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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
DOI 10.1016/j.ebiom.2018.10.068PMC PMC6306312
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Fig. 1 Study design and plasma miRNA profiling in TLE. (a) Schematic showing overview of study. Plasma samples were collected from controls (C) and from epilepsy patients at baseline (EBS) and after seizures (EAS) during in-patient video-EEG monitoring in Marburg (MAR) and Dublin (DUB). Samples were run individually on the OpenArray platform (OA) or pooled for RNA-seq. From these a selection of 15 miRNAs were validated using individual miRNA assays during a confirmation phase using the original samples and then three miRNAs were advanced for validation in a larger cohort of plasma samples from controls, TLE patients as well as samples from patients with genetic generalized epilepsy (GGE), frontal lobe epilepsy (FLE), psychogenic non-epileptic seizures (PNES) and status epilepticus (SE). An extended validation phase involved testing three miRNAs through a set of animal and human studies. (b) Barplots showing the number of unique miRNA identified in each sample using OA profiling. (c) Venn diagram indicating overlap of differentially expressed miRNA identified by OA analysis of the MAR samples and five miRNAs that were differentially expressed. (d) Summary bar chart on RNA-seq data from the MAR sample analysis. Panel to right shows a heatmap and unsupervised clustering by sample and miRNA for the 50 miRNAs with highest coefficient of variation. (e) Summary RNA-seq data and heat map from DUB sample analysis. (f) Graphic displaying miRNAs from the RNA-seq data with common direction of altered expression which were selected for validation.
Fig. 1 Study design and plasma miRNA profiling in TLE. (a) Schematic showing overview of study. Plasma samples were collected from controls (C) and from epilepsy patients at baseline (EBS) and after seizures (EAS) during in-patient video-EEG monitoring in Marburg (MAR) and Dublin (DUB). Samples were run individually on the OpenArray platform (OA) or pooled for RNA-seq. From these a selection of 15 miRNAs were validated using individual miRNA assays during a confirmation phase using the original samples and then three miRNAs were advanced for validation in a larger cohort of plasma samples from controls, TLE patients as well as samples from patients with genetic generalized epilepsy (GGE), frontal lobe epilepsy (FLE), psychogenic non-epileptic seizures (PNES) and status epilepticus (SE). An extended validation phase involved testing three miRNAs through a set of animal and human studies. (b) Barplots showing the number of unique miRNA identified in each sample using OA profiling. (c) Venn diagram indicating overlap of differentially expressed miRNA identified by OA analysis of the MAR samples and five miRNAs that were differentially expressed. (d) Summary bar chart on RNA-seq data from the MAR sample analysis. Panel to right shows a heatmap and unsupervised clustering by sample and miRNA for the 50 miRNAs with highest coefficient of variation. (e) Summary RNA-seq data and heat map from DUB sample analysis. (f) Graphic displaying miRNAs from the RNA-seq data with common direction of altered expression which were selected for validation.CC BY-NC-ND · PubMed Central
Fig. 2 Validation of miRNAs miR-27a-3p, −328-3p and 654-3p as epilepsy biomarkers.(a) Box-and-whiskers plots of the expression of miRNAs -27a-3p, 328-3p and 654-3p in plasma from the discovery cohort (MAR + DUB) samples (n = 32 per group). * p < .05; ** p < .01 compared to indicated group. (b) Validation of the three miRNAs in an expanded cohort that included controls (n = 110), TLE patients (n = 102), patients with genetic generalized epilepsy (GGE, n = 23), frontal lobe epilepsy (FLE, n = 10), psychogenic non-epileptic seizures (PNES, n = 15) and status epilepticus (SE, n = 6). (c) ROC plots of the plasma data for TLE and GGE samples compared to controls. Note, combinations of miRNAs resulted in slightly higher biomarker performance compared to single miRNA assessments. *p < .05 compared to controls.
Fig. 2 Validation of miRNAs miR-27a-3p, −328-3p and 654-3p as epilepsy biomarkers.(a) Box-and-whiskers plots of the expression of miRNAs -27a-3p, 328-3p and 654-3p in plasma from the discovery cohort (MAR + DUB) samples (n = 32 per group). * p < .05; ** p < .01 compared to indicated group. (b) Validation of the three miRNAs in an expanded cohort that included controls (n = 110), TLE patients (n = 102), patients with genetic generalized epilepsy (GGE, n = 23), frontal lobe epilepsy (FLE, n = 10), psychogenic non-epileptic seizures (PNES, n = 15) and status epilepticus (SE, n = 6). (c) ROC plots of the plasma data for TLE and GGE samples compared to controls. Note, combinations of miRNAs resulted in slightly higher biomarker performance compared to single miRNA assessments. *p < .05 compared to controls.CC BY-NC-ND · PubMed Central
Fig. 3 miRNA biomarker performance in experimental epilepsy. (a) (left) Schematic of studies in mice in which miRNA performance was analyzed. Plasma was collected from mice two weeks after status epilepticus (SE) at a time when mice display spontaneous recurrent seizures (SRS). In a second experiment, already-epileptic mice were dosed with AEDs carbamazepine (CBZ) or diazepam (DZM). Graphs (right) show relative expression of miR-27a-3p and miR-328-3p in control and epileptic (Epi) mice and in epileptic mice dosed with the two AEDs (n = 5–9 per group). Note, both miRNAs were lower in epileptic mouse plasma consistent with the human data while plamsa levels of the miRNA were not significantly changed by AED exposure. (b) (left) Schematic showing experimental design in which mice were given a known disease-modifying miRNA therapy (anti-mir) immediately after status epilepticus to reduce epilepsy development. Samples were taken two weeks later. Graphs (right) show relative expression of miR-27a-3p and miR-654-3p in control and epileptic mice (n = 4–5 per group). Note, partial reversal of miRNA expression pattern in mice given the disease-modifying antimir therapy. *p < .05 compared to indicated group.
Fig. 3 miRNA biomarker performance in experimental epilepsy. (a) (left) Schematic of studies in mice in which miRNA performance was analyzed. Plasma was collected from mice two weeks after status epilepticus (SE) at a time when mice display spontaneous recurrent seizures (SRS). In a second experiment, already-epileptic mice were dosed with AEDs carbamazepine (CBZ) or diazepam (DZM). Graphs (right) show relative expression of miR-27a-3p and miR-328-3p in control and epileptic (Epi) mice and in epileptic mice dosed with the two AEDs (n = 5–9 per group). Note, both miRNAs were lower in epileptic mouse plasma consistent with the human data while plamsa levels of the miRNA were not significantly changed by AED exposure. (b) (left) Schematic showing experimental design in which mice were given a known disease-modifying miRNA therapy (anti-mir) immediately after status epilepticus to reduce epilepsy development. Samples were taken two weeks later. Graphs (right) show relative expression of miR-27a-3p and miR-654-3p in control and epileptic mice (n = 4–5 per group). Note, partial reversal of miRNA expression pattern in mice given the disease-modifying antimir therapy. *p < .05 compared to indicated group.CC BY-NC-ND · PubMed Central
Fig. 4 Copy number analysis of miRNAs in human plasma and prototype miRNA detector.(a) Graphs show miRNA copy number determined using digital PCR for miRNAs -27a-3p, 328-3p and 654-3p in control (C), TLE, GGE and PNES groups. Note that TLE samples showed the expected lower levels of miR-27a-3p in TLE as well as GGE patients compared to controls and miR-328-3p levels were also lower in TLE patients. In contrast, levels of the miRNAs were not different to controls in PNES patients. (b)tornado; image of fully assembled miRNA detector disc with integrated electrodes (cropped to view one section). Specific chambers contain target miRNA strands, wash steps, platinum (Pt) nanoparticle-labeled miRNA probe strands, the electrode chamber and a waste chamber with an overflow system. Expanded box (middle panel) shows schematic of the hybridisation of miRNA strands on surface of working electrode inside chamber. Graph (right) shows amperometric i-t curves comparing the response of a fully complementray samdwich assay with a target strand of 1 nM (black line) with a bare gold electrode (red line), a gold electrode with capture miRNA only (cRNA, green line) and a gold electrode with capture miRNA and target miRNA (tRNA, blue line potential applied is −0.25 V in 1 mM DPBS. The difference in current before and after the addition of 20 μM H2O2 is displayed and shows the large differnece in current when the assay is fully complementary. (c) Graphs show results for a set of samples run in the tornado with (d), graphs showing raw data from tornado for all three miRNAs for a representative sample from each group. Note, that the result for tornado follows closely the abundance of the three miRNAs determined by dPCR.
Fig. 4 Copy number analysis of miRNAs in human plasma and prototype miRNA detector.(a) Graphs show miRNA copy number determined using digital PCR for miRNAs -27a-3p, 328-3p and 654-3p in control (C), TLE, GGE and PNES groups. Note that TLE samples showed the expected lower levels of miR-27a-3p in TLE as well as GGE patients compared to controls and miR-328-3p levels were also lower in TLE patients. In contrast, levels of the miRNAs were not different to controls in PNES patients. (b)tornado; image of fully assembled miRNA detector disc with integrated electrodes (cropped to view one section). Specific chambers contain target miRNA strands, wash steps, platinum (Pt) nanoparticle-labeled miRNA probe strands, the electrode chamber and a waste chamber with an overflow system. Expanded box (middle panel) shows schematic of the hybridisation of miRNA strands on surface of working electrode inside chamber. Graph (right) shows amperometric i-t curves comparing the response of a fully complementray samdwich assay with a target strand of 1 nM (black line) with a bare gold electrode (red line), a gold electrode with capture miRNA only (cRNA, green line) and a gold electrode with capture miRNA and target miRNA (tRNA, blue line potential applied is −0.25 V in 1 mM DPBS. The difference in current before and after the addition of 20 μM H2O2 is displayed and shows the large differnece in current when the assay is fully complementary. (c) Graphs show results for a set of samples run in the tornado with (d), graphs showing raw data from tornado for all three miRNAs for a representative sample from each group. Note, that the result for tornado follows closely the abundance of the three miRNAs determined by dPCR.CC BY-NC-ND · PubMed Central
Fig. 5 Analysis of the mechanism of miRNA transport in plasma increases diagnostic yield.(a) Schematic showing study design. A subset of plasma samples were processed to assess total, exosomal and AGO2-bound miRNA. (b) Graphs show miRNA expression within exosomal and AGO2-bound fractions relative to the total for each of the three miRNAs. Note that a majority of the miRNA was detected in the exosomal fraction. Also, the relative fraction of miR-328-3p in the AGO2 fraction selectively increased in samples obtained after seizures relative to baseline. (c) ROC plots of the exosomal data for TLE compared to controls. Note, combinations of exosomal miRNAs resulted in higher biomarker performance.
Fig. 5 Analysis of the mechanism of miRNA transport in plasma increases diagnostic yield.(a) Schematic showing study design. A subset of plasma samples were processed to assess total, exosomal and AGO2-bound miRNA. (b) Graphs show miRNA expression within exosomal and AGO2-bound fractions relative to the total for each of the three miRNAs. Note that a majority of the miRNA was detected in the exosomal fraction. Also, the relative fraction of miR-328-3p in the AGO2 fraction selectively increased in samples obtained after seizures relative to baseline. (c) ROC plots of the exosomal data for TLE compared to controls. Note, combinations of exosomal miRNAs resulted in higher biomarker performance.CC BY-NC-ND · PubMed Central
Fig. 6 Cellular localization of miR-27a-3p and miR-328-3p in human brain. (a, b) Panels show in situ hybridization for miR-27a-3p and miR-328-3p in human hippocampal tissue sections from TLE patients. Note neuronal localization of both miRNAs and nuclear localization. (c) Specificity controls showing absence of staining with a scrambled probe and staining for U6 as a positive control.
Fig. 6 Cellular localization of miR-27a-3p and miR-328-3p in human brain. (a, b) Panels show in situ hybridization for miR-27a-3p and miR-328-3p in human hippocampal tissue sections from TLE patients. Note neuronal localization of both miRNAs and nuclear localization. (c) Specificity controls showing absence of staining with a scrambled probe and staining for U6 as a positive control.CC BY-NC-ND · PubMed Central
Fig. 7 Targets and pathways regulated by miRNA biomarkers of epilepsy.(a) Chord diagram showing the validated and predicted targets of hsa-miR-27a-3p, hsa-miR-328-3p and hsa-miR-654-3p. The three miRNA and their mRNA targets are represented by coloured sectors around the outside of the circle. The two outer ‘orbits’ highlight transcription factors (inner grey sectors) and mRNA targets that have been associated with epilepsy (outer red sectors). Sectors are linked if a miRNA-target interaction (MTI) has been demonstrated between the miRNA and mRNA target. MTIs that have been validated with ‘strong evidence’ as classified by miRTarBase are highlighted by links with black borders. The width of each link corresponds to the MTI score associated with that link, as described in the Methods (MTIs with wider links have higher scores). (b) Bar chart showing the -log10(adjusted p-value) of significantly enriched (adjusted p-value <.001) PANTHER pathways for the 95 targets shown in (A). The number of targets in each pathway relative to the total number of proteins in that pathway is shown in parentheses.
Fig. 7 Targets and pathways regulated by miRNA biomarkers of epilepsy.(a) Chord diagram showing the validated and predicted targets of hsa-miR-27a-3p, hsa-miR-328-3p and hsa-miR-654-3p. The three miRNA and their mRNA targets are represented by coloured sectors around the outside of the circle. The two outer ‘orbits’ highlight transcription factors (inner grey sectors) and mRNA targets that have been associated with epilepsy (outer red sectors). Sectors are linked if a miRNA-target interaction (MTI) has been demonstrated between the miRNA and mRNA target. MTIs that have been validated with ‘strong evidence’ as classified by miRTarBase are highlighted by links with black borders. The width of each link corresponds to the MTI score associated with that link, as described in the Methods (MTIs with wider links have higher scores). (b) Bar chart showing the -log10(adjusted p-value) of significantly enriched (adjusted p-value <.001) PANTHER pathways for the 95 targets shown in (A). The number of targets in each pathway relative to the total number of proteins in that pathway is shown in parentheses.CC BY-NC-ND · PubMed Central

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Research works printed but not linked: 60

  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.
ORCID
Mooney, Catherine
Venue
eBioMedicine
Publisher
Elsevier BV
Published
2018-12
Volume
38
Pages
127-141
ISSN
2352-3964
DOI
10.1016/j.ebiom.2018.10.068
PMC
PMC6306312
Cited by
106
References
3
Language
en
Licence
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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