- Research article
- Open access
- Published:
Network topology of NaV1.7 mutations in sodium channel-related painful disorders
BMC Systems Biology volume 11, Article number: 28 (2017)
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 (Δ value = mutant value -WT value ). Pathogenic NaV1.7 mutations showed significantly higher variation of |ΔB ct | compared to benign variants and polymorphisms. Using the cut-off value ±0.26 calculated by receiver operating curve analysis, we found that ΔB 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 |ΔB ct | value as a potential in-silico marker.
Background
SCN9A gene encodes the alpha-subunit of voltage-gated sodium channel NaV1.7 that is expressed in dorsal root ganglion (DRG) nociceptors and in sympathetic neurons. NaV1.7 is folded into four homologous domains, each containing six transmembrane helices (S1-S6). S1–S4 helices form the voltage-sensing domain (VSD) and highly conserved basic residues in S4 sense the electric field across the membrane. S5–S6 helices with the re-entrant extracellular loop in between form the pore domain (PD) [1]. Membrane depolarisation induces a conformational change in the VSD that, through the S4-S5 linker, is transmitted to the PD and prompt the gate to open, allowing the passage of sodium ions through the pore [2]. Opening and closing of the channel modulate the subthreshold membrane potential of nociceptors and play a key role in regulating their firing.
Missense mutations in SCN9A have been associated to a spectrum of painful conditions in humans [3], including inherited erythromelalgia (IEM), [4–13], paroxysmal extreme pain disorder (PEPD) [14–17], and small fibre neuropathy (SFN) [18, 19]. Voltage-clamp recording, performed in transfected cell lines and DRG neurons in vitro, showed that IEM-related mutations enhance the activation of NaV1.7 through a hyperpolarising shift and a slower deactivation that keeps the channel open longer once it is activated [3], thus generating a larger-than-normal inward sodium current, with greater biophysical changes at higher temperature [20]. PEPD-related NaV1.7 mutations impair channel inactivation and prolong action potentials and repetitive nociceptor firing in response to provoking stimuli, such as stretching and exposure to cold temperatures [14, 16, 21]. NaV1.7 mutations identified in SFN patients display a spectrum of electrophysiological signatures, including impaired slow inactivation, depolarised slow and fast inactivation and enhanced resurgent currents [18].
Overall, all the disease-related NaV1.7 mutations are pro-excitatory for the NaV1.7 channel, thus increasing nociceptor excitability. For those NaV1.7 mutations that have been studied by structural modelling, the gain-of-function effect stems from functionally significant changes in the biomolecular structure of NaV1.7 channel [22–24]. Accordingly, gain-of-function mutations found in IEM, PEPD, and SFN patients might be expected to produce functionally significant changes in the protein structure of NaV1.7, whereas single nucleotide polymorphisms (SNPs) or variants not associated with disease would not be expected to modify the NaV1.7 protein structure in functionally significant ways. Previous NaV1.7 structural modelling, combined with functional studies, showed that the disruption of the hydrophobic ring by the F1449V [24] or the in-frame deletion Leu955Del [22] contribute to destabilizing the NaV1.7 closed-state. These studies suggest that homology modelling is a useful tool to predict functional changes in the biomolecular structure of Nav1.7. However, the nature and extent of interatomic bond variations in NaV1.7 protein structure caused by amino acid changes have not been examined over a spectrum of mutations and SNPs.
Structural modelling combined with network theory has been widely exploited in studying protein structure to identify the emergent features of global connectivity. Indeed, several studies have used network theory to provide important insights in the local topology of interactions from a global prospective with examples from the field of allosteric communication pathways [25], protein-protein interactions [26], catalytic site residues in enzymes [27] and protein-folding mechanisms [28]. Several methods have been proposed in the literature to transform the protein structures into a network by considering: (a) the C-alpha/C-beta atoms in the amino acid residues, as in a protein backbone network [29] (b) description of the atomic contacts between residues that also feature correlated motions [30–32] or (c) weak and strong non-covalent protein structure network considering atom-atom interaction at the side chain level which has been proven to provide valuable biological insights [30, 33, 34]. These studies have shown that network analysis of a protein can yield a useful method to characterize the topology of the constituent amino acid residues. Protein topologies and interaction connectivity could often produce distinct small-world networks proprieties [28, 35, 36], thus having high local connectivity of residue nodes with a smaller number of long-range residue-residue interactions.
In the present study, we aimed at elucidating specific interatomic bond variations caused by amino acid changes in NaV1.7 structure by using a network-based method. We tested the hypothesis that mutations associated with IEM, PEPD and SFN cause specific types of interatomic bonds variation of NaV1.7 that can be quantified by a network-based theoretical approach able to reduce the complexity of the three-dimensional protein architecture to one-dimensional graphs [28].
Methods
Protocol description
The overall method is summarized in Fig. 1. Our methodology can be encapsulated in a protocol that has two main components: homology modelling and topology analysis. The main steps of the current protocol are: (A) Homology modelling of NaV1.7 WT based on the bacterial NavAb sodium channel template. (B) Energy minimization and structure refinement of the protein structure (C) In-silico mutagenesis is performed for pathogenetic and control group (nABN/hSNPs) mutations (Table 1). (D) Construction of inter-residue network based on weak and strong noncovalent interactions (E) Network centrality calculation and (F) the difference between mutated and WT mutated (Δ value = mutant value -WT value ).
NaV1.7 homology modelling
A homology model of the closed-state pore domain of the NaV1.7 was generated using the crystal structure of the bacterial Arcobacter bultzeri NaV channel NaVAb [37] as a template with the human sequence NM_002977.3 through the MEMOIR server [38]. Gap region (269-340, DI) between template-target alignment and interdomain loop regions (416-726, DI-DII; 967-1175, DII-DIII; 1458-1498, DIII-DIV) were excluded from in-silico mutagenesis (Fig. 1A). The NaVAb template shared 28% sequence identity for DI, 24% for DII, 28% for DIII and 28% for DIV (overall 27% sequence identity). The four homologous domains were modelled in the clockwise direction viewed from the extracellular side as previously suggested [39, 40]. Ab-initio modelling was performed to extend the S6 helices of the PD using the Iterative Threading ASSEmbly Refinement (I-TASSER) server [41]. The final model was subjected to energy minimization and model refinement using the YAMBER force field [42] and the Fragment-Guided Molecular Dynamics (FG-MD) server [43]. The NaV1.7 WT model was subjected to stereochemical analysis with RAMPAGE server (http://services.mbi.ucla.edu/). RAMPAGE provides results in a graphical form that shows the number of residues falling in favoured region, allowed region and in outlier region.
In-Silico Mutagenesis of NaV1.7 pathogenetic and control mutations
We performed in-silico mutagenesis via WT domain replacement of NaV1.7 mutations found in IEM, PEPD or SFN patients in which gain-of-function was demonstrated by cell electrophysiology assay and that do not alter the biophysical properties of the channel (nABN). To increase the number of control variants, we added missense SNPs identified between SCN9a homologous genes sharing >90% nucleotide sequence identity using the NCBI HomoloGene Database [44]. We constructed the phylogenetic tree of the multiple sequence alignment using ClustalW via neighbor joining method (Additional file 1: S1 Text; https://www.ebi.ac.uk/Tools/phylogeny/clustalw2_phylogeny/). The mutated models were further subjected to energy minimization and model refinement using the YAMBER force field [42] and the FG-MD server (Fig. 1B) [43]. Such hSNPs have previously been used in similar studies [45–47]. All the mutations and SNPs are reported in Additional file 2: Table S1.
Transforming NaV1.7 structure into residue interaction graphs
NaV1.7 structures were transformed into mathematical graphs by identifying interatomic bonds between the amino acids. The amino acid residues form the nodes and inter-node contact interaction form the edges of the graph (Fig. 1D). We identified the interatomic bonds (hydrophobic, hydrogen bonds, salt-bridges, cation-π and π-π stacking interactions) between two residues i and j as long as the atom-atom distance between them was less than 5.0 Å using the commands “ListIntAtom” and “ListIntBo” via YASARA software (Yet Another Scientific Artificial Reality Application, www.yasara.org). Hydrophobic contacts between residues were considered in the following atom groups: (a) the first carbon of CH3-, -CH2- and CHC3 (b) sp2 carbons (phenolic rings). π-π stacking were considered between (a) sp2 carbons with a hydrogen and (b) carbon, nitrogen, oxygen or sulphur atoms in planar phenolic rings. Cation-π formation was considered to be a π-π contact with the difference being that one of the interaction partners is a cation. The de novo network construction for each mutant and WT models is achieved considering the predicted binary interatomic bonds identified through YASARA software.
Topological metrics and network visualization
We computed some of the most well-known network centrality measures for each mutant and WT network NaV1.7 graph using the Cytoscape plugin NetworkAnalyzer [48], namely:
Betweenness Centrality (B ct ) and edge Betweenness centrality (EB ct ): B ct [49] is defined as the fraction of shortest pathways between all pairs of nodes of the network that go through that node. Let G = (N, E) a graph, where N is the set of the nodes and E is the set of the edges. For each node n and m in N, let d (n, m) the distance between n and m. We define
where s, t ∈N, σst (n) is the number of shortest paths from s to t that n lies on, and σst denotes the number of shortest paths from s to t. It accounts the importance of a node facilitating interactions between other nodes. For example, a node with high Bct can operate as a bridge on many shortest paths between other nodes in the network. It is a measure of how powerful a node is able to transfer (high B ct ) or interrupt (low B ct ) the spread of information on the fastest connection between two nodes. Similarly, the EB ct of an edge is the number of shortest paths between pairs of nodes that run along it. We define:
Where N = set of nodes; E = set of edges; \( {\upsigma_{{\mathrm{n}}_{\mathrm{i}}}}_{{\mathrm{n}}_{\mathrm{j}}} \) = number of shortest paths between ni and nj; \( {\upsigma_{{\mathrm{n}}_{\mathrm{i}}}}_{{\mathrm{n}}_{\mathrm{j}}}\ \left(\mathrm{e}\right) \) = number of shortest paths between ni and nj which pass through e ∊ E;
Degree (D): D [49] of a node (k) is defined as the total number of nodes that it is directly connected to;
Clustering Coefficient (CC ct ): Clustering Coefficient [49] is a metric commonly employed to identify well-connected sub-components in network which represents the interconnectivity of neighbors of the node. It measures the degree to which nodes tend to cluster together and is defined as the fraction of triangles around a node among the total number of possible triangles. We define
where kn is the number of neighbors of n and en is the number of connected pairs between all neighbors of n;
Closeness centrality (C ct ): Cct is defined as the sum of the inverted distances, i.e. farness, to all other nodes in the graph. It captures the basic intuition that the closer a node is to all other nodes in terms of path length, the more important it is. Mathematically, C ct of a node n is defined as the inverse of the sum of shortest paths from n to all other nodes m in network. We define
Eccentricity (E ct ): Ect measures the distance between a node n and the most distance node m; if the E ct of the node n is low, this means that all other nodes are in proximity whereas a high E ct means that there is at least one node (and all its neighbors) that is far from node n. We define E ct maximum non-infinite length of a shortest path between n and another node in the network. We define
Network centrality measure variation
For each network centrality measures we calculated the difference between mutant and WT values defined as Δvalue (Δvalue = mutant value – WT value). The NaV1.7 amino acid network was visualized using Cytoscape’s Organic layout, which is a force-directed layout algorithm similar to the Fruchterman-Reingold approach [50].
Statistical analysis
Statistical analyses were performed using the R statistical Package [51]. Data are indicated as mean ± SD. Statistical significance was determined by the Wilcoxon signed-ranked test (p <0.05). The receiver operating characteristics (ROC) curve was used to assess the discriminatory power of centrality measure variations between pathogenetic NaV1.7 mutations and control groups (nABN and hSNPs). The upper-angle of ROC corresponding to the best sensitivity and specificity was used to identify the best cut-off value.
Results
NaV1.7 interatomic structure graph design
We performed homology modelling to construct the tertiary structure of the closed-state NaV1.7 sodium channel (Fig. 1). We constructed the atomic model of NaV1.7 sodium channel using the MEMOIR server [38] based on the crystal structure of the bacterial Arcobacter bultzeri NaV channel NaVAb as a template with the human sequence NM_002977.3. The first four helices S1–S4 form the VSD and the last two helices S5–S6 form the PD (Fig. 2a and b). Gap region (269-340, S5-6 extracellular linker in DI) between template-target alignment and interdomain loop regions (416-726, DI-DII; 967-1175, DII-DIII; 1458-1498, DIII-DIV) were excluded from in-silico mutagenesis. The four homologous domains were modelled in the clockwise direction viewed from the extracellular side as suggested previously [39, 40]. Ab-initio modelling was performed to extend the S6 helices of the PD using the Iterative Threading ASSEmbly Refinement (I-TASSER) server [41]. The final model was subjected to energy minimization and model refinement using the YAMBER force field [42] and the Fragment-Guided Molecular Dynamics (FG-MD) server [43] (Additional file 3: NaV1.7 pdb file). The RAMPAGE results for the NaV1.7 model showed 88.5% residues in most favored region (Additional file 4: Figure S1), 9% (90 residues) in allowed region and 2.5% (25 residues) in outlier region. A good quality Ramachandran plot has over 90% residues in the most favoured regions [52] therefore Ramachandran plot of NaV.17 it is close to a good quality model (88.5% residues in most favoured regions).
We performed in-silico mutagenesis for 18 mutations causing IEM, 6 mutations causing SFN, 6 mutations causing PEPD (Additional file 2: Table S1), 4 mutations not causing biophysical abnormalities (nABN) in the channel (N1245S: [53]; L1267V: [53]; V1428I and T920N: Waxman, Dib-Hajj and Mantegazza, unpublished observations) and 49 SNPs identified among human and homologous mammalian (hSNPs) SCN9A genes with >90% sequence identity (Additional file 5: S2 Text). All the disease-related mutations had previously been characterized by electrophysiological assays, and found to confer gain-of-function changes to the NaV1.7 channel (Additional file 2: Table S1). The WT and mutant NaV1.7 structures were transformed into undirected graphs by the identification of hydrophobic, cation-π and π-π stacking interactions and hydrogen bonds (H-bonds) among the amino acids. In the resulting graph, amino acids are the nodes and their interactions are the edges (Fig. 2c).
Analyses of the interatomic variations caused by gain-of-function mutations
Previous studies showed that gain-of-function mutations change the biophysical properties of the channel NaV1.7 [4–9, 14–16, 18, 54] but the underlying interatomic variations are yet to be investigated. We analyzed the interatomic variations by calculating the network centrality parameters (B ct , D, CC ct , C ct , E ct ; see methods for detailed definitions) of WT and mutated residues and the value of the variation (Δ value = mutant value - WT value , ΔB ct , ΔD, ΔCC ct , ΔC ct , ΔE ct ) associated with each gain-of-function NaV1.7 mutation, nABN and hSNP. B ct is a measure of the centrality of a node n defined as the fraction of shortest pathways between all pairs of nodes (s, t) of the network that go through that node n [49, 55]. D of a node n is defined as the total number of nodes that it is directly connected to [49, 55]. CC ct is a metric commonly employed to identify well-connected sub-components in network which represents the interconnectivity of neighbors of a node n [35, 49]. C ct is defined as the sum of the inverted distances of a node n, i.e. farness, to all other nodes in the graph. It captures the basic intuition that the closer a node is to all other nodes, the more important it is [56]. Eccentricity (E ct ) of a node n is the greatest distance from a node n to any other node m [55].
Figure 3a-e show the profile of the topological parameters B ct , D, CC ct , C ct , and E ct in WT and mutated residues. The graphs show that both gain-of-function mutations and nABN/hSNPs modify the D values (Fig. 3c and Additional file 6: Figure S2) and CC ct values (Fig. 3d and Additional file 7: Figure S3) in a wide range but without significant differences between the groups (gain-of-function mean ∆D = 4.30 ± 5.15; nABN and hSNP mean ∆D = 2.27 ± 2.1; p > 0.05 by Wilcoxon signed-ranked test; gain-of-function mean ∆CC ct = 0.15 ± 0.20; nABN and hSNP mean ∆CC ct = 0.20 ± 0.25; p > 0.05 by Wilcoxon signed-ranked test). Smaller variations were observed in C ct values (Fig. 3e and Additional file 8: Figure S4) and E ct values (Fig. 3f and Additional file 9: Figure S5) without significant differences between the groups (gain-of-function mean ∆C ct = 0.65 ± 0.94; nABN and hSNP mean ∆C ct = 0.71 ± 1.51; p > 0.05 by Wilcoxon signed-ranked test; gain-of-function mean ∆E ct = 1.53 ± 3.75; nABN and hSNP mean ∆E ct = 2.05 ± 4.62; p > 0.05 by Wilcoxon signed-ranked test). Overall, ΔD, ΔCC ct , ΔC ct , ΔE ct did not differ significantly between gain-of-function mutations and nABN and hSNPs.
We next analysed B ct values and found that pathogenic NaV1.7 mutations are characterized by higher variations of ΔB ct compared with non-pathogenic mutations and polymorphisms (Fig. 3a; Table 1 and 2). Indeed, |ΔB ct | was significantly higher in gain-of-function mutations compared with nABN and hSNPs (gain-of-function mean ∆B ct = 1.14 ± 1.40; nABN and hSNP mean ∆B ct = 0.19 ± 0.28; p < 0.001 by Wilcoxon signed-ranked test; Fig. 3b). ΔB ct variations associated with Nav1.7 pathogenetic mutations and nABN variants are exemplified in the structural modeling shown in the Fig. 4.
Figure 4a and b shows the B ct topological proprieties of the F216S mutation associated to IEM [11, 57]. In the WT protein, F216 is located in VSD (S4) of DI and is predicted to mediates hydrophobic interactions with V194, V195, F198, T202 (S3, DI) and L219 (S4, DI). F216 is also predicted to mediate two H-bonds: F216[NH] with L213[CO] and F216[CO] with L219[NH] residues (S4, DI). Upon mutation, the hydrophobic interaction between F216S (S4) and the S3 residues (DI; VSD) are interrupted. The H-bonds F216[NH] with L213[CO] are interrupted. New H-bonds between S216[NH] and A212[CO] are created. All these changes yield negative B ct variation (ΔB ct = -1.71, Fig. 4a and b; Additional file 10: S1 YASARA; Additional file 11: S2 YASARA). L858H is another IEM-associated mutation [4, 9, 17]. In the WT protein, L858 is located in S4-S5 and is predicted to interacts with I234 (DI; S4-S5), V861 (DII; S4-S5), N950, L951 and V947 (DII; S6) through hydrophobic bonds and through H-bonds formed by L858[CO] and L862[NH]) (DII; S4-S5). L858H mutation interrupts hydrophobic interaction with I234 (DI; S4-S5), V861 (DII; S4-S5), N950 (DII; S6) and forms new H-bonds by H858[NH] and A854[CO] (DII; S4-S5) and by H858[CO] with V947[NH] (DII; S6) leading to a negative ΔB ct value (-1.85) (Fig. 4c and d; Additional file 12: S3 YASARA; Additional file 13: S4 YASARA). L1267V is an example of nABN variant that is located in the VSD of DIII which is highly conserved between human and SCN9A homologous genes (Additional file 5: S2 Text). L1267 interacts with V1263 through H-bonds formed by L1267[NH] and V1263[CO]. Upon mutation, V1267 forms new hydrophobic bond with V1263 which does not cause B ct variation (ΔB ct =0) (Fig. 4e and f; Additional file 14:S5 YASARA; Additional file 15:S6 YASARA).
Figure 5 shows the network inter-residue connectivity of the IEM-associated mutations I848T and N395K, both characterized by very high ΔB ct values. I848 is located in S4-S5 (DII) and I848T causes a significant hyperpolarising shift in activation, a slow deactivation and an increased response to small-ramp depolarisations in DRG nociceptors [4, 9, 17, 58, 59]. I848 is predicted to interact with S4-S5 (DII) and pore (DIII; S6) through I845 and F1435, which have with very high B ct values (3.4 and 6.6, respectively). Upon mutation, the interatomic bond interactions between DII (S4-S5) and DIII (pore; S6) are interrupted and therefore ΔB ct shifts to a negative value (-5.83) showing lower EBct values (Fig. 5a and b, Additional file 16: Figure S6). Conversely, N395K mutation forms interdomain hydrophobic (S4-S5; DI and DIV, Pore; DIV) and H-bonds (S4-S5; DIV and S6; DIV), leading to a positive ΔB ct (5) and higher EBct values.
ΔBct distinguishes with high specificity pathogenic NaV1.7 mutations from variants not causing disease
The in-silico topological analyses described in Figures 4 and 5 was computed for all the pain disorder-related mutations (18 causing IEM, 6 causing SFN and 6 causing PEPD), and for all the 4 nABN and 49 hSNPs variants showed in Fig. 2c. The results showed that the only topological parameter that differs significantly between gain-of-function mutations and non-pathogenic amino acid changes is the |ΔB ct | value (Fig. 3). Indeed, 83% of nABN variants and hSNPs were characterized by |ΔB ct | values <0.26. The remaining 17% showed |ΔB ct | values >0.26 (42, V795I; 57, M1532V; 59, Y1537N; 63, V1565I; 68, V1613I; 73, I1399D; 67, T1590R; 81, D1662A; 82, G1674A) (Fig. 6a and Table 2). According to our NaV1.7 model structure, most of nABN and hSNPs, which are evolutionary variable, are located in VSD and P-loop domains and are predicted to be exposed to the lipid interface (Fig. 6b).
Twenty-three out of 30 (77%) gain-of-function NaV1.7 mutations had |ΔB ct | > 0.26 and are located in VSD, Pore and S4-S5 of DI, DII, DIII and DIV domains (Table 1). The remaining 7 mutations (23%) had |ΔB ct | <0.26 (1, I136V; 2, R185H; 8, M1532I; 9, W1538R; 17, V1298F; 19, V1299F; 20, P1308L) (Fig. 6a and Table 1). These pathogenetic mutations with small |ΔB ct | variation are located in VSD of DI (2, R185H) and DIII (8, M1532I; 9 W1538R) or in S4-S5 linker of DIII (17, V1298F; 19, V1299F; 20, P1308L), are highly evolutionary conserved residues (Additional file 5: S2 Text) and are predicted to be exposed outside the core of the channel (exception: I136V; Fig. 6b-c).
According to these results, we hypothesized that ΔB ct might provide enough sensitivity and specificity to distinguish gain-of-function mutations from control variants. Using the cut-off value (ΔB ct ± 0.26) that maximizes sensitivity and specificity, ΔB ct correctly classified 44 out of 53 controls variants (nABN and hSNPs) and 23 out of 30 gain-of-function mutations, yielding 76% sensitivity and 83% specificity. The area under the ROC curve analysis for the ΔB ct scores was 0.81 (Fig. 6c, 95% confidence interval CI = 0.70–0.91).
Discussion
Many phenomena can be modelled as collections of elements that interact through a complex set of connections. Network theory has become one of the most successful frameworks for studying these phenomena [60] and has led to major advances in our understanding of ecological systems [61], social and communication networks [62], brain connectivity [63] and metabolic and gene regulatory pathways in living cells [64].
Using network theory, protein structure can be described as mathematical graphs [28] that represent the interatomic connections. The topological features of amino acid residues, named nodes, can be described using centrality measures that define the reciprocal relationship in terms of connectivity and capability to influence other nodes within the network. We focused on the topological analysis of NaV1.7 gain-of-function mutations identified in patients with painful disorders. We considered a homology model of the NaV1.7 pore in the closed state and calculated the interaction of the nodes within the network through several measures of topology.
Our findings show that ΔB ct values tend to be significantly higher in NaV1.7 pain-related mutations than in control groups (nABN and hSNPs). B ct represents the influence that the shortest communication pathways have on the overall interatomic connections. Nodes with high B ct value could efficiently integrate signals (e.g. energy) and the reduction of B ct value caused by single amino acid substitutions suggests that the signalling transfer capability of the network is decreased. Conversely, the increase of B ct value suggests that a mutated node could facilitate the load transfer through the shortest communication pathways. Therefore, changes in ΔB ct reflect increased or decreased potential for connectivity of amino acid within the protein and provides numerical values about how single amino acid substitutions might act as a bottleneck for specific nodes linking different parts of the network. Previous studies of network topological parameters revealed that effective allosteric communications can be primarily provided by structurally stable residues that exhibit high B ct [65]. Therefore, B ct might provide a novel and useful tool for identifying allosteric hotspots in comparison with other centrality measures as previously suggested [25, 66].
Using the cut-off value (ΔB ct ± 0.26) that maximizes sensitivity and specificity, our data show that ΔB ct correctly classified 44 out of 53 controls variants (nABN and hSNPs) and 23 out of 30 gain-of-function mutations, yielding 76% sensitivity and 83% specificity. The area under the ROC curve value for the ΔB ct scores was 0.81 (Fig. 6c, 95% confidence interval CI = 0.70–0.91). By contrast, our data show that none of other topological parameters (D, CC ct , C ct , and E ct ) differ significantly between controls and gain-of-function NaV1.7 mutations. Although these data suggest that the pain-related NaV1.7 gain-of-function mutations do not have significant effects on the degree of connectivity, local clustering connectivity of the neighbour nodes (i.e. their tendency to cluster together) and eccentricity (i.e. how far is each node from any other node within the network), it is important to consider that our results derive from homology modelling constructed on the closed-state pore domain of NaV1.7. A given residue may have a number of distinct interaction networks within the channel protein throughout the gating cycle, thus our modeling captures a snap shot of these interactions, and future studies are needed to further investigate interaction networks within the channel protein throughout the gating cycle.
Our NaV1.7 modeling also suggests a link between ΔB ct value and the buried or exposed nature of an amino acid substitution. Indeed, gain-of-function mutations predicted to be buried inside or close to the core of the channel have higher |ΔB ct | than the overall mean |ΔB ct | =1.14 (3, S211P; 4, F216S; 11, I234T; 5, I228M;12, S241T; 13, I848T; 15, L858H; 16, L858F; 24, N395K; 27, V872G; 30, A1746G) or the cut-off value (>0.26) (6, I739V; 25, V400M; 29, F1449V). Conversely, gain-of-function mutations predicted to face the lipid interface (exception: I136V) have lower |ΔB ct | than the overall mean |ΔB ct | (1.14) (14, G856D; 26, A863P; 28, M932L; 21, V1316A; 18, V1298D; 10, G1607R; 23, A1632E; exception: M1627K) or the cut-off value |ΔB ct | (<0.26) (17, V1298F; 19, V1299F; 20, P1308L; 2, R185H; 8, V1532I; 9, W1538R). Similarly, most of the control variants (nABN/hSNPs) predicted to face the lipid interface have low |ΔB ct | (<0.26) (Fig. 6b and c). Hence, in our NaV1.7 model, mutations predicted to be buried into the core of the channel show higher |ΔB ct | than those exposed at the interface of the membrane. This finding suggests that lipophilic interactions within the cell membrane may be disturbed by the mutations. Additional studies are required to more definitively assess the changes in lipophilic interactions that are produced by these mutations. Irrespective of the underlying mechanistic/molecular explanation, some pathogenic mutations would be missed by our method. Thus, ΔB ct should be regarded as a novel in-silico screening tool in addition to existing common predictive algorithms (e.g. Polyphen-2 [67], SIFT [68]) that could help in selecting pathogenetic mutations for functional testing.
Conclusions
Our findings demonstrate that most of the pathogenic NaV1.7 mutations identified in patients affected by severe painful disorders could be predicted, according to our homology modelling, to cause profound changes in the amino acid connectivity of the channel. Such modification may underpin the gain-of-function effects measurable in DRG nociceptors by electrophysiological assays. Based on these findings, we propose to consider Bct may therefore be a marker of pathogenic shift in the mutant channels, though prospective experimental studies will be required to validate its effectiveness and its biological meaning.
Abbreviations
- B ct :
-
Betweenness centrality
- CC ct :
-
Clustering coefficient
- C ct :
-
Closeness
- D:
-
Degree
- DRG:
-
Dorsal root ganglion
- EB ct :
-
Edge betweeness centrality
- E ct :
-
Eccentricity
- H-bonds:
-
Hydrogen bonds
- hSNPs:
-
Homologous single nucleotide polymorphisms
- IEM:
-
Inherited erythromelalgia
- nABN:
-
Variants not causing biophysical abnormalities of the channel
- PD:
-
Pore domain
- PEPD:
-
Paroxysmal extreme pain disorder
- ROC:
-
Receiver operating characteristics
- SCN9A:
-
Sodium channel, voltage-gated, type IX, alpha subunit
- SFN:
-
Painful small fibre neuropathy
- VSD:
-
Voltage-sensing domain
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Acknowledgements
We acknowledge the other members of the PROPANE (Probing the role of sodium channels in painful neuropathies) study group: Michela Taiana (Italy), Margherita Marchi (Italy), Raffaella Lombardi (Italy), Daniele Cazzato (Italy), Filippo Martinelli Boneschi (Italy), Andrea Zauli (Italy), Ferdinando Clarelli (Italy), Silvia Santoro (Italy), Ignazio Lopez (Italy), Angelo Quattrini (Italy), Janneke Hoeijmakers (The Netherlands), Maurice Sopacua (The Netherlands), Bianca de Greef (The Netherlands), Hubertus Julius Maria Smeets (The Netherlands), Rowida Al Momani (The Netherlands), Jo Michel Vanoevelen (The Netherlands), Ivo Eijkenboom (The Netherlands), Sandrine Cestèle (France), Oana Chever (France), Rayaz Malik (United Kingdom), Mitra Tavakoli (United Kingdom), Dan Ziegler (Germany).
Funding
The study was financed by institutional funding (IRCCS Foundation Carlo Besta Neurological Institute, Ricerca Corrente), the Italian Ministry of Health (Giovani Ricercatori, n. GR-2010/208) and the European Union 7th Framework Programme (grant 602273).
Availability of data and materials
The supporting data of this article are included within the article and its Additional files 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 and 15.
Authors’ contributions
DK perfomed designed the study, performed homology modeling, network analysis and wrote the manuscript. JS and MNX wrote and participated in data interpretation. BG performed statistical analysis and prepared the figures. YY, RLW, RS, PL, CGF, MG, ISJM, SDD, MM, SGW revised the manuscript. GL conceived the study and participated in data interpretation. All the authors participated, read and approved the manuscript.
Competing interests
The authors declare that they have no competing interests.
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Additional files
Additional file 1: S1.
Text. Phylogenenetic tree of human SCN9A and homologous genes. (DOCX 35 kb)
Additional file 2: Table S1.
NaV1.7 mutations associated to IEM, SFN and PEPD. (DOCX 50 kb)
Additional file 3:
NaV1.7 pdf File. (PDB 1288 kb)
Additional file 4: Figure S1.
Ramachandran plot of NaV1.7 WT. (DOCX 233 kb)
Additional file 5: S2.
Text. Pairwise sequence alignment between human SCN9A and homologous genes. (DOCX 34 kb)
Additional file 6: Figure S2.
Degree variation (∆D) in NaV1.7 mutations compared to WT. (DOCX 1691 kb)
Additional file 7: Figure S3.
Clustering coefficient variation (∆CC ct ) in NaV1.7 mutations compared to WT. (DOCX 2798 kb)
Additional file 8: Figure S4.
Closeness Centrality variation (∆C ct ) in NaV1.7 mutations compared to WT.A (DOCX 2801 kb)
Additional file 9: Figure S5.
Eccentricity centrality variation (∆E ct ) in NaV1.7 mutations compared to WT. (DOCX 2709 kb)
Additional file 10: S1.
YASARA. Structural modelling of F216 NaV1.7 variant and their interatomic bonds. (SCE 709 kb)
Additional file 11: S2.
YASARA. Structural modelling of S216 NaV1.7 variant and their interatomic bonds. (SCE 706 kb)
Additional file 12: S3.
YASARA. Structural modelling of L858 NaV1.7 variant and their interatomic bonds. (SCE 707 kb)
Additional file 13: S4.
YASARA. Structural modelling of H858 NaV1.7 variant and their interatomic bonds. (SCE 775 kb)
Additional file 14: S5.
YASARA. Structural modelling of L1267 NaV1.7 variant and their interatomic bonds. (SCE 707 kb)
Additional file 15: S5.
YASARA. Structural modelling of V1267 NaV1.7 variant and their interatomic bonds. (SCE 706 kb)
Additional file 16: Figure S6.
Structural modelling variants and their interatomic bonds of I848T and N395K. (DOCX 747 kb)
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Kapetis, D., Sassone, J., Yang, Y. et al. Network topology of NaV1.7 mutations in sodium channel-related painful disorders. BMC Syst Biol 11, 28 (2017). https://doi.org/10.1186/s12918-016-0382-0
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DOI: https://doi.org/10.1186/s12918-016-0382-0