Genome-scale model reveals metabolic basis of biomass partitioning in a model diatom

PLoS ONE

Volumn 11, Issue 5, 2016, Pages

  Levering, Jennifer ^a   Broddrick, Jared ^a,b   Dupont, Christopher L ^c   Peers, Graham ^d   Beeri, Karen ^c   Mayers, Joshua ^e   Gallina, Alessandra A ^a,d   Allen, Andrew E ^c,f   Palsson, Bernhard O ^a   Zengler, Karsten ^a  

^a UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

^b UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

^c J CRAIG VENTER INSTITUTE   (United States)

^d Veterinary Dentistry and Oral Surgery   (United States)

^e CHALMERS UNIVERSITY OF TECHNOLOGY   (Sweden)

^f INTEGRATIVE OCEANOGRAPHY DIVISION   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CARBOHYDRATE; GLUTAMINE; LIPID; ORNITHINE; PROTEIN;

ARTICLE; BIOCHEMICAL COMPOSITION; BIOINFORMATICS; BIOMASS PARTITIONING; CARBON PARTITIONING; CELLULAR DISTRIBUTION; CHLOROPLAST; CONTROLLED STUDY; ENDOSYMBIOSIS; GENETIC ASSOCIATION; GENOME ANALYSIS; INFRARED SPECTROSCOPY; METABOLIC ENGINEERING; METABOLISM; METABOLITE; NONHUMAN; PHAEODACTYLUM TRICORNUTUM; PHOTOSYNTHESIS; PREDICTION; PROTEIN LOCALIZATION; SIMULATION; BIOLOGICAL MODEL; BIOMASS; CELL FRACTIONATION; DIATOM; ENZYMOLOGY; GENETICS; GENOME; MITOCHONDRION; PLASTID;

BIOMASS; DIATOMS; GENOME; MITOCHONDRIA; MODELS, BIOLOGICAL; PLASTIDS; SUBCELLULAR FRACTIONS;

EID: 84968571681     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0155038     Document Type: Article

References (84)

1. Nelson DM, Tréguer P, Brzezinski MA, Leynaert A, Quéguiner B. Production and dissolution of biogenic silica in the ocean: revised global estimates, comparison with regional data and relationship to biogenic sedimentation. Global Biogeochem Cycles. 1995; 9: 359-372. doi: 10.1029/95GB01070
2. Bowler C, Allen AE, Badger JH, Grimwood J, Jabbari K, Kuo A, et al. The Phaeodactylum genome reveals the evolutionary history of diatom genomes. Nature. 2008; 456: 239-244. doi: 10.1038/nature07410 PMID: 18923393
3. Hockin NL, Mock T, Mulholland F, Kopriva S, Malin G. The response of diatom central carbon metabolism to nitrogen starvation is different from that of green algae and higher plants. Plant Physiol. 2012; 158: 299-312. doi: 10.1104/pp.111.184333 PMID: 22065419
4. Dolatabadi JEN, de la Guardia M. Applications of diatoms and silica nanotechnology in biosensing, drug and gene delivery, and formation of complex metal nanostructures. Trends Anal Chem. 2011; 30: 1538-1548. doi: 10.1016/j.trac.2011.04.015
5. Allen AE, Dupont CL, Oborník M, Horák A, Nunes-Nesi A, McCrow JP, et al. Evolution and metabolic significance of the urea cycle in photosynthetic diatoms. Nature. 2011; 473: 203-207. doi: 10.1038/nature10074 PMID: 21562560
6. Armbrust EV, Berges JA, Bowler C, Green BR, Martinez D, Putnam NH, et al. The genome of the diatom Thalassiosira pseudonana: ecology, evolution, and metabolism. Science. 2004; 306: 79-86. doi: 10.1126/science.1101156 PMID: 15459382
7. Bozarth A, Maier U-G, Zauner S. Diatoms in biotechnology: modern tools and applications. Appl Microbiol Biotechnol. 2009; 82: 195-201. doi: 10.1007/s00253-008-1804-8 PMID: 19082585
8. Karas BJ, Molparia B, Jablanovic J, Hermann WJ, Lin Y-C, Dupont CL, et al. Assembly of eukaryotic algal chromosomes in yeast. J Biol Eng. 2013; 7: 30. doi: 10.1186/1754-1611-7-30 PMID: 24325901
9. Weyman PD, Beeri K, Lefebvre SC, Rivera J, McCarthy JK, Heuberger AL, et al. Inactivation of Phaeodactylum tricornutum urease gene using transcription activator-like effector nuclease-based targeted mutagenesis. Plant Biotechnol J. 2015; 13: 460-470. doi: 10.1111/pbi.12254 PMID: 25302562
10. Daboussi F, Leduc S, Maréchal A, Dubois G, Guyot V, Perez-Michaut C, et al. Genome engineering empowers the diatom Phaeodactylum tricornutum for biotechnology. Nat Commun. 2014; 5: 3831. doi: 10.1038/ncomms4831 PMID: 24871200
11. Karas BJ, Diner RE, Lefebvre SC, McQuaid J, Phillips APR, Noddings CM, et al. Designer diatom episomes delivered by bacterial conjugation. Nat Commun. 2015; 6: 6925. doi: 10.1038/ncomms7925 PMID: 25897682
12. Siaut M, Heijde M, Mangogna M, Montsant A, Coesel S, Allen A, et al. Molecular toolbox for studying diatom biology in Phaeodactylum tricornutum. Gene. 2007; 406: 23-35. doi: 10.1016/j.gene.2007.05.022 PMID: 17658702
13. De Riso V, Raniello R, Maumus F, Rogato A, Bowler C, Falciatore A. Gene silencing in the marine diatom Phaeodactylum tricornutum. Nucleic Acids Res. 2009; 37: e96. doi: 10.1093/nar/gkp448 PMID: 19487243
14. Thiele I, Palsson BØ. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc. Nature Publishing Group; 2010; 5: 93-121.
15. Lewis NE, Nagarajan H, Palsson BO. Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol. 2012; 81: 291-305. doi: 10.1038/nrmicro2737
16. Kim TY, Sohn SB, Kim Y Bin, Kim WJ, Lee SY. Recent advances in reconstruction and applications of genome-scale metabolic models. Curr Opin Biotechnol. 2012; 23: 617-623. doi: 10.1016/j.copbio.2011.10.007 PMID: 22054827
17. The Uniprot Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 2015; 43: D204-D212. doi: 10.1093/nar/gku989 PMID: 25348405
18. Krogh A, Larsson B, von Heijne G, Sonnhammer ELL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol. 2001; 305: 567-580. doi: 10.1006/jmbi.2000.4315 PMID: 11152613
19. Claros MG, Vincens P. Computational method to predict mitochondrially imported proteins and their targeting sequences. Eur J Biochem. 1996; 241: 779-786. doi: 10.1111/j.1432-1033.1996.00779.x PMID: 8944766
20. Bendtsen JD, Nielsen H, von Heijne G, Brunak S. Improved prediction of signal peptides: SignalP 3.0. J Mol Biol. 2004; 340: 783-795. doi: 10.1016/j.jmb.2004.05.028 PMID: 15223320
21. Petersen TN, Brunak S, von Heijne G, Nielsen H. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods. 2011; 8: 785-786. doi: 10.1038/nmeth.1701 PMID: 21959131
22. 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. 2000; 300: 1005-1016. doi: 10.1006/jmbi.2000.3903 PMID: 10891285
23. Gschloessl B, Guermeur Y, Cock JM. HECTAR: a method to predict subcellular targeting in heterokonts. BMC Bioinformatics. 2008; 9: 393. doi: 10.1186/1471-2105-9-393 PMID: 18811941
24. Cokol M, Nair R, Rost B. Finding nuclear localization signals. EMBO Rep. 2000; 1: 411-415. doi: 10.1093/embo-reports/kvd092 PMID: 11258480
25. Mayers JJ, Flynn KJ, Shields RJ. Rapid determination of bulk microalgal biochemical composition by Fourier-Transform Infrared spectroscopy. Bioresour Technol. 2013; 148: 215-220. doi: 10.1016/j.biortech.2013.08.133 PMID: 24050924
26. Ghosh S, Gepstein S, Heikkila JJ, Dumbroff EB. Use of a scanning densitometer or an ELISA plate reader for measurement of nanogram amounts of protein in crude extracts from biological tissues. Anal Biochem. 1988; 169: 227-233. doi: 10.1016/0003-2697(88)90278-3 PMID: 2454592
27. DuBois M, Gilles KA, Hamilton JK, Rebers PA, Smith F. Colorimetric method for determination of sugars and related substances. Anal Chem. 1956; 28: 350-356. doi: 10.1021/ac60111a017
28. Templeton DW, Quinn M, Van Wychen S, Hyman D, Laurens LML. Separation and quantification of microalgal carbohydrates. J Chromatogr A. 2012; 1270: 225-234.PMID: 23177152
29. Bligh EG, Dyer WJ. A rapid method of total lipid extraction and purification. Can J Biochem Physiol. 1959; 37: 911-917. PMID: 13671378
30. Lourenço SO, Barbarino E, Lavín PL, Lanfer Marquez UM, Aidar E. Distribution of intracellular nitrogen in marine microalgae: calculation of new nitrogen-to-protein conversion factors. J Phycol. 1998; 34: 798-811. doi: 10.1080/0967026032000157156
31. Brown MR. The amino-acid and sugar composition of 16 species of microalgae used in mariculture. J Exp Mar Bio Ecol. 1991; 145: 79-99.
32. Owens TG, Wold ER. Light-harvesting function in the diatom Phaeodactylum tricornutum: II. Distribution of excitation energy between the photosystems. Plant Physiol. 1986; 80: 732-738. doi: 10.1104/pp.80.3.739 PMID: 16664694
33. Veith T, Büchel C. The monomeric photosystem I-complex of the diatom Phaeodactylum tricornutum binds specific fucoxanthin chlorophyll proteins (FCPs) as light-harvesting complexes. Biochim Biophys Acta. 2007; 1767: 1428-1435. doi: 10.1016/j.bbabio.2007.09.004 PMID: 18028870
34. Fidalgo JP, Cid A, Abalde J, Herrero C. Culture of the marine diatom Phaeodactylum tricornutum with different nitrogen sources: growth, nutrient conversion and biochemical composition. Cah Biol Mar. 1995; 36: 165-173.
35. Abdullahi AS, Underwood GJC, Gretz MR. Extracellular matrix assembly in diatoms (Bacillariophyceae). V. Environmental effects on polysaccharide synthesis in the model diatom, Phaeodactylum tricornutum. J Phycol. 2006; 42: 363-378. doi: 10.1111/j.1529-8817.2006.00193.x
36. Willis A, Chiovitti A, Dugdale TM, Wetherbee R. Characterization of the extracellular matrix of Phaeodactylum tricornutum (Bacillariophyceae): structure, composition, and adhesive characteristics. J Phycol. 2013; 49: 937-949. doi: 10.1111/jpy.12103 PMID: 27007317
37. Abida H, Dolch L-J, Meï C, Villanova V, Conte M, Block MA, et al. Membrane glycerolipid remodeling triggered by nitrogen and phosphorus starvation in Phaeodactylum tricornutum. Plant Physiol. 2015; 167: 118-136. doi: 10.1104/pp.114.252395 PMID: 25489020
38. Mus F, Toussaint J-P, Cooksey KE, Fields MW, Gerlach R, Peyton BM, et al. Physiological and molecular analysis of carbon source supplementation and pH stress-induced lipid accumulation in the marine diatom Phaeodactylum tricornutum. Appl Microbiol Biotechnol. 2013; 97: 3625-3642. doi: 10.1007/s00253-013-4747-7 PMID: 23463245
39. Chang R, Ghamsari L, Manichaikul A, Hom E, Balaji S, Fu W, et al. Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism. Mol Syst Biol. 2011; 7: 518. doi: 10.1038/msb.2011.52 PMID: 21811229
40. Nogales J, Gudmundsson S, Knight EM, Palsson BO, Thiele I. Detailing the optimality of photosynthesis in cyanobacteria through systems biology analysis. Proc Natl Acad Sci U S A. 2012; 109: 2678-2683. doi: 10.1073/pnas.1117907109 PMID: 22308420
41. Knoop H, Gründel M, Zilliges Y, Lehmann R, Hoffmann S, Lockau W, et al. Flux balance analysis of cyanobacterial metabolism: the metabolic network of Synechocystis sp. PCC 6803. PLoS Comput Biol. 2013; 9: e1003081. doi: 10.1371/journal.pcbi.1003081 PMID: 23843751
42. Sauls JT, Buescher JM. Assimilating genome-scale metabolic reconstructions with modelBorgifier. Bioinformatics. 2014; 30: 1036-1038. doi: 10.1093/bioinformatics/btt747 PMID: 24371155
43. Agren R, Liu L, Shoaie S, Vongsangnak W, Nookaew I, Nielsen J. The RAVEN Toolbox and its use for generating a genome-scale metabolic model for Penicillium chrysogenum. PLoS Comput Biol. 2013; 9: e1002980. doi: 10.1371/journal.pcbi.1002980 PMID: 23555215
44. Schellenberger J, Que R, Fleming RMT, Thiele I, Orth JD, Feist AM, et al. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc. 2011; 6: 1290-1307. doi: 10.1038/nprot.2011.308 PMID: 21886097
45. Fabris M, Matthijs M, Rombauts S, Vyverman W, Goossens A, Baart GJE. The metabolic blueprint of Phaeodactylum tricornutum reveals a eukaryotic Entner-Doudoroff glycolytic pathway. Plant J. 2012; 70: 1004-1014. doi: 10.1111/j.1365-313X.2012.04941.x PMID: 22332784
46. Kanehisa M, Goto S, Sato Y, Kawashima M, Furumichi M, Tanabe M. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 2014; 42: D199-D205. doi: 10.1093/nar/gkt1076 PMID: 24214961
47. Ren Q, Chen K, Paulsen IT. TransportDB: a comprehensive database resource for cytoplasmic membrane transport systems and outer membrane channels. Nucleic Acids Res. 2007; 35: D274-D279. doi: 10.1093/nar/gkl925 PMID: 17135193
48. Saier MH, Reddy VS, Tamang DG, Västermark Å. The transporter classification database. Nucleic Acids Res. 2014; 42: D251-D258. doi: 10.1093/nar/gkt1097 PMID: 24225317
49. Schellenberger J, Park JO, Conrad TM, Palsson BØ. BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinformatics. 2010; 11: 213. doi: 10.1186/1471-2105-11-213 PMID: 20426874
50. King ZA, Dräger A, Ebrahim A, Sonnenschein N, Lewis NE, Palsson BO. Escher: a web application for building, sharing, and embedding data-rich visualizations of biological pathways. PLOS Comput Biol. 2015; 11: e1004321. doi: 10.1371/journal.pcbi.1004321 PMID: 26313928
51. Orth JD, Thiele I, Palsson BØ. What is flux balance analysis? Nat Biotechnol. 2010; 28: 245-248. doi: 10.1038/nbt.1614 PMID: 20212490
52. Geider RJ, Osborne BA, Raven JA, Osbonie BA, Raven JA. Growth, photosynthesis and maintenance metabolic cost in the diatom Phaeodactylum tricornutum at very low light levels. J Phycol. 1986; 22: 39-48. doi: 10.1111/j.1529-8817.1986.tb02513.x
53. Lewis NE, Hixson KK, Conrad TM, Lerman JA, Charusanti P, Polpitiya AD, et al. Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol Syst Biol. 2010; 6: 390. doi: 10.1038/msb.2010.47 PMID: 20664636
54. ZoBell CE. The assimilation of ammonium nitrogen by Nitzschia Closterium and other marine phytoplankton. Proc Natl Acad Sci U S A. 1935; 21: 517-522.PMID: 16577680
55. Hayward J. Studies on the growth of Phaeodactylum tricornutum (Bohlin) I. The effect of certain organic nitrogenous substances on growth. Physiol Plant. 1965; 18: 201-207. Available: http://onlinelibrary.wiley.com/doi/10.1111/j.1399-3054.1965.tb06883.x/abstract
56. Kettles NL, Kopriva S, Malin G. Insights into the regulation of DMSP synthesis in the diatom Thalassiosira pseudonana through APR activity, proteomics and gene expression analyses on cells acclimating to changes in salinity, light and nitrogen. PLoS One. 2014; 9: e94795. doi: 10.1371/journal.pone.0094795 PMID: 24733415
57. Sunaga Y, Maeda Y, Yabuuchi T, Muto M, Yoshino T, Tanaka T. Chloroplast-targeting protein expression in the oleaginous diatom Fistulifera solaris JPCC DA0580 toward metabolic engineering. J Biosci Bioeng. 2015; 119: 28-34. doi: 10.1016/j.jbiosc.2014.06.008 PMID: 25043335
58. Gruber A, Vugrinec S, Hempel F, Gould SB, Maier U-G, Kroth PG. Protein targeting into complex diatom plastids: functional characterisation of a specific targeting motif. Plant Mol Biol. 2007; 64: 519-530. doi: 10.1007/s11103-007-9171-x PMID: 17484021
59. Liaud MF, Lichtlé C, Apt K, Martin W, Cerff R. Compartment-specific isoforms of TPI and GAPDH are imported into diatom mitochondria as a fusion protein: evidence in favor of a mitochondrial origin of the eukaryotic glycolytic pathway. Mol Biol Evol. 2000; 17: 213-223. doi: 10.1093/oxfordjournals.molbev.a026301 PMID: 10677844
60. Domergue F, Spiekermann P, Lerchl J, Beckmann C, Kilian O, Kroth PG, et al. New insight into Phaeodactylum tricornutum fatty acid metabolism. Cloning and functional characterization of plastidial and microsomal delta12 fatty acid desaturases. Plant Physiol. 2003; 131: 1648-1660.PMID: 12692324
61. Apt KE, Zaslavkaia L, Lippmeier JC, Lang M, Kilian O, Wetherbee R, et al. In vivo characterization of diatom multipartite plastid targeting signals. J Cell Sci. 2002; 115: 4061-4069. doi: 10.1242/jcs.00092 PMID: 12356911
62. Tanaka Y, Nakatsuma D, Harada H, Ishida M, Matsuda Y. Localization of soluble beta-carbonic anhydrase in the marine diatom Phaeodactylum tricornutum. Sorting to the chloroplast and cluster formation on the girdle lamellae. Plant Physiol. 2005; 138: 207-217. doi: 10.1104/pp.104.058982 PMID: 15849303
63. Kilian O, Kroth PG. Presequence acquisition during secondary endocytobiosis and the possible role of introns. J Mol Evol. 2004; 58: 712-721. doi: 10.1007/s00239-004-2593-z PMID: 15461428
64. Tachibana M, Allen AE, Kikutani S, Endo Y, Bowler C, Matsuda Y. Localization of putative carbonic anhydrases in two marine diatoms, Phaeodactylum tricornutum and Thalassiosira pseudonana. Photosynth Res. 2011; 109: 205-221. doi: 10.1007/s11120-011-9634-4 PMID: 21365259
65. Feist AM, Palsson BO. The biomass objective function. Curr Opin Microbiol. 2010; 13: 344-349. doi: 10.1016/j.mib.2010.03.003 PMID: 20430689
66. Chauton MS, Winge P, Brembu T, Vadstein O, Bones AM. Gene regulation of carbon fixation, storage, and utilization in the diatom Phaeodactylum tricornutum acclimated to light/dark cycles. Plant Physiol. 2013; 161: 1034-1048. doi: 10.1104/pp.112.206177 PMID: 23209127
67. Levitan O, Dinamarca J, Zelzion E, Lun DS, Guerra LT, Kim MK, et al. Remodeling of intermediate metabolism in the diatom Phaeodactylum tricornutum under nitrogen stress. Proc Natl Acad Sci U S A. 2015; 112: 412-417. doi: 10.1073/pnas.1419818112 PMID: 25548193
68. Raven JA. The role of vacuoles. New Phytol. 1987; 106: 357-422.
69. Kroth PG, Chiovitti A, Gruber A, Martin-Jezequel V, Mock T, Parker MS, et al. A model for carbohydrate metabolism in the diatom Phaeodactylum tricornutum deduced from comparative whole genome analysis. PLoS One. 2008; 3: e1426. doi: 10.1371/journal.pone.0001426 PMID: 18183306
70. Singh D, Carlson R, Fell D, Poolman M. Modelling metabolism of the diatom Phaeodactylum tricornutum. Biochem Soc Trans. 2015; 43: 1182-1186. doi: 10.1042/BST20150152 PMID: 26614658
71. Hunt KA, Folsom JP, Taffs RL, Carlson RP. Complete enumeration of elementary flux modes through scalable demand-based subnetwork definition. Bioinformatics. 2014; 30: 1569-1578. doi: 10.1093/bioinformatics/btu021 PMID: 24497502
72. Kim J, Fabris M, Baart G, Kim MK, Goossens A, Vyverman W, et al. Flux balance analysis of primary metabolism in the diatom Phaeodactylum tricornutum. Plant J. 2015; doi: 10.1111/tpj.13081
73. de Oliveira Dal'Molin CG, Quek L-E, Palfreyman RW, Brumbley SM, Nielsen LK. AraGEM, a genome-scale reconstruction of the primary metabolic network in Arabidopsis. Plant Physiol. 2010; 152: 579-589. doi: 10.1104/pp.109.148817 PMID: 20044452
74. Hay JO, Shi H, Heinzel N, Hebbelmann I, Rolletschek H, Schwender J. Integration of a constraint-based metabolic model of Brassica napus developing seeds with 13C-metabolic flux analysis. Front Plant Sci. 2014; 5: 1-18. doi: 10.3389/fpls.2014.00724
75. Gomes de Oliveira Dal'Molin C, Quek L-E, Palfreyman RW, Nielsen LK. AlgaGEM-a genome-scale metabolic reconstruction of algae based on the Chlamydomonas reinhardtii genome. BMC Genomics. 2011; 12: S5. doi: 10.1186/1471-2164-12-S4-S5
76. Saha R, Suthers PF, Maranas CD. Zea mays iRS1563: a comprehensive genome-scale metabolic reconstruction of maize metabolism. PLoS One. 2011; 6: e21784. doi: 10.1371/journal.pone.0021784 PMID: 21755001
77. Osterlund T, Nookaew I, Bordel S, Nielsen J. Mapping condition-dependent regulation of metabolism in yeast through genome-scale modeling. BMC Syst Biol. 2013; 7: 36. doi: 10.1186/1752-0509-7-36 PMID: 23631471
78. Thiele I, Swainston N, Fleming RMT, Hoppe A, Sahoo S, Aurich MK, et al. A community-driven global reconstruction of human metabolism. Nat Biotechnol. 2013; 31: 419-425. doi: 10.1038/nbt.2488 PMID: 23455439
79. Sigurdsson MI, Jamshidi N, Steingrimsson E, Thiele I, Palsson BO. A detailed genome-wide reconstruction of mouse metabolism based on human Recon 1. BMC Syst Biol. 2010; 4: 140. doi: 10.1186/1752-0509-4-140 PMID: 20959003
80. Levering J, Broddrick J, Zengler K. Engineering of oleaginous organisms for lipid production. Curr Opin Biotechnol. 2015; 36: 32-39. doi: 10.1016/j.copbio.2015.08.001 PMID: 26319892
81. Yu ET, Zendejas FJ, Lane PD, Gaucher S, Simmons BA, Lane TW. Triacylglycerol accumulation and profiling in the model diatoms Thalassiosira pseudonana and Phaeodactylum tricornutum (Baccilariophyceae) during starvation. J Appl Phycol. 2009; 21: 669-681. doi: 10.1007/s10811-008-9400-y
82. Su W, Jakob T, Wilhelm C. The impact of nonphotochemical quenching of fluorescence on the photon balance in diatoms under dynamic light conditions. J Phycol. 2012; 48: 336-346. doi: 10.1111/j.1529-8817.2012.01128.x PMID: 27009723
83. Bailleul B, Berne N, Murik O, Petroutsos D, Prihoda J, Tanaka A, et al. Energetic coupling between plastids and mitochondria drives CO2 assimilation in diatoms. Nature. 2015; 524: 366-369. doi: 10.1038/nature14599 PMID: 26168400
84. d'Ippolito G, Sardo A, Paris D, Vella FM, Adelfi MG, Botte P, et al. Potential of lipid metabolism in marine diatoms for biofuel production. Biotechnol Biofuels. 2015; 8: 28. doi: 10.1186/s13068-015-0212-4 PMID: 25763104