Zinc is an essential trace metal for oceanic primary producers with the highest concentrations in polar oceans. However, its role in the biological functioning and adaptive evolution of polar phytoplankton remains enigmatic. Here, we have applied a combination of evolutionary genomics, quantitative proteomics, co-expression analyses and cellular physiology to suggest that model polar phytoplankton species have a higher demand for zinc because of elevated cellular levels of zinc-binding proteins. We propose that adaptive expansion of regulatory zinc-finger protein families, co-expanded and co-expressed zinc-binding proteins families involved in photosynthesis and growth in these microalgal species and their natural communities were identified to be responsible for the higher zinc demand. The expression of their encoding genes in eukaryotic phytoplankton metatranscriptomes from pole-to-pole was identified to correlate not only with dissolved zinc concentrations in the upper ocean but also with temperature, suggesting that environmental conditions of polar oceans are responsible for an increased demand of zinc. These results suggest that zinc plays an important role in supporting photosynthetic growth in eukaryotic polar phytoplankton and that this has been critical for algal colonization of low-temperature polar oceans.
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The Microglena sp. genome assembly data were deposited in NCBI GenBank (under BioProject accession PRJNA787402 and genome accession JAJSRW000000000). All raw transcriptome sequencing data of Microglena sp. were deposited into the Sequence Read Archive (under BioProject accession PRJNA814737). The mass spectrometry proteomics data of Microglena sp. and C. reinhardtii have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the iProX partner repository with the dataset identifier PXD032702. Source data are provided with this paper.
Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281, 237 (1998).
Anbar, A. D. & Knoll, A. H. Proterozoic ocean chemistry and evolution: a bioinorganic bridge? Science 297, 1137–1142 (2002).
Saito, M. A., Sigman, D. M. & Morel, F. M. M. The bioinorganic chemistry of the ancient ocean: the co-evolution of cyanobacterial metal requirements and biogeochemical cycles at the Archean–Proterozoic boundary? Inorg. Chim. Acta 356, 308–318 (2003).
Morel, F. M. M., Lam, P. J. & Saito, M. A. Trace metal substitution in marine phytoplankton. Annu. Rev. Earth Planet Sci. 48, 491–517 (2020).
Morel, F. M. & Price, N. M. The biogeochemical cycles of trace metals in the oceans. Science 300, 944–947 (2003).
Twining, B. S. & Baines, S. B. The trace metal composition of marine phytoplankton. Annu. Rev. Mar. Sci. 5, 191–215 (2013).
Ho, T.-Y. et al. The elemental composition of some marine phytoplankton. J. Phycol. 39, 1145–1159 (2003).
Ellwood, M. J. Wintertime trace metal (Zn, Cu, Ni, Cd, Pb and Co) and nutrient distributions in the subantarctic zone between 40–52°S; 155–160°E. Mar. Chem. 112, 107–117 (2008).
Zhao, Y., Vance, D., Abouchami, W. & de Baar, H. J. W. Biogeochemical cycling of zinc and its isotopes in the Southern Ocean. Geochim. Cosmochim. Acta 125, 653–667 (2014).
John, S. G., Helgoe, J. & Townsend, E. Biogeochemical cycling of Zn and Cd and their stable isotopes in the Eastern Tropical South Pacific. Mar. Chem. 201, 256–262 (2018).
Middag, R., de Baar, H. J. W. & Bruland, K. W. The relationships between dissolved zinc and major nutrients phosphate and silicate along the GEOTRACES GA02 transect in the West Atlantic Ocean. Glob. Biogeochem. Cy. 33, 63–84 (2019).
Sunda, W. G. & Huntsman, S. A. Feedback interactions between zinc and phytoplankton in seawater. Limnol. Oceanogr. 37, 25–40 (1992).
Sunda, W. G. & Huntsman, S. A. Cobalt and zinc interreplacement in marine phytoplankton: biological and geochemical implications. Limnol. Oceanogr. 40, 1404–1417 (1995).
Vance, D. et al. Silicon and zinc biogeochemical cycles coupled through the Southern Ocean. Nat. Geosci. 10, 202 (2017).
Weber, T., John, S., Tagliabue, A. & DeVries, T. Biological uptake and reversible scavenging of zinc in the global ocean. Science 361, 72 (2018).
Roshan, S., DeVries, T., Wu, J. & Chen, G. The internal cycling of zinc in the ocean. Glob. Biogeochem. Cy. 32, 1833–1849 (2018).
Scott, C. et al. Bioavailability of zinc in marine systems through time. Nat. Geosci. 6, 125–128 (2012).
Mock, T. et al. Evolutionary genomics of the cold-adapted diatom Fragilariopsis cylindrus. Nature 541, 536–540 (2017).
Blaby-Haas, C. E. & Merchant, S. S. Comparative and functional algal genomics. Annu. Rev. Plant Biol. 70, 605–638 (2019).
Zhang, Z. H. et al. Adaptation to extreme Antarctic environments revealed by the genome of a sea ice green alga. Curr. Biol. 30, 3330–3341 (2020).
Clarke, A. et al. The Southern Ocean benthic fauna and climate change: a historical perspective. Philos. Trans. R. Soc. Lond. B 338, 299–309 (1992).
Klug, A. The discovery of zinc fingers and their applications in gene regulation and genome manipulation. Annu. Rev. Biochem. 79, 213–231 (2010).
Krishna, S. S., Majumdar, I. & Grishin, N. V. Structural classification of zinc fingers: survey and summary. Nucleic Acids Res. 31, 532–550 (2003).
Barlow, P. N. et al. Structure of the C3HC4 domain by 1H-nuclear magnetic resonance spectroscopy: a new structural class of zinc-finger. J. Mol. Biol. 237, 201–211 (1994).
Stephens, T. G. et al. Genomes of the dinoflagellate Polarella glacialis encode tandemly repeated single-exon genes with adaptive functions. BMC Biol. 18, 56 (2020).
Aranda, M. et al. Genomes of coral dinoflagellate symbionts highlight evolutionary adaptations conducive to a symbiotic lifestyle. Sci. Rep. 6, 39734 (2016).
Liu, H. et al. Symbiodinium genomes reveal adaptive evolution of functions related to coral–dinoflagellate symbiosis. Commun. Biol. 1, 95 (2018).
Shoguchi, E. et al. Draft assembly of the Symbiodinium minutum nuclear genome reveals dinoflagellate gene structure. Curr. Biol. 23, 1399–1408 (2013).
Shoguchi, E. et al. Two divergent Symbiodinium genomes reveal conservation of a gene cluster for sunscreen biosynthesis and recently lost genes. BMC Genomics 19, 458 (2018).
Hoppe, C. J. M., Flintrop, C. M. & Rost, B. The Arctic picoeukaryote Micromonas pusilla benefits synergistically from warming and ocean acidification. Biogeosciences 15, 4353–4365 (2018).
Ferguson, R. E. et al. Housekeeping proteins: a preliminary study illustrating some limitations as useful references in protein expression studies. Proteomics 5, 566–571 (2005).
Aslam, S. N. et al. Identifying metabolic pathways for production of extracellular polymeric substances by the diatom Fragilariopsis cylindrus inhabiting sea ice. ISME J. 12, 1237–1251 (2018).
Valenzuela, J. J. et al. Ocean acidification conditions increase resilience of marine diatoms. Nat. Commun. 9, 2328 (2018).
Martin, K. et al. The biogeographic differentiation of algal microbiomes in the upper ocean from pole to pole. Nat. Commun. 12, 5483 (2021).
Mock, Thomas. Sea of Change: Eukaryotic Phytoplankton Communities in the Arctic Ocean. United States. https://doi.org/10.25585/1488054
Duncan, A. et al. Metagenome-assembled genomes of phytoplankton communities across the Arctic Circle and Atlantic Oceans. Microbiome 10 https://doi.org/10.1186/s40168-022-01254-7 (2022).
Persi, E., Wolf, Y. I. & Koonin, E. V. Positive and strongly relaxed purifying selection drive the evolution of repeats in proteins. Nat. Commun. 7, 13570 (2016).
Mock, T. & Gradinger, R. Determination of Arctic ice algal production with a new in situ incubation technique. Mar. Ecol. Prog. Ser. 177, 15–26 (1999).
Rühle, T., Hemschemeier, A., Melis, A. & Happe, T. A novel screening protocol for the isolation of hydrogen producing Chlamydomonas reinhardtii strains. BMC Plant Biol. 8, 107 (2008).
Crawford, D. W. et al. Influence of zinc and iron enrichments on phytoplankton growth in the northeastern subarctic Pacific. Limnol. Oceanogr. 48, 1583–1600 (2003).
Provasoli, L. Media and prospects for the cultivation of marine algae. In Cultures and Collections of Algae. Proc. US-Japan Conference, Hakone, 12-15 September 1966 (eds Watanabe, A & Hattori, A.) 63–75 (Japanese Society of Plant Physiology, 1968).
Ranallo-Benavidez, T. R., Jaron, K. S. & Schatz, M. C. GenomeScope 2.0 and Smudgeplot for reference-free profiling of polyploid genomes. Nat. Commun. 11, 1432 (2020).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Ye, C. X. et al. DBG2OLC: efficient assembly of large genomes using long erroneous reads of the third generation sequencing technologies. Sci. Rep. 6, 31900 (2016).
Chin, C. S. et al. Phased diploid genome assembly with single-molecule real-time sequencing. Nat. Methods 13, 1050–1054 (2016).
Qin, M. et al. LRScaf: improving draft genomes using long noisy reads. BMC Genomics 20, 955 (2019).
Ruan, J. & Li, H. Fast and accurate long-read assembly with wtdbg2. Nat. Methods 17, 155–158 (2020).
Servant, N. et al. HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol. 16, 259 (2015).
Ellinghaus, D., Kurtz, S. & Willhoeft, U. LTRharvest, an efficient and flexible software for de novo detection of LTR retrotransposons. BMC Bioinf. 9, 18 (2008).
Xu, Z. & Wang, H. LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Res. 35, W265–W268 (2007).
Ou, S. J. & Jiang, N. LTR_retriever: a highly accurate and sensitive program for identification of long terminal repeat retrotransposons. Plant Physiol. 176, 1410–1422 (2018).
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinf. 11, 119 (2010).
Stanke, M., Schöffmann, O., Morgenstern, B. & Waack, S. Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources. BMC Bioinf. 7, 62 (2006).
Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).
Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).
Haas, B. J. et al. Automated eukaryotic gene structure annotation using EVidenceModeler and the program to assemble spliced alignments. Genome Biol. 9, R7 (2008).
Mitchell, A. L. et al. InterPro in 2019: improving coverage, classification and access to protein sequence annotations. Nucleic Acids Res. 47, D351–D360 (2018).
Rice, P., Longden, I. & Bleasby, A. EMBOSS: The European Molecular Biology Open Software Suite. Trends Genet. 16, 276–277 (2000).
Kimura, M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120 (1980).
Emms, D. M. & Kelly, S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 20, 238 (2019).
Zhang, Z. et al. KaKs_Calculator: calculating Ka and Ks through model selection and model averaging. Genom. Proteom. Bioinf. 4, 259–263 (2006).
Kumar, S., Stecher, G., Suleski, M. & Hedges, S. B. TimeTree: a resource for timelines, timetrees, and divergence times. Mol. Biol. Evol. 34, 1812–1819 (2017).
Yang, Z. H. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).
Wiśniewski, J. R., Hein, M. Y., Cox, J. & Mann, M. A ‘proteomic ruler’ for protein copy number and concentration estimation without spike-in standards. Mol. Cell Proteom. 13, 3497–3506 (2014).
Huntemann, M. et al. The standard operating procedure of the DOE-JGI Metagenome Annotation Pipeline (MGAP v.4). Stand. Genomic Sci. 10, 86 (2016).
Fu, L. et al. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152 (2012).
Bushnell, B. BBMap: A Fast, Accurate, Splice-aware Aligner (Lawrence Berkeley National Laboratory, 2014).
Löytynoja, A. Phylogeny-aware Alignment with PRANK: Multiple Sequence Alignment Methods (Humana Press, 2014).
Castresana, J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol. Biol. Evol. 17, 540–552 (2000).
This work was supported by the national key research and development programme of China (2018YFD0900305), the Marine S&T Fund of Shandong Province for the Pilot National Laboratory for Marine Science and Technology (Qingdao) (2021QNLM050103-1), National Natural Science Foundation of China (41676145, 32000404), Central Public-interest Scientific Institution Basal Research Fund, YSFRI, CAFS (20603022020019, 20603022021019), China Agriculture Research System (CARS-50), Taishan Scholars Funding of Shandong Province, Young Taishan Scholars Program (tsqn202103136). The metatranscriptome sequencing was conducted by the US Department of Energy (DOE)–Joint Genome Institute, a DOE Office of Science User Facility, which is supported by the Office of Science of the DOE under contract no. DE-AC02-05CH1123. A.T. was supported by the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant no. 724289). T.M. acknowledges funding from the US DOE–Joint Genome Institute (grant no. 532, Community Science Program) and the Natural Environment Research Council (grant nos. NE/K004530/1 and NE/R000883/1). T.M. and C.v.O. acknowledge partial funding from the School of Environmental Sciences at the University of East Anglia, Norwich Research Park, United Kingdom.
The authors declare no competing interests.
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Extended Data Fig. 1 Morphology and phylogenetic relationship of Microglena sp. YARC.
a, b, Optical microscope (a) and transmission electron microscopy (b) of Microglena sp. YARC. c, A maximum likelihood tree of green algae based on the five chloroplast protein-coding genes (atpB, psaA, psaB, psbC and rbcL) using maximum likelihood method. The tree with branches showing bootstrap support > 50.
Extended Data Fig. 2 Genome characters of Microglena sp. YARC.
a, Estimation of the genome size by flow cytometry. The maize diploid cells (genome size 2.3 Gb) were used as reference. The relative fluorescence values of maize diploid cells were 55.15 ± 2.43, and the relative fluorescence value of Microglena haploid cells were 15.11 ± 0.73. Based on the ratio of fluorescence values, the genome size of Microglena was estimated to be about 1.26 ± 0.02 Gb. b, K-mer estimation of the genome size of Microglena sp. YARC. c, Cytogenetic studies showed that Microglena sp. YARC has n = 6 chromosomes. d, The Hi-C assisted assembly of Microglena sp. pseudomolecules. Heatmap showing Hi-C interactions under the resolution of 200 kb, and the antidiagonal pattern for the intrachromosomal interactions may reflect the Rabl configuration of chromatins. e,f, Ks (e) and 4dtv (f) distribution of the whole Microglena sp. genome.
Extended Data Fig. 3 Comparison of total zinc-finger domains in bacteria and fish genomes.
a, Total zinc-finger domains comparison between psychrophillic (blue) and mesophilic bacteria (tawny). b, Zinc-finger comparison between two Antarctic fishes and four Mesophilic fishes. Statics analysis of two-sided Duncan’s test showed no significant difference between each species. For all boxplots, box bounds represent the first and third quartiles and whiskers 1.5× the interquartile range; the centre line represents the median.
Extended Data Fig. 4
Phylogenetic and expansion analysis of carbonic anhydrase encoding genes in green algae and diatoms.
Extended Data Fig. 5 Expansion of light-harvesting proteins in Microglena sp. YARC.
a, Expansion of chlorophyll A-B binding protein domains as a function of total annotated domains for green algal, diatom and dinoflagellate genomes. Solid for polar algae and hollow for non-polar algae. b, Unrooted genealogy of LHC genes in Microglena (Red), Chlamydomonas reinhardtii (blue), Chlamydomonas eustigma (green), Volvox carteri (yellow) and Gonium pectorale (purple).
Extended Data Fig. 6 Upper 100 m annually averaged dissolved zinc (nmol L-1) from the PISCES model with observations taken within the upper 100 m overlain as coloured circles.
Sampling locations for this study are indicated with red crosses.
Supplementary Methods and Tables 1–7.
Supplementary Data 1, PFAM and Interproscan annotation of zinc-finger-containing LTRs; 2, Protein copy number of Microglena sp. YARC; 3, Protein copy number of Chlamydomonas reinhardtii; 4, Copy number of zinc-binding proteins of Microglena sp. YARC; 5, Copy number of zinc-binding proteins of Chlamydomonas reinhardtii; 6 Co-expression gene pairs of Microglena sp. YARC.
Source Data Fig. 2
Original source data.
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Ye, N., Han, W., Toseland, A. et al. The role of zinc in the adaptive evolution of polar phytoplankton. Nat Ecol Evol 6, 965–978 (2022). https://doi.org/10.1038/s41559-022-01750-x
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