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Dual-center, dual-platform microRNA profiling identifies potential plasma biomarkers of adult temporal lobe epilepsy

Raoof, Rana, Bauer, Sebastian, El Naggar, Hany, Connolly, Niamh M.C., Brennan, Gary P., Brindley, Elizabeth, Hill, Thomas, McArdle, Hazel, Spain, Elaine, Forster, Robert J., Prehn, Jochen H.M., Hamer, Hajo, Delanty, Norman, Rosenow, Felix, Mooney, Catherine, Henshall, David C.·2018·eBioMedicine

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  1. Moshe, 2015, Epilepsy: New advances, Lancet, 385, 884, 10.1016/S0140-6736(14)60456-6 · DOI
  2. Engel, 2013, Epilepsy biomarkers, Epilepsia, 54 Suppl 4, 61, 10.1111/epi.12299 · DOI · Paper page
  3. Walker, 2016, WONOEP appraisal: Molecular and cellular biomarkers for epilepsy, Epilepsia, 57, 1354, 10.1111/epi.13460 · DOI
  4. Muhlhofer, 2017, MRI-negative temporal lobe epilepsy-what do we know?, Epilepsia, 58, 727, 10.1111/epi.13699 · DOI
  5. Chowdhury, 2008, Misdiagnosis in epilepsy: A review and recognition of diagnostic uncertainty, Eur. J. Neurol., 15, 1034, 10.1111/j.1468-1331.2008.02260.x · DOI
  6. Brown, 2011, Psychogenic nonepileptic seizures, Epilepsy Behav., 22, 85, 10.1016/j.yebeh.2011.02.016 · DOI
  7. Pitkanen, 2016, Advances in the development of biomarkers for epilepsy, Lancet Neurol., 15, 843, 10.1016/S1474-4422(16)00112-5 · DOI
  8. Walker, 2017, Molecular isoforms of high-mobility group box 1 are mechanistic biomarkers for epilepsy, J. Clin. Invest., 127, 2118, 10.1172/JCI92001 · DOI
  9. Rao, 2013, MicroRNAs as biomarkers for CNS disease, Front. Mol. Neurosci., 6, 39, 10.3389/fnmol.2013.00039 · DOI · Paper page
  10. Hunt, 2015, MicroRNA detection: Current technology and research strategies, Annu Rev Anal Chem (Palo Alto, Calif), 8, 217, 10.1146/annurev-anchem-071114-040343 · DOI
  11. Henshall, 2016, MicroRNAs in epilepsy: Pathophysiology and clinical utility, Lancet Neurol., 15, 1368, 10.1016/S1474-4422(16)30246-0 · DOI
  12. Korotkov, 2017, Systematic review and meta-analysis of differentially expressed miRNAs in experimental and human temporal lobe epilepsy, Sci. Rep., 7, 11592, 10.1038/s41598-017-11510-8 · DOI
  13. Mooney, 2016, EpimiRBase: a comprehensive database of microRNA-epilepsy associations, Bioinformatics, 32, 1436, 10.1093/bioinformatics/btw008 · DOI
  14. Wang, 2015, Genome-wide circulating microRNA expression profiling indicates biomarkers for epilepsy, Sci. Rep., 5, 9522, 10.1038/srep09522 · DOI · Paper page
  15. Wang, 2015, Circulating microRNAs are promising novel biomarkers for drug-resistant epilepsy, Sci. Rep., 5, 10201, 10.1038/srep10201 · DOI
  16. An, 2016, Elevated serum miR-106b and miR-146a in patients with focal and generalized epilepsy, Epilepsy Res., 127, 311, 10.1016/j.eplepsyres.2016.09.019 · DOI
  17. Surges, 2016, Changes in serum miRNAs following generalized convulsive seizures in human mesial temporal lobe epilepsy, Biochem. Biophys. Res. Commun., 481, 13, 10.1016/j.bbrc.2016.11.029 · DOI
  18. Yan, 2017, Altered microRNA profiles in plasma exosomes from mesial temporal lobe epilepsy with hippocampal sclerosis, Oncotarget, 8, 4136, 10.18632/oncotarget.13744 · DOI
  19. Raoof, 2017, Cerebrospinal fluid microRNAs are potential biomarkers of temporal lobe epilepsy and status epilepticus, Sci. Rep., 7, 3328, 10.1038/s41598-017-02969-6 · DOI · Paper page
  20. Mestdagh, 2014, Evaluation of quantitative miRNA expression platforms in the microRNA quality control (miRQC) study, Nat. Methods, 11, 809, 10.1038/nmeth.3014 · DOI · Paper page
  21. Bencurova, 2017, MicroRNA and mesial temporal lobe epilepsy with hippocampal sclerosis: Whole miRNome profiling of human hippocampus, Epilepsia, 58, 1782, 10.1111/epi.13870 · DOI
  22. Peterson, 2014, Common features of microRNA target prediction tools, Front. Genet., 5, 23, 10.3389/fgene.2014.00023 · DOI
  23. Pritchard, 2012, MicroRNA profiling: Approaches and considerations, Nat. Rev. Genet., 13, 358, 10.1038/nrg3198 · DOI · Paper page
  24. Cheng, 2013, Plasma processing conditions substantially influence circulating microRNA biomarker levels, PLoS One, 8, e64795, 10.1371/journal.pone.0064795 · DOI · Paper page
  25. Mooney, 2015, High throughput qPCR expression profiling of circulating microRNAs reveals minimal sex- and sample timing-related variation in plasma of healthy volunteers, PLoS One, 10, e0145316, 10.1371/journal.pone.0145316 · DOI
  26. Kirschner, 2011, Haemolysis during sample preparation alters microRNA content of plasma, PLoS One, 6, e24145, 10.1371/journal.pone.0024145 · DOI · Paper page
  27. El-Khoury, 2016, Assessing cellular and circulating miRNA recovery: the impact of the RNA isolation method and the quantity of input material, Sci. Rep., 6, 19529, 10.1038/srep19529 · DOI
  28. Huber, 2015, Orchestrating high-throughput genomic analysis with bioconductor, Nat. Methods, 12, 115, 10.1038/nmeth.3252 · DOI
  29. McCall, 2014, On non-detects in qPCR data, Bioinformatics, 30, 2310, 10.1093/bioinformatics/btu239 · DOI
  30. Vandesompele, 2002, Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes, Genome Biol., 3, 10.1186/gb-2002-3-7-research0034 · DOI · Paper page
  31. Benjamini, 1995, Controlling the false discovery rate: a practical and powerful approach to multiple testing, J R Stat Soc B Methodol, 57, 289, 10.1111/j.2517-6161.1995.tb02031.x · DOI · Paper page
  32. Robinson, 2010, edgeR: A bioconductor package for differential expression analysis of digital gene expression data, Bioinformatics, 26, 139, 10.1093/bioinformatics/btp616 · DOI · Paper page
  33. Jimenez-Mateos, 2011, miRNA Expression profile after status epilepticus and hippocampal neuroprotection by targeting miR-132, Am. J. Pathol., 179, 2519, 10.1016/j.ajpath.2011.07.036 · DOI
  34. Jimenez-Mateos, 2012, Silencing microRNA-134 produces neuroprotective and prolonged seizure-suppressive effects, Nat. Med., 18, 1087, 10.1038/nm.2834 · DOI
  35. McArdle, 2017, "TORNADO" - theranostic One-step RNA detector; microfluidic disc for the direct detection of microRNA-134 in plasma and cerebrospinal fluid, Sci. Rep., 7, 1750, 10.1038/s41598-017-01947-2 · DOI
  36. Kinahan, 2014, Event-triggered logical flow control for comprehensive process integration of multi-step assays on centrifugal microfluidic platforms, Lab Chip, 14, 2249, 10.1039/C4LC00380B · DOI
  37. Chou, 2016, miRTarBase 2016: Updates to the experimentally validated miRNA-target interactions database, Nucleic Acids Res., 44, D239, 10.1093/nar/gkv1258 · DOI
  38. Agarwal, 2015, Predicting effective microRNA target sites in mammalian mRNAs, elife, 4, 10.7554/eLife.05005 · DOI · Paper page
  39. Wong, 2015, miRDB: an online resource for microRNA target prediction and functional annotations, Nucleic Acids Res., 43, D146, 10.1093/nar/gku1104 · DOI
  40. Gu, 2014, Circlize Implements and enhances circular visualization in R, Bioinformatics, 30, 2811, 10.1093/bioinformatics/btu393 · DOI
  41. Zhang, 2015, AnimalTFDB 2.0: A resource for expression, prediction and functional study of animal transcription factors, Nucleic Acids Res., 43, D76, 10.1093/nar/gku887 · DOI
  42. Kuleshov, 2016, Enrichr: a comprehensive gene set enrichment analysis web server 2016 update, Nucleic Acids Res., 44, W90, 10.1093/nar/gkw377 · DOI · Paper page
  43. Mi, 2016, PANTHER version 10: Expanded protein families and functions, and analysis tools, Nucleic Acids Res., 44, D336, 10.1093/nar/gkv1194 · DOI · Paper page
  44. Klein, 2015, Inter-individual variation in the effect of antiepileptic drugs in the intrahippocampal kainate model of mesial temporal lobe epilepsy in mice, Neuropharmacology, 90, 53, 10.1016/j.neuropharm.2014.11.008 · DOI
  45. Spain, 2015, Direct, non-amplified detection of microRNA-134 in plasma from epilepsy patients, RSC Adv., 5, 90071, 10.1039/C5RA16352H · DOI
  46. Kan, 2012, Genome-wide microRNA profiling of human temporal lobe epilepsy identifies modulators of the immune response, Cell. Mol. Life Sci., 69, 3127, 10.1007/s00018-012-0992-7 · DOI
  47. Brennan, 2016, Dual and opposing roles of microRNA-124 in epilepsy are mediated through inflammatory and NRSF-dependent gene networks, Cell Rep., 14, 2402, 10.1016/j.celrep.2016.02.042 · DOI
  48. Wang, 2012, Comparing the MicroRNA spectrum between serum and plasma, PLoS One, 7, e41561, 10.1371/journal.pone.0041561 · DOI
  49. van Vliet, 2017, Standardization procedure for plasma biomarker analysis in rat models of epileptogenesis: Focus on circulating microRNAs, Epilepsia, 58, 2013, 10.1111/epi.13915 · DOI
  50. Fromm, 2015, A uniform system for the annotation of vertebrate microRNA genes and the evolution of the human microRNAome, Annu. Rev. Genet., 49, 213, 10.1146/annurev-genet-120213-092023 · DOI
  51. Miller-Delaney, 2015, Differential DNA methylation profiles of coding and non-coding genes define hippocampal sclerosis in human temporal lobe epilepsy, Brain, 138, 616, 10.1093/brain/awu373 · DOI
  52. Gorter, 2014, Hippocampal subregion-specific microRNA expression during epileptogenesis in experimental temporal lobe epilepsy, Neurobiol. Dis., 62, 508, 10.1016/j.nbd.2013.10.026 · DOI · Paper page
  53. McKiernan, 2012, Reduced mature microRNA levels in association with dicer loss in human temporal lobe epilepsy with hippocampal sclerosis, PLoS One, 7, e35921, 10.1371/journal.pone.0035921 · DOI · Paper page
  54. Kretschmann, 2015, Different microRNA profiles in chronic epilepsy versus acute seizure mouse models, J. Mol. Neurosci., 55, 466, 10.1007/s12031-014-0368-6 · DOI
  55. Risbud, 2013, Changes in microRNA expression in the whole hippocampus and hippocampal synaptoneurosome fraction following pilocarpine induced status epilepticus, PLoS One, 8, e53464, 10.1371/journal.pone.0053464 · DOI · Paper page
  56. Engel, 2007, Elevated p53 and lower MDM2 expression in hippocampus from patients with intractable temporal lobe epilepsy, Epilepsy Res., 77, 151, 10.1016/j.eplepsyres.2007.09.001 · DOI
  57. Engel, 2010, Loss of p53 results in protracted electrographic seizures and development of an aggravated epileptic phenotype following status epilepticus, Cell Death Dis., 1, e79, 10.1038/cddis.2010.55 · DOI
  58. Liou, 2003, To die or not to die for neurons in ischemia, traumatic brain injury and epilepsy: a review on the stress-activated signaling pathways and apoptotic pathways, Prog. Neurobiol., 69, 103, 10.1016/S0301-0082(03)00005-4 · DOI
  59. Venereau, 2016, HMGB1 as biomarker and drug target, Pharmacol. Res., 111, 534, 10.1016/j.phrs.2016.06.031 · DOI
  60. Moldovan, 2014, Methodological challenges in utilizing miRNAs as circulating biomarkers, J. Cell. Mol. Med., 18, 371, 10.1111/jcmm.12236 · DOI · Paper page

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Metadata

Authors
Raoof, R., Bauer, S., El Naggar, H., Connolly, N. M. C., Brennan, G. P., Brindley, E., Hill, T., McArdle, H., Spain, E., Forster, R. J., Prehn, J. H. M., Hamer, H., Delanty, N., Rosenow, F., Mooney, C., & Henshall, D. C.
Venue
eBioMedicine
Publisher
Elsevier BV
Published
2018-12
Volume
38
Pages
127-141
ISSN
2352-3964
Cited by
106
References
3
Language
en
Funders
  • European Union's ‘Seventh Framework’ Programme (602130)
  • Science Foundation Ireland (SFI/13/IA/1891, SFI/14/ADV/RC2721, 12/COEN/18, 12/RC/2272, 16/RC/3948)

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