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Cerebrospinal fluid microRNAs are potential biomarkers of temporal lobe epilepsy and status epilepticus

Raoof, Rana, Jimenez-Mateos, Eva M., Bauer, Sebastian, Tackenberg, Björn, Rosenow, Felix, Lang, Johannes, Onugoren, Müjgan Dogan, Hamer, Hajo, Huchtemann, Tessa, Körtvélyessy, Peter, Connolly, Niamh M. C., Pfeiffer, Shona, Prehn, Jochen H. M., Farrell, Michael A., O’Brien, Donncha F., Henshall, David C., Mooney, Catherine·2017·Scientific Reports
DOI 10.1038/s41598-017-02969-6PMC PMC5468228
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Abstract

There is a need for diagnostic biomarkers of epilepsy and status epilepticus to support clinical examination, electroencephalography and neuroimaging. Extracellular microRNAs may be potentially ideal biomarkers since some are expressed uniquely within specific brain regions and cell types. Cerebrospinal fluid offers a source of microRNA biomarkers with the advantage of being in close contact with the target tissue and sites of pathology. Here we profiled microRNA levels in cerebrospinal fluid from patients with temporal lobe epilepsy or status epilepticus, and compared findings to matched controls. Differential expression of 20 microRNAs was detected between patient groups and controls. A validation phase included an expanded cohort and samples from patients with other neurological diseases. This identified lower levels of miR-19b in temporal lobe epilepsy compared to controls, status epilepticus and other neurological diseases. Levels of miR-451a were higher in status epilepticus compared to other groups whereas miR-21-5p differed in status epilepticus compared to temporal lobe epilepsy but not to other neurological diseases. Targets of these microRNAs include proteins regulating neuronal death, tissue remodelling, gliosis and inflammation. The present study indicates cerebrospinal fluid contains microRNAs that can support differential diagnosis of temporal lobe epilepsy and status epilepticus from other neurological and non-neurological diseases.

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Figures

Figure 1 Bar plot showing the number of microRNA detected in each sample (A) before and (B) after filtering to remove any microRNA not present in at least 50% of samples. (C) Principal component analysis (PCA) plot. The PCA was performed on samples after imputing missing values, normalisation of Ct values and batch correction for sample origin. (D) Heat map and unsupervised two-way hierarchical clustering by sample and microRNA. Dendrograms were constructed using complete linkage method with Euclidean distance measure.
Figure 1 Bar plot showing the number of microRNA detected in each sample (A) before and (B) after filtering to remove any microRNA not present in at least 50% of samples. (C) Principal component analysis (PCA) plot. The PCA was performed on samples after imputing missing values, normalisation of Ct values and batch correction for sample origin. (D) Heat map and unsupervised two-way hierarchical clustering by sample and microRNA. Dendrograms were constructed using complete linkage method with Euclidean distance measure.CC BY · PubMed Central
Figure 2 Validation of microRNA. (A–F) Boxplots showing the distribution of normalised Ct following Taqman individual microRNA assays of miR-19b-3p, miR-21-5p, miR-204-5p, miR-223-3p, miR-451a and miR-886-3p in each of the four sets of samples: CH (N = 25), CND (N = 25), SE (N = 16) and TLE (N = 14). Note, the data shown are normalized and the lower the value the greater the relative expression of the microRNA. Outliers not graphed for purposes of data presentation are shown as circles. (G) log2(FC) and adjusted p-value for validated microRNA. CH: controls with headache, TLE: temporal lobe epilepsy, SE: status epilepticus, CND: controls with other neurological diseases.
Figure 2 Validation of microRNA. (A–F) Boxplots showing the distribution of normalised Ct following Taqman individual microRNA assays of miR-19b-3p, miR-21-5p, miR-204-5p, miR-223-3p, miR-451a and miR-886-3p in each of the four sets of samples: CH (N = 25), CND (N = 25), SE (N = 16) and TLE (N = 14). Note, the data shown are normalized and the lower the value the greater the relative expression of the microRNA. Outliers not graphed for purposes of data presentation are shown as circles. (G) log2(FC) and adjusted p-value for validated microRNA. CH: controls with headache, TLE: temporal lobe epilepsy, SE: status epilepticus, CND: controls with other neurological diseases.CC BY · PubMed Central
Figure 3 Determination of microRNA copy number in CSF. (A–C) Graphs depicting detection of miR-451a, miR-21-5p and miR-19b-3p per µl of dPCR reaction assay in serially diluted cDNA samples confirming suitability for measuring microRNA copy numbers in CSF. (D) Representative chips show the number of miR-451a positive wells in cDNA isolated from CSF samples for each group. Blue dots represent positive signal from amplified miR-451a. (E,F) Copy numbers for miR-451a, miR-21-5p and miR-19b-3p in representative CSF samples from the four groups (n = 3/group).
Figure 3 Determination of microRNA copy number in CSF. (A–C) Graphs depicting detection of miR-451a, miR-21-5p and miR-19b-3p per µl of dPCR reaction assay in serially diluted cDNA samples confirming suitability for measuring microRNA copy numbers in CSF. (D) Representative chips show the number of miR-451a positive wells in cDNA isolated from CSF samples for each group. Blue dots represent positive signal from amplified miR-451a. (E,F) Copy numbers for miR-451a, miR-21-5p and miR-19b-3p in representative CSF samples from the four groups (n = 3/group).CC BY · PubMed Central
Figure 4 ROC analysis using binomial logistic regression to combine microRNAs for (A–D) TLE samples versus CH, CND and (E–H) SE samples versus CH, CND and TLE samples.
Figure 4 ROC analysis using binomial logistic regression to combine microRNAs for (A–D) TLE samples versus CH, CND and (E–H) SE samples versus CH, CND and TLE samples.CC BY · PubMed Central
Figure 5 Relative expression of Argonaute2 (A), exosome (E) and total (T) microRNA in Alzheimer’s disease (AD), multiple sclerosis (MS), SE and TLE samples. (D) AUC following ROC analysis using binomial logistic regression to combine microRNAs for SE samples versus CH, CND and TLE samples, and TLE samples versus CH, CND and SE samples, for Argonaute2 bound microRNA and exosomal microRNA.
Figure 5 Relative expression of Argonaute2 (A), exosome (E) and total (T) microRNA in Alzheimer’s disease (AD), multiple sclerosis (MS), SE and TLE samples. (D) AUC following ROC analysis using binomial logistic regression to combine microRNAs for SE samples versus CH, CND and TLE samples, and TLE samples versus CH, CND and SE samples, for Argonaute2 bound microRNA and exosomal microRNA.CC BY · PubMed Central
Figure 6 Cellular localization of a microRNA biomarker of SE. Panels show representative in situ hybridization for miR-451a in human hippocampal tissue sections from SE and TLE patients. Note astrocyte location of miR-451a in SE tissue whereas expression is low-to-undetectable in TLE samples and in the negative control (microRNA probe omitted).
Figure 6 Cellular localization of a microRNA biomarker of SE. Panels show representative in situ hybridization for miR-451a in human hippocampal tissue sections from SE and TLE patients. Note astrocyte location of miR-451a in SE tissue whereas expression is low-to-undetectable in TLE samples and in the negative control (microRNA probe omitted).CC BY · PubMed Central
Figure 7 MicroRNA target prediction and pathway enrichment analysis. (A) MicroRNA-Target Interaction Network showing targets with strong experimental evidence from miRTarBase for miR-19b-3p, miR-21-5p and miR-451a. Edge thickness corresponds to the number of publications that validated the miRNA-mRNA interaction. Pink/red coloured targets are also targets of one or more of the functionally-validated epilepsy-associated microRNAs (see Materials & Methods). The network was generated using Cytoscape software. (B,C) Removal of GO term redundancy and semantic visualisation of the significantly associated GO molecular function (B) and biological processes (C) (adjusted p-value < 0.05) using REVIGO. The scatterplot shows the cluster representatives (i.e. terms remaining after the redundancy reduction) in a two dimensional space derived by applying multidimensional scaling to a matrix of the GO terms’ semantic similarities (the axes have no intrinsic meaning). Bubble colour indicates the log10(adjusted p-value) (legend in upper right-hand corner of B); size indicates the frequency of the GO term in the underlying Homo sapiens database (bubbles of more general terms are larger). (D) Bar plot showing the −log10(adjusted p-value) of significantly enriched (adjusted p-value < 0.001) PANTHER pathways for validated targets of miR-19b-3p, miR-21-5p and miR-451a. The number of targets in a given pathway is shown in parentheses.
Figure 7 MicroRNA target prediction and pathway enrichment analysis. (A) MicroRNA-Target Interaction Network showing targets with strong experimental evidence from miRTarBase for miR-19b-3p, miR-21-5p and miR-451a. Edge thickness corresponds to the number of publications that validated the miRNA-mRNA interaction. Pink/red coloured targets are also targets of one or more of the functionally-validated epilepsy-associated microRNAs (see Materials & Methods). The network was generated using Cytoscape software. (B,C) Removal of GO term redundancy and semantic visualisation of the significantly associated GO molecular function (B) and biological processes (C) (adjusted p-value < 0.05) using REVIGO. The scatterplot shows the cluster representatives (i.e. terms remaining after the redundancy reduction) in a two dimensional space derived by applying multidimensional scaling to a matrix of the GO terms’ semantic similarities (the axes have no intrinsic meaning). Bubble colour indicates the log10(adjusted p-value) (legend in upper right-hand corner of B); size indicates the frequency of the GO term in the underlying Homo sapiens database (bubbles of more general terms are larger). (D) Bar plot showing the −log10(adjusted p-value) of significantly enriched (adjusted p-value < 0.001) PANTHER pathways for validated targets of miR-19b-3p, miR-21-5p and miR-451a. The number of targets in a given pathway is shown in parentheses.CC BY · PubMed Central

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Metadata

Authors
Raoof, R., Jimenez-Mateos, E. M., Bauer, S., Tackenberg, B., Rosenow, F., Lang, J., Onugoren, M. D., Hamer, H., Huchtemann, T., Körtvélyessy, P., Connolly, N. M. C., Pfeiffer, S., Prehn, J. H. M., Farrell, M. A., O’Brien, D. F., Henshall, D. C., & Mooney, C.
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2017-06-12
Volume
7
Issue
1
ISSN
2045-2322
DOI
10.1038/s41598-017-02969-6
PMC
PMC5468228
Cited by
109
References
10
Language
en
Licence

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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