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An Integrative Model of Carbon and Nitrogen Metabolism in a Common Deep-Sea Sponge (Geodia barretti)

de Kluijver, Anna; Bart, Martijn C; van Oevelen, Dick; de Goeij, Jasper M; Leys, Sally P; Maier, Sandra R; Maldonado, Manuel; Soetaert, Karline; Verbiest, Sander; Middelburg, Jack J

ABSTRACT

Deep-sea sponges and their microbial symbionts transform various forms of carbon (C) and nitrogen (N) via several metabolic pathways, which, for a large part, are poorly quantified. Previous flux studies on the common deep-sea sponge Geodia barretti consistently revealed net consumption of dissolved organic carbon (DOC) and oxygen (O2) and net release of nitrate (NO3). Here we present a biogeochemical metabolic network model that, for the first time, quantifies C and N fluxes within the sponge holobiont in a consistent manner, including many poorly constrained metabolic conversions. Using two datasets covering a range of individual G. barretti sizes (10–3,500 ml), we found that the variability in metabolic rates partially resulted from body size as O2 uptake allometrically scales with sponge volume. Our model analysis confirmed that dissolved organic matter (DOM), with an estimated C:N ratio of 7.7 ± 1.4, is the main energy source of G. barretti. DOM is primarily used for aerobic respiration, then for dissimilatory NO3 reduction to ammonium (NH+4) (DNRA), and, lastly, for denitrification. Dissolved organic carbon (DOC) production efficiencies (production/assimilation) were estimated as 24 ± 8% (larger individuals) and 31 ± 9% (smaller individuals), so most DOC was respired to carbon dioxide (CO2), which was released in a net ratio of 0.77–0.81 to O2 consumption. Internally produced NH+4 from cellular excretion and DNRA fueled nitrification. Nitrification-associated chemoautotrophic production contributed 5.1–6.7 ± 3.0% to total sponge production. While overall metabolic patterns were rather independent of sponge size, (volume-)specific rates were lower in larger sponges compared to smaller individuals. Specific biomass production rates were 0.16% day–1 in smaller compared to 0.067% day–1 in larger G. barretti as expected for slow-growing deep-sea organisms. Collectively, our approach shows that metabolic modeling of hard-to-reach, deep-water sponges can be used to predict community-based biogeochemical fluxes and sponge production that will facilitate further investigations on the functional integration and the ecological significance of sponge aggregations in deep-sea ecosystems.

ACKNOWLEDGEMENTS Titus Rombouts and Pieter Slot (UvA) and Sharyn Ossebaar (NIOZ) are acknowledged for their analytical assistance with the nutrient measurements of the incubation experiments. We thank Asimenia Gavriilidou and Detmer Sipkema (WUR, SponGES) for their input on the microbial genome in G. barretti. We thank last Hans Tore Rapp (UiB) for the excellent project coordination. We would like to thank CF and CR for their valuable and constructive reviews. AUTHOR CONTRIBUTIONS AK developed the model, performed the data analyses and model analyses, prepared the figures, and wrote the manuscript. MB and JG designed and conducted the incubation experiments, performed nutrient analyses, and contributed to the writing of the manuscript. SV contributed to model conceptualization and development. JM acquired funding and contributed to the conceptualization of the model, interpretation, and writing of the manuscript. MM contributed to the conceptualization of the model, interpretation, and writing. SL and SM contributed data for model development. KS contributed technically with model implementation and analyses. DO designed the model, contributed to model development and interpretation, and writing of the manuscript. All authors contributed to the article and approved the submitted version. SUPPLEMENTARY MATERIAL The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmars.2020.596251/full#supplementary-material 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. DATA AVAILABILITY STATEMENT The dataset of Leys et al. (2018) is available at University of Alberta Education and Research Archive: doi: 10.7939/R3057D48V. The dataset of Bart et al. (2020b) will be made available at Pangaea. The model input files and R scripts are available on Zenodo: doi: 10.5281/zenodo.4139792. FUNDING This research has been performed in the scope of the EU SponGES project, which received funding from the European Union's Horizon 2020 Research and Innovation Program under grant agreement no. 679849. Further support included ERC starting grant agreement no. 715513 to JG and the Netherlands Earth System Science Center to JM. This document reflects only the authors' views, and the Executive Agency for Small and Medium-sized Enterprises (EASME) is not responsible for any use that may be made of the information it contains.
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