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Dual-center, dual-platform microRNA profiling identifies potential plasma biomarkers of adult temporal lobe epilepsy
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“Several of the same miRNAs are differentially expressed in baseline or post-seizure blood samples from patients with drug-resistant epilepsy (Enright et al., 2018; Brennan et al., 2020; Leontariti et al., 2020; Martins-Ferreira et al., 2020), including in AGO-bound fractions (Raoof et al., 2018).”
From Brain cell-specific origin of circulating microRNA biomarkers in experimental temporal lobe epilepsy (2023) · cites this paper as Raoof et al., 2018
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“AGO-bound miRNAs represent a major pool of circulating miRNAs (Arroyo et al., 2011), and measuring AGO-bound miRNAs may enhance the ability to distinguish brain disease subtypes (Raoof et al., 2017), and identify post-seizure from baseline samples in plasma from epilepsy patients (Raoof et al., 2018).”
From Brain cell-specific origin of circulating microRNA biomarkers in experimental temporal lobe epilepsy (2023) · cites this paper as Raoof et al., 2018
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pROC: an open-source package for R and S+ to analyze and compare ROC curves
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-77 · Cited by 13,697
Distribution of miRNA expression across human tissues
Ludwig, N., Leidinger, P., Becker, K., Backes, C., Fehlmann, T., Pallasch, C., Rheinheimer, S., Meder, B., Stähler, C., Meese, E., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw116 · Cited by 968
Jovičić, A., Roshan, R., Moisoi, N., Pradervand, S., Moser, R., Pillai, B., & Luthi-Carter, R. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.0600-12.2013 · Cited by 251
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Research works printed but not linked: 60
- Moshe, 2015, Epilepsy: New advances, Lancet, 385, 884, 10.1016/S0140-6736(14)60456-6 · DOI
- Engel, 2013, Epilepsy biomarkers, Epilepsia, 54 Suppl 4, 61, 10.1111/epi.12299 · DOI · Paper page
- Walker, 2016, WONOEP appraisal: Molecular and cellular biomarkers for epilepsy, Epilepsia, 57, 1354, 10.1111/epi.13460 · DOI
- Muhlhofer, 2017, MRI-negative temporal lobe epilepsy-what do we know?, Epilepsia, 58, 727, 10.1111/epi.13699 · DOI
- 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
- Brown, 2011, Psychogenic nonepileptic seizures, Epilepsy Behav., 22, 85, 10.1016/j.yebeh.2011.02.016 · DOI
- Pitkanen, 2016, Advances in the development of biomarkers for epilepsy, Lancet Neurol., 15, 843, 10.1016/S1474-4422(16)00112-5 · DOI
- Walker, 2017, Molecular isoforms of high-mobility group box 1 are mechanistic biomarkers for epilepsy, J. Clin. Invest., 127, 2118, 10.1172/JCI92001 · DOI
- Rao, 2013, MicroRNAs as biomarkers for CNS disease, Front. Mol. Neurosci., 6, 39, 10.3389/fnmol.2013.00039 · DOI · Paper page
- 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
- Henshall, 2016, MicroRNAs in epilepsy: Pathophysiology and clinical utility, Lancet Neurol., 15, 1368, 10.1016/S1474-4422(16)30246-0 · DOI
- 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
- Mooney, 2016, EpimiRBase: a comprehensive database of microRNA-epilepsy associations, Bioinformatics, 32, 1436, 10.1093/bioinformatics/btw008 · DOI
- Wang, 2015, Genome-wide circulating microRNA expression profiling indicates biomarkers for epilepsy, Sci. Rep., 5, 9522, 10.1038/srep09522 · DOI · Paper page
- Wang, 2015, Circulating microRNAs are promising novel biomarkers for drug-resistant epilepsy, Sci. Rep., 5, 10201, 10.1038/srep10201 · DOI
- 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
- 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
- Yan, 2017, Altered microRNA profiles in plasma exosomes from mesial temporal lobe epilepsy with hippocampal sclerosis, Oncotarget, 8, 4136, 10.18632/oncotarget.13744 · DOI
- 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
- 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
- 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
- Peterson, 2014, Common features of microRNA target prediction tools, Front. Genet., 5, 23, 10.3389/fgene.2014.00023 · DOI
- Pritchard, 2012, MicroRNA profiling: Approaches and considerations, Nat. Rev. Genet., 13, 358, 10.1038/nrg3198 · DOI · Paper page
- Cheng, 2013, Plasma processing conditions substantially influence circulating microRNA biomarker levels, PLoS One, 8, e64795, 10.1371/journal.pone.0064795 · DOI · Paper page
- 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
- Kirschner, 2011, Haemolysis during sample preparation alters microRNA content of plasma, PLoS One, 6, e24145, 10.1371/journal.pone.0024145 · DOI · Paper page
- 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
- Huber, 2015, Orchestrating high-throughput genomic analysis with bioconductor, Nat. Methods, 12, 115, 10.1038/nmeth.3252 · DOI
- McCall, 2014, On non-detects in qPCR data, Bioinformatics, 30, 2310, 10.1093/bioinformatics/btu239 · DOI
- 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
- 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
- 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
- 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
- Jimenez-Mateos, 2012, Silencing microRNA-134 produces neuroprotective and prolonged seizure-suppressive effects, Nat. Med., 18, 1087, 10.1038/nm.2834 · DOI
- 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
- 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
- Chou, 2016, miRTarBase 2016: Updates to the experimentally validated miRNA-target interactions database, Nucleic Acids Res., 44, D239, 10.1093/nar/gkv1258 · DOI
- Agarwal, 2015, Predicting effective microRNA target sites in mammalian mRNAs, elife, 4, 10.7554/eLife.05005 · DOI · Paper page
- Wong, 2015, miRDB: an online resource for microRNA target prediction and functional annotations, Nucleic Acids Res., 43, D146, 10.1093/nar/gku1104 · DOI
- Gu, 2014, Circlize Implements and enhances circular visualization in R, Bioinformatics, 30, 2811, 10.1093/bioinformatics/btu393 · DOI
- 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
- 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
- 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
- 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
- Spain, 2015, Direct, non-amplified detection of microRNA-134 in plasma from epilepsy patients, RSC Adv., 5, 90071, 10.1039/C5RA16352H · DOI
- 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
- 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
- Wang, 2012, Comparing the MicroRNA spectrum between serum and plasma, PLoS One, 7, e41561, 10.1371/journal.pone.0041561 · DOI
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Venereau, 2016, HMGB1 as biomarker and drug target, Pharmacol. Res., 111, 534, 10.1016/j.phrs.2016.06.031 · DOI
- Moldovan, 2014, Methodological challenges in utilizing miRNAs as circulating biomarkers, J. Cell. Mol. Med., 18, 371, 10.1111/jcmm.12236 · DOI · Paper page
Cited by
Sorted by citations
Epigenetic genes and epilepsy — emerging mechanisms and clinical applications
Van Loo, K. M. J., Carvill, G. L., Becker, A. J., Conboy, K., Goldman, A. M., Kobow, K., Lopes-Cendes, I., Reid, C. A., van Vliet, E. A., & Henshall, D. C. (2022) · Nature Reviews Neurology · doi:10.1038/s41582-022-00693-y · Cited by 68
Self-citationBrain cell-specific origin of circulating microRNA biomarkers in experimental temporal lobe epilepsy
Brindley, E., Heiland, M., Mooney, C., Diviney, M., Mamad, O., Hill, T. D. M., Yan, Y., Venø, M. T., Reschke, C. R., Batool, A., et al. (2023) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2023.1230942 · Cited by 21
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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
- 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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