Network topology of NaV1.7 mutations in sodium channel-related painful disorders

Dimos Kapetis, Dimos Kapetis, Jenny Sassone, Jenny Sassone, Yang Yang, Yang Yang, Barbara Galbardi, Markos N. Xenakis, Markos N. Xenakis, Ronald L. Westra, Ronald L. Westra, Radek Szklarczyk, Patrick Lindsey, Catharina G. Faber, Catharina G. Faber, Monique Gerrits, Ingemar S.J. Merkies, Ingemar S.J. Merkies, Sulayman D. Dib-Hajj, Sulayman D. Dib-HajjMassimo Mantegazza, Stephen G. Waxman, Stephen G. Waxman, Giuseppe Lauria, Michela Taiana, Margherita Marchi, Raffaella Lombardi, Daniele Cazzato, Filippo Martinelli Boneschi, Andrea Zauli, Ferdinando Clarelli, Silvia Santoro, Ignazio Lopez, Angelo Quattrini, Janneke Hoeijmakers, Maurice Sopacua, Bianca de Greef, Hubertus Julius Maria Smeets, Rowida Al Momani, Jo Michel Vanoevelen, Ivo Eijkenboom, Sandrine Cestèle, Oana Chever, Rayaz Malik, Mitra Tavakoli, Dan Ziegler

Research output: Contribution to journalArticlepeer-review

Abstract

© 2017 The Author(s). Background: Gain-of-function mutations in SCN9A gene that encodes the voltage-gated sodium channel NaV1.7 have been associated with a wide spectrum of painful syndromes in humans including inherited erythromelalgia, paroxysmal extreme pain disorder and small fibre neuropathy. These mutations change the biophysical properties of NaV1.7 channels leading to hyperexcitability of dorsal root ganglion nociceptors and pain symptoms. There is a need for better understanding of how gain-of-function mutations alter the atomic structure of Nav1.7. Results: We used homology modeling to build an atomic model of NaV1.7 and a network-based theoretical approach, which can predict interatomic interactions and connectivity arrangements, to investigate how pain-related NaV1.7 mutations may alter specific interatomic bonds and cause connectivity rearrangement, compared to benign variants and polymorphisms. For each amino acid substitution, we calculated the topological parameters betweenness centrality (B ct ), degree (D), clustering coefficient (CC ct ), closeness (C ct ), and eccentricity (E ct ), and calculated their variation ( value = mutant value -WT value ). Pathogenic NaV1.7 mutations showed significantly higher variation of |B ct | compared to benign variants and polymorphisms. Using the cut-off value ±0.26 calculated by receiver operating curve analysis, we found that B ct correctly differentiated pathogenic NaV1.7 mutations from variants not causing biophysical abnormalities (nABN) and homologous SNPs (hSNPs) with 76% sensitivity and 83% specificity. Conclusions: Our in-silico analyses predict that pain-related pathogenic NaV1.7 mutations may affect the network topological properties of the protein and suggest |B ct | value as a potential in-silico marker.
Original languageEnglish
JournalBMC Systems Biology
Volume11
Issue number1
DOIs
Publication statusPublished - Feb 24 2017

Keywords

  • Network analysis
  • Neuropathic pain
  • Sodium channel
  • Structural modeling

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