Abstract
The surface ocean is the largest sunlit environment on Earth where marine microalgae are known as the main drivers of global productivity. However, rhodopsin phototrophs are actually the most abundant metabolic group, suggesting a major role in the biogeochemical cycles. While previous studies have shown that rhodopsin-containing bacterioplankton thrive in the most severely nutrient-depleted environments, growing evidence suggest that this type of phototrophy may also be relevant in nutrient-rich environments. To examine its role in productive waters, we investigated the monthly rhodopsin dynamics in the upwelling system of the Southern California Bight by measuring retinal–the photoreactive chromophore essential for rhodopsin function–in seawater. Unlike oligotrophic regions, rhodopsin levels peaked during the highly productive spring phytoplankton bloom, coinciding with the highest chlorophyll concentrations. Heterotrophic bacterial abundances, particularly within the order Flavobacteriales, correlated strongly with rhodopsin concentrations, allowing us to build linear models to predict rhodopsin distributions in a productive environment. Metagenomic data further showed that Flavobacteriales also dominated the rhodopsin gene pool when the highest rhodopsin levels were recorded, underscoring their key contribution to light-driven energy capture. Overall, our findings reveal that rhodopsin phototrophy plays a substantial role in productive marine systems, broadening its recognized importance far beyond oligotrophic oceans.
Data availability
Source data are provided with this paper. 16S rDNA amplicon and shotgun sequencing data are available on Genbank (https://www.ncbi.nlm.nih.gov/genbank/) under the Bioproject PRJNA1040444. Metagenome Assembled Genomes (MAGs) are available on Figshare https://doi.org/10.6084/m9.figshare.2985686691 Source data are provided with this paper.
References
Falkowski, P. G. The role of phytoplankton photosynthesis in global biogeochemical cycles. Photosynth Res. 39, 235–258 (1994).
Larkum, A., Ritchie, R. & Raven, J. J. P. Living off the sun: chlorophylls, bacteriochlorophylls and rhodopsins. Photosynthetica 56, 11–43 (2018).
Karl, D. M. Solar energy capture and transformation in the sea. Elementa 2, 000021 (2014).
Béjà, O. et al. Bacterial rhodopsin: evidence for a new type of phototrophy in the sea. Science 289, 1902–1906 (2000).
Martinez, A., Bradley, A. S., Waldbauer, J. R., Summons, R. E. & DeLong, E. F. Proteorhodopsin photosystem gene expression enables photophosphorylation in a heterologous host. Proc. Natl. Acad. Sci. USA 104, 5590–5595 (2007).
Finkel, O. M., Béjà, O. & Belkin, S. Global abundance of microbial rhodopsins. ISME J. 7, 448–451 (2013).
Kandori, H. Ion-pumping microbial rhodopsins. Front. Mol. Biosci. 2, 52 (2015).
Pinhassi, J., DeLong, E. F., Béjà, O., Gonzalez, J. M. & Pedrós-Alió, C. Marine bacterial and archaeal ion-pumping rhodopsins: genetic diversity, physiology, and ecology. Microbiol. Mol. Biol. Rev. 80, 929–954 (2016).
Gómez-Consarnau, L. et al. Microbial rhodopsins are major contributors to the solar energy captured in the sea. Sci. Adv. 5, eaaw8855 (2019).
Kirchman, D. L. & Hanson, T. E. Bioenergetics of photoheterotrophic bacteria in the oceans. Environ. Microbiol. Rep. 5, 188–199 (2013).
Morris, R. M. et al. Comparative metaproteomics reveals ocean-scale shifts in microbial nutrient utilization and energy transduction. ISME J. 4, 673–685 (2010).
Martínez-García, S. & Pinhassi, J. Adaptations of microorganisms to low nutrient environments: managing life in the oligotrophic ocean. In Encyclopedia of Microbiology 4th edn, (ed. Schmidt, T. M.) (Academic Press, Oxford, 2019).
Brindefalk, B. et al. Distribution and expression of microbial rhodopsins in the Baltic Sea and adjacent waters. Environ. Microbiol. 18, 4442–4455 (2016).
Nguyen, D. et al. Winter diversity and expression of proteorhodopsin genes in a polar ocean. ISME J. 9, 1835–1845 (2015).
Campbell, B. J., Waidner, L. A., Cottrell, M. T. & Kirchman, D. L. Abundant proteorhodopsin genes in the North Atlantic Ocean. Environ. Microbiol. 10, 99–109 (2008).
Dubinsky, V. et al. Metagenomic analysis reveals unusually high incidence of proteorhodopsin genes in the ultraoligotrophic Eastern Mediterranean Sea. Environ. Microbiol. 19, 1077–1090 (2017).
Lami, R., Cottrell, M. T., Campbell, B. J. & Kirchman, D. L. Light-dependent growth and proteorhodopsin expression by Flavobacteria and SAR11 in experiments with Delaware coastal waters. Environ. Microbiol. 11, 3201–3209 (2009).
Teeling, H. et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science 336, 608–611 (2012).
Steindler, L., Schwalbach, M. S., Smith, D. P., Chan, F. & Giovannoni, S. J. Energy starved Candidatus Pelagibacter ubique substitutes light-mediated ATP production for endogenous carbon respiration. PLoS ONE 6, e19725 (2011).
Sieradzki, E. T., Fuhrman, J. A., Rivero-Calle, S. & Gómez-Consarnau, L. Proteorhodopsins dominate the expression of phototrophic mechanisms in seasonal and dynamic marine picoplankton communities. PeerJ 6, e5798 (2018).
Giovannoni, S. J. SAR11 bacteria: the most abundant plankton in the oceans. Ann. Rev. Mar. Sci. 9, 231–255 (2017).
Gómez-Consarnau, L. et al. Proteorhodopsin light-enhanced growth linked to vitamin-B1 acquisition in marine Flavobacteria. ISME J. 10, 1102–1112 (2016).
Gómez-Consarnau, L. et al. Light stimulates growth of proteorhodopsin-containing marine Flavobacteria. Nature 445, 210–213 (2007).
Hassanzadeh, B. et al. Microbial rhodopsins are increasingly favoured over chlorophyll in high nutrient low chlorophyll waters. Environ. Microbiol. Rep. 13, 401–406 (2021).
Andrew, S. M. et al. Widespread use of proton-pumping rhodopsin in Antarctic phytoplankton. Proc. Natl. Acad. Sci. USA 120, e2307638120 (2023).
Longhurst, A., Sathyendranath, S., Platt, T. & Caverhill, C. An estimate of global primary production in the ocean from satellite radiometer data. J. Plankton Res. 17, 1245–1271 (1995).
Capone, D. G. & Hutchins, D. A. Microbial biogeochemistry of coastal upwelling regimes in a changing ocean. Nat. Geosci. 6, 711–717 (2013).
Chow, C.-E. T. et al. Temporal variability and coherence of euphotic zone bacterial communities over a decade in the Southern California Bight. ISME J. 7, 2259 (2013).
Cram, J. A. et al. Seasonal and interannual variability of the marine bacterioplankton community throughout the water column over ten years. ISME J. 9, 563 (2015).
Countway, P. D., Vigil, P. D., Schnetzer, A., Moorthi, S. D. & Caron, D. A. Seasonal analysis of protistan community structure and diversity at the USC Microbial Observatory (San Pedro Channel, North Pacific Ocean). Limnol. Oceanogr. 55, 2381–2396 (2010).
Maresca, J. A., Miller, K. J., Keffer, J. L., Sabanayagam, C. R. & Campbell, B. J. Distribution and diversity of rhodopsin-producing microbes in the Chesapeake Bay. Appl. Environ. Microbiol. 84, 00137–00118 (2018).
Gómez-Consarnau, L., Needham, D. M., Weber, P. K., Fuhrman, J. A. & Mayali, X. Influence of Light on particulate organic matter utilization by attached and free-living marine bacteria. Front. Microbiol. 10, 1204 (2019).
Strauss, J. et al. Plastid-localized xanthorhodopsin increases diatom biomass and ecosystem productivity in iron-limited surface oceans. Nat. Microbiol. 8, 2050–2066 (2023).
Bar-Shalom, R. et al. Rhodopsin-mediated nutrient uptake by cultivated photoheterotrophic Verrucomicrobiota. ISME J. 17, 1063–1074 (2023).
Bergauer, K. et al. Organic matter processing by microbial communities throughout the Atlantic water column as revealed by metaproteomics. Proc. Natl. Acad. Sci. USA 115, E400–E408 (2018).
Arístegui, J. et al. Variability in plankton community structure, metabolism, and vertical carbon fluxes along an upwelling filament (Cape Juby, NW Africa). Prog. Oceanogr. 62, 95–114 (2004).
Giovannoni, S. J. et al. Proteorhodopsin in the ubiquitous marine bacterium SAR11. Nature 438, 82–85 (2005).
Béjà, O., Spudich, E. N., Spudich, J. L., Leclerc, M. & DeLong, E. F. Proteorhodopsin phototrophy in the ocean. Nature 411, 786–789 (2001).
Lauro, F. M. et al. The genomic basis of trophic strategy in marine bacteria. Proc. Natl. Acad. Sci. USA 106, 15527–15533 (2009).
Olson, D. K. et al. Proteorhodopsin variability and distribution in the North Pacific Subtropical Gyre. ISME J. 12, 1047–1060 (2018).
Rozenberg, A., Inoue, K., Kandori, H. & Béjà, O. Microbial rhodopsins: the last two decades. Annu. Rev. Microbiol. 75, 427–447 (2021).
Mannen, K. et al. Multiple roles of a conserved glutamate residue for unique biophysical properties in a new group of microbial rhodopsins homologous to TAT rhodopsin. J. Mol. Biol. 436, 168331 (2024).
Kolber, Z. Energy cycle in the ocean: powering the microbial world. Oceanography 20, 79–88 (2007).
Vader, A., Laughinghouse, H. D., Griffiths, C., Jakobsen, K. S. & Gabrielsen, T. M. Proton-pumping rhodopsins are abundantly expressed by microbial eukaryotes in a high-Arctic fjord. Environ. Microbiol. 20, 890–902 (2018).
Marchetti, A. et al. Marine diatom proteorhodopsins and their potential role in coping with low iron availability. ISME J. 9, 2745–2748 (2015).
Fernández-Gomez, B. et al. Ecology of marine Bacteroidetes: a comparative genomics approach. ISME J. 7, 1026–1037 (2013).
Arandia‐Gorostidi, N. et al. Light supports cell‐integrity and growth rates of taxonomically diverse coastal photoheterotrophs. Environ. Microbiol. 22, 3823–3837 (2020).
González, J. M. et al. Genome analysis of the proteorhodopsin-containing marine bacterium Polaribacter sp. MED152 (Flavobacteria). Proc. Natl. Acad. Sci. USA 105, 8724–8729 (2008).
Mary, I. et al. Light enhanced amino acid uptake by dominant bacterioplankton groups in surface waters of the Atlantic Ocean. FEMS Microbiol. Ecol. 63, 36–45 (2008).
Gómez-Pereira, P. R. et al. Comparable light stimulation of organic nutrient uptake by SAR11 and Prochlorococcus in the North Atlantic subtropical gyre. ISME J. 7, 603–614 (2013).
Knap, A., Michaels, A., Close, A., Ducklow, H. & Dickson, A. Protocols for the Joint Global Ocean Flux Study (JGOFS) Core Measurements. IOC Manuals and Guides No. 29, UNESCO, Paris (1994).
Schlitzer, R. Ocean Data View. https://odv.awi.de (2018).
Strickland, J. D. & Parsons, T. R. A Practical Handbook of Seawater Analysis. 2nd ed. Bulletin No. 167. (Ottawa: Fisheries Research Board of Canada, 1972).
Cruaud, P., Rasplus, J.-Y., Rodriguez, L. J. & Cruaud, A. High-throughput sequencing of multiple amplicons for barcoding and integrative taxonomy. Sci. Rep. 7, 1–12 (2017).
Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).
Andrews, S. FastQC: a quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc (2010).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Li, D. et al. MEGAHIT v1. 0: a fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods 102, 3–11 (2016).
Arkin, A. P. et al. KBase: the United States department of energy systems biology knowledgebase. Nat. Biotechnol. 36, 566–569 (2018).
West, P. T., Probst, A. J., Grigoriev, I. V., Thomas, B. C. & Banfield, J. F. Genome-reconstruction for eukaryotes from complex natural microbial communities. Genome Res. 28, 569–580 (2018).
Zhu, W., Lomsadze, A. & Borodovsky, M. Ab initio gene identification in metagenomic sequences. Nucleic Acids Res. 38, e132 (2010).
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 11, 1–11 (2010).
Mirdita, M., Steinegger, M., Breitwieser, F., Söding, J. & Levy Karin, E. Fast and sensitive taxonomic assignment to metagenomic contigs. Bioinformatics 37, 3029–3031 (2021).
Li, W. et al. The EMBL-EBI bioinformatics web and programmatic tools framework. Nucleic Acids Res. 43, W580–W584 (2015).
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).
Suzek, B. E. et al. UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics 31, 926–932 (2015).
Levy Karin, E., Mirdita, M. & Söding, J. MetaEuk—sensitive, high-throughput gene discovery, and annotation for large-scale eukaryotic metagenomics. Microbiome 8, 1–15 (2020).
Groussman, R. D., Blaskowski, S., Coesel, S. N. & Armbrust, E. V. MarFERReT, an open-source, version-controlled reference library of marine microbial eukaryote functional genes. Sci. Data 10, 926 (2023).
Chaumeil, P. A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the genome taxonomy database. Bioinformatics 6, 1925–1927 (2020).
Vasimuddin, M., Misra, S., Li, H. & Aluru, S. Efficient architecture-aware acceleration of BWA-MEM for multicore systems. In 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS) 314–324 (IEEE, Rio de Janeiro, Brazil, 2019). https://doi.org/10.1109/IPDPS.2019.00041.
Li, H. The sequence alignment/map (SAM) format and SAMtools 1000 Genome Project data processing subgroup. Bioinformatics 25, 1 (2009).
Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).
Ernst, O. P. et al. Microbial and animal rhodopsins: structures, functions, and molecular mechanisms. Chem. Rev. 8, 126–163 (2014).
Nagata, T. & Inoue, K. Rhodopsins at a glance. J. Cell Sci. 134, jcs258989 (2021).
Yamauchi, Y. et al. Molecular properties of a DTD channelrhodopsin from Guillardia theta. Biophys. Physicobiol. 14, 57–66 (2017).
Bulzu, P., Kavagutti, V. S., Andrei, A. & Ghai, R. The evolutionary kaleidoscope of rhodopsins. mSystems 7, e00405–22 (2022).
Needham et al. A distinct lineage of giant viruses brings a rhodopsin photosystem to unicellular marine predators. Proc. Natl. Acad. Sci. USA 116, 20574–20583 (2019).
Man, D. et al. Diversification and spectral tuning in marine proteorhodopsins. EMBO J. 15, 725–731 (2003).
Katoh, K., Misawa, K., Kuma, K. & Miyata, T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).
Castresana, J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol. Biol. Evol. 17, 540–552 (2000).
Johnson, L. S., Eddy, S. R. & Portugaly, E. Hidden Markov model speed heuristic and iterative HMM search procedure. BMC Bioinf. 11, 431 (2010).
Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).
Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).
Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nat. Methods 11, 1144–1146 (2014).
Pan, S., Zhao, X.-M. & Coelho, L. P. SemiBin2: self-supervised contrastive learning leads to better MAGs via deep learning. Bioinformatics 39, i21–i29 (2023).
Uritskiy, G. V., DiRuggiero, J. & Taylor, J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6, 158 (2018).
Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 11, 2864–2868 (2017).
Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).
Cantalapiedra, C. P., Hernandez-Plaza, A., Letunic, I., Bork, P. & Huerta-Cepas, J. eggNOG-mapper v2: functional annotation, orthology assignments, and domain prediction at the metagenomic scale. Mol. Bio. Evol. 38, 5825–5829 (2021).
Aroney, S. T. N. et al. CoverM: read alignment statistics for metagenomics. Bioinformatics 41, btaf147 (2025).
Cuevas-Cruz, M. et al. Annotated metagenome-assembled genomes (MAGs) from the Southern California Bight. Data sets. Figshare https://doi.org/10.6084/m9.figshare.29856866.
Acknowledgements
We thank Yamne Ortega Saad, Hiram Zayola and Lidia Montiel for their assistance in DNA extractions, statistical and bioinformatics analyses, and the crew on board the R/V Yellowfin for sample collection. This project was partially funded by the United States National Science Foundation grant (NSF, OCE1924464 and OCE-2220546), the Ministry of Economy and Competitiveness – Spanish Agencia Estatal de Investigación PID2023-152792NB-I00 and the United States-Israel Binational Science Foundation (BSF, No. 2019612).
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L.G.-C., B.H., and S.A.S.-W. designed research, L.G.-C., B.H., and S.A.S.-W. collected and processed samples, L.G.-C., B.H., E.V., M.C.-C., J.A., R.L., F.L., A.L.-L., L.S. and S.A.S.-W. performed research and analyzed data; L.G.-C., B.H., and S.A.S.-W. wrote the paper with input from the other coauthors.
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Gómez-Consarnau, L., Hassanzadeh, B., Villarreal, E. et al. Unexpected microbial rhodopsin dynamics in sync with phytoplankton blooms.
Nat Commun (2025). https://doi.org/10.1038/s41467-025-67474-1
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DOI: https://doi.org/10.1038/s41467-025-67474-1
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