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FHM3 in familial hemiplegic migraine is more resistant to mutation than FHM1 and FHM2
Journal of the Neurological Sciences 277 (2009) 76–79
Contents lists available at ScienceDirect
Journal of the Neurological Sciences j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / j n s
FHM3 in familial hemiplegic migraine is more resistant to mutation than FHM1 and FHM2 Viroj Wiwanitkit ⁎ Wiwanitkit House, Bangkhae, Bangkok, 10160, Thailand
a r t i c l e
i n f o
Article history: Received 10 July 2008 Accepted 14 October 2008 Available online 13 November 2008 Keywords: Familial hemiplegic migraine Mutation
a b s t r a c t Familial hemiplegic migraine (FHM) is a rare subtype of migraine with aura and transient hemiplegia. CACNA1A (FHM1) gene, the ATP1A2 (FHM2) and the SCN1A (FHM3) are reported for their correlation to FHM. Here, a bioinformatics analysis was done to study the risk positions for mutation within the amino acid sequence of the three mentioned molecules. In this work, the author can identify many mutant prone positions within the studied FHM. Of interest, the author detected that FHM3 is a high resistant molecule when compared to FHM1 and FHM2. © 2008 Elsevier B.V. All rights reserved.
1. Introduction
2. Materials and methods
Familial hemiplegic migraine (FHM) is a rare clinical subtype of migraine with aura and transient hemiplegia [1]. FHM mutations are reported in 3 different discreted genes including the CACNA1A (FHM1) gene, the ATP1A2 (FHM2) and the SCN1A (FHM3) gene and seem to pose an autosomal-dominant mode of inheritance [1]. In 2006, Freilinger and Dichgans stated for the roles of CACNA1A, ATP1A2, and SCN1A in the pathophysiology of cortical spreading depression, which was the likely correlate of migraine aura [2]. At present, molecular biology permits a definite diagnosis of FHM although there is still controversy on the phenotypal variability and physiopathogenic mechanisms [3]. Presently, structural and function prediction are great advents in the bioinformatics era. An interesting bioinformatics procedure is determination of mutation prone position within a sequence [4,5]. Generally, defected sites within proteins often usually pose specific short linear peptide motifs that are cordial for functional characterization. Determination of those positions within the amino acid sequence is accepted as a good prediction for specific weak linkages in a specific protein [4,5]. Presently, this kind of analysis is probable by use of advanced bioinformatics methods. In this work, the author used a bioinformatics computational analysis to study the determine positions complying peptide motifs in the amino acid sequence of the three mentioned particles, FHM1, FHM2 and FHM3. The results are shown and discussed for further clinical importance.
The protocol for this study is the accepted published protocol from recent reference works [8–10]. The primary sequences derivation was from searching the database Expert Protein Analysis System (ExPASY) [6] which was a computational proteomics server of the Swiss Institute of Bioinformatics (SIB) for computational analysis of protein sequences, protein structures and two dimensional PAGE results [6]. Searching for the amino acid sequence of FHM1, FHM2 and FHM3 was firstly done and three derived sequences were used for further study on weak linkage. Then GlobPlot, a web service that allows determine the possible tendency within the submitted protein for order/globularity and disorder was used to determine weak linkage in FHM1, FHM2 and FHM3 [7]. For processing, GlobPlot server used Biopython. org software to analyzed sequence and description of the polypeptide from an ExPASy server and it can identify inter-domain segments containing linear motifs, and also possible defected regions that do not contain any recognized domain [7]. Weak linkages in a protein, which are prone for mutation, are those identified positions [4,5].
⁎ Tel./fax: +66 24132436. E-mail address: [email protected]. 0022-510X/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.jns.2008.10.012
3. Result From primary ExPaSy searching, FHM1, FHM2 and FHM3 were derived for further study on mutation prone position. From processing by GlobPlot, identified motifs of FHM1, FHM2 and FHM3 are presented in Fig. 1. For FHM1, the positions 4–43, 123–133, 259–298, 324–333, 412–416, 544–551, 638–653, 678–684, 829–842, 872–884, 898–909, 923–958, 965–974, 986–1009, 1036–1069, 1085–1114, 1121–1200, 1210–1228, 1270–1276, 1476–1486, 1771–1782, 1865–1872, 1994– 2026, 2049–2070, 2079–2088, 2106–2130, 2177–2206 and 2218– 2236 are determined as resistant positions to mutation. For FHM2, the positions 1–8, 55–76, 215–220, 250–256, 400–415, 438–443 and 558–
V. Wiwanitkit / Journal of the Neurological Sciences 277 (2009) 76–79
77
Fig. 1. Predicted positions that comply peptide motifs peptide motifs within amino acid sequence of FHM1, FHM2 and FHM3. (Each alphabet letter within the figure represents an amino acid and each capital letter represents specific amino acid with resistant to mutation.)
78
V. Wiwanitkit / Journal of the Neurological Sciences 277 (2009) 76–79
Fig. 1 (continued ).
566 are determined as resistant positions to mutation. For FHM3, all positions are determined as resistant positions to mutation. 4. Discussion Cutrer and Huerter recently noted that the episodes of transient focal neurologic symptoms, known as aura, occur in association with migraine headache in about 11.9 million people in the United States [11]. van de Ven et al. noted that the pathogenesis of the aura and headache phases was well described, but the mechanism by which migraine attacks are triggered was unknown [12]. van de Ven et al. proposed that identifying corresponding genes and deciphering their function could help to unravel the triggering mechanisms for migraine attacks [12]. FHM is an uncommon monogenic subtype of migraine with aura and there are 3 identified corresponding genes for this specific neurological disorder. Identification of the mutation points within FHM1, FHM2 and FHM3 can be useful for further step to understand the pathogenesis of FHM. However, the determination for mutation points within FHM via classical in vitro experiments usually requires a long time and has very low chance to get the positive case, therefore, application of computational bioinformatics technology can be applied [13]. In this work, the author used a bioinformatics algorithm to predict the position in the amino acid sequences of FHM that can be mutated. The standard protein database tool EXPASY was use for primary data searching and the reliability of this primary searching is accepted [14]. For secondary searching of the mutation, the GlobPlot was used and this technique is also acceptable and already published for reliability of the technique [8–10]. In this work, the author derived many positions within FHM. Some are known positions and the others are firstly detected. Of interest, the
author detected that FHM3 is a high resistant molecule. Indeed, the FHM3 is a new identified molecule that is related to FHM and there is only a few data on it. In addition, there is only a few cases of mutated FHM3 comparing to FHM1 and FHM2 indicating its resistant nature. The results can be useful for better understanding on molecular pathogenesis of FHM. However, some limitations of this work should be mentioned. Although it is stated that the FHM3 gene is more resistant to mutations than FHM1 and FHM2, it does not take into account that more than 150 mutations in the FHM3 gene spread throughout all domains of the gene, cause disease. One may argue that the majority of FHM mutations cause childhood epilepsy, but the recent understanding of overlap between FHM and epilepsy shows that there is much overlap also in underlying molecular mechanisms. Furthermore, predict functional and clinical consequences based on the context of an amino acid. One might argue that the rationale is perhaps meaningful to certain extent if it is dealing with gain-offunction mutations. For FHM1, until now, these mutations are all gainof-function mutations. For FHM2 this definitely is not the case. Any bioinformatics on FHM2 may be less meaningful. For FHM3, it is less certain whether mutations will be gain or loss-of-function mutations. References [1] Thomsen LL, Kirchmann M, Bjornsson A, Stefansson H, Jensen RM, Fasquel AC, et al. The genetic spectrum of a population-based sample of familial hemiplegic migraine. Brain Feb 2007;130(Pt 2):346–56. [2] Freilinger T, Dichgans M. Genetics of migraine. Nervenarzt. Oct 2006;77(10):1186, 1188-95. [3] Haan J, Terwindt GM, van den Maagdenberg AM, Stam AH, Ferrari MD. A review of the genetic relation between migraine and epilepsy. Cephalalgia Feb 2008;28(2): 105–13. [4] Lee C, Wang Q. Bioinformatics analysis of alternative splicing. Brief Bioinform 2005;6:23–33.
V. Wiwanitkit / Journal of the Neurological Sciences 277 (2009) 76–79 [5] Levin JM, Penland RC, Stamps AT, Cho CR. Using in silico biology to facilitate drug development. Novartis Found Symp 2002;247:222–38. [6] Gasteiger E, Gattiker A, Hoogland C, Ivanyi I, Appel RD, Bairoch A. ExPASy: the proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res 2003;31:3784–8. [7] Linding R, Russell RB, Neduva V, Gibson TJ. GlobPlot: exploring protein sequences for globularity and disorder. Nucleic Acids Res 2003;31:3701–8. [8] Wiwanitkit V. Where is the weak linkage in the globin chain? Int J Nanomed 2006;1:109–10. [9] Wiwanitkit V. Weak linkage in androgen receptor: identification of mutationprone points. Fertil Steril Oct 11 2007 [Electronic publication ahead of print].
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[10] Wiwanitkit V. Mutation-prone points in thrombin receptor. J Thromb Thrombolysis Apr 2008;25(2):190–2. [11] Cutrer FM, Huerter K. Migraine aura. Neurologist May 2007;13(3):118–25. [12] van de Ven RC, Kaja S, Plomp JJ, Frants RR, van den Maagdenberg AM, Ferrari MD. Genetic models of migraine. Arch Neurol May 2007;64(5):643–6. [13] Golaz O, Wilkins MR, Sanchez JC, Appel RD, Hochstrasser DF, Williams KL. Identification of proteins by their amino acid composition: an evaluation of the method. Electrophoresis 1996;17:573–9. [14] Linding R, Schymkowitz J, Rousseau F, Diella F, Serrano L. A comparative study of the relationship between protein structure and beta-aggregation in globular and intrinsically disordered proteins. J Mol Biol 2004;342:345–53.
Contents lists available at ScienceDirect
Journal of the Neurological Sciences j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / j n s
FHM3 in familial hemiplegic migraine is more resistant to mutation than FHM1 and FHM2 Viroj Wiwanitkit ⁎ Wiwanitkit House, Bangkhae, Bangkok, 10160, Thailand
a r t i c l e
i n f o
Article history: Received 10 July 2008 Accepted 14 October 2008 Available online 13 November 2008 Keywords: Familial hemiplegic migraine Mutation
a b s t r a c t Familial hemiplegic migraine (FHM) is a rare subtype of migraine with aura and transient hemiplegia. CACNA1A (FHM1) gene, the ATP1A2 (FHM2) and the SCN1A (FHM3) are reported for their correlation to FHM. Here, a bioinformatics analysis was done to study the risk positions for mutation within the amino acid sequence of the three mentioned molecules. In this work, the author can identify many mutant prone positions within the studied FHM. Of interest, the author detected that FHM3 is a high resistant molecule when compared to FHM1 and FHM2. © 2008 Elsevier B.V. All rights reserved.
1. Introduction
2. Materials and methods
Familial hemiplegic migraine (FHM) is a rare clinical subtype of migraine with aura and transient hemiplegia [1]. FHM mutations are reported in 3 different discreted genes including the CACNA1A (FHM1) gene, the ATP1A2 (FHM2) and the SCN1A (FHM3) gene and seem to pose an autosomal-dominant mode of inheritance [1]. In 2006, Freilinger and Dichgans stated for the roles of CACNA1A, ATP1A2, and SCN1A in the pathophysiology of cortical spreading depression, which was the likely correlate of migraine aura [2]. At present, molecular biology permits a definite diagnosis of FHM although there is still controversy on the phenotypal variability and physiopathogenic mechanisms [3]. Presently, structural and function prediction are great advents in the bioinformatics era. An interesting bioinformatics procedure is determination of mutation prone position within a sequence [4,5]. Generally, defected sites within proteins often usually pose specific short linear peptide motifs that are cordial for functional characterization. Determination of those positions within the amino acid sequence is accepted as a good prediction for specific weak linkages in a specific protein [4,5]. Presently, this kind of analysis is probable by use of advanced bioinformatics methods. In this work, the author used a bioinformatics computational analysis to study the determine positions complying peptide motifs in the amino acid sequence of the three mentioned particles, FHM1, FHM2 and FHM3. The results are shown and discussed for further clinical importance.
The protocol for this study is the accepted published protocol from recent reference works [8–10]. The primary sequences derivation was from searching the database Expert Protein Analysis System (ExPASY) [6] which was a computational proteomics server of the Swiss Institute of Bioinformatics (SIB) for computational analysis of protein sequences, protein structures and two dimensional PAGE results [6]. Searching for the amino acid sequence of FHM1, FHM2 and FHM3 was firstly done and three derived sequences were used for further study on weak linkage. Then GlobPlot, a web service that allows determine the possible tendency within the submitted protein for order/globularity and disorder was used to determine weak linkage in FHM1, FHM2 and FHM3 [7]. For processing, GlobPlot server used Biopython. org software to analyzed sequence and description of the polypeptide from an ExPASy server and it can identify inter-domain segments containing linear motifs, and also possible defected regions that do not contain any recognized domain [7]. Weak linkages in a protein, which are prone for mutation, are those identified positions [4,5].
⁎ Tel./fax: +66 24132436. E-mail address: [email protected]. 0022-510X/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.jns.2008.10.012
3. Result From primary ExPaSy searching, FHM1, FHM2 and FHM3 were derived for further study on mutation prone position. From processing by GlobPlot, identified motifs of FHM1, FHM2 and FHM3 are presented in Fig. 1. For FHM1, the positions 4–43, 123–133, 259–298, 324–333, 412–416, 544–551, 638–653, 678–684, 829–842, 872–884, 898–909, 923–958, 965–974, 986–1009, 1036–1069, 1085–1114, 1121–1200, 1210–1228, 1270–1276, 1476–1486, 1771–1782, 1865–1872, 1994– 2026, 2049–2070, 2079–2088, 2106–2130, 2177–2206 and 2218– 2236 are determined as resistant positions to mutation. For FHM2, the positions 1–8, 55–76, 215–220, 250–256, 400–415, 438–443 and 558–
V. Wiwanitkit / Journal of the Neurological Sciences 277 (2009) 76–79
77
Fig. 1. Predicted positions that comply peptide motifs peptide motifs within amino acid sequence of FHM1, FHM2 and FHM3. (Each alphabet letter within the figure represents an amino acid and each capital letter represents specific amino acid with resistant to mutation.)
78
V. Wiwanitkit / Journal of the Neurological Sciences 277 (2009) 76–79
Fig. 1 (continued ).
566 are determined as resistant positions to mutation. For FHM3, all positions are determined as resistant positions to mutation. 4. Discussion Cutrer and Huerter recently noted that the episodes of transient focal neurologic symptoms, known as aura, occur in association with migraine headache in about 11.9 million people in the United States [11]. van de Ven et al. noted that the pathogenesis of the aura and headache phases was well described, but the mechanism by which migraine attacks are triggered was unknown [12]. van de Ven et al. proposed that identifying corresponding genes and deciphering their function could help to unravel the triggering mechanisms for migraine attacks [12]. FHM is an uncommon monogenic subtype of migraine with aura and there are 3 identified corresponding genes for this specific neurological disorder. Identification of the mutation points within FHM1, FHM2 and FHM3 can be useful for further step to understand the pathogenesis of FHM. However, the determination for mutation points within FHM via classical in vitro experiments usually requires a long time and has very low chance to get the positive case, therefore, application of computational bioinformatics technology can be applied [13]. In this work, the author used a bioinformatics algorithm to predict the position in the amino acid sequences of FHM that can be mutated. The standard protein database tool EXPASY was use for primary data searching and the reliability of this primary searching is accepted [14]. For secondary searching of the mutation, the GlobPlot was used and this technique is also acceptable and already published for reliability of the technique [8–10]. In this work, the author derived many positions within FHM. Some are known positions and the others are firstly detected. Of interest, the
author detected that FHM3 is a high resistant molecule. Indeed, the FHM3 is a new identified molecule that is related to FHM and there is only a few data on it. In addition, there is only a few cases of mutated FHM3 comparing to FHM1 and FHM2 indicating its resistant nature. The results can be useful for better understanding on molecular pathogenesis of FHM. However, some limitations of this work should be mentioned. Although it is stated that the FHM3 gene is more resistant to mutations than FHM1 and FHM2, it does not take into account that more than 150 mutations in the FHM3 gene spread throughout all domains of the gene, cause disease. One may argue that the majority of FHM mutations cause childhood epilepsy, but the recent understanding of overlap between FHM and epilepsy shows that there is much overlap also in underlying molecular mechanisms. Furthermore, predict functional and clinical consequences based on the context of an amino acid. One might argue that the rationale is perhaps meaningful to certain extent if it is dealing with gain-offunction mutations. For FHM1, until now, these mutations are all gainof-function mutations. For FHM2 this definitely is not the case. Any bioinformatics on FHM2 may be less meaningful. For FHM3, it is less certain whether mutations will be gain or loss-of-function mutations. References [1] Thomsen LL, Kirchmann M, Bjornsson A, Stefansson H, Jensen RM, Fasquel AC, et al. The genetic spectrum of a population-based sample of familial hemiplegic migraine. Brain Feb 2007;130(Pt 2):346–56. [2] Freilinger T, Dichgans M. Genetics of migraine. Nervenarzt. Oct 2006;77(10):1186, 1188-95. [3] Haan J, Terwindt GM, van den Maagdenberg AM, Stam AH, Ferrari MD. A review of the genetic relation between migraine and epilepsy. Cephalalgia Feb 2008;28(2): 105–13. [4] Lee C, Wang Q. Bioinformatics analysis of alternative splicing. Brief Bioinform 2005;6:23–33.
V. Wiwanitkit / Journal of the Neurological Sciences 277 (2009) 76–79 [5] Levin JM, Penland RC, Stamps AT, Cho CR. Using in silico biology to facilitate drug development. Novartis Found Symp 2002;247:222–38. [6] Gasteiger E, Gattiker A, Hoogland C, Ivanyi I, Appel RD, Bairoch A. ExPASy: the proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res 2003;31:3784–8. [7] Linding R, Russell RB, Neduva V, Gibson TJ. GlobPlot: exploring protein sequences for globularity and disorder. Nucleic Acids Res 2003;31:3701–8. [8] Wiwanitkit V. Where is the weak linkage in the globin chain? Int J Nanomed 2006;1:109–10. [9] Wiwanitkit V. Weak linkage in androgen receptor: identification of mutationprone points. Fertil Steril Oct 11 2007 [Electronic publication ahead of print].
79
[10] Wiwanitkit V. Mutation-prone points in thrombin receptor. J Thromb Thrombolysis Apr 2008;25(2):190–2. [11] Cutrer FM, Huerter K. Migraine aura. Neurologist May 2007;13(3):118–25. [12] van de Ven RC, Kaja S, Plomp JJ, Frants RR, van den Maagdenberg AM, Ferrari MD. Genetic models of migraine. Arch Neurol May 2007;64(5):643–6. [13] Golaz O, Wilkins MR, Sanchez JC, Appel RD, Hochstrasser DF, Williams KL. Identification of proteins by their amino acid composition: an evaluation of the method. Electrophoresis 1996;17:573–9. [14] Linding R, Schymkowitz J, Rousseau F, Diella F, Serrano L. A comparative study of the relationship between protein structure and beta-aggregation in globular and intrinsically disordered proteins. J Mol Biol 2004;342:345–53.