# Microbial Ecotoxicology Advances to Improve Environmental and Human Health Under Global Change

Aurélie Cébron, Dimitrios Georgios Karpouzas, Fabrice Martin-Laurent, Soizic Morin, Carmen Palacios, Mechthild Schmitt-Jansen

► To cite this version:

Dimitrios Georgios Karpouzas, Fabrice Martin-Laurent, Soizic Morin, Carmen Palacios, Mechthild Schmitt-Jansen (Dir.). Microbial Ecotoxicology Advances to Improve Environmental and Human Health Under Global Change. Frontiers Media SA, 2022, Frontiers Research Topics, 978-2-88974-921-8. 10.3389/978-2-88974-921-8. hal-03683456

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Distributed under a Creative Commons Attribution 4.0 International License
MICROBIAL ECOTOXICOLOGY ADVANCES
TO IMPROVE ENVIRONMENTAL AND
HUMAN HEALTH UNDER GLOBAL CHANGE

EDITED BY: Fabrice Martin-Laurent, Aurélie Cebron,
Mechthild Schmitt-Jansen, Dimitrios Georgios Karpouzas,
Soizic Morin and Carmen Palacios

PUBLISHED IN: Frontiers in Microbiology

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ISSN 1664-8714
ISBN 978-2-88974-921-8
DOI 10.3389/978-2-88974-921-8

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MICROBIAL ECOTOXICOLOGY ADVANCES
TO IMPROVE ENVIRONMENTAL AND
HUMAN HEALTH UNDER GLOBAL CHANGE

Topic Editors:

**Fabrice Martin-Laurent**, Institut National de la Recherche Agronomique (INRA),
France

**Aurélie Cebron**, UMR7360 Laboratoire Interdisciplinaire des Environnements
Continentaux (LIEC), France

**Mechthild Schmitt-Jansen**, Helmholtz Centre for Environmental Research,
Helmholtz Association of German Research Centres (HZ), Germany

**Dimitrios Georgios Karpouzas**, University of Thessaly, Greece

**Soizic Morin**, INRAE Nouvelle-Aquitaine Bordeaux, France

**Carmen Palacios**, Université de Perpignan Via Domitia, France

**Citation:** Martin-Laurent, F., Cebron, A., Schmitt-Jansen, M., Karpouzas, D. G., Morin, S., Palacios, C., eds. (2022). Microbial Ecotoxicology Advances to Improve Environmental and Human Health Under Global Change.

Lausanne: Frontiers Media SA. doi: 10.3389/978-2-88974-921-8
Table of Contents

**06 Editorial: Microbial Ecotoxicology Advances to Improve Environmental and Human Health Under Global Change**

Aurélie Cébron, Dimitrios Georgios Karpouzas, Fabrice Martin-Laurent, Soizic Morin, Carmen Palacios and Mechthild Schmitt-Jansen

**09 Comparison of Novel and Established Nitrification Inhibitors Relevant to Agriculture on Soil Ammonia- and Nitrite-Oxidizing Isolates**

Evangelia S. Papadopoulou, Eleftheria Bachtsevani, Eleni Lampronikou, Eleni Adamou, Afroditi Katsaouni, Sotirios Vasileiadis, Cécile Thion, Urania Menkissoglu-Spiroudi, Graeme W. Nicol and Dimitrios G. Karpouzas

**24 Assessing the Effects of β-Triketone Herbicides on the Soil Bacterial and hppd Communities: A Lab-to-Field Experiment**

Clémence Thiour-Mauprizez, Marion Devers-Lamrani, David Bru, Jérémie Béguet, Aymé Spor, Arnaud Mounier, Lionel Alletto, Christophe Calvayrac, Lise Barthelmebs and Fabrice Martin-Laurent

**34 Relative Influence of Plastic Debris Size and Shape, Chemical Composition and Phytoplankton-Bacteria Interactions in Driving Seawater Plastisphere Abundance, Diversity and Activity**

Jingguang Cheng, Justine Jacquin, Pascal Conan, Mireille Pujo-Pay, Valérie Barbe, Matthieu George, Pascale Fabre, Stéphane Bruzaud, Alexandra Ter Halle, Anne-Leila Meistertzheim and Jean-François Ghiglione

**51 Variability of the Atmospheric PM₁₀ Microbiome in Three Climatic Regions of France**

Abdoulaye Samaké, Jean M. F. Martins, Aurélie Bonin, Gaëlle Uzu, Pierre Taberlet, Sébastien Conil, Olivier Favez, Alexandre Thomasson, Benjamin Chazeau, Nicolas Marchand and Jean-Luc Jaffrezo

**66 Graphene-Based Nanomaterials Modulate Internal Biofilm Interactions and Microbial Diversity**

Lauris Evariste, Paul Braylé, Florence Mouchet, Jérôme Silvestre, Laury Gauthier, Emmanuel Flahaut, Eric Pinelli and Maialen Barret

**81 Antibiotrophy: Key Function for Antibiotic-Resistant Bacteria to Colonize Soils—Case of Sulfamethazine-Degrading Microbacterium sp. C448**

Loren Billet, Stéphane Pesce, Nadine Rouard, Aymé Spor, Laurianne Paris, Martin Leremboure, Arnaud Mounier, Pascale Besse-Hoggan, Fabrice Martin-Laurent and Marion Devers-Lamrani

**94 Environmental Concentrations of Sulfonamides Can Alter Bacterial Structure and Induce Diatom Deformities in Freshwater Biofilm Communities**

Laura Kergoat, Pascale Besse-Hoggan, Martin Leremboure, Jérémie Beguet, Marion Devers, Fabrice Martin-Laurent, Matthieu Masson, Soizic Morin, Amélie Roinat, Stéphane Pesce and Chloé Bonnineau

**106 Side Effects of Pesticides and Metabolites in Groundwater: Impact on Denitrification**

Caroline Michel, Nicole Baran, Laurent André, Mickael Charron and Catherine Joulian
**123 Relative Weight of Organic Waste Origin on Compost and Digestate 16S rRNA Gene Bacterial Profilings and Related Functional Inferences**
Axel Aigle, Emilie Bourgeois, Laurence Marjolet, Sabine Houot, Dominique Patureau, Emmanuel Doelsch, Benoit Cournoyer and Wessam Galia

**141 Biofilm Formation, Production of Matrix Compounds and Biosorption of Copper, Nickel and Lead by Different Bacterial Strains**
Md. Manjurul Haque, Md Khaled Mosharaf, Md. Amdadul Haque, Md. Zahid Hasan Tanvir and Md. Khairul Alam

**160 Description of Microbial Communities of Phosphate Mine Wastes in Morocco, a Semi-Arid Climate, Using High-Throughput Sequencing and Functional Prediction**
Najoua Mghazli, Laila Sbabou, Rachid Hakkou, Ahmed Ouhammou, Mariam El Adnani and Odile Bruneel

**177 Microbial Diversity and Activity During the Biodegradation in Seawater of Various Substitutes to Conventional Plastic Cotton Swab Sticks**
Justine Jacquin, Nolwenn Callac, Jingguang Cheng, Carolane Giraud, Yonko Gorand, Clement Denoual, Mireille Pujo-Pay, Pascal Conan, Anne-Leila Meistertzheim, Valerie Barbe, Stéphane Bruzaud and Jean-François Ghiglione

**192 Bioleaching of E-Waste: Influence of Printed Circuit Boards on the Activity of Acidophilic Iron-Oxidizing Bacteria**
Juan Anaya-Garzon, Agathe Hubau, Catherine Joulian and Anne-Gwénaëlle Guezennec

**204 Bioaugmentation With a Consortium of Bacterial Sodium Lauryl Ether Sulfate-Degraders for Remediation of Contaminated Soils**
Ludovica Rolando, Anna Barra Caracciolo, Paola Grenni, Livia Mariani, Jasmin Rauseo, Francesca Spataro, Gian Luigi Garbini, Andrea Visca and Luisa Patrolecco

**214 The Response of Extracellular Polymeric Substances Production by Phototrophic Biofilms to a Sequential Disturbance Strongly Depends on Environmental Conditions**
Emilie Loustau, Joséphine Leflaive, Claire Boscus, Quentin Amalric, Jessica Ferriol, Olga Oleinikova, Oleg S. Pokrovsky, Elisabeth Girbal-Neuhauser and Jean-Luc Rols

**228 Diversity, Functions and Antibiotic Resistance of Sediment Microbial Communities From Lake Geneva Are Driven by the Spatial Distribution of Anthropogenic Contamination**
Emilie Lyautey, Chloé Bonnineau, Patrick Billard, Jean-Luc Loizeau, Emmanuel Naffrechoux, Ahmed Tlili, Edward Topp, Benoît J.D. Ferrari and Stéphane Pesce

**243 Driving Factors of Geosmin Appearance in a Mediterranean River Basin: The Ter River Case**
Carmen Espinosa, Meritxell Abril, Èlia Bretxa, Marta Jutglar, Sergio Ponsà, Núria Sellarès, Lídia Vendrell-Puigmitjà, Laia Llenas, Marc Ordeix and Lorenzo Proia
**257 Microbial Transformation of Chlordecone and Two Transformation Products Formed During in situ Chemical Reduction**

Jennifer Hellal, Pierre-Loïc Saaidi, Sébastien Bristeau, Marc Crampon, Delphine Muselet, Oriane Della-Negra, Aourell Mauffret, Christophe Mouvet and Catherine Joulian

**270 Bacterial Abundance, Diversity and Activity During Long-Term Colonization of Non-biodegradable and Biodegradable Plastics in Seawater**

Charlene Odobel, Claire Dussud, Lena Philip, Gabrielle Derippe, Marion Lauters, Boris Eyheraguibel, Gaëtan Burgaud, Alexandra Ter Halle, Anne-Leila Meistertzheim, Stephane Bruzaud, Valerie Barbe and Jean-François Ghiglione

**286 Dynamics of Soil Microbial Communities During Diazepam and Oxazepam Biodegradation in Soil Flooded by Water From a WWTP**

Marc Crampon, Coralie Soulier, Pauline Sidoli, Jennifer Hellal, Catherine Joulian, Mickaël Charron, Quentin Guillemoto, Géraldine Picot-Colbeaux and Marie Pettenati

**305 Resistance and Resilience of Soil Nitrogen Cycling to Drought and Heat Stress in Rehabilitated Urban Soils**

Mehdi Fikri, Catherine Joulian, Mikael Motelica-Heino, Marie-Paule Norini and Jennifer Hellal
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# Editorial: Microbial Ecotoxicology Advances to Improve Environmental and Human Health Under Global Change

Aurélie Cébron<fnref n="1" />*, Dimitrios Georgios Karpouzas², Fabrice Martin-Laurent³, Soizic Morin⁴, Carmen Palacios⁵,⁶ and Mechthild Schmitt-Jansen⁷

**Keywords:** microbial communities, impact of pollutants, environmental risk assessment, multiple stressors, biodegradation, bio-processes, ecosystem rehabilitation

## Editorial on the Research Topic

### Microbial Ecotoxicology Advances to Improve Environmental and Human Health Under Global Change

Microbial Ecotoxicology is an interdisciplinary science at the intersection of microbial ecology, toxicology, ecotoxicology, and analytical chemistry (Ghiglione et al., 2016; Shahsavari et al., 2017; Pesce et al., 2020). This Research Topic was proposed aiming to present the full range of research currently in place in an emerging topic like microbial ecotoxicology. The research focus of microbial ecotoxicology spans from the assessment of the impact of various contaminants on microbial communities to the development of new bio-processes and includes studies on the microbial biodegradation of contaminants and rehabilitation of contaminated environments. In this Research Topic “Microbial Ecotoxicology Advances to Improve Environmental and Human Health Under Global Change,” we have collected 21 original articles presenting research on a range of contaminants (heavy metals, nanomaterials, biogenic and synthetic contaminants such as pesticides, herbicides, medicines, plastics and other agrochemicals). Presented research assesses their impacts on microbial diversity and activity, and their biodegradation. Contributions included the usage of microbial technologies to remove contaminants from contaminated environments and evaluation of essential microbial functions in contaminated and rehabilitated environments.

1. Microbial communities support several ecosystem functions and thus play a key role not only in biogeochemical cycles but also in a wide range of ecosystem services (Falkowski et al., 2008). Exposure of microbial communities to a range of contaminants can modify their abundance, composition and activity (Tang et al., 2019; Fei et al., 2020; Noyer et al., 2020) with consequences on the ecosystem functions they deliver as well as on higher levels of biological organization. In this Research Topic, changes in microbial community structure and functions under contamination stress were demonstrated in soils (Thiour-Mauprivez et al.), groundwater (Michel et al.), freshwater (Lyautey et al.; Kergoat et al.; Evariste et al.) and seawater (Cheng et al.). In Lake Geneva, the local anthropogenic contamination (organic matter, trace metals, PAH and PCB) induced lower bacterial and archaeal diversity but higher levels of many microbial

**OPEN ACCESS**

Edited and reviewed by:
Eric Altermann,
AgResearch Ltd, New Zealand

*Correspondence:
Aurélie Cébron
aurelie.cebron@univ-lorraine.fr

**Specialty section:**
This article was submitted to Microbiotechnology, a section of the journal Frontiers in Microbiology

**Received:** 06 February 2022

**Accepted:** 23 February 2022

**Published:** 23 March 2022

**Citation:**
Cébron A, Karpouzas DG, Martin-Laurent F, Morin S, Palacios C and Schmitt-Jansen M (2022) Editorial: Microbial Ecotoxicology Advances to Improve Environmental and Human Health Under Global Change. *Front. Microbiol.* 13:870404. doi: 10.3389/fmicb.2022.870404

<footnote n="1">¹ Université de Lorraine, CNRS, LIEC, F-54000 Nancy, France, ² Laboratory of Plant and Environmental Biotechnology, Department of Biochemistry and Biotechnology, University of Thessaly, Larissa, Greece, ³ Agroécologie, INRAE, Institut Agro, Université de Bourgogne, Université de Bourgogne Franche-Comté, F-21000 Dijon, France, ⁴ UR EABX, INRAE, F-33612 Cestas, France, ⁵ Univ. Perpignan Via Domitia, CEFREM, UMR5110, F-66860, Perpignan, France, ⁶ CNRS, CEFREM, UMR5110, F-66860, Perpignan, France, ⁷ Helmholtz-Centre for Environmental Research—UFZ, Department of Bioanalytical Ecotoxicology, Leipzig, Germany</footnote>
activities (respiration, denitrification, methanogenesis, phosphatase and beta-glucosidase) and high abundance of antibiotic resistance genes (Lyautey et al.). Further, microbial activity (nitrification, Papadopoulou et al.; or denitrification, Michel et al.) was affected while functional population abundance was not impacted by synthetic pesticides. In other cases, the effect of antibiotics and graphene-based nanomaterials on the bacterial and diatom community composition, viability, physiology and interactions in biofilms were observed (Kergoat et al.; Evariste et al.). Unexpectedly, teratogenic effects of sulfonamide antibiotics on diatoms within periphyton were reported for the first time (Kergoat et al.). However, other studies reported no marked impact of other synthetic pesticide groups like β-triketone herbicides toward bacterial communities (Thiour-Mauprizez et al.) or limited effects of plastic size and shape on the abundance, diversity and activity of bacterial communities on the plastic surfaces (Cheng et al.).

Only few studies have addressed how cumulative stressors can alter ecosystem services. In this Research Topic, combined stressors studies have shown that ecosystems already stressed by the presence of contaminants may become more sensitive to additional environmental stresses and functional consequences amplified (Loustau et al.; Fikri et al.). The effect of droughts following a previous exposure to copper in biofilms, highlighted the importance of considering not only direct but also indirect effects of global changes (Loustau et al.). Moreover, low resistance but high resilience of an ecosystem after exposure to multiple stressors on already degraded soils was shown (Fikri et al.).

2. Due to their often-demonstrated capacities to transform and degrade a large range of substances including organic pollutants, microbial communities play a key role in the environmental fate of pollutants by regulating their persistence and mitigating related ecotoxicological impacts in the environment (Holliger et al., 1997; Haritash and Kaushik, 2009; Singh and Singh, 2016; Mohanan et al., 2020). Biodegradation of pesticides, medicines, plastics, antibiotics and foaming agents were studied in different environments to evaluate bioremediation and auto-depuration of impacted sites (Odobel et al.; Jacquin et al.; Hellal et al.; Crampon et al., Rolando et al.; Billet et al.). *In situ*, the microbial degradation of benzodiazepines was evaluated in soils (Crampon et al.) and the degradation of bio-based and fossil-based plastics in seawater was explored, where potential degraders were additionally identified (Odobel et al.). Transformation of the pesticide chlordecone and of two of its transformation products by microbial enrichment culture was assessed to identify degradation pathways, degradation key players and transformation products formed by microbial activities (Hellal et al.). The use of bacterial isolates or consortia capable of efficiently degrading organic contaminants such as antibiotics (Billet et al.) or foaming agents (Rolando et al.) in environmental matrices was explored. Finally, recommendations to improve future remediation strategies of polluted environments were proposed.

3. The cutting-edge research produced by microbial ecotoxicologists meets the demands of policy makers and the society in large. Specifically, it contributes to the tremendous challenges caused by intensive anthropogenic activities that threaten both environmental and human health worldwide. To bridge research to end-users, ecological engineering technologies are developed based on microbial technologies to support a more sustainable world. They aim to improve the management of contaminated environments and to bring up new treatment processes (Peng et al., 2018; Quintella et al., 2019; Bhatt et al., 2021). Here, new methods were developed to qualify and improve waste treatment and the quality of natural resources (Espinosa et al.; Anaya-Garzon et al.; Aigle et al.; Haque et al.). First, a better management of water quality (such as cyanobacterial metabolites affecting the taste of drinking water) through the monitoring of microbial development was proposed (Espinosa et al.). Bacterial activities can help to treat and recover wastes, such as agricultural and urban organic wastes *via* anaerobic digestion (Aigle et al.), e-waste treatment through metal bioleaching (Anaya-Garzon et al.) or metal biosorption by bacterial biofilms (Haque et al.).

Furthermore, after remediation or rehabilitation of contaminated sites, the recovery of the functioning of the rehabilitated ecosystem has to be assessed (Fikri et al.; Mghazli et al.). For example, the covering of acidic tailing with alkaline phosphate mine wastes was tested as a rehabilitation scenario of abandoned mines, and the status of microbial community diversity and functions were evaluated (Mghazli et al.). Nowadays, degraded urban soils can be ecologically rehabilitated by adding various materials to soils in order to restore the microbial functions involved in the C, P and N cycles (Fikri et al.).

Finally, emerging contaminants are constantly being introduced into the environment because of the implementation of new technologies in various industrial sectors and of the lack of prevention of possible contaminations issued from these new technologies. The fate and the impact of these emerging contaminants are often not well described and there is a need for monitoring. In particular, some environmental compartments are less monitored than others. For instance, the atmosphere is rarely considered in monitoring studies. In response to this, Samaké et al. demonstrated the importance of monitoring biogenic organic aerosol.

In summary, microbial ecotoxicology addresses several research challenges such as (i) providing an in-depth analysis of the changes imposed in the structure and functions of microbial communities under contamination, (ii) disentangling the complexity of environmental systems characterized of various interactors (toxicants, targets, multiple stressors), (iii) assessing the potential of contaminant biodegradation and utilization of the catabolic capacities of microbial communities for bioremediation of contaminated sites. Fundamental discoveries feed current applied developments including risk assessment using microbes in a changing world, development and validation of new methods to qualify environmental quality, guidelines for environmental policies and ecological
engineering technologies based on microbial technologies. This Research Topic brings together original results concerning these challenging questions as well as articles addressing the latest advances in microbial ecotoxicology.

We are delighted to present this Research Topic in Frontiers in Microbiology. We hope that this e-book will be interesting and useful to the readers of Frontiers in Microbiology while highlighting the value of focusing on microbial ecotoxicology to broaden our knowledge on contaminants effect, biodegradation and treatment.

## AUTHOR CONTRIBUTIONS

AC wrote the first version of the editorial manuscript. All authors contributed to manuscript write, revision, read,

and approved the submitted version. All authors were co-editors of the Research Topic: *Microbial Ecotoxicology Advances to Improve Environmental and Human Health Under Global Change*.

## ACKNOWLEDGMENTS

This Research Topic was promoted by the International Network on Microbial Ecotoxicology—EcotoxicoMic (https://ecotoxicomic.org/), following the second International Conference on Microbial Ecotoxicology held online in October, 2020. We thank all participating authors and reviewers for having made this Research Topic a success.

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Bhatt, P., Bhatt, K., Sharma, A., Zhang, W., Mishra, S., and Chen, S. (2021). Biotechnological basis of microbial consortia for the removal of pesticides from the environment. *Critic. Rev. Biotechnol.* 41, 317–338. doi: 10.1080/07388551.2020.1853032

Falkowski, P. G., Fenchel, T., and Delong, E. F. (2008). The microbial engines that drive Earth's biogeochemical cycles. *Science* 320, 1034–1039. doi: 10.1126/science.1153213

Fei, Y., Huang, S., Zhang, H., Tong, Y., Wen, D., Xia, X., et al. (2020). Response of soil enzyme activities and bacterial communities to the accumulation of microplastics in an acid cropped soil. *Sci. Total Environ.* 707, 135634. doi: 10.1016/j.scitotenv.2019.135634

Ghiglione, J. F., Martin-Laurent, F., and Pesce, S. (2016). Microbial ecotoxicology: an emerging discipline facing contemporary environmental threats. *Environ. Sci. Pollut. Res.* 23, 3981–3983. doi: 10.1007/s11356-015-5763-1

Haritash, A. K., and Kaushik, C. P. (2009). Biodegradation aspects of polycyclic aromatic hydrocarbons (PAHs): a review. *J. Hazard. Mater.* 169, 1–15. doi: 10.1016/j.jhazmat.2009.03.137

Holliger, C., Gaspard, S., Glod, G., Heijman, C., Schumacher, W., Schwarzenbach, R. P., et al. (1997). Contaminated environments in the subsurface and bioremediation: organic contaminants. *FEMS Microbiol. Rev.* 20, 517–523. doi: 10.1111/j.1574-6976.1997.tb00334.x

Mohanan, N., Montazer, Z., Sharma, P. K., and Levin, D. B. (2020). Microbial and enzymatic degradation of synthetic plastics. *Front. Microbiol.* 11, 2837. doi: 10.3389/fmicb.2020.580709

Noyer, M., Reoyo-Prats, B., Aubert, D., Bernard, M., Verneau, O., and Palacios, c. (2020). Particle-attached riverine bacteriome shifts in a pollutant-resistant and pathogenic community during a Mediterranean extreme storm event. *Sci. Total Environ.* 732, 139047. doi: 10.1016/j.scitotenv.2020.139047

Peng, W., Li, X., Xiao, S., and Fan, W. (2018). Review of remediation technologies for sediments contaminated by heavy metals. *J. Soils Sediments* 18, 1701–1719. doi: 10.1007/s11368-018-921-7

Pesce, S., Ghiglione, J. F., Topp, E., and Martin-Laurent, F. (2020). Editorial: Microbial Ecotoxicology. *Front Microbiol.* 20, 9. doi: 10.3389/978-2-88963-881-9

Quintella, C. M., Mata, A. M., and Lima, L. C. (2019). Overview of bioremediation with technology assessment and emphasis on fungal bioremediation of oil contaminated soils. *Journal of environmental management* 241, 156–166. doi: 10.1016/j.jenvman.2019.04.019

Shahsavari, E., Aburto-Medina, A., Khudur, L. S., Taha, M., and Ball, A. S. (2017). "From microbial ecology to microbial ecotoxicology," in *Microbial Ecotoxicology* (Cham: Springer), pp. 17–38

Singh, B., and Singh, K. (2016). Microbial degradation of herbicides. *Critic. Rev. Microbiol*, 42, 245–261. doi: 10.3109/1040841X.2014.929564

Tang, J., Zhang, J., Ren, L., Zhou, Y., Gao, J., Luo, L., et al. (2019). Diagnosis of soil contamination using microbiological indices: a review on heavy metal pollution. *J. Environ. Manage*, 242, 121–130. doi: 10.1016/j.jenvman.2019.04.061

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

**Publisher's Note:** All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Copyright © 2022 Cébron, Karpouzas, Martin-Laurent, Morin, Palacios and Schmitt-Jansen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
<figure><img src="image_5.png" /></figure>

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# Comparison of Novel and Established Nitrification Inhibitors Relevant to Agriculture on Soil Ammonia- and Nitrite-Oxidizing Isolates

Evangelia S. Papadopoulou¹*, Eleftheria Bachtsevani¹, Eleni Lampronikou¹, Eleni Adamou¹, Afroditi Katsaouni¹, Sotirios Vasileiadis¹, Cécile Thion², Urania Menkissoglu-Spiroudi³, Graeme W. Nicol² and Dimitrios G. Karpouzas¹

¹ Laboratory of Plant and Environmental Biotechnology, Department of Biochemistry and Biotechnology, University of Thessaly, Larissa, Greece, ² Laboratoire Ampère, École Centrale de Lyon, University of Lyon, Ecully, France, ³ Pesticide Science Laboratory, School of Agriculture, Forestry and Environment, Faculty of Agriculture, Aristotle University of Thessaloniki, Thessaloniki, Greece

OPEN ACCESS

**Edited by:**
Akihiko Terada,
Tokyo University of Agriculture
and Technology, Japan

**Reviewed by:**
Haoran Duan,
The University of Queensland,
Australia
Sanin Musovic,
Danish Technological Institute (DTI),
Denmark

*Correspondence:
Evangelia S. Papadopoulou
evapapadopoulou@bio.uth.gr

**Specialty section:**
This article was submitted to
Microbiotechnology,
a section of the journal
Frontiers in Microbiology

**Received:** 08 July 2020
**Accepted:** 16 October 2020
**Published:** 04 November 2020

**Citation:**
Papadopoulou ES, Bachtsevani E, Lampronikou E, Adamou E, Katsaouni A, Vasileiadis S, Thion C, Menkissoglu-Spiroudi U, Nicol GW and Karpouzas DG (2020) Comparison of Novel and Established Nitrification Inhibitors Relevant to Agriculture on Soil Ammonia- and Nitrite-Oxidizing Isolates. *Front. Microbiol.* 11:581283.
doi: 10.3389/fmicb.2020.581283

Nitrification inhibitors (NIs) applied to soil reduce nitrogen fertilizer losses from agro-ecosystems. NIs that are currently registered for use in agriculture appear to selectively inhibit ammonia-oxidizing bacteria (AOB), while their impact on other nitrifiers is limited or unknown. Ethoxyquin (EQ), a fruit preservative shown to inhibit ammonia-oxidizers (AO) in soil, is rapidly transformed to 2,6-dihydro-2,2,4-trimethyl-6-quinone imine (QI), and 2,4-dimethyl-6-ethoxy-quinoline (EQNL). We compared the inhibitory potential of EQ and its derivatives with that of dicyandiamide (DCD), nitropyrin (NP), and 3,4-dimethylpyrazole-phosphate (DMPP), NIs that have been used in agricultural settings. The effect of each compound on the growth of AOB (*Nitrosomonas europaea*, *Nitrosospira multiformis*), ammonia-oxidizing archaea (AOA; "Candidatus Nitrosocosmicus franklandus," "Candidatus Nitrosotalea sinensis"), and a nitrite-oxidizing bacterium (NOB; *Nitrobacter* sp. NHB1), all being soil isolates, were determined in liquid culture over a range of concentrations by measuring nitrite production or consumption and qPCR of *amoA* and *nxrB* genes, respectively. The degradation of NIs in the liquid cultures was also determined. In all cultures, EQ was transformed to the short-lived QI (major derivative) and the persistent EQNL (minor derivative). They all showed significantly higher inhibition activity of AOA compared to AOB and NOB isolates. QI was the most potent AOA inhibitor (EC₅₀ = 0.3–0.7 µM) compared to EQ (EC₅₀ = 1–1.4 µM) and EQNL (EC₅₀ = 26.6–129.5 µM). The formation and concentration of QI in EQ-amended cultures correlated with the inhibition patterns for all isolates suggesting that it was primarily responsible for inhibition after application of EQ. DCD and DMPP showed greater inhibition of AOB compared to AOA or NOB,
with DMPP being more potent (EC₅₀ = 221.9–248.7 µM vs EC₅₀ = 0.6–2.1 µM). NP was the only NI to which both AOA and AOB were equally sensitive with EC₅₀s of 0.8–2.1 and 1.0–6.7 µM, respectively. Overall, EQ, QI, and NP were the most potent NIs against AOA, NP, and DMPP were the most effective against AOB, while NP, EQ and its derivatives showed the highest activity against the NOB isolate. Our findings benchmark the activity range of known and novel NIs with practical implications for their use in agriculture and the development of NIs with broad or complementary activity against all AO.

**Keywords:** nitrification inhibitors, ammonia-oxidizing bacteria, ammonia-oxidizing archaea, nitrite-oxidizing bacteria, ethoxyquin, quinone imine

INTRODUCTION

Modern agricultural systems depend heavily on large inputs of synthetic N fertilizers to maintain crop productivity and meet the increasing global food demand (Fowler et al., 2018). However, ca. 70% of the annual global input of 100 Tg N fertilizer is lost from agricultural ecosystems due to nitrification and subsequent denitrification processes leading to groundwater and atmospheric pollution (Raun and Johnson, 1999). To minimize N losses and improve N use efficiency in soil, nitrification inhibitors (NIs), compounds known to reduce the activity of nitrifying prokaryotes, are routinely incorporated into N-stabilized fertilizers (Abbasi and Adams, 1998; Moir et al., 2007).

Hundreds of compounds have been identified that inhibit nitrifying prokaryotes (Bédard and Knowles, 1989; McCarty, 1999) including plant-derived molecules (Coskun et al., 2017), aliphatic and aromatic n-alkynes (Taylor et al., 2015; Wright et al., 2020), statins (Zhao et al., 2020), and PTIO (2-phenyl-4,4,5,5-tetramethylimidazoline-1-oxyl 3-oxide; Martens-Habbena et al., 2015). Many of these are used as selective inhibitors of ammonia-oxidizing bacteria (AOB; e.g., octyne) or archaea (AOA; e.g., PTIO) in laboratory cultures, soil microcosms or slurries, but are not suitable for use in an agricultural setting due to rapid degradation in soil or application in a gaseous state. Only three compounds have gained importance for practical use as NIs in agriculture: 2-chloro-6-(trichloromethyl) pyridine (nitrapyrin; NP; Goring, 1962), dicyandiamide (DCD; Solansky, 1982), and 3,4-dimethylpyrazole phosphate (DMPP; Zerulla et al., 2001). All three are presumed to act as Cu chelators interfering with ammonia monooxygenase (AMO), a key enzyme in the first and rate-limiting step of nitrification (Ruser and Schulz, 2015). In addition, NP was also proposed to serve as a weak mechanism-based or “suicide” inhibitor (Vannelli and Hooper, 1992). However, the precise mode of action of these NIs has yet to be fully elucidated.

When NIs were first introduced in agriculture, soil nitrification was considered a two-step process carried out

by AOB and nitrite-oxidizing bacteria (NOB). AOB oxidize ammonia to hydroxylamine (NH₂OH) using AMO, which is further oxidized to nitric oxide (NO) and finally nitrite (NO₂⁻). NOB subsequently transform NO₂⁻ to nitrate (NO₃⁻) using nitrite oxidoreductase (NXR; Coskun et al., 2017; Beeckman et al., 2018). However, over the last 15 years, other groups were demonstrated to contribute to soil nitrification including AOA (Leininger et al., 2006; Zhang et al., 2012), and recently “comammox” *Nitrospira*, (Wang et al., 2019; Li et al., 2019a) that perform complete oxidation of ammonia to nitrate within an individual cell (Daim et al., 2015; van Kessel et al., 2015). Isolation of soil AOA strains confirmed their role in soil ammonia oxidation (Tourna et al., 2011; Lehtovirta-Morley et al., 2014), while all *Nitrospira* strains isolated from soil are non-comammox strains.

Despite these breakthroughs in our understanding of the microbiology and biochemistry of nitrification, current knowledge regarding the spectrum of activity and the inhibition thresholds of NIs used in agriculture on soil ammonia-oxidizers (AO) is limited. The use of inhibition assays with pure cultures of a diverse range of soil-derived strains is a necessary benchmarking step to define the exact spectrum of activity of NIs destined for use in agriculture. Most culture inhibition assays have focused on AOB (e.g., Bélser and Schmidt, 1981; Vannelli and Hooper, 1992) or tested NIs not broadly applied in agricultural settings on soil AOA strains (i.e., allylthiourea, n-aliphatic alkynes, and simvastatin; Wright et al., 2020; Zhao et al., 2020). Others have explored the activity of NIs of agricultural relevance on AOA (Jung et al., 2011; Kim et al., 2012), but only three provided a systematic assessment and inhibition thresholds for AOA soil strains like “Candidatus Nitrosocosmicus agrestis” (Liu et al., 2019), “Candidatus Nitosotalea devanaterra” (Lehtovirta-Morley et al., 2013), and *Nitrososphaera viennensis* (Shen et al., 2013). In addition, most NIs are known to act on the ammonia oxidation step of nitrification (Bédard and Knowles, 1989), hence their activity on NOB remains unknown. The variation in sensitivity of AOA and AOB to different types of NIs, combined with their contribution to nitrification in distinct ecological niches (Prosser and Nicol, 2012; Kits et al., 2017), implies a potential suboptimal efficiency of the NIs currently used in agriculture, and stresses the need for the discovery of novel NIs with a broader range of activity against all microorganisms contributing to nitrification.

<footnote>**Abbreviations:** NIs, nitrification inhibitors; EQ, ethoxyquin; QI, 2,6-dihydro-2,2,4-trimethyl-6-quinone imine; EQNL, 2,4-dimethyl-6-ethoxyquinoline; DCD, dicyandiamide; NP, nitrapyrin; DMPP, 3,4-dimethylpyrazole phosphate; AOB, ammonia-oxidizing bacteria; AOA, ammonia-oxidizing archaea; AO, ammonia-oxidizers; NOB, nitrite-oxidizing bacteria; comammox, complete ammonia-oxidizing bacteria; AMO, ammonia monooxygenase; EPS, extracellular polymeric substances.</footnote>
In previous soil microcosm studies we showed that ethoxyquin (EQ; 1,2-dihydro-6-ethoxy-2,2,4-trimethylquinoline), an antioxidant used as preservative in fruit-packaging plants, and its derivative 2,6-dihydro-2,2,4-trimethyl-6-quinone imine (QI), strongly inhibited the activity of AOB and AOA (Papadopoulou et al., 2016). EQ in soil is rapidly transformed to QI and 2,4-dimethyl-6-ethoxyquinoline (EQNL; Karas et al., 2015). The potential capacity of EQ to be rapidly transformed in soil to potent NIs is of particular interest, considering that the spectrum and the duration of inhibition are desirable attributes of NIs used in agricultural practice.

We aimed to determine the inhibitory potency of EQ and its derivatives on representative isolates of diverse and globally distributed lineages of soil AOB and AOA in liquid culture, in comparison to NIs widely used in agricultural settings (NP, DCD, and DMPP). We expanded our liquid inhibition assays to NOB to gain insights on the impact of NIs on a microbial group functionally associated with ammonia oxidation, and directly linked to nitrogen loss from disturbed agricultural ecosystems in the form of nitrate production. Specifically, we used (i) AOB strains *Nitrosomonas europaea* and *Nitrosospira multiformis*, belonging to AOB clusters 7 and 3, respectively, (Purkhold et al., 2000), with cluster 3 often being the dominant AOB lineage in soil ecosystems (Kowalchuk and Stephen, 2001); (ii) AOA strains "Candidatus Nitrosocosmicus franklandus" (Lehtovirta-Morley et al., 2016) and "Candidatus Nitrosotalea sinensis" (Lehtovirta-Morley et al., 2014), occupying contrasting ecological niches and representing widely distributed neutrophilic and acidophilic AOA lineages, respectively, (Herbold et al., 2017), and (iii) one NOB, *Nitrobacter* sp. NHB1 (de Boer et al., 1991) as a representative of one of the two dominant NOB lineages found in soil (Daimis et al., 2016), with *Nitrobacter* strains typically having greater nitrite oxidation activity compared to *Nitrospira*, and dominating activity under excess nitrogen supply (e.g., fertilized soils; Xia et al., 2011; Nowka et al., 2015). While previous studies have examined the effective concentration of different NIs on ammonia oxidizer isolates, this study also examined the degradation of NIs during laboratory incubation.

# MATERIALS AND METHODS

## Microbial Strains, Growth Conditions and Chemicals

All strains were grown aerobically in the dark without shaking. AOB *N. europaea* ATCC25978 and *N. multiformis* ATCC25196 were grown at 28°C in Skinner and Walker's medium (Skinner and Walker, 1961) containing 1 mM $NH_4^+$ [$(NH_4)_2SO_4$] and phenol red (0.5 mg L$^{-1}$) as a pH indicator. AOA "Ca. N. franklandus" C13 and "Ca. N. sinensis" ND2, were incubated at 35°C in a medium supplemented with 1 mM $NH_4^+$ ($NH_4Cl$). The former was cultured in HEPES-buffered modified freshwater medium (pH 7.5; Lehtovirta-Morley et al., 2014), while the latter was grown in freshwater medium (pH 5.2; Lehtovirta-Morley et al., 2011). *Nitrobacter* sp. strain NHB1 was grown at 28°C in freshwater medium (pH 5.2; Lehtovirta-Morley et al., 2011) supplemented with 0.5 mM $NO_2^-$ (NaNO$_2$).

Analytical standards of DCD (99% purity), NP (≥98%), and EQ (95%) were purchased from Sigma-Aldrich (Germany), while DMPP (99.1%) analytical standard was provided by BASF Hellas. The oxidation derivatives of EQ, QI, and EQNL were synthesized as described by Thorisson et al. (1992). The chemical structures of all studied compounds are shown in **Supplementary Figure 1**.

## Liquid Culture Assays

The activity of all NIs was determined in liquid batch cultures over a range of concentrations to establish relevant inhibition thresholds per strain and compound. Preliminary assays with a broad range of concentrations for each NI and isolate ($NO_2^-$ production) dictated the range of NI concentrations that will allow calculation of inhibition thresholds. Cultures were established in triplicate for each strain × NI × concentration combination in 100-mL Duran bottles containing 50 mL of growth medium and inoculated with a 1 or 2% (v/v) transfer of exponentially growing cultures of AOB or AOA/NOB, respectively. EQ, QI, EQNL, and NP were added to the cultures as filter sterilized dimethyl sulfoxide (DMSO) solutions due to their low water solubility (≤60 mg L$^{-1}$ at 20°C). The final concentration of DMSO in all cultures was 0.1% (v/v), which did not exert a significant inhibitory effect to any of the isolates tested (data not shown), in line with previous studies with the same isolates (Wright et al., 2020; Zhao et al., 2020). DCD and DMPP were dissolved in sterile $dH_2O$ before addition of 25 µl (0.5% v/v). All NIs were added to batch cultures at the beginning of the exponential growth phase. For all assays, triplicate cultures with the same inoculum not amended with NIs were included. Upon inoculation all liquid batch cultures were sampled at regular time intervals to determine the effect of NIs on the activity and growth of nitrifying microorganisms by measuring changes in nitrite concentrations and the abundance of *amoA* (AO) or *nxrB* (NOB) genes, respectively.

## Nitrite Measurements and Gene Abundance Quantification

Nitrite concentrations were determined colorimetrically at 540 nm in a 96-well plate format assay by diazotizing and coupling with Griess reagent (Shinn, 1941). *amoA* and *nxrB* gene abundance was determined in a Biorad CFX Real-Time PCR system. DNA was extracted from a cell pellet obtained from 2-ml aliquots of the microbial cultures using the tissue DNA extraction kit (Macherey-Nagel, Germany). The *amoA* genes of AOB and AOA was amplified with primers *amoA-1F*/*amoA-2R* (Rotthauwe et al., 1997) and Arch-*amoAF*/Arch-*amoAR* (Francis et al., 2005), respectively, as described by Rousidou et al. (2013), and the *nxrB* gene of *Nitrobacter* was quantified with primers *nxrB-1F* and *nxrB-1R* (Vanparys et al., 2007). All qPCR assays used the following thermal cycling conditions: 95°C for 3 min, followed by 40 cycles of 95°C for 30 s, 57°C for 20 s, 72°C for 30 s, with a final dissociation curve analysis. The abundance of *amoA* and *nxrB* genes were determined via external standard curves as described by Rousidou et al. (2013). qPCR amplification efficiencies ranged from 80.3% to 109.4%, with r² values ≥ 0.98.
## Nitrification Inhibitors Extraction

Ethoxyquin, QI, EQNL, and NP residues were extracted from liquid media by mixing 0.3 mL liquid culture with 0.7 mL of acetonitrile. Residues of DCD and DMPP were extracted by mixing 0.1 mL liquid culture with 0.9 mL of ddH₂O water and methanol, respectively. The derived mixtures were vortexed for 30 s and stored at -20°C until analysis. Recovery tests at three concentration levels (in the range of the tested concentrations) showed recoveries of >80% for all compounds studied.

## Chromatographic Analyses

High performance liquid chromatography (HPLC) analyses were performed in a Shimadzu LC-20ADHPLC system equipped with an UV/VIS PDA detector. A Shimadzu GVP-ODs (4.6 mm by 150 mm, 5 µm) pre-column, connected to a RP Shimadzu VPODs (4.6 mm × 150 mm, 5 µm) column, was used for NI separation. The injection volume was 20 µl. The flow rate of the mobile phase was set at 0.8 mL min⁻¹ for DCD and at 1 mL min⁻¹ for all other NIs. Column temperature was set at 40°C for DCD and DMPP, and at 25°C for all the other NIs. Mixtures of acetonitrile and ammonia [0.25% (vol/vol)] or ortho-phosphoric acid [0.1% (vol/vol)] were used at a ratio of 70:30 (vol/vol) for mobile phases in the analyses of EQ, QI, EQNL, and NP, respectively, and detection was achieved at 225, 245, 230, and 269 nm, respectively. Similarly, chromatographic separation of DCD and DMPP was achieved using ddH₂O (100%) and a mixture of methanol and ortho-phosphoric acid [0.1% (vol/vol)] solution 50:50 by volume, respectively. DCD and DMPP residues were detected at 218 nm and 225 nm, respectively.

## Calculation of Inhibition Threshold Levels (EC₅₀)

In this study, EC₅₀ describes the concentration of the inhibitor that reduces half of the activity (nitrite accumulation or consumption) of AO or NOB. Dose-response modeling was performed using normalized data whereby nitrite concentration values were divided by the mean value of the matching control. Analyses were carried out using the dose response curves (drc) v3.0-1 package (Ritz and Streibig, 2005) of the R software (R Core Team, 2020). A brief description of the tested models can be found in Ritz et al. (2016). An empirical modeling approach was initially used for selecting the best fitting model according to tested goodness of fit indices (see **Supplementary Material**), followed by the choice of the four-parameter log logistic model as the best compromise among tested models for comparing endpoint values.

## Data Analysis

Nitrite and qPCR data were subjected to one-way ANOVA, followed by Tukey's *post hoc* test (*P* < 0.05). Variance between the EC₅₀ values of the different NIs for one strain and between different strains for a given NI was analyzed by one-way ANOVA, and Duncan *post hoc* test (*P* < 0.05). The four kinetic models proposed by the FOCUS working group on pesticide degradation kinetics (FOCUS, 2006) [single first order kinetic (SFO), biphasic models hockey stick (HS), first order

multi-compartment (FOMC), and double first order in parallel (DFOP) models] were used to calculate NI degradation kinetic parameters (DT₅₀, kᵣₐg). Curve fitting was performed with the mkin v0.9.47.1 package (Ranke, 2018) of the R Studio v4.0.2 software (R Core Team, 2020).

## RESULTS

### The Impact and Degradation of EQ, QI, and EQNL in AO and NOB Cultures
#### Effects on the Activity and Growth of AO and NOB Isolates

Ethoxyquin fully inhibited the activity of *N. europaea*, *N. multiformis*, and *Nitrobacter* sp. NHB1 only at the highest tested concentration of 460 µM (Figure 1). In contrast, the activity of “Ca. N. franklandus” and “Ca. N. sinensis” was significantly reduced by EQ at concentrations ≥4.6 µM and ≥0.46 µM (*p* ≤ 0.05), respectively, while complete inhibition of “Ca. N. sinensis” was evident at levels ≥4.6 µM. Growth inhibition profiles of AO isolates corresponded with NO₂⁻ production (Figure 1). In contrast to activity measurements, a significant reduction (*p* ≤ 0.05) in the growth of *Nitrobacter* sp. NHB1 was observed at the end of the incubation period for all EQ concentration levels.

2,6-dihydro-2,2,4-trimethyl-6-quinone imine fully inhibited ammonia oxidation by *N. europaea* and *N. multiformis* at concentrations ≥270 µM and ≥135 µM, respectively, (Figure 2). The activity of AOA was significantly reduced (*p* ≤ 0.05) at all QI concentrations, with little or no activity at concentrations ≥2.7 µM, and a gradual recovery observed only for “Ca. N. franklandus” at the lowest concentration level (0.27 µM). Nitrite consumption by *Nitrobacter* sp. NHB1 was significantly suppressed at concentrations ≥135 µM (*p* ≤ 0.05), though a persistent inhibitory effect was evident only at 540 µM. The inhibition of QI on AO growth concurred with the NO₂⁻ production patterns, unlike NOB where QI persistently inhibited the growth of *Nitrobacter* sp. NHB1 at concentrations ≥135 µM (Figure 2).

2,4-dimethyl-6-ethoxyquinoline only temporarily inhibited *N. europaea* activity at the highest concentration tested, 500 µM, while at the same concentration level *N. multiformis* activity was fully inhibited (Supplementary Figure 2). Ammonia oxidation by “Ca. N. franklandus” and “Ca. N. sinensis” was significantly reduced at concentrations ≥125 µM and ≥25 µM (*p* ≤ 0.05), respectively, and complete inhibition occurred at 500 µM and ≥125 µM, respectively. Nitrite oxidation by *Nitrobacter* sp. NHB1 was completely inhibited by EQNL only at the highest tested concentrations of 500 µM (Supplementary Figure 2). While the inhibition of AOB growth was congruent with NO₂⁻ production, the impact of EQNL on the growth of “Ca. N. franklandus” was not fully consistent with the activity measurements, and no significant differences among the different concentrations were observed at the end of the incubation period (day 22), probably due to the decreased number of living cells at EQNL concentrations ≤25 µM. Variations in the growth
<figure><img src="image_6.png" /></figure>

inhibition pattern of EQNL was observed also for *Nitrobacter* sp. NHB1 which was not significantly (*p* = 0.063) affected by EQNL even at the highest tested concentration (500 µM; **Supplementary Figure 2**).

## Degradation Patterns of EQ, QI, and EQNL in Liquid Culture

In the liquid cultures of all tested isolates, EQ was rapidly transformed to QI and EQNL (**Figure 1** and **Supplementary Figure 3**). QI and EQNL constituted 10.4–34.9% and 1.1–4.5%, respectively, of the total amount of EQ recovered at the onset of inhibition in the liquid cultures amended with

the highest concentration of EQ (460 µM; **Figure 1**). The degradation half-life (DT₅₀) for the sum of EQ + QI + EQNL in cultures supplemented with 460 µM of EQ ranged from 2.1 days for *Nitrobacter* sp. NHB1 to 60.1 days for *N. multiformis* (**Supplementary Table 1**).

The degradation of QI, when added directly into liquid culture, was best described by the SFO kinetic model (x² ≤ 15, r² ≥ 0.75). QI showed limited persistence and a weak dose-dependent degradation pattern with DT₅₀ = 0.05–1.52 days at the lowest concentration level (2.7 µM), and 2.23–5.65 days at the highest concentration level (540 µM; **Figure 2** and **Supplementary Table 1**). In contrast, EQNL
<figure><img src="image_7.png" /><figcaption>**FIGURE 2** | The effect of different concentrations of QI on the activity and growth of AOB *N. europaea* and *N. multiformis*, AOA “Ca. N. franklandus” and “Ca. N. sinensis” and NOB *Nitrobacter* sp. NHB1, determined by nitrite production, or consumption and the abundance of *amoA* or *nrbB* genes. The degradation pattern of QI applied over a range of concentrations is also presented. Error bars represent the standard error of the mean of triplicate cultures. At each time point, bars designated by different lower-case letters are significantly different at the 5% level.</figcaption></figure>

persisted in the liquid cultures throughout the experiment (extrapolated $DT_{50} > 1000$ days; **Supplementary Figure 2** and **Supplementary Table 1**).

## The Impact and Degradation of DCD on AO and NOB Cultures

Dicyandiamide significantly inhibited ($p < 0.05$) the activity of both AOB strains at concentrations of 250 µM and 500 µM, with complete inhibition observed only at 500 µM (**Figure 3**). These concentrations had a reduced or no effect on the two AOA strains, with the activity of “Ca. N. franklandus” and “Ca. N. sinensis” being significantly inhibited ($p < 0.05$) at concentrations $≥1$ mM and $≥0.5$ mM, respectively. However, persistent inhibition was evident only at concentrations $≥2.5$ mM and $≥1$ mM, respectively, (**Figure 3**). Nitrite oxidation by *Nitrobacter* sp. was significantly inhibited ($p < 0.05$) by DCD

only at the highest concentration tested (100 mM). The growth inhibition patterns of AOB and NOB were congruent with the $NO_2^-$ production patterns. This was not the case for AOA where “Ca. N. franklandus” growth was significantly reduced at 0.5 mM ($p ≤ 0.05$; **Figure 3**). DCD did not show a dose-dependent degradation pattern and was rather persistent with $DT_{50}$ values ranging from 45.9 to >1000 days (**Figure 3** and **Supplementary Table 1**).

## The Impact and Degradation of NP on AO and NOB Cultures

Nitrapyrin completely inhibited the activity of both *N. europaea* and *N. multiformis* at concentrations $≥5$ µM (**Figure 4**). The activity of “Ca. N. franklandus” and “Ca. N. sinensis” was significantly reduced at concentrations $≥1$ µM and $≥5$ µM ($p ≤ 0.05$), with complete inhibition observed at $≥5$ µM
<figure><img src="image_8.png" /><figcaption>**FIGURE 3** | The effect of different concentrations of DCD on the activity and growth of AOB *N. europaea* and *N. multiformis*, AOA "Ca. N. franklandus" and "Ca. N. sinensis" and NOB *Nitrobacter* sp. NHB1, determined by nitrite production, or consumption and the abundance of *amoA* or *nxrB* genes. The degradation pattern of DCD applied over a range of concentrations is also provided. Error bars represent the standard error of the mean of triplicate cultures. At each time point, bars designated with different lower-case letters are significantly different at the 5% level.</figcaption></figure>

and ≥25 µM, respectively, (Figure 4). The activity of *Nitrobacter* sp. NHB1 was fully suppressed at concentrations ≥100 µM (Figure 4). The growth inhibition patterns of all tested isolates concurred with the NO₂⁻ production patterns. NP rapidly degraded in all liquid cultures with DT₅₀ values ranging from 0.12 to 12.5 days (Figure 4 and Supplementary Table 1).

## The Impact and Degradation of DMPP on AO and NOB Cultures

3,4-dimethylpyrazole phosphate induced complete inhibition of nitrite production by *N. europaea* and *N. multiformis* at concentrations ≥10 µM and ≥1 µM, respectively, (Figure 5). The pattern of AOB growth inhibition was congruent with

NO₂⁻ production, except for a weak (22.9 ± 3.9%) but significant (*p* < 0.05) inhibition of *N. multiformis* growth at 0.1 µM compared to the control. Conversely, DMPP significantly inhibited the activity of both AOA isolates at higher concentrations of ≥0.5 mM (*p* ≤ 0.05), with complete inhibition of "Ca. N. franklandus" and Ca. N. sinensis" occurring only at 5 mM and ≥1 mM, respectively, (Figure 5). In certain cases, the impact of DMPP on nitrite production was not concomitant with growth patterns, with DMPP concentrations ≥0.5 mM inducing a persistent reduction in *amoA* gene abundance of "Ca. N. franklandus" (Figure 5). DMPP completely inhibited nitrite oxidation by *Nitrobacter* sp. NHB1 only at the highest tested concentrations of 25 mM, while its growth was significantly suppressed at concentrations ≥5 mM (*p* < 0.05; Figure 5). DMPP
<figure><img src="image_9.png" /><figcaption>**FIGURE 4** | The effect of different concentrations of NP on the activity and growth of AOB *N. europaea* and *N. multiformis*, AOA "Ca. N. franklandus" and "Ca. N. sinensis" and NOB *Nitrobacter* sp. NHB1, determined by nitrite production, or consumption and the abundance of *amoA* or *nxrB* genes. The degradation pattern of NP applied over a range of concentrations is also presented. Error bars represent the standard error of the mean of triplicate cultures. At each time point, bars designated with different lower-case letters are significantly different at the 5% level.</figcaption></figure>

was rather persistent in liquid cultures with its DT₅₀ values ranging from 14.34 to >1000 days without a dose-dependent degradation pattern (Figure 5 and Supplementary Table 1).

## Comparison of NIs Activity Based on Calculated EC₅₀ Values

The two AOB isolates showed equivalent EC₅₀ values for the NIs tested (Figure 6) with the exception of EQ derivatives, where significantly higher EC₅₀ values were observed for *N. europaea* compared to *N. multiformis* for both QI and EQNL (*p* < 0.001). DMPP and NP were the most potent inhibitors of *N. europaea*, followed by EQ, QI, and DCD which were not significantly different, with EQNL being the weakest inhibitor (EC₅₀ = 181.4 ± 23.3 µM). For *N. multiformis*, DMPP, NP, and QI were equally effective inhibitors, followed by EQ, DCD, and

EQNL. The two AOA strains exhibited contrasting responses, with "Ca. N. franklandus" having decreased sensitivity to DCD and DMPP compared to "Ca. N. sinensis." EQ, its derivatives and NP were equally effective inhibitors of both AOA isolates, with QI having the lowest EC₅₀ values (0.3 ± 0.0-0.7 ± 0.4 µM), while DCD and DMPP were the weakest AOA inhibitors (Figure 6). EQ, its derivatives and NP were equally suppressive toward *Nitrobacter* sp. NHB1, while DMPP and DCD showed no appreciable inhibition.

## DISCUSSION

This study is the first to investigate the inhibitory effect of EQ, a novel NI of potential agricultural relevance, and its oxidation derivatives, QI and EQNL, on soil nitrifiers grown
<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>
in pure cultures and demonstrate greater inhibition of all three compounds on AOA compared to AOB isolates. In all cultures, EQ was rapidly transformed to QI and EQNL, with the former being the major but least persistent derivative, while the latter being the minor but more persistent derivative, and was consistent with previous studies in soil (Karas et al., 2015; Papadopoulou et al., 2016). Considering that (i) in all cultures, QI showed equivalent or higher inhibitory activity compared to its parent compound, and (ii) in EQ-amended cultures, QI was formed at concentrations equal or higher than those expected to induce an inhibitory effect on the AO tested, and EQNL was formed at levels substantially lower than those expected to result in an inhibitory effect on the AO tested (Figure 1 and Supplementary Table 2), we suggest that QI is the main determinant for the persistent inhibitory effect of EQ on AO and NOB, in line with our previous soil studies (Papadopoulou et al., 2016). The higher inhibition potential of QI for AOA compared to AOB isolates, contradicts our previous soil studies, where equivalent inhibitory effects against both groups were observed. Although direct comparisons between soil and culture studies cannot be made, we suggest that the concentrations of QI formed in soil samples (up to 86.1 µmol Kg⁻¹ dwt soil) probably reached or exceeded its inhibition threshold levels for both AO groups.

In contrast to EQ and its derivatives, DCD and DMPP exhibited higher inhibitory activity toward AOB isolates as observed by Shen et al. (2013) who reported greater inhibition by DCD on *N. multiformis* compared to the AOA *N. viennensis*. Of these two NIs, DMPP showed greater inhibitory activity toward both AOB isolates. Data on the inhibitory activity of DMPP toward soil-derived cultures of AOB and AOA strains are scarce. Liu et al. (2019) recently reported greater inhibition by DMPP (EC₅₀ = 448 µM) compared to DCD (EC₅₀ = 947.1 µM) to "Ca. Nitrosocosmicus agrestis," a soil strain closely related to "Ca. N. franklandus." Unlike DMPP, there are several reports on the inhibitory activity of DCD on soil AOA and AOB cultures with DCD strongly inhibiting *Nitrososphaera* sp. JG1 (Kim et al., 2012) and *Ca. Nitrosarchaeum koreensis* MY1 (Jung et al., 2011) at 0.5 mM which was in the same range to the two AOA strains examined here. Lehtovirta-Morley et al. (2013) showed that DCD induced a significant inhibition of "Ca. Nitrosotalea devanaterra" at 1 mM, compared to 0.5 mM needed for the inhibition of "Ca. N. sinensis" in our study. Others reported EC₅₀ values of 950 µM for "Ca. N. agrestis" (Liu et al., 2019), and 940.6 µM for *N. viennensis* (Shen et al., 2013) compared to 1568.5 µM observed here for "Ca. N. franklandus." For AOB, Shen et al. (2013) reported an EC₅₀ of 80.3 µM for DCD on *N. multiformis* compared to 248.7 µM observed in our study for the same strain. Although there are no obvious differences between the two studies explaining this variation, the salt and concentration of ammonium was different which may have affected the growth characteristics of *N. multiformis*.

Nitrapyrin was the only tested NI that showed an equivalent and strong inhibitory effect toward both AOB and AOA isolates, suppressing their activity at concentrations ≥0.5–5 µM and ≥1–5 µM, respectively. This in line with previous studies which showed inhibition of AOB (*Nitrosomonas* sp., *Nitrosospira* sp., *Nitrosolobus* sp., *N. europaea*, *N. multiformis*) and AOA strains

(Nitrososphaera sp. JG1, *Nitrosarchaeum koreensis* MY1) at levels varying from 0.86 µM for AOB (Bélser and Schmidt, 1981) to 10 µM for both AOB and AOA (Jung et al., 2011; Kim et al., 2012; Martens-Habbena et al., 2015). Comparison with other AOA isolates indicates that inhibition characteristics are similar between strains belonging to the same phylogenetic group. For example, Lehtovirta-Morley et al. (2013) demonstrated that NP halted the activity of "Ca. N. devanaterra ND1" at concentrations ≥10 µM compared to ≥5 µM for "Ca. N. sinensis (ND2)" in our study, while Liu et al. (2019) reported an EC₅₀ of 0.6 µM for "Ca. N. agrestis" compared to 1 µM for "Ca. N. franklandus" in our study. However, in contrast to our findings for NP inhibition of *N. multiformis* (EC₅₀ 0.8 ± 0.3 µM), Shen et al. (2013) reported a much weaker inhibitory effect for the same strain (EC₅₀ > 173 µM). In addition to the minor differences in cultivation conditions between the two studies, Shen et al. added solid NP directly into the cultures to achieve concentrations in the range of 40–173 µM, with the highest level corresponding to the upper limit of NP water solubility at 20°C (40 mg L⁻¹), entailing a risk for precipitation of the active compound.

The considerable range in the inhibitory concentrations of the tested NIs may indicate differences in their mode of action not considered previously. For example, DCD, DMPP, and NP, all considered as Cu-chelators, varied in their ability to inhibit AOA (Lehtovirta-Morley et al., 2013; Shen et al., 2013; Liu et al., 2019). In addition, NP has also been proposed to function as an alternative AMO substrate, generating 6-chloropicolinic acid which irreversibly deactivates ammonia oxidation (Vannelli and Hooper, 1992). This inhibitory mechanism proposed for NP may offer an explanation for its rather universal inhibitory activity toward AOA and AOB. Both EQ and its derivatives possess high-antioxidative capacity acting as free radical scavengers (Błaszczyk et al., 2013). As EQ and its degradation product QI showed similar inhibitory effects to NO-scavengers (e.g., PTIO; Martens-Habbena et al., 2015), their efficiency against AOA may be due to a similar mode of action. Alternatively, as QI is a strong antioxidant, it could be involved in oxidative stress-related cell disruption particularly in AOA, with AOB being capable of coping with oxidative stress using catalases, enzymes which are largely absent in AOA (Kim et al., 2016).

In addition to the contrasting differences in sensitivity between AOA and AOB to all NIs tested (except for NP), we also observed differences in the sensitivity between the two AOA or two AOB strains examined. For QI and EQNL, *N. multiformis* was consistently more sensitive than *N. europaea*, and for DCD and DMPP, "Ca. N. sinensis" was consistently more sensitive than "Ca. N. franklandus." Studies on the comparative sensitivity of AOB isolates to chemicals, including NIs, are scant. Brandt et al. (2001) reported a higher sensitivity of *N. multiformis* over *N. europaea* to linear alkylbenzene sulfonate surfactants. The different sensitivity of the two AOB isolates to EQ derivatives is probably related to differences in the physiology of these isolates. Comparative genomic and proteomic analysis of *N. europaea* and *N. multiformis* showed that the two strains possess a largely different set of stress response proteins, alkyl hyperoxide reductase vs superoxide dismutase and rubrerythrin, respectively, that might exhibit different efficiencies to stress imposed by QI
and EQNL (Zorz et al., 2018). Alternatively, *N. europaea* has a greater array of membrane protein transporters, potentially enabling a greater efflux of toxic chemicals (Zorz et al., 2018).

The different sensitivities of the two tested AOA isolates to DCD and DMPP are also associated with the contrasting ecophysiologies (Lehtovirta-Morley et al., 2014, 2016). In line with our findings, previous studies comparing the two strains reported a higher sensitivity of “*Ca*. N. sinensis” to both simvastatin (Zhao et al., 2020) and 3,5-dichloraniline (Vasileiadis et al., 2018). The higher tolerance of “*Ca*. N. franklandus” to DCD and DMPP might be associated with its capacity to produce extracellular polymeric substances (EPS) leading to aggregate formation that block the hydrophilic NIs DCD and DMPP of accessing the surface of cells engulfed into hydrophobic EPS (Gao et al., 2007). This production of EPS is a feature shared by all *Ca*. Nitrosocosmicus isolates (Jung et al., 2016; Lehtovirta-Morley et al., 2016; Sauder et al., 2017; Alves et al., 2019; Liu et al., 2019) and has been reported as a protection mechanism of AOB against NIs (Powell and Prosser, 1991).

The comparative analysis of the inhibitory range of the tested NIs highlights the practical implications of our findings. The two most widely used NIs, DCD, and DMPP, showed high inhibitory activity only to AOB, the latter being the most potent AOB inhibitor together with NP. While NP is the only NI currently used in agriculture that demonstrates equal inhibition of both AOB and AOA, it is not currently registered for use in Europe. These findings have serious practical implications for nitrification inhibition in agricultural soils with acidic to neutral pH, which constitute 30% of the World's soils (pH < 5.5) and a large fraction of European agricultural soils (mean soil pH = 5.8; Fabian et al., 2014), and where ammonia oxidation is often dominated by AOA (Zhao et al., 2020). Differences in the inhibition thresholds between AOA and AOB could affect agricultural practice, as AOA may contribute to nitrogen fertilizer loss under conditions when AOB are inhibited (Hink et al., 2018). Conversely, universal inhibitory effects on both AOB and AOA, and perhaps commomox bacteria recently reported to be inhibited by NP (primarily), DCD and DMPP in soil microcosm studies (Li et al., 2019b), suggest that nitrification inhibition would not be compromised by functional redundancy. Alternatively, the use of mixtures of NIs exhibiting complementary activity against different AO groups or targeting different parts of the ammonia oxidation pathway could be as efficient as using broad range NIs. In this regard, the potential agricultural use of EQ as a novel NI, applied alone or in combination with NIs selective to AOB (i.e., DMPP) could be promising, considering its low cost (equivalent or lower than that of DCD and NP; Blaszczyk et al., 2013), and its unique feature to be transformed in soil to QI, a highly potent inhibitor of AOA and whose activity to AOB is comparable with that of NIs currently used in agricultural settings such as DCD. Although extrapolating from pure culture tests to predicted effects in soil should be performed with caution, based on our liquid culture assays, a soil concentration of 10 mg Kg⁻¹ of EQ (corresponding to 14.4 Kg ha⁻¹, assuming incorporation of the NI to the top 5 cm of the soil profile of a field site of 1 ha, and soil bulk density 1.3 g cm³) could have a universal inhibitory effect on both AOA and AOB, while 0.06 mg Kg⁻¹ (<0.1 Kg ha⁻¹)

would be required to achieve effective inhibition AOA only. This is consistent with earlier studies that showed that EQ, when applied in soil at concentration levels (50 mg Kg⁻¹) simulating a wastewater disposal scenario, resulted in inhibition of both AOA and AOB (Papadopoulou et al., 2016). Such application rates are in the same range as those of established NIs, providing the first evidence for the feasibility of EQ use in an agricultural setting. On-going studies will determine the effective dose rates of EQ under full-scale agricultural conditions.

There is less known about the direct effects of NIs on NOB, despite their important regulatory function in the overall nitrification process (Daimis et al., 2016). NOB are closely associated with AOs and their activity results in the rapid conversion of potentially toxic nitrite to nitrate (Matsumoto et al., 2009), an important nitrogen source for plants and aerobic soil microorganisms (Koch et al., 2015). In disturbed agricultural ecosystems such as fertilized soils, NOB-derived nitrate production contributes to N losses and environmental pollution through nitrate leaching and subsequent denitrification processes (Raun and Johnson, 1999). We demonstrated that DMPP and DCD were not active against *Nitrobacter* sp. NHB1, in contrast to NP, EQ, and its derivatives which were suppressive to the tested isolate in µM concentration levels. Our study was the first to provide data regarding the impact of DCD, DMPP, EQ, and its derivatives on a pure NOB culture, while NP previously applied at rates up to 50 µM did not inhibit the nitrite-oxidizing activity of the widely distributed *Nitrobacter agilis* (Matsuba et al., 2003), in line with our results.

The impact of NP, EQ, and its derivatives on the activity of both AOs and NOB could affect the total nitrogen balance, and the direction and degree of nitrogen transformation during the nitrification process. This could have serious practical and ecological implications in cases where NIs inhibit NOB to a greater extent than AOs. This would lead to possible NO₂⁻ accumulation in soil and increased NO₂⁻ driven N₂O production (Venterea et al., 2015) with reciprocal effects for the environment and plant productivity. However, our findings suggest a lower (DCD, NP, DMPP, and QI) or equivalent (EQ) inhibition potential of NIs against *Nitrobacter* sp. compared to AOs (Figure 6). Further studies extended to other NOB, including the widely distributed and diverse *Nitrospira*-like bacteria, would determine the full inhibitory potential of NIs on soil NOB.

In parallel, we determined the degradation and transformation of the tested NIs to identify potential links between the duration of exposure (persistence) and the effects observed. The total residues of EQ showed limited persistence in the AOA and NOB cultures (DT₅₀ = 2.4–8.7 days), and low to moderate persistence in the AOB cultures (DT₅₀ = 8.7–60.1 days), a difference most likely attributed to abiotic factors such as medium pH (acidic for *Ca*. N. sinensis and *Nitrobacter* sp. vs. alkaline for *N. europaea* and *N. multiformis*) rather than an enzymatic transformation, considering the autotrophic lifestyle of the tested isolates (Kim et al., 2016), the lack of genetic repertoire for the catabolism of organic pollutants (Chain et al., 2003; Norton et al., 2008), and the recalcitrance of EQ to biotic degradation under aerobic and anaerobic conditions (Shah et al., 2005). However, a direct
interaction of these compounds with the tested organisms cannot be fully excluded. The three commercial NIs showed remarkably different stability in the liquid cultures. DCD showed moderate to high persistence ($DT_{50s} = 44.5$ to >1000 days) with the lowest $DT_{50}$ values observed in the liquid cultures of *Nitrobacter* sp. NHB1, suggesting a potential interaction with this strain. Microbial mineralization of DCD by pure cultures of soil isolated bacteria has been previously reported (Hallinger et al., 1990; Hawser and Haselwandter, 1990). Later studies suggested an enzymatic hydrolysis of the NI catalyzed by microbial ureases (Estermaier et al., 1992). This was not confirmed in our study since no significant differences in the stability of DCD between urease-positive (*N. multiformis*, *Ca. N. franklandus*) and urease-negative (*N. europaea*, *Ca. N. sinensis*, and *Nitrobacter* sp. NHB1) AOs were observed. Abiotic parameters such as temperature or pH could potentially influence the stability of DCD in soil (Amberger, 1986; Hallinger et al., 1990; Kelliher et al., 2008). However, in our studies we did not observe any clear effects of temperature (28°C for AOB and NOB, and 35°C for AOA) and pH (7.5-8.0 for AOB and *Ca. N. franklandus*, and 5.2 for *Ca. N. sinensis* and *Nitrobacter* sp. NHB1) on DCD stability in our liquid culture conditions. NP degraded rapidly ($DT_{50} = 0.12-12.5$ days) in all liquid cultures. In contrast, DMPP showed a high persistence in all liquid cultures, except of *Nitrobacter* where a great variation in its persistence was evident. Considering that *Nitrobacter* sp. NHB1 and AOA were cultured in media of similar content and pH, the above variation was most probably driven by interaction between DMPP and the bacterium. Genomic analysis suggested limited catabolic capacity of aromatic compounds by *Nitrobacter* strains (Starkenburg et al., 2008), although some studies have shown an appreciable degradation of crude oil by *Nitrobacter* (Jong and Okpokwasili, 2012). Overall, we did not observe any clear correlations between NIs persistence and inhibition potency, except for the lower persistence of DCD and DMPP in the liquid cultures of *Nitrobacter* sp. NHB1 which coincided with the limited activity of these NIs to the bacterium.

## CONCLUSION

We compared the inhibition potential of EQ, a novel NI, and those currently used in agricultural practice, on the activity and growth of soil-derived AOA, AOB, and NOB isolates grown in liquid culture. EQ, and primarily its major derivative QI, showed high potency against AOA, in contrast to DCD and DMPP (the only NIs currently registered for use in Europe) which were inhibitory to AOB only. Conversely, NP showed an equally high inhibitory activity against both AOA and AOB isolates. EQ, QI, and NP were the most potent AOA and NOB inhibitors, unlike DCD and DMPP, which demonstrated no activity. DMPP and NP were the most potent AOB inhibitors, with EQ, QI, and DCD showing lower but still appreciable inhibitory activity. Our study (i) offers benchmarking knowledge of the activity range of currently used in agriculture and potentially new NIs to soil AO and *Nitrobacter* NOB, whose response to NIs were unknown, (ii) introduces a novel potential NI, EQ, which

possesses desirable characteristics, including transformation into a highly potent NI (QI) characterized by high inhibitory activity against AOA compared to currently registered NIs in Europe, and (iii) demonstrates the different sensitivity of AOA and AOB to NIs, which indicates that novel strategies for effective nitrification inhibition should rely on new broad-range NIs, or more likely, mixtures of NIs with complementary activity against different nitrifier groups. Future work will focus on the elucidation of EQ and QI inhibitory mechanisms, and on the evaluation of their environmental and agronomic performance under diverse edaphic and climatic conditions and on soils with different microbial communities.

## DATA AVAILABILITY STATEMENT

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

## AUTHOR CONTRIBUTIONS

EP planned, performed, and supervised the experiments, carried out data analysis, drafted and revised the final manuscript. EB, EL, EA, and AK participated in parts of the culture inhibition assays. SV performed the modeling for the calculation of $EC_{50}$ and $DT_{50}$ values, constructed and edited data figures, and reviewed the manuscript. CT planned and participated in part of the culture inhibition assays. UM-S supervised the chemical synthesis of EQ derivatives and reviewed the manuscript. GN provided nitrifying cultures, supervised part of the culture inhibition assays, and reviewed the manuscript. DK conceived the experimental rationale, co-supervised the experiments, and reviewed the manuscript. All authors contributed to the article and approved the submitted version.

## FUNDING

This work is part of the project "NITRIC - Looking up for Novel nITRification Inhibitors: New stories with old Compounds" which has received funding from the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology (GSRT), under grant agreement No. 1229. CT and GN were funded by the AXA Research Fund.

## ACKNOWLEDGMENTS

This manuscript has been released as a pre-print at [bioRxiv], (Papadopoulou et al., 2020 at doi: https://doi.org/10.1101/2020.04.07.023168).

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2020.581283/full#supplementary-material
**Supplementary Figure 1** | The chemical structures of the tested nitrification inhibitors (NIs).

**Supplementary Figure 2** | The effect of different concentrations of EQNL on the activity and growth of AOB *N. europaea* and *N. multiformis*, AOA "Ca. N. franklandus" and "Ca. N. sinensis" and NOB *Nitrobacter* sp. NHB1, determined by nitrite production or consumption and the abundance of *amoA* or *nrbB* genes. The degradation pattern of EQNL applied at a range of concentrations in the liquid cultures of the nitrifying isolates, is also presented. Error bars represent the standard error of the mean of triplicate cultures. Within each time point bars designated by different lower-case letters are significantly different at the 5% level.

**Supplementary Figure 3** | The degradation and transformation patterns of EQ in the liquid cultures of "Ca. N. franklandus" (a, b) and "Ca. N. sinensis" (c, d)

amended with 4.6 µM (a, c) and 46 µM of EQ (b, d). Each value is the mean of triplicates ± standard error. Bars designated by one asterisk show the concentration of EQ and its oxidative derivatives at the onset of inhibition, while bars designated by two asterisks indicate the time point when maximum QI concentrations were observed.

**Supplementary Table 1** | DT₅₀ values (days) of the different nitrification inhibitors (NIs) tested per nitrifying isolate and NI concentrations used. DT₅₀ values were calculated by fitting the best fitting kinetic model to the degradation data.

**Supplementary Table 2** | Mean concentrations ± standard errors (µM) of QI and EQNL formed in liquid cultures of nitrifying isolates amended with EQ at the (i) onset of inhibition, and (ii) time of detection of their maximum concentration levels. The timepoint (days) at which each measurement was taken is given in brackets.

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**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2020 Papadopoulou, Bachtsevani, Lampronikou, Adamou, Katsaouni, Vasileiadis, Thion, Menkissoglu-Spiroudi, Nicol and Karpouzas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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# Assessing the Effects of β-Triketone Herbicides on the Soil Bacterial and hppd Communities: A Lab-to-Field Experiment

Clémence Thiour-Mauprivez¹,²,³, Marion Devers-Lamrani³, David Bru³, Jérémie Béguet³, Aymé Spor³, Arnaud Mounier³, Lionel Alletto⁴, Christophe Calvayrac¹,², Lise Barthelmebs¹,² and Fabrice Martin-Laurent³*

¹ Biocapteurs-Analyses-Environnement, Université de Perpignan Via Domitia, Perpignan, France, ² Laboratoire de Biodiversité et Biotechnologies Microbiennes, USR 3579 Sorbonne Universités (UPMC) Paris 6 et CNRS Observatoire Océanologique, Banyuls-sur-Mer, France, ³ Agroécologie, AgroSup Dijon, INRAE, Université Bourgogne Franche-Comté, Dijon, France, ⁴ Université de Toulouse, INRAE, UMR AGIR, Castanet-Tolosan, France

## OPEN ACCESS

**Edited by:**
Christopher Bagwell,
Pacific Northwest National Laboratory
(DOE), United States

**Reviewed by:**
Giorgia Pertile,
Institute of Agrophysics (PAN), Poland
Malgorzata Baćmaga,
University of Warmia and Mazury
in Olsztyn, Poland

**\*Correspondence:**
Fabrice Martin-Laurent
fabrice.martin@inrae.fr

**Specialty section:**
This article was submitted to
Microbiotechnology,
a section of the journal
Frontiers in Microbiology

**Received:** 25 September 2020

**Accepted:** 07 December 2020

**Published:** 11 January 2021

**Citation:**
Thiour-Mauprivez C,
Devers-Lamrani M, Bru D, Béguet J,
Spor A, Mounier A, Alletto L,
Calvayrac C, Barthelmebs L and
Martin-Laurent F (2021) Assessing
the Effects of β-Triketone Herbicides
on the Soil Bacterial and hppd
Communities: A Lab-to-Field
Experiment.
Front. Microbiol. 11:610298.
doi: 10.3389/fmicb.2020.610298

Maize cultivators often use β-triketone herbicides to prevent the growth of weeds in their fields. These herbicides target the 4-HPPD enzyme of dicotyledons. This enzyme, encoded by the *hppd* gene, is widespread among all living organisms including soil bacteria, which are considered as “non-target organisms” by the legislation. Within the framework of the pesticide registration process, the ecotoxicological impact of herbicides on soil microorganisms is solely based on carbon and nitrogen mineralization tests. In this study, we used more extensive approaches to assess with a lab-to-field experiment the risk of β-triketone on the abundance and the diversity of both total and *hppd* soil bacterial communities. Soil microcosms were exposed, under lab conditions, to 1× or 10× the recommended dose of sulcotrione or its commercial product, Decano®. Whatever the treatment applied, sulcotrione was fully dissipated from soil after 42 days post-treatment. The abundance and the diversity of both the total and the *hppd* bacterial communities were not affected by the herbicide treatments all along the experiment. Same measurements were led in real agronomical conditions, on three different fields located in the same area cropped with maize: one not exposed to any plant protection products, another one exposed to a series of plant protection products (PPPs) comprising mesotrione, and a last one exposed to different PPPs including mesotrione and tembotrione, two β-triketones. In this latter, the abundance of the *hppd* community varied over time. The diversity of the total and the *hppd* communities evolved over time independently from the treatment received. Only slight but significant transient effects on the abundance of the *hppd* community in one of the tested soil were observed. Our results showed that tested β-triketones have no visible impact toward both total and *hppd* soil bacteria communities.

**Keywords:** soil, microorganisms, β-triketone, herbicide, hppd gene, lab-to-field, non-target organisms
# INTRODUCTION

Soils are known as complex and dynamic habitats where a wide diversity of species lives. Soils could potentially shelter about a quarter of the world biodiversity (Decaëns et al., 2006). Among their inhabitants, microorganisms constitute a huge part of this biodiversity with several billions of bacterial species and more than a hundred thousand fungi species living in one gram of soil (Schloss and Handelsman, 2006). These microorganisms contribute to a series of complex processes and support key soil ecosystem functions associated to food supply, climate regulation or biochemical cycles among others (Wall et al., 2013; Graham et al., 2016). It then appears important to preserve and protect soil microorganisms and their habitats. In 2010, the European Commission officially recognized the need to protect soils and raised them as a non-renewable resource, as this was already the case for water (European Commission, 2010). The European soil biodiversity expert group identified the main pressures on soil organisms and human intensive exploitation appeared as a major threat (Gardi et al., 2013). This intensive exploitation is irremediably linked to the use of plant protection products (PPPs), applied in conventional agriculture to protect crops from various pests. Among them, herbicides are still widely used to control weeds which enter in competition with crops. Some of them, developed during these last two decades are inspired by the biomimetism. They mimicked active natural products and are consequently considered as “eco-friendly” (Cantrell et al., 2012).

This is the case for β-triketone herbicides, for which the chemical structure derived from a natural phytotoxin obtained from the Californian bottlebrush plant, *Callistemon citrinus* (Mitchell et al., 2001). Three β-triketone active substances are mainly used as herbicides on maize cultures: sulcotrione, mesotrione, and tembotrione (Schulz et al., 1993; Mitchell et al., 2001; Lee et al., 2008). These molecules inhibit the 4-hydroxyphenylpyruvate dioxygenase (HPPD) and lead to bleaching and death of weeds (Dayan et al., 2007; Rocaboy-Faquet et al., 2014). This enzyme is not only found in plants but in almost all living organisms, including microorganisms where it takes part to the tyrosine degradation pathway (Moran, 2005). The *hppd* gene, coding the targeted enzyme, is described in about 2000 bacterial species (Thiour-Mauprizez et al., 2020). Thus, soil bacteria, classified as “non-target organisms” by current EU regulation for pesticide authorization, might be impacted by β-triketones, with a possible domino effect on microbial functions supporting soil ecosystem services (Thiour-Mauprizez et al., 2019). In this context, studies assessing the ecotoxicological impact of β-triketone herbicides on soil total bacteria have recently been led (Romdhane et al., 2016a, 2019). In order to get a closer look to the direct effect of β-triketone, we report here the assessment of its ecotoxicological effect on the abundance and the diversity of the total bacterial community and the bacterial community harboring the *hppd* gene. These parameters were measured in a lab-to-field experiment in which lab conditions were led with the active substance at higher doses than the recommended ones, to test the “worst-case scenario” and field experiments were led with recommended field doses (RfDs), in frame with a “realistic scenario of exposure,” as recommended

by the European Food Safety Authority (Ockleford et al., 2017). Our lab-to-field experiment complies the two-tiered approach to assess the possible toxicity of β-triketones on the *hppd* bacterial community which harbors the gene encoding the enzyme targeted by these herbicides.

Under lab conditions, soil microcosms were treated with 1× or 10× the RfD of sulcotrione using the active ingredient or the formulated product (Decano®) or not treated (control). Under these conditions, dissipation studies of the active substance or the formulated PPP in soil microcosms were carried out in order to estimate the scenario of exposure of soil microorganisms. Under field conditions, samples were collected in maize crops exposed to PPP treatments comprising several β-triketones or not treated at all (control). For both lab and field experiments, the abundance and the diversity of both the total and the *hppd* bacterial communities were estimated by quantitative PCR (qPCR) and high throughput sequencing, respectively.

# MATERIALS AND METHODS

## Laboratory Experiment

### Reagents

Standard of sulcotrione [(S) 98.8% purity], a weak acidic herbicide with pKa value of 2.87 and MW = 328.7 g.mol⁻¹ was purchased from Sigma-Aldrich, France. Formulated sulcotrione [Decano® (D)] was purchased from (SAPEC Agro, France). Methanol and acetonitrile (HPLC quality) were purchased from Carlo Erba, dichloromethane for pesticide analysis from Riedel-de-Haën GmbH, trifluoroacetic acid (99.0% purity) and hydrochloric acid (38.0%) from Aldrich and water was Milli-Q quality.

## Soil Sampling and Microcosm Set-up Under Lab Conditions

Soil samples were collected from the surface layer (0–20 cm) of an arable field located in Perpignan, France. According to the World Reference Base of Soil Resources, it is a sandy loam clay-rich soil composed of 16.2% clay, 29.1% silt, and 54.7% sand. Granulometry was measured according to the particle size 5 fractions with no decarbonization method (NF X 31-107). Soil contains 27.5 g/kg of organic matter, 15.99 g/kg of organic carbon, and 1.25 g/kg of nitrogen. These contents were measured by dry combustion according to the standard methods NF ISO 10694 and NF ISO 13878. Soil physicochemical characteristics are 99 meq/kg Cation Exchange Capacity (CEC), 260.3% Ca2/CEC and water pH 8.04. CEC was measured according to the Metson method (NF X 31-130) and water pH was determined using a glass electrode (NF ISO 10390). Soil samples were sieved to 2 mm and soil moisture was of 3%. Soil samples were divided in microcosm of 20 g of soil each and treated or not with active ingredient {2-[2-chloro-4-(methylsulfonyl)benzoyl]-1,3-cyclohexanedione} (S) or formulated compound [Decano® (D)]. For each treatment (sulcotrione or Decano®), twelve microcosms were treated at 1× RfD (1.5 µg/g), twelve were treated at 10× RfD (15 µg/g) and twelve remained untreated (control). These doses were chosen to fulfill the two-tier scenario
of exposure. Soil moisture was adjusted at 22% relative humidity. Soil microcosm were incubated at 21 ± 2°C in the dark. Every 2 days, microcosms were ventilated and soil moisture was adjusted if needed under sterile conditions. Soil samples were collected after 30 min of treatment (day 0), 7, 14, and 42 days of incubation and immediately stored at -20°C for further analyses. Three replicates were prepared for each treatment (S control, S 1× RfD, S 10× RfD, D control, D 1× RfD, and D 10× RfD).

# Herbicide Quantification

## Extraction step

This analytical method was an adaptation of the procedure previously published by Chaabane et al., 2008. In order to follow active substance or formulated PPP dissipation in soil, 10 g of soil (triplicates) for each condition were extracted twice with 30 mL of acetonitrile/0.1 M hydrochloric acid (90/10; V/V) under agitation at 100 rpm orbital shaking for 50 min and then filtered on a Whatman filter GF/A 47 mm. The organic filtrate was evaporated at 30°C and the acidic aqueous solution was then extracted twice with 6.0 mL of dichloromethane. The organic phase was evaporated to dryness and then solubilized with 3.0 mL of methanol. The final extract was analyzed by High Performance Liquid Chromatography (HPLC).

## Chromatographic analysis

Soil extracts treated with sulcotrione were analyzed using a VWR Hitachi LaChrom apparatus consisted of auto-sampler L-2200 and HTA L-2130 pump modules equipped with a Kinetex C18 column (5 µm, 150 mm × 3 mm) and a DAD L-2450 UV/Vis detector set at 285 nm. For sulcotrione, the mobile phase consisted of a mixture of water acidified with 0.1% acetic acid (AW) and acetonitrile (ACN) delivered at a flow-rate of 0.5 mL/min with an isocratic mode 70/30 (AW/ACN) for 15 min. For soil extracts treated with Decano®, the mobile phase was the same as the one used for sulcotrione but was delivered at a flow-rate of 1 mL/min with an isocratic mode 80/20 (AW/ACN) for 8 min.

## Analytical performance

Recovered analytics were evaluated by spiking soil samples replicates (n = 5) with increasing doses (0.1–75 µg/g) of analytical standards of sulcotrione or formulated sulcotrione (Decano®) prior to extraction. The mean recovery rates were estimated as 85 ± 10 and 52 ± 4%, for sulcotrione and Decano®, respectively. The limit of quantification, defined as the sample concentration required to give a signal-to-noise ratio of 5:1 was evaluated to 0.1 µg/g for both sulcotrione and Decano®.

# Field Experiment

Soil samples were collected from the surface layer (0–20 cm) of three arable fields located near Tarbes, France. These fields were not part of an experimental parcel, they were exploited by farmers and, thus, were treated at RfDs of PPPs with a crop protection program which we did not choose. Field 1 has not received any PPPs for 3 years and was historically cultivated with maize but is now cultivated with walnut tree and will constitute the control field. Field 2 was sown with maize on the 30th April 2019 and

was treated with Camix® (containing 40 g/L mesotrione, applied at 2.5 L/ha) on the 23th May 2019 and with Auxo® (containing 50 g/L tembotrione, applied at 0.6 L/ha), Nisshin (nicosulfuron-based, 0.6 L/ha), Banvel (dicamba-based, 0.2 L/ha), and Sakol (alkylpolyglucoside-based, 0.1 L/ha) on the 3rd June 2019. Three samplings were made over one crop cycle on these three fields with three replicates made of seven composite-samples each done using an auger. The first sampling time was before seedling and treatments on the 16th September 2018 (September 2018). The second was made after treatment for field 3 whereas in the field 2, it was made between the first and the second treatment on the 6th June 2019 (June 2019). For all the fields, the last sampling campaign was led on the 28th August 2019 (August 2019). Soil properties and chemical characteristics are described in the **Supplementary Table 1**. Soil samples were stored at -20°C until use.

# Ecotoxicological Impact of β-Triketone on Soil Bacterial Communities

## Soil DNA Extraction

At each sampling time of the experiment, nucleic acids were extracted with RNeasy PowerSoil DNA Elution Kit (Qiagen). DNA quality was checked and concentrations quantified by Quant-iT™ PicoGreen™ (Thermo Fischer Scientific, France). A mean of 0.19 ± 0.18 and 0.38 ± 0.29 µg/g of soil were extracted from laboratory and field samples, respectively. DNA extracts were stored at -20°C until use.

# Abundance of Total and hppd Bacterial Communities

Prior to quantification, inhibition tests were performed as previously described in Petric et al. (2011), in order to detect the presence of DNA co-extracts possibly inhibiting the qPCR assays. Samples were then diluted at 1 ng/µl. Amplification of both *hppd* and 16S rRNA genes were carried out using a ViiA™ 7 (Thermo Fisher Scientific, France) in a final reaction mixture of 15 µL. The mixture contained SYBR Green PCR Master Mix (Takyon™ Low ROX SYBR® 2× Mastermix Blue, Eurogentec, Belgium), 250 ng of T4 gp32 (Qiogene, MP Biomedicals, France), 10 µM of each *hppd* primer (HIAF and FFER, Thiour-Mauprivez et al., 2020), or 1 µM of each 16S rRNA primer (341F and 534R, Muyzer et al., 1996) and 1 ng of soil DNA template. qPCR runs were as follow for the amplification of the *hppd* gene: *Taq* polymerase enzyme activation step for 3 min at 95°C, then 40 cycles of 10 s of denaturation at 95°C, 30 s of annealing at 54°C, elongation for 30 s at 72°C, and a melting curve stage with 15 s at 95°C, 1 min at 68°C, and 15 s at 95°C (data were collected at this step). Run parameters used for the amplification of 16S rRNA gene were previously described by Romdhane et al. (2016a). Standard curves were generated using serial dilutions of linearized plasmid pGEM-T easy vector system containing each standard gene sequence (ranging from 10² to 10⁷ copies per qPCR reaction). For each condition tested, two qPCR assays were conducted. In each assay, qPCR calibration was performed in triplicate and three no-template controls (NTC) were also included.
## Diversity of Total and *hppd* Bacterial Communities

Total and *hppd* bacterial diversities were monitored using a high throughput sequencing of 16S rRNA and *hppd* amplicons, respectively (Illumina MiSeq, Microsynth). A two-step PCR procedure was used to amplify the *hppd* gene from obtained DNA extracts. In the first step, 27 cycles of amplification were performed in duplicate using the NGS_FFER-NGS_HIAF primer pair (Thiour-Mauprizez et al., 2020). Duplicates were then pooled and a second step of PCR was conducted, in duplicate, using 1 µL of the previous PCR as template to carry out eight cycles of amplification with barcoded primers. To amplify the 16S rRNA gene from obtained DNA extracts, 20 cycles of amplification were performed in duplicate using the U341F-805R primer pair (Takahashi et al., 2014). Duplicates were then pooled and a second step of PCR was conducted, in duplicate, using 1 µL of the previous PCR as template to carry out 15 cycles of amplification with barcoded primers. After pooling, amplicon size was checked by electrophoresis on a 2% agarose gel. 25 µL of PCR products were normalized using SequalPrepTM Normalization Plate Kit (Invitrogen) and 10 µL of each sample were sent to Microsynth for sequencing. 16S rRNA amplicon data analysis were conducted as described in Romdhane et al. (2016a). For the *hppd* amplicon data analysis, reads were quality-controlled and assembled using PEAR (Zhang et al., 2014). Unassembled reads and once-assembled reads outside the expected range were discarded. Sequences were corrected and converted into protein sequences using framebot (Wang et al., 2013), then clustered into OTUs using cd-hit (Li and Godzik, 2006). Representative *hppd* sequences for each OTU were aligned using Muscle (Edgar, 2004) and the maximum likelihood phylogeny was calculated using FastTree 2 algorithm (Price et al., 2009). Several α-diversity indices (PD whole tree, Simpson reciprocal, and Shannon indices) were then calculated using rarefied OTU tables at the depth of 6000 sequences per sample in the microcosms treated with sulcotrione, 15,000 sequences per sample in those treated with Decano® and 20,000 sequences per sample in the soils sampled directly from fields considering the 16S rRNA amplicons. Same indices were calculated using rarefied OTU tables of 2300 sequences per sample in the microcosms treated with sulcotrione, 2000 sequences per sample in those treated with Decano® and 4200 sequences per sample in the soils sampled directly from fields considering the *hppd* amplicons. Principal Coordinate Analysis (PCoA) plots were generated in QIIME based on weighted UniFrac distance matrix and coordinates were used to draw 3D figures.

## Statistical Analysis

Statistical analysis were conducted using R software and a significance threshold was set at a *p*-value of 0.05 (R Development Core Team, 2005). For qPCR results, data were log-transformed. Data normality was verified by a Shapiro-Wilk test. The homogeneity of variances of the residues was verified by a Levene test. Data were subjected to an analysis of variance (ANOVA) and Tukey test was performed for each treatment and each time point. Not all data showed a normal distribution. Consequently, non-parametric test (Kruskal-Wallis test) was carried out on

non-normal distributed data. Weighted or unweighted UniFrac distance matrices were subjected to a PERMANOVA analysis using the ADONIS function from R package "vegan" (Oksanen et al., 2020). Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) was performed to select discriminant OTUs between different treatments using the function "splsda" from R package mixOmics (Rohart et al., 2017). A homemade R script was used to identify the discriminant OTUs by performing an ANOVA on each of these OTUs.

## RESULTS

### Fate of Sulcotrione and Decano® in Soil Microcosms

In order to decipher the exposure scenario of bacteria in soil microcosms, kinetics of dissipation of sulcotrione and Decano® in soils were measured at each time point (Table 1). Sulcotrione and Decano® dissipation kinetics could be reasonably described by a first-order kinetics ($C = C_0e^{-kt}$) for the application at 1x RfD. Half-life time of sulcotrione in Perpignan soil was estimated at ca 4 days for 1x RfD. Regarding Decano®, half-life time in Perpignan soil was evaluated at ca 1.5 days for the 1x RfD condition. For the application at 10x RfD, we were only able to estimate graphically the half-life time of sulcotrione at ca 14 days and of Decano® at ca 18 days (Supplementary Figure 1). Anyhow, sulcotrione and Decano® both reached an unquantifiable concentration after 42 days of exposure in Perpignan soil, whatever the starting concentration of RfD applied.

### Impact of Sulcotrione and Decano® on the Abundance of the Total and the *hppd* Bacterial Community

The impact of sulcotrione and Decano® on the abundance of the total and the *hppd* bacterial community in the soil microcosms was monitored by qPCR. Sequence copy numbers ranged from 8.99 × 10³ to 3.82 × 10⁵ sequences of 16S rRNA per nanogram of DNA and from 4.81 × 10³ to 2.75 × 10⁵ sequences of *hppd* per nanogram of DNA. The abundances of both the total and the *hppd* bacterial communities were not affected by the β-triketone treatments applied. Similarly, the relative abundance of *hppd* sequences per 1000 sequences of 16S rRNA (Figure 1) was not affected by any of the β-triketone treatments applied. In both cases, only a time effect was observable with relative abundance measured at day 0 different from the other sampling days

<table><caption>TABLE 1 | Main environmental parameters of sulcotrione and Decano® estimated by modeling their kinetics of dissipation in Perpignan soil.</caption><thead><tr><th>Treatment</th><th>Sulcotrione</th><th colspan="2">Decano®</th></tr></thead><tbody><tr><td>RfD</td><td>1</td><td>10</td><td>1</td><td>10</td></tr><tr><td>k (day)</td><td>0.36 ± 0.02</td><td>n.d.</td><td>0.43 ± 0.03</td><td>n.d.</td></tr><tr><td>DT<sub>50</sub> (day)</td><td>1.94 ± 0.08</td><td>n.d.</td><td>1.63 ± 0.11</td><td>n.d.</td></tr><tr><td>R<sup>2</sup></td><td>0.81 ± 0.10</td><td>n.d.</td><td>0.99 ± 0.01</td><td>n.d.</td></tr></tbody></table>
<figure><img src="image_13.png" /></figure>

**FIGURE 1** | *hppd* sequence copy number per 1000 sequences of 16S rRNA. Soil microcosms (A) treated with sulcotrione; (B) with Decano®. Values shown are averages of *n* = 3 biological replicates for each series. Error bars are calculated from the standard deviation for each series. ANOVA and Kruskal-Wallis test *p* < 0.05 were considered as significant and, if needed, indicated by a small letter.

(ANOVA, *p* = 0.001) for microcosms treated with sulcotrione and relative abundance measured at day 42 different from those measured at 0, 7, or 14 days (Kruskal-Wallis test, *p* = 0.002) for microcosms treated with Decano®.

## Impact of Sulcotrione and Decano® on the Diversity of the *hppd* Bacterial Community

To further investigate the possible ecotoxicological impact of sulcotrione and Decano® on the diversity of total and *hppd* bacterial communities, the 16S rRNA and the *hppd* amplicons were amplified from soil DNA extracts and sequenced. These sequences were grouped into 2849 OTUs and 1688 OTUs at 80% amino-acid sequence identity threshold for sulcotrione and Decano® treatment respectively. α-Diversity was estimated for each treatment by a range of indices depicted in **Table 2**. No significant differences neither overtime nor in response to the RfD applied was shown for all tested treatments. Same measurements were made on the total bacterial α-diversity by sequencing the 16S rRNA gene. Sequences were grouped in 4099 OTUs in the soil treated with sulcotrione and in 4767 OTUs in the soil treated with Decano® at 94% nucleotide sequence identity threshold. A time effect was highlighted but no significant differences due to the treatment applied were shown (**Supplementary Table 2**). Together, these results indicate that the α-diversity of both the total and the *hppd* communities are not affected by sulcotrione or Decano® in our experimental conditions.

The ecotoxicological impact of sulcotrione and Decano® on the *hppd* bacterial β-diversity of soil microcosms was evaluated. For each treatment, the analysis of the PCoA, representing the weighted UniFrac distances, showed no significant difference in the *hppd* bacterial community composition between RfD applied (Control, 1× RfD, or 10× RfD) and sampling time (0, 7, 14, 42 days after treatment) (**Figure 2**).

Altogether, the results obtained for α- and β-diversity measurements indicate that the diversity of the *hppd* bacterial

community was not modified over time or according to the RfD applied. Same conclusions were made on the measurements of the total bacterial diversity, monitored by 16S rRNA Illumina sequencing (**Supplementary Figure 2**).

<table><caption>**TABLE 2** | Richness and diversity indices of the *hppd* bacterial community calculated for soil microcosms exposed for 0, 7, 14, and 42 days to sulcotrione (S, in brown) or Decano® (D, in blue) applied at different concentrations (control, 1× RfD, and 10× RfD).</caption><thead><tr><th>Day after treatment</th><th>Treatment</th><th>Observed species</th><th>PD whole tree</th><th>Simpson reciprocal</th></tr></thead><tbody><tr><td rowspan="6">0</td><td>S control</td><td>211 ± 24</td><td>62 ± 5</td><td>18 ± 6</td></tr><tr><td>S 1× RfD</td><td>188 ± 26</td><td>55 ± 7</td><td>16 ± 2</td></tr><tr><td>S 10× RfD</td><td>184 ± 49</td><td>54 ± 13</td><td>15 ± 8</td></tr><tr><td>D control</td><td>196 ± 17</td><td>52 ± 4</td><td>16 ± 2</td></tr><tr><td>D 1× RfD</td><td>216 ± 38</td><td>59 ± 8</td><td>26 ± 17</td></tr><tr><td>D 10× RfD</td><td>200 ± 15</td><td>54 ± 2</td><td>17 ± 1</td></tr><tr><td rowspan="6">7</td><td>S control</td><td>165 ± 21</td><td>49 ± 6</td><td>9 ± 2</td></tr><tr><td>S 1× RfD</td><td>192 ± 28</td><td>56 ± 6</td><td>16 ± 6</td></tr><tr><td>S 10× RfD</td><td>167 ± 11</td><td>49 ± 3</td><td>11 ± 1</td></tr><tr><td>D control</td><td>206 ± 24</td><td>55 ± 6</td><td>21 ± 7</td></tr><tr><td>D 1× RfD</td><td>208 ± 17</td><td>57 ± 4</td><td>26 ± 14</td></tr><tr><td>D 10× RfD</td><td>233 ± 12</td><td>63 ± 1</td><td>26 ± 1</td></tr><tr><td rowspan="6">14</td><td>S control</td><td>177 ± 28</td><td>50 ± 7</td><td>11 ± 1</td></tr><tr><td>S 1× RfD</td><td>168 ± 18</td><td>48 ± 5</td><td>9 ± 1</td></tr><tr><td>S 10× RfD</td><td>180 ± 35</td><td>52 ± 7</td><td>10 ± 2</td></tr><tr><td>D control</td><td>204 ± 3</td><td>55 ± 2</td><td>18 ± 1</td></tr><tr><td>D 1× RfD</td><td>230 ± 24</td><td>61 ± 5</td><td>26 ± 11</td></tr><tr><td>D 10× RfD</td><td>225 ± 17</td><td>59 ± 5</td><td>25 ± 5</td></tr><tr><td rowspan="6">42</td><td>S control</td><td>176 ± 21.2</td><td>51 ± 5</td><td>11 ± 2</td></tr><tr><td>S 1× RfD</td><td>167 ± 25</td><td>50 ± 6</td><td>12 ± 2</td></tr><tr><td>S 10× RfD</td><td>199 ± 31</td><td>57 ± 8</td><td>14 ± 4</td></tr><tr><td>D control</td><td>211 ± 1</td><td>57 ± 0</td><td>19 ± 1</td></tr><tr><td>D 1× RfD</td><td>213 ± 7</td><td>57 ± 1</td><td>19 ± 2</td></tr><tr><td>D 10× RfD</td><td>204 ± 11</td><td>57 ± 3</td><td>16 ± 2</td></tr></tbody></table>

Mean values ± confidence intervals are shown. No significant difference for each treatment and each time point was shown (Kruskal-Wallis test *p* < 0.05 were considered as significant and, if needed, indicated by a small letter).
Abundance and Diversity of the Total and the *hppd* Bacterial Community in Agricultural Soils Exposed to β-Triketone Herbicides

The ecotoxicological impact of PPP treatments on the abundance, the diversity and the composition of the total and the *hppd* bacterial communities was also assessed under real agronomical conditions.

Sequence copy numbers ranged from 1.21 × 10⁵ to 4.63 × 10⁵ sequences of 16S rRNA per nanograms of DNA and from

7.90 × 10⁴ to 2.62 × 10⁵ sequences of *hppd* per ng of DNA. The relative abundance of *hppd* gene copies per 1000 sequences of 16S rRNA is shown in **Figure 3A**. An ANOVA test suggest that whatever the treatment applied, values recorded in the samples collected in September 2018 were significantly higher than that of the two others (June 2019 and August 2019) (*p* < 0.01). Considering the relative abundance of *hppd* found in each treated soil, only values observed in field 2 were statistically different from those measured in the others: values recorded in June 2019 (ANOVA, *p* < 0.01) were significantly lower than those of September 2018. This was no longer observed on samples of

<figure><img src="image_14.png" /><figcaption>**FIGURE 2** | UniFrac analysis of the effect of sulcotrione (A) and Decano® (B) applied at different concentrations (control, 1 × RfD, and 10 × RfD) on the *hppd* bacterial community composition of soil microcosms at 0, 7, 14, and 42 days. The first three axes of the PCoA of the weighted UniFrac distance matrix of *hppd* amplicon Illumina sequencing are shown. The percent of variance explained by each axis is given.</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>**FIGURE 3** | Analysis of the abundance (A), the α-diversity (B), and the β-diversity (C) of the *hppd* bacterial community in soils of agricultural exposed to PPPs (fields 2 and 3) or not (field 1) collected at different sampling times over a 1-year crop cycle (September 2018, June 2019, August 2019). (A) *hppd* sequence copy number per 1000 sequences of 16S rRNA gene. Values shown are averages of *n* = 3 biological replicates for each series. Error bars are calculated from the standard deviation for each series. ANOVA *p* < 0.05 was considered as significant and, if needed, indicated by a small letter. (B) Richness and diversity indices of the *hppd* bacterial community (mean values ± confidence intervals). ANOVA tests were conducted and *p* < 0.05 was considered as significant and indicated by a different letter. (C) UniFrac analysis on the *hppd* bacterial community composition.</figcaption></figure>
August 2019, which were not statistically different from those obtained in September 2018 (ANOVA, *p* = 0.09) or in June 2019 (ANOVA, *p* = 0.17).

The *hppd* sequences were grouped into 1562 OTUs at 80% amino-acid sequence identity threshold. α-Diversity was estimated for each condition by a range of indices depicted in **Figure 3B**. The observed species and the Phylogenetic Diversity (PD) indices calculated in field 2 were significantly lower than those of field 1 and field 3, no matter what the sampling date (ANOVA, *p* < 0.01). Same measurements were made on the total bacterial α-diversity and they led to the similar conclusions: the three indices calculated in the case of field 2 were significantly lower from those calculated for fields 1 and 3, respectively (ANOVA, *p* < 0.05, **Supplementary Figure 3**). The total and the *hppd* bacterial α-diversities are different in field 2 than in the other two fields. On June 2019, the observed species and the PD whole tree indices calculated on the *hppd* bacterial α-diversities within field 1 were significantly different from those calculated on September 2018 (ANOVA, *p* < 0.01) but not from those measured in August 2019 (ANOVA, *p*<sub>observed species</sub> = 0.66 and *pPD_whole_tree* = 0.88). It has to be noticed that the α-diversity of the total bacterial community was also modified over time (ANOVA, *p* < 0.05, **Supplementary Figure 3**).

The *hppd* bacterial β-diversity is illustrated on **Figure 3C** by a PCoA representing the weighted UniFrac distances. In addition, an sPLS-DA was performed in parallel to an ANOVA test performed on each *hppd* OTU taken separately. All of these tests showed no statistical differences between soils: the β-diversity of *hppd* bacterial community remained unchanged among time and between the different studied soils. Same tests were performed on the total bacterial β-diversity obtained by sequencing the 16S rRNA gene with sequences grouped in 5739 OTUs at 94% nucleotide sequence identity threshold. Whatever the soil considered, no significant differences were found (data not shown).

DISCUSSION

The aim of this study was to evaluate, in a lab-to-field
experimental design, the ecotoxicological impact of β-triketone
herbicides on the abundance, the composition and the diversity of
total and *hppd* bacterial communities. Under lab conditions, the
worst-case scenario (up to 10 times the agronomical dose) as well
as the effect of the formulation (sulcotrione vs Decano®) were
tested. Under field conditions, agronomical scenario exposure
including the application of several PPP treatments over a 1-year
cropping cycle was tested.

Estimation of the Fate and
Ecotoxicological Impact of Sulcotrione
and Decano® on the Total and hppd
Bacterial Communities Under Lab
Conditions

Under lab conditions, soil microcosms were exposed for 42 days
to 1× or 10× RfD of sulcotrione or Decano®. Kinetics of

dissipation of the active substance were established. Although
the dissipation of sulcotrione in the Perpignan soil has already
been monitored in several studies (Chaabane et al., 2008;
Romdhane et al., 2019), this is the first time that the kinetic of
dissipation of the active substance of Decano® was measured
in this soil. Even if the recovery rate of sulcotrione from
microcosms treated with Decano® was not optimal (52%),
one could observe that, 42 days after application, sulcotrione
was fully dissipated from both batches of microcosms. Our
results are consistent with previous studies where similar
concentrations were applied, showing a complete dissipation
of sulcotrione within 45 days in Perpignan soil (Calvayrac
et al., 2012; Romdhane et al., 2016b, 2019). Observed DT₅₀
at 1× RfD were in the range of those measured in other
studies (4–7 days in dark) confirming that sulcotrione is rapidly
dissipated from soils (Barchanska et al., 2016). Concomitantly to
sulcotrione dissipation and, as previously observed by Romdhane
et al. (2016b), CMBA, the principal transformation product of
sulcotrione, is accumulated in Perpignan soil in both microcosm
batches. As expected, for both Decano® and sulcotrione, at a
higher dose rate of application, dissipation lasted longer than at
lower rate of application.

The effect of sulcotrione and of Decano® on the abundance
and the diversity of both the total and *hppd* soil bacterial
communities was measured by qPCR and amplicon Illumina
sequencing, respectively. Both treatments had no effect on the
abundance of the total soil bacterial community. This observation
is in accordance with that of Romdhane et al. (2016a) showing
that natural triketone (i.e., leptospermone) has either no effect or
very short-lasting effect on the abundance of the total bacterial
community. It has to be noticed that mesotrione, another
β-triketone, was shown to transiently modify the abundance and
the diversity of both soil bacterial and fungal communities (Du
et al., 2018). It suggests that β-triketone ecotoxicological effect
on microorganisms depends on the active compound, on the soil
type and on their interactions. Similarly, the two treatments, at
both doses, had no effect on the composition and structure of the
total bacterial community. This is in agreement with Romdhane
et al. (2019) who reported no change of α- and β-diversities of
the total bacterial community in the soil of Perpignan exposed
to either 1× or 10× RfD of sulcotrione. Interestingly, one
can observe that different studies carried out on the same
soil at different times with different sequencing technologies
(454 pyrosequencing vs Illumina sequencing) under similar
conditions led to similar conclusions showing the reliability of
such approaches, as already suggested (Luo et al., 2012) and
consolidating our observations.

Soil bacterial *hppd* abundance and diversity have already been
monitored in Perpignan soil (Thiour-Mauprivez et al., 2020) but,
to our best knowledge, this was the first time they were monitored
in response to a sulcotrione and Decano® exposure. As observed
for the total bacterial community, both treatments applied at both
doses have no effect on the abundance and diversity of the *hppd*
bacterial community. Therefore, one can conclude that, under
our experimental conditions, the *hppd* bacterial community
harboring the gene target of β-triketone herbicides is insensitive
to formulated sulcotrione or to the active substance. The low
persistence of sulcotrione in Perpignan soil indicates that soil
bacterial community is only shortly exposed to β-triketone and
such a short exposure is not enough to modify the abundance
or the diversity of the soil bacterial community. In addition,
detoxification mechanisms such as efflux transporters, known as
unspecific drivers of resistance of bacteria to antibiotics, could
work as a detoxification mechanism against β-triketones and even
could stop them entering the cell. Such mechanisms have already
been observed in an *Escherichia coli* strain exposed to glyphosate
(Staub et al., 2012). However, another β-triketone formulated
product called Callisto® (mesotrione) is shown to transiently
inhibit the nitrification process in bacteria when applied at
10× RfD, probably because of a direct effect (Crouzet et al.,
2016). This further reinforces the assumption that like for other
herbicidal active substances, the β-triketone ecotoxicological
effect on microorganisms depends on the properties of active
compound, of soil type and of their interactions.

Estimation of the Ecotoxicological Effect of β-Triketone on the Abundance, Composition, and Diversity of Both Total- and hppd-Bacterial Communities Under Field Conditions

The lab-to-field assessment of the ecotoxicological impact of active substances on soil microorganisms has been proposed as a gold standard to conduct environmental risk assessment of pesticides (Karpouzas et al., 2016). Consequently, in parallel to lab-experiments, the impact of β-triketone PPPs, including mesotrione- and/or tembotrione-based PPPs was measured on the abundance, the composition and the diversity of both the total and the *hppd* bacterial communities in field samples collected over 1 year cropping cycle.

The main result of this field study concerns one of the
tested fields (field 2), in which the *hppd* bacterial community
abundance decreased after the first treatment with mesotrione-
based herbicide (Camix®) to finally increase after the second
treatment with tembotrione-, dicamba-, nicosulfuron-, and
alkylpolyglucoside-based herbicides but without reaching the
level of abundance measured initially. This suggests a partial
resilience of the *hppd* community in this field. This observed
time effect might be due to different factors such as climatic
conditions or the phenology of the crop. It could also be explained
by the PPP treatments applied to this field. However, we are
not able to conclude whether the abundance decrease is due
to mesotrione because: (i) Camix® is composed, in addition
to mesotrione, of S-metolachlor (400 g/L) and benoxacor
(20 g/L); (ii) the α-diversity of this field is significantly lower
than those measured in fields 1 and 3, making a comparison
uneasy. Regarding the β-diversity measured in this field, it
remained unchanged and identical to the other fields all along
the cropping cycle.

This field experiment reveals the difficulty to define reliable
references when working on formulated product composed of
several active compounds with different action modes and of
several other compounds entering in their formulation that
are usually bound by confidentiality by the agrochemical firms.

Considering this, it becomes evident that the conclusions of *a priori* risk assessment carried out under control conditions for a single active compound cannot be expanded for a formulated PPP applied in combination with others in the field all along the cropping cycle. This study also highlights the difficulty to define reliable controls when working on real agronomical situations encountered on farms. Indeed, in our experimental design, we chose a control field not exposed to β-triketones located closely to the other two sampled fields cropped with maize and exposed to herbicides. Even if the soils of chosen fields are subjected to the same pedoclimatic conditions and harbor similar physicochemical properties, differences in the bacterial composition were readily observed at the first sampling date. This raises the question of the reference to interpret the changes in response to the PPP treatments applied. Consequently, there is a need to define a normal operating range (NOR) for each studied indicator as it has been evaluated on diverse indicators (soil microbial biomass and bacterial diversity) in a 13 km² parcel on 278 soils all spaced from 215 meters (Constancias et al., 2015). The fully randomized block design that is usually applied in agronomical assay has been shown in the past to be of interest to estimate the impact of several active substances applied one by one on soil microorganisms (Papadopoulou et al., 2016; Storck et al., 2018), but it seems not appropriate to evaluate the risk encountered in-farm at a systemic level which is far more complex with a combination of PPPs applied simultaneously or one after the other all along the cropping cycle.

CONCLUSION

Our study provides a comprehensive lab-to-field assessment
of the ecotoxicity of β-triketone herbicides (active compound
and formulated one) on both the total and *hppd* bacterial
communities. Our lab-experiment confirmed the low persistence
of the active substance in both sulcotrione and Decano®
treated soil microcosms. Under our experimental conditions,
both treatments at agronomical doses have no effect on the
abundance, the composition and the diversity of both total and
*hppd* bacterial communities. Our field-experiment led to similar
conclusions thereby comforting the eco-friendly reputation of
β-triketone herbicides.

DATA AVAILABILITY STATEMENT

The datasets presented in this study can be found in online
repositories. The names of the repository/repositories and
accession number(s) can be found below: NCBI, accession
number: PRJNA666084 (https://www.ncbi.nlm.nih.gov/sra/
?term=PRJNA666084).

AUTHOR CONTRIBUTIONS

CT-M, LB, FM-L, MD-L, and CC conceived and designed the
research. CT-M and CC conducted the chemical analysis. CT-M,
MD-L, AS, and AM conducted the bioinformatics analysis.
JB and DB conducted the PCR experiments. LA conducted the field sampling. CT-M, FM-L, and LB wrote the manuscript. All authors read and approved the manuscript.

## FUNDING

This work was supported by the Région Occitanie and European Funds for Regional Development (FEDER). Authors also

thank Perpignan University Via Domitia for their financial support (BQR, 2018).

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2020.610298/full#supplementary-material

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**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Thiour-Mauprizez, Devers-Lamrani, Bru, Béguet, Spor, Mounier, Alletto, Calvayrac, Barthelmebs and Martin-Laurent. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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# Relative Influence of Plastic Debris Size and Shape, Chemical Composition and Phytoplankton-Bacteria Interactions in Driving Seawater Plastisphere Abundance, Diversity and Activity

Jingguang Cheng¹, Justine Jacquin¹, Pascal Conan¹, Mireille Pujo-Pay¹, Valérie Barbe², Matthieu George³, Pascale Fabre³, Stéphane Bruzaud⁴, Alexandra Ter Halle⁵, Anne-Leila Meistertzheim⁶ and Jean-François Ghiglione¹*

¹ UMR 7621, CNRS, Laboratoire d'Océanographie Microbienne, Observatoire Océanologique de Banyuls-sur-Mer, Sorbonne Université, Banyuls-sur-Mer, France, ² Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, Evry, France, ³ Laboratoire Charles Coulomb (L2C), UMR 5221 CNRS-UM, Place Eugène Bataillon, Montpellier, France, ⁴ Institut de Recherche Dupuy de Lôme (IRDL), Université Bretagne Sud, UMR CNRS 6027, Lorient, France, ⁵ IMRCP, Université de Toulouse, CNRS, Toulouse, France, ⁶ SAS Plastic@Sea, Observatoire Océanologique de Banyuls-sur-Mer, Banyuls-sur-Mer, France

The thin film of life that inhabits all plastics in the oceans, so-called “plastisphere,” has multiple effects on the fate and impacts of plastic in the marine environment. Here, we aimed to evaluate the relative influence of the plastic size, shape, chemical composition, and environmental changes such as a phytoplankton bloom in shaping the plastisphere abundance, diversity and activity. Polyethylene (PE) and polylactide acid (PLA) together with glass controls in the forms of meso-debris (18 mm diameter) and large-microplastics (LMP; 3 mm diameter), as well as small-microplastics (SMP) of 100 µm diameter with spherical or irregular shapes were immerged in seawater during 2 months. Results of bacterial abundance (confocal microscopy) and diversity (16S rRNA Illumina sequencing) indicated that the three classical biofilm colonization phases (primo-colonization after 3 days; growing phase after 10 days; maturation phase after 30 days) were not influenced by the size and the shape of the materials, even when a diatom bloom (Pseudo-nitzschia sp.) occurred after the first month of incubation. However, plastic size and shape had an effect on bacterial activity (³H leucine incorporation). Bacterial communities associated with the material of 100 µm size fraction showed the highest activity compared to all other material sizes. A mature biofilm developed within 30 days on all material types, with higher bacterial abundance on the plastics compared to glass, and distinct bacterial assemblages were detected on each material type. The diatom bloom event had a great impact on the plastisphere of all

OPEN ACCESS

**Edited by:**
Aurélie Cebron,
UMR7360 Laboratoire
Interdisciplinaire des Environnements
Continentaux (LIEC), France

**Reviewed by:**
Inga Vanessa Kirstein,
Aalborg University, Denmark
Luciano Procópio,
Federal University of Rio de Janeiro,
Brazil
Daniela Batista,
University of Minho, Portugal

*Correspondence:
Jean-François Ghiglione
ghiglione@obs-banyuls.fr

**Specialty section:**
This article was submitted to
*Microbiotechnology,*
a section of the journal
*Frontiers in Microbiology*

Received: 25 September 2020

Accepted: 11 December 2020

Published: 13 January 2021

**Citation:**
Cheng J, Jacquin J, Conan P, Pujo-Pay M, Barbe V, George M, Fabre P, Bruzaud S, Ter Halle A, Meistertzheim A-L and Ghiglione J-F (2021) Relative Influence of Plastic Debris Size and Shape, Chemical Composition and Phytoplankton-Bacteria Interactions in Driving Seawater Plastisphere Abundance, Diversity and Activity. *Front. Microbiol.* 11:610231.
doi: 10.3389/fmicb.2020.610231
materials, resulting in a drastic change in diversity and activity. Our results showed that the plastic size and shape had relatively low influence on the plastisphere abundance, diversity, and activity, as compared to the plastic composition or the presence of a phytoplankton bloom.

Keywords: plastic litter, plastisphere, biofilm, biofouling, colonization, microbial ecotoxicology

INTRODUCTION

Plastic pollution has become a global environmental problem
affecting all parts of oceans worldwide, including the most remote
areas such as deep seafloor or polar regions. In this area, the
longevity of the plastics is estimated to be hundreds to thousands
of years (Barnes et al., 2009; Lusher et al., 2015; Kane et al.,
2020). Vast accumulation zones have been identified in the five
subtropical oceanic gyres (Van Sebille et al., 2015), but also in
the Mediterranean Sea that has been proposed as the sixth great
accumulation zone for marine litter (Cózar et al., 2015). Variation
in quantity and composition was observed throughout the
different environmental compartments: polyethylene (PE) and
polypropylene (PP) were mostly observed in epipelagic waters,
whereas polyamide and polyester dominated in sediments. These
variations have been explained through the differences in density,
surface area, and the size of plastic litter (Chubarenko et al., 2016;
Kowalski et al., 2016; Schwarz et al., 2019).

Once entering the environment, plastic litter is subjected to
degradation caused by a combination of mechanical abrasion,
photo- or thermal-oxidation, hydrolysis and biodegradation
(Andrady, 2003). Plastic degradation results in the formation
of tiny plastic fragments of <5 mm size, so-called "secondary
microplastics" that differ from the "primary microplastics" that
are designed and produced as purpose, for example in industrial
cleaners and personal care products. Larger plastics are classically
categorized into meso-debris (5 mm-2 cm) and macro-debris
(>2 cm) for large-scale and long-term monitoring of plastic
litter across countries and environments (Thompson et al., 2009).
Over the estimated 5.25 trillion particles are afloat in the global
ocean, 34.8% of which are small microplastics (SMP; 330 µm-
1 mm), 57.5% of large microplastics (LMP; 1-5 mm), 7% of
meso-plastics, and 0.2% of macro-plastics (Eriksen et al., 2014).
Large debris have been shown to have adverse effects on fish,
seabirds, and other top consumers, whereas microplastics are
suitable for ingestion by smaller organisms at lower trophic levels
(Wang et al., 2019).

When directly released at sea, plastics are primarily colonized
by microorganisms that form dense biofilms on their surfaces, the
so-called “plastisphere” (Zettler et al., 2013). The plastisphere has
multiple effects on the fate and impacts of plastic in the marine
environment. First, the biofilm growing on the surface and inside
plastic cracks can contribute to a loss of physical integrity, a
phenomenon called “biodeterioration” that play a significant role
on the breakdown of large plastic debris into microplastics when
coupled with abiotic degradation (Sabev et al., 2006; Dussud
and Ghiglione, 2014). Second, the biofouling may increase or
decrease the buoyancy of the plastic particles, rendering them
susceptible to vertical transport (Kooi et al., 2017; Kane et al.,

2020). Third, extracellular polymeric substances produced by
the biofilm contribute to co-aggregation of microorganisms and
detritus together with microplastics, thus resulting in an increase
or decrease of sedimentation rates of algal bloom (such as
diatoms or cryptophytes) with important impact on ecosystem
functioning (Long et al., 2015; Severin et al., 2017). Fourth,
biofilms alter the physico-chemical properties of plastics and
increase further colonization by metazoan larvae (Hadfield, 2011;
Ghiglione and Laudet, 2020). Fifth, biofilms can host pathogens
species that can be transported across the marine environment
by plastic dispersion and thus participate to the diffusion of
infectious diseases (Zettler et al., 2013; Kirstein et al., 2016).
And finally, plastic biodegradation is promoted by the biofilm by
secreting extracellular enzymes able to transform polymers into
oligomers and monomers (“biofragmentation”), which can serve
as carbon source for microbial growth (“bio-assimilation”) that
may result in the complete mineralization of polymers into CO2
and H2O (“biomineralization”) (Jacquin et al., 2019).

A growing literature is reporting the large diversity of microorganisms that composed the plastisphere, which differed from the surrounding communities living in a free-living state (Zettler et al., 2013; Bryant et al., 2016; Pinto et al., 2019), or attached to organic particles (Dussud et al., 2018b), sediment particles (Basili et al., 2020) or other substrates such as wood, cellulose or glass (Kirstein et al., 2018; Oberbeckmann et al., 2018; Ogonowski et al., 2018). The reasons for the preferential attachment of specific communities to plastic particles is still enigmatic. Within the plastisphere communities directly sampled at sea, several factors such as plastic type (polyethylene, polypropylene, polystyrene), geographical location or seasons appeared to differentiate the biofilm communities (Amaral-Zettler et al., 2015; Oberbeckmann et al., 2018). Other factors such as hydrophobicity, topography, roughness, crystallinity, and surface charge may play a role in the selection of bacterial community in the early stages of colonization, which is a crucial step for the following colonizing communities by modifying the material-specific surface properties (Rummel et al., 2017). Most of the above studies focused on microbial diversity and abundance, but only one evaluated the corresponding activity of the microorganisms that form the biofilm (Dussud et al., 2018a). Moreover, only one observation based on field study tested the influence of plastic size and shape (Frère et al., 2018) that is often mentioned as having a crucial role in shaping the biofilm (Oberbeckmann et al., 2015; Harrison et al., 2018), but no specifically designed experiment was dedicated to this question so far.

The aim of this study was to test how much plastisphere was
influenced by different polymer composition (PE and PLA), sizes
(SMP, LMP, and meso-plastics of 100 µm, 3 mm, and 1.8 cm
in diameter, respectively) and shape (spherical vs. irregular in the case of SMP). During the 2-months incubation in natural seawater from the NW Mediterranean Sea, we also evaluated the impact of environmental change such as a phytoplankton bloom. We hypothesized that the plastisphere may be more influenced by phytoplankton-bacteria interactions rather than the plastic composition, size or shape. Temporal variations of bacterial abundance (confocal microscopy), diversity (16S rRNA sequencing) and heterotrophic activity (radiolabeled leucine incorporation) were measured on all plastic types, but also compared to glass of similar size and shape, as well as to the surrounding seawater.

# MATERIALS AND METHODS

## Preparation of Polymers of Different Composition, Size and Shape

High density-polyethylene (HDPE) and Poly L lactic acid (PLA) were supplied by the Good Fellow company (Avilés, Spain) in a form of film of 10 and 50 µm thickness (± 20%), respectively. Circular pieces of respectively 3 mm and 1.8 cm in diameter were cut using a hole puncher. Glass coverslip (soda lime composition) were supplied by the Verres Vagner company (Toulouse, France) in circular form with 1.8 cm and 3 mm diameter of 170 µm thickness.

Irregular PLA and glass microbeads were obtained by cryo-grinding the polymer and glass films described above (SPEX sample Prep), which were further sieved with ethanol in order to recover the microparticles for which the size was ranging from 90 to 125 µm. Material for HDPE irregular microbeads were obtained from Good Fellow films with 1 mm thickness to ensure the 3-dimensional structure, and then reduced in size by cryo-grinding as described above.

Spherical HDPE microbeads of size distribution between 96-125 µm were commercially available (CPMS-0.96, Cospheric™). Spherical PLA microbeads were obtained as pellets and transformed in spherical microbeads by solvent emulsion-evaporation technique (O'Donnell and McGinity, 1997). It consisted in dissolving the polymer in a volatile organic solvent immiscible with water (dichloromethane), then introducing this solution into an aqueous solution containing an emulsifier as poly(vinyl alcohol) (PVA, 2%). The emulsion was finally placed under moderate magnetic stirring for 24 h at atmospheric pressure and ambient temperature, in order to allow the microbeads to harden, until complete evaporation of the organic solvent. The spherical PLA microbeads were collected by wet sieving between 90 and 125 µm, rinsed with permuted water and lyophilized until further use. Spherical glass microbeads were mainly made up with soda lime and commercially available (Good Fellow company, Avilés, Spain).

Granulometry analysis using a laser diffraction particle size analyzer (Mastersizer 2000 model with a Scirocco 2000 module, Malvern, United Kingdom) showed a gaussian distribution of the microbeads that always peaked at 100 µm for all polymers for spherical or non-spherical beads. Before the experiment, all the materials (including irregular microbeads (IR), spherical regular

microbeads (RE) of average 100 µm diameter as well as films of 3 mm and 1.8 cm in diameter) were washed for 1 h with ethanol followed by 3 rounds of vortex (1 min) and sonic bath (3 min) and then dried under the sterile hood.

## Experiment Setup

Each material type (spherical or irregular 100 µm microbeads, 3 mm and 1.8 cm films of PE, PLA and glass, respectively) was placed separately in 12 identical glass tanks of 2 L capacity (Plastic@Sea, Banyuls-sur-mer, France), in which seawater was continually renewed (flow rate was set on 20 mL min⁻<fnref n="1" />) by direct pumping at 14 m depth in Banyuls bay closed to the SOLA observatory station (NW Mediterranean Sea, France). Three additional tanks containing circulating seawater served as controls. Seawater was pre-filtered with 20 µm porosity filters (Dutscher™, France) to remove inorganic matter and potential predator in front of each tank. The tanks were placed in a dark room and illuminated from above in a 12/12 h light/dark rhythm by Lumivie LED RAL G2-SBM lamps (Zoomalia, France) with a nominal luminous flux of 1860 lm each. The experiment started from 13 August 2019, and samples were taken after 3, 10, 30, and 66 days (designated as D3, D10, D30, and D66, hereafter).

## Seawater Environmental Variables

Temperature, salinity, inorganic nutrients, chlorophyll *a*, and particulate organic carbon were weekly recorded *in situ* at the SOLA station (0.5 miles off the coast) in the framework of the French national coastal monitoring program "Service d'Observation en Milieu Littoral" (SOMLIT) according to protocols previously described (Ghiglione et al., 2005; Blanchet et al., 2017) and available on the SOMLIT website<fnref n="1" />. All samples were processed after sampling within 30 min.

## Confocal Microscopy and Flow Cytometry

For each sampling date, triplicate samples were fixed with 1% (v/v) glutaraldehyde for 30 min before freezing until analysis. Samples were stained with a 4,6-diamidino-2-phenylindole (DAPI) solution (final concentration 10% [v/v], Sigma Aldrich, France) for at least 5 min in the dark at ambient temperature before confocal microscopy observations (TCS SP8 confocal laser scanning microscope, Leica, Germany). Photomultiplier tubes (PMT3) detector was used for detecting the fluorescence signal and transmitted light detector (TLD) was used to capture the white light signal. The light intensity was compensated for the microbeads, and the Z-Step size were set on 1 µm to get regularly spaced cross sections. For each sample, 3 beads or 3 pieces of films were used for counting the bacterial abundance using the Image J software (Abramoff et al., 2004). For the regular spherical microbeads, the surface area was calculated using a simple geometrical formula. For irregular microbeads, the surface area was estimated using two different methods. First, a simple geometrical calculation based on the overall shape of the particle, i.e., ellipsoidal, cylindrical or conic. The main dimensions used

<footnote n="1">¹http://somlit.epoc.u-bordeaux1.fr/</footnote>
for the surface area calculation were measured from optical images. In addition, for a series of samples, the surface was calculated via a full reconstruction of the particle surface using 1 µm separated confocal microscopy cross-sections, using the image-J software. A 10% agreement was found between the two methods (geometrical estimation and 3D volume reconstruction) for the samples studied, thus validating the use of a simple geometrical method for irregular beads and giving us the surface measurement uncertainty. Cell counts were verified using the Gwyddion software (Neèas and Klapetek, 2012) threshold filter and grains numbering. Manual counting was performed on a series of samples to double check the cell counts accuracy, which was found to be of 10%. Cell counts were then expressed as the number of cells over surface area (in cells mm⁻²) with an accuracy of 20%.

In parallel, 1 mL of seawater from the control tank was also fixed using the same procedure. A volume of 500 µL control seawater was mixed with the nucleic acid dye SYBR Green I (final concentration 0.05% [v/v], Sigma Aldrich) for 15 min at room temperature in the dark. Cell counts were performed with a FACSCanto II flow cytometer (BD Bioscience, San Jose, CA, United States) equipped with a blue laser (488-nm, air-cooled, 20-mW solid state), as previously described (Mével et al., 2008).

## Heterotrophic Bacterial Production

Bacterial production was measured in triplicate for each material at each sampling time by ³H-leucine incorporation (Dussud et al., 2018a). In brief, the films with the size of 3 and 18 mm were rinsed with sterile filtered seawater using a wash bottle before transferring to the microtubes containing 1.5 mL sterile filtered seawater (0.2 µm pore size, polycarbonate filter, Nucleopore). Microbeads were collected on a membrane filter with 10 µm pore size membrane filter (LCWG02500, Mitex™) and then rinsed with sterile filtered seawater, and the seawater was removed by air pumping for 30 s to get accurate sample weight. Different mass of materials (15 mg for PE, 18 mg for PLA, and 33 mg for glass microbeads) were collected in order to homogenize the total surface areas, and then transferred into microtubes before adding 1.5 mL of sterile filtered seawater. A cell detachment pre-treatment was applied for all the materials using 3 cycles of 3 min sonication bath (Delta Sonic, France) followed by 3 min vortex at maximum speed (Skyline, Elmi Ltd., Russia). Immediately after cell-detachment, ³H-leucine (125.6 Ci mmol⁻¹, Perkin Elmer™) were added at 1 nmol L⁻¹ final concentration (completed with cold leucine to 150 nmol L⁻¹), which consisted of 1.5 ml sterile seawater containing the film or microbeads and detached bacteria. For seawater samples from the control tanks, ³H-leucine was added at a final concentration of 4.3 nmol L⁻¹ to 1.5 mL of control seawater. All the samples were incubated in the dark at 18°C for 3 h. The empirical conversion factor of 1.55 ngC pmol⁻¹ of incorporated leucine was used to calculate the bacterial heterotrophic production (Kirchman, 2001; Céa et al., 2015). For each microbeads on each sampling date, the percentage of hydration of the beads were assessed by weighting 3 additional aliquots (wet weight), that were frozen, lyophilized and weighted a second time to measure dry weight. Knowing the wet weight and the percentage

of hydration of each material during the kinetic, the bacterial activity was expressed per dry weight.

Knowing the number of cells (N) per unit area for each sample and the average specific surface $R_{SV}$ (i.e., surface to volume ratio) for each type of microbeads, we expressed the heterotrophic bacterial production as the carbon produced per unit area per unit time ($ngC mm^{-2} h^{-1}$) or the specific bacterial activity as the carbon produced per cell per unit time ($fgC cell^{-1} h^{-1}$). This data representation allows to meaningfully compare materials having different shapes, i.e., different surface to volume ratios.

Additionally, we used bacterial production data derived from leucine incorporation to estimate the bacterial growth rate on plastisphere, using a conversion factor of 12 fgC per cell as previously described (Fukuda et al., 1998).

## DNA Extraction, PCR, Sequencing and Data Analysis

On each sampling date, triplicates of plastic or glass were harvested from each sample by using the same method described above for the bacterial production and immediately stored at -80°C. Triplicates of 2 L seawater from control tanks were also obtained using 0.2 µm pore size polycarbonate filters (47 mm diameter, Nucleopore). The microbial genomic DNA extraction were followed with classic phenol-chloroform protocol (Ghiglione et al., 1999; Marty et al., 2012) and DNA was quantified by DeNovix (DS-11 series, United States). Polymerase chain reaction (PCR) amplification was done using universal small subunit ribosomal RNA (SSU rRNA) primers (515Y, 5'-GTGYCAGCMGCCGCGGTAA-3'; 926R, 5'-CCGYCAATTYMTTTRAGTTT-3') which has been shown to be well-suited for quantitative profiling of marine natural communities by simultaneously amplifying templates from Bacteria, Archaea and Eukaryota in a single PCR reaction (Parada et al., 2016; Yeh et al., 2019). Illumina MiSeq sequencing were performed at Genoscope (Evry, France) for the 156 samples, corresponding to the 144 samples of PE, PLA and glass (3 substrate * 4 sampling date * 4 size fraction * 3 replicates) and the 12 seawater samples (4 sampling date * 3 replicates). All the SSU rRNA data are available in the NCBI SRA repository (accession number PRJNA663787).

Processing of SSU rRNA sequences was performed using the DADA2 R package (Callahan et al., 2016). Forward and reverse 16S reads were trimmed off before error correction and denoising step. Paired reads were merged (average length from 367 to 377 bp) and all the singletons and chimeras were removed. 18S reads are 575-595 bp, which is too long for forward and reverse reads to overlap. As recommended by Parada et al. (2016) and Yeh et al. (2019), we trimmed the 18S forward reads to fixed length (220 bp) and the reverse reads was discarded before error correction and denoising step. Denoised reads were concatenated and chimeric reads were discarded. The 16S and 18S amplicon sequence variants (ASVs) were assigned with SILVA 128 database (Quast et al., 2013). The taxonomic affiliation of ASVs of interest were further verified against sequences from the NCBI database using the BLASTnt. The ASVs corresponding to eukaryotes, archaea, chloroplast and mitochondria were removed and all the
samples were rarified to the same number (ngseed = 1) before
the analyses on bacteria using the phyloseq R package (McMurdie
and Holmes, 2013). The α-diversity was performed using the
alpha() function from the microbiome R package. Square root
transformation was performed for the β-diversity analyses and
hypotheses test. The taxonomy compositions were visualized
using the histogram and bubble plot with the ggplot2 R package
(Wickham, 2016).

To investigate the unique and shared bacterial community
between biofilm and seawater, the reads from plastic or glass
with different size fractions were pooled and rarefied into the
same number to that of seawater. Two levels of comparison were
conducted: in ASV level (presence or absence) and in tags levels.

Statistical Analyses

An unweighted-pair group method with arithmetic (UPGMA)
dendrogram based on Bray-Curtis similarities was used for
visualization of beta-diversity. A similarity profile test (SIMPROF,
PRIMER 6) was performed on the null hypothesis that a specific
sub-cluster can be recreated by permuting the entry species and
samples. The significant branch was used as a prerequisite for
defining bacterial cluster.

The significance of the factor size (including irregular and
regular microbeads), the substrate and the date were analyzed
using the global or pairwise permutational multivariate analysis
of variance (PERMANOVA) (Anderson and Walsh, 2013) using
the *adonis*() function with the vegan R package (Oksanen
et al., 2007), the homogeneity of variances was tested using
the *betadisper*() function. The *p* value was adjusted with the
Benjamin-Hochberg method. To test the bacterial community
relationship between seawater and plastic, a Mantel test was
performed in Vegan using the *mantel*() based on Pearson
correlation method.

Three-way analyses of variance (ANOVA) were performed
for statistical analyses with the type I sum of square applied
for the results on bacterial abundance, bacterial production,
bacterial activity and bacterial growth rate. Tukey's *post hoc* test
was used when necessary. Relative importance of each predictor
(in R square) for ANOVA results was further determined with
function of *calc.relimp*() from relaimpo R package (Grömping,
2006). When the data did not meet homogeneity, the Welch's
ANOVA was chosen for the analyses of α-diversity and the ASVs
comparison on results of the bubble plot. Games-Howell test was
used as *post hoc* test.

RESULTS

Environmental Conditions

During the studied period (13th August – 20th October),
the environmental conditions in the Bay of Banyuls
were characteristic of an autumn situation in a temperate
Mediterranean area. The surface water temperature remained
stable (from 20 to 23°C) as well as the salinity that stayed around
38 (Supplementary Figure 1A).

As expected in late summer, concentrations of silicate were low (~0.5 µM) or often close to the detection limit for nitrate and

phosphate (<0.05 µM and <0.02, respectively) (Supplementary
Figure 1B). Two events were clearly identified during the period
and marked by a nutrient enrichment in the water column.
The first had a limited magnitude on 24th September and the
second was more important at the very end of the experiment
on 25th October with concentrations of 2.4, 0.93, and 0.18 µM in
Si(OH)₄, NO₃ and PO₄, respectively.

These nutrient inputs due to the first mixing inducing the
disruption of the water column stratification were responsible for
an increase in particulate matter in the water column as shown in
**Supplementary Figure 1C** in terms of Total Suspended Matter
(TSM), and especially in terms of chlorophyll. After a low and
homogeneous concentration during August and the beginning of
September (range between 0.1 and 0.2 mg m⁻³ of chlorophyll
a), we observed 2 peaks (0.5 and 0.6 mg m⁻³ of chlorophyll a)
characteristic of a coastal autumn bloom situation.

Microbial Cell Counts and Shape

Confocal microscopy revealed a large diversity of morphological
forms including spherical, rod-shaped or spiral-shaped bacterial
like structure at the surface of PE, PLA and glass, for which
rod-shaped and spiral-shaped structure was more observed on
D3 and D10 compared to D30 and D66 (Figure 1). Typical
morphotypes of diatoms appeared at D66 and were not visible
before. Confocal microscopy was also useful to confirm the size
distribution of microbeads between 90 and 125 µm, as well as
their shape (regular versus irregular).

Triplicate samples analyzed by confocal microscopy allowed
us to follow the changes in bacterial counts for each material
type and size. The data highlighted three distinct phases of
biofilm formation: primo-colonization, growth, and maturation
(Figure 2). Interestingly, the three distinct phases were found
whatever the material type or size. Three-way ANOVA revealed
significant difference in bacterial counts according to the
sampling date and material type, but not within material
sizes (material sizes containing the two shapes of IR and RE)
(R² = 0.29, 0.29, and 0.01, respectively). PE presented the largest
bacterial abundance on average together with PLA, whereas
it appeared to be ten-fold smaller on glass. A rapid primo-
colonization was observed after D3, with average abundance of
3.0 × 10³, 1.6 × 10³, and 0.3 × 10³ cells mm⁻² for PE, PLA and
glass, respectively. Slight growth was observed between D3 and
D10 for PE, PLA and glass, where the bacterial abundance for
glass samples remained relatively low (8.6 × 10³, 2.8 × 10³, and
0.9 × 10³ cells mm⁻², respectively). We observed a significant
increase in bacterial abundance between D10 and D30. Since
we did not find significant increase in bacterial counts at a
later time point (between D30 and D66, *p* > 0.05), it indicates
that the maturation phase corresponding to the stabilization of
bacterial abundance was reached for all material types from D30
and until the end of the experiment (D66). On average the
mature biofilm was of 2.5 × 10⁴, 1.5 × 10⁴, and 0.2 × 10⁴
cells mm⁻² for PE, PLA and glass, respectively, with non-
significant differences between size and shapes within each
material type. It should be noted that PLA regular microbeads
was excluded from the statistical analysis for bacterial abundance
and activity, because we noted a significantly lower abundance
<figure><img src="image_17.png" /><figcaption>**FIGURE 1** | Confocal microscopy for polyethylene irregular microbeads of around 100 µm diameter (PE IR) and 3 mm film (PE 3 mm) at days 3 (D3), 10 (D10), 30 (D30), and 66 (D66). Scale bar: 50 µm. Arrows are pointing typical shape of diatoms that appeared at D66 only.</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>**FIGURE 2** | Bacterial cell counts per surface area (mm⁻²) for PE, PLA and glass on mesoplastics (18 mm diameter), large microplastics (3 mm diameter) and small microplastics microbeads (100 µm diameter) with irregular (IR) or regular spherical shapes (RE) during the course of the experiment at days 3 (D3), 10 (D10), 30 (D30), and 66 (D66).</figcaption></figure>

compared to other PLA samples, which was much closer to those of the glass samples. Indeed, we realized that the use of surfactants in the home-made synthesis of PLA regular beads could be responsible for this behavior, as surfactant could be able to remain at the beads surfaces and considerably modify their hydrophobicity.

Diatoms with the average length around 11 µm were observed only on D66 on all materials, with higher abundance for 3 mm and 1.8 cm sizes on PE (3.5 × 10³ cells mm⁻²) and PLA (2.4 × 10³ cells mm⁻²) compared to glass (0.8 × 10³ cells mm⁻²). They were rarely visible on spherical or regular microplastics.

The bacterial counts from seawater controls remained stable from 4.5 × 10⁴ to 1.0 × 10⁵ cells per mL all along the experiment.

## Heterotrophic Bacterial Production

Heterotrophic bacterial production (BP) was measured alongside the time course of the experiment and it was expressed in units of incorporated carbon per square millimeter per hour (pgC mm⁻² h⁻¹) (Figure 3A). Three-way ANOVA showed that all the factors (sampling date, material type, and size) could significantly impact the BP, being the sampling date and material type factors explaining more variation than the material size (material sizes
<figure><img src="image_19.png" /><figcaption>**FIGURE 3** | Bacterial heterotrophic production (BP, pgC mm⁻² h⁻¹) (A) and bacterial activity (fg C cell⁻¹ h⁻¹) (B) for PE, PLA and glass on larger pieces (18 mm diameter), microplastics (3 mm diameter) and irregular (IR) or regular spherical (RE) microbeads (100 µm diameter) during the course of the experiment at days 3 (D3), 10 (D10), 30 (D30), and 66 (D66).</figcaption></figure>

containing the two shapes of IR and RE) (R² = 0.20, 0.25 and 0.07, respectively). Significant increase has been observed from the primo-colonization phase (on D3) to the growing phase (on D10). Interestingly, the BP did not show significant differences between D10 and D30, even though there was a significant increase in bacterial abundance (Figure 2). The maturation phase showed a significant increase of BP from D30 to D66. Differences in BP were also observed on the three sample types, with PE being significantly higher than PLA and glass. In average, BP of PE is 16 and 110 times higher than that of PLA and glass by comparing within each material size. The BP of PE, PLA and glass have increased from 3.2, 0.2, and 0.1 pgC mm⁻² h⁻¹ at the primo-colonization phase (D3) to 35.8, 6.8, and 0.7 pgC mm⁻² h⁻¹ at the maturation phase (D66), with intermediate maturation phase showing 6.8, 2.7, and 0.1 pgC mm⁻² h⁻¹, respectively (D30). Besides, BP difference from material type was also observed, with LMP of 3 mm size being higher than

the rest of the samples, and the regular SMP microplastics of 100 µm size presenting the lowest values. Generally, PE and glass in regular SMP were 11 and 6 times lower than other material sizes (also containing form). PLA regular SMP were 150 times lower than other sizes or shape. The BP for seawater ranged from 88 to 150 pgC mL h⁻¹ without significant difference between sampling dates.

Bacterial specific activity was further calculated by dividing the BP by the bacterial abundance in the unit of incorporated carbon per cell per hour (fgC cell⁻¹ h⁻¹). Three-way ANOVA also showed that all the factors (sampling date, material type, and size) could significantly impact the specific activity, the material type explaining more variation than the sampling date and material size (material sizes containing the two shapes of IR and RE) (R² = 0.08, 0.27, and 0.07, respectively). Interestingly, BP on the primo-colonization phase of D3 and growing phase of D10 and maturation phase of D66 were significantly higher
than during the maturation phase at D30 (*p* < 0.05) (Figure 3B). When it came to the material type, specific activity of PE showed significantly higher levels than PLA and glass, which confirmed that the material type could influence the bacterial specific activity on plastics. The BP of PE, PLA and glass in the average of the first two sampling dates (D3 and D10) were 1.9, 0.3, and 0.4 fgC cell⁻¹ h⁻¹, respectively, decreased to D30 (0.45, 0.15, and 0.20 fgC cell⁻¹ h⁻¹, respectively), and increased until D66 (2.5, 1.5, and 0.2 fgC cell⁻¹ h⁻¹, respectively). The material size and shape could also impact the specific activity, no difference was found between the size of 18 mm and irregular microplastic of 100 µm, while BP of the samples in 3 mm showed higher activity, and regular microbeads of 100 µm showing the lowest.

Accordingly, the bacterial growth rate on PE was higher than on PLA and glass. The bacterial growth rate of PE, PLA and glass had the average of the first two sampling dates (D3 and D10) with 3.8, 0.6, and 0.8 day⁻¹, respectively, decreased to D30 (0.9, 0.3, and 0.4 day⁻¹), and drastically increased until D66 (5.2, 3.0, and 0.5 day⁻¹, respectively). The growth rate measured in seawater was 2.8, 6.4, 2.0, and 5.2 day⁻¹ for D3, D10, D30, and D66, respectively.

## Diversity Indexes

Illumina MiSeq DNA sequencing generated 3 823 342 sequences tags, falling into 5293 ASVs after randomly resampling to 5177 sequences per sample to provide statistical robustness when comparing diversity measures among samples. DNA quantity were not sufficient to allow the sequencing of several samples at the early colonization stage (D3) that failed for glass samples including regular microbeads of 100 µm, 3 and 1.8 mm size, and also 3 mm for PE and PLA. A total of 123 samples were used for the following analyses, excluding also two samples (irregular PE microbeads and seawater) with low number of reads at D30. Rarefaction analysis suggested that all samples reached a stationary phase (data not shown).

Chao 1 estimator showed that the richness increased drastically in seawater during the course of the experiment, ranging on average from 570 to 1110 ASVs at D3 and D66, respectively. A slight Chao 1 estimator increased was also found from D3 (average of 363, 265, and 267, respectively) to D66 (468, 395, and 288, respectively) for PE, PLA and glass, indicating that on the primo-colonization phase, a handful of bacterial species already colonizing the plastic surface (Figure 4A). Seawater sample showed significant higher Chao1 richness than PE, PLA, and glass. No difference was found on the Pielou index between seawater and PE, PLA or glass (Welch's *p* > 0.05), where evenness increased for all materials and seawater samples at the end of the experiment (Figure 4B). Shannon diversity index of PE and glass was significantly higher than PLA (average of 4.6, 4.5, and 4.3, respectively) and no significant difference could be found in relation to the size of the different materials (Welch's ANOVA test) (Figure 4C). High correlation of the temporal dynamic of Shannon index and Pielou evenness was found between 3 material types and seawater diversity (Pearson correlation, *p* = 0.018, *r* = 0.75; and *p* = 0.03, *r* = 0.71, respectively).

<figure><img src="image_20.png" /><figcaption>**FIGURE 4** | Alpha-diversity indices of (A) richness (Chao1), (B) evenness (Pielou), and (C) diversity (Shannon) of all the materials (PE, PLA, glass) and the surrounding seawater during the course of the experiment at days 3 (D3), 10 (D10), 30 (D30), and 66 (D66). The boxplots show the median between triplicate samples of different sizes for each material (heavy horizontal line inside the boxes), the box represents the first and third quartiles and unfilled circles indicate outliers. Lowercase letters (a, b, c) denote statistically different groups (*p* < 0.05).</figcaption></figure>

## Bacterial Community Structure

Unweighted-pair group method with arithmetic dendrogram based on Bray-Curtis dissimilarities showed that each of the triplicate samples in each sampling time and condition clustered together (except for one sample D10-PE-IR), confirming the homogeneity within the triplicates and also the proper
<figure><img src="image_21.png" /></figure>

sampling strategy, rigorous DNA extraction, sequencing and data processing (Figure 5).

Clear dissimilarities were found between bacterial communities in seawater and material types (PA, PLA, glass) (dissimilarity > 85%) and between samples corresponding to before (D3 to D30) and after the diatom bloom (D66) within each cluster (dissimilarity > 75%). Similarity profile

testing (SIMPROF) showed these groups to be highly significant (*p* < 0.001).

Global PERMANOVA analyses with all samples confirmed the variance were highly explained by sampling date, to a less extent by chemical composition and material size (R² = 0.39, 0.14, and 0.05 for PE, PLA, and glass, respectively, *p* < 0.01) (Supplementary Table 1). Pairwise PERMANOVA analyses