Spatial Reconstruction of TRPC-Mechanoreceptors of the Ctenophore Mnemiopsis leidyi A. Agassiz, 1865
- 作者: Kuznetsov A.1,2, Vtyurina D.3
-
隶属关系:
- Kovalevsky Institute of Biology of the Southern Seas, Russian Academy of Sciences
- Sevastopol State University
- Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences
- 期: 卷 57, 编号 4 (2023)
- 页面: 726-735
- 栏目: БИОИНФОРМАТИКА
- URL: https://journals.rcsi.science/0026-8984/article/view/138727
- DOI: https://doi.org/10.31857/S0026898423040122
- EDN: https://elibrary.ru/QLQZZG
- ID: 138727
如何引用文章
详细
Ctenophore Mnemiopsis leidyi A. Agassiz, 1865 responds to gentle mechanical stimulation with intense luminescence; however, the mechanism of this phenomenon is unknown. We searched for possible mechanosensitive receptors that initiate signal transduction resulting in photoprotein luminescence. The three ortholog genes of mouse (5z96) and Drosophila (5vkq) TRPC-proteins, such as ML234550a-PA (860 aa), ML03701a-PA (828 aa) and ML038011a-PA (1395 aa), were found in the M. leidyi genome. The latter protein contains a long ankyrin helix consisting of 16 ANK domains. Study of the annotated domains and the network of interactions between the interactome proteins suggests that the ML234550a-PA and ML03701a-PA proteins carry out cytoplasmic, but ML038011a-PA provides intranuclear transduction of mechanical signals. Spatial reconstruction of the studied proteins revealed differences in their structure, which may be related to various functions of these proteins in the cell. The question of which of these proteins is involved in the initiation of luminescence after mechanical stimulation is discussed.
作者简介
A. Kuznetsov
Kovalevsky Institute of Biology of the Southern Seas, Russian Academy of Sciences; Sevastopol State University
Email: vtyurinad@gmail.com
Russia, 299011, Sevastopol; Russia, 299053, Sevastopol
D. Vtyurina
Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences
编辑信件的主要联系方式.
Email: vtyurinad@gmail.com
Russia, 119991, Moscow
参考
- Himmel N.J., Cox D.N. (2020) Transient receptor potential channels: current perspectives on evolution, structure, function and nomenclature. Proc. Biol. Sci. 287(1933), 20201309. https://doi.org/10.1098/rspb.2020.1309
- Cao E. (2020) Structural mechanisms of transient receptor potential ion channels. J. Gen. Physiol. 152(3), e201811998. https://doi.org/10.1085/jgp.201811998
- Samanta A., Hughes T.E., Moiseenkova-Bell V.Y. (2018) Transient receptor potential (TRP) channels. Subcell. Biochem. 87, 141‒165. https://doi.org/10.1007/978-981-10-7757-9_6
- Nilius B., Owsianik G. (2011) The transient receptor potential family of ion channels. Genome Biol. 12(3), 218. https://doi.org/10.1186/gb-2011-12-3-218
- Lehnert B.P., Santiago C., Huey E.L., Emanuel A.J., Renauld S., Africawala N., Alkislar I., Zheng Y., Bai L., Koutsioumpa C., Hong J.T., Magee A.R., Harvey C.D., Ginty D.D. (2021) Mechanoreceptor synapses in the brainstem shape the central representation of touch. Cell. 184(22), 5608‒5621. https://doi.org/10.1016/j.cell.2021.09.023
- Robinson C.V., Rohacs T., Hansen S.B. (2019) Tools for understanding nanoscale lipid regulation of ion channels. Trends Biochem. Sci. 44(9), 795‒806. https://doi.org/10.1016/j.tibs.2019.04.00
- Liang X., Sun L., Liu Z. (2017) Mechanosensory transduction in Drosophila melanogaster. Singapore: Springer, pp. 82. https://doi.org/10.1007/978-981-10-6526-2
- Ryan J.F., Pang K., Schnitzler C.E., Nguyen A.D., Moreland R.T., Simmons D.K., Koch B.J., Francis W.R., Havlak P., NISC Comparative Sequencing Program; Smith S.A., Putnam N.H., Haddock S.H., Dunn C.W., Wolfsberg T.G., Mullikin J.C., Martindale M.Q., Baxevanis A.D. (2013) The genome of the ctenophore Mnemiopsis leidyi and its implications for cell type evolution. Science. 342(6164), 1242592. https://doi.org/10.1126/science.1242592
- Moroz L.L. (2015) Convergent evolution of neural systems in ctenophores. J. Exp. Biol. 218(4), 598‒611. https://doi.org/10.1242/jeb.110692
- Moroz L.L., Kohn A.B. (2016) Independent origins of neurons and synapses: insights from ctenophores. Philos. Trans. R. Soc. B. 371(1685), 20150041. https://doi.org/10.1098/rstb.2015.0041.
- Moroz L.L. (2021) Multiple origins of neurons from secretory cells. Front. Cell Dev. Biol. 9, 669087. https://doi.org/10.3389/fcell.2021.669087
- Aronova M.Z. (2009) Structural models of “simple” sense organs by the example of the first Metazoa. J. Evol. Biochem. Phys. 45(2), 179‒196. https://doi.org/10.1134/S0022093009020017
- Jékely G., Godfrey-Smith P., Keijzer F. (2021) Reafference and the origin of the self in early nervous system evolution. Philos. Trans. R. Soc. B. 376(1821), 20190764. https://doi.org/10.1098/rstb.2019.0764
- Bagriantsev S.N., Gracheva E.O., Gallagher P.G. (2014) Piezo proteins: regulators of mechanosensation and other cellular processes. J. Biol. Chem. 289(46), 31673‒31681. https://doi.org/10.1074/jbc.R114.612697
- Madeira F., Park Y.M., Lee J., Buso N., Gur T., Madhusoodanan N., Basutkar P., Tivey A.R.N., Potter S.C., Finn R.D., Lopez R. (2019) The EMBL-EBI search and sequence analysis tools APIs in 2019. Nucl. Acids Res. 2(47), W636‒W641. https://doi.org/10.1093/nar/gkz268
- Chevenet F., Brun C., Bañuls A.L., Jacq B., Christen R. (2006) TreeDyn: towards dynamic graphics and annotations for analyses of trees. BMC Bioinformatics. 10(7), 439. https://doi.org/10.1186/1471-2105-7-439
- Kyte J., Doolittle R.F. (1982) A simple method for displaying the hydropathic character of a protein. J. Mol. Biol. 157(1), 105‒132. https://doi.org/10.1016/0022-2836(82)90515-0
- Mistry J., Chuguransky S., Williams L., Qureshi M., Salazar G.A., Sonnhammer E.L., Tosatto S.C.E., Paladin L., Raj S., Richardson L.J., Finn R.D., Bateman A. (2021) Pfam: The protein families database in 2021. Nucl. Acids Res. 49(D1), D412‒D419. https://doi.org/10.1093/nar/gkaa913
- Szklarczyk D., Gable A.L., Nastou K.C., Lyon D., Kirsch R., Pyysalo S., Doncheva N.T., Legeay M., Fang T., Bork P., Jensen L.J., von Mering C. (2021) The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucl. Acids Res. 49(D1), D605‒D612. https://doi.org/10.1093/nar/gkaa1074
- Kelley L.A., Mezulis S., Yates C.M., Wass M.N., Sternberg M.J. (2015) The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protoc. 10(6), 845‒858. https://doi.org/10.1038/nprot.2015.053
- Sayle R.A., Milner-White E.J. (1995) RASMOL: biomolecular graphics for all. Trends Biochem. Sci. 20(9), 374‒376. https://doi.org/10.1016/S0968-0004(00)89080-5
- Pettersen E.F., Goddard T.D., Huang C.C., Couch G.S., Greenblatt D.M., Meng E.C., Ferrin T.E. (2004) UCSF Chimera – a visualization system for exploratory research and analysis. J. Comput. Chem. 25(13), 1605‒1612. https://doi.org/10.1002/jcc.20084
- Jin P., Bulkley D., Guo Y., Zhang W., Guo Z., Huynh W., Wu S., Meltzer S., Cheng T., Jan L.Y., Jan Y.N., Cheng Y. (2017) Electron cryo-microscopy structure of the mechanotransduction channel NOMPC. Nature. 547(7661), 118‒122. https://doi.org/10.1038/nature22981
- Duan J., Li J., Zeng B., Chen G.L., Peng X., Zhang Y., Wang J., Clapham D.E., Li Z., Zhang J. (2018) Structure of the mouse TRPC4 ion channel. Nat. Commun. 9(1), 1‒10. https://doi.org/10.1038/s41467-018-05247-9
- Ray A., Lindahl E., Wallner B. (2012) Improved model quality assessment using ProQ2. BMC Bioinform. 13(1), 1‒12. https://doi.org/10.1186/1471-2105-13-224
- Russell S., Norvigb P. (2010) Intelligence Artificielle: Avec Plus de 500 Exercices. Pearson Education, France.
- Ward J.J., McGuffin L.J., Bryson K., Buxton B.F., Jones D.T. (2004) The DISOPRED server for the prediction of protein disorder. Bioinformatics. 20(13), 2138‒2139. https://doi.org/10.1093/bioinformatics/bth195
- Jones D.T., Cozzetto D. (2015) DISOPRED3: precise disordered region predictions with annotated protein-binding activity. Bioinformatics. 31(6), 857‒863. https://doi.org/10.1093/bioinformatics/btu744
- Perissinotti P.P., Martínez-Hernández E., Piedras-Rentería E.S. (2021) TRPC1/5-Cav3 complex mediates leptin-induced excitability in hypothalamic neurons. Front. Neurosci. 15, 679078. https://doi.org/10.3389/fnins.2021.679078
- Watson R.A. (2006) Compositional Evolution: The Impact of Sex, Symbiosis, and Modularity on the Gradualist Frame-work of Evolution. Vienna Series in Theoretical Biology: A Bradford Book. 344 p. ISBN-10: 9780262232432
- Oteiza P., Baldwin M.W. (2021) Evolution of sensory systems. Curr. Opin. Neurobiol. 71, 52‒59. https://doi.org/10.1016/j.conb.2021.08.005
- Li H. (2017) TRP channel classification. Adv. Exp. Med. Biol. 976, 1‒8. https://doi.org/10.1007/978-94-024-1088-4_1
- Hellmich U.A., Gaudet R. (2014) Structural biology of TRP channels. Handb. Exp. Pharmacol. 223, 963‒990. https://doi.org/10.1007/978-3-319-05161-1_10
- Venkatachalam K., Montell C. (2007) TRP channels. Annu. Rev. Biochem. 76, 387‒417. https://doi.org/10.1146/annurev.biochem.75.103004.142819
- Voets T. (2012) Quantifying and modeling the temperature-dependent gating of TRP channels. Rev. Physiol. Biochem. Pharmacol. 162, 91‒119. https://doi.org/10.1007/112_2011_5
- Coste B., Mathur J., Schmidt M., Earley T.J., Ranade S., Petrus M.J., Dubin A.E., Patapoutian A. (2010) Piezo1 and Piezo2 are essential components of distinct mechanically activated cation channels. Science. 330(6000), 55‒60. https://doi.org/10.1126/science.1193270
- Peng G., Shi X., Kadowaki T. (2015) Evolution of TRP channels inferred by their classification in diverse animal species. Mol. Phylogenet. Evol. 84, 145‒157. https://doi.org/10.1016/j.ympev.2014.06.016
- Voets T., Nilius B. (2003) TRPs make sense. J. Membr. Biol. 192(1), 1‒8. https://doi.org/10.1007/s00232-002-1059-8
- Voets T., Talavera K., Owsianik G., Nilius B. (2005) Sensing with TRP channels, Nat. Chem. Biol. 1(2), 85‒92. https://doi.org/10.1038/nchembio0705-85
- Kadowaki T. (2015) Evolutionary dynamics of metazoan TRP channels. Pflugers Arch. 467(10), 2043‒2053. https://doi.org/10.1007/s00424-015-1705-5