Spatial Reconstruction of TRPC-Mechanoreceptors of the Ctenophore Mnemiopsis leidyi A. Agassiz, 1865

封面

如何引用文章

全文:

开放存取 开放存取
受限制的访问 ##reader.subscriptionAccessGranted##
受限制的访问 订阅存取

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

  1. 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
  2. Cao E. (2020) Structural mechanisms of transient receptor potential ion channels. J. Gen. Physiol. 152(3), e201811998. https://doi.org/10.1085/jgp.201811998
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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.
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. Russell S., Norvigb P. (2010) Intelligence Artificielle: Avec Plus de 500 Exercices. Pearson Education, France.
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. Li H. (2017) TRP channel classification. Adv. Exp. Med. Biol. 976, 1‒8. https://doi.org/10.1007/978-94-024-1088-4_1
  33. 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
  34. Venkatachalam K., Montell C. (2007) TRP channels. Annu. Rev. Biochem. 76, 387‒417. https://doi.org/10.1146/annurev.biochem.75.103004.142819
  35. 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
  36. 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
  37. 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
  38. Voets T., Nilius B. (2003) TRPs make sense. J. Membr. Biol. 192(1), 1‒8. https://doi.org/10.1007/s00232-002-1059-8
  39. 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
  40. Kadowaki T. (2015) Evolutionary dynamics of metazoan TRP channels. Pflugers Arch. 467(10), 2043‒2053. https://doi.org/10.1007/s00424-015-1705-5

补充文件

附件文件
动作
1. JATS XML
2.

下载 (1MB)
3.

下载 (101KB)
4.

下载 (151KB)
5.

下载 (970KB)
6.

下载 (1MB)
7.

下载 (753KB)

版权所有 © А.В. Кузнецов, Д.Н. Втюрина, 2023

##common.cookie##