Genomic adaptation of the picoeukaryote Pelagomonas calceolata to iron-poor oceans revealed by a chromosome-scale genome sequence.
Journal
Communications biology
ISSN: 2399-3642
Titre abrégé: Commun Biol
Pays: England
ID NLM: 101719179
Informations de publication
Date de publication:
16 09 2022
16 09 2022
Historique:
received:
13
12
2021
accepted:
02
09
2022
entrez:
16
9
2022
pubmed:
17
9
2022
medline:
21
9
2022
Statut:
epublish
Résumé
The smallest phytoplankton species are key actors in oceans biogeochemical cycling and their abundance and distribution are affected with global environmental changes. Among them, algae of the Pelagophyceae class encompass coastal species causative of harmful algal blooms while others are cosmopolitan and abundant. The lack of genomic reference in this lineage is a main limitation to study its ecological importance. Here, we analysed Pelagomonas calceolata relative abundance, ecological niche and potential for the adaptation in all oceans using a complete chromosome-scale assembled genome sequence. Our results show that P. calceolata is one of the most abundant eukaryotic species in the oceans with a relative abundance favoured by high temperature, low-light and iron-poor conditions. Climate change projections based on its relative abundance suggest an extension of the P. calceolata habitat toward the poles at the end of this century. Finally, we observed a specific gene repertoire and expression level variations potentially explaining its ecological success in low-iron and low-nitrate environments. Collectively, these findings reveal the ecological importance of P. calceolata and lay the foundation for a global scale analysis of the adaptation and acclimation strategies of this small phytoplankton in a changing environment.
Identifiants
pubmed: 36114260
doi: 10.1038/s42003-022-03939-z
pii: 10.1038/s42003-022-03939-z
pmc: PMC9481584
doi:
Substances chimiques
Nitrates
0
Iron
E1UOL152H7
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
983Informations de copyright
© 2022. The Author(s).
Références
Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281, 237–240 (1998).
pubmed: 9657713
doi: 10.1126/science.281.5374.237
Boyce, D. G., Lewis, M. R. & Worm, B. Global phytoplankton decline over the past century. Nature 466, 591–596 (2010).
pubmed: 20671703
doi: 10.1038/nature09268
Henson, S. A., Cael, B. B., Allen, S. R. & Dutkiewicz, S. Future phytoplankton diversity in a changing climate. Nat. Commun. 12, 5372 (2021).
pubmed: 34508102
pmcid: 8433162
doi: 10.1038/s41467-021-25699-w
Vaulot, D., Eikrem, W., Viprey, M. & Moreau, H. The diversity of small eukaryotic phytoplankton (≤3 μm) in marine ecosystems. FEMS Microbiol. Rev. 32, 795–820 (2008).
pubmed: 18564290
doi: 10.1111/j.1574-6976.2008.00121.x
Agawin, N. S. R., Duarte, C. M. & Agustí, S. Nutrient and temperature control of the contribution of picoplankton to phytoplankton biomass and production. Limnol. Oceanogr. 45, 591–600 (2000).
doi: 10.4319/lo.2000.45.3.0591
Morán, X. A. G., López-Urrutia, Á., Calvo-Díaz, A. & Li, W. K. W. Increasing importance of small phytoplankton in a warmer ocean. Glob. Change Biol. 16, 1137–1144 (2010).
doi: 10.1111/j.1365-2486.2009.01960.x
Li, W. K. W., McLaughlin, F. A., Lovejoy, C. & Carmack, E. C. Smallest algae thrive as the arctic ocean freshens. Science 326 https://doi.org/10.1126/science.1179798 (2009).
Benner, I., Irwin, A. J. & Finkel, Z. V. Capacity of the common Arctic picoeukaryote Micromonas to adapt to a warming ocean. Limnol. Oceanography Lett. 5, 221–227 (2020).
Sunda, W. G. & Huntsman, S. A. Iron uptake and growth limitation in oceanic and coastal phytoplankton. Mar. Chem. 50, 189–206 (1995).
doi: 10.1016/0304-4203(95)00035-P
Raven, J. A. The twelfth Tansley Lecture. Small is beautiful: the picophytoplankton. Funct. Ecol. 12, 503–513 (1998).
doi: 10.1046/j.1365-2435.1998.00233.x
Morel, F. M. M. & Price, N. M. The biogeochemical cycles of trace metals in the oceans. Science 300, 944–947 (2003).
pubmed: 12738853
doi: 10.1126/science.1083545
Gao, X., Bowler, C. & Kazamia, E. Iron metabolism strategies in diatoms. J. Exp. Bot. 72, 2165–2180 (2021).
pubmed: 33693565
pmcid: 7966952
doi: 10.1093/jxb/eraa575
Caputi, L. et al. Community-level responses to iron availability in open ocean plankton ecosystems. Glob. Biogeochemical Cycles 33, 391–419 (2019).
doi: 10.1029/2018GB006022
Carradec, Q. et al. A global ocean atlas of eukaryotic genes. Nat. Commun. 9, 373 (2018).
pubmed: 29371626
pmcid: 5785536
doi: 10.1038/s41467-017-02342-1
Morrissey, J. et al. A novel protein, ubiquitous in marine phytoplankton, concentrates iron at the cell surface and facilitates uptake. Curr. Biol. 25, 364–371 (2015).
pubmed: 25557662
doi: 10.1016/j.cub.2014.12.004
Moore, C. M. et al. Processes and patterns of oceanic nutrient limitation. Nat. Geosci. 6, 701–710 (2013).
doi: 10.1038/ngeo1765
Kumar, A. & Bera, S. Revisiting nitrogen utilization in algae: a review on the process of regulation and assimilation. Bioresour. Technol. Rep. 12, 100584 (2020).
doi: 10.1016/j.biteb.2020.100584
Smith, S. R. et al. Evolution and regulation of nitrogen flux through compartmentalized metabolic networks in a marine diatom. Nat. Commun. 10, 4552 (2019).
pubmed: 31591397
pmcid: 6779911
doi: 10.1038/s41467-019-12407-y
Berg, G. M., Glibert, P. M., Lomas, M. W. & Burford, M. A. Organic nitrogen uptake and growth by the chrysophyte Aureococcus anophagefferens during a brown tide event. Mar. Biol. 129, 377–387 (1997).
doi: 10.1007/s002270050178
Andersen, R. A., Saunders, G. W., Paskind, M. P. & Sexton, J. P. Ultrastructure and 18s rRNA gene sequence for Pelagomonas calceolata gen. et sp. nov. and the description of a new algal class, the pelagophyceae classis nov. J. Phycol. 29, 701–715 (1993).
doi: 10.1111/j.0022-3646.1993.00701.x
Choi, C. J. et al. Seasonal and geographical transitions in eukaryotic phytoplankton community structure in the Atlantic and Pacific Oceans. Front. Microbiol. 11, 542372 (2020).
pubmed: 33101224
pmcid: 7554337
doi: 10.3389/fmicb.2020.542372
Duerschlag, J. et al. Niche partitioning by photosynthetic plankton as a driver of CO2-fixation across the oligotrophic South Pacific Subtropical Ocean. ISME J 1–12 https://doi.org/10.1038/s41396-021-01072-z (2021).
Worden, A. Z. et al. Global distribution of a wild alga revealed by targeted metagenomics. Curr. Biol. 22, R675–R677 (2012).
pubmed: 22974991
doi: 10.1016/j.cub.2012.07.054
Dimier, C. é, Brunet, C., Geider, R. & Raven, J. Growth and photoregulation dynamics of the picoeukaryote Pelagomonas calceolata in fluctuating light. Limnol. Oceanogr. 54, 823–836 (2009).
doi: 10.4319/lo.2009.54.3.0823
Dupont, C. L. et al. Genomes and gene expression across light and productivity gradients in eastern subtropical Pacific microbial communities. ISME J. 9, 1076–1092 (2015).
pubmed: 25333462
doi: 10.1038/ismej.2014.198
Kang, Y. et al. Transcriptomic responses of four pelagophytes to nutrient (N, P) and light stress. Front. Mar. Sci. 8, 636699 (2021).
Huff, J. T., Zilberman, D. & Roy, S. W. Mechanism for DNA transposons to generate introns on genomic scales. Nature 538, 533–536 (2016).
pubmed: 27760113
pmcid: 5684705
doi: 10.1038/nature20110
Waterhouse, R. M. et al. BUSCO applications from quality assessments to gene prediction and phylogenomics. Mol. Biol. Evol. 35, 543–548 (2018).
pubmed: 29220515
doi: 10.1093/molbev/msx319
Nambiar, M. & Smith, G. R. Repression of harmful meiotic recombination in centromeric regions. Semin Cell Dev. Biol. 54, 188–197 (2016).
pubmed: 26849908
pmcid: 4867242
doi: 10.1016/j.semcdb.2016.01.042
Pessia, E. et al. Evidence for widespread GC-biased gene conversion in eukaryotes. Genome Biol. Evol. 4, 675–682 (2012).
pubmed: 22628461
pmcid: 5635611
doi: 10.1093/gbe/evs052
Chi, J., Mahé, F., Loidl, J., Logsdon, J. & Dunthorn, M. Meiosis gene inventory of four ciliates reveals the prevalence of a synaptonemal complex-independent crossover pathway. Mol. Biol. Evol. 31, 660–672 (2014).
pubmed: 24336924
doi: 10.1093/molbev/mst258
Ramesh, M. A., Malik, S.-B. & Logsdon, J. M. A phylogenomic inventory of meiotic genes; evidence for sex in Giardia and an early eukaryotic origin of meiosis. Curr. Biol. 15, 185–191 (2005).
pubmed: 15668177
Schurko, A. M. & Logsdon, J. M. Using a meiosis detection toolkit to investigate ancient asexual ‘scandals’ and the evolution of sex. Bioessays 30, 579–589 (2008).
pubmed: 18478537
doi: 10.1002/bies.20764
Ibarbalz, F. M. et al. Global trends in marine plankton diversity across kingdoms of life. Cell 179, 1084–1097.e21 (2019).
pubmed: 31730851
pmcid: 6912166
doi: 10.1016/j.cell.2019.10.008
Frémont, P. et al. Restructuring of plankton genomic biogeography in the surface ocean under climate change. Nat. Clim. Chang. 12, 393–401 (2022).
doi: 10.1038/s41558-022-01314-8
Ward, D. M. & Kaplan, J. Ferroportin-mediated iron transport: expression and regulation. Biochim Biophys. Acta 1823, 1426–1433 (2012).
pubmed: 22440327
pmcid: 3718258
doi: 10.1016/j.bbamcr.2012.03.004
Gobler, C. J., Lonsdale, D. J. & Boyer, G. L. A review of the causes, effects, and potential management of harmful brown tide blooms caused by Aureococcus anophagefferens (Hargraves et sieburth). Estuaries 28, 726–749 (2005).
doi: 10.1007/BF02732911
Agusti, S., Lubián, L. M., Moreno-Ostos, E., Estrada, M. & Duarte, C. M. Projected changes in photosynthetic picoplankton in a warmer subtropical ocean. Front. Mar. Sci. 5, 506 (2019).
doi: 10.3389/fmars.2018.00506
Anderson, S. I., Barton, A. D., Clayton, S., Dutkiewicz, S. & Rynearson, T. A. Marine phytoplankton functional types exhibit diverse responses to thermal change. Nat. Commun. 12, 6413 (2021).
pubmed: 34741038
pmcid: 8571312
doi: 10.1038/s41467-021-26651-8
Martin, J. H. et al. Testing the iron hypothesis in ecosystems of the equatorial Pacific Ocean. Nature 371, 123–129 (1994).
doi: 10.1038/371123a0
Shi, D., Xu, Y., Hopkinson, B. M. & Morel, F. M. M. Effect of ocean acidification on iron availability to marine phytoplankton. Science 327, 676–679 (2010).
pubmed: 20075213
doi: 10.1126/science.1183517
McQuaid, J. B. et al. Carbonate-sensitive phytotransferrin controls high-affinity iron uptake in diatoms. Nature 555, 534–537 (2018).
pubmed: 29539640
doi: 10.1038/nature25982
Turnšek, J. et al. Proximity proteomics in a marine diatom reveals a putative cell surface-to-chloroplast iron trafficking pathway. eLife 10, e52770 (2021).
pubmed: 33591270
pmcid: 7972479
doi: 10.7554/eLife.52770
Urzica, E. I. et al. Systems and trans-system level analysis identifies conserved iron deficiency responses in the plant lineage[W][OA]. Plant Cell 24, 3921–3948 (2012).
pubmed: 23043051
pmcid: 3517228
doi: 10.1105/tpc.112.102491
Mao, X. et al. Diversity, prevalence, and expression of cyanase genes (cynS) in planktonic marine microorganisms. ISME J. 16, 602–605 (2022).
pubmed: 34408267
doi: 10.1038/s41396-021-01081-y
Ou, L., Cai, Y., Jin, W., Wang, Z. & Lu, S. Understanding the nitrogen uptake and assimilation of the Chinese strain of Aureococcus anophagefferens (Pelagophyceae). Algal Res. 34, 182–190 (2018).
doi: 10.1016/j.algal.2018.07.019
Shu, C. J., Ulrich, L. E. & Zhulin, I. B. The NIT domain: a predicted nitrate-responsive module in bacterial sensory receptors. Trends Biochem Sci. 28, 121–124 (2003).
pubmed: 12633990
doi: 10.1016/S0968-0004(03)00032-X
Wu, S. Q., Chai, W., Lin, J. T. & Stewart, V. General nitrogen regulation of nitrate assimilation regulatory gene nasR expression in Klebsiella oxytoca M5al. J. Bacteriol. 181, 7274–7284 (1999).
pubmed: 10572131
pmcid: 103690
doi: 10.1128/JB.181.23.7274-7284.1999
Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 3, 95–98 (2016).
pubmed: 27467249
pmcid: 5846465
doi: 10.1016/j.cels.2016.07.002
Li, R., Li, Y., Kristiansen, K. & Wang, J. SOAP: short oligonucleotide alignment program. Bioinformatics 24, 713–714 (2008).
pubmed: 18227114
doi: 10.1093/bioinformatics/btn025
Alberti, A. et al. Viral to metazoan marine plankton nucleotide sequences from the Tara Oceans expedition. Sci. Data 4, 170093 (2017).
pubmed: 28763055
pmcid: 5538240
doi: 10.1038/sdata.2017.93
Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).
pubmed: 23071270
doi: 10.1093/bioinformatics/bts611
Kim, D., Song, L., Breitwieser, F. P. & Salzberg, S. L. Centrifuge: rapid and sensitive classification of metagenomic sequences. Genome Res. https://doi.org/10.1101/gr.210641.116 (2016).
Vurture, G. W. et al. GenomeScope: fast reference-free genome profiling from short reads. Bioinformatics 33, 2202–2204 (2017).
pubmed: 28369201
pmcid: 5870704
doi: 10.1093/bioinformatics/btx153
Vaser, R. & Šikić, M. Yet another de novo genome assembler. BioRxiv. https://doi.org/10.1101/656306 (2019).
Liu, H. et al. SMARTdenovo: a de novo assembler using long noisy reads. Gigabyte 2021, 1–9 (2021).
doi: 10.46471/gigabyte.15
Kolmogorov, M., Yuan, J., Lin, Y. & Pevzner, P. A. Assembly of long, error-prone reads using repeat graphs. Nat. Biotechnol. 37, 540–546 (2019).
pubmed: 30936562
doi: 10.1038/s41587-019-0072-8
Ruan, J. & Li, H. Fast and accurate long-read assembly with wtdbg2. Nat. Methods 17, 155–158 (2020).
pubmed: 31819265
doi: 10.1038/s41592-019-0669-3
Wick, R. R., Schultz, M. B., Zobel, J. & Holt, K. E. Bandage: interactive visualization of de novo genome assemblies. Bioinformatics 31, 3350–3352 (2015).
pubmed: 26099265
pmcid: 4595904
doi: 10.1093/bioinformatics/btv383
Vaser, R., Sović, I., Nagarajan, N. & Šikić, M. Fast and accurate de novo genome assembly from long uncorrected reads. Genome Res 27, 737–746 (2017).
pubmed: 28100585
pmcid: 5411768
doi: 10.1101/gr.214270.116
Aury, J.-M. & Istace, B. Hapo-G, haplotype-aware polishing of genome assemblies with accurate reads. NAR Genomics Bioinform. 3, lqab034 (2021).
Benson, G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 27, 573–580 (1999).
pubmed: 9862982
pmcid: 148217
doi: 10.1093/nar/27.2.573
Morgulis, A., Gertz, E. M., Schäffer, A. A. & Agarwala, R. A fast and symmetric DUST implementation to mask low-complexity DNA sequences. J. Comput Biol. 13, 1028–1040 (2006).
pubmed: 16796549
doi: 10.1089/cmb.2006.13.1028
Smit, A. F. A., Hubley, R. & Green, P. RepeatMasker. http://repeatmasker.org/ (2013).
Price, A. L., Jones, N. C. & Pevzner, P. A. De novo identification of repeat families in large genomes. Bioinformatics 21, i351–i358 (2005).
pubmed: 15961478
doi: 10.1093/bioinformatics/bti1018
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
pubmed: 20110278
pmcid: 2832824
doi: 10.1093/bioinformatics/btq033
Pedersen, B. S. & Quinlan, A. R. Mosdepth: quick coverage calculation for genomes and exomes. Bioinformatics 34, 867–868 (2018).
pubmed: 29096012
doi: 10.1093/bioinformatics/btx699
Schulz, M. H., Zerbino, D. R., Vingron, M. & Birney, E. Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics 28, 1086–1092 (2012).
pubmed: 22368243
pmcid: 3324515
doi: 10.1093/bioinformatics/bts094
Zerbino, D. R. & Birney, E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 18, 821–829 (2008).
pubmed: 18349386
pmcid: 2336801
doi: 10.1101/gr.074492.107
Li, H. et al. The sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
pubmed: 19505943
pmcid: 2723002
doi: 10.1093/bioinformatics/btp352
Marchler-Bauer, A. et al. CDD: NCBI’s conserved domain database. Nucleic Acids Res. 43, D222–D226 (2015).
pubmed: 25414356
doi: 10.1093/nar/gku1221
Niang, G. et al. METdb: A genomic reference database for marine species. F1000Research, https://doi.org/10.7490/f1000research.1118000.1 (2020).
Kent, W. J. BLAT–the BLAST-like alignment tool. Genome Res. 12, 656–664 (2002).
pubmed: 11932250
pmcid: 187518
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).
pubmed: 2231712
doi: 10.1016/S0022-2836(05)80360-2
Birney, E., Clamp, M. & Durbin, R. GeneWise and genomewise. Genome Res. 14, 988–995 (2004).
pubmed: 15123596
pmcid: 479130
doi: 10.1101/gr.1865504
Stanke, M. et al. AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res. 34, W435–W439 (2006).
pubmed: 16845043
pmcid: 1538822
doi: 10.1093/nar/gkl200
Dubarry, M. et al. Gmove a tool for eukaryotic gene predictions using various evidences. F1000Research, https://doi.org/10.7490/f1000research.1111735.1 (2016).
Sibbald, S. J., Lawton, M. & Archibald, J. M. Mitochondrial genome evolution in pelagophyte algae. Genome Biol. Evol. 13, evab018 (2021).
Quevillon, E. et al. InterProScan: protein domains identifier. Nucleic Acids Res. 33, W116–W120 (2005).
pubmed: 15980438
pmcid: 1160203
doi: 10.1093/nar/gki442
Buchfink, B., Reuter, K. & Drost, H.-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods 18, 366–368 (2021).
pubmed: 33828273
pmcid: 8026399
doi: 10.1038/s41592-021-01101-x
Aramaki, T. et al. KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 36, 2251–2252 (2020).
pubmed: 31742321
doi: 10.1093/bioinformatics/btz859
Delmont, T. O. et al. Functional repertoire convergence of distantly related eukaryotic plankton lineages abundant in the sunlit ocean. Cell Genomics 2, 100123 (2022).
doi: 10.1016/j.xgen.2022.100123
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
pubmed: 19451168
pmcid: 2705234
doi: 10.1093/bioinformatics/btp324
Pesant, S. et al. Open science resources for the discovery and analysis of Tara Oceans data. Sci. Data 2, 150023 (2015).
pubmed: 26029378
pmcid: 4443879
doi: 10.1038/sdata.2015.23
Aumont, O., Ethé, C., Tagliabue, A., Bopp, L. & Gehlen, M. PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies. Geoscientific Model Dev. 8, 2465–2513 (2015).
doi: 10.5194/gmd-8-2465-2015
Clayton, S. et al. Biogeochemical versus ecological consequences of modeled ocean physics. Biogeosciences 14, 2877–2889 (2017).
doi: 10.5194/bg-14-2877-2017
Ravindra, K., Rattan, P., Mor, S. & Aggarwal, A. N. Generalized additive models: building evidence of air pollution, climate change and human health. Environ. Int. 132, 104987 (2019).
pubmed: 31398655
doi: 10.1016/j.envint.2019.104987
Günther, F. & Fritsch, S. neuralnet: training of neural networks. R. J. 2, 30–38 (2010).
doi: 10.32614/RJ-2010-006
Gobler, C. J. et al. Niche of harmful alga Aureococcus anophagefferens revealed through ecogenomics. Proc. Natl Acad. Sci. USA 108, 4352–4357 (2011).
pubmed: 21368207
pmcid: 3060233
doi: 10.1073/pnas.1016106108
Guo, L. et al. Genome assembly of Nannochloropsis oceanica provides evidence of host nucleus overthrow by the symbiont nucleus during speciation. Commun. Biol. 2, 1–12 (2019).
doi: 10.1038/s42003-019-0500-9
Bowler, C. et al. The Phaeodactylum genome reveals the evolutionary history of diatom genomes. Nature 456, 239–244 (2008).
pubmed: 18923393
doi: 10.1038/nature07410
Armbrust, E. V. et al. The genome of the diatom thalassiosira pseudonana: ecology, evolution, and metabolism. Science 306, 79–86 (2004).
pubmed: 15459382
doi: 10.1126/science.1101156
Worden, A. Z. et al. Green evolution and dynamic adaptations revealed by genomes of the marine picoeukaryotes micromonas. Science 324, 268–272 (2009).
pubmed: 19359590
doi: 10.1126/science.1167222
Palenik, B. et al. The tiny eukaryote Ostreococcus provides genomic insights into the paradox of plankton speciation. PNAS 104, 7705–7710 (2007).
pubmed: 17460045
pmcid: 1863510
doi: 10.1073/pnas.0611046104
Moreau, H. et al. Gene functionalities and genome structure in Bathycoccus prasinos reflect cellular specializations at the base of the green lineage. Genome Biol. 13, R74 (2012).
pubmed: 22925495
pmcid: 3491373
doi: 10.1186/gb-2012-13-8-r74
Read, B. A. et al. Pan genome of the phytoplankton Emiliania underpins its global distribution. Nature 499, 209–213 (2013).
pubmed: 23760476
doi: 10.1038/nature12221