Evidence for widespread cytoplasmic structuring into mesoscale condensates.
Journal
Nature cell biology
ISSN: 1476-4679
Titre abrégé: Nat Cell Biol
Pays: England
ID NLM: 100890575
Informations de publication
Date de publication:
Mar 2024
Mar 2024
Historique:
received:
31
01
2022
accepted:
23
01
2024
medline:
18
3
2024
pubmed:
1
3
2024
entrez:
29
2
2024
Statut:
ppublish
Résumé
Compartmentalization is an essential feature of eukaryotic life and is achieved both via membrane-bound organelles, such as mitochondria, and membrane-less biomolecular condensates, such as the nucleolus. Known biomolecular condensates typically exhibit liquid-like properties and are visualized by microscopy on the scale of ~1 µm (refs.
Identifiants
pubmed: 38424273
doi: 10.1038/s41556-024-01363-5
pii: 10.1038/s41556-024-01363-5
doi:
Substances chimiques
Proteome
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
346-352Subventions
Organisme : NIGMS NIH HHS
ID : R35 GM128813
Pays : United States
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer Nature Limited.
Références
Banani, S. F., Lee, H. O., Hyman, A. A. & Rosen, M. K. Biomolecular condensates: organizers of cellular biochemistry. Nat. Rev. Mol. Cell Biol. 18, 285 (2017).
pubmed: 28225081
pmcid: 7434221
doi: 10.1038/nrm.2017.7
Shin, Y. & Brangwynne, C. P. Liquid phase condensation in cell physiology and disease. Science 357, aaf4382 (2017).
doi: 10.1126/science.aaf4382
Hnisz, D., Shrinivas, K., Young, R. A., Chakraborty, A. K. & Sharp, P. A. A phase separation model for transcriptional control. Cell 169, 13–23 (2017).
pubmed: 28340338
pmcid: 5432200
doi: 10.1016/j.cell.2017.02.007
Ivanov, P., Kedersha, N. & Anderson, P. Stress granules and processing bodies in translational control. Cold Spring Harb. Perspect. Biol. 11, a032813 (2019).
pubmed: 30082464
pmcid: 6496347
doi: 10.1101/cshperspect.a032813
Wippich, F. et al. Dual specificity kinase DYRK3 couples stress granule condensation/dissolution to mTORC1 signaling. Cell 152, 791–805 (2013).
pubmed: 23415227
doi: 10.1016/j.cell.2013.01.033
Chong, P. A. & Forman-Kay, J. D. Liquid–liquid phase separation in cellular signaling systems. Curr. Opin. Struct. Biol. 41, 180–186 (2016).
pubmed: 27552079
doi: 10.1016/j.sbi.2016.08.001
Alberti, S. & Dormann, D. Liquid–liquid phase separation in disease. Annu. Rev. Genet 53, 171–194 (2019).
pubmed: 31430179
doi: 10.1146/annurev-genet-112618-043527
Tsang, B., Pritisanac, I., Scherer, S. W., Moses, A. M. & Forman-Kay, J. D. Phase separation as a missing mechanism for interpretation of disease mutations. Cell 183, 1742–1756 (2020).
pubmed: 33357399
doi: 10.1016/j.cell.2020.11.050
Fei, J. et al. Quantitative analysis of multilayer organization of proteins and RNA in nuclear speckles at super resolution. J. Cell Sci. 130, 4180–4192 (2017).
pubmed: 29133588
pmcid: 5769577
Go, C. D. et al. A proximity-dependent biotinylation map of a human cell. Nature 595, 120–124 (2021).
pubmed: 34079125
doi: 10.1038/s41586-021-03592-2
Hubstenberger, A. et al. P-body purification reveals the condensation of repressed mRNA regulons. Mol. Cell 68, 144–157 (2017).
Jain, S. et al. ATPase-modulated stress granules contain a diverse proteome and substructure. Cell 164, 487–498 (2016).
pubmed: 26777405
pmcid: 4733397
doi: 10.1016/j.cell.2015.12.038
Markmiller, S. et al. Context-dependent and disease-specific diversity in protein interactions within stress granules. Cell 172, 590–604 (2018).
Youn, J. Y. et al. High-density proximity mapping reveals the subcellular organization of mRNA-associated granules and bodies. Mol. Cell 69, 517–532 (2018).
Pancsa, R., Vranken, W. & Meszaros, B. Computational resources for identifying and describing proteins driving liquid–liquid phase separation. Brief. Bioinform. 22, bbaa408 (2021).
pubmed: 33517364
pmcid: 8425267
doi: 10.1093/bib/bbaa408
Vernon, R. M. & Forman-Kay, J. D. First-generation predictors of biological protein phase separation. Curr. Opin. Struct. Biol. 58, 88–96 (2019).
pubmed: 31252218
doi: 10.1016/j.sbi.2019.05.016
Lohka, M. J. & Maller, J. L. Induction of nuclear envelope breakdown, chromosome condensation, and spindle formation in cell-free extracts. J. Cell Biol. 101, 518–523 (1985).
pubmed: 3926780
doi: 10.1083/jcb.101.2.518
Hannak, E. & Heald, R. Investigating mitotic spindle assembly and function in vitro using Xenopus laevis egg extracts. Nat. Protoc. 1, 2305–2314 (2006).
pubmed: 17406472
doi: 10.1038/nprot.2006.396
Sonnett, M., Yeung, E. & Wuhr, M. Accurate, sensitive, and precise multiplexed proteomics using the complement reporter ion cluster. Anal. Chem. 90, 5032–5039 (2018).
pubmed: 29522331
pmcid: 6220677
doi: 10.1021/acs.analchem.7b04713
Johnson, A., Stadlmeier, M. & Wuhr, M. TMTpro complementary ion quantification increases plexing and sensitivity for accurate multiplexed proteomics at the MS2 level. J. Proteome Res. 20, 3043–3052 (2021).
pubmed: 33929851
pmcid: 8330405
doi: 10.1021/acs.jproteome.0c00813
McAlister, G. C. et al. MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. Anal. Chem. 86, 7150–7158 (2014).
pubmed: 24927332
pmcid: 4215866
doi: 10.1021/ac502040v
Meszaros, B. et al. PhaSePro: the database of proteins driving liquid–liquid phase separation. Nucleic Acids Res. 48, D360–D367 (2020).
pubmed: 31612960
van Mierlo, G. et al. Predicting protein condensate formation using machine learning. Cell Rep. 34, 108705 (2021).
pubmed: 33535034
doi: 10.1016/j.celrep.2021.108705
Ning, W. et al. DrLLPS: a data resource of liquid–liquid phase separation in eukaryotes. Nucleic Acids Res. 48, D288–D295 (2020).
pubmed: 31691822
doi: 10.1093/nar/gkz1027
You, K. et al. PhaSepDB: a database of liquid–liquid phase separation related proteins. Nucleic Acids Res. 48, D354–D359 (2020).
pubmed: 31584089
doi: 10.1093/nar/gkz847
Li, Q. et al. LLPSDB: a database of proteins undergoing liquid–liquid phase separation in vitro. Nucleic Acids Res. 48, D320–D327 (2019).
pmcid: 6943074
doi: 10.1093/nar/gkz778
Dignon, G. L., Best, R. B. & Mittal, J. Biomolecular phase separation: from molecular driving forces to macroscopic properties. Annu. Rev. Phys. Chem. 71, 53–75 (2020).
pubmed: 32312191
pmcid: 7469089
doi: 10.1146/annurev-physchem-071819-113553
Brangwynne, C. P., Tompa, P. & Pappu, R. V. Polymer physics of intracellular phase transitions. Nat. Phys. 11, 899–904 (2015).
doi: 10.1038/nphys3532
Kar, M. et al. Phase-separating RNA-binding proteins form heterogeneous distributions of clusters in subsaturated solutions. Proc. Natl Acad. Sci. USA 119, e2202222119 (2022).
pubmed: 35787038
pmcid: 9282234
doi: 10.1073/pnas.2202222119
Mittag, T. & Pappu, R. V. A conceptual framework for understanding phase separation and addressing open questions and challenges. Mol. Cell 82, 2201–2214 (2022).
pubmed: 35675815
pmcid: 9233049
doi: 10.1016/j.molcel.2022.05.018
Eisenberg, E. & Levanon, E. Y. Human housekeeping genes, revisited. Trends Genet. 29, 569–574 (2013).
pubmed: 23810203
doi: 10.1016/j.tig.2013.05.010
Thomas, L., Putnam, A. & Folkmann, A. Germ granules in development. Development 150, dev.201037 (2023).
doi: 10.1242/dev.201037
Neil, C. R. et al. L-bodies are RNA–protein condensates driving RNA localization in Xenopus oocytes. Mol. Biol. Cell 32, ar37 (2021).
pubmed: 34613784
pmcid: 8694076
doi: 10.1091/mbc.E21-03-0146-T
Ma, W. & Mayr, C. A membraneless organelle associated with the endoplasmic reticulum enables 3′ UTR-mediated protein–protein interactions. Cell 175, 1492–1506 (2018).
Lee, D. S. et al. Size distributions of intracellular condensates reflect competition between coalescence and nucleation. Nat. Phys. 19, 586–596 (2023).
pubmed: 37073403
pmcid: 10104779
doi: 10.1038/s41567-022-01917-0
Molliex, A. et al. Phase separation by low complexity domains promotes stress granule assembly and drives pathological fibrillization. Cell 163, 123–133 (2015).
pubmed: 26406374
pmcid: 5149108
doi: 10.1016/j.cell.2015.09.015
Kato, M. et al. Cell-free formation of RNA granules: low complexity sequence domains form dynamic fibers within hydrogels. Cell 149, 753–767 (2012).
pubmed: 22579281
pmcid: 6347373
doi: 10.1016/j.cell.2012.04.017
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
doi: 10.1023/A:1010933404324
Breiman, L. Bagging predictors. Mach. Learn. 24, 123–140 (1996).
doi: 10.1007/BF00058655
Mardia, K. V. Fisher’s pioneering work on discriminant analysis and its impact on AI. Preprint at arXiv 2309.04774 (2023).
Walsh, I., Martin, A. J., Di Domenico, T. & Tosatto, S. C. ESpritz: accurate and fast prediction of protein disorder. Bioinformatics 28, 503–509 (2012).
pubmed: 22190692
doi: 10.1093/bioinformatics/btr682
Castello, A. et al. Comprehensive identification of RNA-binding domains in human cells. Mol. Cell 63, 696–710 (2016).
pubmed: 27453046
pmcid: 5003815
doi: 10.1016/j.molcel.2016.06.029
UniProt, C. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 49, D480–D489 (2021).
doi: 10.1093/nar/gkaa1100
Huntley, R. P. et al. The GOA database: gene ontology annotation updates for 2015. Nucleic Acids Res. 43, D1057–1063 (2015).
Bolognesi, B. et al. A concentration-dependent liquid phase separation can cause toxicity upon increased protein expression. Cell Rep. 16, 222–231 (2016).
pubmed: 27320918
pmcid: 4929146
doi: 10.1016/j.celrep.2016.05.076
Vernon, R. M. et al. Pi–Pi contacts are an overlooked protein feature relevant to phase separation. eLife 7, e31486 (2018).
pubmed: 29424691
pmcid: 5847340
doi: 10.7554/eLife.31486
Zarin, T. et al. Proteome-wide signatures of function in highly diverged intrinsically disordered regions. eLife 8, e46883 (2019).
pubmed: 31264965
pmcid: 6634968
doi: 10.7554/eLife.46883
Zwicker, D., Seyboldt, R., Weber, C. A., Hyman, A. A. & Jülicher, F. Growth and division of active droplets provides a model for protocells. Nat. Phys. 13, 408–413 (2017).
doi: 10.1038/nphys3984
Honerkamp-Smith, A. R., Veatch, S. L. & Keller, S. L. An introduction to critical points for biophysicists; observations of compositional heterogeneity in lipid membranes. Biochim. Biophys. Acta 1788, 53–63 (2009).
pubmed: 18930706
doi: 10.1016/j.bbamem.2008.09.010
Wlizla, M., McNamara, S. & Horb, M. E. Generation and care of Xenopus laevis and Xenopus tropicalis embryos. Methods Mol. Biol. 1865, 19–32 (2018).
pubmed: 30151756
pmcid: 6396978
doi: 10.1007/978-1-4939-8784-9_2
Ubbels, G. A., Hara, K., Koster, C. H. & Kirschner, M. W. Evidence for a functional role of the cytoskeleton in determination of the dorsoventral axis in Xenopus laevis eggs. J. Embryol. Exp. Morphol. 77, 15–37 (1983).
pubmed: 6689175
Good, M. C. & Heald, R. Preparation of cellular extracts from xenopus eggs and embryos. Cold Spring Harb. Protoc. 2018, 097055 (2018).
doi: 10.1101/pdb.prot097055
Sawin, K. E. & Mitchison, T. J. Mitotic spindle assembly by two different pathways in vitro. J. Cell Biol. 112, 925–940 (1991).
pubmed: 1999463
doi: 10.1083/jcb.112.5.925
Nguyen, T. et al. Differential nuclear import sets the timing of protein access to the embryonic genome. Nat. Commun. 13, 5887 (2022).
pubmed: 36202846
pmcid: 9537182
doi: 10.1038/s41467-022-33429-z
3DFilterHolderDesigns. GitHub https://github.com/wuhrlab/3DFilterHolderDesigns (2021).
Grant, I. M. et al. The Xenopus ORFeome: a resource that enables functional genomics. Dev. Biol. 408, 345–357 (2015).
pubmed: 26391338
pmcid: 4684507
doi: 10.1016/j.ydbio.2015.09.004
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
Abràmoff, M. D., Magalhães, P. J. & Ram, S. J. Image processing with ImageJ. Biophoton. Int. 11, 36–42 (2004).
Gupta, M., Sonnett, M., Ryazanova, L., Presler, M. & Wühr, M. Quantitative proteomics of Xenopus embryos I, sample preparation. Methods Mol. Biol. 1865, 175–194 (2018).
pubmed: 30151767
pmcid: 6564683
doi: 10.1007/978-1-4939-8784-9_13
Hughes, C. S. et al. Single-pot, solid-phase-enhanced sample preparation for proteomics experiments. Nat. Protoc. 14, 68–85 (2019).
pubmed: 30464214
doi: 10.1038/s41596-018-0082-x
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
Edwards, A. & Haas, W. Multiplexed quantitative proteomics for high-throughput comprehensive proteome comparisons of human cell lines. Methods Mol. Biol. 1394, 1–13 (2016).
pubmed: 26700037
doi: 10.1007/978-1-4939-3341-9_1
Ruepp, A. et al. CORUM: the comprehensive resource of mammalian protein complexes—2009. Nucleic Acids Res. 38, D497–D501 (2010).
pubmed: 19884131
doi: 10.1093/nar/gkp914
Youn, J. Y. et al. Properties of stress granule and p-body proteomes. Mol. Cell 76, 286–294 (2019).
pubmed: 31626750
doi: 10.1016/j.molcel.2019.09.014
Krogh, A., Larsson, B., von Heijne, G. & Sonnhammer, E. L. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol. 305, 567–580 (2001).
pubmed: 11152613
doi: 10.1006/jmbi.2000.4315
Sonnhammer, E. L., von Heijne, G. & Krogh, A. A hidden Markov model for predicting transmembrane helices in protein sequences. Proc. Int. Conf. Intell. Syst. Mol. Biol. 6, 175–182 (1998).
Sonnett, M., Gupta, M., Nguyen, T. & Wühr, M. Quantitative proteomics for Xenopus embryos II, data analysis. Methods Mol. Biol. 1865, 195–215 (2018).
pubmed: 30151768
pmcid: 6534117
doi: 10.1007/978-1-4939-8784-9_14
Huttlin, E. L. et al. A tissue-specific atlas of mouse protein phosphorylation and expression. Cell 143, 1174–1189 (2010).
pubmed: 21183079
pmcid: 3035969
doi: 10.1016/j.cell.2010.12.001
Rad, R. et al. Improved monoisotopic mass estimation for deeper proteome coverage. J. Proteome Res. 20, 591–598 (2021).
pubmed: 33190505
doi: 10.1021/acs.jproteome.0c00563
Sashimi. Source Forge http://sashimi.svn.sourceforge.net/viewvc/sashimi/ (2023).
Eng, J. K., McCormack, A. L. & Yates, J. R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass. Spectrom. 5, 976–989 (1994).
pubmed: 24226387
doi: 10.1016/1044-0305(94)80016-2
Fortriede, J. D. et al. Xenbase: deep integration of GEO and SRA RNA-seq and ChIP-seq data in a model organism database. Nucleic Acids Res. 48, D776–D782 (2020).
pubmed: 31733057
Elias, J. E. & Gygi, S. P. Target–decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Methods 4, 207–214 (2007).
pubmed: 17327847
doi: 10.1038/nmeth1019
TMTProC. GitHub https://github.com/wuhrlab/TMTProC (2021).
Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2018).
pmcid: 6323896
doi: 10.1093/nar/gky1106
Andrews, S. FastQC. Babraham Bioinformatics, Babraham Institute http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).
Krueger, F. Trim galore. A wrapper tool around Cutadapt and FastQC to consistently apply quality and adapter trimming to FastQ files Babraham Bioinformatics, Babraham Institute http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/ (2015).
http://www.xenbase.org/entry/
Dobin, A. & Gingeras, T. R. Optimizing RNA-seq mapping with STAR. Methods Mol. Biol. 1415, 245–262 (2016).
pubmed: 27115637
doi: 10.1007/978-1-4939-3572-7_13
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1–21 (2014).
doi: 10.1186/s13059-014-0550-8
Szklarczyk, D. et al. The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 51, D638–d646 (2023).
pubmed: 36370105
doi: 10.1093/nar/gkac1000
Liu, Y., Zhou, J. & White, K. P. RNA-seq differential expression studies: more sequence or more replication? Bioinformatics 30, 301–304 (2014).
pubmed: 24319002
doi: 10.1093/bioinformatics/btt688