微小 RNA 和伴海马硬化的内侧颞叶癫痫：人类海马全微小 RNA 组谱_Journal of Epilepsy

Authors：

PetraBencurova , JiriBaloun , KaterinaMusilova , 张乐　译 , 慕洁　审

Corresponding author：

PetraBencurova, Email:

Keywords：

内侧颞叶癫痫; 微小 RNA 分析; 下一代测序技术; miRNA 特异性-实时定量聚合酶链式反应; 海马硬化; 靶点预测

DOI：

10.7507/2096-0247.20180030

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

内侧颞叶癫痫（mesial temporal lobe epilepsy, mTLE）是一种严重的以反复癫痫发作为特征的神经系统疾病。mTLE 通常伴有海马神经元变性导致的海马硬化（Hippocampal sclerosis，HS）, 它是与 mTLE 患者耐药性相关的最常见的形态学改变。对 mTLE+HS 的认识不足使其治疗复杂化。mTLE+HS 的潜在病理机制可能涉及异常的基因表达调控，包括涉及微小 RNA（microRNA, miRNA）的转录后网络。 miRNA 表达调控在包括癫痫的多种疾病中都有报道。但是，mTLE+HS 的 miRNA 谱暂未被完全了解，需要进一步解决。在此，为了揭示 miRNA 的异常表达，我们关注了 33 例 mTLE+HS 患者和 9 具对照者尸检的海马 miRNA 分析。研究中，通过联合两种不同的 miRNA 分析方法，以及下一代测序技术和 miRNA 特异性实时定量聚合酶链式反应，我们显著降低了技术相关的偏移（最常见的是 miRNA 分析数据的假阳性）。这些方法结合起来明确并验证了 20 例癫痫患者海马中 miRNA 的表达改变。在 mTLE+HS 患者中，有 19 例 miRNA 上调，1 例下调。其中 9 种 miRNA 以往没有报道与癫痫有联系，19 例异常表达的 miRNA 可能调节与癫痫相关的靶点和通路（例如：钾离子通道，γ-氨基丁酸，神经营养因子信号传导和轴突导向）。研究通过明确 miRNA 在 mTLE+HS 中表达，扩展了当前对 mTLE+HS 中 miRNA 介导的基因表达调控的认知，包括 9 个新发现的 miRNA 异常表达及其可能的靶点。这些发现进一步提升了基于 microRNA 的生物标志物或疗法的可能性。

Citation： PetraBencurova, JiriBaloun, KaterinaMusilova, 张乐　译, 慕洁　审. 微小 RNA 和伴海马硬化的内侧颞叶癫痫：人类海马全微小 RNA 组谱. Journal of Epilepsy, 2018, 4(2): 162-173. doi: 10.7507/2096-0247.20180030 Copy

1.	Engel J. A proposed diagnostic scheme for people with epileptic seizures and with epilepsy: report of the ILAE task force on classification and terminology. Epilepsia, 2001, 42(6): 796-803.
2.	Thom M. Hippocampal sclerosis: progress since sommer. Brain Pathol, 2009, 19(4): 565-572.
3.	Jimenez-Mateos EM, Henshall DC. Epilepsy and microRNA. Neuroscience, 2013, 238(2): 218-229.
4.	Bartel D. MicroRNAs genomics, biogenesis, mechanism, and function. Cell, 2004, 116(2): 281-297.
5.	Coolen M, Bally-Cuif L. MicroRNAs in brain development and physiology. Curr Opin Neurobiol, 2009, 19(5): 461-470.
6.	Kan AA, van Erp S, Derijck AA, et al. Genome-wide microRNA profiling of human temporal lobe epilepsy identifies modulators of the immune response. Cell Mol Life Sci, 2012, 69(18): 3127-3145.
7.	Alsharafi WA, Xiao B, Abuhamed MM, et al. miRNAs: biological and clinical determinants in epilepsy. Front Mol Neurosci, 2015, 8: 59.
8.	Mestdagh P, Hartmann N, Baeriswyl L, et al. Evaluation of quantitative miRNA expression platforms in the microRNA quality control (miRQC) study. Nat Methods, 2014, 11(8): 809-815.
9.	Wieser HG. ILAE Commission on Neurosurgery of Epilepsy. ILAE Commission Report. Mesial temporal lobe epilepsy with hippocampal sclerosis. Epilepsia, 2004, 45(6): 695-714.
10.	Vitsios DM, Enright AJ. Chimira: analysis of small RNA sequencing data and microRNA modifications. Bioinformatics, 2015, 31(20): 3365-3367.
11.	R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Core Team, 2016.
12.	Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol, 2010, 11(10): R106.
13.	Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 2010, 26(1): 139-140.
14.	Smyth GK. limma: linear models for microarray data. In Gentleman R, Carey VJ, Huber W, et al. (Eds) Bioinformatics and computational biology solutions using r and bioconductor. New York, NY: Springer-Verlag, 2005: 397-420.
15.	Benes V, Collier P, Kordes C, et al. Identification of cytokine-induced modulation of microRNA expression and secretion as measured by a novel microRNA specific qPCR assay. Sci Rep, 2015, 5: 11590.
16.	Bustin SA, Benes V, Garson JA, et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem, 2009, 55(4): 611-622.
17.	Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods, 2001, 25(4): 402-408.
18.	Vlachos IS, Zagganas K, Paraskevopoulou MD, et al. DIANA-miRPath V3.0: deciphering microRNA function with experimental support. Nucleic Acids Res, 2015, 43(W1): 460-466.
19.	Ewing B, Hillier L, Wendl MC, et al. Base-calling of automated sequencer traces using phred I. Accuracy assessment. Genome Res, 1998, 8(3): 175-185.
20.	Nassirpour R, Mathur S, Gosink MM, et al. Identification of tubular injury microRNA biomarkers in urine: comparison of next-generation sequencing and qPCR-based profiling platforms. BMC Genom, 2014, 15: 485.
21.	Aronica E, Fluiter K, Iyer A, et al. Expression pattern of miR-146a, an inflammation-associated microRNA, in experimental and human temporal lobe epilepsy. Eur J Neurosci, 2010, 31(6): 1100-1107.
22.	Jimenez-Mateos EM, Bray I, Sanz-Rodriguez A, et al. miRNA expression profile after status epilepticus and hippocampal neuroprotection by targeting miR-132. Am J Pathol, 2011, 179(5): 2519-2532.
23.	Jimenez-Mateos EM, Engel T, Merino-Serrais P, et al. Silencing microRNA-134 produces neuroprotective and prolonged seizure-suppressive effects. Nat Med, 2012, 18(7): 1087-1094.
24.	Peng J, Omran A, Ashhab MU, et al. Expression patterns of miR-124, miR-134, miR-132, and miR-21 in an immature rat model and children with mesial temporal lobe epilepsy. J Mol Neurosci, 2013, 50(2): 291-297.
25.	Dudoit S, Yang YH, Callow MJ, et al. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Stat Sin, 2002, 12: 111-139.
26.	Wong N, Wang X. miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic Acids Res, 2015, 43(Database issue): 146-152.
27.	Mc Kiernan RC, Jimenez-Mateos EM, Bray I, et al. Reduced mature microRNA levels in association with dicer loss in human temporal lobe epilepsy with hippocampal sclerosis. PLoS One, 2012, 7(5): e35921.
28.	Roncon P, Soukupova M, Binaschi A, et al. MicroRNA profiles in hippocampal granule cells and plasma of rats with pilocarpine-induced epilepsy-comparison with human epileptic samples. Sci Rep, 2015, 5: 14143.
29.	Sørensen SS, Nygaard AB, Christensen T. miRNA expression profiles in cerebrospinal fluid and blood of patients with alzheimer’s disease and other types of dementia-an exploratory study. Transl Neurodegener, 2016, 5: 6.
30.	Vallelunga A, Ragusa M, Di Mauro S, et al. Identification of circulating microRNAs for the differential diagnosis of Parkinson’s disease and multiple system atrophy. Front Cell Neurosci, 2014, 8: 156.
31.	Wang C, Ji B, Cheng B, et al. Neuroprotection of microRNA in neurological disorders (Review). Biomed Rep, 2014, 2(5): 611-619.
32.	Meissner L, Gallozzi M, Balbi M, et al. Temporal profile of microRNA expression in contused cortex after traumatic brain injury in mice. J Neurotrauma, 2016, 33(8): 713-720.
33.	Jovičić A, Roshan R, Moisoi N, et al. Comprehensive expression analyses of neural cell-type-specific miRNAs identify new determinants of the specification and maintenance of neuronal phenotypes. J Neurosci, 2013, 33(12): 5127-5137.
34.	Kakimoto Y, Kamiguchi H, Ochiai E, et al. MicroRNA stability in postmortem FFPE tissues: quantitative analysis using autoptic samples from acute myocardial infarction patients. PLoS One, 2015, 10(6): e0129338.
35.	Kandratavicius L, Balista PA, Lopes-Aguiar C, et al. Animal models of epilepsy: use and limitations. Neuropsychiatr Dis Treat, 2014, 10: 1693-1705.
36.	Di Nuzzo M, Mangia S, Maraviglia B, et al. Physiological bases of the K+ and the glutamate/GABA hypotheses of epilepsy. Epilepsy Res, 2014, 108(6): 995-1012.
37.	Holtmaat AJ, Gorter JA, De Wit J, et al. Transient downregulation of sema3a mrna in a rat model for temporal lobe epilepsy: a novel molecular event potentially contributing to mossy fiber sprouting. Exp Neurol, 2003, 182(1): 142-150.
38.	Xu B, Michalski B, Racine R, et al. Continuous infusion of neurotrophin-3 triggers sprouting, decreases the levels of TrkA and Trk C, and inhibits epileptogenesis and activity-dependent axonal growth in adult rats. Neuroscience, 2002, 115(4): 1295-1308.
39.	Cupertino RB, Kappel DB, Bandeira CE, et al. SNARE complex in developmental psychiatry: neurotransmitter exocytosis and beyond. J Neural Transm, 2016, 123(8): 867-883.
40.	Pralhada Rao R, Vaidyanathan N, Rengasamy M, et al. Sphingolipid metabolic pathway: an overview of major roles played in human diseases. J Lipids, 2013, 2013: 1-12.

1. Engel J. A proposed diagnostic scheme for people with epileptic seizures and with epilepsy: report of the ILAE task force on classification and terminology. Epilepsia, 2001, 42(6): 796-803.
2. Thom M. Hippocampal sclerosis: progress since sommer. Brain Pathol, 2009, 19(4): 565-572.
3. Jimenez-Mateos EM, Henshall DC. Epilepsy and microRNA. Neuroscience, 2013, 238(2): 218-229.
4. Bartel D. MicroRNAs genomics, biogenesis, mechanism, and function. Cell, 2004, 116(2): 281-297.
5. Coolen M, Bally-Cuif L. MicroRNAs in brain development and physiology. Curr Opin Neurobiol, 2009, 19(5): 461-470.
6. Kan AA, van Erp S, Derijck AA, et al. Genome-wide microRNA profiling of human temporal lobe epilepsy identifies modulators of the immune response. Cell Mol Life Sci, 2012, 69(18): 3127-3145.
7. Alsharafi WA, Xiao B, Abuhamed MM, et al. miRNAs: biological and clinical determinants in epilepsy. Front Mol Neurosci, 2015, 8: 59.
8. Mestdagh P, Hartmann N, Baeriswyl L, et al. Evaluation of quantitative miRNA expression platforms in the microRNA quality control (miRQC) study. Nat Methods, 2014, 11(8): 809-815.
9. Wieser HG. ILAE Commission on Neurosurgery of Epilepsy. ILAE Commission Report. Mesial temporal lobe epilepsy with hippocampal sclerosis. Epilepsia, 2004, 45(6): 695-714.
10. Vitsios DM, Enright AJ. Chimira: analysis of small RNA sequencing data and microRNA modifications. Bioinformatics, 2015, 31(20): 3365-3367.
11. R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Core Team, 2016.
12. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol, 2010, 11(10): R106.
13. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 2010, 26(1): 139-140.
14. Smyth GK. limma: linear models for microarray data. In Gentleman R, Carey VJ, Huber W, et al. (Eds) Bioinformatics and computational biology solutions using r and bioconductor. New York, NY: Springer-Verlag, 2005: 397-420.
15. Benes V, Collier P, Kordes C, et al. Identification of cytokine-induced modulation of microRNA expression and secretion as measured by a novel microRNA specific qPCR assay. Sci Rep, 2015, 5: 11590.
16. Bustin SA, Benes V, Garson JA, et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem, 2009, 55(4): 611-622.
17. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods, 2001, 25(4): 402-408.
18. Vlachos IS, Zagganas K, Paraskevopoulou MD, et al. DIANA-miRPath V3.0: deciphering microRNA function with experimental support. Nucleic Acids Res, 2015, 43(W1): 460-466.
19. Ewing B, Hillier L, Wendl MC, et al. Base-calling of automated sequencer traces using phred I. Accuracy assessment. Genome Res, 1998, 8(3): 175-185.
20. Nassirpour R, Mathur S, Gosink MM, et al. Identification of tubular injury microRNA biomarkers in urine: comparison of next-generation sequencing and qPCR-based profiling platforms. BMC Genom, 2014, 15: 485.
21. Aronica E, Fluiter K, Iyer A, et al. Expression pattern of miR-146a, an inflammation-associated microRNA, in experimental and human temporal lobe epilepsy. Eur J Neurosci, 2010, 31(6): 1100-1107.
22. Jimenez-Mateos EM, Bray I, Sanz-Rodriguez A, et al. miRNA expression profile after status epilepticus and hippocampal neuroprotection by targeting miR-132. Am J Pathol, 2011, 179(5): 2519-2532.
23. Jimenez-Mateos EM, Engel T, Merino-Serrais P, et al. Silencing microRNA-134 produces neuroprotective and prolonged seizure-suppressive effects. Nat Med, 2012, 18(7): 1087-1094.
24. Peng J, Omran A, Ashhab MU, et al. Expression patterns of miR-124, miR-134, miR-132, and miR-21 in an immature rat model and children with mesial temporal lobe epilepsy. J Mol Neurosci, 2013, 50(2): 291-297.
25. Dudoit S, Yang YH, Callow MJ, et al. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Stat Sin, 2002, 12: 111-139.
26. Wong N, Wang X. miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic Acids Res, 2015, 43(Database issue): 146-152.
27. Mc Kiernan RC, Jimenez-Mateos EM, Bray I, et al. Reduced mature microRNA levels in association with dicer loss in human temporal lobe epilepsy with hippocampal sclerosis. PLoS One, 2012, 7(5): e35921.
28. Roncon P, Soukupova M, Binaschi A, et al. MicroRNA profiles in hippocampal granule cells and plasma of rats with pilocarpine-induced epilepsy-comparison with human epileptic samples. Sci Rep, 2015, 5: 14143.
29. Sørensen SS, Nygaard AB, Christensen T. miRNA expression profiles in cerebrospinal fluid and blood of patients with alzheimer’s disease and other types of dementia-an exploratory study. Transl Neurodegener, 2016, 5: 6.
30. Vallelunga A, Ragusa M, Di Mauro S, et al. Identification of circulating microRNAs for the differential diagnosis of Parkinson’s disease and multiple system atrophy. Front Cell Neurosci, 2014, 8: 156.
31. Wang C, Ji B, Cheng B, et al. Neuroprotection of microRNA in neurological disorders (Review). Biomed Rep, 2014, 2(5): 611-619.
32. Meissner L, Gallozzi M, Balbi M, et al. Temporal profile of microRNA expression in contused cortex after traumatic brain injury in mice. J Neurotrauma, 2016, 33(8): 713-720.
33. Jovičić A, Roshan R, Moisoi N, et al. Comprehensive expression analyses of neural cell-type-specific miRNAs identify new determinants of the specification and maintenance of neuronal phenotypes. J Neurosci, 2013, 33(12): 5127-5137.
34. Kakimoto Y, Kamiguchi H, Ochiai E, et al. MicroRNA stability in postmortem FFPE tissues: quantitative analysis using autoptic samples from acute myocardial infarction patients. PLoS One, 2015, 10(6): e0129338.
35. Kandratavicius L, Balista PA, Lopes-Aguiar C, et al. Animal models of epilepsy: use and limitations. Neuropsychiatr Dis Treat, 2014, 10: 1693-1705.
36. Di Nuzzo M, Mangia S, Maraviglia B, et al. Physiological bases of the K+ and the glutamate/GABA hypotheses of epilepsy. Epilepsy Res, 2014, 108(6): 995-1012.
37. Holtmaat AJ, Gorter JA, De Wit J, et al. Transient downregulation of sema3a mrna in a rat model for temporal lobe epilepsy: a novel molecular event potentially contributing to mossy fiber sprouting. Exp Neurol, 2003, 182(1): 142-150.
38. Xu B, Michalski B, Racine R, et al. Continuous infusion of neurotrophin-3 triggers sprouting, decreases the levels of TrkA and Trk C, and inhibits epileptogenesis and activity-dependent axonal growth in adult rats. Neuroscience, 2002, 115(4): 1295-1308.
39. Cupertino RB, Kappel DB, Bandeira CE, et al. SNARE complex in developmental psychiatry: neurotransmitter exocytosis and beyond. J Neural Transm, 2016, 123(8): 867-883.
40. Pralhada Rao R, Vaidyanathan N, Rengasamy M, et al. Sphingolipid metabolic pathway: an overview of major roles played in human diseases. J Lipids, 2013, 2013: 1-12.

Previous Article
伴中央颞区棘波自限性癫痫患儿的认知功能：一项系统评价和荟萃分析
Next Article
SCN1A 基因突变致 Dravet 综合征的临床诊断学特征

Journal of Epilepsy

微小 RNA 和伴海马硬化的内侧颞叶癫痫：人类海马全微小 RNA 组谱

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content