Tag: Cryoelectron microscopy

  • Sanes, J. R. & Lichtman, J. W. Development of the vertebrate neuromuscular junction. Annu. Rev. Neurosci. 22, 389–442 (1999).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Missias, A. C., Chu, G. C., Klocke, B. J., Sanes, J. R. & Merlie, J. P. Maturation of the acetylcholine receptor in skeletal muscle: regulation of the AChR γ-to-ε switch. Dev. Biol. 179, 223–238 (1996).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Mishina, M. et al. Molecular distinction between fetal and adult forms of muscle acetylcholine receptor. Nature 321, 406–411 (1986).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Sakmann, B. & Brenner, H. R. Change in synaptic channel gating during neuromuscular development. Nature 276, 401–402 (1978).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Hesselmans, L. F. G. M., Jennekens, F. G. I., Van Den Oord, C. J. M., Veldman, H. & Vincent, A. Development of innervation of skeletal muscle fibers in man: relation to acetylcholine receptors. Anatomical Rec. 236, 553–562 (1993).

    Article 
    CAS 

    Google Scholar
     

  • Liu, Y. et al. Essential roles of the acetylcholine receptor γ-subunit in neuromuscular synaptic patterning. Development 135, 1957–1967 (2008).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Jaramillo, F., Vicini, S. & Schuetze, S. M. Embryonic acetylcholine receptors guarantee spontaneous contractions in rat developing muscle. Nature 335, 66–68 (1988).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Kidokoro, Y. & Saito, M. Early cross-striation formation in twitching Xenopus myocytes in culture. Proc. Natl Acad. Sci. USA 85, 1978–1982 (1988).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, B. G. X. et al. Combination of agrin and laminin increase acetylcholine receptor clustering and enhance functional neuromuscular junction formation In vitro. Dev. Neurobiol. 76, 551–565 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Madhavan, R. & Peng, H. B. A synaptic balancing act: local and global signaling in the clustering of ACh receptors at vertebrate neuromuscular junctions. J. Neurocytol. 32, 685–696 (2003).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Cetin, H., Beeson, D., Vincent, A. & Webster, R. The structure, function, and physiology of the fetal and adult acetylcholine receptor in muscle. Front. Mol. Neurosci. 13, 581097 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rimer, M., Mathiesen, I., Lømo, T. & McMahan, U. J. γ-AChR/ε-AChR switch at agrin-induced postsynaptic-like apparatus in skeletal muscle. Mol. Cell. Neurosci. 9, 254–263 (1997).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Nayak, T. K., Chakraborty, S., Zheng, W. & Auerbach, A. Structural correlates of affinity in fetal versus adult endplate nicotinic receptors. Nat. Commun. 7, 11352 (2016).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Nayak, T. K. et al. Functional differences between neurotransmitter binding sites of muscle acetylcholine receptors. Proc. Natl Acad. Sci. USA 111, 17660–17665 (2014).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Nayak, T. K. & Auerbach, A. Asymmetric transmitter binding sites of fetal muscle acetylcholine receptors shape their synaptic response. Proc. Natl Acad. Sci. USA 110, 13654–13659 (2013).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bouzat, C., Bren, N. & Sine, S. M. Structural basis of the different gating kinetics of fetal and adult acetylcholine receptors. Neuron 13, 1395–1402 (1994).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Herlitze, S., Villarroel, A., Witzemann, V., Koenen, M. & Sakmann, B. Structural determinants of channel conductance in fetal and adult rat muscle acetylcholine receptors. J. Physiol. 492, 775–787 (1996).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rubin, L. L., Schuetze, S. M., Weill, C. L. & Fischbach, G. D. Regulation of acetylcholinesterase appearance at neuromuscular junctions in vitro. Nature 283, 264–267 (1980).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Lomo, T., Massoulie, J. & Vigny, M. Stimulation of denervated rat soleus muscle with fast and slow activity patterns induces different expression of acetylcholinesterase molecular forms. J. Neurosci. 5, 1180 (1985).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Takahashi, M. et al. Spontaneous muscle action potentials fail to develop without fetal‐type acetylcholine receptors. EMBO Rep. 3, 674–681-681 (2002).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cetin, H. et al. Rapsyn facilitates recovery from desensitization in fetal and adult acetylcholine receptors expressed in a muscle cell line. J. Physiol. 597, 3713–3725 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Matthews-Bellinger, J. & Salpeter, M. M. Distribution of acetylcholine receptors at frog neuromuscular junctions with a discussion of some physiological implications. J. Physiol. 279, 197–213 (1978).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Unwin, N. Acetylcholine receptor channel imaged in the open state. Nature 373, 37–43 (1995).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Rahman, M. M. et al. Structure of the native muscle-type nicotinic receptor and inhibition by snake venom toxins. Neuron 106, 952–962.e955 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Takai, T. et al. Cloning, sequencing and expression of cDNA for a novel subunit of acetylcholine receptor from calf muscle. Nature 315, 761–764 (1985).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Yang, K. et al. CHRNE compound heterozygous mutations in congenital myasthenic syndrome: a case report. Medicine 97, e0347 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rodríguez Cruz, P. M., Palace, J. & Beeson, D. The neuromuscular junction and wide heterogeneity of congenital myasthenic syndromes. Int. J. Mol. Sci. 19, 1677 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gilhus, N. E. in Handbook of Clinical Neurology Vol. 195 (ed. Younger, D. S.) 635–652 (Elsevier, 2023).

  • Rahman, M. M. et al. Structural mechanism of muscle nicotinic receptor desensitization and block by curare. Nat. Struct. Mol. Biol. 29, 386–394 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zarkadas, E. et al. Conformational transitions and ligand-binding to a muscle-type nicotinic acetylcholine receptor. Neuron 110, 1358–1370.e1355 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Lindstrom, J. M. & Lambert, E. H. Content of acetylcholine receptor and antibodies bound to receptor in myasthenia gravis, experimental autoimmune myasthenia gravis, and Eaton‐Lambert syndrome. Neurology 28, 130–130 (1978).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Einarson, B., Gullick, W., Conti-Tronconi, B., Ellisman, M. & Lindstrom, J. Subunit composition of bovine muscle acetylcholine receptor. Biochemistry 21, 5295–5302 (1982).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Nys, M. et al. The molecular mechanism of snake short-chain α-neurotoxin binding to muscle-type nicotinic acetylcholine receptors. Nat. Commun. 13, 4543 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Moore, M. A. & McCarthy, M. P. Snake venom toxins, unlike smaller antagonists, appear to stabilize a resting state conformation of the nicotinic acetylcholine receptor. Biochim. Biophys. Acta 1235, 336–342 (1995).

    Article 
    PubMed 

    Google Scholar
     

  • Polak, R. L., Sellin, L. C. & Thesleff, S. Acetylcholine content and release in denervated or botulinum poisoned rat skeletal muscle. J. Physiol. 319, 253–259 (1981).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rich, M. M. & Pinter, M. J. Sodium channel inactivation in an animal model of acute quadriplegic myopathy. Ann. Neurol. 50, 26–33 (2001).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Fucile, S., Sucapane, A., Grassi, F., Eusebi, F. & Engel, A. G. The human adult subtype ACh receptor channel has high Ca2+ permeability and predisposes to endplate Ca2+ overloading. J. Physiol. 573, 35–43 (2006).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ragozzino, D., Barabino, B., Fucile, S. & Eusebi, F. Ca2+ permeability of mouse and chick nicotinic acetylcholine receptors expressed in transiently transfected human cells. J. Physiol. 507, 749–758 (1998).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Imoto, K. et al. Rings of negatively charged amino acids determine the acetylcholine receptor channel conductance. Nature 335, 645–648 (1988).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Unwin, N. Refined structure of the nicotinic acetylcholine receptor at 4Å resolution. J. Mol. Biol. 346, 967–989 (2005).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Hansen, S. B., Wang, H.-L., Taylor, P. & Sine, S. M. An ion selectivity filter in the extracellular domain of Cys-loop receptors reveals determinants for ion conductance. J. Biol. Chem. 283, 36066–36070 (2008).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gharpure, A. et al. Agonist selectivity and ion permeation in the α3β4 ganglionic nicotinic receptor. Neuron 104, 501–511.e506 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hussy, N., Lukas, W. & Jones, K. A. Functional properties of a cloned 5-hydroxytryptamine ionotropic receptor subunit: comparison with native mouse receptors. J. Physiol. 481, 311–323 (1994).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Walsh, R. M. et al. Structural principles of distinct assemblies of the human α4β2 nicotinic receptor. Nature 557, 261–265 (2018).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tapia, L., Kuryatov, A. & Lindstrom, J. Ca2+ permeability of the (alpha4)3(beta2)2 stoichiometry greatly exceeds that of (alpha4)2(beta2)3 human acetylcholine receptors. Mol. Pharmacol. 71, 769–776 (2007).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Dwyer, T. M., Adams, D. J. & Hille, B. The permeability of the endplate channel to organic cations in frog muscle. J. Gen. Physiol. 75, 469–492 (1980).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Zhu, H. & Gouaux, E. Architecture and assembly mechanism of native glycine receptors. Nature 599, 513–517 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Liu, X. & Wang, W. Asymmetric gating of a human hetero-pentameric glycine receptor. Nat. Commun. 14, 6377 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Labarca, C. et al. Channel gating governed symmetrically by conserved leucine residues in the M2 domain of nicotinic receptors. Nature 376, 514–516 (1995).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Filatov, G. N. & White, M. M. The role of conserved leucines in the M2 domain of the acetylcholine receptor in channel gating. Mol. Pharmacol. 48, 379 (1995).

    CAS 
    PubMed 

    Google Scholar
     

  • Engel, A. G., Shen, X.-M., Selcen, D. & Sine, S. M. Congenital myasthenic syndromes: pathogenesis, diagnosis, and treatment. Lancet Neurol. 14, 420–434 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jahn, K. et al. Deactivation and desensitization of mouse embryonic- and adult-type nicotinic receptor channel currents. Neurosci. Lett. 307, 89–92 (2001).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Basak, S., Gicheru, Y., Rao, S., Sansom, M. S. P. & Chakrapani, S. Cryo-EM reveals two distinct serotonin-bound conformations of full-length 5-HT3A receptor. Nature 563, 270–274 (2018).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Noviello, C. M. et al. Structure and gating mechanism of the α7 nicotinic acetylcholine receptor. Cell 184, 2121–2134.e2113 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sine, S. M. et al. Naturally occurring mutations at the acetylcholine receptor binding site independently alter ACh binding and channel gating. J. Gen. Physiol. 120, 483–496 (2002).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sine, S. M. et al. Mechanistic diversity underlying fast channel congenital myasthenic syndromes. Ann. N.Y. Acad. Sci. 998, 128–137 (2003).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Shen, X.-M. et al. Mutations causing slow-channel myasthenia reveal that a valine ring in the channel pore of muscle AChR is optimized for stabilizing channel gating. Hum. Mutat. 37, 1051–1059 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ealing, J. et al. Mutations in congenital myasthenic syndromes reveal an ε subunit C-terminal cysteine, C470, crucial for maturation and surface expression of adult AChR. Hum. Mol. Genet. 11, 3087–3096 (2002).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • de la Rosa, G., Corrales-García, L. L., Rodriguez-Ruiz, X., López-Vera, E. & Corzo, G. Short-chain consensus alpha-neurotoxin: a synthetic 60-mer peptide with generic traits and enhanced immunogenic properties. Amino Acids 50, 885–895 (2018).

    Article 
    PubMed 

    Google Scholar
     

  • Punjani, A., Rubinstein, J. L., Fleet, D. J. & Brubaker, M. A. cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 14, 290–296 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Punjani, A., Zhang, H. & Fleet, D. J. Non-uniform refinement: adaptive regularization improves single-particle cryo-EM reconstruction. Nat. Methods 17, 1214–1221 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Sanchez-Garcia, R. et al. DeepEMhancer: a deep learning solution for cryo-EM volume post-processing. Commun. Biol. 4, 874 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kimanius, D., Dong, L., Sharov, G., Nakane, T. & Scheres, S. H. W. New tools for automated cryo-EM single-particle analysis in RELION-4.0. Biochem. J 478, 4169–4185 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Jamali, K. et al. Automated model building and protein identification in cryo-EM maps. Nature, https://doi.org/10.1038/s41586-024-07215-4 (2024).

  • Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr. D Biol. Crystallogr. 60, 2126–2132 (2004).

    Article 
    ADS 
    PubMed 

    Google Scholar
     

  • Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D Biol. Crystallogr. 66, 213–221 (2010).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chen, V. B. et al. MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr. D Biol. Crystallogr. 66, 12–21 (2010).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Pettersen, E. F. et al. UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Sievers, F. & Higgins, D. G. Clustal omega. Curr. Protoc. Bioinformatics 48, 3.13.11–13.13.16 (2014).

    Article 

    Google Scholar
     

  • Smart, O. S., Neduvelil, J. G., Wang, X., Wallace, B. A. & Sansom, M. S. P. HOLE: a program for the analysis of the pore dimensions of ion channel structural models. J. Mol. Graphics 14, 354–360 (1996).

    Article 
    CAS 

    Google Scholar
     

  • Pettersen, E. F. et al. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci. 30, 70–82 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Shevchenko, A., Wilm, M., Vorm, O. & Mann, M. Mass spectrometric sequencing of proteins from silver-stained polyacrylamide gels. Anal. Chem. 68, 850–858 (1996).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Morales-Perez, C. L., Noviello, C. M. & Hibbs, R. E. Manipulation of subunit stoichiometry in heteromeric membrane proteins. Structure 24, 797–805 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Basak, S. et al. Cryo-EM structure of 5-HT3A receptor in its resting conformation. Nat. Commun. 9, 514 (2018).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shen, X.-M. et al. Mutations causing congenital myasthenia reveal principal coupling pathway in the acetylcholine receptor ε-subunit. JCI Insight 3, e97826 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Engel, A. G. et al. New mutations in acetylcholine receptor subunit genes reveal heterogeneity in the slow-channel congenital myasthenic syndrome. Hum. Mol. Genet. 5, 1217–1227 (1996).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Fidzianska, A., Ryniewicz, B., Shen, X.-M. & Engel, A. G. IBM-type inclusions in a patient with slow-channel syndrome caused by a mutation in the AChR epsilon subunit. Neuromuscul. Disord. 15, 753–759 (2005).

    Article 
    PubMed 

    Google Scholar
     

[ad_2]

Source link

  • Propofol rescues voltage-dependent gating of HCN1 channel epilepsy mutants

    [ad_1]

  • Kaupp, U. B. & Seifert, R. Molecular diversity of pacemaker ion channels. Annu. Rev. Physiol. 63, 235–257 (2001).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • DiFrancesco, D. Pacemaker mechanisms in cardiac tissue. Annu. Rev. Physiol. 55, 455–472 (1993).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Biel, M., Wahl-Schott, C., Michalakis, S. & Zong, X. Hyperpolarization-activated cation channels: from genes to function. Physiol. Rev. 89, 847–885 (2009).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Tibbs, G. R. et al. An anchor-tether ‘hindered’ HCN1 inhibitor is antihyperalgesic in a rat spared nerve injury neuropathic pain model. Br. J. Anaesth. 131, 745–763 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Bleakley, L. E. et al. Cation leak underlies neuronal excitability in an HCN1 developmental and epileptic encephalopathy. Brain 144, 2060–2073 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lyashchenko, A. K., Redd, K. J., Yang, J. & Tibbs, G. R. Propofol inhibits HCN1 pacemaker channels by selective association with the closed states of the membrane embedded channel core. J. Physiol. 583, 37–56 (2007).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Poduri, A. HCN1 gain-of-function mutations—a new cause of epileptic encephalopathy. Epilepsy Curr. 14, 348–349 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Marini, C. et al. HCN1 mutation spectrum: from neonatal epileptic encephalopathy to benign generalized epilepsy and beyond. Brain 141, 3160–3178 (2018).

    Article 
    PubMed 

    Google Scholar
     

  • Lee, C. H. & MacKinnon, R. Structures of the human HCN1 hyperpolarization-activated channel. Cell 168, 111–120 e111 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lee, C. H. & MacKinnon, R. Voltage sensor movements during hyperpolarization in the HCN channel. Cell 179, 1582–1589 e1587 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mannikko, R., Elinder, F. & Larsson, H. P. Voltage-sensing mechanism is conserved among ion channels gated by opposite voltages. Nature 419, 837–841 (2002).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Vemana, S., Pandey, S. & Larsson, H. P. S4 movement in a mammalian HCN channel. J. Gen. Physiol. 123, 21–32 (2004).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wu, X., Cunningham, K. P., Ramentol, R., Perez, M. E. & Larsson, H. P. Similar voltage-sensor movement in spHCN channels can cause closing, opening, or inactivation. J. Gen. Physiol. 155, e202213170 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mandala, V. S. & MacKinnon, R. Voltage-sensor movements in the Eag Kv channel under an applied electric field. Proc. Natl Acad. Sci. USA 119, e2214151119 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Whicher, J. R. & MacKinnon, R. Structure of the voltage-gated K+ channel Eag1 reveals an alternative voltage sensing mechanism. Science 353, 664–669 (2016).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kim, D. M. & Nimigean, C. M. Voltage-gated potassium channels: a structural examination of selectivity and gating. Cold Spring Harb. Perspect. Biol. 8, a029231 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dai, G., Aman, T. K., DiMaio, F. & Zagotta, W. N. The HCN channel voltage sensor undergoes a large downward motion during hyperpolarization. Nat. Struct. Mol. Biol. 26, 686–694 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wu, X., Ramentol, R., Perez, M. E., Noskov, S. Y. & Larsson, H. P. A second S4 movement opens hyperpolarization-activated HCN channels. Proc. Natl Acad. Sci. USA 118, e2102036118 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lu, Z., Klem, A. M. & Ramu, Y. Coupling between voltage sensors and activation gate in voltage-gated K+ channels. J. Gen. Physiol. 120, 663–676 (2002).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Long, S. B., Campbell, E. B. & Mackinnon, R. Voltage sensor of Kv1.2: structural basis of electromechanical coupling. Science 309, 903–908 (2005).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Lorinczi, E. et al. Voltage-dependent gating of KCNH potassium channels lacking a covalent link between voltage-sensing and pore domains. Nat. Commun. 6, 6672 (2015).

    Article 
    ADS 
    MathSciNet 
    PubMed 

    Google Scholar
     

  • Fernandez-Marino, A. I., Harpole, T. J., Oelstrom, K., Delemotte, L. & Chanda, B. Gating interaction maps reveal a noncanonical electromechanical coupling mode in the Shaker K+ channel. Nat. Struct. Mol. Biol. 25, 320–326 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • de la Pena, P., Dominguez, P. & Barros, F. Gating mechanism of Kv11.1 (hERG) K+ channels without covalent connection between voltage sensor and pore domains. Pflugers Arch. 470, 517–536 (2018).

    Article 
    PubMed 

    Google Scholar
     

  • Flynn, G. E. & Zagotta, W. N. Insights into the molecular mechanism for hyperpolarization-dependent activation of HCN channels. Proc. Natl Acad. Sci. USA 115, E8086–E8095 (2018).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cowgill, J. & Chanda, B. Mapping electromechanical coupling pathways in voltage-gated ion channels: challenges and the way forward. J. Mol. Biol. 433, 167104 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rivolta, I., Binda, A., Masi, A. & DiFrancesco, J. C. Cardiac and neuronal HCN channelopathies. Pflugers Arch. 472, 931–951 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Butler, K. M., da Silva, C., Alexander, J. J., Hegde, M. & Escayg, A. Diagnostic yield from 339 epilepsy patients screened on a clinical gene panel. Pediatr. Neurol. 77, 61–66 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bleakley, L. E. & Reid, C. A. HCN1 epilepsy: from genetics and mechanisms to precision therapies. J. Neurochem. https://doi.org/10.1111/jnc.15928 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Thollon, C. et al. Use-dependent inhibition of hHCN4 by ivabradine and relationship with reduction in pacemaker activity. Br. J. Pharmacol. 150, 37–46 (2007).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Lu, X., Smaill, J. B. & Ding, K. New promise and opportunities for allosteric kinase inhibitors. Angew. Chem. Int. Ed. Engl. 59, 13764–13776 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Kim, E. D. et al. Allosteric drug discrimination is coupled to mechanochemical changes in the kinesin-5 motor core. J. Biol. Chem. 285, 18650–18661 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhu, S. et al. Structural and dynamic mechanisms of GABAA receptor modulators with opposing activities. Nat. Commun. 13, 4582 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ramirez, D., Zuniga, R., Concha, G. & Zuniga, L. HCN channels: new therapeutic targets for pain treatment. Molecules 23, 2094 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cacheaux, L. P. et al. Impairment of hyperpolarization-activated, cyclic nucleotide-gated channel function by the intravenous general anesthetic propofol. J. Pharmacol. Exp. Ther. 315, 517–525 (2005).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Saponaro, A. et al. Gating movements and ion permeation in HCN4 pacemaker channels. Mol. Cell 81, 2929–2943 e2926 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tanguay, J., Callahan, K. M. & D’Avanzo, N. Characterization of drug binding within the HCN1 channel pore. Sci. Rep. 9, 465 (2019).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Faulkner, C., Santos-Carballal, D., Plant, D. F. & de Leeuw, N. H. Atomistic molecular dynamics simulations of propofol and fentanyl in phosphatidylcholine lipid bilayers. ACS Omega 5, 14340–14353 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Joyce, R. L. et al. Alkylphenol inverse agonists of HCN1 gating: H-bond propensity, ring saturation and adduct geometry differentially determine efficacy and potency. Biochem. Pharmacol. 163, 493–508 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shintre, C. et al. Human hyperpolarization activated cyclic nucleotide gated ion channel 4. Zenodo https://doi.org/10.5281/zenodo.1434068 (2018).

  • Schmidpeter, P. A. M. et al. Anionic lipids unlock the gates of select ion channels in the pacemaker family. Nat. Struct. Mol. Biol. 29, 1092–1100 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hung, A. et al. Biophysical analysis of an HCN1 epilepsy variant suggests a critical role for S5 helix Met-305 in voltage sensor to pore domain coupling. Prog. Biophys. Mol. Biol. 166, 156–172 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Ludwig, A., Zong, X., Jeglitsch, M., Hofmann, F. & Biel, M. A family of hyperpolarization-activated mammalian cation channels. Nature 393, 587–591 (1998).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Decher, N., Chen, J. & Sanguinetti, M. C. Voltage-dependent gating of hyperpolarization-activated, cyclic nucleotide-gated pacemaker channels: molecular coupling between the S4-S5 and C-linkers. J. Biol. Chem. 279, 13859–13865 (2004).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Flynn, G. E. & Zagotta, W. N. Molecular mechanism underlying phosphatidylinositol 4,5-bisphosphate-induced inhibition of SpIH channels. J. Biol. Chem. 286, 15535–15542 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bruening-Wright, A., Elinder, F. & Larsson, H. P. Kinetic relationship between the voltage sensor and the activation gate in spHCN channels. J. Gen. Physiol. 130, 71–81 (2007).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ramentol, R., Perez, M. E. & Larsson, H. P. Gating mechanism of hyperpolarization-activated HCN pacemaker channels. Nat. Commun. 11, 1419 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Valley, C. C. et al. The methionine-aromatic motif plays a unique role in stabilizing protein structure. J. Biol. Chem. 287, 34979–34991 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ryu, S. & Yellen, G. Charge movement in gating-locked HCN channels reveals weak coupling of voltage sensors and gate. J. Gen. Physiol. 140, 469–479 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Porro, A. et al. Do the functional properties of HCN1 mutants correlate with the clinical features in epileptic patients? Prog. Biophys. Mol. Biol. 166, 147–155 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Boonsimma, P. et al. Exome sequencing as first-tier genetic testing in infantile-onset pharmacoresistant epilepsy: diagnostic yield and treatment impact. Eur. J. Hum. Genet. 31, 179–187 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Kim, J. J. et al. Shared structural mechanisms of general anaesthetics and benzodiazepines. Nature 585, 303–308 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zivanov, J., Nakane, T. & Scheres, S. H. W. Estimation of high-order aberrations and anisotropic magnification from cryo-EM data sets in RELION-3.1. IUCrJ 7, 253–267 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zivanov, J. et al. New tools for automated high-resolution cryo-EM structure determination in RELION-3. eLife 7, e42166 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kimanius, D., Dong, L., Sharov, G., Nakane, T. & Scheres, S. H. W. New tools for automated cryo-EM single-particle analysis in RELION-4.0. Biochem. J. 478, 4169–4185 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Zheng, S. Q. et al. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 14, 331–332 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wagner, T. et al. SPHIRE-crYOLO is a fast and accurate fully automated particle picker for cryo-EM. Commun. Biol. 2, 218 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sanchez-Garcia, R. et al. DeepEMhancer: a deep learning solution for cryo-EM volume post-processing. Commun. Biol. 4, 874 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Liebschner, D. et al. Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix. Acta Crystallogr. D Struct. Biol. 75, 861–877 (2019).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D Biol. Crystallogr. 66, 486–501 (2010).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Casanal, A., Lohkamp, B. & Emsley, P. Current developments in Coot for macromolecular model building of electron cryo-microscopy and crystallographic data. Protein Sci. 29, 1069–1078 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Croll, T. I. ISOLDE: a physically realistic environment for model building into low-resolution electron-density maps. Acta Crystallogr. D Struct. Biol. 74, 519–530 (2018).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pettersen, E. F. et al. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci. 30, 70–82 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Smart, O. S., Neduvelil, J. G., Wang, X., Wallace, B. A. & Sansom, M. S. HOLE: a program for the analysis of the pore dimensions of ion channel structural models. J. Mol. Graph. 14, 354–360 (1996).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Liu, Y. et al. CB-Dock2: improved protein–ligand blind docking by integrating cavity detection, docking and homologous template fitting. Nucleic Acids Res. 50, W159–W164 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, S., Xie, J., Pei, J. & Lai, L. CavityPlus 2022 update: an integrated platform for comprehensive protein cavity detection and property analyses with user-friendly tools and cavity databases. J. Mol. Biol. 435, 168141 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Maglic, J. B. & Lavendomme, R. MoloVol: an easy-to-use program for analyzing cavities, volumes and surface areas of chemical structures. J. Appl. Crystallogr. 55, 1033–1044 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Laskowski, R. A. & Swindells, M. B. LigPlot+: multiple ligand-protein interaction diagrams for drug discovery. J. Chem. Inf. Model. 51, 2778–2786 (2011).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Jubb, H. C. et al. Arpeggio: a web server for calculating and visualising interatomic interactions in protein structures. J. Mol. Biol. 429, 365–371 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Procter, J. B. et al. Alignment of biological sequences with Jalview. Methods Mol. Biol. 2231, 203–224 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Webb, B. & Sali, A. Comparative protein structure modeling using MODELLER. Curr. Protoc. Bioinformatics 54, 5.6.1–5.6.37 (2016).

    Article 
    PubMed 

    Google Scholar
     

  • Humphrey, W., Dalke, A. & Schulten, K. VMD: visual molecular dynamics. J. Mol. Graph. 14, 33–38 (1996).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Wu, E. L. et al. CHARMM-GUI Membrane Builder toward realistic biological membrane simulations. J. Comput. Chem. 35, 1997–2004 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kasimova, M. A. et al. Helix breaking transition in the S4 of HCN channel is critical for hyperpolarization-dependent gating. eLife 8, e53400 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Elbahnsi, A. et al. Interplay between VSD, pore, and membrane lipids in electromechanical coupling in HCN channels. eLife 12, e80303 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Best, R. B. et al. Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone ɸ, ψ and side-chain χ1 and χ2 dihedral angles. J. Chem. Theory Comput. 8, 3257–3273 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W. & Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79, 926–935 (1983).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Arcario, M. J., Mayne, C. G. & Tajkhorshid, E. Atomistic models of general anesthetics for use in in silico biological studies. J. Phys. Chem. B 118, 12075–12086 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Olsson, M. H., Sondergaard, C. R., Rostkowski, M. & Jensen, J. H. PROPKA3: consistent treatment of internal and surface residues in empirical pKa predictions. J. Chem. Theory Comput. 7, 525–537 (2011).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Abraham, M. J. et al. GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1-2, 19–25 (2015).

    Article 
    ADS 

    Google Scholar
     

  • Tribello, G. A., Bonomi, M., Branduardi, D., Camilloni, C. & Bussi, G. PLUMED 2: new feathers for an old bird. Comput. Phys. Commun. 185, 604–613 (2014).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Darden, T. A., York, D. M. & Pedersen, L. G. Particle mesh Ewald: an Nlog(N) method for Ewald sums in large systems. J. Chem. Phys. 98, 10089–10092 (1993).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Hess, B., Bekker, H., Berendsen, H. J. C. & Fraaije, J. G. E. M. LINCS: a linear constraint solver for molecular simulations. J. Comput. Chem. 18, 1463–1472 (1997).

  • Kim, E. D. et al. Propofol rescues voltage-dependent gating of HCN1 channel epilepsy mutants. Zenodo https://doi.org/10.5281/zenodo.11528212 (2024).

  • [ad_2]

    Source link

  • Molecular mechanism of ligand gating and opening of NMDA receptor

    [ad_1]

  • Hansen, K. B. et al. Structure, function, and pharmacology of glutamate receptor ion channels. Pharmacol. Rev. 73, 298–487 (2021).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mony, L. & Paoletti, P. Mechanisms of NMDA receptor regulation. Curr. Opin. Neurobiol. 83, 102815 (2023).

    CAS 
    PubMed 

    Google Scholar
     

  • Zhou, C. & Tajima, N. Structural insights into NMDA receptor pharmacology. Biochem. Soc. Trans. 51, 1713–1731 (2023).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mayer, M. L., Westbrook, G. L. & Guthrie, P. B. Voltage-dependent block by Mg2+ of NMDA responses in spinal cord neurones. Nature 309, 261–263 (1984).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Nowak, L., Bregestovski, P., Ascher, P., Herbet, A. & Prochiantz, A. Magnesium gates glutamate-activated channels in mouse central neurones. Nature 307, 462–465 (1984).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Jahr, C. E. & Stevens, C. F. Glutamate activates multiple single channel conductances in hippocampal neurons. Nature 325, 522–525 (1987).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • MacDermott, A. B., Mayer, M. L., Westbrook, G. L., Smith, S. J. & Barker, J. L. NMDA-receptor activation increases cytoplasmic calcium concentration in cultured spinal cord neurones. Nature 321, 519–522 (1986).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Kauer, J. A., Malenka, R. C. & Nicoll, R. A. NMDA application potentiates synaptic transmission in the hippocampus. Nature 334, 250–252 (1988).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Karakas, E. & Furukawa, H. Crystal structure of a heterotetrameric NMDA receptor ion channel. Science 344, 992–997 (2014).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lee, C. H. et al. NMDA receptor structures reveal subunit arrangement and pore architecture. Nature 511, 191–197 (2014).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Michalski, K. & Furukawa, H. Structure and function of GluN1-3A NMDA receptor excitatory glycine receptor channel. Sci. Adv. 10, eadl5952 (2024).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tajima, N. et al. Activation of NMDA receptors and the mechanism of inhibition by ifenprodil. Nature 534, 63–68 (2016).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jalali-Yazdi, F., Chowdhury, S., Yoshioka, C. & Gouaux, E. Mechanisms for zinc and proton inhibition of the GluN1/GluN2A NMDA receptor. Cell 175, 1520–1532 e1515 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lu, W., Du, J., Goehring, A. & Gouaux, E. Cryo-EM structures of the triheteromeric NMDA receptor and its allosteric modulation. Science 355, eaal3729 (2017).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhu, S. et al. Mechanism of NMDA receptor inhibition and activation. Cell 165, 704–714 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chou, T. H., Tajima, N., Romero-Hernandez, A. & Furukawa, H. Structural basis of functional transitions in mammalian NMDA receptors. Cell 182, 357–371 e313 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chou, T. H. et al. Structural insights into binding of therapeutic channel blockers in NMDA receptors. Nat. Struct. Mol. Biol. 29, 507–518 (2022).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, Y. et al. Structural basis of ketamine action on human NMDA receptors. Nature 596, 301–305 (2021).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Song, X. et al. Mechanism of NMDA receptor channel block by MK-801 and memantine. Nature 556, 515–519 (2018).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chou, T.-H., Kang, H., Simorowski, N., Traynelis, S. F. & Furukawa, H. Structural insights into assembly and function of GluN1-2C, GluN1-2A-2C, and GluN1-2D NMDARs. Mol. Cell 82, 4548–4563.e4544 (2022).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Furukawa, H., Singh, S. K., Mancusso, R. & Gouaux, E. Subunit arrangement and function in NMDA receptors. Nature 438, 185–192 (2005).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Jespersen, A., Tajima, N., Fernandez-Cuervo, G., Garnier-Amblard, E. C. & Furukawa, H. Structural insights into competitive antagonism in NMDA receptors. Neuron 81, 366–378 (2014).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Furukawa, H. & Gouaux, E. Mechanisms of activation, inhibition and specificity: crystal structures of the NMDA receptor NR1 ligand-binding core. EMBO J. 22, 2873–2885 (2003).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Esmenjaud, J. B. et al. An inter-dimer allosteric switch controls NMDA receptor activity. EMBO J. 38, e99894 (2019).

    PubMed 

    Google Scholar
     

  • Karakas, E., Simorowski, N. & Furukawa, H. Subunit arrangement and phenylethanolamine binding in GluN1/GluN2B NMDA receptors. Nature 475, 249–253 (2011).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Karakas, E., Simorowski, N. & Furukawa, H. Structure of the zinc-bound amino-terminal domain of the NMDA receptor NR2B subunit. EMBO J. 28, 3910–3920 (2009).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Regan, M. C. et al. Structural elements of a pH-sensitive inhibitor binding site in NMDA receptors. Nat. Commun. 10, 321 (2019).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Stroebel, D. et al. A novel binding mode reveals two distinct classes of NMDA receptor GluN2B-selective antagonists. Mol. Pharmacol. 89, 541–551 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kazi, R., Dai, J., Sweeney, C., Zhou, H. X. & Wollmuth, L. P. Mechanical coupling maintains the fidelity of NMDA receptor-mediated currents. Nat. Neurosci. 17, 914–922 (2014).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tajima, N. et al. Development and characterization of functional antibodies targeting NMDA receptors. Nat. Commun. 13, 923 (2022).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Romero-Hernandez, A., Simorowski, N., Karakas, E. & Furukawa, H. Molecular basis for subtype specificity and high-affinity zinc inhibition in the GluN1-GluN2A NMDA receptor amino-terminal domain. Neuron 92, 1324–1336 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Amin, J. B. et al. Two gates mediate NMDA receptor activity and are under subunit-specific regulation. Nat. Commun. 14, 1623 (2023).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jones, K. S., VanDongen, H. M. & VanDongen, A. M. The NMDA receptor M3 segment is a conserved transduction element coupling ligand binding to channel opening. J. Neurosci. 22, 2044–2053 (2002).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Twomey, E. C., Yelshanskaya, M. V., Grassucci, R. A., Frank, J. & Sobolevsky, A. I. Channel opening and gating mechanism in AMPA-subtype glutamate receptors. Nature 549, 60–65 (2017).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chen, S. et al. Activation and desensitization mechanism of AMPA receptor-TARP complex by cryo-EM. Cell 170, 1234–1246.e14 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Herguedas, B. et al. Architecture of the heteromeric GluA1/2 AMPA receptor in complex with the auxiliary subunit TARP gamma8. Science 364, eaav9011 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jin, R. et al. Crystal structure and association behaviour of the GluR2 amino-terminal domain. EMBO J. 28, 1812–1823 (2009).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sun, Y. et al. Mechanism of glutamate receptor desensitization. Nature 417, 245–253 (2002).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Mothet, J. P. et al. d-Serine is an endogenous ligand for the glycine site of the N-methyl-d-aspartate receptor. Proc. Natl Acad. Sci. USA 97, 4926–4931 (2000).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ribeiro, C. S., Reis, M., Panizzutti, R., de Miranda, J. & Wolosker, H. Glial transport of the neuromodulator d-serine. Brain Res. 929, 202–209 (2002).

    CAS 
    PubMed 

    Google Scholar
     

  • Fukasawa, Y. et al. Identification and characterization of a Na(+)-independent neutral amino acid transporter that associates with the 4F2 heavy chain and exhibits substrate selectivity for small neutral d– and l-amino acids. J. Biol. Chem. 275, 9690–9698 (2000).

    CAS 
    PubMed 

    Google Scholar
     

  • Hill, M. D. et al. SAGE-718: a first-in-class N-methyl-d-aspartate receptor positive allosteric modulator for the potential treatment of cognitive impairment. J. Med. Chem. 65, 9063–9075 (2022).

    CAS 
    PubMed 

    Google Scholar
     

  • Hanson, J. E. et al. Therapeutic potential of N-methyl-d-aspartate receptor modulators in psychiatry. Neuropsychopharmacology 49, 51–66 (2024).

    CAS 
    PubMed 

    Google Scholar
     

  • Furukawa, H., Simorowski, N. & Michalski, K. Effective production of oligomeric membrane proteins by EarlyBac-insect cell system. Methods Enzymol. 653, 3–19 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Regan, M. C. et al. Structural mechanism of functional modulation by gene splicing in NMDA receptors. Neuron 98, 521–529 e523 (2018).

    MathSciNet 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Punjani, A., Rubinstein, J. L., Fleet, D. J. & Brubaker, M. A. cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 14, 290–296 (2017).

    CAS 
    PubMed 

    Google Scholar
     

  • Pettersen, E. F. et al. UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).

    CAS 
    PubMed 

    Google Scholar
     

  • Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D 66, 486–501 (2010).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D 66, 213–221 (2010).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yuan, H., Erreger, K., Dravid, S. M. & Traynelis, S. F. Conserved structural and functional control of N-methyl-d-aspartate receptor gating by transmembrane domain M3. J. Biol. Chem. 280, 29708–29716 (2005).

    CAS 
    PubMed 

    Google Scholar
     

  • Eswar, N. et al. Comparative protein structure modeling using modeller. Curr. Protoc. Bioinformatics 15, 5.6.1–5.6.37 (2006).


    Google Scholar
     

  • Lindorff-Larsen, K. et al. Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins Struct. Funct. Bioinformatics 78, 1950–1958 (2010).

    CAS 

    Google Scholar
     

  • Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W. & Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79, 926–935 (1983).

    ADS 
    CAS 

    Google Scholar
     

  • Jämbeck, J. P. M. & Lyubartsev, A. P. Derivation and systematic validation of a refined all-atom force field for phosphatidylcholine lipids. J. Phys. Chem. B 116, 3164–3179 (2012).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Darden, T., York, D. & Pedersen, L. Particle mesh Ewald: an N·log(N) method for Ewald sums in large systems. J. Chem. Phys. 98, 10089–10092 (1993).

    ADS 
    CAS 

    Google Scholar
     

  • Hess, B., Bekker, H., Berendsen, H. J. C. & Fraaije, J. G. E. M. LINCS: a linear constraint solver for molecular simulations. J. Comput. Chem. 18, 1463–1472 (1997).

    CAS 

    Google Scholar
     

  • Nosé, S. A unified formulation of the constant temperature molecular dynamics methods. J. Chem. Phys. 81, 511–519 (1984).

    ADS 

    Google Scholar
     

  • Parrinello, M. & Rahman, A. Polymorphic transitions in single crystals: a new molecular dynamics method. J. Appl. Phys. 52, 7182–7190 (1981).

    ADS 
    CAS 

    Google Scholar
     

  • Kumar, S., Rosenberg, J. M., Bouzida, D., Swendsen, R. H. & Kollman, P. A. The weighted histogram analysis method for free-energy calculations on biomolecules. I. The method. J. Comput. Chem. 13, 1011–1021 (1992).

    CAS 

    Google Scholar
     

  • [ad_2]

    Source link

  • Author Correction: Extensive halogen-mediated ozone destruction over the tropical Atlantic Ocean

    [ad_1]

  • Department of Chemistry, University of York, Heslington, York, UK

    Katie A. Read, Lucy J. Carpenter & Sarah J. Moller

  • School of Chemistry, University of Leeds, Leeds, UK

    Anoop S. Mahajan, Dwayne E. Heard, Hilke Oetjen, Michael J. Pilling & John M. C. Plane

  • School of Earth and the Environment (SEE), University of Leeds, Leeds, UK

    Mathew J. Evans & James B. McQuaid

  • Instituto Nacional de Meteorologia Geofísica (INMG), Delegação de São Vicente, Monte, Mindelo, Cape Verde

    Bruno V. E. Faria & Luis Mendes

  • National Centre for Atmospheric Science (NCAS), University of York, Heslington, York, UK

    James R. Hopkins, James D. Lee & Alastair C. Lewis

  • Earth and Space Science Division, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA

    Alfonso Saiz-Lopez

  • [ad_2]

    Source link

  • Structural basis for pegRNA-guided reverse transcription by a prime editor

    [ad_1]

    Sample preparation

    The PE2 (nSpCas9–engineered M-MLV RT), Cas9 (H840A) and M-MLV RTΔRNaseH (D200N/T306K/W313F/T330P) genes were PCR-amplified from pCMV-PE2 (Addgene plasmid 132775) and assembled separately into pET-based expression vectors with an N-terminal His6-SUMO-tag. The PE6a–d expression plasmids were constructed by replacing the RT gene in the PE2 expression plasmid with the synthesized PE6 RT genes (Eurofins Genomics), respectively. Mutations were introduced by a PCR-based method, and sequences were confirmed by DNA sequencing (Supplementary Table 3). After the plasmids were transformed into Escherichia coli Rosetta 2 (DE3), the E. coli cells were cultured at 37 °C until the optical density at 600 nm (OD600 nm) reached 0.8, and protein expression was induced at 20 °C for 18–20 h by the addition of 1 mM isopropyl-β-d-thiogalactopyranoside (Nacalai Tesque). The E. coli cells were collected by centrifugation, lysed by sonication in buffer A (20 mM Tris-HCl, pH 8.0, 1 M NaCl and 20 mM imidazole), and clarified by centrifugation. The clarified lysate was incubated with Ni-NTA Superflow resin (Qiagen) at 4 °C for 1 h and loaded into an Econo-Column (Bio-Rad). After the resin was washed with buffer A and buffer B (20 mM Tris-HCl, pH 8.0, 300 mM NaCl and 20 mM imidazole), the protein was eluted with buffer C (20 mM Tris-HCl, pH 8.0, 300 mM NaCl and 300 mM imidazole). The eluted protein was incubated with SUMO protease (produced in-house) at 4 °C overnight, and then loaded onto a HiTrap Heparin column (GE Healthcare) equilibrated with buffer D (20 mM Tris-HCl, pH 8.0 and 300 mM NaCl). The bound protein was eluted with a linear gradient of 0.3–2 M NaCl and further purified on a HiLoad 16/600 Superdex 200 pg column (GE Healthcare) equilibrated with buffer E (20 mM Tris-HCl, pH 8.0, 500 mM NaCl, 2 mM MgCl2 and 1 mM DTT). The peak fractions were collected and stored at −80 °C until use.

    pegRNA preparation

    Templates for in vitro transcription were prepared by annealing a forward T7 promoter oligonucleotide with an oligonucleotide containing the reverse complement of the T7 promoter and a pegRNA sequence (Supplementary Table 3). The in vitro transcription reaction was performed at 37 °C overnight, in 50 mM Tris-HCl, pH 8.0, 40 mM KCl, 20 mM MgCl2, 5 mM each NTP, 10 mM GMP, 5 mM DTT, 2 mM spermidine, 1 U ml−1 inorganic pyrophosphatase (Sigma), 80 µg ml−1 T7 RNA polymerase (produced in-house) and 20 nM template. The transcribed pegRNA was purified by 8% denaturing urea-PAGE, extracted from gel slices with Tris borate–EDTA buffer (Takara) and then ethanol precipitated. The pegRNA pellet was dissolved in nuclease-free water and stored at −20 °C.

    In vitro prime editing assay

    All in vitro prime editing reactions were performed using 5′-Cy5-labelled pre-nicked DNA substrates. These DNA substrates were annealed with three oligonucleotides (5′-Cy5-NTS, NTS-3′ and TS; 1:1:1 molar ratio for Fig. 1c and 1:1.5:1 molar ratio for the other experiments) (Supplementary Table 1) by heating to 95 °C for 2 min followed by slowly cooling to room temperature. For the pegRNA-MM, TS-MM was used in place of TS. When using untethered PE in place of PE2 in the reaction, purified dSpCas9 and purified RTΔRH were mixed at a molar ratio of 1:1 and handled like PE2 in the subsequent steps. The PE2–pegRNA complex (1.6 μM or 3.0 μM) was prepared by mixing the purified PE2 and pegRNA at 37 °C for 3 min. The binary complex (5 μl) was mixed with the 5′-Cy5-labelled pre-nicked DNA substrate (5 μl, 200 nM final concentration) and incubated at 37 °C for 10 min in PE reaction buffer (20 mM HEPES-NaOH, pH 7.5, 100 mM NaCl, 5% glycerol, 3 mM MgCl2, 0.2 mM EDTA and 5 mM DTT) supplemented with 250 μM each dNTP or U-Stall Solution (250 μM ddATP, 250 μM dTTP, 250 μM dGTP and 250 μM dCTP). The reaction was stopped by the addition of quench buffer containing EDTA (0.5 mM final concentration) and Proteinase K (60 ng). Aliquots (2 μl) were mixed with quench buffer (3 μl), and the reaction products were separated on 10% or 15% Novex PAGE TBE–urea gels (Invitrogen) and then visualized using an Amersham Imager 600 (GE Healthcare). The reverse transcription efficiencies of each group were calculated using Image J (ref. 23). In vitro prime editing experiments were performed at least three times.

    Cryo-EM sample preparation

    The 51-nt pre-nicked DNA substrates for cryo-EM samples were prepared by annealing three nucleotides (5′-NTS+3nt, NTS-3′ and TS-MM; 1:1:1 molar ratio). For the pre-initiation and initiation complexes, 5′-NTS was used in place of 5′-NTS+3nt (Supplementary Table 2). The dSpCas9–RTΔRH–pegRNA–target DNA complexes were reconstituted by incubating the purified dSpCas9, RTΔRH, the 115-nt or 137-nt pegRNA-MM and the 51-nt pre-nicked DNA substrate at a molar ratio of 6:6:8:3 at 37 °C for 30 min in PE reconstitution buffer (20 mM HEPES-NaOH, pH 7.5, 100 mM NaCl, 2.5% glycerol and 2 mM MgCl2), supplemented with 250 μM ddATP (for the initiation state) or U-Stall Solution (for the other states). The dSpCas9–pegRNA–target DNA complex (the pre-initiation complex) was reconstituted similarly without RTΔRH. The reconstituted complexes were purified by size-exclusion chromatography on a Superdex 200 Increase 10/300 column (GE Healthcare) equilibrated with buffer F (20 mM Tris-HCl, pH 8.0, 150 mM NaCl, 2 mM MgCl2 and 1 mM DTT). The purified complex solution (A260 = 4.6–11) was applied to Au 300 mesh R1.2/1.3 grids (Quantifoil), which were freshly glow-discharged with 3 μl amylamine, using a Vitrobot Mark IV (FEI) at 4 °C and 100% humidity, with a waiting time of 10 s and a blotting time of 4 s. The grids were then plunge-frozen in liquid ethane cooled at liquid nitrogen temperature.

    Cryo-EM data collection

    Cryo-EM data for the initiation, pre-initiation and elongation (28-nt) complexes were collected using a Titan Krios G3i microscope (Thermo Fisher Scientific) and for the other complexes using a Titan Krios G4 microscope (Thermo Fisher Scientific), both running at 300 kV and equipped with a Gatan Quantum-LS Energy Filter (GIF) and a Gatan K3 Summit direct electron detector in electron counting mode (University of Tokyo). All movies were recorded at a nominal magnification of 105,000×, corresponding to a calibrated pixel size of 0.83 Å, with a total dose of approximately 50 electrons per Å2 per 48 frames. The data were automatically acquired using the EPU software (Thermo Fisher Scientific). The dose-fractionated movies of the pre-initiation and elongation (28-nt) complexes were subjected to beam-induced motion correction and dose weighting using MotionCor2 (ref. 24) in RELION v.3.1.1 (ref. 25); those of the termination and initiation complexes were processed using patch motion correction in cryoSPARC v.3.3.2 (ref. 26); and those of the elongation complex (16-nt) were handled using patch motion correction in cryoSPARC v.4.2.1. The contrast transfer function (CTF) parameters for the termination and initiation complexes, the pre-initiation and elongation (16-nt) complexes and the elongation (28-nt) complex were estimated using patch-based CTF estimation in cryoSPARC versions 3.3.2, 4.2.1 and v4.4, respectively.

    Single-particle cryo-EM data processing

    Data for the termination and initiation complexes were processed using cryoSPARC v3.3.2 and v4.2.1. Data for the pre-initiation and elongation (16-nt) complexes and the elongation (28-nt) complex were processed using cryoSPARC v4.2.1 and v4.4, respectively. All reported resolutions are based on the gold-standard Fourier shell correlation with a cut-off of 0.14327, and the local resolution was estimated with BlocRes28 in cryoSPARC.

    For the termination complex, 1,112,419 particles were selected using a Topaz picking model from the 4,363 motion-corrected and dose-weighted micrographs, and extracted at a pixel size of 3.32 Å. These particles were subjected to two rounds of two-dimensional (2D) classification to separate 671,078 promising particles from junk particles. Then, 500,000 particles were randomly selected from each particle set, and subsequently used for ab initio reconstruction to generate good initial and junk maps. All of the extracted particles were further curated by three rounds of heterogeneous refinement with two good initial and two junk maps, while updating the two good reference maps. The 248,187 particles in the best class were re-extracted at a pixel size of 1.30 Å and subsequently refined using non-uniform refinement29 with optimization of the CTF value, resulting in the 3.00-Å overall map. Particle subtraction was performed on the refined particles using a mask around the Cas9–pegRNA scaffold region, and the signal-subtracted particles were used for local refinement (rotation search extent 5 deg, shift search extent 2 Å, initial lowpass resolution 8 Å) with a local mask around the RTΔRH, resulting in the 3.48-Å local map. Finally, the overall and local maps were merged into the final composite map, using the vop maximum command in UCSF ChimeraX30.

    For the initiation complex, 2,532,892 particles were chosen using a Topaz picking model from the 5,266 motion-corrected and dose-weighted micrographs, and extracted at a pixel size of 3.32 Å, as described above. These particles were subjected to two rounds of 2D classification to select 1,607,568 promising particles, which were further curated through three rounds of heterogeneous refinement, as described above. The 656,084 particles in the best class were re-extracted at a pixel size of 1.30 Å and then subjected to 3D classification (five classes, target resolution = 4 Å, PCA initialization mode) with a focus mask around the RTΔRH. The 118,125 particles in the best class were refined using non-uniform refinement, resulting in the 3.12-Å overall map. To further improve the local resolution around the RTΔRH, particle subtraction and local refinement were performed as described above, resulting in the 4.10-Å local map around the RTΔRH. Finally, the overall and local maps were merged into the final composite map, using the vop maximum command in UCSF ChimeraX.

    For the pre-initiation complex, 3,357,907 particles were selected using a Topaz picking model from the 8,154 motion-corrected and dose-weighted micrographs, and extracted at a pixel size of 3.32 Å. These particles were subjected to two rounds of 2D classification to select 1,382,881 promising particles, which were further curated through three rounds of heterogeneous refinement in a similar manner to the procedure used for the termination complex. The 976,259 particles in the good classes were re-extracted with a pixel size of 1.30 Å and subsequently refined using non-uniform refinement, resulting in a 3.11-Å map, in which the density for the PBS–NTS heteroduplex was, however, unresolved. Therefore, the aligned particles were subjected to 3D classification (five classes, target resolution = 5 Å, PCA initialization mode) with a focus mask around the position of the PBS–NTS heteroduplex in the initiation complex. The 197,777 particles in the best class were refined using non-uniform refinement with optimization of the CTF value, resulting in the final 3.22-Å overall map.

    For the elongation complex (16-nt), 3,208,543 particles were chosen using a Topaz picking model from the 7,932 motion-corrected and dose-weighted micrographs, and extracted at a pixel size of 3.32 Å. These particles were subjected to two rounds of 2D classification to select 2,262,020 promising particles, which were further curated through three rounds of heterogeneous refinement in a similar manner to the procedure used for the termination complex. The 924,985 particles in the two good classes were re-extracted at a pixel size of 1.30 Å and subjected to 3D classification (four classes, target resolution = 6 Å, PCA initialization mode) with a focus mask around the RTΔRH. The 133,711 particles in the best class were then refined with a manually generated solvent mask just before non-uniform refinement with optimization of the CTF value, resulting in the 3.10-Å overall map. To further improve the local resolution around the RTΔRH, particle subtraction and local refinement were performed as described for the termination complex, resulting in the 6.06-Å local map around the RTΔRH. Finally, the overall and local maps were merged into the final composite map, using the vop maximum command in UCSF ChimeraX.

    For the elongation complex (28-nt), 4,851,974 particles were selected using a Topaz picking model from the 9,872 motion-corrected and dose-weighted micrographs, and extracted at a pixel size of 3.32 Å. These particles were subjected to two rounds of 2D classification to select 2,800,847 promising particles, which were further curated through three rounds of heterogeneous refinement in a similar manner to the procedure used for the termination complex. The 702,552 particles in the best class were re-extracted at a pixel size of 1.15 Å and subjected to 3D classification (six classes, target resolution = 4 Å, PCA initialization mode) with a focus mask around the RTΔRH and the RNA–DNA heteroduplex along with Cas9. The 104,057 particles in the best class were refined using non-uniform refinement with optimization of the CTF value, resulting in the final 3.19-Å overall map. To further improve the local resolution around the RTΔRH, particle subtraction and local refinement were performed as described for the termination complex, resulting in the 4.54-Å local map around the RTΔRH. Finally, the overall and local maps were merged into the final composite map, using the vop maximum command in UCSF ChimeraX.

    Model building and validation

    The model of the termination complex was built using the cryo-EM structure of the SpCas9–sgRNA–target DNA complex in the checkpoint state (PDB 7Z4L; ref. 12) and the crystal structure of apo-M-MLV RT (PDB 4MH8; ref. 17) as the reference models, followed by manual model building using Coot (ref. 31) against the final density map sharpened using DeepEMhancer. The models of the other complexes were built using the model of the termination complex as the reference, followed by manual model building using Coot and ISOLDE (ref. 32) against the final density map sharpened using DeepEMhancer or local-resolution filtering in cryoSPARC. All models were refined using phenix.real_space_refine v.1.20.1 (ref. 33) with secondary structure and base pair restraints. The structure validation was performed using MolProbity in the PHENIX package34. The EMRinger score35 and 3DFSC sphericity36 were calculated by PHENIX and by the 3DFSC Processing Server (https://3dfsc.salk.edu/upload/info/), respectively. The statistics of the 3D reconstruction and model refinement are summarized in Extended Data Table 1. The cryo-EM density map figures were generated using UCSF ChimeraX. Molecular graphics figures were prepared using UCSF ChimeraX and CueMol (http://www.cuemol.org).

    Mammalian prime editing assay

    HEK293FT cells were purchased from Thermo Fisher Scientific (R70007) and maintained in DMEM-GlutaMAX (Thermo Fisher Scientific, 10569044) with 1× penicillin–streptomycin (Thermo Fisher Scientific, 15140122) and 10% FBS (VWR, 97068-085) at 37 °C with 5% CO2. The cells were seeded at a density of 2 × 104 cells per well in 96-well plates for transfection. Transfections were performed using Lipofectamine 3000 (Thermo Fisher Scientific, L3000015) when cells reached around 90% confluency. In total, 200 ng plasmids, including 150 ng PE plasmid with 50 ng pegRNA plasmid for PE2, or 135 ng PE plasmid and 50 ng pegRNA with 15 ng sgRNA plasmid for PE3, were transfected into each well. Three wells were transfected for each condition. Three days after transfection, genomic DNA was extracted using 50 µl QuickExtract DNA extraction solution (Lucigen, QE09050) by cycling at 65 °C for 15 min, 68 °C for 15 min and 95 °C for 10 min. Two rounds of PCR were conducted to amplify target sites with NEBNext High-Fidelity 2× PCR Master Mix (NEB, M0541L). For the first round of PCR, 2.5 µl of cell lysate was used as the template in 10-µl PCR reactions under the following thermal cycling conditions: one cycle, 98 °C, 30 s; 12 cycles, 98 °C, 10 s, 69 °C, 20 s, 72 °C, 30 s; one cycle, 72 °C, 2 min; 4 °C hold. For the second round of PCR, 1 µl of PCR product from the first round was used as the template in 10-µl PCR reactions under the following thermal cycling conditions: one cycle, 98 °C, 30 s; 18 cycles, 98 °C, 10 s, 63 °C, 20 s, 72 °C, 30 s; one cycle, 72 °C, 5 min; 4 °C hold. All amplicons were sequenced using a MiSeq Reagent Kit v.2, 300-cycle (Illumina, MS-102-2002). The prime editing efficiency was quantified using the published CRISPResso2 pipeline37.

    Reporting summary

    Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

    [ad_2]

    Source link

  • High-resolution in situ structures of mammalian respiratory supercomplexes

    [ad_1]

    Preparation of porcine mitochondria and cryo-EM grids

    Mitochondria were isolated from porcine hearts following a modified version of a protocol originally described by A. L. Smith57. Before mitochondrial extraction, the pig hearts were subjected to three distinct treatment conditions: (1) fresh—immediately placed on ice for all subsequent procedures; (2) mild—incubated at room temperature for 40 min and put on ice to quickly cool down for isolation; and (3) harsh—incubated at room temperature for more than 4 h before being cooled on ice. The isolated mitochondria were then resuspended in a solution containing 0.25 M sucrose, 10 mM Tris-buffered with H2SO4 and 0.2 mM EDTA at pH 7.8. The suspension was adjusted to achieve a final optical density at 600 nm of 1.3 absorbance units.

    For cryo-EM grid preparation, 3.3 μl of the mitochondrial suspension was applied to each Quantifoil holey carbon grid (R2/1, 300 mesh gold). Grids were incubated for 5 s in a Vitrobot Mark IV (Thermo Fisher Scientific) chamber maintained at 8 °C and 95% relative humidity. Excess solution was blotted using standard Vitrobot filter paper before the grids were rapidly plunged into liquid ethane at a temperature of approximately −170 °C.

    Cryo-ET data collection

    Grids were initially screened for optimal ice conditions using a 200 kV Glacios microscope (Thermo Fisher Scientific) at the Yale Science Hill Electron Microscopy Facility. Selected grids were subsequently transferred to a 300 kV Titan Krios microscope (Thermo Fisher Scientific), equipped with a Bioquantum Energy Filter and a K3 direct electron detector (Gatan), for high-resolution data acquisition at the Yale West Campus Electron Microscopy Facility. Automated data collection was facilitated using SerialEM software58 and Gatan DigitalMicrograph. All images were captured in superresolution mode, with a physical pixel size of 6.1 Å (effectively 3.05 Å in superresolution). A total of eight tilt series were collected, targeting a relatively high defocus range, from −6 µm to −10 µm, for better contrast to guarantee a more reliable initial reconstruction. A grouped dose-symmetric scheme, spanning from −60° to 60° at 2° increments, was used for tilt series acquisition, with an accumulated dose of 100 e2.

    Cryo-ET reconstruction and subtomogram averaging

    Tomogram reconstruction was streamlined using custom scripts. Initial frame alignment was performed using MotionCorr2 (ref. 59), followed by micrograph binning at a factor of two. Tilt series stacks were generated using in-house scripts. All tilt series were aligned and reconstructed using AreTomo 1.2.5 (ref. 60). Initial contrast transfer function (CTF) parameters were estimated with GCTF61 and cryoSPARC62. Raw micrographs and reconstructed results were visualized and diagnosed using IMOD63 and ChimeraX64.

    Individual SC particles were picked in EMAN2 (ref. 65). Metadata preparation yielded 12,000 subtomogram particles in RELION-4.0 (ref. 66) with a binning factor of 2 (pixel size 12.2 Å). Following two rounds of 3D classification, 806 SC particles were selected for final refinement, resulting in a 37 Å subtomogram averaging map. Resolution was assessed using Fourier shell correlation with a threshold of 0.143 in RELION-4.0 (ref. 66). The averaged map was backprojected onto the original tomogram using the subtomo2Chimera code, available at https://github.com/builab/subtomo2Chimera.

    Single-particle cryo-EM data collection

    Automated data acquisition was performed using either a Glacios or a Titan Krios electron microscope (Thermo Fisher Scientific). The Glacios was equipped with a K3 direct electron detector (Gatan) and operated at 200 kV at a pixel size of 0.434 Å in superresolution mode, with an objective aperture of 100 μm. The Titan Krios, also equipped with a K3 direct electron detector, was operated at 300 kV at a pixel size of 0.416 Å in superresolution mode with a Gatan energy filter. Automatic data collection was facilitated using the SerialEM software package58. Multishot acquisition parameters were set at 3 × 3 holes per imaging location, with four exposures per hole at 200 kV and five exposures per hole at 300 kV. The total electron dose was fractionated to 42 e2 for the Glacios and 50 e2 for the Titan Krios, distributed across 45 frames at 40 ms per frame. Defocus parameters ranged from −1.0 μm to −3.0 μm for the 200 kV dataset and from −1.3 μm to −3.0 μm for the 300 kV datasets. Details of the data collection are summarized in Supplementary Tables 1–6.

    Preprocessing

    For all datasets, motion correction was performed using MotionCor2 (ref. 59) or cryoSPARC62. The CTF of each motion-corrected micrograph was estimated using Gctf61 or cryoSPARC62. Particles were picked with Gautomatch or cryoSPARC using an iterative sorting strategy as described below. Cryo-EM scripts used for real-time data transfer and on-the-fly preprocessing can be downloaded from https://github.com/JackZhang-Lab.

    Overall particle selection and sorting strategy

    Owing to the challenges posed by low signal-to-noise ratios and a highly congested macromolecular environment (Extended Data Fig. 1a), traditional particle selection methodologies were insufficient for generating datasets amenable to reliable two-dimensional (2D) classification, ab initio three-dimensional (3D) reconstruction and subsequent local refinement. To address this issue, we implemented an iterative strategy to optimize particle selection and sorting. The approach involved several rounds of iterative 2D particle picking, 2D classification and 3D analyses including ab initio 3D reconstruction, 3D classification and multilevel local refinement. Unlike the conventional particle selection approach, our strategy used Gautomatch and cryoSPARC62 for template matching to gradually increase the resolution of 3D projections as the reconstructions were progressively improved over cycles. We used several independent sources of references to cross-validate the final results. To maximize the yield of high-quality particles, particles from the classes that show clear features of SCs in all cycles were merged for subsequent 3D cross-classification. More details of the strategy are explained in the following sections.

    Initial 3D reconstruction with surrounding membranes (type A)

    Conventional 2D classification failed to generate meaningful class averages using images selected from our in situ cryo-EM micrographs of mitochondria for three main reasons: (1) thick samples that led to low signal-to-noise ratios and large defocus variations, (2) a crowded environment that affected particle detection and alignment, and (3) strong membrane signals that dominated the alignment, leading to blurred averages of protein regions (Extended Data Fig. 1b).

    To address this, we initially used the strong membrane signals and focused on the side views surrounded by membranes using 2D classification. These side views in principle contained sufficient orientational information for a complete 3D reconstruction. At the outset, protein signals were completely averaged out in the 2D classification, whereas the membranes were well aligned owing to the strong side-view signals (Extended Data Fig. 1c). We then conducted several cycles of 2D classification to focus only on particles exhibiting clear membrane signals.

    Through comprehensive 2D analyses, we found that regions potentially harbouring mitochondrial SCs exhibited special features of local curvature. Specifically, these regions were characterized by membrane signals that seemed to be concave towards the matrix direction, indicative of the presence of CIII2 (Extended Data Fig. 1c–e). By merging particles from classes with characteristic concave membranes surrounding CIII2 and conducting further 2D classification, we achieved improved 2D averages showing clear membrane features around CIII2 (Extended Data Fig. 1d). Notably, extra protein densities adjacent to CIII2 were obvious, probably representing CI or CIV densities. However, it was unclear how many types of respiratory SC exist in native mitochondria and whether CI, CIII2 and CIV always appear in the form of SCs or just partially.

    To further address these observations and obtain unbiased density maps, we used four independent methods to generate initial references: (1) cryo-ET subvolume averaging (Supplementary Fig. 1), (2) ab initio reconstruction using particles assigned to the 2D averages with visible protein densities (Extended Data Fig. 1f) and characteristic CIII2 membrane features (Extended Data Fig. 1g), (3) ab initio 3D reconstruction using particles after membrane signal subtraction (Extended Data Fig. 1h), and (4) models generated from random selection of unsorted particles or random noise (Extended Data Fig. 1i). All these references were combined for 3D classification and subsequently used for local refinement and focused classification (Extended Data Fig. 1i). Given that in situ cryo-EM datasets are more heterogeneous than conventional single-particle datasets, we included ‘false references’ generated from approach (4) for better classification. Finally, particles corresponding to classes showing clear features of type-A SC were re-extracted and merged for further classification and refinement (Extended Data Fig. 1i).

    Cross-classification of multiple SCs

    Around the reconstructed SC I1III2IV1 (type A) map, we observed extra densities, clearly indicating that more proteins bound to the type-A SC to form larger SCs. We suspected that more types of SC existed in native mitochondrial membranes. Preliminary results from both large single-particle 3D classification at low resolutions and cryo-ET subvolume averaging confirmed this speculation. To further improve the accuracy of 3D classification for high-resolution refinement, we deliberately provided extra false references generated from random subdatasets using discarded particles from previous cycles. These false references served to randomly absorb low-quality and falsely picked particles, leading to a relatively clean dataset for the target class. We then accumulated particles classified into good classes, defined by clear secondary structures, over several cycles. Owing to the crowded mitochondrial environment, misclassified and misaligned particles were always present. To address this, we reorganized the particles by merging those that fell into classes generating similar 3D maps. We selected multiple references from different classes, including those considered ‘bad’ and reperformed 3D classification on each subdataset. Afterwards, we recombined all subsets of different classes that were considered ‘good’ and reclassified them. On the basis of these results, we then merged all the particles belonging to a specific target from previous cycles and performed a further cycle of 3D classification on the merged dataset. This further classification used high-resolution references generated from previous classification cycles and local refinement to discard low-quality or misclassified particles.

    After numerous rounds of cross-classification followed by local refinement, we identified various other types of SC, including the three other main classes: type B (I1III2IV2), type O (I2III2IV2) and type X (I2III4IV2). In addition to the four main classes, other classes such as I1III2, I4III4IV4 and even higher-order assemblies were observed; however, they were not subject to further refinement in this study owing to the low population. Subsequently, we cross-validated our classification results by providing a set of references lacking the correct form of the SC for subclassification of each class. We also performed further reference-free 2D classification after 3D classification and refinement to verify different forms of SC. This allowed us to visualize the distinct features of the four main classes from 2D averages directly, without imposing any references. Only datasets converging to the correct form of supercomplex, regardless of the initial references used, were included in the final multilevel local refinement and focused 3D classification.

    Multilevel local refinement and focused 3D classification

    A hierarchical masking strategy was used for local refinement on all four main types of SC. Specifically, the mask size was incrementally reduced to focus on distinct regions of each type of respiratory SC, ensuring stable local refinement. We partitioned the type-A SC into five principal domains: (1) CI hydrophilic region, (2) CI hydrophobic region, (3) CIII2, (4) CIV and (5) lipid environment.

    Before the multilevel local refinement, the type-A SC was refined to 3.39 Å overall using images binned two times (1.664 Å per pixel after binning) with 1,113,902 high-quality particles. This included type B, type O and type X, as they all share the type-A region. We recentred and re-extracted these particles, generating 1,050,463 final particles for subsequent local refinement (particles near the edges were excluded after re-extraction). Initially, the resolutions of CI, CIII2 and CIV worsened slightly (approximately 3.5 Å) after the first cycle of refinement using the unbinned particles (0.832 Å per pixel). Further improvement was achieved by optimizing several local refinement parameters, including optimization of mask sizes, global CTF, local CTF refinement, local angular refinement and non-uniform refinement67.

    By iteratively applying these techniques, we refined the maps of the hydrophilic region of CI and the hydrophobic regions of CI, CIII2 and CIV to average resolutions of 2.46 Å, 2.58 Å, 2.31 Å and 2.66 Å, respectively (Supplementary Fig. 2 and Supplementary Tables 1–6). Even smaller regional masks, focused on CI and CIII2, further improved local resolutions. Local resolutions in most of the protein regions of CIII2 ranged from 1.8 to 2.4 Å (Supplementary Fig. 2c). Focused classification and refinement for specific subdomains, such as the Q/QH2 binding sites, yielded further improvements that aided in model building. For more complex regions, such as the lipid environment surrounding the transmembrane regions of the SCs and Q/QH2 binding sites, further levels of focused classification and local refinement were performed. To ensure seamless integration of adjacent regions, all local masks were manually created so that pairs of adjacent masks contained sufficiently large areas for the generation of final composite maps using the smaller regions individually refined. All locally refined segments were integrated into a composite map in ChimeraX64.

    Similar multilevel refinement approaches were used to determine the structures of other forms of respiratory SC. Detailed parameters and refinement results are summarized in Supplementary Figs. 2–5 and Supplementary Tables 1–6.

    Membrane signal detection and weakening

    One of the critical bottlenecks limiting high-resolution cryo-EM reconstruction of membrane proteins in their native environment is the severe signal interference from surrounding membranes. This interference can significantly affect several steps in cryo-EM data analysis, including ab initio reconstruction, Euler angle determination, and 2D and 3D classification, as well as refinement of alignment parameters. To address this issue, we developed a computational toolkit to detect membrane signals from 2D averages, estimate the local geometry of detected membranes, and suppress or remove these signals to substantially improve the alignment reliability of mitochondrial complexes in native membrane environments.

    Initially, we generated a series of 15–30 computationally simulated 2D projections of lipid bilayers, with local curvatures ranging from 0 nm−1 to 0.02 nm−1. These simulated 2D membranes served as templates for detection of the side-view signals of mitochondrial membranes using Gautomatch. Subsequently, three to five cycles of 2D classification were performed to discard low-quality and non-membrane particles, resulting in a subset of particles showing clear side views of lipid bilayers. We then estimated the approximate orientation and centre of each individual lipid bilayer on the basis of its corresponding 2D average using the Radon transform. Local curvature was determined by maximizing the cross-correlation between each 2D average and a series of simulated lipid bilayers. These curves were rotated and translated using alignment parameters from 2D classification generated by cryoSPARC62. Centres of each membrane segment were refined by maximizing the normalized cross-correlation between the raw image and transformed 2D average. Using these estimated parameters, we approximated the principal signals of each membrane segment by locally averaging the image intensities along the membrane curve within a soft mask, which was around 25% larger than the typical lipid bilayer we estimated. Membrane signals that had dominated the alignment in the raw images were weakened to enhance protein signal contributions for subsequent reconstruction, alignment, classification and local refinement. This improved the signal contributions from protein regions for the initial alignment, akin to the critical effects observed in our previously described microtubule signal subtraction method68,69. Finally, alignment and classification parameters were applied to the raw images along with membrane signals for subsequent local refinement and focused classification.

    Membrane modelling and geometry analysis

    The in situ mitochondrial respiratory chain complexes largely preserved the native state of the membrane architecture, as evidenced by exceptionally clear density maps (Extended Data Fig. 2) compared with previously published in vitro structures. This high fidelity in density was observable in both the final 3D reconstructions and the post-3D-refinement 2D class averages, enabling direct modelling of native membrane structures.

    The model building for the inner membrane structures surrounding the mitochondrial SCs involved a four-step procedure. First, discrete points were sampled from the raw signals in a given density map—such as the type-A SC—on the basis of binarized membrane density. A 2D plane was fitted by least-square minimization; the normal vector of each SC was estimated and the coordinate system was rotated so that this vector aligned with the z axis. Second, these sampled discrete points were used to generate two smooth, curved surfaces with a thickness of around 4 nm. Third, planar phospholipid bilayer structures were generated to match the geometry of these estimated surfaces. Finally, the information from the second and third steps was integrated to geometrically deform each planar membrane structure into a smooth, curved surface.

    To optimize the initial sampling for membrane model building, we categorized the membrane structures surrounding the protein into three distinct groups: structured lipids, surface-associated lipids and generic bilayer lipids. The first category, structured lipids, included lipids that are closely associated with the transmembrane regions of the protein. This close association enabled identification and direct atomic-level modelling of these specific lipid species, which have also been observed in previously reported structures purified using detergent. The second category, surface-associated lipids, comprised lipids situated around the immediate periphery of the protein, forming a pseudo-lattice structure. Within this lattice, partial phosphatidyl head groups and hydrophobic tails could be discerned. Our in situ density maps allowed us to unambiguously determine the locations of individual lipids in this category; however, the current quality of the density maps does not permit identification of the specific types of lipid present. The third category, generic bilayer lipids, represented a region farther from the protein where only the density features corresponding to the bilayers could be observed. We used a generic phospholipid membrane model to approximate the probable horizontal positions of the phosphatidyl headgroups. Owing to the fluid nature of the lipid bilayer and the high level of noise in the density maps, the central positions of these generic bilayer lipids may still vary among different subclasses even after focused classification. However, the average geometric features and the central locations of the membranes were notably consistent across each of the four main types of SC. Therefore, these generic bilayer lipids were used solely for calibrating the central locations and orientations of the phospholipid bilayer, rather than representing the actual positions of individual phospholipid molecules within the bilayer of each SC. This approach facilitated analysis of the overall geometric changes among the SCs, albeit not at the level of individual phospholipid molecule structures.

    To achieve a sufficiently smooth model for the generic bilayer lipids, we performed real-space refinement of the initial structures using the Coot software70. The refined structures were subjected to further smoothing using a local Gaussian filter to minimize residual noise in localized membrane regions. This step enabled precise estimation of the contour map and the local curvature at each point (Fig. 2b). We used the CHARM-GUI web service71 to generate a simulated rectangular planar phospholipid bilayer. This planar structure was then mapped on to the curved surfaces that were obtained after Gaussian smoothing. This mapping process yielded a curved membrane model that optimally fit the density map. From these estimated surfaces, information about the local geometry of the membranes surrounding the mitochondrial SCs could be directly retrieved for subsequent geometry analyses and comparisons.

    Model building, refinement and validation

    The atomic models were built manually using Coot72. First, high-resolution structures of bovine CI (PDB: 7QSK), bovine CIII2 (PDB: 2A06) and bovine CIV (PDB: 5XDQ) were fitted into the corresponding map as a rigid body using ChimeraX64. Then, the fitted model was manually mutated, adjusted and real-space refined to correct errors in local regions to best match the density maps using Coot72. The final model was refined using phenix.real_space_refine73 with geometric constraints and validated using MolProbity74. Figures were generated using UCSF ChimeraX64 and PyMOL.

    Reporting summary

    Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

    [ad_2]

    Source link

  • Membraneless channels sieve cations in ammonia-oxidizing marine archaea

    [ad_1]

  • Könneke, M. et al. Isolation of an autotrophic ammonia-oxidizing marine archaeon. Nature 437, 543–546 (2005).

    Article 
    ADS 
    PubMed 

    Google Scholar
     

  • Hutchins, D. A. & Capone, D. G. The marine nitrogen cycle: new developments and global change. Nat. Rev. Microbiol. 20, 401–414 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Karner, M. B., DeLong, E. F. & Karl, D. M. Archaeal dominance in the mesopelagic zone of the Pacific Ocean. Nature 409, 507–510 (2001).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Könneke, M. et al. Ammonia-oxidizing archaea use the most energy-efficient aerobic pathway for CO2 fixation. Proc. Natl Acad. Sci. USA 111, 8239–8244 (2014).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kraft, B. et al. Oxygen and nitrogen production by an ammonia-oxidizing archaeon. Science 375, 97–100 (2022).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Wan, X. S. et al. Ambient nitrate switches the ammonium consumption pathway in the euphotic ocean. Nat. Commun. 9, 915 (2018).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Martens-Habbena, W., Berube, P. M., Urakawa, H., de la Torre, J. R. & Stahl, D. A. Ammonia oxidation kinetics determine niche separation of nitrifying Archaea and Bacteria. Nature 461, 976–979 (2009).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Urakawa, H., Martens-Habbena, W. & Stahl, D. A. in Nitrification 115–155 (John Wiley & Sons, 2011).

  • Qin, W. et al. Nitrosopumilus maritimus gen. nov., sp. nov., Nitrosopumilus cobalaminigenes sp. nov., Nitrosopumilus oxyclinae sp. nov., and Nitrosopumilus ureiphilus sp. nov., four marine ammonia-oxidizing archaea of the phylum Thaumarchaeota. Int. J. Syst. Evol. Microbiol. 67, 5067–5079 (2017).

    Article 
    PubMed 

    Google Scholar
     

  • Bharat, T. A. M., von Kügelgen, A. & Alva, V. Molecular logic of prokaryotic surface layer structures. Trends Microbiol. 29, 405–415 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Albers, S. V. & Meyer, B. H. The archaeal cell envelope. Nat. Rev. Microbiol. 9, 414–426 (2011).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Li, P. N. et al. Nutrient transport suggests an evolutionary basis for charged archaeal surface layer proteins. ISME J. 12, 2389–2402 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, P. N., Herrmann, J., Wakatsuki, S. & van den Bedem, H. Transport properties of nanoporous, chemically forced biological lattices. J. Phys. Chem. B 123, 10331–10342 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Nakagawa, T. & Stahl, D. A. Transcriptional response of the archaeal ammonia oxidizer Nitrosopumilus maritimus to low and environmentally relevant ammonia concentrations. Appl. Environ. Microbiol. 79, 6911–6916 (2013).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Qin, W. et al. Stress response of a marine ammonia-oxidizing archaeon informs physiological status of environmental populations. ISME J. 12, 508–519 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • von Kügelgen, A. et al. In Situ structure of an intact lipopolysaccharide-bound bacterial surface layer. Cell 180, 348–358 (2020).

    Article 

    Google Scholar
     

  • Bharat, T. A. M. et al. Structure of the hexagonal surface layer on Caulobacter crescentus cells. Nat. Microbiol. 2, 17059 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • von Kügelgen, A., Alva, V. & Bharat, T. A. M. Complete atomic structure of a native archaeal cell surface. Cell Rep. 37, 110052 (2021).

    Article 

    Google Scholar
     

  • Zivanov, J. et al. A Bayesian approach to single-particle electron cryo-tomography in RELION-4.0. eLife 11, e83724 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jarrell, K. F. et al. N-linked glycosylation in Archaea: a structural, functional, and genetic analysis. Microbiol. Mol. Biol. Rev. 78, 304–341 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Herdman, M. et al. High-resolution mapping of metal ions reveals principles of surface layer assembly in Caulobacter crescentus cells. Structure 30, 215–228 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Baranova, E. et al. SbsB structure and lattice reconstruction unveil Ca2+ triggered S-layer assembly. Nature 487, 119–122 (2012).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • von Kügelgen, A. et al. Interdigitated immunoglobulin arrays form the hyperstable surface layer of the extremophilic bacterium Deinococcus radiodurans. Proc. Natl Acad. Sci. USA 120, e2215808120 (2023).

    Article 

    Google Scholar
     

  • Gambelli, L. et al. Structure of the two-component S-layer of the archaeon Sulfolobus acidocaldarius. eLife 13, e84617 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gambelli, L. et al. Architecture and modular assembly of Sulfolobus S-layers revealed by electron cryotomography. Proc. Natl Acad. Sci. USA 116, 25278–25286 (2019).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Fioravanti, A. et al. Structure of S-layer protein Sap reveals a mechanism for therapeutic intervention in anthrax. Nat. Microbiol. 4, 1805–1814 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Bharat, T. A. M., Tocheva, E. I. & Alva, V. The cell envelope architecture of Deinococcus: HPI forms the S-layer and SlpA tethers the outer membrane to peptidoglycan. Proc. Natl Acad. Sci. USA 120, e2305338120 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kim, J.-G. et al. Spindle-shaped viruses infect marine ammonia-oxidizing thaumarchaea. Proc. Natl Acad. Sci. USA 116, 15645–15650 (2019).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Song, W. et al. PyLipID: a Python package for analysis of protein-lipid interactions from molecular dynamics simulations. J. Chem. Theory Comput. 18, 1188–1201 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Xue, L. et al. Visualizing translation dynamics at atomic detail inside a bacterial cell. Nature 610, 205–211 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tegunov, D., Xue, L., Dienemann, C., Cramer, P. & Mahamid, J. Multi-particle cryo-EM refinement with M visualizes ribosome-antibiotic complex at 3.5 Å in cells. Nat. Methods 18, 186–193 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hollocher, T. C., Tate, M. E. & Nicholas, D. J. Oxidation of ammonia by Nitrosomonas europaea. Definite 18O-tracer evidence that hydroxylamine formation involves a monooxygenase. J. Biol. Chem. 256, 10834–10836 (1981).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Hodgskiss, L. H. et al. Unexpected complexity of the ammonia monooxygenase in archaea. ISME J. 17, 588–599 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Vajrala, N. et al. Hydroxylamine as an intermediate in ammonia oxidation by globally abundant marine archaea. Proc. Natl Acad. Sci. USA 110, 1006–1011 (2013).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Leininger, S. et al. Archaea predominate among ammonia-oxidizing prokaryotes in soils. Nature 442, 806–809 (2006).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Buchholz, T.-O. et al. in Methods in Cell Biology Vol. 152 (eds Müller-Reichert, T. & Pigino, G.) 277–289 (Academic, 2019).

  • Buchholz, T.-O., Jordan, M., Pigino, G. & Jug, F. Cryo-CARE: Content-aware image restoration for cryo-transmission electron microscopy data. In Proc. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) 502–506 (IEEE, 2019).

  • Sanchez-Garcia, R. et al. DeepEMhancer: a deep learning solution for cryo-EM volume post-processing. Commun. Biol. 4, 874 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Schouten, S. et al. Intact membrane lipids of “Candidatus Nitrosopumilus maritimus,” a cultivated representative of the cosmopolitan mesophilic group I Crenarchaeota. Appl. Environ. Microbiol. 74, 2433–2440 (2008).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Leavitt, W. et al. Archaeal lipid hydrogen isotopes in a marine thaumarchaeon. Earth Space Sci. Open Arch. https://doi.org/10.1002/essoar.10512169.1 (2022).

  • von Kügelgen, A., van Dorst, S., Alva, V. & Bharat, T. A. M. A multidomain connector links the outer membrane and cell wall in phylogenetically deep-branching bacteria. Proc. Natl Acad. Sci. USA 119, e2203156119 (2022).

    Article 

    Google Scholar
     

  • Wan, W. et al. Structure and assembly of the Ebola virus nucleocapsid. Nature 551, 394–397 (2017).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hagen, W. J. H., Wan, W. & Briggs, J. A. G. Implementation of a cryo-electron tomography tilt-scheme optimized for high resolution subtomogram averaging. J. Struct. Biol. 197, 191–198 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mastronarde, D. N. Automated electron microscope tomography using robust prediction of specimen movements. J. Struct. Biol. 152, 36–51 (2005).

    Article 
    PubMed 

    Google Scholar
     

  • Kremer, J. R., Mastronarde, D. N. & McIntosh, J. R. Computer visualization of three-dimensional image data using IMOD. J. Struct. Biol. 116, 71–76 (1996).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Rohou, A. & Grigorieff, N. CTFFIND4: fast and accurate defocus estimation from electron micrographs. J. Struct. Biol. 192, 216–221 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Agulleiro, J. I. & Fernandez, J. J. Tomo3D 2.0—exploitation of advanced vector extensions (AVX) for 3D reconstruction. J. Struct. Biol. 189, 147–152 (2015).

    Article 
    PubMed 

    Google Scholar
     

  • Bharat, T. A. M. et al. Cryo-electron tomography of Marburg virus particles and their morphogenesis within infected cells. PLoS Biol. 9, e1001196 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Turoňová, B., Schur, F. K. M., Wan, W. & Briggs, J. A. G. Efficient 3D-CTF correction for cryo-electron tomography using NovaCTF improves subtomogram averaging resolution to 3.4 Å. J. Struct. Biol. 199, 187–195 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ilca, S. L. et al. Multiple liquid crystalline geometries of highly compacted nucleic acid in a dsRNA virus. Nature 570, 252–256 (2019).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Abrishami, V. et al. Localized reconstruction in Scipion expedites the analysis of symmetry mismatches in cryo-EM data. Prog. Biophys. Mol. Biol. 160, 43–52 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Zheng, S. Q. et al. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 14, 331–332 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zivanov, J. et al. New tools for automated high-resolution cryo-EM structure determination in RELION-3. eLife 7, e42166 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bepler, T. et al. Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs. Nat. Methods 16, 1153–1160 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zivanov, J., Nakane, T. & Scheres, S. H. W. Estimation of high-order aberrations and anisotropic magnification from cryo-EM data sets in RELION-3.1. IUCrJ 7, 253–267 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Scheres, S. H. RELION: implementation of a Bayesian approach to cryo-EM structure determination. J. Struct. Biol. 180, 519–530 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tan, Y. Z. et al. Addressing preferred specimen orientation in single-particle cryo-EM through tilting. Nat. Methods 14, 793–796 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D 66, 486–501 (2010).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Murshudov, G. N. et al. REFMAC5 for the refinement of macromolecular crystal structures. Acta Crystallogr. D 67, 355–367 (2011).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Burnley, T., Palmer, C. M. & Winn, M. Recent developments in the CCP-EM software suite. Acta Crystallogr. D 73, 469–477 (2017).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Liebschner, D. et al. Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix. Acta Crystallogr. D 75, 861–877 (2019).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Yamashita, K., Palmer, C. M., Burnley, T. & Murshudov, G. N. Cryo-EM single-particle structure refinement and map calculation using Servalcat. Acta Crystallogr. D 77, 1282–1291 (2021).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Pettersen, E. F. et al. UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Pettersen, E. F. et al. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci. 30, 70–82 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • The PyMOL molecular graphics system v.2.0 (Schrödinger, 2015).

  • Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Qu, K. et al. Structure and architecture of immature and mature murine leukemia virus capsids. Proc. Natl Acad. Sci. USA 115, E11751–E11760 (2018).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ermel, U. H., Arghittu, S. M. & Frangakis, A. S. ArtiaX: an electron tomography toolbox for the interactive handling of sub-tomograms in UCSF ChimeraX. Protein Sci. 31, e4472 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zimmermann, L. et al. A completely reimplemented MPI bioinformatics Toolkit with a new HHpred server at its core. J. Mol. Biol. 430, 2237–2243 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinform. 10, 421 (2009).

    Article 

    Google Scholar
     

  • Steinegger, M. et al. HH-suite3 for fast remote homology detection and deep protein annotation. BMC Bioinform. 20, 473 (2019).

    Article 

    Google Scholar
     

  • Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Teufel, F. et al. SignalP 6.0 predicts all five types of signal peptides using protein language models. Nat. Biotechnol. 40, 1023–1025 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rice, P., Longden, I. & Bleasby, A. EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet. 16, 276–277 (2000).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Frickey, T. & Lupas, A. CLANS: a Java application for visualizing protein families based on pairwise similarity. Bioinformatics 20, 3702–3704 (2004).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Kozlowski, L. P. IPC 2.0: prediction of isoelectric point and pKa dissociation constants. Nucleic Acids Res. 49, W285–W292 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • [ad_2]

    Source link

  • Pro-CRISPR PcrIIC1-associated Cas9 system for enhanced bacterial immunity

    [ad_1]

  • Marraffini, L. A. CRISPR–Cas immunity in prokaryotes. Nature 526, 55–61 (2015).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Makarova, K. S. et al. Evolutionary classification of CRISPR–Cas systems: a burst of class 2 and derived variants. Nat. Rev. Microbiol. 18, 67–83 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Wang, J. Y. & Doudna, J. A. CRISPR technology: a decade of genome editing is only the beginning. Science 379, eadd8643 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Altae-Tran, H. et al. The widespread IS200/IS605 transposon family encodes diverse programmable RNA-guided endonucleases. Science 374, 57–65 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Garneau, J. E. et al. The CRISPR/Cas bacterial immune system cleaves bacteriophage and plasmid DNA. Nature 468, 67–71 (2010).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Hampton, H. G., Watson, B. N. J. & Fineran, P. C. The arms race between bacteria and their phage foes. Nature 577, 327–336 (2020).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Malone, L. M., Birkholz, N. & Fineran, P. C. Conquering CRISPR: how phages overcome bacterial adaptive immunity. Curr. Opin. Biotechnol. 68, 30–36 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Shivram, H., Cress, B. F., Knott, G. J. & Doudna, J. A. Controlling and enhancing CRISPR systems. Nat. Chem. Biol. 17, 10–19 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Davidson, A. R. et al. Anti-CRISPRs: protein inhibitors of CRISPR–Cas systems. Annu. Rev. Biochem. 89, 309–332 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Arnold, B. J., Huang, I. T. & Hanage, W. P. Horizontal gene transfer and adaptive evolution in bacteria. Nat. Rev. Microbiol. 20, 206–218 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Strecker, J. et al. RNA-activated protein cleavage with a CRISPR-associated endopeptidase. Science 378, 874–881 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Saito, M. et al. Dual modes of CRISPR-associated transposon homing. Cell 184, 2441–2453 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Schuler, G., Hu, C. & Ke, A. Structural basis for RNA-guided DNA cleavage by IscB-ωRNA and mechanistic comparison with Cas9. Science 376, 1476–1481 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wilkinson, M. et al. Structure of the DNA-bound spacer capture complex of a type II CRISPR–Cas system. Mol. Cell 75, 90–101 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dhingra, Y., Suresh, S. K., Juneja, P. & Sashital, D. G. PAM binding ensures orientational integration during Cas4-Cas1-Cas2-mediated CRISPR adaptation. Mol. Cell 82, 4353–4367 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Altae-Tran, H. et al. Uncovering the functional diversity of rare CRISPR–Cas systems with deep terascale clustering. Science 382, eadi1910 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yamada, M. et al. Crystal structure of the minimal Cas9 from Campylobacter jejuni reveals the molecular diversity in the CRISPR–Cas9 systems. Mol. Cell 65, 1109–1121 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Coleman, G. A. et al. A rooted phylogeny resolves early bacterial evolution. Science 372, eabe0511 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Senissar, M., Manav, M. C. & Brodersen, D. E. Structural conservation of the PIN domain active site across all domains of life. Protein Sci. 26, 1474–1492 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Park, D. et al. Crystal structure of proteolyzed VapBC and DNA-bound VapBC from Salmonella enterica Typhimurium LT2 and VapC as a putative Ca2+-dependent ribonuclease. FASEB J. 34, 3051–3068 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Ernits, K. et al. The structural basis of hyperpromiscuity in a core combinatorial network of type II toxin-antitoxin and related phage defense systems. Proc. Natl Acad. Sci. USA 120, e2305393120 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cofsky, J. C., Soczek, K. M., Knott, G. J., Nogales, E. & Doudna, J. A. CRISPR–Cas9 bends and twists DNA to read its sequence. Nat. Struct. Mol. Biol. 29, 395–402 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pacesa, M. et al. R-loop formation and conformational activation mechanisms of Cas9. Nature 609, 191–196 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hibshman, G. N. et al. Unraveling the mechanisms of PAMless DNA interrogation by SpRY-Cas9. Nat. Commun. 15, 3663 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yang, M. et al. Nonspecific interactions between SpCas9 and dsDNA sites located downstream of the PAM mediate facilitated diffusion to accelerate target search. Chem. Sci. 12, 12776–12784 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jayaraman, V., Toledo‐Patiño, S., Noda‐García, L. & Laurino, P. Mechanisms of protein evolution. Protein Sci. 31, e4362 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Koonin, E. V., Makarova, K. S., Wolf, Y. I. & Krupovic, M. Evolutionary entanglement of mobile genetic elements and host defence systems: guns for hire. Nat. Rev. Genet. 21, 119–131 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Pawluk, A., Davidson, A. R. & Maxwell, K. L. Anti-CRISPR: discovery, mechanism and function. Nat. Rev. Microbiol. 16, 12–17 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Maxwell, K. L. The anti-CRISPR story: a battle for survival. Mol. Cell 68, 8–14 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Marino, N. D., Pinilla-Redondo, R., Csörgő, B. & Bondy-Denomy, J. Anti-CRISPR protein applications: natural brakes for CRISPR–Cas technologies. Nat. Methods 17, 471–479 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jia, N. & Patel, D. J. Structure-based functional mechanisms and biotechnology applications of anti-CRISPR proteins. Nat. Rev. Mol. Cell Biol. 22, 563–579 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Nayfach, S. et al. A genomic catalog of Earth’s microbiomes. Nat. Biotechnol. 39, 499–509 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Couvin, D. et al. CRISPRCasFinder, an update of CRISRFinder, includes a portable version, enhanced performance and integrates search for Cas proteins. Nucleic Acids Res. 46, W246–W251 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cantalapiedra, C. P., Hernández-Plaza, A., Letunic, I., Bork, P. & Huerta-Cepas, J. eggNOG-mapper v2: functional annotation, orthology assignments, and domain prediction at the metagenomic scale. Mol. Biol. Evol. 38, 5825–5829 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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 (2018).

    Article 
    PubMed Central 

    Google Scholar
     

  • Eddy, S. R. A new generation of homology search tools based on probabilistic inference. Genome Inform. 23, 205–211 (2009).

    PubMed 

    Google Scholar
     

  • Sievers, F. et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 7, 539 (2011).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, C., Shine, M., Pyle, A. M. & Zhang, Y. US-align: universal structure alignments of proteins, nucleic acids, and macromolecular complexes. Nat. Methods 19, 1109–1115 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • R Core Team. R: A Language and Environment for Statistical Computing http://www.R-project.org/ (R Foundation for Statistical Computing, 2013).

  • Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://doi.org/10.48550/arXiv.1802.03426 (2018).

  • Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pettersen, E. F. et al. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci. 30, 70–82 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Minh, B. Q. et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37, 1530–1534 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293–W296 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hug, L. A. et al. A new view of the tree of life. Nat. Microbiol. 1, 16048 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10, giab008 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Robinson, J. T., Thorvaldsdottir, H., Turner, D. & Mesirov, J. P. igv.js: an embeddable JavaScript implementation of the Integrative Genomics Viewer (IGV). Bioinformatics 39, btac830 (2022).

    Article 
    PubMed Central 

    Google Scholar
     

  • Sun, A. et al. The compact Casπ (Cas12l) ‘bracelet’ provides a unique structural platform for DNA manipulation. Cell Res. 33, 229–244 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Magoč, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Crooks, G. E., Hon, G., Chandonia, J. M. & Brenner, S. E. WebLogo: a sequence logo generator. Genome Res. 14, 1188–1190 (2004).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zheng, S. Q. et al. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 14, 331–332 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Punjani, A., Rubinstein, J. L., Fleet, D. J. & Brubaker, M. A. cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 14, 290–296 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Punjani, A. & Fleet, D. J. 3DFlex: determining structure and motion of flexible proteins from cryo-EM. Nat. Methods 20, 860–870 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sanchez-Garcia, R. et al. DeepEMhancer: a deep learning solution for cryo-EM volume post-processing. Commun. Biol. 4, 874 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr. D 60, 2126–2132 (2004).

    Article 
    ADS 
    PubMed 

    Google Scholar
     

  • Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D 66, 213–221 (2010).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • [ad_2]

    Source link

  • Kainate receptor channel opening and gating mechanism

    [ad_1]

  • Hansen, K. B. et al. Structure, function, and pharmacology of glutamate receptor ion channels. Pharmacol. Rev. 73, 298–487 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jane, D. E., Lodge, D. & Collingridge, G. L. Kainate receptors: pharmacology, function and therapeutic potential. Neuropharmacology 56, 90–113 (2009).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Huettner, J. E. Kainate receptors and synaptic transmission. Prog. Neurobiol. 70, 387–407 (2003).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Lerma, J. Roles and rules of kainate receptors in synaptic transmission. Nat. Rev. Neurosci. 4, 481–495 (2003).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Lerma, J. & Marques, J. M. Kainate receptors in health and disease. Neuron 80, 292–311 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Vincent, P. & Mulle, C. Kainate receptors in epilepsy and excitotoxicity. Neuroscience 158, 309–323 (2009).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Lauri, S. E., Ryazantseva, M., Orav, E., Vesikansa, A. & Taira, T. Kainate receptors in the developing neuronal networks. Neuropharmacology 195, 108585 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Negrete-Diaz, J. V., Falcon-Moya, R. & Rodriguez-Moreno, A. Kainate receptors: from synaptic activity to disease. FEBS J. 289, 5074–5088 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Bhangoo, S. K. & Swanson, G. T. Kainate receptor signaling in pain pathways. Mol. Pharmacol. 83, 307–315 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Nair, J. D., Wilkinson, K. A., Henley, J. M. & Mellor, J. R. Kainate receptors and synaptic plasticity. Neuropharmacology 196, 108540 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Chalupnik, P. & Szymanska, E. Kainate receptor antagonists: recent advances and therapeutic perspective. Int. J. Mol. Sci. 24, 1908 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Meyerson, J. R. et al. Structural basis of kainate subtype glutamate receptor desensitization. Nature 537, 567–571 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kumari, J., Vinnakota, R. & Kumar, J. Structural and functional Insights into GluK3–kainate receptor desensitization and recovery. Sci. Rep. 9, 10254 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kumari, J. et al. Structural dynamics of the GluK3–kainate receptor neurotransmitter binding domains revealed by cryo-EM. Int. J. Biol. Macromol. 149, 1051–1058 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Khanra, N., Brown, P. M., Perozzo, A. M., Bowie, D. & Meyerson, J. R. Architecture and structural dynamics of the heteromeric GluK2/K5 kainate receptor. eLife 10, e66097 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Selvakumar, P. et al. Structural and compositional diversity in the kainate receptor family. Cell Rep. 37, 109891 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gangwar, S. P., Yen, L. Y., Yelshanskaya, M. V. & Sobolevsky, A. I. Positive and negative allosteric modulation of GluK2 kainate receptors by BPAM344 and antiepileptic perampanel. Cell Rep. 42, 112124 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bogdanovic, N., Segura-Covarrubias, G., Zhang, L. & Tajima, N. Structural dynamics of GluK2 kainate receptors in apo and partial agonist bound states. Preprint at Research Square https://doi.org/10.21203/rs.3.rs-3592604/v1 (2023).

  • Valbuena, S. & Lerma, J. Non-canonical signaling, the hidden life of ligand-gated ion channels. Neuron 92, 316–329 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Wenthold, R. J., Trumpy, V. A., Zhu, W.-S. & Petralia, R. S. Biochemical and assembly properties of GluR6 and KA2, two members of the kainate receptor family, determined with subunit-specific antibodies. J. Biol. Chem. 269, 1332–1339 (1994).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Copits, B. A., Robbins, J. S., Frausto, S. & Swanson, G. T. Synaptic targeting and functional modulation of GluK1 kainate receptors by the auxiliary neuropilin and tolloid-like (NETO) proteins. J. Neurosci. 31, 7334–7340 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tang, M. et al. Neto1 is an auxiliary subunit of native synaptic kainate receptors. J. Neurosci. 31, 10009–10018 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, W. et al. A transmembrane accessory subunit that modulates kainate-type glutamate receptors. Neuron 61, 385–396 (2009).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Swanson, G. T. et al. Differential activation of individual subunits in heteromeric kainate receptors. Neuron 34, 589–598 (2002).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Brown, P. M., Aurousseau, M. R., Musgaard, M., Biggin, P. C. & Bowie, D. Kainate receptor pore-forming and auxiliary subunits regulate channel block by a novel mechanism. J. Physiol. 594, 1821–1840 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mott, D. D., Rojas, A., Fisher, J. L., Dingledine, R. J. & Benveniste, M. Subunit-specific desensitization of heteromeric kainate receptors. J. Physiol. 588, 683–700 (2010).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Barberis, A., Sachidhanandam, S. & Mulle, C. GluR6/KA2 kainate receptors mediate slow-deactivating currents. J. Neurosci. 28, 6402–6406 (2008).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Fisher, J. L. & Mott, D. D. The auxiliary subunits Neto1 and Neto2 reduce voltage-dependent inhibition of recombinant kainate receptors. J. Neurosci. 32, 12928–12933 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Fisher, J. L. The auxiliary subunits Neto1 and Neto2 have distinct, subunit-dependent effects at recombinant GluK1-and GluK2-containing kainate receptors. Neuropharmacology 99, 471–480 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sheng, N., Shi, Y. S., Lomash, R. M., Roche, K. W. & Nicoll, R. A. Neto auxiliary proteins control both the trafficking and biophysical properties of the kainate receptor. eLife 4, e11682 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Straub, C., Zhang, W. & Howe, J. R. Neto2 modulation of kainate receptors with different subunit compositions. J. Neurosci. 31, 8078–8082 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bowie, D. Ion-dependent gating of kainate receptors. J. Physiol. 588, 67–81 (2010).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Mollerud, S., Frydenvang, K., Pickering, D. S. & Kastrup, J. S. Lessons from crystal structures of kainate receptors. Neuropharmacology 112, 16–28 (2017).

    Article 
    PubMed 

    Google Scholar
     

  • Mayer, M. L. Structural biology of kainate receptors. Neuropharmacology 190, 108511 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Armstrong, N. & Gouaux, E. Mechanisms for activation and antagonism of an AMPA-sensitive glutamate receptor: crystal structures of the GluR2 ligand binding core. Neuron 28, 165–181 (2000).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Jin, R. et al. Crystal structure and association behaviour of the GluR2 amino-terminal domain. EMBO J. 28, 1812–1823 (2009).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sobolevsky, A. I., Rosconi, M. P. & Gouaux, E. X-ray structure, symmetry and mechanism of an AMPA-subtype glutamate receptor. Nature 462, 745–756 (2009).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Twomey, E. C., Yelshanskaya, M. V., Grassucci, R. A., Frank, J. & Sobolevsky, A. I. Channel opening and gating mechanism in AMPA-subtype glutamate receptors. Nature 549, 60–65 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chen, S. et al. Activation and desensitization mechanism of AMPA receptor–TARP complex by cryo-EM. Cell 170, 1234–1246.e1214 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Everts, I. et al. Lectin-induced inhibition of desensitization of the kainate receptor GluR6 depends on the activation state and can be mediated by a single native or ectopic N-linked carbohydrate side chain. J. Neurosci. 19, 916–927 (1999).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Fay, A. M. & Bowie, D. Concanavalin-A reports agonist-induced conformational changes in the intact GluR6 kainate receptor. J. Physiol. 572, 201–213 (2006).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gonzalez, C. U., Carillo, E., Berka, V. & Jayaraman, V. Structural arrangement produced by concanavalin A binding to homomeric GluK2 receptors. Membranes 11, 613 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Montano Romero, A., Bonin, C. & Twomey, E. C-SPAM: an open-source time-resolved specimen vitrification device with light-activated molecules. IUCrJ 11, 16–22 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Rubinstein, J. L. et al. Shake-it-off: a simple ultrasonic cryo-EM specimen-preparation device. Acta Crystallogr. D 75, 1063–1070 (2019).

    Article 
    CAS 

    Google Scholar
     

  • Biedermann, J., Braunbeck, S., Plested, A. J. R. & Sun, H. Nonselective cation permeation in an AMPA-type glutamate receptor. Proc. Natl Acad. Sci. USA 118, e2012843118 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yelshanskaya, M. V., Patel, D. S., Kottke, C. M., Kurnikova, M. G. & Sobolevsky, A. I. Opening of glutamate receptor channel to subconductance levels. Nature 605, 172–178 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wollmuth, L. P. & Sobolevsky, A. I. Structure and gating of the glutamate receptor ion channel. Trends Neurosci. 27, 321–328 (2004).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Twomey, E. C., Yelshanskaya, M. V., Grassucci, R. A., Frank, J. & Sobolevsky, A. I. Structural bases of desensitization in AMPA receptor–auxiliary subunit complexes. Neuron 94, 569–580.e565 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Klykov, O., Gangwar, S. P., Yelshanskaya, M. V., Yen, L. & Sobolevsky, A. I. Klykov, O., Gangwar, S. P., Yelshanskaya, M. V., Yen, L. & Sobolevsky, A. I. Structure and desensitization of AMPA receptor complexes with type II TARP γ5 and GSG1L. Mol. Cell 81, 4771–4783.e7 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Herguedas, B. et al. Mechanisms underlying TARP modulation of the GluA1/2-γ8 AMPA receptor. Nat. Commun. 13, 734 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Twomey, E. C. & Sobolevsky, A. I. Structural mechanisms of gating in ionotropic glutamate receptors. Biochemistry 57, 267–276 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Durr, K. L. et al. Structure and dynamics of AMPA receptor GluA2 in resting, pre-open, and desensitized states. Cell 158, 778–792 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yelshanskaya, M. V., Li, M. & Sobolevsky, A. I. Structure of an agonist-bound ionotropic glutamate receptor. Science 345, 1070–1074 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Goehring, A. et al. Screening and large-scale expression of membrane proteins in mammalian cells for structural studies. Nat. Protoc. 9, 2574–2585 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Punjani, A., Rubinstein, J. L., Fleet, D. J. & Brubaker, M. A. cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 14, 290–296 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Bepler, T. et al. Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs. Nat. Methods 16, 1153–1160 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pettersen, E. F. et al. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci. 30, 70–82 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D 66, 486–501 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pettersen, E. F. et al. UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Afonine, P. V. et al. Towards automated crystallographic structure refinement with phenix.refine. Acta Crystallogr. D 68, 352–367 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Smart, O. S., Neduvelil, J. G., Wang, X., Wallace, B. A. & Sansom, M. S. HOLE: a program for the analysis of the pore dimensions of ion channel structural models. J. Mol. Graphics 14, 354–360 (1996).

    Article 
    CAS 

    Google Scholar
     

  • Jo, S., Kim, T., Iyer, V. G. & Im, W. CHARMM-GUI: a web-based graphical user interface for CHARMM. J. Comput. Chem. 29, 1859–1865 (2008).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Wu, E. L. et al. CHARMM-GUI Membrane Builder toward realistic biological membrane simulations. J. Comput. Chem. 35, 1997–2004 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Case, D. A. et al. AmberTools. J. Chem. Inf. Model. 63, 6183–6191 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, J., Wolf, R. M., Caldwell, J. W., Kollman, P. A. & Case, D. A. Development and testing of a general amber force field. J. Comput. Chem. 25, 1157–1174 (2004).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Lindorff-Larsen, K. et al. Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins 78, 1950–1958 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dickson, C. J. et al. Lipid14: the Amber lipid force field. J. Chem. Theory Comput. 10, 865–879 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ryckaert, J.-P., Ciccotti, G. & Berendsen, H. J. C. Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J. Comput. Phys. 23, 327–341 (1977).

    Article 
    CAS 

    Google Scholar
     

  • Darden, T., York, D. & Pedersen, L. Particle mesh Ewald: an Nlog(N) method for Ewald sums in large systems. J. Chem. Phys. 98, 10089–10092 (1993).

    Article 
    CAS 

    Google Scholar
     

  • Roe, D. R. & Cheatham, T. E. 3rd PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J. Chem. Theory Comput. 9, 3084–3095 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Humphrey, W., Dalke, A. & Schulten, K. VMD: visual molecular dynamics. J. Mol. Graph. 14, 33–38 (1996).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • [ad_2]

    Source link

  • Molecular mechanism of choline and ethanolamine transport in humans

    [ad_1]

    Generation of inducible HEK293 stable cell lines

    The complementary DNAs of full-length wild-type FLVCR1 (human SLC49A1, NCBI reference sequence NM_014053) and FLVCR2 (human SLC49A2, NCBI reference sequence NM_017791) were cloned into pcDNA5/FRT/TO (Invitrogen) vectors, respectively. The gene for both FLVCRs was modified by a C-terminal FLAG fusion tag. Further details are found in sequence data provided in Supplementary Tables 1 and 2. The recombinant Flp-In T-REx293-FLVCR1 and Flp-In T-REx293-FLVCR2 cell lines were generated by using a tetracycline-inducible and commercially available Flp-In T-REx293 host-cell line system from Invitrogen. Flp-In T-REx293 cells were cultured in high-glucose Dulbecco’s Modified Eagle’s Medium (DMEM; Sigma-Aldrich) supplemented with 10% fetal bovine serum (FBS; Gibco), 1% Pen/Strep (Gibco), 1 μg ml−1 of Zeocin (Gibco) and 15 μg ml−1 of blasticidin S hydrochloride (AppliChem) at 37 °C in an atmosphere of 5% CO2. Cells were periodically tested negative for mycoplasma contamination. For stable integration, the pcDNA5/FRT-FLVCR1-FLAG and pcDNA5/FRT-FLVCR2-FLAG vectors were cotransfected with the Flp recombinase encoding expression vector pOG44 (Invitrogen) at a 1:13 mass ratio, respectively. All transfection procedures were performed with Lipofectamine 2000 reagent according to the manufacturer’s instructions (Invitrogen). To select for stable clones, transfected cells were cultivated with growth medium containing 100 μg ml−1 of hygromycin B (AppliChem).

    Transport assays in HEK293 cells

    HEK293 cells were cotransfected with pcDNA3.1 plasmid and human FLVCR1 or FLVCR2 and human choline kinase A (CHKA) for choline transport assays or ethanolamine kinase 1 (ETNK1) for ethanolamine transport assays using Lipofectamine 2000 reagent (Invitrogen). Cells were periodically tested negative for mycoplasma contamination. After 24 h post-transfection, cells were incubated with DMEM containing 20 μM [3H]choline or 2.5 μM [14C]ethanolamine. The cells were incubated at 37 °C and 5% CO2 for 1 h for uptake of the ligands. The cells were subsequently washed with ice-cold plain DMEM and lysed with RIPA buffer (Thermo Scientific) by shaking at room temperature for 30 min. The cell lysates were quantified by scintillation counter Tri-Carb (Perkin Elmer). Radioactive signals from cell lysates were normalized to total protein levels. For dose curve assays, indicated concentrations of choline and ethanolamine were incubated with the cells for 1 h at 37 °C. For time-course assays, the cells were incubated with 20 μM [3H]choline or with 2.5 μM [14C]ethanolamine. The transport assays were stopped at indicated time points by adding ice-cold plain DMEM. For testing transport activity of FLVCR1 mutants, 20 μM [3H]choline and 2.5 μM [14C]ethanolamine were used. For testing transport activity of FLVCR2 mutants, 100 μM [3H]choline and 2.5 μM [14C]ethanolamine were used. For transport assays of HEK293 cells overexpressing FLVCR1 or FLVCR2 without co-expressing with CHKA or ETNK1, 20 μM [3H]choline and 2.5 μM [14C]ethanolamine were used.

    For transport assays under indicated pH conditions, the following buffers were used: pH 8.5 buffer (140 mM NaCl, 20 mM Tris-HCl pH 8.5, 2 mM CaCl2, 1 g l−1 of d-glucose), pH 6.5 buffer (140 mM NaCl, 20 mM MES pH 6.5, 2 mM CaCl2, 1 g l−1 of d-glucose) or pH 7.5 buffer (140 mM NaCl, 20 mM HEPES-NaOH, 2 mM CaCl2, 1 g l−1 of d-glucose). For sodium-free buffer, buffer containing 140 mM KCl, 20 mM HEPES-KOH pH 7.5, 2 mM CaCl2, 1 g l−1 of d-glucose was used. In these assays, 20 μM [3H]choline or 2.5 μM [14C]ethanolamine was used and the assays were stopped after 15 min of incubation with the ligands. Radioactive signals from cell lysates were normalized to total protein levels. Total protein was quantified using Pierce BCA Protein Assay Kit (Thermo Scientific).

    Immunofluorescent staining

    HEK293 cells were seeded onto 24-well plates with coverslips and maintained in DMEM (Gibco) supplemented with 10% fetal bovine serum and 1% penicillin–streptomycin. HEK293 cells were cotransfected with FLVCR1 or FLVCR2 with membrane expressing GFP (Addgene: catalogue no. 14757) using Lipofectamine 2000 reagent (Invitrogen). The inducible HEK293 stable cell lines overproducing FLVCR1 or FLVCR2 were seeded onto Millicell EZ SLIDE eight-well glass slides (Millipore), respectively. The stable cell lines were induced at 80% confluence by adding a final concentration of 2 μg ml−1 of doxycycline hydrochloride. The protein overproduction was carried out for 24 h. For permeabilization and staining, cells were washed with PBS twice and fixed in 4% PFA for 15 min at room temperature, followed by washing with PBS twice and permeabilized in PBST (PBS with 0.5% Triton-X) for 15 min at room temperature. For immunofluorescent staining, the HEK293 cells were subsequently washed with PBS and blocked in 5% normal goat serum for 1 h before staining with FLVCR1 and FLVCR2 polyclonal antibodies at 1:250 dilutions for 1 h and then with Alexa Fluor 555 (A-21428, Invitrogen) as secondary antibody at 1:500 dilutions for 1 h. The cells were counter-stained with DAPI (Thermo Scientific) and imaged with a laser confocal microscope (Zeiss LSM710). The overproduction stable cells were treated with the same protocol but stained with monoclonal ANTI-FLAG M2-FITC (F4049, Sigma-Aldrich) at 10 μg ml−1 in TBS at room temperature for 1 h against their FLAG-tags and MitoTracker Red CMXRos (M7512, Invitrogen) for mitochondria localization. The cells were mounted using ProLong Diamond mounting medium with DAPI (P36966, Invitrogen) and imaged with laser confocal microscope (Confocal Microscope Leica STELLARIS 5).

    Structure-guided mutagenesis

    To generate the mutant plasmids for FLVCR1 and FLVCR2, an overlapping PCR approach was used. The mutated cDNA of FLVCR1 or FLVCR2 was cloned into pcDNA3.1 for overexpression. The mutations were validated by Sanger sequencing. To test the transport activity of these mutants, the mutant plasmid was either cotransfected with CHKA for choline transport assay or ETNK1 for ethanolamine transport assay. After 24 h of post-transfections, cells were washed with DMEM and incubated with DMEM containing 20 μM [3H]choline or 2.5 μM [14C]ethanolamine for FLVCR1 mutants and 100 μM [3H]choline or 2.5 μM [14C]ethanolamine for FLVCR2 mutants. The assays were stopped after 1 h of incubation at 37 °C. Radioactive signal of each mutant was normalized to the total protein levels.

    Choline export assay

    To examine the export function, FLVCR1 and FLVCR2 plasmids were expressed in HEK293 cells without cotransfection with CHKA or ETNK1. The cells were then incubated with 200 μM [3H]choline or 100 μM [14C]ethanolamine for 2 h to prepack the cells with the ligand. Subsequently, the cells were washed to remove the ligands left over in the medium and incubated with choline/ethanolamine-free medium for 1 h at 37 °C for the release of prepacked ligand. The cells were washed and collected for quantification of radioactive signals. Samples after 2 h of incubation with the radioactive ligand were collected to determine the levels of radioactive levels before the release and used for control.

    Metabolomic analysis

    Adult livers (aged 3–6 months) from controls (FLVCR1f/f and FLVCR1f/+-Mx1-Cre) and conditional FLVCR1-knockout (FLVCR1f/f-Mx1-Cre) mice were used for metabolomic analysis. All mice were maintained under specific pathogen-free conditions with free access to food and water with 12 h dark–light cycle. Briefly, the mice were perfused with PBS to remove blood before organ collection. Liver samples were snap-frozen before being shipped for metabolomics by Metabolon. The levels of metabolites were expressed as relative amount. Studies involving mice were reviewed and approved by the University of Washington Institutional Animal Care and Use Committee under protocol number 2001-13.

    Production and purification of the human FLVCR1 and FLVCR2

    For protein production, the Flp-In T-REx293-FLVCR1 and Flp-In T-REx293-FLVCR2 cell lines were cultured in roller bottles (Greiner Bio-One) in growth media containing 100 μg ml−1 of hygromycin B for 14 d under the above-mentioned conditions. Gene expression was induced at 100% confluence by adding a final concentration of 2 μg ml−1 of doxycycline hydrochloride. After 72 h, cells were harvested with Accutase solution (Sigma-Aldrich) and stored at −80 °C until further use. Harvested cells were suspended in cold lysis buffer containing 25 mM Tris pH 7.4, 150 mM NaCl and 0.1 g ml−1 of SigmaFast ethylenediaminetetraacetic acid (EDTA)-free protease inhibitor (Sigma-Aldrich) and disrupted by stirring under high-pressure nitrogen atmosphere (750 MPa) for 45 min at 4 °C in a cell-disruption vessel (Parr Instrument). The cell lysate was centrifuged at 8,000g at 4 °C for 15 min. Subsequently, the low-velocity supernatant was centrifuged at 220,000g at 4 °C for 60 min. Pelleted membranes were resuspended and stored in a storage buffer containing 25 mM Tris pH 7.4, 150 mM NaCl, 10% glycerol (v/v) and 0.1 g ml−1 of SigmaFast EDTA-free protease inhibitor (Sigma-Aldrich).

    All purification steps of both FLVCRs were performed at 4 °C. Isolated membranes were solubilized with 1% (w/v) lauryl maltose neopentyl glycol (LMNG; GLYCON Biochemicals) with gentle stirring for 1 h. The insoluble membrane fraction was removed through ultracentrifugation at 220,000g for 1 h. Subsequently, the supernatant was incubated with ANTI-FLAG M2 Affinity Gel resin (Millipore) for 1 h. The resin was pre-equilibrated with a buffer containing 50 mM Tris pH 7.4, 150 mM NaCl and 0.02% LMNG (w/v). The washing step was performed using 20 column volumes of wash buffer (50 mM Tris pH 7.4, 150 mM NaCl, 5% (v/v) glycerol and 0.02% LMNG). The protein was eluted from the M2 resin with 10 column volumes of the same buffer supplemented with 4 mM FLAG Peptide (Millipore). The eluted sample was concentrated and subjected to a Superdex 200 Increase 10/300 column (Cytiva) equilibrated with size exclusion chromatography buffer (50 mM Tris pH 7.4, 150 mM NaCl and 0.001% (w/v) LMNG). Peak fractions were pooled, concentrated to 1.5 mg ml−1 using an Amicon 50 kDa cut-off concentrator (Millipore) and stored for further analysis.

    Immunoblotting

    Affinity-purified proteins were subjected to SDS–polyacrylamide gel electrophoresis and immunoblotting. FLAG-tagged FLVCR1 and FLVCR2 were detected using anti-FLAG (F3165, Sigma-Aldrich) at 1:1,000 dilution. Anti-mouse IgG conjugated with alkaline phosphatase (A9316, Sigma-Aldrich) was used as secondary antibody at 1:5,000 dilution. Native FLVCR1 and FLVCR2 proteins were detected by polyclonal FLVCR1 and FLVCR2 antibodies raised in-house at 1:1,000 dilution. GAPDH antibody (sc-32233, Santa Cruz) was used as loading control at 1:4,000 dilution. IRDye 680LT (926-32212, Li-COR Biosciences) was used as secondary antibody for detection.

    Tryptophan fluorescence measurement

    Tryptophan fluorescence measurements were carried out using Prometheus Panta (NanoTemper Technologies). Purified protein samples were diluted with dilution buffer containing 50 mM HEPES pH 7.4, 150 mM NaCl and 0.001% (w/v) LMNG to 1 μM. Buffers with different concentrations of choline or betaine were prepared by serial dilutions in dilution buffer containing 4 mM of the compounds. The protein samples were mixed with an equal volume of dilution buffer or the compound-containing buffer with a final protein concentration of 0.5 μM and then incubated at room temperature for 15 min. A volume of 10 μl of mixed solution was used per Prometheus high-sensitivity capillary (NanoTemper Technologies). Recorded F350/F330 was analysed by using Python libraries including pandas, numpy, scipy and seaborn in Visual Studio Code (Microsoft). Three technical replicates were recorded for data analysis. The custom python code used for data analysis is publicly available through https://doi.org/10.5281/zenodo.10938397.

    Cryo-EM sample preparation

    To collect cryo-EM data of FLVCR1 and FLVCR2 in different sample conditions, different combinations of FLVCR proteins and putative substrate molecules were prepared. For both as-isolated samples of FLVCRs, the protein concentration was adjusted to approximately 1.5 mg ml−1 and subjected to plunge freezing. For samples supplemented with choline, purified proteins were adjusted to 1.5 mg ml−1 and choline was added at a final concentration of 1 mM. For FLVCR1 samples supplemented with ethanolamine, purified proteins were adjusted to 1.5 mg ml−1 and ethanolamine was added at a final concentration of 1 mM. The samples were incubated for 10 min at room temperature before plunge freezing. Identical plunge freezing conditions were applied for all samples: 300 mesh R1.2/1.3 copper grids (Quantifoil) were washed in chloroform and subsequently glow-discharged with a PELCO easiGlow device at 15 mA for 90 s. A volume of 4 µl sample was applied to a grid and blotting was performed for 4 s at 4 °C, 100% relative humidity with nominal blot force 20 immediately before freezing in liquid ethane, using a Vitrobot Mark IV device (Thermo Scientific).

    Cryo-EM image recording

    For each cryo-EM sample, a dataset was recorded in energy-filtered transmission electron microscopy mode using either a Titan Krios G3i or a Krios G4 microscope (Thermo Scientific), both operated at 300 kV. Electron-optical alignments were adjusted with EPU software 3.0–3.4 (Thermo Scientific). Images were recorded using automation strategies of EPU 3.0–3.4 in electron counting mode with either a Gatan K3 (installed on Krios G3i) or a Falcon4 (installed on Krios G4) direct electron detector. For Gatan K3 detector, a nominal magnification of 105,000, corresponding to a calibrated pixel size of 0.837 Å was used and dose fractionated videos (80 frames) were recorded at an electron flux of approximately 15 e pixel−1 s−1 for 4 s, corresponding to a total dose of about 80 e A−2. For Falcon4 detector, a nominal magnification of 215,000, corresponding to a calibrated pixel size 0.573 Å was used, dose fractionated videos were recorded in electron-event representation format at an electron flux of approximately 4 e pixel−1 s−1 for 5 s, corresponding to a total dose of about 50 e A−2. Images were recorded between −1.1 and −2.0 µm nominal defocus. Data collection quality was monitored through EPU v.3.0-3.4 and CryoSparc Live (v.3.0 and 4.0)33.

    Cryo-EM image processing

    For each acquired dataset, the same cryo-EM image processing approach was applied: MotionCor2 was used to correct for beam-induced motion and to generate dose-weighted images34. Gctf was used to determine the contrast transfer function (CTF) parameters and perform correction steps35. Images with estimated poor resolution (more than 4 Å) and severe astigmatism (more than 400 Å) were removed at this step. Particles were picked by TOPAZ and used for all further processing steps36. Two-dimensional classification, initial model generation, three-dimensional (3D) classification, CTF refinement, Bayesian polishing, 3D sorting and final map reconstructions were performed using RELION (v.3.1 and 4.0) or cryoSPARC (v.3.0 and 4.0)33,37,38. In the data processing pipeline, 3D autorefine jobs were conducted following each 3D classification or 3D sorting round for all resulted classes, to carefully assess the resulting density maps for quality and resolution through both metrics and visual inspection. Data processing was only proceeded with those maps that seemed promising for further refinement stages. Fourier shell correlation (FSC) curves and local-resolution estimation were generated in RELION or cryoSPARC for individual final maps. A schematic overview of our processing workflow and a summary of map qualities are shown and Supplementary Figs. 3–5.

    Model building and geometry refinement

    The first atomic models of FLVCR1 and FLVCR2 were built into the respective electron microscopy density maps of the as-isolated state in Coot (v0.8) or ISOLDE within ChimeraX (v.1.5 and 1.6)39,40,41, using the AlphaFold predicted structures as initial templates42. After manual backbone tracing and docking of side chains, real-space refinement in Phenix was performed (v.1.18)43. Refinement results were manually inspected and corrected if required. These models were used as templates to build all subsequent atomic models. The finalized models were validated by MolProbity implemented in Phenix44. Map-to-model cross-validation was performed in Phenix (v.1.18). FSC0.5 was used as cut-off to define resolution. The comprehensive information on the Cryo-EM data collection, refinement and validation statistics is shown in Extended Data Table 1. The finalized models of the two FLVCR proteins in different states were visualized using ChimeraX and used as starting structures for molecular dynamics simulations.

    Molecular dynamics simulations

    All molecular dynamics simulations were performed using the GROMACS 2022.4 (ref. 45) software. The protein structures were embedded in a lipid bilayer with 75% POPE and 25% POPG with CHARMM-GUI46 and solvated in TIP3P water with 150 mM NaCl. The CHARMM36m force field47 was used with the improved WYF parameters for cation–π interactions, in particular of the choline and ethanolamine ligands48. The systems were minimized for 5,000 steepest-descent steps and equilibrated for 250 ps of molecular dynamics in an NVT ensemble and for 1.625 ns in an NPT ensemble. Position restraints of 4,000 and 2,000 kJ mol−1 nm−2 in the backbone and side chain heavy atoms, respectively, were gradually released during equilibration. The z-positions of membrane phosphates, as well as lipid dihedrals, were initially restrained with force constants of 1,000 kJ mol−1 nm−2, which were gradually released during equilibration. The initial time step of 1 fs was increased to 2 fs during NPT equilibration. Long-range electrostatic interactions were treated with particle-mesh Ewald49 with a real-space cut-off of 1.2 nm. Van-der-Waals interactions were cut-off beyond a distance of 1.2 nm. The LINCS algorithm50 was used to constrain the bonds involving hydrogen atoms. During equilibration, a constant temperature of 310 K was maintained with the Berendsen thermostat51, using a coupling constant of 1 ps. Constant pressure of 1 bar was established with a semi-isotropic Berendsen barostat and a coupling constant of 5 ps. In the production runs, a Nosé–Hoover thermostat52 and a Parrinello–Rahman barostat were used53.

    We used our cryo-EM structures as initial models for simulations of as-isolated and choline-bound inward-facing FLVCR1, as-isolated and choline-bound inward-facing FLVCR2 and as-isolated outward-facing FLVCR2. We set the protonation state of each residue as predicted for pH 7.0 using the PROPKA server54. An initial structure of choline-bound outward-facing FLVCR2 was generated by aligning the as-isolated outward-facing FLVCR2 to the choline-bound inward-facing FLVCR2 and maintaining choline in the cavity. In choline entry simulations, the as-isolated structures were used with 380 mM choline in solution. For simulations of ethanolamine-bound FLVCR1, the choline within the cavity of the cryo-EM structure was replaced by this ligand. Simulations with deprotonated ethanolamine were performed as well and results are included in Supplementary Figs. 11 and 12. Choline and ethanolamine release simulations were interrupted after ligand exit from the cavity and hence have variable duration. For all other systems, each replica was run for 1 µs. A summary of all simulations performed in this study is provided in Supplementary Table 3 (table with technical information). Time-resolved distance calculations for all replicas not included in the main figures are shown in Supplementary Fig. 11. Minimum atom-pair distances were calculated as the minimum distance over all pairs of atoms in two stated groups, including hydrogens (for example, ligand and certain defined side chains).

    Alanine substitution mutations were introduced using PyMol55 and simulated with identical parameters as those applied in the corresponding wild-type simulations. In FLVCR1 and FLVCR2, alanine mutations were introduced in the cavity residues W125FLVCR1 and W102FLVCR2, respectively.

    For the MM/PBSA calculations, we used gmx_MMPBSA56 with dielectric constants of 7.0, 80.0 and 4.0 for the membrane, solvent and protein, respectively, and the default surface tension of 0.0072 kcal mol−1 nm−2. We estimated entropies using the interaction entropy method57. The contributions of W125FLVCR1 and W102FLVCR2 to the binding energy were estimated by alanine scanning.

    Visual molecular dynamics58 and MDAnalysis59 were used to visualize and analyse the trajectories, respectively. An assessment of the reliability and reproducibility of our simulations is provided in Supplementary Table 4.

    Interior tunnels and cavities

    Tunnels and cavities were mapped with MOLE v.2.5 (ref. 60) with a bottleneck radius of 1.2 Å, bottleneck tolerance 3 Å, origin radius 5 Å, surface radius 10 Å, probe radius 5 Å and an interior threshold of 1.1 Å.

    We calculated the volume of the cavity using CASTp61 with a bottleneck radius of 1.4 Å. Residues 297–320 and 512–516 were removed from the FLVCR1 model to avoid the misattribution of the volume between internal loops to the cavity volume. Analogously, residues 272–296 and 487–502 were not included in the cavity volume calculation of FLVCR2.

    Sequence alignments

    Multiple sequence alignments of FLVCR1 and FLVCR2 from Homo sapiens, Felis catus, Mus musculus and Sus scrofa were performed using Clustal Omega62.

    Reporting summary

    Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

    [ad_2]

    Source link