Carbonate-sensitive phytotransferrin controls high-affinity iron uptake in diatoms


In vast areas of the ocean, the scarcity of iron controls the growth and productivity of phytoplankton1,2. Although most dissolved iron in the marine environment is complexed with organic molecules3, picomolar amounts of labile inorganic iron species (labile iron) are maintained within the euphotic zone4 and serve as an important source of iron for eukaryotic phytoplankton and particularly for diatoms5. Genome-enabled studies of labile iron utilization by diatoms have previously revealed novel iron-responsive transcripts6,7, including the ferric iron-concentrating protein ISIP2A8, but the mechanism behind the acquisition of picomolar labile iron remains unknown. Here we show that ISIP2A is a phytotransferrin that independently and convergently evolved carbonate ion-coordinated ferric iron binding. Deletion of ISIP2A disrupts high-affinity iron uptake in the diatom Phaeodactylum tricornutum, and uptake is restored by complementation with human transferrin. ISIP2A is internalized by endocytosis, and manipulation of the seawater carbonic acid system reveals a second-order dependence on the concentrations of labile iron and carbonate ions. In P. tricornutum, the synergistic interaction of labile iron and carbonate ions occurs at environmentally relevant concentrations, revealing that carbonate availability co-limits iron uptake. Phytotransferrin sequences have a broad taxonomic distribution8 and are abundant in marine environmental genomic datasets9,10, suggesting that acidification-driven declines in the concentration of seawater carbonate ions will have a negative effect on this globally important eukaryotic iron acquisition mechanism.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Transferrin and phytotransferrin are functional analogues with a common origin.
Figure 2: Characterization of phytotransferrin ISIP2A (P. tricornutum).
Figure 3: The synergistic interaction of Fe′ and CO32− determines Fe′ uptake rates.
Figure 4: pH and CO2 manipulation of P. tricornutum induces expression of ISIP2A.


  1. 1

    Moore, C. M. et al. Processes and patterns of oceanic nutrient limitation. Nat. Geosci. 6, 701–710 (2013)

    ADS  CAS  Article  Google Scholar 

  2. 2

    Boyd, P. W. et al. Mesoscale iron enrichment experiments 1993–2005: synthesis and future directions. Science 315, 612–617 (2007)

    ADS  CAS  Google Scholar 

  3. 3

    Gledhill, M. & Buck, K. N. The organic complexation of iron in the marine environment: a review. Front. Microbiol. 3, 69 (2012)

    PubMed  PubMed Central  Google Scholar 

  4. 4

    Barbeau, K., Rue, E. L., Bruland, K. W. & Butler, A. Photochemical cycling of iron in the surface ocean mediated by microbial iron(iii)-binding ligands. Nature 413, 409–413 (2001)

    ADS  CAS  PubMed  Google Scholar 

  5. 5

    Morel, F. M. M., Kustka, A. B. & Shaked, Y. The role of unchelated Fe in the iron nutrition of phytoplankton. Limnol. Oceanogr. 53, 400–404 (2008)

    ADS  CAS  Google Scholar 

  6. 6

    Allen, A. E. et al. Whole-cell response of the pennate diatom Phaeodactylum tricornutum to iron starvation. Proc. Natl Acad. Sci. USA 105, 10438–10443 (2008)

    ADS  CAS  PubMed  Google Scholar 

  7. 7

    Lommer, M. et al. Genome and low-iron response of an oceanic diatom adapted to chronic iron limitation. Genome Biol. 13, R66 (2012)

    PubMed  PubMed Central  Google Scholar 

  8. 8

    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)

    CAS  PubMed  Google Scholar 

  9. 9

    Marchetti, A. et al. Comparative metatranscriptomics identifies molecular bases for the physiological responses of phytoplankton to varying iron availability. Proc. Natl Acad. Sci. USA 109, E317–E325 (2012)

    CAS  PubMed  Google Scholar 

  10. 10

    Bertrand, E. M. et al. Phytoplankton–bacterial interactions mediate micronutrient colimitation at the coastal Antarctic sea ice edge. Proc. Natl Acad. Sci. USA 112, 9938–9943 (2015)

    ADS  CAS  PubMed  Google Scholar 

  11. 11

    Lambert, L. A., Perri, H., Halbrooks, P. J. & Mason, A. B. Evolution of the transferrin family: conservation of residues associated with iron and anion binding. Comp. Biochem. Physiol. 142, 129–141 (2005)

    Google Scholar 

  12. 12

    Aisen, P., Leibman, A. & Zweier, J. Stoichiometric and site characteristics of the binding of iron to human transferrin. J. Biol. Chem. 253, 1930–1937 (1978)

    CAS  PubMed  Google Scholar 

  13. 13

    Baker, H. M., Anderson, B. F. & Baker, E. N. Dealing with iron: common structural principles in proteins that transport iron and heme. Proc. Natl Acad. Sci. USA 100, 3579–3583 (2003)

    ADS  CAS  PubMed  Google Scholar 

  14. 14

    Fisher, M., Gokhman, I., Pick, U. & Zamir, A. A structurally novel transferrin-like protein accumulates in the plasma membrane of the unicellular green alga Dunaliella salina grown in high salinities. J. Biol. Chem. 272, 1565–1570 (1997)

    CAS  PubMed  Google Scholar 

  15. 15

    Anderson, M. A. & Morel, F. M. The influence of aqueous iron chemistry on the uptake of iron by the coastal diatom Thalassiosira weissflogii. Limnol. Oceanogr. 27, 789–813 (1982)

    ADS  CAS  Google Scholar 

  16. 16

    Bruns, C. M. et al. Structure of Haemophilus influenzae Fe+3-binding protein reveals convergent evolution within a superfamily. Nat. Struct. Biol. 4, 919–924 (1997)

    CAS  PubMed  Google Scholar 

  17. 17

    Och, L. M. & Shields-Zhou, G. A. The Neoproterozoic oxygenation event: environmental perturbations and biogeochemical cycling. Earth Sci. Rev. 110, 26–57 (2012)

    ADS  CAS  Google Scholar 

  18. 18

    Smith, S. R. et al. Transcriptional orchestration of the global cellular response of a model pennate diatom to diel light cycling under iron limitation. PLoS Genet. 12, e1006490 (2016)

    PubMed  PubMed Central  Google Scholar 

  19. 19

    Karas, B. J. et al. Designer diatom episomes delivered by bacterial conjugation. Nat. Commun. 6, 6925 (2015)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  20. 20

    Harding, C., Heuser, J. & Stahl, P. Receptor-mediated endocytosis of transferrin and recycling of the transferrin receptor in rat reticulocytes. J. Cell Biol. 97, 329–339 (1983)

    CAS  PubMed  Google Scholar 

  21. 21

    Fisher, M., Zamir, A. & Pick, U. Iron uptake by the halotolerant alga Dunaliella is mediated by a plasma membrane transferrin. J. Biol. Chem. 273, 17553–17558 (1998)

    CAS  PubMed  Google Scholar 

  22. 22

    Schlabach, M. R. & Bates, G. W. The synergistic binding of anions and Fe3+ by transferrin. Implications for the interlocking sites hypothesis. J. Biol. Chem. 250, 2182–2188 (1975)

    CAS  PubMed  Google Scholar 

  23. 23

    Halbrooks, P. J., Mason, A. B., Adams, T. E., Briggs, S. K. & Everse, S. J. The oxalate effect on release of iron from human serum transferrin explained. J. Mol. Biol. 339, 217–226 (2004)

    CAS  PubMed  Google Scholar 

  24. 24

    Doney, S. C ., Fabry, V. J ., Feely, R. A. & Kleypas, J. A. Ocean acidification: the other CO2 problem. Annu. Rev. Mar. Sci. 1, 169–192 (2009)

    ADS  Google Scholar 

  25. 25

    Sunda, W. & Huntsman, S. Effect of pH, light, and temperature on Fe–EDTA chelation and Fe hydrolysis in seawater. Mar. Chem. 84, 35–47 (2003)

    CAS  Google Scholar 

  26. 26

    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)

    ADS  CAS  PubMed  Google Scholar 

  27. 27

    Allen, M. D., del Campo, J. A., Kropat, J. & Merchant, S. S. FEA1, FEA2, and FRE1, encoding two homologous secreted proteins and a candidate ferrireductase, are expressed coordinately with FOX1 and FTR1 in iron-deficient Chlamydomonas reinhardtii. Eukaryot. Cell 6, 1841–1852 (2007)

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28

    Sasaki, T., Kurano, N. & Miyachi, S. Cloning and characterization of high-CO2-specific cDNAs from a marine microalga, Chlorococcum littorale, and effect of CO2 concentration and iron deficiency on the gene expression. Plant Cell Physiol. 39, 131–138 (1998)

    CAS  PubMed  Google Scholar 

  29. 29

    Saito, M. A., Goepfert, T. J. & Ritt, J. T. Some thoughts on the concept of colimitation: three definitions and the importance of bioavailability. Limnol. Oceanogr. 53, 276–290 (2008)

    ADS  CAS  Google Scholar 

  30. 30

    Lis, H., Shaked, Y., Kranzler, C., Keren, N. & Morel, F. M. M. Iron bioavailability to phytoplankton: an empirical approach. ISME J. 9, 1003–1013 (2015)

    CAS  PubMed  Google Scholar 

  31. 31

    Caron, D. A. et al. Probing the evolution, ecology and physiology of marine protists using transcriptomics. Nat. Rev. Microbiol. 15, 6–20 (2017)

    CAS  PubMed  Google Scholar 

  32. 32

    Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013)

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33

    Gouy, M., Guindon, S. & Gascuel, O. SeaView version 4: a multiplatform graphical user interface for sequence alignment and phylogenetic tree building. Mol. Biol. Evol. 27, 221–224 (2010)

    CAS  PubMed  Google Scholar 

  34. 34

    Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014)

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35

    Lartillot, N., Lepage, T. & Blanquart, S. PhyloBayes 3: a Bayesian software package for phylogenetic reconstruction and molecular dating. Bioinformatics 25, 2286–2288 (2009)

    CAS  PubMed  Google Scholar 

  36. 36

    Berney, C. & Pawlowski, J. A molecular time-scale for eukaryote evolution recalibrated with the continuous microfossil record. Proc. R. Soc. B 273, 1867–1872 (2006)

    CAS  PubMed  Google Scholar 

  37. 37

    Parfrey, L. W., Lahr, D. J., Knoll, A. H. & Katz, L. A. Estimating the timing of early eukaryotic diversification with multigene molecular clocks. Proc. Natl Acad. Sci. USA 108, 13624–13629 (2011)

    ADS  CAS  PubMed  Google Scholar 

  38. 38

    Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39

    Petersen, T. N., Brunak, S., von Heijne, G. & Nielsen, H. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat. Methods 8, 785–786 (2011)

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40

    Emanuelsson, O., Nielsen, H., Brunak, S. & von Heijne, G. Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. J. Mol. Biol. 300, 1005–1016 (2000)

    CAS  PubMed  Google Scholar 

  41. 41

    Nielsen, H., Engelbrecht, J., Brunak, S. & von Heijne, G. Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Protein Eng. 10, 1–6 (1997)

    CAS  PubMed  Google Scholar 

  42. 42

    Möller, S., Croning, M. D. R. & Apweiler, R. Evaluation of methods for the prediction of membrane spanning regions. Bioinformatics 17, 646–653 (2001)

    Google Scholar 

  43. 43

    Pierleoni, A., Martelli, P. L. & Casadio, R. PredGPI: a GPI-anchor predictor. BMC Bioinformatics 9, 392 (2008)

    PubMed  PubMed Central  Google Scholar 

  44. 44

    Sunda, W. G., Price, N. M. & Morel, F. M. M. in Algal Culturing Techniques (ed. Anderson. R. A. ) 35–63 (Elsevier, 2005)

  45. 45

    Zaslavskaia, L. A., Lippmeier, J. C., Kroth, P. G., Grossman, A. R. & Apt, K. E. Transformation of the diatom Phaeodactylum tricornutum (Bacillariophyceae) with a variety of selectable marker and reporter genes. J. Phycol. 36, 379–386 (2000)

    CAS  Google Scholar 

  46. 46

    Gibson, D. G. et al. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 6, 343–345 (2009)

    CAS  Google Scholar 

  47. 47

    Falciatore, A., Casotti, R., Leblanc, C., Abrescia, C. & Bowler, C. Transformation of nonselectable reporter genes in marine diatoms. Mar. Biotechnol. 1, 239–251 (1999)

    CAS  PubMed  Google Scholar 

  48. 48

    Poulsen, N. & Kröger, N. A new molecular tool for transgenic diatoms: control of mRNA and protein biosynthesis by an inducible promoter–terminator cassette. FEBS J. 272, 3413–3423 (2005)

    CAS  PubMed  Google Scholar 

  49. 49

    Weyman, P. D. et al. Inactivation of Phaeodactylum tricornutum urease gene using transcription activator-like effector nuclease-based targeted mutagenesis. Plant Biotechnol. J. 13, 460–470 (2015)

    CAS  PubMed  Google Scholar 

  50. 50

    Hudson, R. J. M. & Morel, F. M. M. Distinguishing between extra- and intracellular iron in marine phytoplankton. Limnol. Oceanogr. 34, 1113–1120 (1989)

    ADS  CAS  Google Scholar 

  51. 51

    Dickson, A. G., Sabine, C. L., & Christian, J. R. (eds) Guide to Best Practices for Ocean CO 2 Measurements (North Pacific Marine Science Organization, 2007)

  52. 52

    Kustka, A. B., Allen, A. E. & Morel, F. M. Sequence analysis and transcriptional regulation of iron acquisition genes in two marine diatoms. J. Phycol. 43, 715–729 (2007)

    CAS  Google Scholar 

  53. 53

    Dutta, D., Williamson, C. D., Cole, N. B. & Donaldson, J. G. Pitstop 2 is a potent inhibitor of clathrin-independent endocytosis. PLoS ONE 7, e45799 (2012)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  54. 54

    Carter, B. R., Radich, J. A., Doyle, H. L. & Dickson, A. G. An automated system for spectrophotometric seawater pH measurements. Limnol. Oceanogr. Methods 11, 16–27 (2013)

    Google Scholar 

  55. 55

    Lewis, E ., Wallace, D. & Allison, L. J. Program developed for CO 2 System Calculations (Carbon Dioxide Information Analysis Center, 1998)

  56. 56

    Slinker, B. K. The statistics of synergism. J. Mol. Cell. Cardiol. 30, 723–731 (1998)

    CAS  PubMed  Google Scholar 

  57. 57

    R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, Austria, 2014)

Download references


We thank J. Badger for early contributions to phylogenetic analyses, A. Dickson for pH analysis, K. Forsch for CSV measurements and E. Bertrand for trace-metal clean techniques. This study was supported by the National Science Foundation (NSF-MCB-1024913, NSF-ANT-1043671 and NSF-OCE-0727997), United States Department of Energy Genomics Science program (DE-SC00006719 and DE-SC0008593), and the Gordon and Betty Moore Foundation grant GBMF3828 (A.E.A.); NSF-1557928 (A.B.K.); and the Czech Science Foundation, project 15-17643S (M.O. and A.H.).

Author information




J.B.M., A.B.K., M.O. and A.E.A. designed the study and interpreted the results. J.B.M., M.O. and A.H. generated and analysed phylogenetic and molecular clock data. J.B.M. and B.J.K. generated mutant cell lines, J.B.M. and A.B.K. with assistance from K.A.B. performed physiology experiments. J.B.M. performed microscopy, and H.Z. performed western blots. T.K. and A.J.A. analysed inorganic carbon species. J.B.M. and J.P.M. conducted statistical analyses. J.B.M. wrote the paper with input from A.E.A., A.B.K., M.O., J.P.M, A.J.A. and K.A.B. All authors discussed the results and commented on the manuscript.

Corresponding author

Correspondence to Andrew E. Allen.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks S. Amin, E. DeLong and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Figure 1 Conservation of active site amino acids.

a, Left, conservation of putative iron-coordinating (red) and carbonate-coordinating (green) amino acids among phylogenetic groups, filled circles indicate >85% conservation, unfilled circles indicate <20% conservation; arginine and lysine were permitted at the carbonate-binding site11. Right, logo tag detailing alignment conservation at the anion-binding region. Phosphonate-binding proteins (Bacteria and Thaumarchaea) retain the S/T-rich phosphonate-binding area, whereas transferrin (Euryarchaea and Metazoa) and phytotransferrin have the carbonate-coordinating K/R insertion. A downstream SAG region used to stabilize carbonate in transferrin24 shows some conservation in phytotransferrin, but an upstream threonine (−4 amino acids from the conserved Arg) is not present. b, Active site homology among ISIP2A (PT54465) and two transmembrane-anchored P. tricornutum homologues, PT45708 and PT54466. Amino acid distances based on PT54465, red/green triangles are iron- and carbonate-coordinating amino acids.

Extended Data Figure 2 Estimated divergence times between transferrin, phytotransferrin and PBPs.

Analyses were carried out using PhyloBayes (top) and BEAST (bottom). For the fossil calibration points used in generating the minimum and maximum constraints, see Supplementary Table 1. MYA, million years ago.

Extended Data Figure 3 Bayesian (PhyloBayes) phylogenetic tree contrasted with alternate topology derived using maximum likelihood.

Prasinococcus capsulatus, Pyraminomonas obovata and other chlorophyte algae have copies of transferrin and phytotransferrin, whereas all other non-chlorophyte algae have only phytotransferrin. Branches are colour-coded by phylogenetic group. Scale bar, 0.5 substitutions per position.

Extended Data Figure 4 Characterization of ISIP2A.

a, Western blot of wild-type P. tricornutum compared to ISIP2A-knockout strains (ΔISIP2A). The estimated mass of ISIP2A protein is 57 kDa. b, Specific growth rates of wild type compared to Δ ISIP2A. c, Uptake of 59Fe-ferrioxamine B is unaffected in Δ ISIP2A, suggesting an alternate pathway for uptake of iron–siderophore complexes. d, Effect of a clathrin-mediated endocytosis (CME) inhibitor on short-term 59Fe′ uptake rates, wild-type P. tricornutum. e, Upon addition of iron to iron-limited cells, the membrane-impermeable FM1-43 stain is internalized into vesicle-like inclusions. Scale bar, 5 μm. Pink is plastid auto-fluorescence. b, Data are mean ± s.e.m.; two-sided heteroscedastic t-test, n = biological replicates for wild-type and ISIP2A are indicated in brackets as (WT, ISIP2A) and P values are indicated. 12 pM Fe′ (5, 3), P = 0.008; 22 pM Fe′ (7, 5), P = 0.003; 43 pM Fe′ (6, 4), P = 0.006; 83 pM Fe′ (4, 4), P = 0.007; 165 pM Fe′ (3, 3), P = 0.07. d, Data are mean ± s.e.m.; n = 3 biological replicates; two-sided t-test, P = 0.009.

Extended Data Figure 5 Reconciliation of the carbonate effect with the influence of acidification on seawater iron chemistry.

a, Linearized representation of Fig. 3a, with high iron uptake rates (blue triangles) plotted on the secondary y axis, and pH values for each [CO32−] listed at top. Fe–FOB uptake rates decrease with pH, consistent with the findings of ref. 26, whereas Fe′ uptake rates increase with decreasing pH, inconsistent with the hypothesized effects of acidification on iron–EDTA chemistry26. This inconsistency is resolved when uptake rates are plotted as a function of the synergistic interaction between Fe′ and CO32− (Fig. 3b). b, c, Under CO2-induced acidification, the strong influence of pH on [Fe′] results in a significant correlation of uptake to [Fe′], although the interaction Fe′ and CO32− results in a better fit (Fig. 4a, solid line). d, e, When the change in [Fe′] is constrained relative to pH and [CO32−], uptake rates are positively correlated with [CO32−] and uncorrelated to [Fe′], revealing the influence of the carbonate ion on Fe′ uptake rates. For statistical analyses of the linear regressions, see Extended Data Table 4.

Extended Data Figure 6 Derivation of second-order and constitutive rate constants from P. tricornutum resuspended in NaHCO3-manipulated medium.

a, Regression of the interaction product of Fe′ and CO32−. Regression excludes the two observations in which the medium was not supplemented with NaHCO3. b, 59Fe uptake rates for treatments incubated with low (2–5 pM Fe′, open symbols) and high (20–50 pM Fe′, closed symbols) 59Fe versus [CO32−]. c, Demonstration of reproducibility at low [Fe′]. Data are mean ± s.e.m.; n = 3 biological replicates. d, Uptake rates normalized to [Fe′], plotted against [CO32−]. The slope has units of mol Fe cell−1 h−1 (M Fe′)−1 (M CO32−)−1, equivalent to the pseudo-first-order uptake rate with respect to carbonate. e, Rates of uptake calculated as a function of [Fe′] and [CO32−] versus measured rates.

Extended Data Table 1 Uptake rates compared to the measured (in bold) and derived concentrations of Fe′ and carbonic acid species, in Aquil uptake medium
Extended Data Table 2 Uptake rates compared to measured (in bold) and derived concentrations for both Fe′ and carbonic acid species
Extended Data Table 3 Measured (in bold) and derived values for CO2 and pH manipulation experiments
Extended Data Table 4 Statistical analyses of NaHCO3 and pH and CO2 manipulations

Supplementary information

Supplementary Information

This file contains Supplementary Figure 1 and Supplementary Tables 1-2. (PDF 332 kb)

Life Sciences Reporting Summary (PDF 86 kb)

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

McQuaid, J., Kustka, A., Oborník, M. et al. Carbonate-sensitive phytotransferrin controls high-affinity iron uptake in diatoms. Nature 555, 534–537 (2018).

Download citation

Further reading


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.