Environmental gradients reveal stress hubs pre-dating plant terrestrialization.
Journal
Nature plants
ISSN: 2055-0278
Titre abrégé: Nat Plants
Pays: England
ID NLM: 101651677
Informations de publication
Date de publication:
09 2023
09 2023
Historique:
received:
25
10
2022
accepted:
11
07
2023
medline:
19
9
2023
pubmed:
29
8
2023
entrez:
28
8
2023
Statut:
ppublish
Résumé
Plant terrestrialization brought forth the land plants (embryophytes). Embryophytes account for most of the biomass on land and evolved from streptophyte algae in a singular event. Recent advances have unravelled the first full genomes of the closest algal relatives of land plants; among the first such species was Mesotaenium endlicherianum. Here we used fine-combed RNA sequencing in tandem with a photophysiological assessment on Mesotaenium exposed to a continuous range of temperature and light cues. Our data establish a grid of 42 different conditions, resulting in 128 transcriptomes and ~1.5 Tbp (~9.9 billion reads) of data to study the combinatory effects of stress response using clustering along gradients. Mesotaenium shares with land plants major hubs in genetic networks underpinning stress response and acclimation. Our data suggest that lipid droplet formation and plastid and cell wall-derived signals have denominated molecular programmes since more than 600 million years of streptophyte evolution-before plants made their first steps on land.
Identifiants
pubmed: 37640935
doi: 10.1038/s41477-023-01491-0
pii: 10.1038/s41477-023-01491-0
pmc: PMC10505561
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1419-1438Subventions
Organisme : Deutsche Forschungsgemeinschaft (German Research Foundation)
ID : 514060973
Organisme : Deutsche Forschungsgemeinschaft (German Research Foundation)
ID : SPP 2237
Organisme : Deutsche Forschungsgemeinschaft (German Research Foundation)
ID : BU 2301/6-1
Organisme : Deutsche Forschungsgemeinschaft (German Research Foundation)
ID : HO 2793/5-1
Organisme : Deutsche Forschungsgemeinschaft (German Research Foundation)
ID : FE 446/14-1
Organisme : Deutsche Forschungsgemeinschaft (German Research Foundation)
ID : GRK 2172-PRoTECT
Organisme : EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)
ID : 852725
Organisme : Ministry of Education - Singapore (MOE)
ID : T2EP30122-0001
Informations de copyright
© 2023. The Author(s).
Références
Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on Earth. Proc. Natl Acad. Sci. USA 115, 6506–6511 (2018).
pubmed: 29784790
pmcid: 6016768
doi: 10.1073/pnas.1711842115
Lenton, T. M. et al. Earliest land plants created modern levels of atmospheric oxygen. Proc. Natl Acad. Sci. USA 113, 9704–9709 (2016).
pubmed: 27528678
pmcid: 5024600
doi: 10.1073/pnas.1604787113
Wodniok, S. et al. Origin of land plants: do conjugating green algae hold the key? BMC Evol. Biol. 11, 104 (2011).
pubmed: 21501468
pmcid: 3088898
doi: 10.1186/1471-2148-11-104
Wickett, N. J. et al. Phylotranscriptomic analysis of the origin and early diversification of land plants. Proc. Natl Acad. Sci. USA 111, E4859–E4868 (2014).
pubmed: 25355905
pmcid: 4234587
doi: 10.1073/pnas.1323926111
Puttick, M. N. et al. The interrelationships of land plants and the nature of the ancestral embryophyte. Curr. Biol. 28, 733–745 (2018).
pubmed: 29456145
doi: 10.1016/j.cub.2018.01.063
One Thousand Plant Transcriptomes Initiative. One thousand plant transcriptomes and the phylogenomics of green plants. Nature 574, 679–685 (2019).
Hess, S. et al. A phylogenomically informed five-order system for the closest relatives of land plants. Curr. Biol. 32, 4473–4482 (2022).
pubmed: 36055238
pmcid: 9632326
doi: 10.1016/j.cub.2022.08.022
Cheng, S. et al. Genomes of subaerial Zygnematophyceae provide insights into land plant evolution. Cell 179, 1057–1067.e14 (2019).
pubmed: 31730849
doi: 10.1016/j.cell.2019.10.019
Feng, X. et al. Chromosome-level genomes of multicellular algal sisters to land plants illuminate signaling network evolution. Preprint at bioRxiv https://doi.org/10.1101/2023.01.31.526407 (2023).
Sekimoto, H. et al. A divergent RWP‐RK transcription factor determines mating type in heterothallic Closterium. N. Phytol. https://doi.org/10.1111/nph.18662 (2023).
doi: 10.1111/nph.18662
Jiao, C. et al. The Penium margaritaceum genome: hallmarks of the origins of land plants. Cell 181, 1097–1111.e12 (2020).
pubmed: 32442406
doi: 10.1016/j.cell.2020.04.019
Golicz, A. A., Batley, J. & Edwards, D. Towards plant pangenomics. Plant Biotechnol. J. 14, 1099–1105 (2016).
pubmed: 26593040
doi: 10.1111/pbi.12499
Gordon, S. P. et al. Extensive gene content variation in the Brachypodium distachyon pan-genome correlates with population structure. Nat. Commun. 8, 2184 (2017).
pubmed: 29259172
pmcid: 5736591
doi: 10.1038/s41467-017-02292-8
Bayer, P. E., Golicz, A. A., Scheben, A., Batley, J. & Edwards, D. Plant pan-genomes are the new reference. Nat. Plants 6, 914–920 (2020).
pubmed: 32690893
doi: 10.1038/s41477-020-0733-0
Umezawa, T. et al. Molecular basis of the core regulatory network in ABA responses: sensing, signaling and transport. Plant Cell Physiol. 51, 1821–1839 (2010).
pubmed: 20980270
pmcid: 2978318
doi: 10.1093/pcp/pcq156
Bowman, J. L., Briginshaw, L. N., Fisher, T. J. & Flores-Sandoval, E. Something ancient and something neofunctionalized—evolution of land plant hormone signaling pathways. Curr. Opin. Plant Biol. 47, 64–72 (2019).
pubmed: 30339930
doi: 10.1016/j.pbi.2018.09.009
Hundertmark, M. & Hincha, D. K. LEA (Late Embryogenesis Abundant) proteins and their encoding genes in Arabidopsis thaliana. BMC Genomics 9, 118–122 (2008).
pubmed: 18318901
pmcid: 2292704
doi: 10.1186/1471-2164-9-118
Carella, P. et al. Conserved biochemical defenses underpin host responses to oomycete infection in an early-divergent land plant lineage. Curr. Biol. 29, 2282–2294.e5 (2019).
pubmed: 31303485
doi: 10.1016/j.cub.2019.05.078
Rieseberg, T. P. et al. Crossroads in the evolution of plant specialized metabolism. Sem. Cell Dev. Biol. 134, 37–58 (2023).
doi: 10.1016/j.semcdb.2022.03.004
de Vries, J., Curtis, B. A., Gould, S. B. & Archibald, J. M. Embryophyte stress signaling evolved in the algal progenitors of land plants. Proc. Natl Acad. Sci. USA 115, E3471–E3480 (2018).
pubmed: 29581286
pmcid: 5899452
doi: 10.1073/pnas.1719230115
Sun, Y. et al. A ligand-independent origin of abscisic acid perception. Proc. Natl Acad. Sci. USA 116, 24892–24899 (2019).
pubmed: 31744875
pmcid: 6900503
doi: 10.1073/pnas.1914480116
Van de Poel, B., Cooper, E. D., Van Der Straeten, D., Chang, C. & Delwiche, C. F. Transcriptome profiling of the green alga Spirogyra pratensis (Charophyta) suggests an ancestral role for ethylene in cell wall metabolism, photosynthesis, and abiotic stress responses. Plant Physiol. 172, 533–545 (2016).
pubmed: 27489312
pmcid: 5074641
doi: 10.1104/pp.16.00299
Rippin, M., Becker, B. & Holzinger, A. Enhanced desiccation tolerance in mature cultures of the streptophytic green alga Zygnema circumcarinatum revealed by transcriptomics. Plant Cell Physiol. 58, 2067–2084 (2017).
pubmed: 29036673
doi: 10.1093/pcp/pcx136
de Vries, J. et al. Heat stress response in the closest algal relatives of land plants reveals conserved stress signaling circuits. Plant J. 103, 1025–1048 (2020).
pubmed: 32333477
doi: 10.1111/tpj.14782
Fürst-Jansen, J. M. R. et al. Submergence of the filamentous Zygnematophyceae Mougeotia induces differential gene expression patterns associated with core metabolism and photosynthesis. Protoplasma 259, 1157–1174 (2022).
pubmed: 34939169
doi: 10.1007/s00709-021-01730-1
Emms, D. M. & Kelly, S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 20, 238 (2019).
pubmed: 31727128
pmcid: 6857279
doi: 10.1186/s13059-019-1832-y
Edel, K. H., Marchadier, E., Brownlee, C., Kudla, J. & Hetherington, A. M. The evolution of calcium-based signalling in plants. Curr. Biol. 27, R667–R679 (2017).
pubmed: 28697370
doi: 10.1016/j.cub.2017.05.020
Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).
pubmed: 19114008
pmcid: 2631488
doi: 10.1186/1471-2105-9-559
Song, J.-Y., Leung, T., Ehler, L. K., Wang, C. & Liu, Z. Regulation of meristem organization and cell division by TSO1, an Arabidopsis gene with cysteine-rich repeats. Development 127, 2207–2217 (2000).
pubmed: 10769244
doi: 10.1242/dev.127.10.2207
Kleine, T. et al. Acclimation in plants—the Green Hub consortium. Plant J. 106, 23–40 (2021).
pubmed: 33368770
doi: 10.1111/tpj.15144
Moreno, J. C., Mi, J., Alagoz, Y. & Al‐Babili, S. Plant apocarotenoids: from retrograde signaling to interspecific communication. Plant J. 105, 351–375 (2021).
pubmed: 33258195
pmcid: 7898548
doi: 10.1111/tpj.15102
Rossini, L., Cribb, L., Martin, D. J. & Langdale, J. A. The maize Golden2 gene defines a novel class of transcriptional regulators in plants. Plant Cell 13, 1231–1244 (2001).
pubmed: 11340194
pmcid: 135554
doi: 10.1105/tpc.13.5.1231
Yasumura, Y., Moylan, E. C. & Langdale, J. A. A conserved transcription factor mediates nuclear control of organelle biogenesis in anciently diverged land plants. Plant Cell 17, 1894–1907 (2005).
pubmed: 15923345
pmcid: 1167540
doi: 10.1105/tpc.105.033191
Waters, M. T. et al. GLK transcription factors coordinate expression of the photosynthetic apparatus in Arabidopsis. Plant Cell 21, 1109–1128 (2009).
pubmed: 19376934
pmcid: 2685620
doi: 10.1105/tpc.108.065250
Timm, S. et al. A cytosolic pathway for the conversion of hydroxypyruvate to glycerate during photorespiration in Arabidopsis. Plant Cell 20, 2848–2859 (2008).
pubmed: 18952776
pmcid: 2590732
doi: 10.1105/tpc.108.062265
Fürst-Jansen, J. M. R., de Vries, S. & de Vries, J. Evo-physio: on stress responses and the earliest land plants. J. Exp. Bot. 71, 3254–3269 (2020).
pubmed: 31922568
pmcid: 7289718
doi: 10.1093/jxb/eraa007
Liu, L.-J. et al. COP1-mediated ubiquitination of CONSTANS is implicated in cryptochrome regulation of flowering in Arabidopsis. Plant Cell 20, 292–306 (2008).
pubmed: 18296627
pmcid: 2276438
doi: 10.1105/tpc.107.057281
Sarid-Krebs, L. et al. Phosphorylation of CONSTANS and its COP 1‐dependent degradation during photoperiodic flowering of Arabidopsis. Plant J. 84, 451–463 (2015).
pubmed: 26358558
doi: 10.1111/tpj.13022
Ordoñez-Herrera, N. et al. The transcription factor COL12 is a substrate of the COP1/SPA E3 ligase and regulates flowering time and plant architecture. Plant Physiol. 176, 1327–1340 (2018).
pubmed: 29187570
doi: 10.1104/pp.17.01207
Dai, S. et al. BROTHER OF LUX ARRHYTHMO is a component of the Arabidopsis circadian clock. Plant Cell 23, 961–972 (2011).
pubmed: 21447790
pmcid: 3082275
doi: 10.1105/tpc.111.084293
Ju, C. et al. Conservation of ethylene as a plant hormone over 450 million years of evolution. Nat. Plants 1, 14004 (2015).
pubmed: 27246051
doi: 10.1038/nplants.2014.4
Kato, Y. & Sakamoto, W. Protein quality control in chloroplasts: a current model of D1 protein degradation in the photosystem II repair cycle. J. Biochem. 146, 463–469 (2009).
pubmed: 19451147
doi: 10.1093/jb/mvp073
Kato, Y., Sun, X., Zhang, L. & Sakamoto, W. Cooperative D1 degradation in the photosystem II repair mediated by chloroplastic proteases in Arabidopsis. Plant Physiol. 159, 1428–1439 (2012).
pubmed: 22698923
pmcid: 3425188
doi: 10.1104/pp.112.199042
Sjögren, L. L. E., Stanne, T. M., Zheng, B., Sutinen, S. & Clarke, A. K. Structural and functional insights into the chloroplast ATP-dependent Clp protease in Arabidopsis. Plant Cell 18, 2635–2649 (2006).
pubmed: 16980539
pmcid: 1626633
doi: 10.1105/tpc.106.044594
Nishimura, K., Kato, Y. & Sakamoto, W. Chloroplast proteases: updates on proteolysis within and across suborganellar compartments. Plant Physiol. 171, 2280–2293 (2016).
pubmed: 27288365
pmcid: 4972267
doi: 10.1104/pp.16.00330
Pfalz, J., Liere, K., Kandlbinder, A., Dietz, K.-J. & Oelmüller, R. pTAC2, -6, and -12 are components of the transcriptionally active plastid chromosome that are required for plastid gene expression. Plant Cell 18, 176–197 (2006).
pubmed: 16326926
pmcid: 1323492
doi: 10.1105/tpc.105.036392
Susek, R. E., Ausubel, F. M. & Chory, J. Signal transduction mutants of arabidopsis uncouple nuclear CAB and RBCS gene expression from chloroplast development. Cell 74, 787–799 (1993).
pubmed: 7690685
doi: 10.1016/0092-8674(93)90459-4
Jiao, Y., Lau, O. S. & Deng, X. W. Light-regulated transcriptional networks in higher plants. Nat. Rev. Genet. 8, 217–230 (2007).
pubmed: 17304247
doi: 10.1038/nrg2049
Chen, R. E. & Thorner, J. Function and regulation in MAPK signaling pathways: lessons learned from the yeast Saccharomyces cerevisiae. Biochim. Biophys. Acta. 1773, 1311–1340 (2007).
pubmed: 17604854
pmcid: 2031910
doi: 10.1016/j.bbamcr.2007.05.003
Nakagami, H., Pitzschke, A. & Hirt, H. Emerging MAP kinase pathways in plant stress signalling. Trends Plant Sci. 10, 339–346 (2005).
pubmed: 15953753
doi: 10.1016/j.tplants.2005.05.009
Rodriguez, M. C. S., Petersen, M. & Mundy, J. Mitogen-activated protein kinase signaling in plants. Annu. Rev. Plant Biol. 61, 621–649 (2010).
pubmed: 20441529
doi: 10.1146/annurev-arplant-042809-112252
Meng, X. & Zhang, S. MAPK cascades in plant disease resistance signaling. Annu. Rev. Phytopathol. 51, 245–266 (2013).
pubmed: 23663002
doi: 10.1146/annurev-phyto-082712-102314
Chen, X. et al. Protein kinases in plant responses to drought, salt, and cold stress. J. Integr. Plant Biol. 63, 53–78 (2021).
pubmed: 33399265
doi: 10.1111/jipb.13061
Sun, Y. et al. Integration of brassinosteroid signal transduction with the transcription network for plant growth regulation in Arabidopsis. Dev. Cell 19, 765–777 (2010).
pubmed: 21074725
pmcid: 3018842
doi: 10.1016/j.devcel.2010.10.010
Planas-Riverola, A. et al. Brassinosteroid signaling in plant development and adaptation to stress. Development 146, dev151894 (2019).
pubmed: 30872266
pmcid: 6432667
doi: 10.1242/dev.151894
Hématy, K. et al. A receptor-like kinase mediates the response of Arabidopsis cells to the inhibition of cellulose synthesis. Curr. Biol. 17, 922–931 (2007).
pubmed: 17540573
doi: 10.1016/j.cub.2007.05.018
Schröder, F., Lisso, J., Lange, P. & Müssig, C. The extracellular EXO protein mediates cell expansion in Arabidopsis leaves. BMC Plant Biol. 9, 20 (2009).
pubmed: 19216774
pmcid: 2661892
doi: 10.1186/1471-2229-9-20
Schindelman, G. et al. COBRA encodes a putative GPI-anchored protein, which is polarly localized and necessary for oriented cell expansion in Arabidopsis. Genes Dev. 15, 1115–1127 (2001).
pubmed: 11331607
pmcid: 312689
doi: 10.1101/gad.879101
Roudier, F., Schindelman, G., DeSalle, R. & Benfey, P. N. The COBRA family of putative GPI-anchored proteins in Arabidopsis. A new fellowship in expansion. Plant Physiol. 130, 538–548 (2002).
pubmed: 12376623
pmcid: 166585
doi: 10.1104/pp.007468
Ko, J.-H., Kim, J. H., Jayanty, S. S., Howe, G. A. & Han, K.-H. Loss of function of COBRA, a determinant of oriented cell expansion, invokes cellular defence responses in Arabidopsis thaliana. J. Exp. Bot. 57, 2923–2936 (2006).
pubmed: 16873454
doi: 10.1093/jxb/erl052
Wolf, S. et al. A receptor-like protein mediates the response to pectin modification by activating brassinosteroid signaling. Proc. Natl Acad. Sci. USA 111, 15261–15266 (2014).
pubmed: 25288746
pmcid: 4210321
doi: 10.1073/pnas.1322979111
Higashi, Y., Okazaki, Y., Myouga, F., Shinozaki, K. & Saito, K. Landscape of the lipidome and transcriptome under heat stress in Arabidopsis thaliana. Sci. Rep. 5, 10533 (2015).
pubmed: 26013835
pmcid: 4444972
doi: 10.1038/srep10533
Mueller, S. P., Krause, D. M., Mueller, M. J. & Fekete, A. Accumulation of extra-chloroplastic triacylglycerols in Arabidopsis seedlings during heat acclimation. J. Exp. Bot. 66, 4517–4526 (2015).
pubmed: 25977236
pmcid: 4507766
doi: 10.1093/jxb/erv226
Gidda, S. K. et al. Lipid droplet-associated proteins (LDAPs) are required for the dynamic regulation of neutral lipid compartmentation in plant cells. Plant Physiol. 170, 2052–2071 (2016).
pubmed: 26896396
pmcid: 4825156
doi: 10.1104/pp.15.01977
Doner, N. M. et al. Arabidopsis thaliana EARLY RESPONSIVE TO DEHYDRATION 7 localizes to lipid droplets via its senescence domain. Front. Plant Sci. 12, 658961 (2021).
pubmed: 33936146
pmcid: 8079945
doi: 10.3389/fpls.2021.658961
Krawczyk, H. E. et al. Heat stress leads to rapid lipid remodeling and transcriptional adaptations in Nicotiana tabacum pollen tubes. Plant Physiol. 189, 490–515 (2022).
pubmed: 35302599
pmcid: 9157110
Listenberger, L. L. & Brown, D. A. Fluorescent detection of lipid droplets and associated proteins. Curr. Protoc. Cell Biol. 35, 24.2.1–24.2.11 (2007).
doi: 10.1002/0471143030.cb2402s35
Kretzschmar, F. K. et al. Identification of low-abundance lipid droplet proteins in seeds and seedlings. Plant Physiol. 182, 1326–1345 (2020).
pubmed: 31826923
doi: 10.1104/pp.19.01255
Lass, A. et al. Adipose triglyceride lipase-mediated lipolysis of cellular fat stores is activated by CGI-58 and defective in Chanarin–Dorfman syndrome. Cell Metab. 3, 309–319 (2006).
pubmed: 16679289
doi: 10.1016/j.cmet.2006.03.005
James, C. N. et al. Disruption of the Arabidopsis CGI-58 homologue produces Chanarin–Dorfman-like lipid droplet accumulation in plants. Proc. Natl Acad. Sci. USA 107, 17833–17838 (2010).
pubmed: 20876112
pmcid: 2955100
doi: 10.1073/pnas.0911359107
Guzha, A., Whitehead, P., Ischebeck, T. & Chapman, K. D. Lipid droplets: packing hydrophobic molecules within the aqueous cytoplasm. Annu. Rev. Plant Biol. 74, 195–223 (2023).
pubmed: 36413579
doi: 10.1146/annurev-arplant-070122-021752
Nishiyama, T. et al. The Chara genome: secondary complexity and implications for plant terrestrialization. Cell 174, 448–464.e24 (2018).
pubmed: 30007417
doi: 10.1016/j.cell.2018.06.033
Zhao, C. et al. Evolution of chloroplast retrograde signaling facilitates green plant adaptation to land. Proc. Natl Acad. Sci. USA 116, 5015–5020 (2019).
pubmed: 30804180
pmcid: 6421419
doi: 10.1073/pnas.1812092116
Honkanen, S. & Small, I. The GENOMES UNCOUPLED1 protein has an ancient, highly conserved role but not in retrograde signalling. New Phytol. 236, 99–113 (2022).
pubmed: 35708656
pmcid: 9545484
doi: 10.1111/nph.18318
Martín, G. et al. Phytochrome and retrograde signalling pathways converge to antagonistically regulate a light-induced transcriptional network. Nat. Commun. 7, 11431 (2016).
pubmed: 27150909
pmcid: 4859062
doi: 10.1038/ncomms11431
Gasulla, F. et al. The response of Asterochloris erici (Ahmadjian) Skaloud et Peksa to desiccation: a proteomic approach. Plant Cell Environ. 36, 1363–1378 (2013).
pubmed: 23305100
doi: 10.1111/pce.12065
Li-Beisson, Y., Thelen, J. J., Fedosejevs, E. & Harwood, J. L. The lipid biochemistry of eukaryotic algae. Prog. Lipid Res. 74, 31–68 (2019).
pubmed: 30703388
doi: 10.1016/j.plipres.2019.01.003
de Vries, J. & Ischebeck, T. Ties between stress and lipid droplets pre-date seeds. Trends Plant Sci. 25, 1203–1214 (2020).
pubmed: 32921563
doi: 10.1016/j.tplants.2020.07.017
The Culture Collection of Algae at the University of Göttingen, Germany (SAG) (The University of Göttingen); https://sagdb.uni-goettingen.de/detailedList.php?str_number=12.97
Friedl, T. & Lorenz, M. The Culture Collection of Algae at Göttingen University (SAG): a biological resource for biotechnological and biodiversity research. Procedia Environ. Sci. 15, 110–117 (2012).
doi: 10.1016/j.proenv.2012.05.015
Ichimura, T. Sexual cell division and conjugation-papilla formation in sexual reproduction of Closterium strigosum. In Proc. 7th International Seaweed Symposium 208–214 (Univ. of Tokyo Press, 1971).
Nichols, H. W. in Handbook of Phycological Methods (ed. Stein J. R.) p. 16–17 (Cambridge Univ. Press, 1973).
Conover, W. J. Practical Nonparametric Statistics 3rd edn (John Wiley & Sons, 1999).
Simon, A. FastQC: a quality control tool for high throughput sequence data (Babraham Bioinformatics, Babraham Institute, 2010); https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).
pubmed: 27312411
pmcid: 5039924
doi: 10.1093/bioinformatics/btw354
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
pubmed: 24695404
pmcid: 4103590
doi: 10.1093/bioinformatics/btu170
Pertea, M., Kim, D., Pertea, G. M., Leek, J. T. & Salzberg, S. L. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat. Protoc. 11, 1650–1667 (2016).
pubmed: 27560171
pmcid: 5032908
doi: 10.1038/nprot.2016.095
Manni, M., Berkeley, M. R., Seppey, M., Simão, F. A. & Zdobnov, E. M. BUSCO update: novel and streamlined workflows along with broader and deeper phylogenetic coverage for scoring of eukaryotic, prokaryotic, and viral genomes. Mol. Biol. Evol. 38, 4647–4654 (2021).
pubmed: 34320186
pmcid: 8476166
doi: 10.1093/molbev/msab199
Shao, M. & Kingsford, C. Accurate assembly of transcripts through phase-preserving graph decomposition. Nat. Biotechnol. 35, 1167–1169 (2017).
pubmed: 29131147
pmcid: 5722698
doi: 10.1038/nbt.4020
Mapleson, D., Venturini, L., Kaithakottil, G. & Swarbreck, D. Efficient and accurate detection of splice junctions from RNA-seq with Portcullis. Gigascience 7, giy131 (2018).
pubmed: 30418570
pmcid: 6302956
doi: 10.1093/gigascience/giy131
Venturini, L., Caim, S., Kaithakottil, G. G., Mapleson, D. L. & Swarbreck, D. Leveraging multiple transcriptome assembly methods for improved gene structure annotation. GigaScience 7, giy093 (2018).
pubmed: 30052957
pmcid: 6105091
doi: 10.1093/gigascience/giy093
Gotoh, O. Direct mapping and alignment of protein sequences onto genomic sequence. Bioinformatics 24, 2438–2444 (2008).
pubmed: 18728043
doi: 10.1093/bioinformatics/btn460
Gotoh, O. A space-efficient and accurate method for mapping and aligning cDNA sequences onto genomic sequence. Nucleic Acids Res. 36, 2630–2638 (2008).
pubmed: 18344523
pmcid: 2377433
doi: 10.1093/nar/gkn105
Li, F.-W. et al. Anthoceros genomes illuminate the origin of land plants and the unique biology of hornworts. Nat. Plants 6, 259–272 (2020).
pubmed: 32170292
pmcid: 8075897
doi: 10.1038/s41477-020-0618-2
Cheng, C.-Y. et al. Araport11: a complete reannotation of the Arabidopsis thaliana reference genome. Plant J. 89, 789–804 (2017).
pubmed: 27862469
doi: 10.1111/tpj.13415
Li, F.-W. et al. Fern genomes elucidate land plant evolution and cyanobacterial symbioses. Nat. Plants 4, 460–472 (2018).
pubmed: 29967517
pmcid: 6786969
doi: 10.1038/s41477-018-0188-8
Wang, S. et al. Genomes of early-diverging streptophyte algae shed light on plant terrestrialization. Nat. Plants 6, 95–106 (2020).
pubmed: 31844283
doi: 10.1038/s41477-019-0560-3
Irisarri, I. et al. Unexpected cryptic species among streptophyte algae most distant to land plants. Proc. Biol. Sci. 288, 20212168 (2021).
pubmed: 34814752
pmcid: 8611356
Merchant, S. S. et al. The Chlamydomonas genome reveals the evolution of key animal and plant functions. Science 318, 245–250 (2007).
pubmed: 17932292
pmcid: 2875087
doi: 10.1126/science.1143609
Hori, K. et al. Klebsormidium flaccidum genome reveals primary factors for plant terrestrial adaptation. Nat. Commun. 5, 3978 (2014).
pubmed: 24865297
doi: 10.1038/ncomms4978
Liang, Z. et al. Mesostigma viride genome and transcriptome provide insights into the origin and evolution of Streptophyta. Adv. Sci. 7, 1901850 (2019).
doi: 10.1002/advs.201901850
Montgomery, S. A. et al. Chromatin organization in early land plants reveals an ancestral association between H3K27me3, transposons, and constitutive heterochromatin. Curr. Biol. 30, 573–588.e7 (2020).
pubmed: 32004456
pmcid: 7209395
doi: 10.1016/j.cub.2019.12.015
Lang, D. et al. The Physcomitrella patens chromosome-scale assembly reveals moss genome structure and evolution. Plant J. 93, 515–533 (2018).
pubmed: 29237241
doi: 10.1111/tpj.13801
Banks, J. A. et al. The Selaginella genome identifies genetic changes associated with the evolution of vascular plants. Science 332, 960–963 (2011).
pubmed: 21551031
pmcid: 3166216
doi: 10.1126/science.1203810
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
Stanke, M., Tzvetkova, A. & Morgenstern, B. AUGUSTUS at EGASP: using EST, protein and genomic alignments for improved gene prediction in the human genome. Genome Biol. 7, S11 (2006).
pmcid: 1810548
doi: 10.1186/gb-2006-7-s1-s11
Hoff, K. J. & Stanke, M. Predicting genes in single genomes with AUGUSTUS. Curr. Protoc. Bioinformatics 65, e57 (2019).
pubmed: 30466165
doi: 10.1002/cpbi.57
Korf, I. Gene finding in novel genomes. BMC Bioinformatics 5, 59 (2004).
pubmed: 15144565
pmcid: 421630
doi: 10.1186/1471-2105-5-59
Kelley, D. R., Liu, B., Delcher, A. L., Pop, M. & Salzberg, S. L. Gene prediction with glimmer for metagenomic sequences augmented by classification and clustering. Nucleic Acids Res. 40, e9 (2012).
pubmed: 22102569
doi: 10.1093/nar/gkr1067
Testa, A. C., Hane, J. K., Ellwood, S. R. & Oliver, R. P. CodingQuarry: highly accurate hidden Markov model gene prediction in fungal genomes using RNA-seq transcripts. BMC Genomics 16, 170 (2015).
pubmed: 25887563
pmcid: 4363200
doi: 10.1186/s12864-015-1344-4
Haas, B. J. et al. Automated eukaryotic gene structure annotation using EVidenceModeler and the program to assemble spliced alignments. Genome Biol. 9, R7 (2008).
pubmed: 18190707
pmcid: 2395244
doi: 10.1186/gb-2008-9-1-r7
Minos—a gene model consolidation pipeline for genome annotation projects. GitHub https://github.com/EI-CoreBioinformatics/minos (2019).
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).
pubmed: 25402007
doi: 10.1038/nmeth.3176
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
pubmed: 27043002
doi: 10.1038/nbt.3519
Kang, Y.-J. et al. CPC2: a fast and accurate coding potential calculator based on sequence intrinsic features. Nucleic Acids Res. 45, W12–W16 (2017).
pubmed: 28521017
pmcid: 5793834
doi: 10.1093/nar/gkx428
Campbell, M. S., Holt, C., Moore, B. & Yandell, M. Genome annotation and curation using MAKER and MAKER-P. Curr. Protoc. Bioinformatics 48, 4.11.1–4.11.39 (2014).
pubmed: 25501943
doi: 10.1002/0471250953.bi0411s48
Eilbeck, K., Moore, B., Holt, C. & Yandell, M. Quantitative measures for the management and comparison of annotated genomes. BMC Bioinformatics 10, 67 (2009).
pubmed: 19236712
pmcid: 2653490
doi: 10.1186/1471-2105-10-67
Dainat, J. et al. AGAT: another gff analysis toolkit to handle annotations in any gtf/gff format. (Version v0.9.2). Zenodo https://www.doi.org/10.5281/zenodo.6621429 (2022).
Huerta-Cepas, J. et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol. Biol. Evol. 34, 2115–2122 (2017).
pubmed: 28460117
pmcid: 5850834
doi: 10.1093/molbev/msx148
Huerta-Cepas, J. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47, D309–D314 (2019).
pubmed: 30418610
doi: 10.1093/nar/gky1085
Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research https://doi.org/10.12688/f1000research.7563.2 (2016).
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
pubmed: 19910308
doi: 10.1093/bioinformatics/btp616
Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).
pubmed: 20196867
pmcid: 2864565
doi: 10.1186/gb-2010-11-3-r25
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
pubmed: 25605792
pmcid: 4402510
doi: 10.1093/nar/gkv007
Phipson, B., Lee, S., Majewski, I. J., Alexander, W. S. & Smyth, G. K. Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression. Ann. Appl. Stat. 10, 946–963 (2016).
pubmed: 28367255
pmcid: 5373812
doi: 10.1214/16-AOAS920
Law, C. W., Chen, Y., Shi, W. & Smyth, G. K.voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).
pubmed: 24485249
pmcid: 4053721
doi: 10.1186/gb-2014-15-2-r29
Liu, R. et al. Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses. Nucleic Acids Res. 43, e97 (2015).
pubmed: 25925576
pmcid: 4551905
doi: 10.1093/nar/gkv412
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2022).
Langfelder, P. & Horvath, S. Fast R functions for robust correlations and hierarchical clustering. J. Stat. Softw. 46, 1–17 (2012).
doi: 10.18637/jss.v046.i11
Yu, G., Wang, L.-G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).
pubmed: 22455463
pmcid: 3339379
doi: 10.1089/omi.2011.0118
Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).
pubmed: 34557778
pmcid: 8454663
Wijesooriya, K., Jadaan, S. A., Perera, K. L., Kaur, T. & Ziemann, M. Urgent need for consistent standards in functional enrichment analysis. PLoS Comput. Biol. 18, e1009935 (2022).
pubmed: 35263338
pmcid: 8936487
doi: 10.1371/journal.pcbi.1009935
Smyth, G. K., Michaud, J. & Scott, H. S. Use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics 21, 2067–2075 (2005).
pubmed: 15657102
doi: 10.1093/bioinformatics/bti270
Amborella Genome Project. et al. The Amborella genome and the evolution of flowering plants. Science 342, 1241089 (2013).
doi: 10.1126/science.1241089
Lamesch, P. et al. The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools. Nucleic Acids Res. 40, D1202–D1210 (2012).
pubmed: 22140109
doi: 10.1093/nar/gkr1090
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
Liu, S. et al. The Brassica oleracea genome reveals the asymmetrical evolution of polyploid genomes. Nat. Commun. 5, 3930 (2014).
pubmed: 24852848
doi: 10.1038/ncomms4930
Wang, X. et al. The genome of the mesopolyploid crop species Brassica rapa. Nat. Genet. 43, 1035–1039 (2011).
pubmed: 21873998
doi: 10.1038/ng.919
The International Brachypodium Initiative.Genome sequencing and analysis of the model grass Brachypodium distachyon. Nature 463, 763–768 (2010).
doi: 10.1038/nature08747
Slotte, T. et al. The Capsella rubella genome and the genomic consequences of rapid mating system evolution. Nat. Genet. 45, 831–835 (2013).
pubmed: 23749190
doi: 10.1038/ng.2669
Blanc, G. et al. The genome of the polar eukaryotic microalga Coccomyxa subellipsoidea reveals traits of cold adaptation. Genome Biol. 13, R39 (2012).
pubmed: 22630137
pmcid: 3446292
doi: 10.1186/gb-2012-13-5-r39
Wan, T. et al. A genome for gnetophytes and early evolution of seed plants. Nat. Plants 4, 82–89 (2018).
pubmed: 29379155
doi: 10.1038/s41477-017-0097-2
Bowman, J. L. et al. Insights into land plant evolution garnered from the Marchantia polymorpha genome . Cell 171, 287–304.e15 (2017).
pubmed: 28985561
doi: 10.1016/j.cell.2017.09.030
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
Ouyang, S. et al. The TIGR Rice Genome Annotation Resource: improvements and new features. Nucleic Acids Res. 35, D883–D887 (2007).
pubmed: 17145706
doi: 10.1093/nar/gkl976
Nystedt, B. et al. The Norway spruce genome sequence and conifer genome evolution. Nature 497, 579–584 (2013).
pubmed: 23698360
doi: 10.1038/nature12211
The Tomato Genome Consortium.The tomato genome sequence provides insights into fleshy fruit evolution. Nature 485, 635–641 (2012).
doi: 10.1038/nature11119
Argout, X. et al. The genome of Theobroma cacao. Nat. Genet. 43, 101–108 (2011).
pubmed: 21186351
doi: 10.1038/ng.736
Palenik, B. et al. The tiny eukaryote Ostreococcus provides genomic insights into the paradox of plankton speciation. Proc. Natl Acad. Sci. USA 104, 7705–7710 (2007).
pubmed: 17460045
pmcid: 1863510
doi: 10.1073/pnas.0611046104
De Clerck, O. et al. Insights into the evolution of multicellularity from the sea lettuce genome. Curr. Biol. 28, 2921–2933.e5 (2018).
pubmed: 30220504
doi: 10.1016/j.cub.2018.08.015
Prochnik, S. E. et al. Genomic analysis of organismal complexity in the multicellular green alga Volvox carteri. Science 329, 223–226 (2010).
pubmed: 20616280
pmcid: 2993248
doi: 10.1126/science.1188800
Wickell, D. et al. Underwater CAM photosynthesis elucidated by Isoetes genome. Nat. Commun. 12, 6348 (2021).
pubmed: 34732722
pmcid: 8566536
doi: 10.1038/s41467-021-26644-7
Marchant, D. B. et al. Dynamic genome evolution in a model fern. Nat. Plants 8, 1038–1051 (2022).
pubmed: 36050461
pmcid: 9477723
doi: 10.1038/s41477-022-01226-7
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).
pubmed: 23329690
pmcid: 3603318
doi: 10.1093/molbev/mst010
Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).
pubmed: 25371430
doi: 10.1093/molbev/msu300
Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A. & Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).
pubmed: 28481363
pmcid: 5453245
doi: 10.1038/nmeth.4285
Hoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35, 518–522 (2018).
pubmed: 29077904
doi: 10.1093/molbev/msx281
Felsenstein, J. Confidence limits on phylogenies: an approach using the bootstrap. Evolution 39, 783–791 (1985).
pubmed: 28561359
doi: 10.2307/2408678
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
pubmed: 22743772
doi: 10.1038/nmeth.2019
Shapiro, S. S. & Wilk, M. B. An analysis of variance test for normality (complete samples). Biometrika 52, 591–611 (1965).
doi: 10.1093/biomet/52.3-4.591
Mann, H. B. & Whitney, D. R. On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 1, 50–60 (1947).
doi: 10.1214/aoms/1177730491
Müller, A. O., Blersch, K. F., Gippert, A. L. & Ischebeck, T. Tobacco pollen tubes—a fast and easy tool for studying lipid droplet association of plant proteins. Plant J. 89, 1055–1064 (2017).
pubmed: 27943529
doi: 10.1111/tpj.13441
Shevchenko, A., Wilm, M., Vorm, O. & Mann, M. Mass spectrometric sequencing of proteins from silver-stained polyacrylamide gels. Anal. Chem. 68, 850–858 (1996).
pubmed: 8779443
doi: 10.1021/ac950914h
Rappsilber, J., Ishihama, Y. & Mann, M. Stop and go extraction tips for matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics. Anal. Chem. 75, 663–670 (2003).
pubmed: 12585499
doi: 10.1021/ac026117i
Rappsilber, J., Mann, M. & Ishihama, Y. Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat. Protoc. 2, 1896–1906 (2007).
pubmed: 17703201
doi: 10.1038/nprot.2007.261
Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).
pubmed: 19029910
doi: 10.1038/nbt.1511
Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740 (2016).
pubmed: 27348712
doi: 10.1038/nmeth.3901
Wang, Z. & Benning, C. Arabidopsis thaliana polar glycerolipid profiling by thin layer chromatography (TLC) coupled with gas-liquid chromatography (GLC). J. Vis. Exp. https://doi.org/10.3791/2518 (2011).
Reich, M. et al. Fatty acid metabolism in the ectomycorrhizal fungus Laccaria bicolor. New Phytol. 182, 950–964 (2009).
pubmed: 19383096
doi: 10.1111/j.1469-8137.2009.02819.x
Miquel, M. & Browse, J. Arabidopsis mutants deficient in polyunsaturated fatty acid synthesis. Biochemical and genetic characterization of a plant oleoyl-phosphatidylcholine desaturase. J. Biol. Chem. 267, 1502–1509 (1992).
pubmed: 1730697
doi: 10.1016/S0021-9258(18)45974-1
Hornung, E. et al. Production of (10E,12Z)-conjugated linoleic acid in yeast and tobacco seeds. Biochim. Biophys. Acta 1738, 105–114 (2005).
pubmed: 16324883
doi: 10.1016/j.bbalip.2005.11.004
Jiao, Y. et al. Improved maize reference genome with single-molecule technologies. Nature 546, 524–527 (2017).
pubmed: 28605751
pmcid: 7052699
doi: 10.1038/nature22971
Clauw, P. et al. Leaf responses to mild drought stress in natural variants of Arabidopsis. Plant Physiol. 167, 800–816 (2015).
pubmed: 25604532
pmcid: 4348775
doi: 10.1104/pp.114.254284
Lu, Z. et al. Genome-wide DNA mutations in Arabidopsis plants after multigenerational exposure to high temperatures. Genome Biol. 22, 160 (2021).
pubmed: 34034794
pmcid: 8145854
doi: 10.1186/s13059-021-02381-4
Suzuki, N. et al. ABA is required for plant acclimation to a combination of salt and heat stress. PLoS ONE 11, e0147625 (2016).
pubmed: 26824246
pmcid: 4733103
doi: 10.1371/journal.pone.0147625
Wang, L. et al. Differential physiological, transcriptomic and metabolomic responses of Arabidopsis leaves under prolonged warming and heat shock. BMC Plant Biol. 20, 86 (2020).
pubmed: 32087683
pmcid: 7036190
doi: 10.1186/s12870-020-2292-y
Zhang, S.-S. et al. Tissue-specific transcriptomics reveals an important role of the unfolded protein response in maintaining fertility upon heat stress in Arabidopsis. Plant Cell 29, 1007–1023 (2017).
pubmed: 28442596
pmcid: 5466030
doi: 10.1105/tpc.16.00916
Elzanati, O., Mouzeyar, S. & Roche, J. Dynamics of the transcriptome response to heat in the moss, Physcomitrella patens. IJMS 21, 1512 (2020).
pubmed: 32098429
pmcid: 7073223
doi: 10.3390/ijms21041512
Jahan, A. et al. Archetypal roles of an abscisic acid receptor in drought and sugar responses in liverworts. Plant Physiol. 179, 317–328 (2019).
pubmed: 30442644
doi: 10.1104/pp.18.00761
Lagercrantz, U. et al. DE‐ETIOLATED1 has a role in the circadian clock of the liverwort Marchantia polymorpha. New Phytol. 232, 595–609 (2021).
pubmed: 34320227
doi: 10.1111/nph.17653
Wu, T.-Y. et al. Evolutionarily conserved hierarchical gene regulatory networks for plant salt stress response. Nat. Plants 7, 787–799 (2021).
pubmed: 34045707
doi: 10.1038/s41477-021-00929-7
Zhang, Y., Parmigiani, G. & Johnson, W. E. ComBat-seq: batch effect adjustment for RNA-seq count data. NAR Genom. Bioinform. 2, lqaa078 (2020).
pubmed: 33015620
pmcid: 7518324
doi: 10.1093/nargab/lqaa078
Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-seq data without a reference genome. Nat. Biotechnol. 29, 644–652 (2011).
pubmed: 21572440
pmcid: 3571712
doi: 10.1038/nbt.1883
Almeida-Silva, F. & Venancio, T. M. cageminer: an R/Bioconductor package to prioritize candidate genes by integrating genome-wide association studies and gene coexpression networks. In Silico Plants 4, diac018 (2022).
doi: 10.1093/insilicoplants/diac018
Jones, P. et al. InterProScan 5: genome-scale protein function classification. Bioinformatics 30, 1236–1240 (2014).
pubmed: 24451626
pmcid: 3998142
doi: 10.1093/bioinformatics/btu031