations were performed using an analytical reversed-phase column. A sample of 10 µL of

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extract was injected for every analysis, at a flow rate of 0.5 mL min-1 with a column temperature of 30 °C and the elution gradient was adopted from Othmani et al. (2016). Briefly, a binary programming of

methanol (MeOH) and water (H2O) was involved at a linear gradient from MeOH/ H2O (40:60, v/v) to

MeOH/ H2O (85:15, v/v) for 3 min and to 100% MeOH for 4 min, followed by a final isocratic step with 100% MeOH for 18 min, finishing by a return to the initial conditions (0.1 min) and equilibration of the column (9.9 min). Regarding the mass spectrometer, operating conditions were set as follows: drying temperature: 350 °C, capillary voltage: 4 kV, nebulizer pressure: 3.45 bars, drying gas: helium at a flow rate of 12 L min-1 (Othmani et al. 2016). Mass spectra acquisition was set at 0.5 Hz from m/Z 50 to 1000.

ii. Pre-processing and processing of data: XCMS Online and the Madison Metabolite Consortium Database

LC/MS data were exported directly from the instrument in .mzXML format. The .mzXML data were pre-processed by XCMS Online (https://xcmsonline.scripps.edu/) (Gowda et al. 2014), an automated cloud-based method, for filtering, identifying the peaks, alignment and retention time correction using the centWave method with signal/noise threshold – 6 and mz difference as 0.01. Single, Pairwise and Multigroup jobs were assigned based on the sample’s requirement. The pre-processed data were uploaded directly as a flat text with a mass/charge ratio, retention time and relative intensity based on mass at a tolerance level of 5 ppm, to the Madison Metabolomics Consortium Database (MMCD http://mmcd.nmrfam.wisc.edu/) (Cui et al. 2008) to search for possible candidates present in our extracts.

4.4 Statistical Analysis

Results are expressed as mean ± standard deviation (SD). The data were analyzed by one-way analysis of variance to determine the significance of the results obtained. The normality of the QQ data was tested using Normal Distribution Comparison of SigmaPlot Version 14.0. Out of the total ‘96’ datasets for each bacterial strain, only the normally distributed datasets were taken for one-way ANOVA. A post-hoc test was performed following one-way ANOVA to compare the significant differences between the groups.

4.5 Results

4.5.1 Algal material - Culture of H. floresii

H. floresii growth was low under pulsed nutrient supply. The macroalgae growth rates ranged from 0.6 to 2.8 % d-1 after 8 weeks of cultivation without significant differences (p > 0.05).

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4.5.2. Optimization of Selective Extraction of surface-associated metabolites

i. DIP Extraction

The main objective was to define the highest possible exposure time in a specific solvent (or a mixture of solvents) in order to maximize the extraction efficiency of surface-associated metabolites without disturbing the cortical cells and extracting whole-cell metabolites. The effect of four organic solvents (n-hexane, dichloromethane, dichloromethane: methanol (1:1 v/v) and methanol) on the surface cells of Halymenia floresii was tested.

Epifluorescence microscopy was used to estimate the cell lysis, as chloroplasts fill the whole cell with auto-fluorescent pigments when disrupted, in comparison with the control (treated only with sterile seawater). All these solvents showed a destructive effect, except n-hexane, which had the advantage of maintaining the integrity of cell membranes up to 60s. Fig 2 illustrates the epifluorescence micrographs of n-hexane dipped cortical cells of H. floresii where only a few cells were lysed for 60s (1-factor ANOVA: F (1,10) = 136.35, p < 0.0001). Fig 3 represents the cell counts for n-hexane dipped H. floresii, whereas with the other solvents the cells were lysed from the initial period of immersion onwards (data not shown). After determining the particular immersion solvent and immersion period, the same method was applied to the rest of the samples. In comparison to other solvents preliminarily tested for DIP, the n-hexane extract was the least concentrated (Table 1).

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Fig 2 Epifluorescence micrographs showing the cortical cells of H. floresii after being dipped in n-hexane for 10s; 20s; 30s; 40s; 50s and 60s and positive control treated in exactly the same way with sterile seawater for 30s.

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Table 1 Concentration of the H. floresii, DIP extracts with different solvents and WCM extracts with n- hexane

Extracts Mean levels of secondary metabolites DIP Extracts (ng cm-2) n-hexane 600 Dichloromethane ND Methanol 76.9 Dichloromethane:methanol (1:1 v/v) 36.9 WCM Extracts – n-hexane (µg mg-1) 10s 29.38 20s 19.00 30s 10.25 40s 7.00 50s 3.25 60s 4.5

Mean levels (n=2) of secondary metabolites. ND = not determined

Fig 3 Number of intact and lysed cells vs. immersion period (in seconds) after dipping in n-hexane counted using ImageJ software. The y-axis represents the Means ± SE of epifluorescence micrographs of H. floresii at ‘5’ different angles (n=3).

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ii. WCM Extraction

Table 1 illustrates the mean levels of secondary metabolites extracted by WCM protocol after DIP extraction with n-hexane. The increase in the immersion period during DIP extraction affected the whole- cell metabolite extraction. The concentration of WCM extracts was observed at 29.3 µg mL-1 after 10s of dipping whereas it was 4.5 µg mL-1 after 60s of dipping.

4.5.3. Quorum Quenching activity of DIP extracts by Bioluminescence – Reporter Assay

i. Identification – Taxonomic affiliation of bacterial strains

The sequences were compared against GenBank database using BLAST. The taxonomic affiliations of the isolated phylotypes (> 97% similarity) are shown in Table 2, which include Vibrio owensii (B3IM), Alteromonas sp. (B7CC), Pseudoalteromonas arabiensis (B4BC), Ruegeria sp. (B4CC), Tenacibaculum sp.(B9BC), Maribacter sp. (B9IM) and Aquimarina sp. (B9.1CC). All the strains belonged to Alphaproteobacteria, Gammaproteobacteria, and Bacteroides. Except for the phylotypes Maribacter sp. (97%) and Alteromonas sp. (99%) all other strains showed 100% similarity to their closest neighbor.

Table 2 Taxonomic affiliation of the isolated phylotypes.

Name Group Closest matching strain in Similarity Designation Genbank B4CC Alphaproteobacteria Ruegeria sp. 100% Ruegeria sp. B3IM Vibrio owensii 100% Vibrio owensii B7CC Gammaproteobacteria Alteromonas sp 99% Alteromonas sp. B4BC Pseudoalteromonas 100 % Pseudoalteromonas arabiensis arabiensis B9BC Tenacibaculum sp. 100% Tenacibaculum sp. B9IM Bacteroidetes Maribacter sp. 97% Maribacter sp. B9.1CC Aquimarina sp. 100% Aquimarina sp.

Based on the 16S rRNA gene sequencing compared against GenBank using NCBI. The assigned reference name of the phylotypes. Bacterial group to which the phylotypes belong, closest neighbor and their similarity in percentage.

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ii. Bioluminescence – Reporter Assay

The quorum quenching activity of crude DIP-n-hexane extract was analyzed using the above- mentioned phylotypes at three different concentrations. Thus, 1,000 ng cm-2 was the original concentration observed on the surface of extracted H. floresii and it was further diluted to obtain 500 ng cm-2and 250 ng cm-2, in order to validate their activity more precisely.

Among the seven phylotypes tested for the quorum quenching activity of DIP-n-hexane crude extract, only four phylotypes responded positively to the assay with decreased luminescence. The results are illustrated in Fig 4 (a-d). The phylotypes that responded positively include Vibrio owensii, Tenacibaculum sp., Maribacter sp. and Aquimarina sp. However, they differ from one another based on the concentrations of the DIP extract and they were statistically analyzed as follows.

In the phylotype, Vibrio owensii (Fig 4a) quenching was more significant at the highest concentration of the extract (1,000 ng cm-2) (1-factor ANOVA: F (3,286) = 59.93, p < 0.0001 and post-hoc t-test (t (148) = 10.03, p < 0.0001) compared to the other concentrations tested. By doubling the volume of the extract (2 µL of 1,000 ng cm-2) the concentration was increased, thus luminescence was higher than that of the control. As the luminescence normally increases with the increased QS signals, at this concentration the quenching activity was not evident. The increased luminescence observed with the increase in the concentration of the extract may be assumed to be an antagonistic approach of the extract.

With Tenacibaculum sp. phylotype in Fig 4b, the quenching was observed to be higher at the lowest concentration of the extract (250 ng cm-2) (t-test (t (120) = -12.57, p < 0.0001) compared to the intermediate concentration (500 ng cm-2) whereas, even though being significant, both of those were inversely related to the highest concentration (1,000 ng cm-2) (t-test (t (120) = 7.22, p < 0.0001) with increased luminescence. The quenching activity observed with B9BC was statistically significant (1-factor ANOVA: F (2,180) = 64.83, p < 0.0001).

The phylotype Maribacter sp. in Fig 4c and Aquimarina sp. (Fig 4d) showed quenching activity in all of the three concentrations tested (1-factor ANOVA: F (3,240) = 21.00, p < 0.0001 and 1-factor ANOVA: F (2,237) = 22.61, p < 0.0001, respectively) but the effect of extract concentration was observed to be insignificant among them.

Thus, DIP_n-hexane extract at the lowest extraction (250 ng cm-2) effectively inhibits the communication signals sent by the phylotypes Tenacibaculum sp. and Maribacter sp. strains whereas for Aquimarina sp. and Vibrio owensii it was required to be at the highest concentration (1,000 ng cm-2). The

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other strains, Alteromonas sp., Pseudoalteromonas arabiensis, and Ruegeria sp. did not show any positive response to the quenching activity tested.

The solvent’s effect was not quite evident with Vibrio owensii whereas with Tenacibaculum sp. and Aquimarina sp. it decreases the luminescence and with Maribacter sp. it increases. The effect of the solvent on QQ behavior compared to the DIP extract is not significant (p>0.05) and shall thus be disregarded.

Fig 4 (a-d) Quorum quenching activity of the DIP extract on the chosen phylotypes. a –Vibrio owensii; b – Tenacibaculum sp.; c – Maribacter sp. and d – Aquimarina sp. No. of cycles (30 minutes per cycle) plotted against relative light units/optical density at 600 nm.

4.5.4. Untargeted metabolomic profiling

The raw DIP LC-MS data in duplicates, were uploaded to the XCMS platform assigning a ‘Single’ job treatment whereas the raw WCM LC-MS data were assigned a ‘Multi’ job as the WCM data includes

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10s, the 20s, 30s, 40s, 50s and 60s individually in duplicate. Both sets of data were treated with the same parameters. Initially, the Total Ion Chromatograms (TIC) of DIP and WCM were overlaid and compared for similar peaks based on their retention time (Fig 5). The WCM chromatogram was quite complex with a large diversity of compounds with high intensity than that of the DIP chromatogram. Most of the WCM extract’s metabolites were detected between 10 to 17 min. The peaks of DIP chromatogram were observed to be less intensified which may be due to the lower concentration of the surface extract. But interestingly we observed ‘2’ peaks of DIP at high intensity at 14.0 – 14.5 min and 17.0 – 17.5 min.

The pre-processed data of DIP and WCM were uploaded separately to the MMCD Mass Bank. For DIP LC-MS data, the Mass Bank proposed ‘83’ metabolites with a one-to-one match of ‘57’ metabolites. For WCM LC-MS data totaled ‘335’ metabolites, with a one-to-one match of ‘219’ metabolites. The proposed list of metabolites for DIP and WCM was quite different from each other where only ‘8’ proposed metabolites were observed to be common among them. The common metabolites of DIP and WCM are shown in Table 3. These proposed metabolites may possibly be smaller compounds as their m/z ratio was observed to be from 200 to 450.

Fig 5 TIC of DIP_n-hexane (red) and WCM_n-hexane (blue) crude extract after retention time correction by XCMS Online platform.

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Table 3 List of common metabolites in DIP and WCM n-hexane extracts proposed by the MMCD mass bank m/z RT Group S.No Compounds ratio (in minutes) Group 1 1 2-furaldehyde 205.11 ND 2 2-(2,4-dichloro-phenoxy)-N-(2-mercapto-ethyl)-acetamide 278.98 12.5–13.0 3 Quercetin 3-sulfate 381.99 17.0-17.5 Group 2 4 Quercetin 7-O-glucoside 464.09 17.5-18.0

Flavonoids 5 Reduced Flavin Mono Nucleotide (FMNH2) 458.12 ND 6 1-deoxy-1-(7,8-dimethyl-2,4-dioxo-3,4-dihydro-2H-benzo[g]pteridin-1-id-10(5H)-yl)-5-O- 458.12 ND phosphonato-D-ribitol Group 3 7 (4-bromophenyl)[4-({(2E)-4-[cyclopropyl(methyl)amino]but-2-enyl}oxy)phenyl]methanone 399.08 14.0-14.5 Halogenated 8 (2E)-N-allyl-4-{[3-(4-bromophenyl)-5-fluoro-1-methyl-1h-indazol-6-yl]oxy}-N-methyl-2- 443.10 14.0-14.5 compounds buten-1-amine

m/z – mass/charge ratio; RT – Retention Time in minutes; ND – Not Detected

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We grouped those common metabolites into three groups. Group 1, that includes Compounds 1 and 2, 2-furaldehyde and 2-(2,4-dichloro-phenoxy)-n-(2-mercapto-ethyl)-acetamide, which were not quite relevant to macroalgal secondary metabolites. Group 2 consisted of flavonoid family members such as, Quercetin 3-sulphate, Quercetin 7-O- glucoside, Reduced Flavin Mononucleotide and 1-deoxy-1-(7,8- dimethyl-2,4-dioxo-3,4-dihydro-2h-benzo[g]pteridin-1-id-10(5h)-yl)-5-o-phosphonato-d-ribitol. Group 3 essentially included halogenated compounds, particularly brominated compounds, (4-bromophenyl)[4- ({(2e)-4-[cyclopropyl(methyl)amino]but-2-enyl}oxy)phenyl]methanone and (2e)-n-allyl-4-{[3-(4- bromophenyl)-5-fluoro-1-methyl-1h-indazol-6-yl]oxy}-n-methyl-2-buten-1-amine. Additionally, we also observed one compound showing mass spectra with bromine isotopic patterns during LC-MS analysis.

4.6 Discussion

H. floresii is a good candidate for recycling inorganic nutrients in a land-based IMTA system, producing biomass whilst reducing the environmental impact of coastal ecosystems through responsible aquaculture practices. Epiphytes and fouling organisms usually induce problems in aquaculture, reducing the yield. The presence of H. floresii in the culture ponds significantly limits the establishment of opportunist green algae usually disturbing the cultures. From these ecological observations, it was interesting to understand these phenomena. So, at first, H. floresii was cultivated under controlled environmental conditions (CC). The growth rate of the CC H. floresii sample was observed to be significantly lesser when compared to the red alga Rhodymenia pseudopalmata, under stress conditions with higher solar radiation (Pliego-Cortés et al. 2019). We found that this might be an ideal environment to study the defense mechanism of H. floresii, as growth and defense were inversely related to each other (Nylund et al. 2013).

4.6.1 Selective extraction of surface-associated metabolites

In order to selectively extract the surface-associated metabolites, we used epifluorescence microscopy to differentiate between the intact and lysed cortical cells of H. floresii after dipping them in different solvents, such as n-hexane, dichloromethane, methanol and dichloromethane: methanol (1:1), without any staining, contrary to the work on the brown alga Taonia atomaria (Othmani et al. 2016). In a similar study using Delisea pulchra, surface metabolite extraction by n-hexane did not show any cell lysis up to 30s but exposure at 60s lysed the cells, whereas cells of Laurencia obtusa remained intact even at 60s (de Nys et al. 1998). For Delisea pulchra and Laurencia obtusa, n-hexane was found to be a suitable solvent for surface-metabolites extraction (de Nys et al. 1998) whereas for Taonia atomaria it was ineffective (Othmani et al. 2016).

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The non-polar solvents, hexane, and dichloromethane (100%) did not discolor algal tissue in different red and brown algae (Kientz et al. 2011) and the same pattern was also seen during DIP extraction of H. floresii. N-hexane has been proved to extract non-polar lipophilic molecules in previous studies (de Nys et al. 1998). In our study, n-hexane also showed extraction of lipophilic metabolites associated on the surface of H. floresii. Jennings and Steinberg (1997) suggested that they have a higher chance of accumulating at the surface, though in the absence of strong interionic interactions they may have a higher probability of diffusion (Saha et al. 2012). As a rule of thumb, the defense molecules associated with the surface are expected to be non-polar in nature, thus they will protect the surface without being easily dissolved in the surrounding seawater. The polar defense molecules are expected to dissolve more easily and this may be metabolically costly for the macroalgae, particularly under culture conditions.

So far, the compounds identified on the surface are polar. Most of the surface-associated molecules identified until now have low to moderate polarity for example, halogenated furanones from Delisea pulchra (de Nys et al. 1998) or fucoxanthin (Saha et al. 2011). These compounds are characterized by a degree of hydrophobicity (Steinberg and de Nys 2002; Harder et al. 2004) which may allow them to diffuse less efficiently in seawater and thus remain close to the surface of the producing organism. On the contrary, DiMethylSulphoPropionate (DMSP) and proline (Saha et al. 2012) described on the surface of Fucus vesiculosus are polar compounds, such molecules may have a high diffusion capacity in the surrounding seawater. Fucoxanthin isolated from the algal surface showed a modulatory effect on epiphytic bacteria at environmentally realistic concentrations (Saha et al. 2011). Proline and DMSP have been shown to play an ecologically relevant role as surface inhibitors against the attachment of bacterial strains isolated from algae.

Solely on the basis of its hydrophilic properties, it is quite difficult to determine whether a single compound may play an ecological role at the algal interface. Physicochemical interactions between such a molecule and the algal surface itself (e.g. ionic interactions or hydrogen bonds) on the one hand, but also with molecules and/or organisms on the surface on the other hand, could enhance or limit its diffusion in seawater. Furthermore, due to the high cell density and the presence of EPS, biofilms are well known for their capacity to limit the diffusion of molecules in water (Stewart 2003). The laboratory experiment where algal incubation in natural seawater was performed showed that the three major surface-associated compounds identified in this study (Group 1, 2 and 3 in Table 3) were released into seawater. This result reinforces the potential of these molecules to be involved in surface interactions.

Although the concentration of the DIP extract was very low, n-hexane was observed to be an efficient solvent in extracting the surface-metabolites of H. floresii. As far as we understand, the surface metabolites on macroalgae studied so far, such as in the red algae Delisea pulchra, Laurencia obtuse,

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Bonnemaisonia asparagoides, in the brown algae Taonia atomaria and in the green alga Caulerpa filiformis, have reported to be at a very low concentration (de Nys et al. 1998; Nylund et al. 2007; Othmani et al. 2016). The concentration of a DIP n-hexane extract of H. floresii was observed to be in accordance with the lower mean surface concentration of secondary metabolites from D. pulchra (de Nys et al. 1998). The chemical defense in macroalgae is metabolically very costly which outweighs the benefits provided through protection against harmful bacterial colonization. Thus, it is not surprising that a low concentration of DIP-n-hexane extract of H. floresii may be costly to the macroalgae (Nylund et al. 2005). In Bonnemaisonia hamifera the chemical defense was observed to be costly in a naturally defended surface compared to that of the undefended macroalgal surface (Nylund et al. 2013).

4.6.2 Influence of DIP n-hexane extract on bacterial communication

The host-specific associations between algae and bacteria suggest that algae may control the associated epibacterial community rather the bacterial biofilm conferring protection on the host alga (Okami, 1986; Kelecom, 2002; Zheng et al. 2005; Lachnit et al. 2009; Mayer et al. 2009; Kientz et al. 2011). Consequently, rather than the symbiotic relationships with the host, it was hypothesized that the dominant communities secret QS-disruptive compounds in order to control the maturation of biofilms (La Barre and Bates 2018). Thus, interfering with the QS systems is always considered to be a potential tool in pathogenicity and disease control. Quorum Quenching, a QS impairment phenomenon, represents an alternative strategy to control bacterial biofilms. Quorum Quenching mechanisms in marine algae mainly include the production of inhibitors or antagonists of signal receptors (Givskov et al, 1996; Kim et al. 2007; Romero et al. 2011). In this work, the promising effect of the crude DIP_n-hexane extract was explored for its quenching behavior of the signal molecules being produced by the bacterial strains. The surface- associated metabolites from H. floresii were assumed to interfere with the establishment of its associated bacteria, which was evident from the quenching activity of the DIP_n-hexane extract on the gram-negative isolates being tested.

Bacterial strains isolated and used in this work basically fall into three different groups such as Alphaproteobacteria, Gammaproteobacteria, and Bacteroides. They are all Gram-negative, a predominant biofilm-forming bacterial group in the marine environment (Brian-Jaisson et al. 2016; A Abdul Malik et al. 2019). Bacteroidetes, which essentially include Tenacibaculum sp., Maribacter sp. and Aquimarina sp. were identified as main epiphytic bacteria in macroalgal surfaces (Wang et al. 2008). Alphaproteobacteria, Ruegeria sp. and Gammaproteobacteria such as Vibrio owensii, Alteromonas sp. and Pseudoalteromonas arabiensis were specifically isolated from the surface of D. pulchra and these phylotypes were observed to be potential opportunistic pathogens in different circumstances in red algae (Kumar et al. 2016).

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Presumably, the QQ behavior of surface metabolites in the crude extract may justify our observation that the surface of H. floresii was remarkably free from any settling organisms which primarily depends on the well-established biofilm. The four isolates that responded positively, included Vibrio owensii, Tenacibaculum sp., Maribacter sp., Aquimarina sp., are known to be causative agents of various diseases. Here, we assumed that these isolates may be opportunistic pathogens to the associated host, H. floresii. Particularly, in D. pulchra, these strains were observed either in bleached or in adjacent bleached areas (Kumar et al. 2016). Vibrio owensii was identified as a causative agent of AHPND (Acute Hepato Pancreatic Necrosis Disease) in Litopenaeus vannamei ponds (Liu et al. 2018). Tenacibaculum maritimum, a worldwide known fish pathogen, is a filamentous biofilm-forming bacteria causing ‘Tenacibaculosis’ in the cultured Cyclopterus lumpus (Småge et al. 2016). Different species of Maribacter were isolated from the bleached surface of D. pulchra (Kumar et al. 2016). Aquimarina latercula had the strongest bleaching pathogenicity on healthy Gracilaria lemaneiformis (Liu et al. 2019). The quenching of the communication signals produced by these bacteria may possibly limit the population from reaching a threshold for activating their respective virulence system. In that case, the surface-associated metabolites from H. floresii may be an alternative strategy in disease control.

4.6.3 Untargeted Metabolomic Profiling

Macroalgae are known to produce a wide variety of chemical compounds, with a range of bioactivity, including sulphated polysaccharides, proteins or peptides or amino acids, lipids, polyphenols and fucoxanthin (Mohamed et al. 2012; Saha et al. 2012). Red algae primarily synthesize isoprenoid and acetogenin derivatives, as well as amino acid, shikimate and nucleic acid derivatives (Amsler 2008). Volatile-halogenated organic compounds such as monoterpenes, sesquiterpenes, diterpenes and meroterpenes, play a significant defensive role (Tiwari and Troy 2015). In order to understand and hypothesize about the metabolites involved in the chemical defense on the surface of H. floresii, the extracts were analyzed by LCMS and the data obtained were processed on the metabolomic platform.

Compound 1, 2-furaldehyde proposed by the Mass Bank has been reported qualitatively as a flavoring component in a range of food items and as a contaminant of the aquatic environment resulting from different industrial processes (World Health Organization (2000)). The 2-furaldehyde, a product of sugar degradation, was identified to be toxic to green microalgae Scenedesmus quadricauda and a freshwater Cyanobacteria Microcystis aeruginosa. Compounds 3, 4, 5 and 6 belong to bioflavonoids, which were naturally involved in several defensive activities. For example, among different flavonoids, Quercetin was present in high percentages in a red alga Gracilaria dendroides, a green alga Ulva reticulata and a brown alga Dictyota ciliolate which exhibited increased antimicrobial activity (Al-Saif et al. 2014), and the bioactivity of Chondrus crispus was mainly contributed by isoflavones (Klejdus et al. 2010).

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In order to confirm the presence of any brominated compounds in H. floresii, such as Compound 7 and 8 as proposed by the Mass Bank, and also to improve the identification of metabolites, we analyzed the extracts obtained from the exploratory test of DIP extraction with solvents like dichloromethane, methanol and dichloromethane: methanol by LC-MS. Interestingly we found one compound showing mass spectrum with bromine isotopic patterns in a methanolic extract of H. floresii which was less intensified. The MS spectrum is shown in Fig 6. The brominated compound in the surface extracts of cultivated H. floresii may remarkably contribute to its chemical defense on the surface. Brominated compounds are widely observed in red algae such as B. asparagoides, D. pulchra, and Asparagopsis taxiformis and have always contributed to several bioactivities. Four different polyhalogenated compounds, with bromine and chlorine isotopic patterns, were observed in the surface extracts of B. asparagoides (Nylund et al. 2010). Recently, two new highly brominated compounds, mahorone and 5-bromomahorone were identified in a DCM: MeOH extract of A. taxiformis and they exhibited mild antibacterial activity against Acinetobacter baumannii, a human pathogen (Greff et al. 2014).

Fig 6 LC-MS spectrum of surface extract showing bromine isotopic pattern (red arrow marks) a characteristic of halogenated metabolite.

The brominated compound in H. floresii may contribute to its antifouling activity by quenching the communication signals of the associated bacteria. Mostly, the QQ behavior of macroalgae was observed to be related to halogenated compounds on the surface. For instance, different polyhalogenated and non- halogenated surface metabolites of B. asparagoides seemed to regulate the epiphytic bacterial community (Nylund et al. 2010). A synthetic halogenated furanone derived from the secondary metabolite of D. pulchra was found to inhibit the AHL-mediated quorum sensing in Pseudomonas aeruginosa (Hentzer et al. 2002). The halogenated furanones from D. pulchra use the same specific receptor binding sites of AHLs, thus

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interfering with important bacterial communication-regulated processes, such as biofilm formation (Harder et al. 2012). α-D-galactopyranosyl-glycerol, betonicine and isoethionic acid produced by the red algae Ahnfeltiopssis flabelliformis, compete with the synthetic AHL signals in a laboratory assay with Agrobacterium tumefaciens (Kim et al. 2007). The unidentified crude extract of the members of Galaxauraceae and Laurencia sp. inhibits the AHL signals (Skindersoe et al. 2008). Bromoperoxidases produced by Laurencia digitata deactivates the AHLs by an oxidation process, where this enzyme is responsible for the formation of brominated compounds in red algae (Wever et al. 2018).

To conclude, we hypothesized different groups of allelopathic secondary metabolites such as brominated compounds, Volatile Halogenated Organic Compounds (VHOCs) like diterpenes and flavonoids assuming that they may contribute to the macroalgal chemical defense on the surface of the red algae Halymenia floresii. The XCMS Online metabolomics platform and the consequent Mass Bank (MMCD) search may be considered as a potential tool for a metabolomics study. The Quorum Quenching activity of the crude extract may justify our proposed hypothesis that it may interfere with the communication of the bacterial community on the surface and thus may prevent biofouling under culture conditions. Rather than the secondary metabolites with antimicrobial activity, the interfering activity of macroalgae on quorum sensing of any opportunist pathogens may help reduce the emergence of resistant strains in aquaculture systems. In that way, H. floresii will be an excellent model in a multi-trophic culture environment.

In the future we aim to work on the defensive role of surface extracts on chemically undefended H. floresii, for example on the bleached surface, to further explore the activity of surface-associated metabolites.

4.7 Acknowledgements

The authors would like to thank E. Caamal Fuentes, L. Taupin for their skilful technical assistance for the chromatographic analyses and V. Avila-Velazquez for the H. floresii cultivation. Financial support from ECOS-Nord CONACYT for the collaboration project M14A03 and PN-CONACYT 2015-01-118 is also acknowledged.

4.8 References

The references of this article are available at the end of the manuscript (‘References’, page no 206)

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5. Chapter IV

Identification and characterisation of the quorum sensing signal of the opportunistic pathogen causing bleaching disease in Halymenia floresii by HPLC/MS method

Will be submitted as a ‘Research Article’ to Marine Drugs_Special Issue “Health Promoting Bioactives from Marine Algae and Their Holobionts”

Shareen et al.

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Identification and characterisation of the quorum sensing signal of the opportunistic pathogen causing bleaching disease in Halymenia floresii by HPLC/MS method

In this part (Chapter IV), we identified the opportunistic pathogen inducing bleaching disease in H. floresii and also characterised its Quorum Sensing (QS) signal (here, homoserine lactone) by HPLC/MS method. This chapter is still under preparation to be submitted as a research article.

Following the identification of the H. floresii epibacteria and differentiating the QS isolates among them (Chapter II), we observed and evaluated the H. floresii surface extract’s interference with their communication signals (Chapter III). In Chapter II we also discussed that different isolates from H. floresii were found to be associated with the strains causing bleaching in other algal species. With this idea, we hypothesized that one or more of the epibacterial strains of H. floresii shall be opportunistic in nature. In order to evaluate our proposed hypothesis, we studied the ability of these strains to induce bleaching in H. floresii. We also examined the presence of any QS signals in any opportunistic pathogen(s), since quorum sensing plays a significant role in causing diseases in macroalgae.

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5.1 Materials and Methods

5.1.1 Algal material

H. floresii, beach-cast (BC) was initially collected from the shores near CINVESTAV Coastal Marine Station at Telchac, Yucatán, México (21.3419° N, 89.2636° W). They were washed thoroughly with natural seawater from the site of the collection in order to remove sand and epiphytes. After packing them in polythene bags, they were transported to the lab in an icebox. Rinsed with sterile seawater (30 psu) and transferred to 25 L acrylic cylinders for acclimatization (15 days) under continuous aeration at a 12:12 light/dark cycle with a light intensity of 110 ± 7.2 µmol photons m-2 s-1. After acclimatization, healthy tips of H. floresii were selected for the tip bleaching assay.

5.1.2 Bacterial isolates

To evaluate the potential capacity of epibacterial strains for induction of thallus bleaching in Halymenia floresii, ‘25’ of the cryopreserved bacterial strains were revived. These strains were previously isolated from the surface of H. floresii and identified by 16S in November 2018. They were maintained in Marine Agar medium in darkness. All cultures were incubated at room temperature. The isolation of the H. floresii epibacterial strains has been detailed in Chapter II.

5.1.3 Tip bleaching assay with single epibacterial strains

H. floresii tips excised from healthy material (n=6 in total for each bacterial strain, each tip was c. 2 – 3 cm long) were individually placed into separate 6-well plate containing 3 mL of sterile seawater (SSW, 30 psu). In order to eliminate epibacterial from these material, two antibiotics, Vancomycin and Cefotaxim at a concentration of 0.1 mg/mL each were added to each well. Further the plates were incubated at 20 °C at a photon flux density of 38.3 µmol photon m-2 s-1 for two days. After this pre-treatment, plates were carefully emptied and H. floresii tips rinsed by washing with 1.5 mL of SSW in order to remove antibiotics. Finally, 3 mL of SSW were added into each well and previously revived and cultured bacteria were immediately inoculated.

Prior to their inoculation, all bacterial cultures were grown in sterile Marine Broth medium

overnight at room temperature in darkness until they had reached an OD600 of 0.2 to 0.3. A volume of 30 µL bacterial cells along with the medium were added into the wells containing H. floresii tips (n = 6), individual plates were used for each strain. Controls consisted of the same volume of sterile bacterial culture medium added into the wells containing H. floresii tips (n=6) and treated as above described. After ‘5’ days of incubation under laboratory conditions all wells were checked under the binocular microscope (magnification factor: 45) and the number of bleached and non-bleached tips in each well were counted

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using a dark background. The relative risk of thallus tip bleaching in all treatments with the addition of bacteria relative to control treatments was calculated as odds ratios of numbers of bleached and non- bleached tips as follows:

Relative risk of bleaching = (Bleached tips in treatments/healthy tips in treatments) / (Bleached tips in control) healthy tips in controls)

The data have been statistically analysed by using Fisher’s exact test for significance. The Mantel- Haenszel extension of Fisher’s exact test has been used for replicated test designs, and Bonferroni correction applied to reduce the risk of type I error. Isolates that turned out to be pathogenic after applying Bonferroni correction (i.e. p < 0.00036) were designated as ‘significant pathogens’ and those which were non-significant after Bonferroni correction (p < 0.05) were called ‘potential pathogens’. Isolates that reduced the risk of thallus tip bleaching were designated as ‘non-pathogens’ or ‘protectors’. Nevertheless, after applying Bonferroni correction isolates that significantly reduced the risk of thallus tip bleaching (p < 0.00036) were also called ‘significant non-pathogens or protectors’ while those that also reduced the tip bleaching (p < 0.05), but were not significant after applying Bonferroni correction are designated as ‘potential non-pathogens or protectors’ (Saha and Weinberger 2019).

5.1.4 Extraction of HomoSerine Lactones (HSLs) from the ‘significant pathogen’

The so called ‘significant pathogen’ identified by the tip-bleaching assay (see Section 5.1.3) was used to extract HSLs (Fig. 1). The bacterium chosen was previously revived on Marine Agar (MA) plates. A single colony was inoculated into 100 mL of Marine Broth (MB) medium and incubated for 24 hours under constant agitation (125 rpm) at 25° C. After 24 hours of culture liquid: liquid extraction was performed to extract the HSLs according to previously described methods (Fletcher et al. 2007; Wang et al. 2011). Briefly, the supernatant (100 mL) was extracted by two equal volumes of acidified ethyl acetate (0.5% of acetic acid) for three times. The solvent was dried under vacuum at 35 °C and the residue was resuspended in acetonitrile. The HSL sample was further dried to remove acetonitrile using nitrogen gas and the dried sample was stored at -20° C until further analysis. The methodology was illustrated in the Fig. 1.

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Fig.1 Schematic illustration of the HSLs extraction process.

5.1.5 Chromatographic conditions – LC-MS QTOF analysis

Samples were resolubilized in 490 µL acetonitrile with 10 µL of internal standard (IS = C4-HSL, C6-HSL, Oxo-C6-HSL, C8-HSL, and C12-HSL, 10 µL of 50 mg/L solution). Calibration standards were prepared at a range of 0.05 – 5 ng/μL and spiked with the same amount of IS. Chromatographic analysis was performed on a liquid chromatography-mass spectrometry system (LC/MS, Ultimate 3000 Dionex - MicroTof QII, Bruker Daltonics). A total of 20 µL were injected into a Gemini C6 phenyl column (250 mm * 4.6 mm * 5 µm, Phenomenex). The column temperature was kept at 40 °C and the flow rate was 0.6 mL/min. Mobile phase A was water/acetonitrile (95/5) and mobile phase B was water/acetonitrile (2/98), both with 10 mM ammonium acetate. The elution program was performed as follows: 0 to 100% B in 15 min, held at 100% B in 5 min, 100 to 0% B in 1 min and then returned to 0% B for the next 9 min.

Mass detector with electrospray ionization (ESI) source was performed as follows: positive mode;

source temperature, 200°C; capillary voltage, 4.5 kV; nebulizer gas (N2) at 2.8 bar and dry gas (N2) at 12 l/min. Mass spectra acquisition was set at two acquisitions/s from m/z 50 to 1,000. LC/MS raw data were

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processed using DataAnalysis 4.1 software and XCMS Online (https://xcmsonline.scripps.edu/) (Gowda et al. 2014).

Acylated homoserine lactones (AHL) extracts were compared with commercial standards of N- Butryl-DL-homoserine lactone (BHL/C4 HSL), N-Hexanoyl-DL-homoserine lactone (HHL/C6 HSL), N- (β-Ketocapropyl)-L-homoserine lactone (3-O-C6-HSL), N-(3-Oxooctanoyl)-L-homoserine lactone (OOHL/3-O-C8 HSL) and N-(3-Oxododecanoyl)-L-homoserine lactone (3-O-C12 HSL) (Sigma) and relative migration factor (Rf) values were calculated. Acylated homoserine lactones were identified based on their retention time and mass spectrum.

5.2 Results

The aim of this part was to differentiate the significant opportunistic pathogen(s) out of the epibacterial community of H. floresii and to identify and characterise the corresponding quorum sensing signal of the opportunistic pathogen(s).

5.3.1 Tip bleaching assay with single epibacterial strains

Of the 25 epibacterial strains isolates evaluated in this work, Vibrio owensii was found to significantly increase the risk of tip bleaching when compared to control treatments without bacterial inoculation. Vibrio owensii was identified as a “significant pathogen” after Bonferroni correction (Table I: Fig. 2 p < 0.00036). Eight additional isolates (Pseudoalteromonas arabiensis; P. mariniglutinosa; Tateyamaria omphalii; Ruegeria sp.; Alteromonas sp. (B7CC and B12CC); Epibacterium sp.; Alteromonadaceae bacterium) had the same effect, but were not significantly pathogenic after Bonferroni correction and were thus considered as ‘potential pathogens’ (Table I; Fig. 2 p < 0.05). Ten out of the 16 remaining isolates were found to significantly reduce the risk of tip bleaching and were considered ‘significant protectors’ (Table I: Fig. 2 p < 0.00036). The last six had the same effect, however after Bonferroni correction they were not significantly protective (Table I; Fig. 2 p < 0.05). Thus, the relative risk of the bacteria analysed that induce or reduce bleaching in H. floresii were compared and followed the pattern of 40% vs 4% for significant non-pathogens or protectors versus pathogens and 24% vs 32% for potential non-pathogens or protectors versus pathogens.

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100

20 Relative Relative of risk bleaching (%)

Fig. 2 Risk of thallus tip bleaching in H. floresii after inoculation of ‘25’ bacterial strains relative to control thalli without bacterial inoculation. No. of replicates (n=6) was assigned for each isolate. Error bars ± in %.

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Table I. Epibacterial strains isolated from Halymenia floresii evaluated for their pathogenicity and/or non- pathogenicity by the tip bleaching assay.

Accession Code Epibacterial strains Origin of the isolates Designation No. (Closest neighbour)

MT176134 B3IM Vibrio owensii IMTA Significantly pathogenic MT176133 B1BC Pseudoalteromonas arctica Beach-Cast Significantly protective MT176135 B4BC Pseudoalteromonas arabiensis Beach-Cast Potentially pathogenic MT176136 B5BC Pseudoalteromonas sp. Beach-Cast Potentially protective MT176138 B6BC Pseudoalteromonas Beach-Cast Potentially pathogenic mariniglutinosa MT176139 B7BC Vibrio owensii Beach-Cast Potentially protective MT176140 B8BC Pseudoalteromonas arctica Beach-Cast Significantly protective MT176141 B9BC Tenacibaculum sp. Beach-Cast Significantly protective MT176144 B2CC Phaeobacter sp. Cultivar Chamber Potentially protective MT176145 B3CC Tateyamaria omphalii Cultivar Chamber Potentially pathogenic MT176146 B4CC Ruegeria sp. Cultivar Chamber Potentially pathogenic MT176147 B7CC Alteromonas sp. Cultivar Chamber Potentially pathogenic MT176149 B8CC Ruegeria lacuscaerulensis Cultivar Chamber Potentially protective MT176150 B8.1CC Alteromonas sp. Cultivar Chamber Significantly protective MT176151 B9CC Spongiimicrobium salis Cultivar Chamber Significantly protective MT176152 B9.1CC Aquimarina sp. Cultivar Chamber Significantly protective MT176154 B10CC Bacillus circulans Cultivar Chamber Significantly protective MT176155 B10.1CC Epibacterium sp. Cultivar Chamber Significantly protective MT176156 B10.2CC Epibacterium sp. Cultivar Chamber Potentially pathogenic MT176157 B12CC Alteromonas sp. Cultivar Chamber Potentially pathogenic MT176158 B13CC Roseobacter sp. Cultivar Chamber Significantly protective MT176159 B14CC Erythrobacter sp. Cultivar Chamber Significantly protective MT176160 B16CC Alteromonas sp. Cultivar Chamber Potentially protective MT176161 B17CC Alteromonadaceae bacterium Cultivar Chamber Potentially pathogenic MT176162 B19CC Ruegeria lacuscaerulensis Cultivar Chamber Potentially protective

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8.3.2 Identification of homoserine lactones (HSLs) from the ‘significant pathogen’

The presence of AHLs was investigated in the acidified ethyl acetate extracts of the significant pathogen, identified here as V. owensii (Accession No. MT176134) after 24h of culture. The QS activity of V. owensii was previously detected by the bioluminescent reporter strain, Escherichia coli pSB406 (for more details, see Chapter II and III). E. coli pSB406 is a lux-based biosensor containing a fusion of rhlRIP: :luxCDABE on a pUC18 plasmid backbone, which enables the detection of a wide range of AHLs differing in the length of their acyl chain (Winson et al. 1998). LC/MS confirmed the presence of N-butyryl-L- homoserine lactone (C4-HSL) in the culture medium of V. owensii grown in MB liquid media. The presence of AHL in the sample was identified according to the retention times of their corresponding standards. The chromatograms generated by the analytical instrument illustrates the separation of the standard and the sample of C4-HSL at a retention time of 6.2 – 7.0 minutes (Fig. 3). This AHL was unambiguously identified by a comparison of its mass spectra with those of pure standards. In Fig. 4, the MS spectrum of the C4- HSL in the standard and the sample are shown as fragments at 102.05 (lactone ring); 172.09 (M+H)+ and at 194.08 (M+Na)+.

The Extracted Ion Chromatogram (EIC) of the AHL extract of V. owensii (significant pathogen), shown in Fig. 2 was previously treated in the XCMS Online Platform (for details see Chapter V). Besides the chemical structure of C4-HSL was depicted in Fig. 5 for a better understanding of its fragments fundamentally based on the lactone moiety.

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Fig. 3 Chromatogram of V. owensii HSL extract pre-processed in XCMS Online platform.

Fig. 4 Chromatogram showing the presences of C4 HSL in the standard (upper panel) and V. owensii extract (lower panel) at a retention time of 6.2 to 7.0 minutes.

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Fig 5. Mass Spectrum showing the fragments of C4 HSL in the standard (upper panel) and V. owensii extract (lower panel) as 102.05 (lactone ring); 172.09 (M+H)+ and 194.08 (M+Na)+.

Fig. 6 Chemical structures corresponding to the mass spectrum at 102.05 (lactone ring); 172.09 (M+H)+ and 194.08 (M+Na)+.

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5.3 Discussion

The epibacterial communities are organized as highly structured biofilms in many different micro- niches and habitats within the marine environment, reaching sufficient abundances to induce quorum sensing-based dialogues (Lami 2019). The recognition of the importance of the bacterial microbiota for other organisms has led to the concept of holobiont, which in turn makes possible the occurrence of bacterial interactions such as quorum sensing (Teplitski et al. 2016; Lami 2019). The elucidation of quorum sensing roles in the holobionts microbial balance can lead to important applications in aquaculture and/or antifouling industries (Lami 2019). From this work, we explored the H. floresii holobiont that allow us to differentiate significant and/or potential pathogens by their ability to induce bleaching. As a result, V. owensii was found to be a significant pathogen inducing bleaching in H. floresii. Subsequently, the extracellular AHL of V. owensii was also identified from liquid: liquid extracts.

V. owensii, a member of Gammaproteobacteria, isolated from cultivated H. floresii was previously identified as a QS bacterium. Quorum sensing behaviour of V. owensii was detected by a bioluminescent assay using a reporter strain, Escherichia coli pSB406 (see Chapter II and III). Subsequent to the identification of V. owensii as a “significant pathogen” inducing bleaching in H. floresii, we were driven to identify the particular QS signal (here, HSL) responsible for its QS activity. The presence of C4-HSL in V. owensii emphasized the QS critical role in the induction of bleaching in H. floresii.

Eight epibacterial isolates from H. floresii, besides V. owensii, also revealed some potential to induce bleaching symptoms. Since these detrimental isolates originated from healthy hosts, it has been proved that the epibacterial community of this alga is obviously composed of opportunistic pathogens that can induce similar bleaching symptoms as described in several red algae (Weinberger et al. 1997; Weinberger 2007; Case et al. 2011; Saha and Weinberger 2019). The “significant” or “potential” pathogens (9 out of 25 strains) include 36% of the total isolates assayed here, whereas, Saha et al. (2019) observed that approximately 5% of the Agarophyton spp. epibiome induced bleaching. However, this calculation is just a representative sample of the whole microbiome as stated by the authors (Saha and Weinberger 2019).

Fernandes et al. (2011) elucidated the ability of Nautella sp. R11 to induce bleaching in D. pulchra and they observed that a combination of virulence factors and QS-dependent regulatory mechanisms enabled a shift from symbiotic to a pathogenic lifestyle of the epibacterial community, especially under environmental conditions unfavorable for the host. As reviewed by Natrah et al. (2011b), several different virulence factors of Vibrio causing diseases in aquaculture systems were strongly related to the AHLs, such as N-hexanoyl-L-homoserine lactone (HHL), N-(3-oxohexanoyl)-L-homoserine lactone (OHHL), N- butanoyl-L-homoserine lactone (BHL), N-3-octanoyl homoserine lactone (OHL), N-(3-oxodecanolyl)-L-

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homoserine Lactone (ODHL), etc. Moreover, V. owensii has been identified as a causative agent of AHPND (Acute Hepato Pancreatic Necrosis Disease) in Litopenaeus vannamei ponds (Liu et al. 2018). Interestingly, the V. owensii strain which was observed to induce bleaching in H. floresii was originated from the surface of the alga, which was cultivated in a land-based IMTA system whereas V. owensii (B7BC) isolated from the beach-cast H. floresii samples failed to induce bleaching.

Previously we also identified that the surface extracts of H. floresii, at its 1-fold natural surface concentration, significantly interfered with the QS signals of V. owensii by reducing the luminescence of the reporter strain, E. coli pSB406. Whereas it increased the luminescence at a higher concentration of the extract, which may be assumed as an antagonistic approach of the surface-extract (A Abdul Malik et al. 2020b). The quorum sensing interference of H. floresii may remarkably contributed by the halogenated metabolites identified to be present on the surface, (4-bromophenyl)[4-({(2E)-4- [cyclopropyl(methyl)amino]but-2-enyl}oxy)phenyl]methanone (Compound 7) and (2E)-N-allyl-4-{[3-(4- bromophenyl)-5-fluoro-1-methyl-1H-indazol-6-yl]oxy}-N-methyl-2-buten-1-amine (Compound 8). Interestingly both these compounds were brominated compounds which are widely observed in red algae such as B. asparagoides, D. pulchra, and Asparagopsis taxiformis and have always contributed for their reported bioactivities (de Nys et al. 1998; Nylund et al. 2010; Greff et al. 2014).

Bacterial infectious diseases produced by Vibrio are the main cause of economic losses in aquaculture. The expression of virulence genes in some Vibrio species is controlled by the diffusion of signal molecules such as AHLs. One of the aquaculture-related pathogenic Vibrio strains, V. owensii VibC- Oc-106, isolated from the seawater, was reported to produce 3-OH-C6-HSL, 3-OH-C7-HSL and C13-HSL (Torres et al. 2018). In our work, it was observed that the macroalgae-associated V. owensii produced the extracellular C4-HSL and, previously, we confirmed its QS activity by a biosensor assay. However, Girard et al. (2017) did not detect any AHLs from strains of V. owensii from natural collections even after using three different biosensors which covered a large spectrum of AHLs (Pseudomonas putida [pKR-C12], E.coli [pJBA-132] and Chromobacterium violaceum [CV026]. QS systems mediated by AHLs have been found in many species of marine pathogenic bacteria and AHLs also seem to play an important role in the host-microbe interactions in the marine environment (Romero et al. 2010).

World production of carrageenophytes its around 160 000 tons of macroalgae (dry weight), with 28 000 tons of carrageenan obtained, valued at USD270 million (Freile-Pelegrín and Robledo 2016). Several studies have shown that the thallus whitening or bleaching is quite common in carrageenophyte farms in which red algae are more vulnerable to this disease symptom. Under cultivation, affected segments were cut off allowing the unaffected thallus to continue to regenerate and regrow, though with reduced overall productivity (Hurtado et al. 2019). Preventing the disease outbreak will be a better approach instead

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of treating or removing the diseased segments after the disease outbreak. Thus, QS interference may be an effective method for preventing and/or inhibiting the virulence of pathogens and hence reduce diseases and increase productivity in aquaculture systems (Zhao et al. 2015). The surface-extracts of H. floresii interfere with the QS activity of V. owensii, as we previously observed thus H. floresii seems to be a candidate to obtain compounds for disease control in aquaculture. Pliego-Cortés et al. (2019) described H. floresii as a efficient cultivar to recycle inorganic nutrients in a land-based IMTA reducing the environmental impact at coastal ecosystems through responsible aquaculture practices. Besides its potential use as a carrageenophyte, H. floresii could possibly play a significant role among green approaches to improve aquaculture productivity and sustainable environmental practices.

5.5 Conclusion and perspectives

The identification of V. owensii as a significant pathogen inducing the risk of bleaching in H. floresii shall be viewed as a shift from their symbiotic to a pathogenic relationship, since the strain used was isolated from healthy algal specimens (microbiont). AHL production in Vibrio owensii is significant as this bacterium is a well-known pathogen in shrimp aquaculture. Besides the AHLs regulation of virulence mechanisms in other Vibrio species described so far (Lilley and Bassler 2000; Zhu et al. 2002; Frans et al. 2011; Girard et al. 2017), our results might increase our understandings of the role of AHLs in the physiology and pathogenicity of these microorganisms (Girard et al. 2017).

As we previously identified that V. owensii is a QS bacterium and that surface-extracts of H. floresii effectively interfere with this QS behaviour, future works shall particularly focus on in vivo experimentation to assess host’s interference on the pathogenicity of V. owensii in H. floresii bleaching. This may contribute to enhance disease control and macroalgal health management in aquaculture systems. In addition, the AHL extracts can be studied further to evaluate the release of other small molecules as has been recently described with Roseobacter clade of macroalgae associated bacteria (Ziesche et al. 2015).

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Screening Halymenia floresii secondary metabolites which underly its surface defence mechanisms

Shareen A Abdul Malik; Gilles Bedoux; Yolanda Freile-Pelegrin; Nathalie Bourgougnon and Daniel Robledo

Submitted as a Research Article to “Metabolites_Special Issue_Seaweed Metabolites Volume 2” Shareen at al. Submitted on 21 September 2020

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Screening Halymenia floresii secondary metabolites which underly its surface defence mechanisms

In the Chapter III, the allelopathic metabolites of Halymenia floresii present on the surface was identified by comparing the surface-associated (DIP) and whole-cell (WCM) metabolites using an untargeted metabolomics approach. By comparing the pooled data of DIP and WCM, we identified an ‘8’ compound through this approach (Section 4.5.4 Table 3) but there was quite a lot of individual set of data resulted from the pre-processing of both the DIP and WCM extracts. In order to widen the knowledge on the metabolic profile of H. floresii, we further proceeded to investigate the potentially active compounds in each set of data. The schematic workflow of this metabolomic work was illustrated in Scheme I and each step involved were detailed in the Chapter V. Here we outline the work of the following chapter. The objectives of this chapter are as follows:

Objectives 1. Metabolite extraction by using selective-extraction of surface-associated (DIP) and whole-cell (WCM) compounds 2. Analyse both extracts by mass spectrometry coupled with liquid chromatography (LC-MS) 3. Process information by untargeted metabolomic data analyses; 4. Propose a metabolic profile with the predicted bioactive metabolites, 5. Use hierarchical clustering of the predicted metabolites.

STEP O

Previously, the surface-associated (DIP) and whole-cell metabolites of H. floresii were extracted by the method of DIP and complete maceration, respectively, as detailed in the Chapter III. Both the DIP and WCM extracts were analyzed by LC/MS (analytical tool) and the raw data were retrieved from the LC/MS system as a .mzXML file (Fig. 24).

Fig. 24 Extraction and analysis of surface (DIP) and whole-cell metabolites of H. floresii

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STEP I

Fig. 25 Pre-processing of the raw data (.mzXML) in XCMS Online platform

STEP II

The processing of the data was performed in Madison Metabolomics Database Consortium (MMCD). The pre-processed XCMS data were downloaded from the platform and ‘flattext’ was created with the retention time (rtmax), mass/charge ratio (mzmax), and relative intensity (maxint). The metabolite search was based on ‘mass’ in a ‘batch mode’ for mixture analysis. The results were downloaded as an ‘excel file’ (Fig. 26).

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Fig. 26 Processing of the data in Madison Metabolomics Consortium Database (MMCD)

STEP III

In this step, both the list of metabolites (DIP and WCM) were compared and the common metabolites were taken for further processing. To post-process the results, we used the SMILES system and exhaustive online search (‘Google’ ‘Google Scholar’) to obtain the corresponding structural descriptors (chemical structures) and the functional descriptors (biochemical class/activity), respectively (Fig. 27).

Fig. 27 Metabolite organization to yield the corresponding structural and functional descriptors of the predicted metabolites

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STEP IV

The downstream statistical analysis was performed to validate and interpret the metabolomic data. This was performed by using the command-line program in ‘R Studio’ and multivariate analysis, Hierarchical Clustering Analysis (HCA), was performed to group the treatments (SMILES and chemical groups identified) that are close to each other in clusters i.e. similar structural descriptors (Fig. 28).

Fig. 28 Statistical analysis to validate the data performed in ‘R Studio’

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Screening Halymenia floresii secondary metabolites which underly its surface defence mechanisms

Shareen A Abdul Malik 1,*; Gilles Bedoux1; Yolanda Freile-Pelegrin2; Nathalie Bourgougnon1 and Daniel Robledo2

1Université Bretagne Sud, EA 3884, LBCM, IUEM, F-56000 Vannes, France; [email protected] (G.B.); [email protected] (N.B.) 2Marine Resources Department, CINVESTAV-Unidad Merida, Mexico; [email protected] (Y.F.P.); [email protected] (D.R.) *Correspondence : shareen.a-abdul-malik-arockiasamy@univ-ubs. fr; Tel: +33 7 82 56 34 94

6.1 Abstract

The surface of Halymenia floresii Mexican Rhodophyta revealed to be clean and remarkably free of epiphytes under culture conditions. The objectives of the present study were the investigation and identification of H. floresii secondary metabolites which could prevent colonization by microorganisms on its surface. Algal metabolites were selectively extracted from surface-associated (DIP) and whole-cell (WCM) samples by using n-hexane, which was previously identified to selectively extract all surface- associated metabolites. In order to propose a global metabolomic fingerprinting of H. floresii, both the DIP and WCM extracts were considered for a four-step metabolomic analysis using mass spectrometry (LC- MS), data pre-processing (XCMS Online), search for putatives in the Madison Metabolomics Consortium Database (MMCD) mass bank, and hierarchical clustering of those putatives. A total of 416 and 110 compounds were detected in the WCM and DIP extracts respectively, among which 41 compounds were present in both extracts. These 41 compounds were characterized for their structures and/or chemical groups (i.e. fatty acids, polysaccharides, peptides, terpenes, sterols, alkaloids, shikimic acid, hydroquinones, aldehydes, ketones, lactones, halogenated or non-halogenated furanones) including their reported effects (i.e. antioxidant, antibacterial, antiviral, antifungal, antitumoral, enzyme inhibitors, cell proliferation inhibitors, insect repellent). As a whole there were 26 compounds that have been identified as secondary metabolites. The structures were encoded into SMILES (for Simplified Molecular Input Line Entry System) strings to build structural descriptors. Further, functional and chemical descriptors were subjected to a Hierarchical Cluster Analysis to investigate the chemical similarities and their possible correlation with bioactivities. By using ‘omic’ studies such as metabolomic fingerprinting, we provide a functional signature of phenotype with biological relevance to advance our understanding of the biosynthetic pathways which

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are involved in this Rhodophyta surface defence mechanism with drugability outcomes. From these results we concluded that H. floresii is a potent source of structurally and functionally diverse compounds.

Keywords: H. floresii, metabolic fingerprinting, epibiose, QSI, XCMS

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6.2 Introduction

Macroalgae are a large and diverse group of marine photosynthetic organisms that are commonly found in coastal areas and they represent a high source of chemical diversity (Gaubert et al. 2019a). Macroalgal metabolome includes diverse primary and secondary metabolites with immediate biochemical and physiological consequences likely to play important ecological functions (Gaubert et al. 2019a, b). They are involved in cell-cell chemical communication with their associated members and play diverse ecological functions in macroalgae (Gaubert et al. 2019b). Some of these compounds exhibit a wide range of therapeutic properties, including anti-cancer, anti-oxidant, anti-inflammatory and anti-diabetic activities (Collins et al. 2016) and thus have been described as an excellent source of biologically active metabolites (Blunt et al. 2015) whose structural diversity makes them invaluable in drug discovery (Pech-Puch et al. 2020). More recently, ‘omic’ techniques have been used to study a wide array of metabolomes, a complete set of metabolites, through three different metabolomics approach, untargeted metabolite profiling, targeted metabolite analysis and metabolite fingerprinting. Untargeted metabolic profiling is used for the identification and (relative) quantification of as many compounds as possible whereas the targeted metabolite analysis focuses on a limited number of metabolites chosen based on prior knowledge (Mishra et al. 2015; Pandey et al. 2015; Patel et al. 2016; Tanna and Mishra 2018). Metabolite fingerprinting is commonly performed to classify samples based on a comprehensive metabolite profiling without identification of individual peaks (Steinfath et al. 2007; Tanna and Mishra 2018). Any metabolomic study shall include one or more of these approaches to identify known or unknown metabolites.

Until now, the chemical profiling of marine algal extracts has been mainly restricted to targeted identification and quantification of selected compound classes with bioactivity (Goulitquer et al. 2012). On the other hand, untargeted metabolomics aims at identifying the extracted metabolites of a sample for revealing novel and unanticipated activities. As a more global metabolomics approach, the fingerprinting can be used as a diagnostic tool to screen the metabolic diversity of any organism (Fiehn 2002; Weckwerth and Morgenthal 2005; Nobeli and Thornton 2006; Ellis et al. 2007; Ivanišević et al. 2011). This technique uses signals from hundreds to thousands of metabolites for a rapid sample classification via statistical analysis (Hegeman 2010; Paix et al. 2019, 2020). As a matter of example, a broad metabolomic study has been performed on the red alga Gracilaria vermiculophylla, in relation to its defence response. Structural elucidation of these metabolites revealed novel eicosanoids as major components, which are part of the innate defence system of G. vermiculophylla against herbivores (Nylund et al. 2011; Goulitquer et al. 2012). Nowadays, studies on macroalgal metabolomic fingerprinting provide useful information on particular compounds and their responses to environmental and/or culture conditions (Gupta et al. 2014). Recently,

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Gaubert et al (2019) (Gaubert et al. 2019b) studied the temporal and spatial variabilities of the metabolome in the brown alga Lobophora by using an untargeted UHPLC-QToF metabolomic fingerprinting approach.

LC-MS based metabolomics is more sensitive than other analytical tools (Hegeman 2010), and has a great potential in relation to the ‘drugability’ of natural products (Zhao et al. 2018). In mass spectrometry- based untargeted metabolomic studies, metabolite’s identification is a key challenge (Zhou et al. 2012). Recently, several advances in database management and processing tools have greatly simplified the most laborious tasks for metabolite identification (Lewis et al. 2012). For example, Madison-Qingdao Metabolomics Consortium Database (MMCD) a web-based metabolomic tool contains data related to biologically relevant small molecules from a variety of species. This database is primarily designed to facilitate the search of metabolites mainly based on mass. (Cui et al. 2008). Hence, MMCD has been widely used for macroalgal metabolomic studies (Goulitquer et al. 2012; Gupta et al. 2014; Kumar et al. 2016; Tanna and Mishra 2018). The mass-based search of metabolites in the databases will primarily result in a putative identification of the metabolites which can be further addressed either by experimental verification of the metabolites using authentic standards or by several computational approaches (Tautenhahn et al. 2007; Cui et al. 2008; Wolf et al. 2010; Horai et al. 2010). MMCD goes with well-accepted computational algorithms that has been developed to ease metabolite identification (Cui et al. 2008; Zhou et al. 2013).

Halymenia floresii, a tropical red macroalga common to the coast of the Yucatan peninsula, Mexico, has been previously identified with economic interest due to its lambda carrageenan content and as an edible seaweed with high potential for cultivation (Pliego-Cortés et al. 2017; Alemañ et al. 2019). Except for the studies of Godinez-Ortega et al. (2012) and Meesala et al. (2018), little is known about H. floresii metabolites. For instance, Godinez-Ortega et al. (2012) described how to modulate H. floresii pigment composition and Chl a, α-carotene, lutein, and phycocyanin content in relation to light quality conditions under laboratory. More recently, a new antimalarial sterol derivative, halymeniaol, was isolated from H. floresii from the west coast of India (Meesala et al. 2018). In our experimental cultivation of H. floresii, we previously observed that both culture tanks and macroalgae surfaces were remarkably free from fouling organisms or epiphytes, even under high concentrations of dissolved inorganic nitrogen. This finding suggested the presence of allelopathic active compounds released by this macroalgae and drove us to selectively extract the surface associated metabolites by using the DIP extraction method proposed by Nys et al. (1998) for evaluating their ability to interfere with the bacterial Quorum Sensing (QS) communication signals. We observed that the DIP surface extracts effectively interfered with the QS signals of the Gram-negative bacterial strains such as Vibrio owensii, Tenacibaculum sp., Maribacter sp., and Aquimarina sp. isolated from the surface of H. floresii. Thus, QS interfering (QSI) activity of the surface extracts could deter settling of organism, which primarily depends on the well-established microbial

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community on the surface of H. floresii. Through LC-MS based untargeted metabolomic fingerprinting, we have also identified that different groups of allelopathic secondary metabolites such as brominated compounds, Volatile Halogenated Organic Compounds (VHOCs) like diterpenes and flavonoids might contribute to such a chemical defence at the algal surface through QSI activity (A Abdul Malik et al. 2020).

Thus, the goal of the present study is to extend this preliminary qualitative screening of H. floresii metabolite to a global untargeted metabolomic fingerprinting of the resulting large data set to investigate its potential to produce bioactive compounds. In order to achieve this, the objectives were framed as follows: (i) metabolite extraction by using selective-extraction of surface-associated (DIP) and whole-cell (WCM) compounds; (ii) analysis of both extracts by mass spectrometry coupled with liquid chromatography (LC-MS); (iii) process information by untargeted metabolomic data analyses; (iv) propose a metabolic profile with the predicted bioactive metabolites, and (v) perform hierarchical clustering of the predicted metabolites in terms of chemical structures, lipophilicity and bioactivity.

6.3 Materials and Methods

6.3.1 Algal material and extractions

H. floresii were cultivated (in CINVESTAV) under controlled environmental conditions in acrylic cultivation cylinders inside a cultivation chamber after several weeks of acclimatization. The cultures used natural seawater, filtered (0.5 microns) and UV-sterilized (UV-filter 0.4 microns) at 30 psu, 22 ± 2° C and pH 8.0 ± 0.3 under continuous aeration at a 12/12 light/dark cycle with an intensity of 110 ± 7.2 µmol photons m-2 s-1. Every week H. floresii was pulse-fed with Provasoli Enriched Seawater Media (0.3%) for 24 h. After eight weeks of cultivation, approximately 60 g fw were harvested and extracted (~ 5 g for each time period in duplicates). In order to selectively extract the surface-associated metabolites, samples were taken for the DIP extraction as previously reported (A Abdul Malik et al. 2020) since the use of n-hexane allows to extract the surface metabolites of H. floresii without lysis of the cortical cell’s wall. Thus, the surface metabolites were gently and selectively extracted using n-hexane, a non-polar solvent, using the dipping method (DIP) previously described by de Nys et al. (1998).

Briefly, H. floresii thalli duplicates were dipped into n-hexane for six durations of 10, 20, 30, 40, 50 and 60 seconds under stirring, and the resulting extracts were pooled together. After the DIP extraction, H. floresii thalli were rinsed with sterile seawater (30 psu) and individually freeze-dried. The lyophilized samples were then exhaustively extracted with n-hexane by maceration for 24 h under agitation at room temperature to yield the Whole Cell Metabolites fraction (WCM). For comparison purposes, the same solvent was used. The extracts were then filtered through Whatman filter to remove any particles and the

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solvent was evaporated under vacuum. Henceforth, the surface metabolites and whole-cell metabolites will be referred to as DIP and WCM, respectively.

6.3.2 LC-MS conditions and data pre-processing

DIP and WCM extracts were analyzed by LC-MS as described in [85]. Briefly, the chemical profiling of H. floresii extracts were analysed by LC/MS using an LC-ESI-Q-TOF-MS (Dionex, Ultimate 3000, Bruker, micrOTOF-QII) system (Bruker Daltonik GmbH, Bremen, Germany). Separations were performed using an analytical reversed-phase column. A sample of 10 µL of the extract was injected for every analysis at a flow rate of 0.5 mL min-1 with a column temperature of 30 °C and the elution gradient

was adopted from Othmani et al. (2016). Binary programming of methanol (MeOH) and water (H2O) was

injected at a linear gradient from MeOH/ H2O (40:60, v/v) to MeOH/ H2O (85:15, v/v) in 3 min and to 100% MeOH in 4 min, followed by a final isocratic step with 100% MeOH for 18 min, finishing by a return to the initial conditions (0.1 min) and equilibration of the column (9.9 min). Regarding the mass spectrometer, operating conditions were set as following: drying temperature: 350 °C, capillary voltage: 4 kV, nebulizer pressure: 3.45 bars, drying gas: helium at a flow rate of 12 L min-1 (Othmani et al. 2016) . Mass spectra acquisition was set to 0.5 Hz from m/z 50 to 1,000.

The raw data files were exported as “.mzXML” format from the Agilent LC/MS instrument used for the analyses. Initially, a free user profile account was created on the XCMS Online home page. The LC/MS raw data in .mzXML format were uploaded into the platform. The DIP LC/MS data in duplicates were uploaded to the assigning a ‘Single’ job treatment whereas the WCM LC/MS data were either assigned a ‘Multi’ job as the WCM data included six extraction periods of 10, 20, 30, 40, 50 and 60 seconds in duplicates or a ‘Single’ job which included individual sets. Both data sets were treated with the same default parameters, UPLC / Bruker Q-TOF positive, with mzdiff of ‘0.01’ and SNR (signal-noise ratio) threshold of ‘6’. The method used for the feature detection is ‘centWave’ with 10 ppm of m/z tolerance and minimum/maximum peak width of 5 seconds/20 seconds. The p-value threshold was set between 0.001 (highly significant) to 0.05 (not significant). After several steps of treatment such as filtration, retention time correction, fill-in missing peak, and annotation of adducts and isotopes, the pre-processed data were downloaded from the platform. For grouping and annotation, the inbuilt CAMERA package was used by the platform. The schematic workflow of this untargeted metabolomics study is displayed in Scheme I.

H. floresii .mzXML data obtained from the LC/MS analysis were fed to the XCMS Online platform that decoded mass spectra based on mass (charge, retention time, and intensity) reported a list of every ‘Single Class’. By using XCMS Online to pre-process LC-MS metabolomic data the retention time was corrected resulting from any experimental drifts from run to run, as the features of ions were defined by

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unique m/z ratio and retention time (Forsberg et al. 2018). One of the most critical challenges in the pre- processing of metabolomic data is that, the ‘.mzXML’ files of LC/MS analysis took a long time to be uploaded to the XCMS Online Platform, as the job sizes increased. This issue shall be addressed by using a data-streaming application XCMStream, an update of the metabolomic platform, which directly uploads data from the instrument computer as it is generated and automatically initiates the job once complete (Smith et al. 2006; Montenegro-Burke et al. 2017).

6.3.3 LC-MS data processing – Madison Metabolomics Consortium Database

H. floresii metabolomic study searched Madison Metabolomics Consortium Database (MMCD) for possible candidates based on the large profile of exact mass since this DB was the most appropriate for algae metabolome (Tohge and Fernie 2009; Goulitquer et al. 2012) . The metabolite search was based on ‘mass’ in a ‘batch mode’ for mixture analysis. Thus, the pre-processed XCMS data were downloaded from the platform and ‘flattext’ was created with the retention time (rtmax), mass/charge ratio (mzmax), and relative intensity (maxint), all at maximum value. The text file was created only with the numerical data, separating each column by a ‘tab space’, without any text. The flattext was uploaded to the database and the ‘Spectral_Type’ was chosen as ‘LC/MS’. The predicted metabolites from the database were individually coded with unique numbers/letters. All the recovered metabolites were searched for any previously reported bioactivity and they were grouped and coded accordingly (Table 1). Typically, the algorithm used to categorize the various metabolites is based on a first sorting key after reported bioactivity, secondary, if no such activity was identified (i.e. no specific activity was reported), metabolites were sorted after the presence of a specific chemical group usually involved in defence mechanisms (like ketones or halogens) and eventually from their biochemical group in its conventional sense. In the case were a particular metabolite exhibited various activities like antibiotic and fungicide and pesticide, it was sorted in the antibiotic class to simplify the post-processing.

Table 1. Codes assigned, based on the bioactivity (functional descriptors) and/or chemical class (chemical descriptors), to the metabolites (DIP and WCM) proposed by the massbank.

Code Biological activity/Biochemical class ATB Antibiotics ATC Anti-cancerous ATF Antifungal ATI Anti-inflammatory ATO Anti-oxidant ATP Antiproliferative ATV Antiviral INH Inhibitors

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DIV Various FUR Furans HAL Halogenated HOR Hormones IME Intermediary Metabolites KET Ketones LIP Lipids NUC Nucleotides PIG Pigments SAC (Poly)-Saccharides VIT Vitamins

6.3.4. Data post-processing – SMILES

Simplified Molecular Input Line Entry System, SMILES, is a chemical notation language and the strings encode a molecular structure as a two-dimensional valence-oriented graph (Weininger 1988). Thus, it is widely used by large chemical databases that can be reverted or derived from chemical structures or CAS (Chemical Abstracts Service) numbers. Cronin et al. (2002) converted the names of the ‘661’ drugs into SMILES strings to calculate their structural descriptors and these SMILES strings were further entered into QSARis software to examine the 2-D descriptors of the structures. SMILES are used to build the structural descriptors of the metabolites proposed by the mass bank. In this work, we used the SMILES system to post-process our results. First, the structures were encoded into SMILES strings according to Weininger (1988) to build structural descriptors. Briefly, the SMILES strings were obtained from the ‘Chemical Resolver Identifier’ website (https://cactus.nci.nih.gov/chemical/structure) by feeding each metabolite’s name. Next, functional and chemical descriptors were added to the selected compounds to fill a matrix with zero or one depending on the lacking or presence of such an effect or chemical group of relevance. The converted structural descriptors of the metabolites were used to build a text file including the ID (previously assigned codes, see above) and their corresponding SMILES strings. This text file was used to build the required structural descriptors by a cheminformatic program, Data Warrior (freely available online and downloaded from http://www.openmolecules.org/). The structural descriptors were downloaded as a ‘.sdf file’, which were further imported for statistical analysis. SMILES hence encode structural descriptors of the molecules that can be matched following strings analysis of their respective Euclidean distances, which is not feasible from their original chemical naming. Therefore, the lowest or highest the Euclidean distance, the shorter or greater their chemical similarity.

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6.3.5 Data Validation & Interpretation – Statistical Analysis

Robust computational tools are essential to analyse and interpret these metabolomics data. After one or more pre-processing and post-processing of metabolomic data, the raw data including structural and functional descriptors are converted into a numerical format that can be used for downstream statistical analysis. Such downstream processing is quite feasible with open source commercial software, R, as it is freely available to the users. R is a software environment for statistical computing, data analysis and graphics, which has now become an essential tool in all areas of bioinformatics research (Grace and Hudson 2016). The ChemmineR package was used for the data post-processing (Cao et al. 2008). An HCA multivariate analysis (Ivanišević et al. 2011; Farag et al. 2012, 2013a, b; Porzel et al. 2014; Rácz et al. 2018), was performed to group the treatments (SMILES and chemical groups identified) that are close to each other in clusters i.e. similar structural descriptors. The HCA of metabolomic data was performed by using the packages ‘ChemmineR’, ‘fmcsR’, ‘dendextend’, and ‘gplots’. They allowed computing dendrograms of the metabolites based on their structural (dis)similarities. The (dis)similarity between each pair of observations was measured using distance measures i.e. Euclidean distance in R according the Ward method, for which, the data were feed as ‘.sdf files’ by using the DataWarrior software. It included the code of each observations, SMILES strings and the structural descriptors. The functional descriptors (Table 1) were separately fed as ‘text’ files.

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Scheme I Schematic workflow of untargeted metabolomics approach on Halymenia floresii

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6.4 Results

6.4.1 LC-MS analyses

Once the LC/MS data were pre-processed by using the XCMS Online platform, results were downloaded from the website. The downloaded files were Extracted Ion Chromatogram (EIC) and Spectrum (spec), which contain EICs of every single peak detected and their spectrum (m/z versus intensity), respectively. Moreover, the ‘RTcor’ file showed the corrected retention time between the samples analysed, from both DIP and WCM as well as among the replicates and ‘Total Ion Chromatogram (TIC)’ file showed the chromatogram of the samples after correcting the retention time. Apart from the above-mentioned files, the platform also produces an excel file ‘XCMS-Report-Annotated-SingleClass’, which includes the following details: m/z (median, minimum and maximum) of the group; retention time (rt) (median; minimum and maximum) in the group; the number of peaks (npeaks); maximum intensity (maxint); isotopes and adducts.

6.4.2 Identifying the H. floresii metabolic diversity

The ‘Mass Batch Search Results’ were downloaded from the MMCD, after being processed. This resulted in ‘110’ and ‘416’ compounds for the surface and whole-cell extracts, respectively. After carefully analysing data by overlaying the compounds which were observed in both extracts and removing duplicated compounds, a set of ‘41’ compounds was retained hereafter labelled as the CMN pool. Most of these compounds exhibit various biological activities as compared with previous studies. Nevertheless, the identification of these macroalgal metabolites is a bottleneck in metabolomic studies, because of the vast metabolite diversity (i.e. 100,000 known and 200,000 unknown metabolites) reported so far (Goulitquer et al. 2012; Gupta et al. 2014). For instance, Paix et al. (2019) (Paix et al. 2019) identified 3065 m/z features composed of 429 variables in the surface extracts of the brown alga, Taonia atomaria however, its raw data were treated with the XCMS package under R environment whereas we used XCMS Online. Such a metabolomic study on marine macroalgae is highly dependent on the availability and quality data and the use of a wide variety of electronic resources.

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Fig. 1 Metabolite counts of the Whole-cell metabolites (WCM); surface metabolites (DIP) and common metabolites (CMN) plotted against Activity/Biochemical classes (symbols are from Table 1).

The metabolic counts of the metabolites originated out of the DIP and WCM, proposed by the massbank was plotted as a histogram in Figure 1. As expected, out of all the biochemical classes, the Intermediary Metabolites (IME) class was observed to be prominent since it features about 17% of the metabolite classes. If Intermediary MEtabolites (IME), LIPids (LIP), NUCleotides (NUC) and (Poly)- SACcharides (SAC) classes are pooled, the cluster then accounts for up to 49%. Besides these, other metabolites including AnTiBiotics (ATB), AnTi-Cancerous (ATC), AnTiFungal (ATF), AnTi- Inflammatory (ATI), AnTi-Oxidant (ATO), AnTiProliferative (ATP), AnTiViral (ATV), FURans (FUR), HALogenated (HAL), KETones; (KET) feature up to 36% of the whole metabolome identified from H. floresii.

The relative occurrence of the metabolites classes from the different extracts, i.e. WCM, DIP, and CMN, is displayed Figure 2. It worth’s to note that all the bioactive classes and the biochemical classes, except AnTiProliferative (ATP), AnTiViral (ATV), HORmones (HOR), KETones (KET), PIGments (PIG) and VITamins (VIT), show up in higher or equal occurrence in the CMN group when compared to the WCM and DIP. Henceforth, only the ‘41’ common metabolites (CMN) are considered as surface

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metabolites (DIP). This is because ‘69’ out of the ‘110’ (110 – 41 = 69) metabolites were not observed in the WCM, and may not necessarily have been originated from the alga.

Fig. 2 Left: Relative class occurrence (%) of the Whole-cell metabolites (WCM); surface-metabolites (DIP) and common metabolites (CMN) plotted against Activity/Biochemical classes (symbols as in Fig. 1). Right: Metabolic count (A) and Relative Class occurrence (B) in % of the Whole-cell metabolites (WCM = 416) and common metabolites (CMN = 41) plotted against Activity/Biochemical classes.

Further analysis was focused on H. floresii metabolites which were identified in both DIP and WCM extracts which feature nearly 10% of all metabolites recovered. Interestingly, antiproliferative, antiviral and biochemical classes such as hormones, ketones and vitamins were not identified in the surface- extract i.e. DIP. Thus, in Figure 2 Right A, the metabolic count between WCM and CMN was compared and the most represented classes were IME followed by NUC and ATB with 70, 60, and 41 metabolites, respectively. Similarly, the relative occurrences of bioactive molecular classes’ in these two extracts were compared and we found that halogenated (HAL), furanones (FUR) and inhibitors (INH) compounds were relatively higher in the CMN (12% vs 3% / 7% vs 3% / 15% vs 8%, respectively) whereas antibiotics (ATB), antioxidant (ATO), anticancerous (ATC) and antifungal (ATF) compounds are present in similar ratios (Figure 2 Right B). On the contrary, ATP, ATV, HOR, KET, PIG, and VIT were under-represented in these extracts.

In Table 1 we present the properties of the ‘41’ CMN putatives with their corresponding codes,

bioactivity/chemical class identified and lipophilicity (Log(KOW)) (Cheng et al. 2007). The putatives names, their actual mass and the chemical formula were also listed (Table 1) as annotated by the MMCD database used. The SMILES strings of the putatives, CMN and WCM were provided in the Supplementary section

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(Table S1). The confidence level (∆) showed the difference between the MW of the compounds (available in the database) and the m/z features of the putatives in our extracts. The experimental m/z features (Exp m/z features) from the pre-processed DIP and WCM data matching the putatives were also listed. The experimental m/z features also allowed us to find the retention time (RT in minutes) and the maximum intensity.

Table 2. Codes assigned to the ‘41’ CMN metabolites, names of the putatives, raw/chemical formula, confidence level (∆), and actual mass as proposed by the database MMCD, experimental m/z features (Exp m/z), retention time (RT in minutes) and maximum intensity resulted from the XCMS Online platform, the corresponding Bioactivity/Biochemical classes of putatives (BIO) and their LOGP3 octanol water partition coefficients (XLogP3-AA). ND – Not Detected.

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Confidence Exp m/z RT (in Maximum XLogP3- BIO S. No. Putative Name Formula level (Delta) Actual Mass features minutes) Intensity AA

311.25; 14.36; 118729; 5-(acetylamino)-2,6-anhydro-3,5-dideoxy-3- S07 C11H18FNO8 0.0009232 311.10 311.29; 22.84; 3024; -3.4 ATB fluoronononic acid 311.01; 20.90 486 (2E)-N-allyl-4-{[3-(4-bromophenyl)-5-fluoro- 443.33; 14.36; 4986; 1-methyl-1H-indazol-6-yl] oxy}-N-methyl-2- C22H23BrFN3O 7.68E-05 443.10 443.34; 18.77; 3311; 5 HAL S20 buten-1-amine 443.22 13.02 89576 S22 Xylarohydroxamate C5H8NO7- 0.00048 194.03 194.11 4.84 12514 -2.6 SAC 399.31; 14.36; 302105; 399.25; 12.69; 12306; (4-bromophenyl) [4-({(2E)-4- 399.22; 9.01; 7091; S44 [cyclopropyl(methyl) amino] but-2-enyl} oxy) C21H22BrNO2 0.00112 399.08 4.8 HAL 399.26; 16.63; 4375; phenyl] methanone 399.27; 13.69; 4094; 399.22 8.61 8449 Reduced Flavin Mono Nucleotide (FMN); 458.35; 14.42; 40048; S49 C17H23N4O9P 0.000319 458.12 -2.3 NUC FMNH2 458.29 12.69 14209 1-deoxy-1-(7,8-dimethyl-2,4-dioxo-3,4- 458.35; 14.42; 40048; S50 dihydro-2H-benzo[g]pteridin-1-id-10(5H)-yl)- C17H23N4O9P 0.000319 458.12 -2.3 IME 458.29 12.69 14209 5-O-phosphonato-D-ribitol 319.28; 14.39; 13412; S72 Ibandronate C9H23NO7P2 0.001058 319.09 -4.1 INH 319.22 13.82 3818 (6-methyl-3,4-dihydro-2H-chromen-2-yl) S102 C11H14O3P- 0.000821 225.06 225.19 10.98 142598 1.1 IME methylphosphinate 325.27; 16.46; 41127; S106 Pancratistatin C14H15NO8 0.0008 325.07 325.27; 14.49; 19715; -1.6 ATC 325.23 14.02 20402 Cyclohexylmethyl 2-formylphenyl hydrogen 298.24; 14.36; 8825; S107 C14H19O5P 0.001421 298.09 2.6 ATB phosphate 298.27 13.42 2232 S108 2-furaldehyde C6H7O6P 5.90E-05 205.99 205.08 12.89 84863 0.4 FUR 354.27; 14.42; 14.42; 354.22; 10.38; 10.38; S123 5-methoxysterigmatocystin C19H14O7 0.00026 354.07 3.3 ATF 354.30; 19.07; 19.07; 354.27 16.63 16.63 167

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354.27; 14.42; 14.42; 354.22; 10.38; 10.38; S125 Trans-5-O-caffeoyl-D-quinate C16H18O9 0.00128 354.09 0.2 ATO 354.30; 19.07; 19.07; 354.27 16.63 16.63 354.27; 14.42; 14.42; 354.22; 10.38; 10.38; S126 Biflorin C16H18O9 0.00128 354.09 -0.7 ATO 354.30; 19.07; 19.07; 354.27 16.63 16.63 354.27; 14.42; 14.42; 354.22; 10.38; 10.38; S127 Scopolin C16H18O9 0.00128 354.09 -1.1 ATF 354.30; 19.07; 19.07; 354.27 16.63 16.63 4-[(1,3-dicarboxy-propylamino)-methyl]-3- S128 hydroxy-2-methyl-5-phosphonooxymethyl- C13H20N2O9P+ 0.000339 379.09 379.28 16.27 8754 -3.8 IME pyridinium (2R,4S)-1-[(4R)-3,4- dihydroxytetrahydrofuran-2-yl]-5- 367.24; 13.76; 5859; S131 [(methylamino)methyl]-1,2,3,4- C11H18N3O9P 0.000919 367.07 -6.3 FUR 367.28 14.42 2189 tetrahydropyrimidine-2,4-diol-5'- monophosphate 2-(6-chloro-3-{[2,2-difluoro-2-(2-pyridinyl) ethyl] amino}-2-oxo-1(2H)-pyrazinyl)-N-[(2- S132 C21H23F3N6O2+2 0.0015296 448.18 448.43 18.93 27649 1.2 HAL fluoro-3-methyl-6-pyridinyl) methyl] acetamide 280.16; 17.66; 6696; 280.17; 12.89; 8257; S140 4,4'-diaminostilbene dihydrochloride C14H14Cl2N2 0.00036 280.05 280.23; 14.42; 2698; 1.6 INH 280.95; 29.62; 1282; 280.95 29.42 1252 464.37; 17.66; 216707; 464.43; 14.46; 39970; S146 11-O-demethylpradinone I C24H16O10 0.00216 464.07 2.7 ATB 464.30; 13.89; 38931; 464.98 28.88 2206 280.16; 17.66; 6696; S152 Adenosine 5'-carboxamide C10H12N6O4 8.00E-05 280.09 -1.5 NUC 280.17; 12.89; 8257;

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280.23; 14.42; 2698; 280.95; 29.62; 1282; 280.95 29.42 1252 414.27; 17.66; 171572; [5-fluoro-2-({[(4,5,7-trifluoro-1,3- 414.32; 14.42; 31188; S155 benzothiazol-2-yl) methyl] amino} carbonyl) C17H10F4N2O4S 0.0009128 414.02 3.2 HAL 414.32; 13.92; 9110; phenoxy] acetic acid 414.36 13.18 45115 2-(2,4-dichloro-phenoxy)-N-(2-mercapto- S158 C10H11Cl2NO2S 0.00084 278.98 ND ND ND 2.8 INH ethyl)-acetamide 227.12; 13.92; 7129; S166 2,4,6-trinitrotoluene C7H5N3O6 0.0011 227.01 227.16; 11.25; 3272; 1.6 DIV 227.12 10.15 1839 348.26; 14.39; 26644; 2-[O-phosphonopyridoxyl]-amino-pentanoic S194 C13H21N2O7P 0.001199 348.10 348.99; 10.55; 4644; -2.6 LIP acid 348.28 17.46 961 404.37; 14.19; 13950; 404.41; 19.10; 3545; S196 Mallotophenone C21H24O8 0.00046 404.14 404.19; 11.95; 3425; 3.6 INH 404.27; 10.88; 2586; 404.28 17.19 2147 N-(2,3,4,5,6-pentafluoro-benzyl)-4-sulfamoyl- S212 C14H9F5N2O3S 0.000124 380.02 380.28 16.24 2234 1.8 HAL benzamide 2'-O-3-aminopropyl cytidine-5'- S213 C12H21N4O8P 0.000901 380.10 380.28 16.24 2234 -5.7 NUC monophosphate 392.29; 31853; 17.66; 392.37; 28633; 14.32; S223 Macarpine C22H18NO6+ 0.00142 392.11 392.31; 8924; 4.4 INH 13.72; 392.11; 3326; 13.05; 9.88 392.30 2539 S250 Euxanthone;1,7-dihydroxyxanthone C13H8O4 0.00022 228.04 228.19 10.15 1987 2.8 PIG

3-[(4-amino-2-methylpyrimidin-5-yl) methyl]- 424.36; 14.36; 6525; S251 5-(2-{[hydroxy(phosphonoamino) phosphoryl] C12H20N5O6P2S+ 0.000378 424.06 424.32; 10.08; 4742; -1.4 ATO oxy} ethyl)-4-methyl-1,3-thiazol-3-ium 424.25 12.55 3441

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381.29; 17.19; 241921; 381.00; 22.37; 3073; S264 Quercetin 3-sulfate C15H10O10S 0.0011 381.99 381.00; 21.94; 2906; 1.6 ATO 381.00; 21.54; 2669; 381.00 21.10 2536 352.25; 12.85; 21465; 352.32; 14.06; 19516; 352.27; 11.68; 4880; 352.97; 29.82; 2634; 352.97; 29.38; 2621; 352.97; 28.61; 2499; 4-(4-deoxy-beta-D-gluc-4-enuronosyl)-D- 352.97; 22.44; 2406; S291 C12H16O12 0.00176 352.06 -3.6 SAC galacturonate 352.97; 24.17; 2457; 352.97; 23.47; 2421; 352.98; 23.74; 2277; 352.97; 23.24; 2345; 352.97; 22.97; 2315; 352.30; 10.42; 3909; 352.97 30.02 2466 [(4Z)-2-(aminomethyl)-4-(4- S316 hydroxybenzylidene)-5-oxo-4,5-dihydro-1H- C13H13N3O4 2.00E-05 275.09 275.25 14.42 4219 -2.4 IME imidazol-1-yl] acetic acid 5-Bromo-2-(4-fluorophenyl)-3- S332 C17H12BrFO2S2 0.0016168 409.94 409.25 13.62 9302 5.3 ATI [4(methylsulfonyl)phenyl] -thiophene 414.27; 17.66; 171572; 414.32; 14.42; 31188; S338 Asperuloside tetraacetates C18H22O11 0.00068 414.11 -2.4 SAC 414.32; 13.92; 9110; 414.36 13.18 45115 478.35; 13.76; 11240; 478.39; 17.63; 10009; S348 Fucofuroeckol B C24H14O11 0.00136 478.05 478.35; 14.05; 8278; 3.8 FUR 478.42; 14.39; 2820; 478.99; 29.98 2368 303.15; 12.89; 22236; S365 Dichlorphenamide C6H6Cl2N2O4S2 0.00116 303.91 0.3 ATB 303.17 8.41 2243

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5'-deoxy-5'-[n-methyl-n-(2-aminooxyethyl) 339.21; 11.85; 15477; S402 C13H21N7O4 0.00034 339.16 -1.8 NUC amino] adenosine 339.34 19.90 10891 412.30; 11.89; 122858; 5-(o-methylaceto)-2-thio-2-deoxy-uridine-5'- 412.34; 12.68; 10161; S435 C12H17N2O10PS 0.000459 412.03 -3.6 NUC monophosphate 412.34; 13.15; 7742; 412.31 17.42 1814

S451 2-methyl-1-hydroxypropyl-ThPP C16H27N4O8P2S+ 0.000102 497.10 497.42 14.42 2338 -0.7 INH

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6.4.3 H. floresii secondary metabolites

The structural and functional diversity of the ‘41’ compounds which correspond to the CMN cluster were further analysed by HCA method to set a structural typology and then, look for to a correspondence with their reported bioactivities, if any. A dendrogram tree was therefore computed by using the Ward clustering method, and tree was cut at a definite height to yield a reasonable number (here 8) of clusters for further analysis. The dendrogram (see Figure. 3) displays the distances (Ward method) calculated from the metabolite’s structural descriptors only. Cutting the tree at a distance of 1.0 yielded ‘8’ clusters numbered hereafter as CI to CVIII roman numbers. The leaf labels which correspond to the ‘26’ compounds for which a defence activity has been reported and are consequently proposed as surface secondary metabolites, are green labelled.

Fig. 3 Dendrogram resulting from the hierarchical cluster analysis (HCA) of ’41’ metabolites ordered into ‘8’ clusters (I -VIII) at a Ward distance of 1.0. The ‘26’ compounds for which a bioactivity has been reported are green labelled.

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To better characterize the chemical similarities within each of the eight clusters, molecular structures are displayed in Figure 4 ordered according to their clustering in Figure 3. Distances were calculated from the presence/absence of chemical patterns within the whole molecular structure according to the SMILES encoding.

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Fig. 4 Clustering of the H. floresii ‘41’ common metabolites (CMN) according to their chemical structures (retrieved from Data Warrior) calculated from their SMILES strings. Numbers correspond to Table 1 ordering.

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In cluster I eleven polyaromatic compounds are ordered into four families. Metabolites S49 and S50 correspond to the same monophosphorylated riboflavin (vitamin B2), protonated and deprotonated respectively. Polyphenols S196 is a dimeric phloroglucinol mallotophenone derivative, S348 fucofuroeckol B, and S264 a quercetin 3-sulfate. Polycyclic aromatic compounds containing naphtoquinone backbone is represented by S146 with a tetracyclic hydrocarbon structure, whereas S123 and S250 are pigments derivatives of xanthone monomer core, and S223 is macarpine a polycyclic aromatic alkaloid. Finally, S155 is a fluorinated compound characterized by an aromatic heterocycle benzothiazole, and S212 is a N-Benzyl- 4-Sulfamoyl-Benzamide. In cluster II we found nucleobase derivatives bearing a phosphorylated furan ring linked to uracil, cytosine or thiouracil (S131, S213, and S435, respectively). Whereas cluster III is a heterogeneous group with two compounds containing adenine backbone (S152 and S402), S22 is a hydroxamic acid derivative, and we also found five poly-substituted glucopyranoside compounds: Scopolin S127, Biflorin S126, Pancratistatin S106, Asperuloside tetra-acetate S338, and galacturonate derivative S291. The last molecule is a 2-fluoro-2-deoxyglucuronic acid S07. In cluster IV five halogenated polyaromatic molecules are grouped (S132, S140, S20, S332, and S44). Cluster V contains two molecules with a phosphono-propyl (S72) and a phenylmethyl ester of phosphoric acid (S107). A structurally heterogeneous group with five compounds that seem to be based on a chemical motif close to a hydroxy- phenyl is found in cluster VI. Cluster VII is constituted of trinitrotoluene (S166) and Dichlorphenamide (S365). Finally, S251 and S451 from cluster VIII are very similar di-phosphorylated structures with amino- pyrimidine linked to substituted thiazole ring and two substituted 3-hydroxy-2-methylpyridine S128 and S194.

As a whole, among the ‘41’ metabolites identified, 15 of them were sorted as intermediary metabolites (IME). Among them, S49 (NUC) and S50 (IME) no dissimilarity in their structures were evident as the Euclidean distance between them is ‘0’. The IMEs, S152 (NUC) and S402 (NUC), S338 (SAC), S22 (SAC), and S291 (SAC), were grouped into Cluster III at a Euclidean distance of 1.0 to 0.5. The latter distance was then further branched out into two groups. At 0.5, S152 and S402 were closely

similar by having ‘purine’ rings with ‘NH2’ groups. The predicted metabolites with antibiotic activity (ATB) such as S146, S07, S107 and S365, were found to be linked to biochemically distinct classes S348 (FUR), S291 (SAC), S72 (INH) and S166 (DIV) at a Euclidean distance below 0.5.

Of the four antioxidant (ATO) metabolites, identified as S264, S126, S125, and S251, predicted in this work, all fell into four different clusters: I, III, VI, and VII, respectively. Moreover, they were closely distanced with different classes of metabolites, such as S264 to S146 (ATB), S126 to S106 (ATC), S251 to S451 (INH) at a Euclidean distance lower than 0.5 and S125 to S316 (IME) higher than 0.5. The INH metabolites, S196 and S223, were observed to be in the same cluster (I) and also at the same distance,

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whereas S140, S72, S158 and S451 fall under different clusters: IV, V, VI and VII, respectively. This shows that there is no significant correlation between the structural properties of the compounds and their reported bioactivities.

6.4.4 Hydrophobicity of the secondary metabolites

As it is well assessed that the lipophilicity of “defence biomolecules” is critical in their mechanism of action, attempts were made to evaluate a possible (Steinberg and de Nys 2002; Harder et al. 2004), or expected, correlation between the compound’s chemical structures and this physico-chemical parameter. Accordingly, a tanglegram was computed which allows comparing two dendrograms calculated from distinct parameters with the same individuals. Here the analysis was restricted to the 26 secondary metabolites identified. Fig. 5 displays the structural dendrograms of chemical (left) and lipophilicity (right) of these metabolites. The tanglegram R algorithm optimize the trees to get the better possible alignment; individuals are next linked by a central line. The match between the two trees is higher as the line gets horizontal. The entanglement function allows to calculate the quality of alignment; values range from 0 to unity, the lower value characterizing a good alignment. Here the entanglement value was of 0.535 that is intermediary.

Fig. 5 Entanglement of chemical (left) and lipophilicity (right) dendrograms. The level of entanglement is 0.535 (intermediate between 0 and 1).

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As a whole, this indicates that there is no straightforward correlation between the molecular structure and its lipophilicity. This point will be further discussed. Similarly, the lipophilic pattern of these secondary metabolites was plotted (see Fig.6) as a function of bioactivity to unveil possible tendencies.

Fig. 6 Lipophilicity of the H. floresii secondary metabolites as a function of the compound’s bioactivity. Octanol-water partition coefficients are calculated from the XLOGP3 method.

Again, it is observed that there is no correlation between lipophilicity and a particular class of activity. All these findings evidence that H. floresii secondary metabolites exhibit a large diversity in the structural, physico-chemical and functional domains.

6.5 Discussion

Metabolomics, a rapidly evolving discipline, applies advanced separation and detection methods to capture the collection of small molecules that characterize metabolic pathways (Kouskoumvekaki and Panagiotou 2010). It can bridge the information gap between the identification of metabolite profile dynamics and their closest phenotypic responses (Kaddurah-Daouk 2006; Holmes et al. 2006; Morvan and Demidem 2007; Kouskoumvekaki and Panagiotou 2010). In order to contribute to the knowledge of secondary metabolites of H. floresii, we performed an untargeted metabolomic approach to putatively identify and propose active metabolites from this species, as a preliminary step towards its metabolomic comprehension. As it is a very first kind of work on H. floresii metabolites with little a priori knowledge,

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the putative identification and peak prediction are completely based on the metabolite database, MMCD. MMCD mainly list the molecular weight of compounds represented in the literature (Tohge and Fernie 2009) and as we made our predictions only on this basis, we regard them as ‘putatives’. As any untargeted study, MMCD did not allow us to identify the individual peaks (more than one experimental m/z features and RT, Table 1), but it greatly assisted to predict these putatives even at very low intensity. Thus, the challenge of low concentration of the surface extracts was efficiently managed.

The first informatic task was to identify the multiple peaks resulting from the chromatographic separation afforded by LC-MS (Tohge and Fernie 2009) and for which we used two different extracts. The untargeted metabolic protocol for H. floresii is described in Scheme I. Initially we used n-hexane, a non- polar lipophilic solvent, to extract the surface-associated (DIP extract) and whole-cell metabolites (WCM extract). Interestingly, n-hexane was observed to extract both the hydrophilic and lipophilic compounds in accordance with Paix et al. (2020) (Paix et al. 2020). These authors also observed that a polar solvent, methanol, was useful to extract the lipophilic compounds rather than hydrophilic ones. Anyway, only 10% of the total classes in the surface-extracts (DIP) of H. floresii were reported. Indeed, surface molecules, when identified as bioactive molecules may play a significant role in the defence mechanisms of H. floresii, since some of these microorganisms first damage multicellular organisms through primary surface adhesion.

With this approach, a fingerprint of ‘41’ H. floresii putatives was proposed from the comparison of DIP and WCM extracts. Confidence in the bioactive putatives identified relies on a robust experimental protocol based on duplicate experiments performed at various extraction durations (10s – 60s). Although to analyse H. floresii crude metabolic extracts we used LC-MS as the sole analytical tool, the increased number of samples (‘26’ samples) resulted in a large data set to process and analyse.

A challenging issue for marine organisms metabolomic analysis is the high number of compounds with unknown structures (Goulitquer et al. 2012). By means of HCA, ‘41’ putatives of H. floresii were clustered into ‘8’ clusters (I-VIII) based on their corresponding structural descriptors i.e. structural (dis)similarity (Figure 4). H. floresii untargeted metabolomic approach revealed a small number of putatives that were structurally similar to different bioactive compounds proposed by the mass bank. The ‘chemical similarity principle’ states that molecules with similar structures may likely exhibit similar biological properties (Kouskoumvekaki and Panagiotou 2010). Based on the metabolite-likeness and biological relevance filters, chemical compounds from virtual screens of large pharmaceutical libraries that are similar to endogenous metabolites stand more chances for being successful drug candidates (Gupta and Aires-de- Sousa 2007; Kong et al. 2009; Kouskoumvekaki and Panagiotou 2010). Thus, H. floresii may become a potential candidate with several bioactive compounds which may be inducible or controlled culture

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conditions without any environmental detriments. As the putatives predicted in this study were based on MS1 future studies should be done to confirm them by MS-MS fragmentation (MS2) using any authentic standards available.

The H. floresii putatives predicted in this study essentially fell under different biochemical classes with characteristic bioactivity based on their chemical structures. Up to ‘41’ putatives are present in the H. floresii metabolome, which was categorized as ATB (AnTiBiotics), and most of them (‘38’) were present in the WCM extract only (Figure 1). Besides this large potential, the macroalgae also exhibit a large diversity of biochemical pathways as deduced from the variety of antibiotics chemical classes. To cite a few, quinones groups (kinobscurinone, romycin, bikaverin, quinquangulin), flavonoids group (isosakuranetin, sakuranetin), benzofuran derivatives (moracin A), coumarin derivatives (chlorobiocin), aminoglycosides (spectinomycin, cyclomycin, manumycin, coformycin), nitrogen heterocycles porphyrines (formylphenyl) or derivatives of tetracyclines. It is also noteworthy that some of the antibacterial molecules have yet been identified in plants like brazilin, phaseolic acid (a plant hormone), adonitoxin, malvin or geniposidic acid. Moreover, they often present a bunch of biological activity as concomitant antibacterial, antifungal (quinquangulin, manumycin) and antiviral (formycin) antioxidant (brazilin), antitumoral (manumycin) effects (French et al. 2018). One can hypothesize that these potentially concomitant effects may be stimulated or activated depending on the stress (culture conditions or competitors) to which the alga is exposed. This multi-dimensional diversity, both structural, and biological should promote further investigations. Indeed, the putatives of H. floresii identified in this study can now be considered as a baseline knowledge for future targeted metabolomic study. This predicted fingerprint can also be used to compare the spatial and temporal differences in the metabolite distribution of H. floresii from different habitats. Rather, these putatives or their derivatives were also observed by various authors in different macroalgal or plant species with vast bioactivity as discussed below.

In cluster I, Fucofuroeckol B (predicted for H. floresii as metabolite S348) isolated from Eisenia bicylis, an edible perennial brown alga, has been shown as a potent anti-inflammatory agent (Lee et al. 2016). Phlorofucofuroeckol-A derived from the brown alga, Ecklonia kurome showed antimicrobial activity by inhibiting Campylobacter jejuni, Escherichia coli, Salmonella enteritidis, Salmonella typhimurium, Vibrio parahaemolyticus (Nagayama et al. 2002; Eom et al. 2012). This compound was clustered together with S146, 11-O-demethylpradinone-I, a member of the Pradinone I antibiotic group with a tetracyclic hydrocarbon structure, a basic structure of anthracyclines as a responsible class of compound (Lee et al. 2016). In the red alga, Halopithys incurva and in the brown algae Fucus spiralis and Treptacantha abies-marina, polyphenols (predicted for H. floresii as S196) and pigments (predicted for H. floresii as S123 and S250) respectively, were reported to exhibit high antioxidant activity by preventing

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oxidative stress under IMTA (Vega et al. 2020). Gracilaria gracilis bio-oil samples were composed of the same compounds classes identified in H. floresii, such as alcohols, ketones, aldehydes fatty acids, esters, aromatics, amino acids, nitrogen-containing heterocyclic compounds and chlorinated/fluorinated compounds (Parsa et al. 2018). Although rarely detected, fluorinated compounds (S155 and S212) shall not be excluded from the macroalgal compounds.

The presence of furans in cluster II suggests interesting applications as a biofuel additive or as a base chemical from which a large variety of bio-based compounds can be produced (Lange et al. 2012; Van Putten et al. 2013). This has been reported for some of the wide diversity of carbohydrates found in macroalgae and their corresponding degradation resulting in furans, such as furfural (degradation of xylose) from P. palmata, laminarin (conversion of glucose) from brown algae, hydroxymethylfurfural (conversion of glucose) and 5-methyl furfural (conversion of rhamnose) from the green alga Ulva spp. (Amin and Prabandano 2017).

In Cluster III, we could able to group, Scopolin (S127), Biflorin (S126), Pancratistatin (S106), Asperuloside tetra-acetate (S338), and galacturonate derivative (S291). Asperuloside tetra-acetate, a terpenoid, found in a terrestrial plant Morinda officinalis (IARC Working Group on the Evaluation of Carcinogenic Risk to Humans. 2002), was clustered together with the galacturonate derivative (S291). Terpenes and polysaccharides are well-known for their bioactivity in macroalgae which essentially includes to combat bacterial invasion (Pérez et al. 2016). Terpenes in algae also act as allelochemical deterrents to herbivores, inhibitors to bacterial biofilm formation, and also act as antioxidants against UV damage (Potin et al. 2002; Shannon and Abu-Ghannam 2016; Dahms and Dobretsov 2017). Recently polysaccharides of the red macroalgae were widely studied for antiviral activity (Bouhlal et al. 2010; Jiao et al. 2011; Gomaa and Elshoubaky 2015; Shi et al. 2017; Peñuela et al. 2018) moreover the depolymerization of the algal polysaccharides also induce protection against fungal and bacterial infections (Vera et al. 2011; Pérez et al. 2016). Pancratistatin (PST) is a natural compound that was isolated from a terrestrial plant Pancratium littorale and shown to exhibit antineoplastic activity (McLachlan et al. 2005)

In cluster V, two different compounds, such as Ibandronate (S72) and Cyclohexylmethyl 2- formylphenyl hydrogen phosphate (S107) were grouped together where they were shown to have anti- resorptive/anti-osteoclastogenesis (Oh et al. 2017) and antibiotic activity (Deprez et al. 2002), respectively, but their role in macroalgae or marine environment were not known. In cluster VI, we observed 2- furaldehyde (S108) linked to Trans-5-o-caffeoyl-d-quinate (S125) and further with [(4Z)-2-(aminomethyl)- 4-(4-hydroxybenzylidene)-5-oxo-4,5-dihydro-1h-imidazol-1-yl] acetic acid (S316). A derivative of S108, 5-hydroxymethyl-2-furaldehyde was previously derived from a marine bacterium Bacillus subtilis isolated from the Gulf of Mannar (India), which inhibits the biofilm formation and virulence of Candida albicans

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(Subramenium et al. 2018) and it was also identified as a contaminant of the aquatic environment resulting from different industrial processes (Cary R, Dobson S 2000). Thus, interestingly this compound shall be extracted from the associated microbiota. However, it shall be ruled out as our solvent of extraction n- hexane was inefficient to lyse the bacterial membrane and extract its metabolites (Kientz et al. 2011). Thus, there is another possibility of the xenobiotic behaviour where the compound is not naturally present in the algae, but being absorbed as a result of contamination.

In cluster VII we observed trinitrotoluene (S166), can be referred to as TNT, an explosive, often present in seawater as a pollutant; Cruz-Uribe et al. (2007) (Cruz-Uribe et al. 2007) found that the red algae, Porphyra yezoensis and Portieria hornemannii, possessed a metabolic route to remove this compound from the seawater and in such process trace amounts of TNT were found within the biomass. Thus, this cluster of metabolites give evidence for the ability of the alga to resist xenobiotics, which shall be considered as a most important ecological activity as contaminants are posing a serious problem in the marine environment. Dichlorphenamide (S365), widely known as a diclofenamide (an ophthalmology drug as per the PubChem),

is a carbonic anhydrase inhibitor. Carbonic anhydrase (CA) is an enzyme for CO2 uptake by a

- photosynthetic organism under external high concentration of HCO3 . CA activity is tightly regulated by

- CO2 availability to reverse CO2/HCO3 and during intense photosynthesis, the CO2 equilibrium is decreased at the algal surface and the red algae have adopted a mechanism to inhibit the CA activity by producing inhibitors (Beer and Bjork 1994; Larsson and Axelsson 1999; Moulin et al. 2011).

Cluster VIII mainly grouped nucleotides derivatives like di-phosphorylated structures with amino- pyrimidine and substituted hydroxymethylpyridine (S124 and S198). Macroalgal originated nucleosides and nucleotides are involved in several key biological processes (Huang et al. 2014) and in red alga Hypnea valentiae has furnished 5-iodo-5-deoxytubercidine, which displayed prominent muscle relaxant property and hypothermia in mice (Kazlauskas et al. 1983; De Koning et al. 2005; Huang et al. 2014). Different pyroglutamic and deoxyguanosine derivatives coupled with bromophenols were obtained in the red alga, Rhodomela confervoides (Zhao et al. 2005).

Five halogenated metabolites identified in our extracts essentially falls into two different clusters. S155 and S212 are observed to be structurally similar with two benzene rings, an amide function, and five fluorines, and they fall into Cluster I whereas S20 and S44 were structurally similar with two phenyls, bromine, and they further linked to S132, instead of ‘Br’, which has ‘Cl’ and fall into Cluster IV. Halogenation often provides these compounds with interesting key features and marine algae hold diverse and unique biosynthetic pathways for the production of halogenated metabolites (Cabrita et al. 2010). Brominated compounds are widely observed in red algae such as Bonnemaisonia asparagoides, Delisea pulchra, and Asparagopsis taxiformis and have always contributed to several bioactivities. Four different

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polyhalogenated compounds, with bromine and chlorine isotopic patterns, were observed in the surface extracts of B. asparagoides (Nylund et al. 2008). Mahorone and 5-bromomahorone were identified in DCM: MeOH extract of A. taxiformis and they exhibited mild antibacterial activity against Acinetobacter baumannii, a human pathogen (Greff et al. 2014).

Among the compounds identified at the algal surface, halogenated ones, furanones, and inhibitors are overrepresented. Interestingly, the first two classes (HAL and FUR) have been identified as potent quorum-sensing-interfering (QSI) compounds (Gonzalez and Keshavan 2006; Huigens 2018) since they mimic the chemical structures of bacterial auto Inducers. Indeed, quorum-sensing allows bacterial biofilms, the sessile “way of life” of bacteria, to regulate gene expression and toxin production. By interfering with the communication within the bacterial populations, the QSI compounds provide to eukaryotes potent antibacterial defences. If one reminds us that biofilms are mainly responsible for fouling on marine organisms, H. floresii appears as a potent source for such compounds. Since the drugability of secondary metabolites drove this study, it is worth noting that bacterial multi-resistance became a global public health challenge. Due to gene transfers among species and strong efflux pumps, “conventional” antibiotics become less and less efficient. This promoted the search for alternative pathways and, in such a context, “messing” with bacterial communication with macroalgal-derived QSI compounds arose as a very promising alternative.

Relatively higher occurrences of bioactive molecular classes found in the WCM and DIP extracts strongly supports the hypothesis that they must be involved in the organism protection against herbivores or epiphytes, as the initial damage to the host begins at their surface (adhesion). Investigations suggest that the primary function of allelopathic (secondary) metabolites of macroalgae is to deter herbivory, however, they may have multiple functions (Renaud et al. 1990; Paul and Ritson-Williams 2008; Fong et al. 2019). Particularly in red algae, these biologically active compounds range in structure from simple aliphatic halo- ketones and brominated phenols to more complex monoterpenes, sesquiterpenes, and diterpenes exhibiting that the strong biological activity has always been linked to the several (closely) related compounds (Hay 1988; Renaud et al. 1990). For example, elatol, a sesquiterpene, one of the well-known major allelopathic metabolites of Laurencia plays important role in ecological interactions, such as antiherbivore activity and potential defence against infection by microorganism (König and Wright 1997; Juagdan et al. 1997; Vairappan 2003; Lhullier et al. 2009; Dos Santos et al. 2010). Previously we also identified that different groups of allelopathic secondary metabolites such as brominated compounds, Volatile Halogenated Organic Compounds (VHOCs) like diterpenes and flavonoids might contribute to the chemical defence on the surface of the H. floresii. And their activity was mainly attributed to the corresponding interference with the surface-associated bacterial communication (A Abdul Malik et al. 2020). Likewise, the large number

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of potentially bioactive molecules of H. floresii shall obviously be related to several ecological functions that require more study on the ‘Systems Biology’ of the predictive putatives.

6.6 Conclusion

H. floresii research is still at its infancy and with very limited knowledge of its metabolomics. With this study, we propose a metabolomic fingerprint for H. floresii as a preliminary step in its metabolomic study. This untargeted study should be further extended including multiple analytical tools and/or carried to a targeted metabolomic study for exploring its bioactive or drug-like compounds, such as promising QSI for counteracting global bacterial multi-resistance. Additionally, the same approach shall be widened to include H. floresii from different habitats, to understand how the environmental (including seasonal) factors influence the stimulation of these secondary metabolites’ biosynthetic pathways.

In our previous study, allelopathic secondary metabolites on the surface of the H. floresii were found to contribute to the macroalgal chemical defence (A Abdul Malik et al. 2020). The metabolites presented in this study were determined for their biological activities in relation to their potential valorization (Table 2) and those bioactivities shall be treated as a primary line of defence of H. floresii as they were observed in the surface extracts of the alga. Irrespective of any predetermined bioactivity the predicted putatives may have the ability to protect the host either by destroying or interfering with certain biotic interactions (for instance QS communication) based on their structure-activity relationship.

A large set of bioactive putatives from H. floresii were screened in this study. The next step is to use targeted analysis for the corresponding metabolic pathways by using the KEGG pathway database (Kanehisa et al. 2019). This in silico approach could benefit from experimental design to enhance the production of a particular QSI under culture conditions. This could benefit from existing standard tests, which allow identifying compounds exhibiting QSI activities (McLean et al. 2004; Rasmussen et al. 2005). Additionally, the presence of brominated compounds, which are well-known for their defensive activity against bacteria and antimethanogenic properties, in H. floresii could make them good ingredient candidates for cattle feeding (Øverland et al. 2019) since they fundamentally eliminates the enteric methane resulting from a high grain diet providing evidence of improved livestock production performance (Nys et al. 2016). H. floresii will be an excellent model in a multi-trophic culture environment as algal secondary metabolites with antimicrobial activity, the interfering activity of macroalgae on quorum sensing of any opportunist pathogens may help reduce the emergence of resistant strains in aquaculture systems.

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6.7 Acknowledgements

The authors thank L. Taupin for her skillful technical assistance for chromatographic analyses. IBT. V. Avila for culturing the Halymenia floresii material used in this experiment. Financial support from ECOS- Nord CONACYT for the collaboration project M14A03 and PN-CONACYT 2015-01-118 are also acknowledged.

6.8 References

The references of this article are available at the end of the manuscript (‘References’, page no. 206)

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7. General Discussion

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7. General Discussion

7.1 Origin of the Thesis

During the experimental cultivation of Halymenia floresii under Integrated MultiTrophic Aquaculture, the establishment of opportunist green algae and the colonisation of sessile invertebrates, which usually disturb this type of cultivation, were not observed. The culture tanks were clean and the surface of the H. floresii was remarkably free from any fouling organisms. This phenomenon could reveal that the presence of H. floresii may prevent the biofilm formation either by releasing allelopathic active compounds that ultimately interfere with the settlement and growth of competitors or its epibacterial community may act as a protective layer, thus preventing any fouling on the host. To assess these hypotheses, we proceeded to investigate the chemical and biological defence mechanisms of H. floresii by means of active secondary metabolites and the associated microbiota, through allelopathy and ‘protective coat’ on the surface of the algae.

The chemical defence of H. floresii was studied differentially by evaluating the surface and whole- cell metabolites, which allowed us to identify the bioactive metabolites on the surface. These were followed by an untargeted metabolomic study that helped us to identify and propose a global metabolomic profile of H. floresii. In this section, the biological defence is emphasized on the microbial defence on the surface of the algae (as detailed in Chapter I). This was analysed by its epibacterial community and its communication. Subsequently, the protective/non-pathogenic and pathogenic bacteria were identified by their ability to induce the risk of bleaching, an algal disease. The analysis of the epibacterial community and the identification of significant pathogens led us to consider the forthcoming disease problems in Aquaculture of algae.

7.2 Holobiont: Halymenia floresii and the associated bacterial community

Throughout their evolutionary history, multicellular organisms have been engaged in symbiotic relationships with microorganisms (Moran 2006). In particular, an assemblage of different organisms forming an ecological functional unit, Holobiont, often defined as the host organisms with all of its microbial symbionts, Microbiont. Through several recent studies, it has become increasingly clear that the microbiota are essential for host functioning although the current knowledge on the functional consequences of the macroalgal holobiont is limited (van der Loos et al. 2019). In this work, we began to explore H. floresii and its associated epibacterial community from different environmental conditions. However, we dealt only with the cultivable strains of H. floresii, a small fraction of all bacteria that were found associated on the surface of the algae, and thus they could be treated as a representative sample of the H. floresii whole microbiome. This is because only 5% of the marine bacterial strains are cultivable and

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in order to perform any assays there is no alternative technique to date to separate the selected microbial components from natural microbial communities (Haglund et al. 2002; Saha and Weinberger 2019).

7.3 The partner: associated bacteria

7.3.1 Characterisation of bacterial community

Thirty-one axenic bacterial strains were isolated from the surfaces of H. floresii, and they belonged to ‘4’ phyla, ‘20’ genera, and ‘25’ species. The main groups of bacterial phyla associated with H. floresii included Gammaproteobacteria (47%) Alphaproteobacteria (37%), Bacteroidetes (13%), Firmicutes (3%) (Fig. 1). Del Olmo et al (2018) observed that Gammaproteobacteria was the dominant phyla in accordance with our results observations. After a review of numerous studies, Hollants et al. (2013) conclude that macroalgal-bacteria interactions primarily consist of Gammaproteobacteria (37%) followed by Bacteroidetes (20%), Alphaproteobacteria (13%), and Firmicutes (10%). We found the same pattern for H. floresii except that Alphaproteobacteria (37%) was followed by Bacteroidetes (13%). Several factors, biological, physical and chemical are likely to influence the colonisation of bacteria and their qualitative and quantitative structuring, and to whether or not to maintain a stable association between algae and their biofilms. Many of the algal-associated microbiome profiles studied so far were structured by variation of

+ several factors such as temperature and nutrients (i.e. NH 4 ) (Florez et al. 2019; Califano et al. 2020); different geographical and seasonal gradients (Bengtsson et al. 2010; Burke et al. 2011; Michelou et al. 2013; Campbell et al. 2015); their physiological and morphological traits during the host life cycle (Bengtsson et al. 2010; Lemay et al. 2018); physiological conditions of the host under optimal or stressing conditions (Marzinelli et al. 2015, 2018; Califano et al. 2020).

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Bacteroidetes… Firmicutes (3%)

Alphaproteobacteria (37%)

Gammaproteobacteria (47%)

Fig. 29 Different classes of bacteria (%) isolated from the surface of H. floresii

Our results provide an important base knowledge as a first step to illustrate the epibacterial community of H. floresii under different environmental conditions, such as Beach Cast (BC), Cultivar Chamber (CC), and Integrated MultiTrophic Aquaculture. This study is the first report on the epibacterial community of H. floresii, particularly under culture conditions, and to date, nothing is known about the diversity and biological potential of H. floresii epibacteria. The epibacterial communities found in H. floresii from different habitats (BC, IMTA, and CC) were quite dissimilar. This could be related to several factors as discussed earlier but maybe mainly attributed to cultivation conditions. The physiological responses to abiotic factors such as temperature, light, water turbulence, and nutrient availability under different conditions are also believed to play a major role in this dissimilarity. Understanding the temporal dynamics of epiphytic bacteria can help identify the possible modifications in the ‘protective layer’ due to external stress factors (Mancuso et al. 2016). In the case of H. floresii, the controlled and uncontrolled culture conditions in CC and IMTA, respectively, might significantly contribute to the stress, whereas in BC, it was mainly caused by the overexposure to increase sunlight (i.e. UV radiation or desiccation). Nevertheless, we may not disregard differences attributed to specimens collected or cultivated (individuals, part of the plant, unnoticed bleached fragments, etc.). The occurrence and richness of epiphytic coverage have been observed to be specific and different in relation to different parts of the host macroalgae leading to a spatial dynamic of the microbiome (Kersen

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et al. 2011). Parrot et al. (2019) used scanning electron microscopy to study the biofilm density on the surface of the brown alga Fucus vesiculosus and showed a lower biofilm thickness at the uppermost tip than at the older thallus regions. We also analysed different parts of the thallus of H. floresii cultivated in controlled conditions (CC), such as apical fronds, mid-thallus and stalk, to understand microbial distribution on the macroalgal surface under the scanning electron microscope. We observed increased microbial load at the mid-thallus than apical fronds showing a spatial variation of the microbiome on the thallus surface. This variation may be due to the overall controlled culture condition of H. floresii. Thus, the spatial and temporal dynamics of macroalgal-associated bacteria shall both be determined by the stressors.

Fig. 30 Scanning electron micrographs of the surface of H. floresii; a – apical fronds; b – mid-thallus; c – stalk.

7.3.2 Influence of the epibacterial community

The surface-associated microbiome of macroalgae plays a crucial role in the growth, morphogenesis, and defence of the macroalgae under favourable environmental conditions (Symbiotism). However, under unfavourable conditions, the microbiome reverses its symbiotic nature affecting the growth and causing diseases leading to macroalgae degradation (Pathogenism) (A Abdul Malik et al. 2020a).

As every organism is associated with microbes, it is becoming increasingly obvious that these associations can greatly influence host health by protecting the host from pathogen intrusion and fouling by the production of bioactive compounds (Egan et al. 2000; Ley et al. 2008; Turnbaugh et al. 2009; Shnit- Orland and Kushmaro 2009; Barott and Rohwer 2012; Greff et al. 2017). Boyd et al. (1999) found that sixty of the ‘280’ strains isolated from different marine macroalgae exhibited antibiotic activities against a series of fouling bacteria and therefore have the potential to control the microbial population on the seaweed surface either by inhibiting their growth or by influencing the tactic behaviour of potentially competing bacteria. Moreover, it is widely accepted that bacterial epiphytes can inhibit the colonisation of surfaces by common fouling organisms (Egan et al. 2000). It has also been suggested that the production of

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antimicrobial compounds by epibiotic bacteria provides a competitive advantage against potential microbial competitors during algal surface colonisation (Franks et al. 2006; Alvarado et al. 2018). Recently, the associated microbiota is functionally regarded as a ‘second skin’ of the host (Wahl et al. 2012) and thus acts as the first line of defence of macroalgae. Based on our results, H. floresii first line of defence it’s essentially composed of at least ‘31’ epibacterial strains identified in our work. Interestingly they were originated from the specimens collected at different habitats revealing the dynamic differences in their association, though we actively dealt with the cultivable strains.

7.4 The Host - H. floresii

As a preliminary step towards the metabolomic comprehension of H. floresii, the untargeted metabolomic approach helped us to identify and predict a global metabolomic profile for H. floresii. By comparing the surface and whole-cell metabolites, the untargeted metabolomics analysis allowed us to identify ‘41’ bioactive metabolites that were grouped into ‘8’ clusters based on their structural (dis)similarity by Hierarchical Cluster Analysis (HCA). The ‘41’ metabolites were categorized into different classes based on their bioactivity and/or biochemical class (Fig. 2). To better characterize the chemical similarities within each of the eight clusters, molecular structures were also found (Chapter IV, Fig. 5). Distances were calculated from the presence/absence of chemical patterns within the whole molecular structure according to the SMILES encoding.

NUC= 5 PIG = 0 SAC= 3 VIT =0 LIP = 2 ATB = 4 KET = 0 ATC = 1

INH = 6 ATF = 2 ATI =1

ATO =3

ATV = 0 ATP = 0 IME = 5 DIV = 1

FUR = 3 HOR = 0 HAL = 5

Fig. 31 Count of Metabolites (‘41’) grouped based on their Activity/Biochemical classes (ATB – Antibiotics; ATC - Anti-cancerous; ATF – Antifungal; ATI - Anti-inflammatory; ATO - Anti-oxidant;

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ATP – Antiproliferative; ATV – Antiviral; INH – Inhibitors; DIV – Various; FUR – Furans; HAL – Halogenated; HOR – Hormones; IME - Intermediary Metabolites; KET – Ketones; LIP – Lipids; NUC – Nucleotides; PIG – Pigments; SAC - (Poly)-Saccharides; VIT - Vitamins)

Relatively higher occurrence of bioactive molecular classes found in the surface extracts of H. floresii strongly supports the hypothesis that they must be involved in the organism protection against herbivores or epiphytes, as the initial damage to the host begins at their surface (adhesion). Investigations suggest that the primary function of allelopathic (secondary) metabolites of macroalgae is to deter herbivory; however, they may have multiple functions (Renaud et al. 1990; Paul and Ritson-Williams 2008; Fong et al. 2019). Previously we also identified that different groups of allelopathic secondary metabolites such as brominated compounds, Volatile Halogenated Organic Compounds (VHOCs) like diterpenes and flavonoids might contribute to the chemical defence on the surface of H. floresii. Their activity was mainly attributed to the corresponding interference with the surface-associated bacterial communication (A Abdul Malik et al. 2020b).

Of the ‘8’ different clusters (Chapter IV Fig. 5) of H. floresii metabolites, cluster I possesses Fucofuroeckol B, which has been previously identified as an antimicrobial compound (Nagayama et al. 2002; Eom et al. 2012). Interestingly, cluster III mainly includes terpenes and polysaccharides (Asperuloside tetra-acetate (S338), and galacturonate derivative (S291)) that are well-known for their bioactivity in macroalgae particularly in combating bacterial invasion (Pérez et al. 2016). Terpenes in algae also act as allelochemical deterrents to herbivores, inhibitors to bacterial biofilm formation, and also act as antioxidants against UV damage (Potin et al. 2002; Shannon and Abu-Ghannam 2016; Dahms and Dobretsov 2017). Cluster VI includes 2-furaldehyde (S108), where a derivative of it 5-hydroxymethyl-2- furaldehyde was previously derived from a marine bacterium Bacillus subtilis isolated from the Gulf of Mannar (India), which inhibits the biofilm formation and virulence of Candida albicans (Subramenium et al. 2018). Thus, interestingly this compound shall be extracted from the associated microbiota. However, it shall be ruled out as our solvent of extraction n-hexane was inefficient to lyse the bacterial membrane

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and extract its metabolites (Kientz et al. 2011). Thus, there is another possibility of the xenobiotic behaviour where the compound is not naturally present in the algae, but being absorbed as a result of contamination. Supporting this hypothesis, we observed the compound trinitrotoluene (S166), under cluster VII, that can be referred to the explosive known as TNT, often present in seawater as a pollutant (Cruz-Uribe et al. 2007). The same authors found that the red algae, Porphyra yezoensis and Portieria hornemannii, possessed a metabolic route to remove this compound from the seawater and in such process trace amounts of TNT were found within the biomass. Thus, this cluster or metabolites give evidence for the ability of the alga to resist xenobiotics, which shall be considered as a most important ecological activity as contaminants are posing a serious problem in the marine environment. Recently the ability of the green alga Ulva under IMTA system was evaluated for its potential uptake of enrofloxacin, a commonly used antibiotic in aquaculture, and the results showed that the alga could assimilate the antibiotic and reached ~5% of the maximum residue limit (Rosa et al. 2020). IMTA systems are believed to play an important role to solve many of the environmental impacts of aquaculture by reaching a balanced ecosystem-based management approach to aquaculture (Chopin 2013, 2017), since the uptake of xenobiotics can have cumulative effects potentiating the problems associated with exposure to such compounds in the target species. In this sense, the ability of H. floresii to uptake or resist the xenobiotics should be considered for bioremediation in aquaculture system.

7.5 Interactions among bacteria and the Host’s Interference

Biological interactions between different marine surface-associated organisms play a major role in the development and maintenance of a biofouling community (Egan et al. 2000), particularly on the macroalgal surface. Such a dynamic epibiotic microbiome protects the surface by forming a protective coat, biofilm (Armstrong et al. 2001) and this itself acts as a biological defence mechanism of the macroalgal host (A Abdul Malik et al. 2020a). A revolutionary discovery of the quorum sensing (QS) system, a mechanism of cooperative behaviour in bacteria (Fuqua et al., 1994) opened a new avenue in the regulation of macroalgal microbiome (Singh and Reddy 2016) as the vital process of biofilm formation is mainly regulated by QS (Paluch et al. 2020).

Of the ‘31’ epibacterial strains of H. floresii, ‘17’ isolates were identified as QS bacteria by the presence of their extracellular QS signals. As the epibacterial components of H. floresii were isolated from healthy specimens, they can be easily inferred to play a protective role on the algal surface. Bacterial interactions with macroalgae are important at two basic levels. It can be beneficial, acting as symbionts (i.e. symbiotism), and can also be detrimental, acting as pathogens (i.e. pathogenism). Both processes have been shown to involve QS (Joint 2006; A Abdul Malik et al. 2020a). In our work, the release of virulence factors

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is also being closely related to the QS and thus there is a possibility that the putative opportunistic pathogenicity of these strains may also be linked to it.

7.5.1 Analysis of positive relation– symbiotism

The macroalgal-associated microbial communities have evolved through a highly competitive environment due to nutrient and space limitation on their host surface, subsequently producing allelochemicals capable of preventing secondary colonisation (Egan et al. 2001, 2008; Wiese et al. 2009; Tujula et al. 2010; Uzair et al. 2018). Thus, as we hypothesized, the microbial biofilms act as a biological defence by protecting the surface from macrofoulers. The epibacterial community of H. floresii identified in this study was obviously from the healthy surfaces of the algae, rather than those collected from stressful environments (Chapter II). Hence, the epibacteria identified were symbiotically associated with the host, H. floresii. According to the symbiosis theories by Lynn Margulis (Cavalier-Smith 1992), we can consider H. floresii and its epibacterial community as a functional entity that referred to symbiotic associations that last for a significant part of an organism’s lifetime (Cavalier-Smith 1992). Moreover, when we trace back to the four-step sequential process of biofouling, after an initial ‘conditioning’ (step I) of the surfaces by organic compounds, this resulted in the subsequent development of bacterial biofilm (step II) (Davis et al. 1989; Wahl 1989; Parsek and Greenberg 2005). With our study, we shall prove that the bacterial biofilm, though regarded as one of the steps towards biofouling, may also acts as a defence mechanism itself by protecting the host surface by symbiotic association.

7.5.2 Putative opportunistic pathogenicity

The pathogenic shift of the macroalgal associated microbiota can be caused by environmental stresses resulting in a ‘Holobiont break-up’ (Ying et al. 2018). In order to survive under such circumstances, marine macroalgae are able to stimulate, inhibit, or compromise QS signals in bacteria (Saurav et al. 2017). The first QS inhibitory compound, Furanones which mimic the bacterial AHL signals, was isolated from the red macroalga Delisea pulchra (Dahms and Dobretsov 2017; Saurav et al. 2017). Thus by ‘eavesdropping’ the bacterial talk (Joint 2006), the macroalgae produce the antagonistic signals which interfere the bacterial communication i.e. Quorum Sensing Interference and control the pathogenic disease outbreak. QSI may be an effective method for inhibiting the virulence of pathogens, further reducing disease in aquaculture systems, and increasing productivity (Zhao et al. 2015).

Mass mortalities of a diverse range of habitat-forming organisms like seagrasses, corals, and macroalgae have been associated with pathogen-induced disease events (Littler and Littler 1995; Rosenberg and Ben-Haim 2002; Case et al. 2011). Obviously, these pathogens are opportunistic in nature by forming an essential part of a ‘holobiont’ (Case et al. 2011; Saha and Weinberger 2019). With a large diversity of

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microorganisms and the prevalence of putative virulence factors in marine bacteria, it may be argued that a specific disease phenotype in the marine environment can be mediated by different bacterial pathogens (Persson et al. 2009; Zinger et al. 2011; Gennari et al. 2012; Lynch and Neufeld 2015; Kumar et al. 2016). The ecology of opportunistic pathogens in marine systems has been recognized as an emerging field. In this regard, opportunistic marine pathogens only cause disease in immune-compromised or stressed hosts (Burge et al. 2013). Among several of the macroalgae diseases studied so far, bleaching (whitening of macroalgal thalli), may be caused by diverse microbial pathogens together with environmentally mediated stress such as climate change and global warming. This results in the subsequent decline of these important habitat-former organisms (Campbell et al. 2011).

7.6 Aquaculture/Disease/Global Change

Marine algae, important dominant habitat-formers, and primary producers in the marine ecosystem (Longford et al. 2019) are highly susceptible to infectious diseases according to increasing evidence (Gardiner et al. 2015). In their natural habitat, several biotic and abiotic parameters affect macroalgal health, whereas under cultivation overall unfavourable environmental conditions may result in disease outbreak (Campbell et al. 2011; Gardiner et al. 2015). Microbiomes of habitat-formers may be impacted by ocean climate change with implications for the health, persistence and resilience of entire marine ecosystems. The impact of climate change is closely linked to the physiological concept of macroalgal health. Relatively little is known about the prevalence and magnitude of diseases in macroalgae (Gachon et al. 2010), but they are predicted to increase under elevated environmental stress (Egan et al. 2014) or could change geographical distribution and virulence of diseases. If diseases directly impact the infected organism and its population, they may additionally have cascading effects on the ecosystem if keystone species are infected (Gachon et al. 2010; Egan and Gardiner 2016). Many diseases are now interpreted as the result of a microbial imbalance and the rise of opportunistic or polymicrobial infections upon host stress (Egan and Gardiner 2016; Dittami et al. 2019). The direct physiological impacts of ocean warming could have led to changes in the microbiomes of important species such as kelps, which may also be a direct response of ocean acidification (Qiu et al. 2019). On the other hand, due to the increasing economic demand of macroalgae, its aquaculture has expanded rapidly during the last 10 years (Cottier-Cook et al. 2016; Alemañ et al. 2019; Shannon and Abu-Ghannam 2019). Despite its large niche market, intensive algal aquaculture might favour disease outbreaks (Gachon et al. 2010). One of the most crucial problems in aquaculture is bacterial diseases (Natrah et al. 2011a) and these microbial infections are being observed to be opportunistic in nature. There is an increased need to find an alternative strategy to control disease outbreak in aquaculture as it raises the production cost and reduces the profit/outcome. Interactions between the hosts and their microbiomes are fundamental for host functioning and resilience (Qiu et al. 2019), and such interactions

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can either be beneficial or detrimental. Any disturbance and disruption of the mutualistic association between host and microbiota can result in diseases by means of holobiont break-up. Through the holobiont approach, the understanding of microbial disease is undergoing a paradigm shift (Longford et al. 2019). For instance, the red alga Agarophyton (Gracilariaceae) possesses a protective epimicrobial community which acts as chemical ‘gardens’ and at the same time deters opportunistic pathogens. Thus, Agaraphyton wisely recruits its microbiota that maintain its overall health and disease management (Saha and Weinberger 2019). The recognition of the bacterial microbiota importance for other organisms lead to the concept of holobiont, which in turn allows the occurrence of bacterial interactions, such as quorum sensing (Teplitski et al. 2016; Lami 2019).

H. floresii epibacteria were assayed in vitro to identify any opportunistic pathogens by their ability to induce bleaching under laboratory condition (more details in Chapter V) and we found that V. owensii has a “significant pathogen” that increased the risk of tip bleaching (Table 1: Fig. 1 p < 0.00036) when compared to control treatments without bacterial inoculation. Eight additional isolates (Pseudoalteromonas arabiensis; P. mariniglutinosa; Tateyamaria omphalii; Ruegeria sp.; Alteromonas sp. (B7CC and B12CC); Epibacterium sp.; Alteromonadaceae bacterium) had the same effect, but were not pathogenic (according to Bonferroni test; Table 1; Fig. 1 p < 0.05) and thus were considered as ‘potential pathogens’. V. owensii, a member of Gammaproteobacteria, isolated from H. floresii cultivated under IMTA was previously identified as a QS bacterium (see Chapter II) and LC/MS confirmed the presence of N-butyryl-L- homoserine lactone (C4-HSL) in the culture medium of V. owensii grown in Marine Broth (MB - liquid) medium (see Chapter IV). The presence of C4-HSL in V. owensii emphasized the QS critical role in the induction of bleaching in H. floresii.

7.7 Future perspectives

Marine macroalgae are consistently exposed to both biotic and abiotic pressures in their natural environments and any external pressure may influence algal physiology leading to the production of metabolites, some of which acting as bioactive compounds (Collins et al. 2016). This potential can be tapped for developing new functional ingredients and health-promoting treatments. Indeed, several therapeutic properties have already been demonstrated for several species of marine macroalgae, including anti-cancer, anti-oxidant, anti-inflammatory, and anti-diabetic activities (Wijesinghe and Jeon 2012; Collins et al. 2016). The range of ailments reported to be treated experimentally by macroalgae or their derivated compounds may become a triggering factor for the development of novel functional ingredients for pharmaceutical purposes (Wijesinghe and Jeon 2012). H. floresii compounds are not the exception since in this exhaustive study we predicted and proposed a list of bioactive metabolites and clustered them based on their structural similarity. These endogenous metabolites have good chances for being successful drug

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candidates based on their metabolite -likeness and biological relevance, as the ‘chemical similarity principle’ states that molecules with similar structures likely exhibit similar biological properties (Kouskoumvekaki and Panagiotou 2010).

Regarding this point, our untargeted metabolomics study of H. floresii revealed the possible presence of compounds with potent pharmaceutical benefits. Among these compounds, the bioactive potential of H. floresii is illustrated in Fig. 3 and discussed below.

In the cluster, I, 11-O-demethylpradinone-I (S146), a member of the Pradinone I antibiotic group with a tetracyclic hydrocarbon structure, was found which is a basic structure of anthracyclines as a responsible class of compound (Lee et al. 2016). Similarly, in cluster III we found Pancratistatin (PST), a natural compound previously isolated from a terrestrial plant Pancratium littorale that has shown antineoplastic activity (McLachlan et al. 2005). In cluster V, two different compounds, Ibandronate (S72) and Cyclohexylmethyl 2-formylphenyl hydrogen phosphate (S107) were grouped together and have been described to show anti-resorptive/anti-osteoclastogenesis (Oh et al. 2017) and antibiotic activities (Deprez et al. 2002). For cluster VIII, mainly nucleotide derivatives groups like di-phosphorylated structures with amino-pyrimidine and substituted hydroxymethylpyridine were found (S124 and S198). These macroalgal compounds are involved in several key biological processes (Huang et al. 2014) and some have displayed prominent muscle relaxant property and hypothermia in mice such as 5-iodo-5-deoxytubercidine from the red alga Hypnea valentiae (Kazlauskas et al. 1983; De Koning et al. 2005; Huang et al. 2014).

On the other hand, bacterial resistance to therapeutic drugs has been a serious issue over the last decades. Thus the quick development of resistance mechanisms by microorganisms due to the overuse of antimicrobials has turned into a major public health issue (Corrêa et al. 2020). Such emergence of antibiotic- resistant bacterial strains has driven attention towards the discovery of alternative innovative strategies to combat pathogens. Since the druggability of secondary metabolites drove this study, it is worth noting that bacterial multi-resistance become a global public health challenge. Due to gene transfers among species and strong efflux pumps, “conventional” antibiotics become less and less efficient. This promoted the search for alternative pathways and, in such a context, “messing” with bacterial communication with macroalgal-derived Quorum Sensing Interference (QSI) compounds arose as a very promising alternative.

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Fig. 32 Bioactive potential of H. floresii identified by untargeted metabolomics study

The challenge in most metabolomic studies is to relate the identified metabolites to their biological activities (Johnson et al. 2016). However, to evaluate the biological roles of one or several metabolites (a metabolic signature), the first step is to determine their function in metabolic pathways and their interconnectivity. In our metabolomic study, the predicted metabolites of H. floresii were exhaustively searched for any related bioactivity using Google Scholar. The KEGG and “Cyc” databases are just some examples of the most popular metabolic pathway databases and were used as a targeted approach to proceed with our baseline study. Once pathways have been reconstructed and analysed, it will be possible to perform comparative and evolutionary analyses of metabolic processes among different lineages with different phenotypes and metabolic capabilities or help to understand peculiarities in metabolic processes, including those at primary and secondary metabolism. These metabolomic data can be considered for systems biology approach in the framework of chemical ecology, which ultimately helps us understand the underlying biotic interactions between the host and the associated microbiota, or to different types of organisms in a community (Goulitquer et al. 2012).

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With this untargeted metabolomics approach, we were able to unveil the potential of H. floresii to produce a vast number of bioactive metabolites under culture conditions. Further questions arise about what may happen if H. floresii culture conditions are changed or disturbed. The metabolic profile of H. floresii arising from this work may help as basic data to compare metabolic spatially and temporally differences, not only in cultivation but also in its natural habitat. H. floresii as a potential carrageenophyte for the food industry and pharmacological purposes (Freile-Pelegrín et al. 2011), can be cultured under favourable culture conditions leading to the improvement of their bioactive secondary metabolites. Under IMTA cultivation, environmental stressors such as temperature, sunlight, nutrient availability (eutrophic or oligotrophic), epibiosis and disease outbreak in any of the trophic levels might result in a putative metabolic shift, exhibiting a high and/or low bioactivity ultimately linking to its chemical defence. With the metabolic profile of H. floresii proposed in this study, this metabolic shift can be assessed. In their natural habitat, H. floresii metabolic shift will help us to understand its ecological role as a host. In aquaculture, it will provide useful information on culture conditions affecting their physiology and metabolism in order to optimize the benefit of its bioactive compounds and their pharmacological purposes.

Additionally, the presence of brominated compounds in H. floresii, which are well-known for their defensive activity against bacteria and antimethanogenic properties, could make them suitable as feed ingredients to cattle (Øverland et al. 2019). Thus, it fundamentally eliminates the enteric methane resulting from a high grain diet providing evidence of improved livestock production performance (de Nys et al. 2016). H. floresii will be an excellent model in a multi-trophic culture environment as algal secondary metabolites with antimicrobial activity, the interfering activity of macroalgae on quorum sensing of any opportunist pathogens may help reduce the emergence of resistant strains in aquaculture systems.

Marine macroalgae are one of the most important economically renewable resources of the oceans. Despite the advancement in macroalgae transcriptomics, genetic regulations controlling various biochemical pathways are still in its inception and largely remain unexplored (Gupta et al. 2014). Further steps, based on the potential of H. floresii, would be to undergo a genetic comparative study of gene activation under biotic and abiotic stresses. This can be done under cultivation in order to track metabolic pathways (and therefore the secondary metabolites) which are actually engaged in counteracting defined stresses as well as the use of ‘Proteomics’ as a highly useful method to explore molecular changes that may occur following any disease incidence (Khan et al. 2018).

World production of carrageenophytes is around 160 000 tons (dry weight), with over 28 000 tons of carrageenan obtained, valued at USD270 million (Freile-Pelegrín and Robledo 2016). Recent reports in commercial carrageenophyte farms show that thallus whitening or bleaching is quite common and most cultivars are quite vulnerable to this disease symptom (Largo et al. 1995). The outbreak of disease resulted

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in diminishing culture stocks and reduced carrageenan quality, which in turn leads to low market value, loss of income, loss of job opportunities and ultimately cause economic loss (Hurtado et al. 2019; Ward et al. 2019). Preventing the disease outbreak will be a better approach instead of treating or removing the diseased segments after the disease outbreak. Thus, QS interference may be an effective method for preventing and/or inhibiting the virulence of pathogens and hence reduce diseases and increase productivity in aquaculture systems (Zhao et al. 2015). As we previously observed how the surface-extracts of H. floresii interfere with the QS activity of V. owensii (see Chapter I), we believe that H. floresii can be a good candidate for disease control in aquaculture. Pliego-Cortés et al. (2017) considered H. floresii as a good candidate to recycle inorganic nutrients in a land-based IMTA by reducing the environmental impact of coastal ecosystems through responsible aquaculture practices. As a potential carrageenophyte, H. floresii could possibly play a significant role among green approaches to improve aquaculture productivity by means of an effective disease mitigation strategy.

7.8 Future possible experimentations

A metagenomic study on H. floresii (beach-cast) collected from the shores of the Yucatan Peninsula is underway. The results of this analysis will complete our understanding of H. floresii holobiont and its ‘core microbial community’ as described for other macroalgae (Singh and Reddy 2016). In this case, the outcome of this work will become part of the fundamental knowledge of H. floresii.

Additionally, this thesis opens several research opportunities that can be addressed in the future, some of which are:

i. Targeted metabolomic approach to isolate and characterize the allelopathic active metabolites

H. floresii metabolic data shall be taken further for an in-depth analysis of each metabolite by selectively isolating them from the extracts or by mapping genes activation. More precisely, the work can be framed to focus on halogenated metabolites, as they are expected to play a significant role in interfering QS system. This requires a tremendous amount of algal material to extract, which may be possible by cultivation.

ii. Attenuation of the QS system in Vibrio owensii to prevent disease outbreak

The interference with QS pathways provides an opportunity to attenuate bacterial pathogenicity and thereby the QS antagonists represent novel therapeutic agents to combat bacterial infections (Meschwitz et al. 2019). In such a case, attenuation of the V. owensii QS system by H. floresii shall be explored further for its application in aquaculture in order to prevent disease outbreak.

iii. Bacteria degrading algal-polysaccharides from the epibiome of H. floresii

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Bacteria degrading algal polysaccharides are key players in the global carbon cycle and in algal biomass recycling (Martin et al. 2015). The enzymes degrading algal polysaccharides are highly expected from the macroalgal-associated bacteria, particularly, surface-associated bacteria (Martin et al. 2014). These bacteria have essentially been found in two phyla, Proteobacteria and Bacteroidetes. H. floresii was previously reported to produce highly diverse sulphated polysaccharides (Robledo and Freile-Pelegrín 2011) and the fact that we also identified the epibacteria under these phyla increased our attention towards their possible unique enzymatic profile that could degrade polysaccharides.

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8. Experimentation Setup

8.1 Media composition i. Luria Bretani Agar (g/L)

Sodium chloride (NaCl) 10 g Tryptone 10 g Yeast extract 5 g Nutrient agar 15 g

ii. Marine Agar (g/L)

Marine broth 40.25 g Nutrient agar 15 g

8.2 Chemicals i. Acidified ethyl acetate (0.5% acetic acid)

Ethyl acetate 995 mL Acetic acid 5 mL

ii. HSL Standard preparation (500 ppm - Stock)

Standards Quantity Acetonitrile BHL/C4 HSL 0.5 mg 1 mL HHL/C6 HSL 0.5 mg 1 mL 3-O-C6-HSL 0.5 mg 1 mL OOHL/3-O-C8 HSL 0.5 mg 1 mL 3-O-C12 HSL 0.5 mg 1 mL

iii. TAE Buffer (50X - Stock)

Tris 2 M 121.14 g/mol Acetic acid 1 M 60.25 EDTA disodium salt 50 mM 372. 24 g/mol

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iv. Ammonium acetate buffer

Ammonium acetate 10 mM 77.08 g/mol

v. Phosphate Buffered Saline (PBS – pH 7.4) (g/L)

Sodium chloride 137 mM 58.44 g/mol Potassium chloride 2.7 mM 74.55 g/mol Sodium phosphate dibasic 10 mM 174.18 g/mol Potassium dihydrogen phosphate 2 mM 136.09 g/mol

8.3 Antibiotics i. Ampicillin sodium salt (100 mg/mL - Stock)

Ampicillin sodium salt 1 g MilliQ water 10 mL

ii. Vancomycin (10 mg/mL - Stock)

Vancomycin hydrochloride 0.5 g MilliQ water 50 mL

iii. Cefotaxim (10 mg/mL - Stock)

Cefotaxim 0.5 g MilliQ water 50 mL

8.4 DNA Extraction Kits

i. DNeasy Blood and Tissue Kit https://www.qiagen.com/fr/resources/resourcedetail?id=63e22fd7-6eed-4bcb-8097- 7ec77bcd4de6&lang=en

ii. ZymoBIOMICS DNA Miniprep Kit https://files.zymoresearch.com/protocols/_d4300t_d4300_d4304_zymobiomics_dna_miniprep_kit.pdf

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8.5 PCR Amplification – 16S

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Defence on surface of Rhodophyta Halymenia floresii: metabolomic profile and interactions with its surface-associated bacteria Shareen Arockiasamy 2020