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

and on behalf of the PROPANE Study Group

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

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
Article number28
JournalBMC Systems Biology
Volume11
Issue number1
DOIs
Publication statusPublished - 24 Feb 2017

Fingerprint

Sodium Channels
Polymorphism
Network Topology
Sodium
Disorder
Mutation
Topology
Betweenness
Centrality
Pain
Amino acids
Substitution reactions
Genes
Proteins
Fibers
NAV1.7 Voltage-Gated Sodium Channel
Computer Simulation
Electric potential
Connectivity
Erythromelalgia

Keywords

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

ASJC Scopus subject areas

  • Structural Biology
  • Modelling and Simulation
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Cite this

Network topology of NaV1.7 mutations in sodium channel-related painful disorders. / and on behalf of the PROPANE Study Group.

In: BMC Systems Biology, Vol. 11, No. 1, 28, 24.02.2017.

Research output: Contribution to journalArticle

and on behalf of the PROPANE Study Group. / Network topology of NaV1.7 mutations in sodium channel-related painful disorders. In: BMC Systems Biology. 2017 ; Vol. 11, No. 1.
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abstract = "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.",
keywords = "Network analysis, Neuropathic pain, Sodium channel, Structural modeling",
author = "{and on behalf of the PROPANE Study Group} and Dimos Kapetis and Jenny Sassone and Yang Yang and Barbara Galbardi and Xenakis, {Markos N.} and Westra, {Ronald L.} and Radek Szklarczyk and Patrick Lindsey and Faber, {Catharina G.} and Monique Gerrits and Merkies, {Ingemar S J} and Dib-Hajj, {Sulayman D.} and Massimo Mantegazza and Waxman, {Stephen G.} and Giuseppe Lauria and Michela Taiana and Margherita Marchi and Raffaella Lombardi and Daniele Cazzato and Boneschi, {Filippo Martinelli} and Andrea Zauli and Ferdinando Clarelli and Silvia Santoro and Ignazio Lopez and Angelo Quattrini and Janneke Hoeijmakers and Maurice Sopacua and {de Greef}, Bianca and Smeets, {Hubertus Julius Maria} and Momani, {Rowida Al} and Vanoevelen, {Jo Michel} and Ivo Eijkenboom and Sandrine Cest{\`e}le and Oana Chever and Rayaz Malik and Mitra Tavakoli and Dan Ziegler",
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AU - and on behalf of the PROPANE Study Group

AU - Kapetis, Dimos

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AU - Galbardi, Barbara

AU - Xenakis, Markos N.

AU - Westra, Ronald L.

AU - Szklarczyk, Radek

AU - Lindsey, Patrick

AU - Faber, Catharina G.

AU - Gerrits, Monique

AU - Merkies, Ingemar S J

AU - Dib-Hajj, Sulayman D.

AU - Mantegazza, Massimo

AU - Waxman, Stephen G.

AU - Lauria, Giuseppe

AU - Taiana, Michela

AU - Marchi, Margherita

AU - Lombardi, Raffaella

AU - Cazzato, Daniele

AU - Boneschi, Filippo Martinelli

AU - Zauli, Andrea

AU - Clarelli, Ferdinando

AU - Santoro, Silvia

AU - Lopez, Ignazio

AU - Quattrini, Angelo

AU - Hoeijmakers, Janneke

AU - Sopacua, Maurice

AU - de Greef, Bianca

AU - Smeets, Hubertus Julius Maria

AU - Momani, Rowida Al

AU - Vanoevelen, Jo Michel

AU - Eijkenboom, Ivo

AU - Cestèle, Sandrine

AU - Chever, Oana

AU - Malik, Rayaz

AU - Tavakoli, Mitra

AU - Ziegler, Dan

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N2 - 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.

AB - 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.

KW - Network analysis

KW - Neuropathic pain

KW - Sodium channel

KW - Structural modeling

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