Genome-wide circulating microRNA expression profiling indicates biomarkers for epilepsy

Jun Wang, Jin-Tai Yu, Lin Tan, Yan Tian, Jing Ma, Chen-Chen Tan, Hui-Fu Wang, Ying Liu, Meng-Shan Tan, Teng Jiang, Lan Tan, Jun Wang, Jin-Tai Yu, Lin Tan, Yan Tian, Jing Ma, Chen-Chen Tan, Hui-Fu Wang, Ying Liu, Meng-Shan Tan, Teng Jiang, Lan Tan

Abstract

MicroRNAs (miRNAs) have been proposed as biomarkers for cancer and other diseases due to their stability in serum. In epilepsy, miRNAs have almost been studied in brain tissues and in animals' circulation, but not in circulation of human. To date, a major challenge is to develop biomarkers to improve the current diagnosis of epilepsy. The aim of this study was to evaluate whether circulating miRNAs can be used as biomarkers for epilepsy. We measured the differences in serum miRNA levels between 30 epilepsy patients and 30 healthy controls in discovery and training phases using Illumina HiSeq2000 sequencing followed by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) assays. The selected miRNAs were then validated in 117 epilepsy patients and 112 healthy controls by qRT-PCR. Let-7d-5p, miR-106b-5p, -130a-3p and -146a-5p were found up-regulated, whereas miR-15a-5p and -194-5p were down-regulated in epilepsy patients compared to controls (P < 0.0001). Among these miRNAs, miR-106b-5p had the best diagnostic value for epilepsy with 80.3% sensitivity and 81.2% specificity. Circulating miRNAs were differentially regulated in epilepsy patients as compared with controls. MiR-106b-5p may serve as a novel, noninvasive biomarker to improve the current diagnosis of epilepsy.

Figures

Figure 1. The circulating miRNAs signatures identified…
Figure 1. The circulating miRNAs signatures identified by Illumina Hiseq2000 sequencing.
The length distribution and frequency percentages of the sequences identified in healthy controls (A) and epilepsy patients (B); RNA species in healthy controls (C) and epilepsy patients (D); and RNA read counts in healthy controls (E) and epilepsy patients (F).
Figure 2. Differential expression levels of significant…
Figure 2. Differential expression levels of significant miRNAs in training phase.
Expression levels of the miRNAs were normalized to spiked-in cel-miR-39 and were calculated utilizing the 2−ΔΔCt method. Mann-Whitney U test was used to determine statistical significance. The black dots and stars represent the outliers. The black dots: Values > Qu + 1.5IQR; the stars: Values > Qu + 3.0IQR. Qu, upper quartile; IQR, inter-quartile range.
Figure 3. Differential expression levels of significant…
Figure 3. Differential expression levels of significant miRNAs in validation phase.
Expression levels of the miRNAs were normalized to spiked-in cel-miR-39 and were calculated utilizing the 2−ΔΔCt method. Mann-Whitney U test was used to determine statistical significance.
Figure 4. Receiver operating characteristic (ROC) curve…
Figure 4. Receiver operating characteristic (ROC) curve analysis using 6 miRNAs selected in validation phase for discriminating epilepsy from healthy controls.
For the up-regulated miRNAs, the normalized expression level of miRNAs (2−ΔΔCt) was selected as the test variable, and for the down-regulated miRNAs, the logarithm of the normalized expression level (2−ΔΔCt) was selected as the test variable. AUC, area under the ROC curve.
Figure 5. Overview of the study design.
Figure 5. Overview of the study design.
qRT-PCR, quantitative reverse transcriptase polymerase chain reaction.

References

    1. Henshall D. C. MicroRNA and epilepsy: profiling, functions and potential clinical applications. Curr Opin Neurol 27, 199–205 (2014).
    1. World Health Organization. . Epilepsy. (2012) Available at: . (Accessed: October 2012).
    1. Esteller M. Non-coding RNAs in human disease. Nat Rev Genet 12, 861–874 (2011).
    1. Chen X. et al. Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases. Cell Res 18, 997–1006 (2008).
    1. ElSharawy A. et al. Genome-wide miRNA signatures of human longevity. Aging Cell 11, 607–616 (2012).
    1. Tan L. et al. Genome-wide serum microRNA expression profiling identifies serum biomarkers for Alzheimer's disease. J Alzheimers Dis 40, 1017–1027 (2014).
    1. Gandhi R. et al. Circulating microRNAs as biomarkers for disease staging in multiple sclerosis. Ann Neurol 73, 729–740 (2013).
    1. Shtilbans A. & Henchcliffe C. Biomarkers in Parkinson's disease: an update. Curr Opin Neurol 25, 460–465 (2012).
    1. Li M. M. et al. Genome-wide microRNA expression profiles in hippocampus of rats with chronic temporal lobe epilepsy. Sci Rep 4, 4734 (2014).
    1. Gorter J. A. et al. Hippocampal subregion-specific microRNA expression during epileptogenesis in experimental temporal lobe epilepsy. Neurobiol Dis 62, 508–520 (2014).
    1. Sun Z. et al. Genome-wide microRNA profiling of rat hippocampus after status epilepticus induced by amygdala stimulation identifies modulators of neuronal apoptosis. PLoS One 8, e78375 (2013).
    1. Bot A. M., Debski K. J. & Lukasiuk K. Alterations in miRNA levels in the dentate gyrus in epileptic rats. PLoS One 8, e76051 (2013).
    1. McKiernan R. C. et al. Reduced mature microRNA levels in association with dicer loss in human temporal lobe epilepsy with hippocampal sclerosis. PLoS One 7, e35921 (2012).
    1. Kan A. A. et al. Genome-wide microRNA profiling of human temporal lobe epilepsy identifies modulators of the immune response. Cell Mol Life Sci 69, 3127–3145 (2012).
    1. Hu K. et al. MicroRNA expression profile of the hippocampus in a rat model of temporal lobe epilepsy and miR-34a-targeted neuroprotection against hippocampal neurone cell apoptosis post-status epilepticus. BMC Neurosci 13, 115 (2012).
    1. Yu S. et al. Circulating microRNA profiles as potential biomarkers for diagnosis of papillary thyroid carcinoma. J Clin Endocrinol Metab 97, 2084–2092 (2012).
    1. Yang C. et al. Identification of seven serum microRNAs from a genome-wide serum microRNA expression profile as potential noninvasive biomarkers for malignant astrocytomas. Int J Cancer 132, 116–127 (2013).
    1. Zhang X. et al. Screening and identification of six serum microRNAs as novel potential combination biomarkers for pulmonary tuberculosis diagnosis. PLoS One 8, e81076 (2013).
    1. Hou J. et al. MicroRNA-146a feedback inhibits RIG-I-dependent Type I IFN production in macrophages by targeting TRAF6, IRAK1, and IRAK2. J Immunol 183, 2150–2158 (2009).
    1. Wang X. J., Cao Q., Zhang Y. & Su X. D. Activation and Regulation of Caspase-6 and Its Role in Neurodegenerative Diseases. Annu Rev Pharmacol Toxicol (2014) [Epub ahead of print].
    1. Lecat A. et al. The c-Jun N-terminal kinase (JNK)-binding protein (JNKBP1) acts as a negative regulator of NOD2 protein signaling by inhibiting its oligomerization process. J Biol Chem 287, 29213–29226 (2012).
    1. Guma M. et al. Antiinflammatory functions of p38 in mouse models of rheumatoid arthritis: advantages of targeting upstream kinases MKK-3 or MKK-6. Arthritis Rheum 64, 2887–2895 (2012).
    1. Sun L., Tan M. S., Hu N., Yu J. T. & Tan L. Exploring the value of plasma BIN1 as a potential biomarker for alzheimer's disease. J Alzheimers Dis 37, 291–295 (2013).
    1. Engel J. Jr et al. Epilepsy biomarkers. Epilepsia 54 Suppl 4, 61–69 (2013).
    1. Jacobs J. et al. High-frequency oscillations (HFOs) in clinical epilepsy. Prog Neurobiol 98, 302–315 (2012).
    1. Zijlmans M. et al. High-frequency oscillations as a new biomarker in epilepsy. Ann Neurol 71, 169–178 (2012).
    1. Moeller F., Stephani U. & Siniatchkin M. Simultaneous EEG and fMRI recordings (EEG-fMRI) in children with epilepsy. Epilepsia 54, 971–982 (2013).
    1. Aronica E. et al. Expression pattern of miR-146a, an inflammation-associated microRNA, in experimental and human temporal lobe epilepsy. Eur J Neurosci 31, 1100–1107 (2010).
    1. Song Y. J. et al. Temporal lobe epilepsy induces differential expression of hippocampal miRNAs including let-7e and miR-23a/b. Brain Res 1387, 134–140 (2011).
    1. Taganov K. D., Boldin M. P., Chang K. J. & Baltimore D. NF-kappaB-dependent induction of microRNA miR-146, an inhibitor targeted to signaling proteins of innate immune responses. Proc Natl Acad Sci U S A 103, 12481–12486 (2006).
    1. Vezzani A., Ravizza T., Balosso S. & Aronica E. Glia as a source of cytokines: implications for neuronal excitability and survival. Epilepsia 49 Suppl 2, 24–32 (2008).
    1. Sheedy F. J. & O'Neill L. A. Adding fuel to fire: microRNAs as a new class of mediators of inflammation. Ann Rheum Dis 67 Suppl 3, iii50–55 (2008).
    1. Sondergaard H. B., Hesse D., Krakauer M., Sorensen P. S. & Sellebjerg F. Differential microRNA expression in blood in multiple sclerosis. Mult Scler 19, 1849–1857 (2013).
    1. Liu F. et al. MicroRNA-106b-5p boosts glioma tumorigensis by targeting multiple tumor suppressor genes. Oncogene 33, 4813–4822(2013).
    1. Zhang J. et al. NF-kappaB-modulated miR-130a targets TNF-alpha in cervical cancer cells. J Transl Med 12, 155 (2014).
    1. Hager M. et al. MicroRNA-130a-mediated down-regulation of Smad4 contributes to reduced sensitivity to TGF-beta1 stimulation in granulocytic precursors. Blood 118, 6649–6659 (2011).
    1. Cimmino A. et al. miR-15 and miR-16 induce apoptosis by targeting BCL2. Proc Natl Acad Sci U S A 102, 13944–13949 (2005).
    1. Roccaro A. M. et al. MicroRNAs 15a and 16 regulate tumor proliferation in multiple myeloma. Blood 113, 6669–6680 (2009).
    1. Soreq L. et al. Small RNA sequencing-microarray analyses in Parkinson leukocytes reveal deep brain stimulation-induced and splicing changes that classify brain region transcriptomes. Frontiers in Molecular Neuroscience 6, 10 (2013).
    1. Engel J. Jr A proposed diagnostic scheme for people with epileptic seizures and with epilepsy: report of the ILAE Task Force on Classification and Terminology. Epilepsia 42, 796–803 (2001).
    1. Mitchell P. S. et al. Circulating microRNAs as stable blood-based markers for cancer detection. Proc Natl Acad Sci U S A 105, 10513–10518 (2008).
    1. McDonald J. S., Milosevic D., Reddi H. V., Grebe S. K. & Algeciras-Schimnich A. Analysis of circulating microRNA: preanalytical and analytical challenges. Clin Chem 57, 833–840 (2011).
    1. Livak K. J. & Schmittgen T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 25, 402–408 (2001).

Source: PubMed

3
Sottoscrivi