Abstract
The identification of mechanisms transforming normal to
seizure-generating tissue after brain injury is key to developing new
antiepileptogenic treatments. MicroRNAs (miRNAs) may act as regulators
and potential treatment targets for epileptogenesis. Here, we undertook
a meta-analysis of changes in miRNA expression in the hippocampal
dentate gyrus (DG) following an epileptogenic insult in three epilepsy
models. We identified 26 miRNAs significantly differentially expressed
during epileptogenesis, and five differentially expressed in chronic
epilepsy. Of these, 13 were not identified in any of the individual
studies. To assess the role of these miRNAs, we predicted their mRNA
targets and then filtered the list to include only target genes
expressed in DG and negatively correlated with miRNA expression.
Functional enrichment analysis of mRNA targets of miRNAs dysregulated
during epileptogenesis suggested a role for molecular processes related
to inflammation and synaptic function. Our results identify new miRNAs
associated with epileptogenesis from existing data, highlighting the
utility of meta-analysis in maximizing value from preclinical data.
Keywords: dentate gyrus, epilepsy, hippocampus, meta-analysis, miRNA,
mRNA
Significance Statement
Meta-analyses of data from human research studies are an invaluable
tool, and the methods to conduct these investigations are well
established. However, meta-analyses of preclinical data are rarely
undertaken, due to the typically small sample sizes and the substantial
heterogeneity between studies. We implemented a meta-analysis of
microRNA (miRNA) expression changes in animal studies of epilepsy. This
is the first study of its kind in the field of epilepsy and one of the
first in preclinical research. Our analyses identify new miRNAs
associated with epileptogenesis and epilepsy, highlighting common
mechanisms across different animal models. These miRNAs and their
predicted effects on gene expression generate new hypotheses about the
causes of epilepsy that will prompt new studies in the field.
Introduction
Epilepsy is a serious, common neurologic disorder primarily
characterized by the occurrence of spontaneous seizures. The treatment
of epilepsy remains as one of the major unmet medical needs in
neurology because, despite of over 20 antiepileptic drugs on the
market, seizures are not controlled in about one third of the patients.
The most common form of epilepsy in adults originates in temporal
structures of the brain (temporal lobe epilepsy, TLE; [40]Hauser et
al., 1993). Epilepsy (TLE in particular) frequently arises as a
consequence of brain injury (“acquired epilepsy”). While acquired
epilepsies are in principle preventable by the therapeutic targeting of
molecular processes underpinning their development (i.e.,
antiepileptogenic therapies), there are currently no established
treatment options for halting the transformation of normal brain tissue
to epileptic ([41]Simonato et al., 2014). Identification of the
mechanisms underlying epileptogenesis would therefore facilitate the
identification of therapies for preventing the development of epilepsy
and may inform new strategies for overcoming drug resistance in
epilepsy more generally ([42]Simonato et al., 2012; [43]2013;
[44]2014).
Recent studies have suggested that microRNAs (miRNAs) play an important
role in the pathogenesis of acquired epilepsy and may represent novel
therapeutic targets ([45]Jimenez-Mateos et al., 2012; [46]Brennan et
al., 2016; for review, see [47]Cattani et al., 2016; [48]Henshall et
al., 2016). miRNAs are a family of small (21-25 nucleotides) noncoding
RNAs, which can modulate various cellular and biological processes by
degrading or repressing translation of specific mRNAs ([49]Bartel,
2004, [50]Guo et al., 2010). In systems analysis, miRNAs and their gene
targets are described as following a “many-to-many” data model, such
that each miRNA may regulate many transcripts and a single transcript
may be regulated by many miRNAs ([51]Ebert and Sharp, 2012). miRNAs
have been implicated in various neuronal functions that are relevant in
the pathogenesis of neurologic diseases, including epilepsy ([52]Tan et
al., 2013; [53]Rajman et al., 2017; for review, see [54]Karnati et al.,
2015).
Interpretation of data investigating the dysregulation of miRNAs in the
brains of patients with epilepsy is challenged by the absence of
appropriate human control brain tissue ([55]Roncon et al., 2016).
Research on the role of miRNAs in epilepsy has therefore focused on the
use of experimental models of epilepsy, revealing changes in the
expression of hippocampal miRNAs at different stages of the
epileptogenic process ([56]Henshall et al., 2016). However,
methodological differences between the various preclinical animal
models of epilepsy have made comparisons between studies difficult and
the identification of common pathways dysregulated in epileptogenesis
and epilepsy problematic. The current preclinical miRNA studies vary in
multiple parameters, including brain region analyzed, animal model,
sample size, microarray platform and analysis technique. Moreover,
these studies are generally substantially underpowered to reliably
detect modest changes in miRNA expression.
One particular concern is that analysis of large brain regions (like
the hippocampus or the cortex) across different studies may confound
interpretation and comparison because of variable cellular composition
(e.g., relating to variable neuronal loss, astrocytosis, microgliosis,
etc.). One way to address this issue could be to focus on a specific
cell population. In this respect, dentate gyrus (DG) granule cells
(GCs) seem particularly attractive as a target of analysis as the DG GC
layer is a compact layer of (almost) identical cells, facilitating the
dissection of a nearly pure cell population (GCs). Moreover, the DG has
been traditionally described as a “gate” to inhibit hippocampal
overexcitation ([57]Chevaleyre and Siegelbaum, 2010) and recently, this
hypothesis found support from new technologies; optogenetic GC
hyperpolarization was found to stop spontaneous seizures, whereas
optogenetic GC activation exacerbated spontaneous seizures, and
activating GCs in nonepileptic animals evoked acute seizures
([58]Krook-Magnuson et al., 2015). Finally, the DG is known to undergo
important functional changes during epileptogenesis (neurogenesis,
mossy fiber sprouting, increased excitation; [59]Pitkänen and Lukasiuk,
2011).
To date, three studies have investigated differential expression of
miRNAs in the DG during the epileptogenesis and the chronic phase of
epilepsy in rats ([60]Bot et al., 2013; [61]Gorter et al., 2014;
[62]Roncon et al., 2015). Each of these studies used a different method
to trigger epileptogenesis: focal electrical stimulation of the lateral
nucleus of the amygdala ([63]Bot et al., 2013); focal electrical
stimulation of the angular bundle, a major afferent pathway from the
entorhinal cortex to the hippocampus ([64]Gorter et al., 2014); and the
systemically administered chemoconvulsant pilocarpine ([65]Roncon et
al., 2015). Interestingly, all these models imply a key involvement of
the DG in the development of epilepsy but via a different epileptogenic
insult: direct activation in the case of angular bundle stimulation,
indirect in amygdala stimulation, and a widespread brain activation in
the case of pilocarpine ([66]Peng and Houser, 2005).
Here, we aimed to overcome some of the limitations related to
individual studies by combining these three studies in a meta-analysis,
the aim being to increase the statistical power for detecting
differentially expressed miRNAs while accounting for study
heterogeneity, ultimately leading to more robust and accurate
predictions of dysregulated downstream pathways ([67]Ramasamy et al.,
2008; [68]Yang et al., 2014). Moreover, this approach offers the
opportunity to identify miRNA changes that are independent of the model
of epilepsy, i.e., more likely to be disease rather than model related.
Our meta-analysis was performed at two time points in the “natural
history” of the experimental disease: epileptogenesis and the chronic
phase of epilepsy.
Materials and Methods
Inclusion criteria and study design
We collected datasets for meta-analysis based on available genome-wide
expression profiles of miRNAs from the DG from epileptic and control
hippocampi during epileptogenesis and chronic epilepsy. To assist the
functional inference of differentially expressed miRNAs we analyzed
publicly available gene expression data obtained from the DG of
epileptic rodents.
To identify relevant studies, we first undertook a systematic search to
identify all published studies of miRNA expression levels and/or gene
expression between cases (epileptic) and controls, in the DG of animal
models of epilepsy. We conducted a PubMed search based on the string:
“(microRNA OR miRNA) AND (dentate gyrus OR dentate cells OR granule
cells) AND epilepsy.” miRNAs or genes expression profiles data obtained
from the whole hippocampi or different brain regions to the DG were not
included in the meta-analysis. This search and inclusion criteria
identified only three relevant articles ([69]Bot et al., 2013;
[70]Gorter et al., 2014; [71]Roncon et al., 2015).
Time points for each stage of epileptogenesis and for the chronic phase
of epilepsy have not been standardized. For the purposes of this study,
we considered 7-8 d after SE as the “epileptogenesis phase” and more
than two months after SE as the “chronic phase.”
The following models were used in the three relevant papers and
considered for the meta-analysis. (1) Pilocarpine model: a microarray
study based on the investigation of miRNAs differentially expressed in
the laser-microdissected GC layer of the DG of the hippocampi of
pilocarpine-induced epileptic rats and matched controls (n = 4), killed
during the late phase of latency, 8 d after SE (n = 5) and in the
chronic stage, 50 d after the first spontaneous seizure (n = 5;
[72]Roncon et al., 2015). (2) Amygdala stimulation: a microarray study
focused on the differential expression of miRNAs and genes in
hand-dissected DG in the amygdala stimulation rat model during the
phase of epileptogenesis, 7 d after the stimulation (n = 5), in the
chronic stage, 60 d after the stimulation (n = 5) and controls (n = 5;
[73]Bot et al., 2013). (3) Perforant path stimulation: a microarray
miRNA study based on the perforant path stimulation rat model of
epilepsy. We analyzed DG samples obtained from stimulated and control
rat hippocampi, during latency (8 d after SE; n = 8) and three to four
months after the stimulation for the chronic stage (n = 6) and controls
(n = 10; [74]Gorter et al., 2014). The datasets considered for the
meta-analysis are summarized in [75]Table 1.
Table 1.
Datasets included in the meta-analysis
[76]Roncon et al. (2015) [77]Bot et al. (2013) [78]Gorter et al. (2014)
[79]Dingledine et al. (2017) [80]Dingledine et al. (2017)
[81]Dingledine et al. (2017)
GEO ID - [82]GSE49849 - [83]GSE47752 [84]GSE47752 [85]GSE47752
Rat model Pilocarpine Amygdala stimulation Angular bundle stimulation
Pilocarpine SSSE Kainate
Sample count epileptogenesiscases:control 5:5 5:5 8:10 6:6 4:5 6:6
Sample count chronic stagecases:control 5:4 5:5 6:10 - - -
Platform Rat miRNA MicroArray kit, Agilent Technologies miRCURY LNA
microRNA Array7th, Exiqon services miRCURY LNA microRNA Array 6th,
Exiqon services GeneChip Rat Genome 230 2.0 Array, Affymetrix GeneChip
Rat Genome 230 2.0 Array, Affymetrix GeneChip Rat Genome 230 2.0 Array,
Affymetrix
miRNA/gene expression data miRNA miRNA and gene expression miRNA Gene
expression Gene expression Gene expression
Tissue collection Laser-microdissected DG Handily dissected DG Handily
dissected DG Laser-microdissected DG Laser-microdissected DG
Laser-microdissected DG
[86]Open in a new tab
Power calculation
The statistical power of cases and controls for each individual model
was calculated using pooled SD of each expressed miRNA. Power to detect
miRNAs differentially expressed at multiple fold changes (1, 1.5, 2,
2.5, 3, 3.5, and 4) were calculated, considering miRNA expression
variability ranging from 20th, 40th, 60th, and 80th to 100th percentile
of the respective SD profiles per model ([87]Fig. 1). Power
calculations were performed using R bioconductor package ‘pwr’ version
1.2.
Figure 1.
[88]Figure 1.
[89]Open in a new tab
Power calculation. Power calculation is plotted as the power (y-axis)
to detect a miRNA with fold change (x-axis) according to the percentile
of the ranked SDs for miRNAs for each study. Across all three models,
the power to detect miRNA with fold change 2 or less falls below 80%
for at least 40% of the miRNAs.
Data processing
From each identified study the following information was extracted:
platform, number of cases and controls, and miRNA expression data at
different time points of the disease. When available, GEO accession
number and gene expression data were extracted ([90]Table 1).
Data transformation
Since different platforms had been used to generate miRNA expression
values, a linear transformation approach was applied to each miRNA
using a Z-score transformation according to the formula:
[MATH: Z–score=Xi-Xδ :MATH]
Where Xi is the normalized intensity data for each miRNA, X is the
average normalized miRNA intensity within a single study, and δ is the
SD of cases and controls within respective studies.
Effect size estimation
A meta-analysis is “a technique for quantitatively combining and
integrating the results of multiple studies on a given topic”
([91]Polit and Beck, 2004). Thus, a key aspect of meta-analysis is to
measure differences and direction of change from quantitative research
studies ([92]Polit and Beck, 2004; [93]Berben et al., 2012). A common
metric used to provide this important information is the effect size
calculation. Accordingly, to give a statistical expression of the
magnitude of the difference between groups (i.e., epileptogenesis vs
controls and chronic stage vs controls) in regard of miRNAs expression,
we estimated the effect size of each individual study defined as the
standardized mean difference (SMD) between cases and controls. The SMD
has been calculated using the Hedge’s method with the following
formula:
[MATH: g=X1-X<
/mrow>2δ :MATH]
Where X[1] is the mean of cases, X[2] the mean of the control group,
and δ is the SD.
Statistical heterogeneity
Different animal models, tissue collection methods, and platforms were
used to generate the datasets. This makes it difficult to directly
compare the data, the risk being of skewing comparison results,
reducing the reliability of measurements of individual miRNA expression
changes ([94]Yang et al., 2014). Statistical heterogeneity was assessed
using Cochrane meta-regression approach calculated by Q test, I^2
statistics, and Tau^2 statistics. These measures were applied at each
dataset to assess the overall heterogeneity ([95]Higgins et al., 2003;
[96]Ioannidis et al., 2007). To test the total variance of each miRNA
within the studies, the Cochran Q test have been run, according to the
formula:
[MATH: Q=k(k-1)<
munderover>∑T-1k(<
/mo>xT-Nk)2∑i-1bxT(k-xT)
:MATH]
Where k is the number of the studies included in the meta-analysis, T
is the number of variables observed, b is the number of miRNAs included
in the test, and N is the total number. A Benjamini-Hochberg (BH)
adjusted p < 0.05 was considered statistically significant for the
Cochrane Q test.
Furthermore, I^2 statistic has been employed to describe the percentage
of the variability in the effect size estimates, following the formula:
[MATH: I2=(Q-dfQ)*100% :MATH]
Where Q is the value derived from the Cochran Q test and df are the
degrees of freedom. Tentatively, I^2 statistic can be considered as an
indicator of heterogeneity, where low, moderate, and high heterogeneity
corresponds to I^2 values of 0–0.3, 0.3–0.7, and 0.7–1, respectively
([97]Higgins et al., 2003).
Finally, to estimate the variance across studies the Tau^2 has been
applied:
[MATH: Tau2=Q-dfC :MATH]
Where Q is the value derived from the Cochran Q test, df are the
degrees of freedom and C is a scaling factor which takes into
consideration that the Q value is the weighted sum of squares.
Meta-analysis
The presence of statistical heterogeneity among the studies led us to
use a random-effect model for the meta-analysis rather than a
fixed-effect model. The pooled effect size (PES) for each miRNA was
obtained applying the random effect size model based on the DerSimonian
and Laird method ([98]DerSimonian and Laird, 1986; [99]DerSimonian and
Kacker, 2007). We generated one forest plot for each miRNA in both the
epileptogenesis and the chronic phase to depict the SMD along with its
95% confidence interval (95% CI) for individual studies as well as the
pooled MD from the meta-analysis.
miRNAs:mRNAs inverse-fold change
miRNAs predominantly act by repressing their target genes by decreasing
target mRNA levels ([100]Bartel, 2004; [101]Guo et al., 2010).
Therefore, we investigated the correlation of mRNAs predicted targets
expression in the available transcriptomic datasets.
To predict miRNA target genes, the miRNA-target interactions were
analyzed with the web-based tool miRwalk ([102]Dweep et al., 2011;
[103]Dweep et al., 2014). The rule that the 5’ region of miRNA from
nucleotides 2-8 (“seed region”) has importance in targeting, is
commonly accepted as the canonical mechanism by which miRNAs
complementary convey functional binding to mRNA targets ([104]Jinek and
Doudna, 2009). However, despite the importance of the seed region, the
3’ end of a mRNA also contributes to the binding in ∼2% of all
preferentially conserved sites ([105]Grimson et al., 2007;
[106]Shkumatava et al., 2009). In addition, some validated miRNAs have
a binding site that exhibits few continuous base pairs in the control
region ([107]Shin et al., 2010). Thus, to figure out the complete
mechanism of miRNA regulation, we expanded the miRNAs-binding site
prediction within the 3’, 5’ untranslated regions (UTRs), and the seed
sequence, with a minimum seed length of seven nucleotides. Furthermore,
to exclude overprediction we applied a comparative analysis by six
prediction programs: miRMap, RNA22, miRanda, RNAhybrid, PICTAR2, and
Targetscan. Following this approach, a candidate mRNA target has to be
identified by all these programs. As we did for the miRNAs, we
conducted PubMed search based on the keywords: “gene expression,
epilepsy, dentate gyrus.” Two studies were identified that were then
included in our analysis ([108]Table 1): [109]Dingledine et al. (2017;
GEO repository accession number: [110]GSE47752) and [111]Bot et al.
(2013; GEO repository accession number: [112]GSE49850). The first
includes gene expression data obtained from laser-microdissected DG of
rats that received different SE models of epilepsy: pilocarpine,
self-sustained SE (SSSE), and kainite, it also included kindling, not
considered in this study not being a post-SE model. We considered in
our analysis only those rats killed 10 d after SE that did not develop
spontaneous seizures, as the best time point matching the
epileptogenesis phase used for the miRNAs meta-analysis (7-8 d after
SE). In [113]Dingledine et al. (2017), the kainate and pilocarpine
models were performed in two independent labs, while the SSSE model
that was performed in only one lab. For studies undertaken in multiple
labs, we combined the p values (obtained from differential expression
analysis) from the independent labs using Fisher’s method. The second
dataset ([114]GSE49850) was obtained from the same amygdala-stimulated
rats used for the miRNA analysis.
To investigate miRNA-mRNA interactions, we included only those mRNAs
that had inverse relationship to miRNA changes in at least three
datasets in the epileptogenesis phase; while for the chronic phase, we
considered all predicted mRNAs that presented an inverse relationship
to miRNA changes in the amygdala stimulation dataset only.
miRNA functional enrichment analysis
Functional enrichment analysis using gene ontology (GO) and pathways
enrichment analysis based on the Kyoto encyclopedia of genes and
genomes (KEGG) database were performed using Webgestalt webserver
([115]Zhang et al., 2005; [116]Wang et al., 2013). The enrichment was
performed with a hypergeometric test separately for the list of
predicted targets based on those miRNAs dysregulated in epileptogenesis
and in the chronic stage. Significant canonical pathways maps were
selected according to a false discovery rate (FDR) < 5%.
To infer functional relationships between miRNAs identified using
meta-analysis, a network of miRNAs based on their ability to target
common pathways has been generated. A connection was made between a
pair of miRNA, if respective mRNA targets belonged to the same pathways
or GO terms that were significantly (FDR < 0.05) enriched for combined
miRNA targets. The network was visualizes using Cytoscape version
2.8.2.
Results
Study design
We included in the meta-analysis all published miRNA expression
datasets from dissected DG of the hippocampus that compared control
(baseline) tissue with tissue from epileptogenesis and chronically
epileptic rats ([117]Fig. 2A). Based on these inclusion criteria, we
identified three datasets that used different epilepsy models
([118]Table 1): (1) pilocarpine ([119]Roncon et al., 2015), (2)
amygdala stimulation ([120]Bot et al., 2013), (3) angular bundle
stimulation ([121]Gorter et al., 2014). We first calculated the power
of each individual study to detect significant changes in miRNA
expression and found that all three individual studies were
substantially underpowered to detect modest fold changes (<2.0) in
miRNA expression ([122]Fig. 1). Of the total number of miRNAs expressed
at any time point in any of the three models, the expression levels of
176 miRNAs were detectable across all three studies ([123]Fig. 2B). The
expression values of these 176 miRNAs were then Z-score transformed and
meta-analyzed across the three studies.
Figure 2.
[124]Figure 2.
[125]Open in a new tab
Study design and data preprocessing. A, Study design. B, Venn diagram
showing miRNAs commonly expressed between the three studies included in
the meta-analysis. C, Statistical heterogeneity estimation. I^2 scores
of commonly expressed miRNAs (n = 176) in epileptogenesis and in
chronic stage. miRNAs are ordered based on the adjusted p value after
meta-analysis. I^2 < 0.3, low heterogeneity; 0.3 < I^2 > 0.7, moderate
heterogeneity; I^2 > 0.7, high heterogeneity. SRS, spontaneous
recurrent seizures; pilo, pilocarpine model; amy stim, amygdala
stimulation model; AB stim, angular bundle stimulation model.
Estimation of statistical heterogeneity
Meta-regression analyses were performed separately for epileptogenesis
and chronic stages of epilepsy for the 176 miRNAs that were expressed
in all three datasets ([126]Fig. 2B). There are two models that are
commonly used to perform meta-analysis, the fixed effect and the random
effects models. The fixed effect model assumes that the effect size is
the same in all studies, while the random effects analysis assumes that
the effect can vary from one study to another. To determine the correct
model for this study, we first estimated statistical heterogeneity
using the Cochrane’s Q test, separately for the epileptogenesis and the
chronic phase. This revealed significant heterogeneity between studies
at both stages of the disease (BH, adjusted p < 0.05). Next, to assess
the proportion of miRNAs that were differentially expressed between
studies, during epileptogenesis and the chronic stage separately, we
calculated the I^2 statistics ([127]Higgins et al., 2003). Of the 176
miRNAs measured across the three studies, 26.14% revealed a high level
of heterogeneity: 31.25% a moderate level and 42.61% a low rate of
heterogeneity during epileptogenesis ([128]Fig. 2C). In the chronic
stage, 18.75% displayed high, 30.11% moderate, and 51.14% low level
heterogeneity ([129]Fig. 2C). Collectively, these observations favor
the use of the random effects model.
Differential expression of miRNAs in the epileptic DG
Using a random effects meta-analysis of miRNA changes in the three
models of epileptogenesis and adopting a stringent correction for
multiple testing to minimize false positives (Bonferroni adjusted p <
0.05), we identified 26 and 5 differentially expressed miRNAs between
control and latency and between control and chronic epilepsy,
respectively. Full results of all these miRNAs including PES
estimations, I^2, Tau^2 and p values are shown in [130]Tables 2,
[131]3. Forest plots for selected miRNA are shown in [132]Figure 3.
Comparing these results with those presented in each original studies
that have been meta-analyzed here, our meta-analysis identified 11
miRNAs differentially expressed in epileptogenesis compared to control
and two miRNAs (i.e., miR-324-3p and miR-130a-3p) in the chronic stage
of epilepsy that were not identified as significantly differentially
expressed in any of the individual studies ([133]Tables 2, [134]3,
miRNAs highlighted in bold). The datasets employed in this
meta-analysis do not allow a precise evaluation of the abundance of
expression of these 26 plus five miRNAs under control conditions, but a
relative abundance estimate based on the internal standards employed in
each study suggests that almost all are expressed at medium to high
abundance in the control DG (only miR-212-5p was expressed at
relatively low levels but upregulated during epileptogenesis).
Table 2.
Differentially expressed microRNAs in the epileptogenesis period
compared with controls
miRNA ESestimation Nominalp value Bonferroni adjustedp value Q
statistics I^2 statistics Tau^2 statistics
miR-212-3p 1.70 6.21^−16 1.10^−13 1.59 0.00 0.00
miR-7a-5p -1.60 9.26^−12 1.64^−09 0.82 0.00 0.00
miR-33-5p -1.57 9.90^−12 1.75^−09 0.22 0.00 0.00
miR-139-5p -1.53 8.34^−11 1.48^−08 0.29 0.00 0.00
miR-344b-2-3p 1.25 5.78^−10 1.02^−07 1.57 0.00 0.00
miR-3573-3p -1.53 1.32^−09 2.33^−07 1.38 0.00 0.00
miR-551b-3p -1.51 2.16^−09 3.83^−07 0.51 0.00 0.00
miR-146a-5p 1.75 3.00^−09 5.31^−07 2.66 0.25 0.25
miR-132-3p 1.85 1.46^−08 2.58^−06 5.28 0.62 0.24
let-7b-3p -1.34 2.46^−07 4.36^−05 7.40 0.73 0.42
miR-212-5p 1.36 4.58^−07 8.11^−05 0.36 0.00 0.00
let-7d-3p -1.42 9.57^−07 0.0002 6.75 0.70 0.38
miR-667-3p -1.37 1.01^−06 0.0002 1.87 0.00 0.00
miR-138-5p -1.40 1.34^−06 0.0002 0.59 0.00 0.00
miR-330-3p -1.37 1.39^−06 0.0002 0.17 0.00 0.00
miR-21-5p 1.85 2.65^−06 0.0005 3.43 0.42 0.17
miR-29c-5p -1.65 5.76^−06 0.0010 2.91 0.31 0.27
miR-335 -1.38 6.01^−06 0.0010 1.41 0.00 0.00
miR-101a-3p -1.32 9.17^−06 0.0016 0.64 0.00 0.00
miR-345-5p -1.29 2.12^−05 0.0037 1.75 0.00 0.00
miR-92b-3p -1.31 3.12^−05 0.0055 1.08 0.00 0.00
miR-150-5p -1.32 3.46^−05 0.0061 1.94 0.00 0.00
miR-136-3p -1.22 3.56^−05 0.0063 0.66 0.00 0.00
miR-324-5p -1.31 4.63^−05 0.0082 2.05 0.02 0.08
miR-153-3p -1.23 6.92^−05 0.0122 0.21 0.00 0.00
miR-383-5p -1.54 0.0002 0.0375 4.00 0.50 0.34
[135]Open in a new tab
miRNAs in italics are upregulated and miRNAs highlighted in bold were
not differentially expressed in individual studies. ES, effect size.
Table 3.
Differentially expressed microRNAs in the chronic stage compared with
controls
miRNA ES estimation Nominalp value Bonferroni adjustedp value Q
statistics I^2 statistics Tau^2 statistics
miR-652-3p -1.56 5.48^−09 9.65^−07 2.1 0.1 0.1
miR-551b-3p -1.52 3.20^−06 0.0006 1.4 0.0 0.0
miR-324-3p -1.40 3.13^−05 0.0055 0.9 0.0 0.0
miR-130a-3p -1.35 6.10^−05 0.0107 0.8 0.0 0.0
miR-148b-3p -1.56 0.0002 0.0317 0.2 0.0 0.0
[136]Open in a new tab
All miRNAs are downregulated, miRNAs highlighted in bold were not
differentially expressed in individual studies. ES, effect size.
Figure 3.
[137]Figure 3.
[138]Open in a new tab
Forest plots of selected miRNA. Forest plots for miR-7a-5p, miR-92b-3p,
miR-101a-3p, miR-138-5p, miR-150-3p, miR-153-3p, miR-335, miR-383-3p,
and miR-3573-3p are shown for the phase of epileptogenesis, and
miR-130a-3p and miR-148b-3p for the chronic stage. For each miRNA, the
effect size of the individual studies is reported as MD and 95% CI. The
% weight refers to random effects analysis. Individual effect sizes are
represented by colored boxes (green for epileptogenesis and blue for
the chronic period) and 95% CI are denoted by black lines. The combined
effect sizes are represented by diamonds, where diamond width
correspond to the 95% CI bounds; boxes and diamonds size is
proportional to effect size estimation precision. For each miRNA, the
weight of the dataset in the combined analysis has been reported in
percentage.
Relationship between miRNAs and mRNAs expression changes
Previous transcriptomic studies in epilepsy models have revealed
dysregulation of many genes in the different phases of the experimental
disease ([139]Lukasiuk and Pitkänen, 2004). It can be hypothesized that
differentially expressed miRNAs may contribute to these alterations
(upregulated miRNAs may downregulate their mRNA targets whereas
downregulation of miRNAs may allow upregulation of their mRNA targets).
To assess the role of differentially expressed miRNAs during the course
of epilepsy, that is, to infer their mRNA regulatory targets, we
explored the potential regulatory relationship between miRNA and mRNA
changes.
First, to predict miRNA target transcripts we used the miRWalk database
([140]Dweep et al., 2011; [141]Dweep et al., 2014), which combines
information across six miRNA target prediction programs (miRMap, RNA22,
miRanda, RNAhybrid, PITCAR2 and Targetscan). As expected, this analysis
identified a very large number of predicted targets but, obviously, the
large majority of these may not be expressed in the DG or may not be
expressed in a negatively correlated fashion relative to the miRNAs.
Therefore, we asked whether the miRNAs that were identified by the
meta-analysis as dysregulated in epileptogenesis and in the chronic
stage were negatively correlated with changes in DG expression in their
predicted mRNA targets. To this end, we took advantage of publicly
available gene expression data generated in three separate datasets
that investigated mRNA expression changes in the DG of rats from the
epilepsy models used in our meta-analysis: pilocarpine (GEO accession:
[142]GSE47752; [143]Dingledine et al., 2017), angular bundle
stimulation (called SSSE in this database; GEO accession:
[144]GSE47752), and amygdala stimulation ([145]Bot et al., 2013; PMID:
24146813). Only the amygdala stimulation mRNA dataset ([146]Bot et al.,
2013) was obtained from the same animals employed for obtaining the
miRNA dataset and included data on the chronic phase. The other
datasets were generated by separate research groups on a separate group
of animals, the experimental procedures were slightly different from
those used in the corresponding miRNAs studies ([147]Gorter et al.,
2014; [148]Roncon et al., 2015) and included mRNA data for these models
related to the epileptogenesis phase only. In addition, [149]Dingledine
et al. (2017) also included datasets for another SE model (kainate)
that we also considered in our analysis.
Analysis of the epileptogenesis data identified inverse relationship,
based on significant fold changes (gene FDR < 0.1), between 22 (of 26)
miRNAs and 122 unique predicted gene targets in at least three of the
four epilepsy models of the Dingledine dataset ([150]Dingledine et al.,
2017) that we considered in this study ([151]Table 4). The mRNAs
encoding the mitogen-activated protein kinase kinase kinase 4 (Map3k4)
and the enhancer of zeste 2 polycomb repressive complex 2 subunit
(Ezh2) were predicted targets and had inverse expression relative to
four miRNAs, i.e., miR-92b-3p, miR-101a-3p, miR-153-3p, and miR-3575-3p
for Map3k4 and miR-92b-3p, miR-101a-3p, miR-138-5p, and miR-153-3p for
Ezh2; synapsin type 2 was inversely correlated to three miRNAs
(miR-101a-3p, miR-139-5p, and miR-551b-3p); the mitogen-activated
protein kinase kinase kinase 14 (Map3k14) and the protein tyrosine
phosphatase, nonreceptor type 5 (Ptpn5) were inversely correlated with
two miRNAs, i.e., miR-7a-5p and miR-138-5p for Map3k14; miR-150-5p and
miR-383-5p for Ptpn5. All the above transcripts were upregulated during
epileptogenesis and predicted targets of miRNAs that were
downregulated. In contrast, miR-132-3p, miR-146a-5p, miR-212-3p, and
miR-212-5p were upregulated during epileptogenesis. The
5-hydroxytryptamine receptor 5 (Htr5b) and the
β-1,3-galactosyltransferase 5 (B3galt5) were predicted targets of
miR-146a-5p and were downregulated. The γ-aminobutiric acid receptor
subunit δ (Gabrd) was a predicted target of and inversely correlated
with miR-212-5p. Finally, we observed that miR-344b-2-3p, let-7d-3p,
miR-21-5p, miR-29c-5p, and miR-324-5p were not anticorrelated with any
of their predicted mRNA targets. Representative graphs for the
anticorrelations between miRNAs and predicted mRNA targets are shown in
[152]Figure 4.
Table 4.
miRNA-mRNA fold changes inverse correlation in epileptogenesis
miRNA name microRNA data
__________________________________________________________________
mRNA targets of respective microRNA (FDR < 0.1)
__________________________________________________________________
Pilocarpine Perforant path stimulation Amygdala stimulation mRNA gene
name Pilocarpine
__________________________________________________________________
Perforant path stimulation
__________________________________________________________________
Amygdala stimulation
__________________________________________________________________
Kainic acid
__________________________________________________________________
miRNA FC Meta-analysis p value FC Adjusted p value FC Adjusted p value
FC Adjusted p value FC Adjusted p value
miR-383-5p -0.50 -0.35 -0.75 6.11^−05 Rab32 0.74 0.01 0.66 0.01 0.28
0.03 0.48 0.06
-0.50 -0.35 -0.75 6.11^−05 Cyb561 1.07 4.62^−05 0.53 0.02 0.66 1.41^−03
0.45 0.09
-0.50 -0.35 -0.75 6.11^−05 Stk40 0.61 0.05 0.58 0.02 0.35 0.04 0.69
0.04
-0.50 -0.35 -0.75 6.11^−05 Ptpn5 0.59 1.68^−03 0.68 0.02 0.42 0.03 0.72
4.39^−04
-0.50 -0.35 -0.75 6.11^−05 Tyms 0.41 0.01 0.81 0.03 0.27 0.06 1.10
3.74^−04
-0.50 -0.35 -0.75 6.11^−05 Ugt1a5 0.18 0.06 0.32 0.08 0.69 7.29^−04
0.01 0.97
-0.50 -0.35 -0.75 6.11^−05 Aif1 0.91 0.04 0.54 0.25 0.70 0.03 0.97 0.02
-0.50 -0.35 -0.75 6.11^−05 Smad3 0.51 0.06 0.19 0.46 0.45 0.03 0.43
0.08
-0.50 -0.35 -0.75 6.11^−05 Rac2 0.44 0.03 0.39 0.31 0.98 0.01 0.61 0.11
-0.50 -0.35 -0.75 6.11^−05 Lcmt1 0.29 0.04 0.25 0.25 0.32 0.02 0.14
0.48
-0.50 -0.35 -0.75 6.11^−05 Mtmr11 0.95 3.62^−04 1.86 0.01 0.05 0.87
1.13 4.82^−05
-0.50 -0.35 -0.75 6.11^−05 Nnat 1.09 0.01 0.86 0.07 0.15 0.59 0.63 0.30
-0.50 -0.35 -0.75 6.11^−05 Casp3 0.24 0.07 0.40 0.05 0.27 0.37 0.25
0.40
miR-153-3p -0.32 -0.42 -0.22 7.95^−04 Arhgap17 0.30 0.02 0.88 0.01 0.43
0.03 0.33 0.08
-0.32 -0.42 -0.22 7.95^−04 Mgst1 0.67 0.07 0.79 0.01 0.69 0.01 0.29
0.67
-0.32 -0.42 -0.22 7.95^−04 Wls 1.45 7.49^−05 0.80 0.01 0.57 0.01 1.75
1.52^−05
-0.32 -0.42 -0.22 7.95^−04 Map3k4 0.09 0.59 0.53 0.03 0.29 0.07 0.02
1.00
-0.32 -0.42 -0.22 7.95^−04 Ezh2 0.16 0.27 0.40 0.07 0.31 0.05 0.04 0.78
-0.32 -0.42 -0.22 7.95^−04 Man2b1 0.29 0.05 0.19 0.63 0.34 0.02 -0.16
0.87
-0.32 -0.42 -0.22 7.95^−04 Zfp521 1.23 1.92^−04 0.74 0.01 0.23 0.22
0.59 3.07^−03
miR-324-5p -0.09 -0.60 -0.29 8.55^−06 Tyrobp 1.26 1.01^−04 0.70 0.26
1.40 4.93^−03 1.31 0.03
-0.09 -0.60 -0.29 8.55^−06 Asph 0.45 0.08 0.45 0.03 0.17 0.13 0.05 0.90
-0.09 -0.60 -0.29 8.55^−06 Cyb5r4 0.36 0.02 0.43 0.07 0.14 0.66 0.50
7.35^−04
miR-150-5p -0.45 -0.45 -0.56 6.17^−05 E2f1 0.89 1.05^−03 0.76 0.01 0.45
0.02 1.11 3.99^−05
-0.45 -0.45 -0.56 6.17^−05 Cyb561 1.07 4.62^−05 0.53 0.02 0.66 1.41^−03
0.45 0.09
-0.45 -0.45 -0.56 6.17^−05 Ppp1r1a 0.35 0.03 0.58 0.02 0.44 0.05 0.73
0.01
-0.45 -0.45 -0.56 6.17^−05 Ptpn5 0.59 1.68^−03 0.68 0.02 0.42 0.03 0.72
4.39^−04
-0.45 -0.45 -0.56 6.17^−05 Tyms 0.41 0.01 0.81 0.03 0.27 0.06 1.10
3.74^−04
-0.45 -0.45 -0.56 6.17^−05 Igsf1 1.03 0.37 0.89 0.04 0.31 0.05 0.50
0.49
-0.45 -0.45 -0.56 6.17^−05 Me3 0.73 0.03 0.61 0.07 0.46 1.41^−03 1.15
4.17^−07
-0.45 -0.45 -0.56 6.17^−05 Ugt1a5 0.18 0.06 0.32 0.08 0.69 7.29^−04
0.01 0.97
-0.45 -0.45 -0.56 6.17^−05 Slc7a14 0.91 3.46^−04 0.42 0.09 0.23 0.06
0.35 0.17
-0.45 -0.45 -0.56 6.17^−05 Tmod3 0.72 0.01 0.61 0.09 0.30 0.10 0.71
0.07
-0.45 -0.45 -0.56 6.17^−05 Tyrobp 1.26 1.01^−04 0.70 0.26 1.40 4.93^−03
1.31 0.03
-0.45 -0.45 -0.56 6.17^−05 Gpnmb 1.54 3.04^−03 0.30 0.68 1.45 0.03 0.81
0.22
-0.45 -0.45 -0.56 6.17^−05 Zmiz1 0.48 2.39^−03 0.33 0.14 0.41 0.03 0.49
0.03
-0.45 -0.45 -0.56 6.17^−05 Skap2 0.73 0.09 0.22 0.37 0.59 0.06 0.57
0.06
-0.45 -0.45 -0.56 6.17^−05 Arhgdib 0.27 0.01 0.48 0.34 0.85 0.03 0.49
0.19
-0.45 -0.45 -0.56 6.17^−05 Ick 0.17 0.04 0.28 0.12 0.27 0.10 -0.01 0.88
-0.45 -0.45 -0.56 6.17^−05 Tmem140 0.80 0.06 0.61 0.05 0.07 0.64 -0.25
0.92
-0.45 -0.45 -0.56 6.17^−05 Trh 1.20 0.01 1.31 0.02 0.99 0.34 2.87 0.01
-0.45 -0.45 -0.56 6.17^−05 Col9a1 0.41 0.02 0.91 0.04 0.03 0.92 1.28
2.67^−04
-0.45 -0.45 -0.56 6.17^−05 Arpp21 0.11 0.07 0.45 0.09 -0.02 0.91 0.63
0.02
miR-92b-3p -1.05 -0.72 -0.37 1.70^−05 Gadd45a -0.20 0.16 1.51 0.01 0.79
0.01 1.33 3.91^−04
-1.05 -0.72 -0.37 1.70^−05 Map3k4 0.09 0.59 0.53 0.03 0.29 0.07 0.02
1.00
-1.05 -0.72 -0.37 1.70^−05 Kcnh2 0.60 0.07 0.63 0.04 0.39 0.09 0.26
0.38
-1.05 -0.72 -0.37 1.70^−05 Ezh2 0.16 0.27 0.40 0.07 0.31 0.05 0.04 0.78
-1.05 -0.72 -0.37 1.70^−05 Wnt10a 0.58 1.89^−03 0.81 0.07 0.46 0.01
1.69 0.01
-1.05 -0.72 -0.37 1.70^−05 Zmiz1 0.48 2.39^−03 0.33 0.14 0.41 0.03 0.49
0.03
-1.05 -0.72 -0.37 1.70^−05 Ick 0.17 0.04 0.28 0.12 0.27 0.10 -0.01 0.88
-1.05 -0.72 -0.37 1.70^−05 Zfp521 1.23 1.92^−04 0.74 0.01 0.23 0.22
0.59 3.07^−03
miR-345-5p -0.23 -0.25 -0.12 1.46^−06 Inpp4b 0.80 0.10 1.15 2.69^−03
0.31 0.07 0.24 0.13
-0.23 -0.25 -0.12 1.46^−06 Arhgap17 0.30 0.02 0.88 0.01 0.43 0.03 0.33
0.08
-0.23 -0.25 -0.12 1.46^−06 Gadd45a -0.20 0.16 1.51 0.01 0.79 0.01 1.33
3.91^−04
-0.23 -0.25 -0.12 1.46^−06 Gcnt1 1.45 3.63^−05 1.61 0.06 0.48 0.01 1.87
4.39^−04
-0.23 -0.25 -0.12 1.46^−06 LOC500956 0.34 0.20 0.50 0.07 0.27 0.02 0.12
0.91
-0.23 -0.25 -0.12 1.46^−06 Cd74 1.10 0.04 1.27 0.07 2.81 0.01 1.24 0.11
-0.23 -0.25 -0.12 1.46^−06 Wnt10a 0.58 1.89^−03 0.81 0.07 0.46 0.01
1.69 0.01
-0.23 -0.25 -0.12 1.46^−06 Skap2 0.73 0.09 0.22 0.37 0.59 0.06 0.57
0.06
-0.23 -0.25 -0.12 1.46^−06 Ss18 0.41 0.02 0.51 0.12 0.58 0.02 0.76
3.58^−04
-0.23 -0.25 -0.12 1.46^−06 Slfn13 0.46 0.04 0.41 0.18 1.53 2.69^−03
0.40 0.12
-0.23 -0.25 -0.12 1.46^−06 Rnd3 0.87 0.01 0.60 0.07 0.13 0.56 0.36 0.13
-0.23 -0.25 -0.12 1.46^−06 Tmem140 0.80 0.06 0.61 0.05 0.07 0.64 -0.25
0.92
miR-101a-3p -0.68 -0.71 -0.19 5.15^−06 Map3k4 0.09 0.59 0.53 0.03 0.29
0.07 0.02 1.00
-0.68 -0.71 -0.19 5.15^−06 Rin2 0.57 0.06 0.43 0.05 0.44 0.03 -0.06
0.95
-0.68 -0.71 -0.19 5.15^−06 Ezh2 0.16 0.27 0.40 0.07 0.31 0.05 0.04 0.78
-0.68 -0.71 -0.19 5.15^−06 Arl4a 0.65 0.02 0.36 0.41 0.40 0.01 0.38
0.09
-0.68 -0.71 -0.19 5.15^−06 Syn2 0.65 0.04 0.55 0.03 0.11 0.21 0.39 0.03
-0.68 -0.71 -0.19 5.15^−06 Nabp1 0.56 0.01 0.81 0.01 0.45 0.11 0.96
0.10
-0.68 -0.71 -0.19 5.15^−06 Arpp21 0.11 0.07 0.45 0.09 -0.02 0.91 0.63
0.02
miR-335 -1.98 -0.39 -0.71 2.47^−05 Efr3a 1.28 0.01 1.40 5.22^−04 0.49
0.01 1.11 4.17^−07
-1.98 -0.39 -0.71 2.47^−05 Rprm 2.68 3.86^−05 2.54 8.57^−04 1.00 0.01
1.53 5.05^−04
-1.98 -0.39 -0.71 2.47^−05 Ackr3 0.90 0.39 2.38 2.69^−03 0.55 3.05^−03
0.90 0.11
-1.98 -0.39 -0.71 2.47^−05 Gcnt1 1.45 3.63^−05 1.61 0.06 0.48 0.01 1.87
4.39^−04
-1.98 -0.39 -0.71 2.47^−05 Fcgr2b 1.87 3.62^−04 0.99 0.20 1.52 0.01
1.39 0.01
-1.98 -0.39 -0.71 2.47^−05 Vim 1.74 0.01 1.01 0.28 0.72 0.05 0.59 0.88
-1.98 -0.39 -0.71 2.47^−05 Arl11 0.86 0.01 0.08 0.75 0.72 0.01 0.71
0.07
-1.98 -0.39 -0.71 2.47^−05 Epsti1 0.92 0.03 0.47 0.35 0.71 0.02 0.61
0.02
-1.98 -0.39 -0.71 2.47^−05 Pycard 0.87 0.01 0.47 0.13 0.47 0.09 0.84
0.01
miR-29c-5p -0.59 -0.41 -0.14 1.48^−07 E2f1 0.89 1.05^−03 0.76 0.01 0.45
0.02 1.11 3.99^−05
-0.59 -0.41 -0.14 1.48^−07 Tmem176b 0.75 0.48 0.67 0.05 0.97 0.01 1.12
0.02
-0.59 -0.41 -0.14 1.48^−07 Sh3bgrl3 0.77 0.01 0.49 0.13 0.48 4.00^−03
0.70 0.01
miR-330-3p -0.99 -0.40 -0.32 1.94^−06 Efr3a 1.28 0.01 1.40 0.00 0.49
0.01 1.11 4.17^−07
-0.99 -0.40 -0.32 1.94^−06 Serinc2 1.16 1.52^−07 1.47 0.01 1.03
7.30^−04 1.43 4.02^−06
-0.99 -0.40 -0.32 1.94^−06 Tmem176b 0.75 0.48 0.67 0.05 0.97 0.01 1.12
0.02
-0.99 -0.40 -0.32 1.94^−06 Arl11 0.86 0.01 0.08 0.75 0.72 0.01 0.71
0.07
-0.99 -0.40 -0.32 1.94^−06 Pycard 0.87 0.01 0.47 0.13 0.47 0.09 0.84
0.01
-0.99 -0.40 -0.32 1.94^−06 Dhrs4 0.20 0.10 0.11 0.57 0.28 0.03 -0.03
0.87
-0.99 -0.40 -0.32 1.94^−06 Cd44 0.11 0.09 0.01 0.99 0.68 0.02 0.18 0.67
-0.99 -0.40 -0.32 1.94^−06 Anks1a 0.72 0.03 0.53 0.05 0.28 0.19 0.17
0.53
-0.99 -0.40 -0.32 1.94^−06 Asph 0.45 0.08 0.45 0.03 0.17 0.13 0.05 0.90
-0.99 -0.40 -0.32 1.94^−06 Cald1 0.30 0.08 0.38 0.09 0.15 0.33 0.07
0.68
miR-138-5p -0.48 -0.29 -0.50 3.98^−08 Rab32 0.74 0.01 0.66 0.01 0.28
0.03 0.48 0.06
-0.48 -0.29 -0.50 3.98^−08 Tpbg 0.83 0.11 1.71 0.01 0.39 0.01 1.70 0.00
-0.48 -0.29 -0.50 3.98^−08 Map3k14 0.11 0.08 0.77 0.03 0.26 0.07 0.20
0.87
-0.48 -0.29 -0.50 3.98^−08 Tcirg1 0.05 0.82 0.36 0.07 0.46 0.06 -0.10
0.89
-0.48 -0.29 -0.50 3.98^−08 Kank2 0.62 0.02 0.71 0.07 0.59 0.02 2.91^−03
0.95
-0.48 -0.29 -0.50 3.98^−08 Ezh2 0.16 0.27 0.40 0.07 0.31 0.05 0.04 0.78
-0.48 -0.29 -0.50 3.98^−08 Spsb1 -0.08 0.22 0.41 0.09 0.40 0.03 0.05
0.99
-0.48 -0.29 -0.50 3.98^−08 C1qc 1.11 1.37^−03 0.42 0.34 0.97 0.08 0.92
0.18
-0.48 -0.29 -0.50 3.98^−08 Sh3bgrl3 0.77 0.01 0.49 0.13 0.48 0.00 0.70
0.01
-0.48 -0.29 -0.50 3.98^−08 Zmiz1 0.48 2.39^−03 0.33 0.14 0.41 0.03 0.49
0.03
-0.48 -0.29 -0.50 3.98^−08 Ly86 0.87 0.03 0.40 0.41 0.97 0.03 0.58 0.05
-0.48 -0.29 -0.50 3.98^−08 Fancd2os 0.66 0.04 0.36 0.18 0.28 0.04 0.85
4.31^−03
-0.48 -0.29 -0.50 3.98^−08 Slc20a1 0.41 0.02 0.14 0.52 0.45 0.00 0.27
0.09
-0.48 -0.29 -0.50 3.98^−08 Rassf5 0.36 0.08 0.00 1.00 0.32 0.05 0.46
0.43
-0.48 -0.29 -0.50 3.98^−08 Cd44 0.11 0.09 0.01 0.99 0.68 0.02 0.18 0.67
-0.48 -0.29 -0.50 3.98^−08 Zfp521 1.23 1.92^−04 0.74 0.01 0.23 0.22
0.59 3.07^−03
-0.48 -0.29 -0.50 3.98^−08 Anks1a 0.72 0.03 0.53 0.05 0.28 0.19 0.17
0.53
-0.48 -0.29 -0.50 3.98^−08 Nnat 1.09 0.01 0.86 0.07 0.15 0.59 0.63 0.30
-0.48 -0.29 -0.50 3.98^−08 Htatip2 0.70 0.01 0.37 0.07 -0.02 0.96 0.79
7.35^−04
-0.48 -0.29 -0.50 3.98^−08 Tnfsf9 0.58 0.03 0.78 0.03 0.06 0.63 0.97
1.99^−04
-0.48 -0.29 -0.50 3.98^−08 Nabp1 0.56 0.01 0.81 0.01 0.45 0.11 0.96
0.10
-0.48 -0.29 -0.50 3.98^−08 Numbl 0.37 0.06 0.40 0.03 0.20 0.14 0.43
0.32
-0.48 -0.29 -0.50 3.98^−08 Cald1 0.30 0.08 0.38 0.09 0.15 0.33 0.07
0.68
-0.48 -0.29 -0.50 3.98^−08 Arpp21 0.11 0.07 0.45 0.09 -0.02 0.91 0.63
0.02
miR-667-3p -0.34 -0.20 -0.49 1.62^−07 Mdm1 0.69 0.04 0.76 2.69^−03 0.30
0.03 0.29 0.07
-0.34 -0.20 -0.49 1.62^−07 Inpp4b 0.80 0.10 1.15 2.69^−03 0.31 0.07
0.24 0.13
-0.34 -0.20 -0.49 1.62^−07 Tjp2 0.40 0.15 0.78 0.02 0.42 0.05 0.19 0.56
-0.34 -0.20 -0.49 1.62^−07 Serping1 1.19 3.39^−03 1.22 0.02 2.12 0.00
1.15 0.15
-0.34 -0.20 -0.49 1.62^−07 Tmem176b 0.75 0.48 0.67 0.05 0.97 0.01 1.12
0.02
-0.34 -0.20 -0.49 1.62^−07 LOC500956 0.34 0.20 0.50 0.07 0.27 0.02 0.12
0.91
-0.34 -0.20 -0.49 1.62^−07 Chi3l1 1.14 0.09 0.02 0.97 0.31 0.08 0.33
0.82
-0.34 -0.20 -0.49 1.62^−07 Laptm5 0.69 0.01 0.31 0.43 1.22 0.01 0.71
0.02
-0.34 -0.20 -0.49 1.62^−07 Ifi30 0.62 0.10 0.11 0.73 0.67 0.07 0.26
0.65
-0.34 -0.20 -0.49 1.62^−07 Fcgr1a 0.33 0.04 0.33 0.26 0.61 0.03 0.33
0.57
-0.34 -0.20 -0.49 1.62^−07 Tmem140 0.80 0.06 0.61 0.05 0.07 0.64 -0.25
0.92
miR-212-5p 2.55 0.27 0.28 1.15^−05 Rasd2 -0.59 1.03^−03 -1.24 1.19^−03
-0.87 0.00 -0.58 3.80^−03
2.55 0.27 0.28 1.15^−05 Gabrd -0.53 0.01 -0.59 0.01 -0.55 0.01 -0.71
0.03
2.55 0.27 0.28 1.15^−05 Hpca -0.74 0.02 -0.64 0.01 -0.22 0.02 -0.40
0.09
2.55 0.27 0.28 1.15^−05 C1ql3 0.14 0.57 -0.47 0.05 -0.38 0.00 -0.61
0.38
2.55 0.27 0.28 1.15^−05 Fat1 -1.05 1.24^−03 -1.01 0.07 -0.19 0.08 -1.55
1.24^−03
let-7b-3p -0.80 -0.41 -5.75 8.36^−06 C1r 0.87 8.05^−04 0.79 0.02 0.81
0.01 0.54 0.01
-0.80 -0.41 -5.75 8.36^−06 Gcnt1 1.45 3.63^−05 1.61 0.06 0.48 0.01 1.87
4.39^−04
-0.80 -0.41 -5.75 8.36^−06 Bdnf 0.45 0.04 0.20 0.69 0.32 0.05 1.17 0.03
-0.80 -0.41 -5.75 8.36^−06 Gpr83 0.59 0.01 0.90 0.06 0.23 0.40 0.43
0.51
-0.80 -0.41 -5.75 8.36^−06 Jup 0.38 0.02 0.45 0.09 0.23 0.29 0.74
1.73^−03
miR-132-3p 1.91 0.80 0.10 4.19^−18 Insig2 -0.37 0.52 -0.81 0.01 -0.34
0.10 -0.15 0.80
miR-146a-5p 4.71 0.37 0.33 4.05^−10 Mthfd1l -0.94 5.30^−04 -0.83 0.01
-0.44 0.01 -1.27 0.04
4.71 0.37 0.33 4.05^−10 Plxdc1 -0.28 0.06 -0.73 0.01 -0.33 0.08 -0.70
7.35^−04
4.71 0.37 0.33 4.05^−10 Htr5b -0.54 0.01 -1.66 0.03 -0.73 0.00 -1.55
3.87^−05
4.71 0.37 0.33 4.05^−10 B3galt5 -1.39 7.49^−05 -0.83 0.04 -0.43 0.05
-1.93 0.01
4.71 0.37 0.33 4.05^−10 Pip5k1b -0.79 0.17 -0.46 0.05 -0.38 0.06 -1.03
0.02
miR-551b-3p -0.78 -0.85 -0.42 9.09^−14 Efr3a 1.28 0.01 1.40 5.22^−04
0.49 0.01 1.11 4.17^−07
-0.78 -0.85 -0.42 9.09^−14 Rprm 2.68 3.86^−05 2.54 8.57^−04 1.00 0.01
1.53 5.05^−04
-0.78 -0.85 -0.42 9.09^−14 Lox 3.11 2.13^−09 2.56 2.69^−03 1.28 0.00
2.66 6.51^−06
-0.78 -0.85 -0.42 9.09^−14 Sox11 1.57 0.01 1.36 0.01 1.23 0.00 1.52
2.72^−03
-0.78 -0.85 -0.42 9.09^−14 Tpbg 0.83 0.11 1.71 0.01 0.39 0.01 1.70
3.06^−03
-0.78 -0.85 -0.42 9.09^−14 C1qc 1.11 1.37^−03 0.42 0.34 0.97 0.08 0.92
0.18
-0.78 -0.85 -0.42 9.09^−14 Syn2 0.65 0.04 0.55 0.03 0.11 0.21 0.39 0.03
-0.78 -0.85 -0.42 9.09^−14 Nabp1 0.56 0.01 0.81 0.01 0.45 0.11 0.96
0.10
-0.78 -0.85 -0.42 9.09^−14 Ntm 0.71 0.05 0.37 0.09 -0.22 0.37 -0.32
0.94
miR-3573-3p -1.02 -0.26 -0.93 4.68^−17 Serping1 1.19 3.39^−03 1.22 0.02
2.12 0.00 1.15 0.15
-1.02 -0.26 -0.93 4.68^−17 Map3k4 0.09 0.59 0.53 0.03 0.29 0.07 0.02
1.00
-1.02 -0.26 -0.93 4.68^−17 Col6a3 0.31 0.01 0.79 0.08 0.35 0.06 0.63
0.09
-1.02 -0.26 -0.93 4.68^−17 Plcxd3 1.94 3.86^−05 1.58 0.17 0.59 0.01
2.02 0.01
-1.02 -0.26 -0.93 4.68^−17 S100a10 1.36 1.68^−03 0.61 0.50 0.80 0.02
1.09 0.43
-1.02 -0.26 -0.93 4.68^−17 Chi3l1 1.14 0.09 0.02 0.97 0.31 0.08 0.33
0.82
-1.02 -0.26 -0.93 4.68^−17 Rbms1 1.24 1.76^−03 0.65 0.17 0.50 0.08 0.59
0.34
-1.02 -0.26 -0.93 4.68^−17 Lgmn 0.41 0.03 0.09 0.77 0.61 0.06 0.22 0.62
-1.02 -0.26 -0.93 4.68^−17 P2ry6 0.17 0.05 0.12 0.72 0.61 0.02 0.50
0.16
-1.02 -0.26 -0.93 4.68^−17 Epb41l4b 0.70 8.37^−04 0.58 0.02 0.17 0.46
0.57 2.98^−03
miR-139-5p -1.40 -0.77 -0.91 4.40^−17 C1s 1.55 1.42^−06 1.15 2.00^−03
0.55 0.02 1.34 3.94^−05
-1.40 -0.77 -0.91 4.40^−17 Tmem176b 0.75 0.48 0.67 0.05 0.97 0.01 1.12
0.02
-1.40 -0.77 -0.91 4.40^−17 Slc7a14 0.91 3.46^−04 0.42 0.09 0.23 0.06
0.35 0.17
-1.40 -0.77 -0.91 4.40^−17 Fcgr2b 1.87 3.62^−04 0.99 0.20 1.52 0.01
1.39 0.01
-1.40 -0.77 -0.91 4.40^−17 C5ar1 0.44 0.02 0.33 0.31 0.24 0.08 0.43
0.28
-1.40 -0.77 -0.91 4.40^−17 Syn2 0.65 0.04 0.55 0.03 0.11 0.21 0.39 0.03
-1.40 -0.77 -0.91 4.40^−17 Anks1a 0.72 0.03 0.53 0.05 0.28 0.19 0.17
0.53
-1.40 -0.77 -0.91 4.40^−17 Mtmr11 0.95 3.62^−04 1.86 0.01 0.05 0.87
1.13 4.82^−05
miR-33-5p -2.40 -0.80 -0.51 4.92^−19 Runx1 1.08 7.92^−04 1.48 2.69^−03
0.60 2.69^−03 1.22 8.95^−04
-2.40 -0.80 -0.51 4.92^−19 Wnt10a 0.58 1.89^−03 0.81 0.07 0.46 0.01
1.69 0.01
-2.40 -0.80 -0.51 4.92^−19 Slc7a14 0.91 3.46^−04 0.42 0.09 0.23 0.06
0.35 0.17
-2.40 -0.80 -0.51 4.92^−19 Fcgr2b 1.87 3.62^−04 0.99 0.20 1.52 0.01
1.39 0.01
-2.40 -0.80 -0.51 4.92^−19 Ly86 0.87 0.03 0.40 0.41 0.97 0.03 0.58 0.05
-2.40 -0.80 -0.51 4.92^−19 Cfh 1.14 0.04 0.91 0.21 0.70 0.03 0.70 0.11
miR-7a-5p -0.91 -0.93 -0.51 3.44^−24 Mdm1 0.69 0.04 0.76 2.69^−03 0.30
0.03 0.29 0.07
-0.91 -0.93 -0.51 3.44^−24 Wls 1.45 7.49^−05 0.80 0.01 0.57 0.01 1.75
1.52^−05
-0.91 -0.93 -0.51 3.44^−24 Tpbg 0.83 0.11 1.71 0.01 0.39 0.01 1.70
3.06^−03
-0.91 -0.93 -0.51 3.44^−24 Serping1 1.19 3.39^−03 1.22 0.02 2.12 0.00
1.15 0.15
-0.91 -0.93 -0.51 3.44^−24 Map3k14 0.11 0.08 0.77 0.03 0.26 0.07 0.20
0.87
-0.91 -0.93 -0.51 3.44^−24 Tmem176b 0.75 0.48 0.67 0.05 0.97 0.01 1.12
0.02
-0.91 -0.93 -0.51 3.44^−24 Dnah12 0.32 0.41 0.65 0.05 0.22 0.03 -0.35
0.91
-0.91 -0.93 -0.51 3.44^−24 Pafah1b3 0.88 0.04 0.84 0.09 0.28 0.01 0.42
0.79
-0.91 -0.93 -0.51 3.44^−24 S100a4 1.44 2.54^−07 0.53 0.17 0.67 0.01
1.05 0.09
-0.91 -0.93 -0.51 3.44^−24 C1qa 1.21 0.05 0.34 0.29 1.27 0.04 1.16
3.03^−03
-0.91 -0.93 -0.51 3.44^−24 Gpnmb 1.54 3.04^−03 0.30 0.68 1.45 0.03 0.81
0.22
-0.91 -0.93 -0.51 3.44^−24 Resp18 0.65 1.68^−03 0.38 0.23 0.28 0.09
0.82 0.02
-0.91 -0.93 -0.51 3.44^−24 Cfh 1.14 0.04 0.91 0.21 0.70 0.03 0.70 0.11
-0.91 -0.93 -0.51 3.44^−24 Arhgdib 0.27 0.01 0.48 0.34 0.85 0.03 0.49
0.19
-0.91 -0.93 -0.51 3.44^−24 Ick 0.17 0.04 0.28 0.12 0.27 0.10 -0.01 0.88
-0.91 -0.93 -0.51 3.44^−24 Nubpl 1.06 1.77^−04 0.88 2.44^−03 0.06 0.64
0.77 0.02
-0.91 -0.93 -0.51 3.44^−24 Arpp21 0.11 0.07 0.45 0.09 -0.02 0.91 0.63
0.02
miR-212-3p 1.09 0.88 0.22 1.74^−47 Insig2 -0.37 0.52 -0.81 0.01 -0.34
0.10 -0.15 0.80
[153]Open in a new tab
Figure 4.
[154]Figure 4.
[155]Open in a new tab
Relationship between selected miRNA and their predicted targets in
different model of TLE. All panels show selected miRNAs-mRNA
anticorrelation based on miRNAs and mRNAs fold changes in
epileptogenesis. A, Inverse relationship between four downregulated
miRNAs (miR-92b-3p, miR-101a-3p, miR-153-3p, and miR-3573-3p) and the
commonly predicted target Map3k4. B, Inverse relationship between the
downregulated miR-138-5p, miR-7a-5p, and the upregulated Map3k14. C,
Inverse relationship between miR-101a-3p, miR-139-3p, miR-551b-3p, and
Syn2. D, Inverse relationship between miR-150-5p, miR-383-5p, and Ptpn.
E, F, Examples of the opposite anticorrelation, the upregulated
miR-146a-5p with the downregulated Htr5b transcript, and the
upregulated miR-212-5p and the downregulated Gabrd transcript.
The relationship between the changes in expression of miRNAs and their
mRNA targets in the chronic stage of epilepsy was analyzed using only
the amygdala stimulation dataset ([156]Bot et al., 2013). We observed
negative correlations (based on fold changes) between all five miRNAs
that emerged as significantly downregulated from the meta-analysis and
29 unique predicted mRNA targets in the dataset. Five of these 29
anticorrelated mRNAs were predicted targets and had inverse expression
relative to two miRNAs and one, the glutamate ionotropic receptor
δ-type subunit 2 (Grid2), was a predicted target and had inverse
expression relative to three miRNAs, namely, miR-130a-3p, miR-148b-3p,
and miR-551b-3p ([157]Table 5). Furthermore, interestingly, three mRNA
targets, the transmembrane protein 176B (Tmem176b), the EFR3 homolog A
(Efr3a), and the zinc finger, MIZ-type containing 1 (Zmiz1) were
downregulated in both epileptogenesis and the chronic stage.
Table 5.
miRNA-mRNA fold changes inverse correlation at the chronic stage
miRNA name Pilocarpine Angular bundle stimulation Amygdala stimulation
mRNA gene name Amygdala Stimulation
__________________________________________________________________
miRNA FC Meta-analysis adjusted p value FC adjusted p value
miR-130a-3p -0.24 -0.32 -0.46 0.0107 Frmd6 0.35 0.0077
-0.24 -0.32 -0.46 0.0107 Grid2 0.31 0.0702
-0.24 -0.32 -0.46 0.0107 Necab3 0.43 0.0202
-0.24 -0.32 -0.46 0.0107 Npepl1 0.22 0.0847
miR-148b-3p -0.24 -0.29 -0.41 0.0317 C1qa 0.81 0.0559
-0.24 -0.29 -0.41 0.0317 Ctsz 0.63 0.0773
-0.24 -0.29 -0.41 0.0317 Flnc 0.53 0.0455
-0.24 -0.29 -0.41 0.0317 Frmd6 0.35 0.0077
-0.24 -0.29 -0.41 0.0317 Grid2 0.31 0.0702
-0.24 -0.29 -0.41 0.0317 Npepl1 0.22 0.0847
-0.24 -0.29 -0.41 0.0317 Tax1bp3 0.36 0.0371
-0.24 -0.29 -0.41 0.0317 Tmem176b 0.91 0.0621
miR-324-3p -0.44 -0.22 -0.27 0.0055 Acss1 0.31 0.0899
-0.44 -0.22 -0.27 0.0055 Atraid 0.23 0.0936
-0.44 -0.22 -0.27 0.0055 Cd9 0.46 0.0380
-0.44 -0.22 -0.27 0.0055 Chi3l1 0.45 0.0455
-0.44 -0.22 -0.27 0.0055 Csf1r 0.80 0.0773
-0.44 -0.22 -0.27 0.0055 Ctsb 0.20 0.0918
-0.44 -0.22 -0.27 0.0055 Gfap 0.93 0.0380
-0.44 -0.22 -0.27 0.0055 Gsap 0.20 0.0843
-0.44 -0.22 -0.27 0.0055 Hmox1 0.19 0.0972
-0.44 -0.22 -0.27 0.0055 Limd2 0.26 0.0817
-0.44 -0.22 -0.27 0.0055 Mex3b 0.33 0.0217
-0.44 -0.22 -0.27 0.0055 Osbpl9 0.19 0.0760
-0.44 -0.22 -0.27 0.0055 Slco2b1 0.46 0.0896
-0.44 -0.22 -0.27 0.0055 Tmem176b 0.91 0.0621
-0.44 -0.22 -0.27 0.0055 Zmiz1 0.34 0.0077
miR-551b-3p -1.30 -0.52 -0.50 0.0006 Csf1r 0.80 0.0773
-1.30 -0.52 -0.50 0.0006 Efr3a 0.60 0.0027
-1.30 -0.52 -0.50 0.0006 Entpd2 0.44 0.0518
-1.30 -0.52 -0.50 0.0006 Grid2 0.31 0.0702
-1.30 -0.52 -0.50 0.0006 Npc2 0.80 0.0882
-1.30 -0.52 -0.50 0.0006 Sox11 0.86 0.0455
miR-652-3p -0.90 -0.34 -0.41 9.65^−07 Cd9 0.46 0.0380
-0.90 -0.34 -0.41 9.65^−07 Hsd3b7 0.31 0.0402
-0.90 -0.34 -0.41 9.65^−07 Tmem176a 0.93 0.0559
[158]Open in a new tab
Recent evidence supports the notion that miRNAs not only decrease
levels of their mRNA targets ([159]Guo et al., 2010), but additionally
may have nuclear functions capable of influencing gene expression, and
which may be reflected by a correlation between a miRNA and its target
gene mRNA levels ([160]Catalanotto et al., 2016). Analysis of the
epileptogenesis data revealed significant correlation (gene FDR < 0.1),
for 21 (of 26) miRNAs and 77 unique predicted gene targets in at least
three of the four epilepsy models of the Dingledine dataset
([161]Dingledine et al., 2017; [162]Table 6). In addition, we found
positive correlations between five of the five miRNAs that were
downregulated in the chronic period and 39 predicted mRNA targets in
the amygdala stimulation dataset ([163]Bot et al., 2013; [164]Table 7).
Interestingly, 29 of the mRNAs identified as potential targets in
epileptogenesis were inversely correlated to some miRNAs and directly
correlated to others (e.g., map3k14 is inversely correlated to
miR-7a-5p and miR-138-5p and directly correlated to miR-212-5p, while
bdnf is inversely correlated to let-7b-3p and directly correlated to
miR-212-5p). This observation prompts the hypothesis that some mRNAs
may be subject to a dual control by different miRNAs at cytosolic and
nuclear level. This hypothesis should be challenged and investigated.
Table 6.
miRNA-mRNA fold changes positive correlation in epileptogenesis
miRNA Name microRNA data
__________________________________________________________________
mRNA targets of respective microRNA (FDR < 0.1)
__________________________________________________________________
Pilocarpine Perforant path stimulation Amygdala stimulation mRNA gene
names Pilocarpine
__________________________________________________________________
Perforant path stimulation
__________________________________________________________________
Amygdala stimulation
__________________________________________________________________
Kainic acid
__________________________________________________________________
miRNA FC Meta-analysis p value FC Adjusted p value FC Adjusted p value
FC Adjusted p value FC Adjusted p value
miR-383-5p -0.50 -0.35 -0.75 6.11^−05 Rasd2 -0.58 0.0038 -1.24 0.0012
-0.59 0.0010 -0.87 0.0034
Sec14l1 -0.59 0.0061 -0.50 0.0425 -0.39 0.0053 -0.20 0.0726
Mpp6 -0.59 0.0295 -0.54 0.0517 -0.39 0.4123 -0.78 0.0510
miR-153-3p -0.32 -0.42 -0.22 0.0008 Mthfd1l -1.27 0.0398 -0.83 0.0102
-0.94 0.0005 -0.44 0.0093
Gdf10 -2.91 8.62^−06 -1.46 0.0306 -1.86 2.27^−07 -1.02 0.0014
Nr4a3 0.12 0.9726 -1.65 0.0721 -1.64 0.0574 -0.65 0.0042
Mettl7a -0.74 0.0066 -0.72 0.0861 -0.87 0.0750 -0.19 0.0500
Ablim2 -0.05 0.9953 -0.78 0.0964 -0.35 0.7323 -0.29 0.0667
miR-324-5p -0.09 -0.60 -0.29 8.552^−06 Ryr1 -1.52 0.0022 -1.39 0.0196
-1.86 2.53^−08 -0.68 0.0014
miR-150-5p -0.45 -0.45 -0.56 6.168^−05 Ddit4l -2.38 0.0063 -1.89 0.0098
-2.00 6.09^−06 -1.09 0.0007
Htr5b -1.55 3.87^−05 -1.66 0.0263 -0.54 0.0096 -0.73 0.0016
Fkbp4 -0.21 0.7718 -0.55 0.0464 -0.45 0.0737 -0.26 0.0775
Pip5k1b -1.03 0.0228 -0.46 0.0492 -0.79 0.1731 -0.38 0.0619
Calml4 -0.60 0.3155 -0.44 0.0839 -0.48 0.1135 -0.38 0.0139
miR-92b-3p -1.05 -0.72 -0.37 1.695^−05 Per2 -0.59 0.8691 -0.46 0.0717
-0.56 0.0253 -0.37 0.0918
miR-345-5p -0.23 -0.25 -0.12 1.464^−06 Rasd2 -0.58 0.0038 -1.24 0.0012
-0.59 0.0010 -0.87 0.0034
Ryr1 -1.52 0.0022 -1.39 0.0196 -1.86 2.53^−08 -0.68 0.0014
Klhl14 -2.36 2.39^−06 -1.66 0.0204 -0.84 0.0006 -0.44 0.0077
B3galt5 -1.93 0.0118 -0.83 0.0442 -1.39 7.49^−05 -0.43 0.0471
Pip5k1b -1.03 0.0228 -0.46 0.0492 -0.79 0.1731 -0.38 0.0619
Rspo3 -0.41 0.5671 -0.41 0.0626 -0.11 0.1711 -0.55 0.0259
Fat1 -1.55 0.0012 -1.01 0.0717 -1.05 0.0012 -0.19 0.0792
miR-101a-3p -0.68 -0.71 -0.19 5.148^−06 Rasd2 -0.58 0.0038 -1.24 0.0012
-0.59 0.0010 -0.87 0.0034
Ddit4l -2.38 0.0063 -1.89 0.0098 -2.00 6.09^−06 -1.09 0.0007
Gdf10 -2.91 8.62^−06 -1.46 0.0306 -1.86 2.27^−07 -1.02 0.0014
Plk5 -2.15 0.0033 -0.59 0.0311 -1.46 0.0013 -1.00 0.0303
Plag1 -0.58 0.0528 -0.45 0.0425 0.06 0.5075 -0.59 0.0453
miR-29c-5p -0.59 -0.41 -0.14 1.477^−07 Crim1 -1.07 0.0084 -0.59 0.0173
-0.87 9.88^−06 -0.31 0.0323
Dnah12 -0.35 0.9054 0.65 0.0492 0.32 0.4073 0.22 0.0340
C5ar1 0.43 0.2817 0.33 0.3103 0.44 0.0181 0.24 0.0841
Slc20a1 0.27 0.0883 0.14 0.5191 0.41 0.0227 0.45 0.0042
miR-330-3p -0.99 -0.40 -0.32 1.935^−06 Gabrd -0.71 0.0282 -0.59 0.0067
-0.53 0.0053 -0.55 0.0070
Ets2 -0.44 0.3991 -0.66 0.0249 -0.44 0.0641 -0.31 0.0095
Gpc3 -1.92 0.0026 -0.85 0.0492 -0.89 0.0006 -1.29 0.0054
miR-138-5p -0.48 -0.29 -0.50 3.983^−08 Rasd2 -0.58 0.0038 -1.24 0.0012
-0.59 0.0010 -0.87 0.0034
Nr4a1 0.24 0.7205 -0.88 0.0125 -0.56 0.4123 -0.32 0.0719
Crim1 -1.07 0.0084 -0.59 0.0173 -0.87 9.88^−06 -0.31 0.0323
Nhlh1 -1.67 0.0023 -1.12 0.0337 -0.63 0.0295 -0.84 0.0097
Nr4a3 0.12 0.9726 -1.65 0.0721 -1.64 0.0574 -0.65 0.0042
miR-667-3p -0.34 -0.20 -0.49 1.62^−07 Rasd2 -0.58 0.0038 -1.24 0.0012
-0.59 0.0010 -0.87 0.0034
Etv5 -0.08 0.9646 -0.63 0.0423 -0.47 0.2965 -0.49 0.0044
miR-212-5p 2.55 0.27 0.28 1.152^−05 Sox11 1.52 0.0027 1.36 0.0092 1.57
0.0063 1.23 0.0011
Serping1 1.15 0.1500 1.22 0.0204 1.19 0.0034 2.12 0.0009
Map3k14 0.20 0.8682 0.77 0.0263 0.11 0.0834 0.26 0.0653
Ptprn 0.83 0.0001 0.56 0.0613 0.38 0.0034 0.33 0.0648
Kank2 0.00 0.9473 0.71 0.0673 0.62 0.0153 0.59 0.0241
Acan 0.30 0.0723 0.37 0.0799 0.17 0.2577 0.37 0.0436
Slc7a14 0.35 0.1717 0.42 0.0877 0.91 0.0003 0.23 0.0568
C1qc 0.92 0.1759 0.42 0.3397 1.11 0.0014 0.97 0.0816
Ly86 0.58 0.0494 0.40 0.4124 0.87 0.0299 0.97 0.0299
Slc20a1 0.27 0.0883 0.14 0.5191 0.41 0.0227 0.45 0.0042
Blnk 0.70 0.0039 0.43 0.1925 0.71 0.0377 0.78 0.0158
Bdnf 1.17 0.0297 0.20 0.6933 0.45 0.0352 0.32 0.0487
Pdlim4 0.19 0.7034 0.15 0.5799 0.15 0.0771 0.46 0.0721
Syn2 0.39 0.0258 0.55 0.0299 0.65 0.0399 0.11 0.2134
Epb41l4b 0.57 0.0030 0.58 0.0237 0.70 0.0008 0.17 0.4555
Nnat 0.63 0.2970 0.86 0.0717 1.09 0.0083 0.15 0.5894
Htatip2 0.79 0.0007 0.37 0.0669 0.70 0.0119 -0.02 0.9557
Trh 2.87 0.0096 1.31 0.0246 1.20 0.0145 0.99 0.3361
Asph 0.05 0.9026 0.45 0.0254 0.45 0.0832 0.17 0.1308
let-7b-3p -0.80 -0.41 -5.75 8.364^−06 Rspo3 -0.41 0.5671 -0.41 0.0626
-0.11 0.1711 -0.55 0.0259
miR-132-3p 1.91 0.80 0.10 4.195^−18 Efr3a 1.11 4.17^−07 1.40 0.0005
1.28 0.0096 0.49 0.0070
Sox11 1.52 0.0027 1.36 0.0092 1.57 0.0063 1.23 0.0011
Wls 1.75 1.52^−05 0.80 0.0108 1.45 7.49^−05 0.57 0.0051
Rin2 -0.06 0.9546 0.43 0.0517 0.57 0.0638 0.44 0.0303
Gpnmb 0.81 0.2150 0.30 0.6840 1.54 0.0030 1.45 0.0259
Zfp521 0.59 0.0031 0.74 0.0052 1.23 0.0002 0.23 0.2169
Asph 0.05 0.9026 0.45 0.0254 0.45 0.0832 0.17 0.1308
miR-146a-5p 4.71 0.37 0.33 4.047^−10 Mdm1 0.29 0.0680 0.76 0.0027 0.69
0.0360 0.30 0.0322
Inpp4b 0.24 0.1291 1.15 0.0027 0.80 0.1003 0.31 0.0735
Arhgap17 0.33 0.0762 0.88 0.0080 0.30 0.0183 0.43 0.0259
Igsf1 0.50 0.4934 0.89 0.0425 1.03 0.3719 0.31 0.0510
Gpat3 0.44 0.4497 0.40 0.0864 -0.04 0.2317 0.16 0.0923
Sowahc 0.53 0.0683 0.45 0.3070 1.09 0.0006 0.28 0.0873
Fcer1g 1.09 0.0112 0.51 0.1380 1.14 0.0002 1.37 0.0059
Slfn13 0.40 0.1166 0.41 0.1757 0.46 0.0429 1.53 0.0027
Zfp521 0.59 0.0031 0.74 0.0052 1.23 0.0002 0.23 0.2169
Anks1a 0.17 0.5280 0.53 0.0492 0.72 0.0323 0.28 0.1948
Trh 2.87 0.0096 1.31 0.0246 1.20 0.0145 0.99 0.3361
Ntm -0.32 0.9428 0.37 0.0861 0.71 0.0527 -0.22 0.3746
Col9a1 1.28 0.0003 0.91 0.0425 0.41 0.0208 0.03 0.9192
miR-551b-3p -0.78 -0.85 -0.42 9.092^−14 Plxdc1 -0.70 0.0007 -0.73
0.0127 -0.28 0.0555 -0.33 0.0823
Ogfrl1 -0.23 0.1736 -0.52 0.0135 -0.55 0.0293 -0.30 0.0563
Htr5b -1.55 3.87^−05 -1.66 0.0263 -0.54 0.0096 -0.73 0.0016
Diaph1 -0.27 0.2293 -0.30 0.0492 0.07 0.3960 -0.23 0.0530
miR-344b-2-3p 3.74 0.11 0.29 5.432^−14 Runx1 1.22 0.0009 1.48 0.0027
1.08 0.0008 0.60 0.0027
Il18 0.31 0.3608 0.52 0.0669 0.38 0.1009 0.51 0.0955
Chi3l1 0.33 0.8228 0.02 0.9731 1.14 0.0881 0.31 0.0785
Fancd2os 0.85 0.0043 0.36 0.1786 0.66 0.0353 0.28 0.0372
P2ry6 0.50 0.1614 0.12 0.7169 0.17 0.0499 0.61 0.0231
Asph 0.05 0.9026 0.45 0.0254 0.45 0.0832 0.17 0.1308
miR-139-5p -1.40 -0.77 -0.91 4.4^−17 Gabrd -0.71 0.0282 -0.59 0.0067
-0.53 0.0053 -0.55 0.0070
Gdf10 -2.91 8.62^−06 -1.46 0.0306 -1.86 2.27^−07 -1.02 0.0014
Rspo3 -0.41 0.5671 -0.41 0.0626 -0.11 0.1711 -0.55 0.0259
miR-33-5p -2.40 -0.80 -0.51 4.916^−19 Smarca2 -0.60 0.2192 -1.53 0.0186
-0.35 0.0904 -0.27 0.0340
Fxyd7 -1.49 7.16^−05 -1.65 0.0186 -0.98 0.0052 -0.30 0.0142
Arg1 -1.24 0.0324 -0.49 0.0984 -0.37 0.0679 -0.50 0.0344
miR-7a-5p -0.91 -0.93 -0.51 3.436^−24 Hpca -0.40 0.0880 -0.64 0.0090
-0.74 0.0220 -0.22 0.0244
miR-212-3p 1.09 0.88 0.22 1.744^−47 Efr3a 1.11 4.17^−07 1.40 0.0005
1.28 0.0096 0.49 0.0070
Sox11 1.52 0.0027 1.36 0.0092 1.57 0.0063 1.23 0.0011
Wls 1.75 1.52^−05 0.80 0.0108 1.45 7.49^−05 0.57 0.0051
Rin2 -0.06 0.9546 0.43 0.0517 0.57 0.0638 0.44 0.0303
Gpnmb 0.81 0.2150 0.30 0.6840 1.54 0.0030 1.45 0.0259
Zfp521 0.59 0.0031 0.74 0.0052 1.23 0.0002 0.23 0.2169
Asph 0.05 0.9026 0.45 0.0254 0.45 0.0832 0.17 0.1308
[165]Open in a new tab
Table 7.
miRNA-mRNA fold changes positive correlation in the chronic period
miRNA name microRNA data
__________________________________________________________________
mRNA targets of respective microRNA (FDR < 0.1)
__________________________________________________________________
Pilocarpine Perforant path stimulation Amygdala stimulation mRNA gene
names Amygdala stimulation Amygdala stimulation
FC Meta-analysis p value FC Adjusted p value
miR-652-3p -0.90 -0.34 -0.41 9.65^−07 Ano2 -0.39 0.0077
Ece2 -0.22 0.0395
Optn -0.26 0.0825
miR-551b-3p -1.30 -0.52 -0.50 0.0006 Clmp -0.36 0.0325
Socs5 -0.36 0.0619
Asic2 -0.26 0.0731
miR-324-3p -0.44 -0.22 -0.27 0.0055 Gdf10 -0.94 0.0083
Gpr176 -0.43 0.0116
Etv5 -0.58 0.0202
Elfn2 -0.25 0.0225
Tcerg1l -0.42 0.0311
Sstr3 -0.33 0.0371
Nr4a3 -0.50 0.0372
Veph1 -0.27 0.0380
Cyp26b1 -0.35 0.0380
Itgb4 -0.50 0.0394
Nefm -0.31 0.0394
Alcam -0.29 0.0547
Grik1 -0.30 0.0555
Clmn -0.30 0.0619
Arg1 -0.30 0.0697
Grik3 -0.72 0.0702
Asic2 -0.26 0.0731
Boc -0.33 0.0772
Ubash3b -0.24 0.0773
Cbarp -0.18 0.0817
miR-148b-3p -0.24 -0.29 -0.41 0.0317 Pip5k1b -0.50 0.0077
Gpr176 -0.43 0.0116
Slit2 -0.39 0.0219
Camk1g -0.43 0.0234
Gpr165 -0.48 0.0326
Hcrtr2 -0.28 0.0371
Htra4 -1.37 0.0380
Vstm2b -0.40 0.0504
Alcam -0.29 0.0547
Ankrd34c -0.44 0.0568
Ppara -0.25 0.0773
Hnrnpm -0.22 0.0988
miR-130a-3p -0.24 -0.32 -0.46 0.0107 Eloc -0.33 0.0380
Htra4 -1.37 0.0380
Trhr -0.75 0.0380
Mthfd1l -0.39 0.0505
Rasd2 -0.47 0.0560
[166]Open in a new tab
Genes that are anticorrelated with miRNAs are enriched for “epileptogenic”
ontology categories
To further investigate the functional role of miRNAs significantly
differentially expressed and anticorrelated with their predicted mRNA
targets, we examined the functional enrichment of the mRNA targets
identified in epileptogenesis and chronic phases of epilepsy.
Target genes that inversely correlated with differentially expressed
miRNAs during the epileptogenesis period were enriched for GO terms
related to synaptic function [like “response to stimulus” (p = 0.0013),
“signaling” (p = 2.68^−05), “signal transduction” (p = 0.0047), and
others] and immunity [like “humoral immune response” (p = 0.0009),
“regulation of immune system process” (p = 0.0013), and others]. In
addition, terms related to complement activation [like “complement
activation” (p = 0.0002) and “complement activation, classical pathway”
(p = 0.0009)] are in prominent position ([167]Fig. 5A; [168]Table 8).
Proteins of the classical complement pathway not only play a role in
the innate immune system, but have been also shown to be released from
neurons, and serve as a new class of synaptic organizers ([169]Yuzaki,
2017). These GO terms are potentially relevant to changes occurring at
the level of the DG in epileptogenesis ([170]Dudek and Sutula, 2007;
[171]Vezzani et al., 2015). In addition, at the level of cell signaling
pathways, analysis of KEGG pathways enriched among the mRNA targets
suggested a key role for the MAPK cascade (p = 0.354; [172]Fig. 5B;
[173]Table 8). Notably, changes in the activation state of kinase
pathways and altered kinase expression patterns have been reported in
the hippocampus by previous studies ([174]Xi et al., 2009). In the
chronic period, the predicted and anticorrelated mRNA targets revealed
enrichment in biological processes that have been previously implicated
in chronic epilepsy ([175]Ludewig et al., 2016; [176]Robel and
Sontheimer, 2016) such as “regulation of dendritic cell
differentiation” (p = 4.8 × 10^−6), “glial cell development” (p = 4.0 ×
10^−4), “proliferation” (p = 6.0 × 10^−4), and “cell proliferation” (p
= 4.0 × 10^−4; [177]Fig. 5C; [178]Table 9). Notably, we performed a
permutation test to check for false positive enrichment in GO term and
KEGG pathway analysis, but this did not change any of the results.
Figure 5.
[179]Figure 5.
[180]Open in a new tab
Functional enrichment of dysregulated miRNA-mRNAs targets modules. A,
Horizontal bar plots (on the left) show the GO enrichment status (top
20 terms) for 112 predicted mRNAs targets that anticorrelate with 22
miRNAs expression level in epileptogenesis (FDR < 5%, hypergeometric
test). The miRNA-mRNA module is represented by a network graph (on the
right) showing the connections between miRNAs based on the function of
their mRNAs predictive targets revealed by the GO enrichment. B,
Horizontal bar plots (on the left) show KEGG enrichment analysis for
predicted mRNAs targets that anticorrelate with miRNAs expression level
in epileptogenesis (FDR < 5%, hypergeometric test). miRNA-mRNA modules
are represented with network plot (on the right) showing the connection
between miRNAs based on the pathways in which are involved their
predicted targets revealed by KEGG analysis. C, D, GO and KEGG
enrichment status for 29 predicted miRNA targets that anticorrelate
with five miRNAs differentially expressed in the chronic stage (FDR <
5%, hypergeometric test).
Table 8.
GO and KEGG enrichment of 122 predicted and anticorrelated mRNAs
targets of 22 miRNAs differentially expressed in epileptogenesis
GO terms Term description GO ID Size of term miRNA target Expected
Enrichment ratio Raw p value FDR
Biological process Single-organism process GO:004469 C = 2489 O = 67 E
= 42.21 R = 1.59 6.29^−08 5.71^−05
Biological process Complement activation GO:000695 C = 11 O = 5 E =
0.19 R = 26.80 5.34^−07 0.0002
Biological process Protein activation cascade GO:007237 C = 13 O = 5 E
= 0.22 R = 22.68 1.45^−06 0.0004
Biological process Humoral immune response GO:000695 C = 16 O = 5 E =
0.27 R = 18.43 4.72^−06 0.0009
Biological process Complement activation, classical pathway GO:000695 C
= 8 O = 4 E = 0.14 R = 29.48 5.14^−06 0.0009
Biological process B cell-mediated immunity GO:001972 C = 32 O = 6 E =
0.54 R = 11.06 1.28^−05 0.0013
Biological process Regulation of immune system process GO:000268 C =
242 O = 15 E = 4.10 R = 3.65 1.05^−05 0.0013
Biological process Response to stimulus GO:005089 C = 2294 O = 59 E =
38.90 R = 1.52 1.25^−05 0.0013
Biological process Immunoglobulin-mediated immune response GO:001606 C
= 32 O = 6 E = 0.54 R = 11.06 1.28^−05 0.0013
Biological process Humoral immune response mediated by circulating
immunoglobulin GO:000245 C = 10 O = 4 E = 0.17 R = 23.59 1.50^−05
0.0014
Biological process Signaling GO:002305 C = 1510 O = 44 E = 25.61 R =
1.72 2.68^−05 0.002
Biological process Single-organism signaling GO:004470 C = 1510 O = 44
E = 25.61 R = 1.72 2.68^−05 0.002
Biological process Signal transduction GO:000716 C = 1308 O = 39 E =
22.18 R = 1.76 6.75^−05 0.0047
Biological process Immune system process GO:000237 C = 446 O = 19 E =
7.56 R = 2.51 0.0001 0.0057
Biological process Positive regulation of immune response GO:005077 C =
90 O = 8 E = 1.53 R = 5.24 0.0001 0.0057
Biological process Cellular response to stimulus GO:005171 C = 1711 O =
46 E = 29.02 R = 1.59 0.0001 0.0057
Biological process Lymphocyte mediated immunity GO:000244 C = 51 O = 6
E = 0.86 R = 6.94 0.0002 0.0086
Biological process Cell communication GO:000715 C = 1570 O = 43 E =
26.63 R = 1.62 0.0002 0.0086
Biological process Positive regulation of response to stimulus
GO:004858 C = 380 O = 17 E = 6.44 R = 2.64 0.0002 0.0086
Biological process Regulation of immune response GO:005077 C = 125 O =
9 E = 2.12 R = 4.25 0.0002 0.0086
Biological process Immune effector process GO:000225 C = 120 O = 9 E =
2.04 R = 4.42 0.0002 0.0086
Biological process Immune response GO:000695 C = 221 O = 12 E = 3.75 R
= 3.20 0.0003 0.0124
Biological process B cell homeostasis GO:000178 C = 9 O = 3 E = 0.15 R
= 19.66 0.0004 0.0151
Biological process Adaptive immune response based on somatic
recombinationof immune receptors built from immunoglobulin superfamily
domains GO:000246 C = 59 O = 6 E = 1.00 R = 6.00 0.0004 0.0151
Biological process Antigen processing and presentation of exogenous
peptide antigen GO:000247 C = 10 O = 3 E = 0.17 R = 17.69 0.0005 0.0175
Biological process Response to lipid GO:003399 C = 342 O = 15 E = 5.80
R = 2.59 0.0005 0.0175
Biological process Adaptive immune response GO:000225 C = 62 O = 6 E =
1.05 R = 5.71 0.0006 0.0202
Biological process Negative regulation of mature B cell apoptotic
process GO:000290 C = 3 O = 2 E = 0.05 R = 39.31 0.0008 0.0234
Biological process Mature B cell apoptotic process GO:000290 C = 3 O =
2 E = 0.05 R = 39.31 0.0008 0.0234
Biological process Regulation of mature B cell apoptotic process
GO:000290 C = 3 O = 2 E = 0.05 R = 39.31 0.0008 0.0234
Biological process Activation of immune response GO:000225 C = 66 O = 6
E = 1.12 R = 5.36 0.0008 0.0234
Biological process Epidermis development GO:000854 C = 92 O = 7 E =
1.56 R = 4.49 0.0009 0.0255
Biological process Positive regulation of immune system process
GO:000268 C = 153 O = 9 E = 2.59 R = 3.47 0.001 0.0267
Biological process Innate immune response GO:004508 C = 94 O = 7 E =
1.59 R = 4.39 0.001 0.0267
Biological process Antigen processing and presentation of peptide
antigen GO:004800 C = 13 O = 3 E = 0.22 R = 13.61 0.0012 0.0294
Biological process Antigen processing and presentation of exogenous
antigen GO:001988 C = 13 O = 3 E = 0.22 R = 13.61 0.0012 0.0294
Biological process Leukocyte mediated immunity GO:000244 C = 71 O = 6 E
= 1.20 R = 4.98 0.0012 0.0294
Biological process Regulation of fibroblast proliferation GO:004814 C =
29 O = 4 E = 0.49 R = 8.13 0.0013 0.0311
Biological process Multicellular organismal process GO:003250 C = 1895
O = 46 E = 32.14 R = 1.43 0.0017 0.0322
Biological process Negative regulation of B cell apoptotic process
GO:000290 C = 4 O = 2 E = 0.07 R = 29.48 0.0017 0.0322
Molecular function Molecular transducer activity GO:006008 C = 309 O =
14 E = 4.99 R = 2.81 0.0004 0.029
Molecular function Signal transducer activity GO:000487 C = 309 O = 14
E = 4.99 R = 2.81 0.0004 0.029
KEGG pathway Staphylococcus aureus infection C = 15 O = 6 E = 0.21 R =
28.48 2.97^−08 9.80^−07
KEGG pathway Complement and coagulation cascades C = 17 O = 6 E = 0.24
R = 25.13 7.19^−08 1.19^−06
KEGG pathway Systemic lupus erythematosus C = 25 O = 5 E = 0.35 R =
14.24 2.10^−05 0.0002
KEGG pathway p53 signaling pathway C = 25 O = 3 E = 0.35 R = 8.55
0.0049 0.027
KEGG pathway Antigen processing and presentation C = 25 O = 3 E = 0.35
R = 8.55 0.0049 0.027
KEGG pathway Pathways in cancer C = 142 O = 7 E = 1.99 R = 3.51 0.0037
0.027
KEGG pathway MAPK signaling pathway C = 123 O = 6 E = 1.73 R = 3.47
0.0075 0.0354
KEGG pathway Pancreatic cancer C = 34 O = 3 E = 0.48 R = 6.28 0.0117
0.0429
KEGG pathway Colorectal cancer C = 33 O = 3 E = 0.46 R = 6.47 0.0108
0.0429
KEGG pathway Lysosome C = 70 O = 4 E = 0.98 R = 4.07 0.0165 0.0495
KEGG pathway Neuroactive ligand-receptor interaction C = 72 O = 4 E =
1.01 R = 3.96 0.0182 0.0495
KEGG pathway Chronic myeloid leukemia C = 38 O = 3 E = 0.53 R = 5.62
0.0159 0.0495
KEGG pathway Natural killer cell-mediated cytotoxicity C = 41 O = 3 E =
0.58 R = 5.21 0.0195 0.0495
[181]Open in a new tab
Table 9.
GO and KEGG enrichment results for the 29 predicted and anticorrelated
mRNAs targets of five miRNAs differentially expressed in the chronic
period
GO terms Term description GO ID Size of term miRNA target Expected
Enrichment ratio Raw p value FDR
Biological process Negative regulation of DC differentiation GO:2001198
C = 2 O = 2 E = 0 R = 576.79 2.88E-06 0.0005
Biological process Regulation of DC differentiation GO:2001199 C = 2 O
= 2 E = 0 R = 576.79 2.88E-06 0.0005
Biological process DC differentiation GO:0097028 C = 13 O = 2 E = 0.02
R = 88.74 0.0002 0.0178
Biological process Glial cell development GO:0021782 C = 69 O = 3 E =
0.12 R = 25.08 0.0002 0.0178
Biological process Regulation of glial cell proliferation GO:0060251 C
= 16 O = 2 E = 0.03 R = 72.10 0.0003 0.0214
Biological process Glial cell proliferation GO:0014009 C = 23 O = 2 E =
0.04 R = 50.16 0.0007 0.0356
Biological process Regulation of immune system process GO:0002682 C =
653 O = 6 E = 1.13 R = 5.30 0.0007 0.0356
Biological process Response to wounding GO:0009611 C = 692 O = 6 E =
1.20 R = 5 0.0009 0.0401
Biological process Negative regulation of DNA binding GO:0043392 C = 31
O = 2 E = 0.05 R = 37.21 0.0013 0.0498
Biological process Oligodendrocyte development GO:0014003 C = 32 O = 2
E = 0.06 R = 36.05 0.0014 0.0498
KEGG pathway Hematopoietic cell lineage C = 124 O = 3 E = 0.07 R =
40.29 5.70E-05 0.001
KEGG pathway Lysosome C = 79 O = 2 E = 0.05 R = 42.16 0.001 0.0015
[182]Open in a new tab
Target genes that directly correlated with differentially expressed
miRNAs during the epileptogenesis period were enriched for GO terms
related to glia proliferation [“regulation of glial cell proliferation”
(p = 0.0001) and “glial cell proliferation” (p = 0.0001)]. In addition,
and as in the inverse correlation analysis, terms related to complement
activation [like “complement activation, classical pathway” (p =
0.0004)] were significantly enriched. In the chronic period, the
positively correlated mRNA targets revealed significant (p < 0.00001)
enrichment in GO terms related to receptor function like “receptor
activity,” “signaling receptor activity,” “G protein-coupled receptor
activity,” “signal transducer activity,” “molecular transducer
activity,” and “transmembrane signaling receptor activity.”
To infer the functional relationships between miRNAs identified as
differentially expressed in the meta-analysis, we created a network of
miRNAs based on their predicted anticorrelated target pathways
([183]Fig. 5). These results highlight that several distinct miRNAs may
contribute to the regulation of functionally related processes and
pathways, and so prioritizing individual miRNAs as potential
therapeutic targets will require downstream experimental analysis.
Discussion
Main findings
The present meta-analysis provides a miRNA differential expression
signature in the DG of rats during epileptogenesis and in the chronic
phase of epilepsy. We identified 26 miRNAs significantly differentially
expressed during epileptogenesis, and five miRNAs significantly
differentially expressed in the chronic phase of epilepsy. We also
identified 11 miRNAs in epileptogenesis and two in chronic epilepsy
that were identified as significantly differentially expressed by the
meta-analysis but not in any of the individual studies. Further, we
explored the negative correlation between the significantly
differentially expressed miRNAs and their predicted mRNA targets in the
same models of epilepsy. We identified 122 predicted mRNAs targets with
an anticorrelated expression relationship to 22 of the 26 miRNAs
significantly differentially expressed in epileptogenesis. Below, we
discuss these findings and their possible implications in the
development and maintenance of epilepsy. Together, we also discuss the
intrinsic limitations of this study that must be taken into account.
Epileptogenesis
Functional annotations of the target genes of miRNAs significantly
differentially expressed during the latent interval between brain
injury and the development of spontaneous seizures (epilepsy) support a
relationship between dysregulated miRNAs and molecular and cellular
reorganizations that are known to occur during epileptogenesis. First,
the GO enrichment analysis identifies many terms that suggest a role
for modulation of synaptic transmission during epileptogenesis. This is
not surprising, given the critical role of the DG in the temporal lobe
seizure network ([184]Krook-Magnuson et al., 2015) and previous
experimental evidence for changes in synaptic efficacy and connections
during epileptogenesis ([185]Dudek and Sutula, 2007). Another set of
terms broadly refers to immunity and inflammation, events that are
deeply associated with epileptogenesis ([186]Vezzani et al., 2015).
This is also supported by the identification of individual
differentially expressed miRNAs (e.g., miR-146a-5p; [187]Aronica et
al., 2010) and mRNA targets (e.g., CD74 and C1r; [188]Teo and Wong,
2010; [189]Zeis et al., 2016) involved in inflammation.
Worthy of note is the enrichment for genes in the MAPK signaling
pathway. Enrichment within the MAPK cascade has been reported during
latency in the pilocarpine model in a hippocampal RNA expression study
based on high-throughput RNA sequencing ([190]Hansen et al., 2014). In
particular, we found robust upregulation of two MAPKs, Map3k14, also
called NIK, and Map3k4, also called MEKK4. Map3k14, a target of
miR-7a-5p and miR-138-5p, mediates the neuron specific suppression of
the nuclear factor κ-B (NF-kB; [191]Mao et al, 2016) that is
upregulated in epilepsy patients ([192]Teocchi et al., 2013) and in an
experimental model of traumatic brain injury ([193]Lipponen et al.,
2016). NF-kB has been linked to traumatic brain injury relevant
outcomes, including epileptogenesis and tissue repair, the hypothesis
being that it plays an antiepileptogenic role ([194]Lipponen et al.,
2016). Thus, Map3k14 activation may favor epileptogenesis and damage.
The transcript of Map3k4, the other upregulated MAPK, is a target of
four significantly downregulated miRNAs (namely, miR-92b-3p,
miR-153-3p, miR-101a-3p, and miR-3573-3p). This enzyme activates the
p38 and JNK pathways that are known to contribute to the apoptotic and
inflammatory responses after kainate injection in mice ([195]Yang et
al., 1997; [196]Jeon et al., 2000).
Activation of each of these suggested proepileptogenic kinase pathways
might be counterbalanced by upregulation of inhibitors such as
phosphatases. In our analysis, the protein tyrosine phosphatase,
nonreceptor type 5 (Ptpn5), also called STEP, was found upregulated,
and anticorrelated with the downregulated miR-150-5p and miR-383-5p.
Contrary to Map3k4, Ptpn5 has been shown to inhibit p38 by selectively
dephosphorylating its activation loop tyrosines and by sequestering it
in the cytosol ([197]Francis et al., 2014). Ptpn5 can also target the
glutamate receptor subunits GluN2b and GluA2 leading to receptor
internalization and decreased synaptic efficiency ([198]Snyder et al.,
2005; [199]Xu et al., 2009). In addition, Ptpn5 inhibits the ERK2
pathway. Whereas p38 downstream molecules lead to the activation of
neuroinflammation and apoptotic processes, the ERK2 cascade triggers
neuronal differentiation and survival through the activation of the
antiapoptotic gene bcl-2 ([200]Cruz and Cruz, 2007).
In addition to above, miRNAs such as miR-101a-3p, miR-551b-3p, and
miR-139-5p may act together to modulate the expression of the predicted
target syn2, a gene that is mutated in epileptic patients
([201]Cavalleri et al., 2007). Synapsin 2 is a member of the synapsin
family composed by synaptic vesicle phosphoproteins that modulate
synaptic transmission and plasticity. Notably, Syn2-knock-out mice show
a decreased vesicle density at inhibitory synapses of DG GCs and are
prone to epileptic seizures ([202]Medrihan et al., 2013). Here, we
found an inverse correlation of three downregulated miRNAs
(miR-101a-3p, miR-551b-3p, and miR-139-5p) with the Syn2 mRNA, which
levels are slightly increased suggesting that Syn2 phophoproteins, at
this stage (i.e., epileptogenesis), are still able to control neuronal
transmission at the DG synapses. Notably, Syn2 interacts with
presynaptic Ca^2+ channels to promote GABA asynchronous release
([203]Medrihan et al., 2013), maintaining the tonic inhibition of
excitatory neurons and contrasting the aberrant network synchronization
that lead to seizures development in the chronic phase. These findings
suggest an antiepileptogenic role of this inverse-correlation. The
dentate cells, in this case, may slow down the miRNA levels to contrast
the upcoming epileptogenic process.
High levels of miR-212-5p may favor the epileptogenic process through
reduced expression of Gabrd. We found that the expression of the
subunit δ of GABA[A] receptors was decreased in DG and
inverse-correlated with the upregulated miR-212-5p.
δ-Subunit-containing receptors are found in extrasynaptic and
perisynaptic locations in hippocampal DG GCs ([204]Wei et al., 2003).
Because of their high affinity for GABA, they mediate tonic GABA[A]
inhibition ([205]Stell et al., 2003). Therefore, a decrease in GABA[A]
receptor δ-subunits may impair tonic GABA inhibition, contributing to
GCs hyperexcitation and seizures onset.
Chronic epilepsy
Our exploration of the chronic stage of epilepsy was more limited than
that for epileptogenesis as anticorrelations (inferred via
statistically significant gene and miRNA fold changes in response to
the disease) with miRNA targets could be evaluated only for the
amygdala stimulation model. This analysis highlighted the
downregulation of five miRNAs and the upregulation of several mRNAs
targets, and identified genes enriched in GO terms related to glial
cells and dendritic cells (DCs). Whereas the proliferation of glia
cells and their contribution to neuroinflammation and hyperexcitability
in chronic epilepsy are well recognized ([206]Devinsky et al., 2013;
[207]Robel and Sontheimer, 2016), the role of DCs in the context of
epilepsy remains elusive. It can be hypothesized that DCs might be
involved in epilepsy by maintaining a chronic inflammatory response
([208]Ludewig et al., 2016).
Among the list of mRNAs predicted targets anticorrelated with
significantly differentially expressed miRNAs in the chronic stage of
epilepsy, of note is the glutamate ionotropic receptor δ type 2
(Grid2), which is anticorrelated with miR-130a-3p, miR-148b-3p, and
miR-551b-3p. The involvement of ionotropic glutamate receptors in
epileptic hyperexcitability is well established, but not much is known
specifically on glutamate ionotropic type δ receptors or the regulation
of this process. Further studies are needed to establish a role of
these receptors in hippocampus and more specifically in epilepsy.
Limitations
The purpose (and, in our view, the strength) of this work was to
maximize information from underpowered individual studies, increasing
power and allowing the identification of a set of miRNAs that, being
significantly and similarly dys-regulated in multiple experimental
models, may be related to the disease rather than specific to a
particular model. The study, however, also has limitations that should
be taken into account.
A technical limitation is that the comparison of datasets in which
tissue was obtained through different methods and that used different
microarray platforms may have led to some miRNAs being detected in one
experimental model and not in another, due to technical differences
related to the assay system. Therefore, we cannot exclude the
possibility that additional miRNAs were significantly dys-regulated.
Other limitations refer to biological aspects. First, miRNAs are only
one mechanism of regulation of gene expression. Other changes may occur
depending on other epigenetic mechanisms (histone modifications, DNA
methylations) or changes in transcription factors. Second, this
analysis has been conducted on one specific hippocampal subarea, the
DG, enriched in a specific cell population, the GCs. Other brain areas
and cell populations may be equally or even more important in
epileptogenesis. Third, due to limitation in the availability of
datasets, we analyzed data from a single time point in all models, but
epileptogenesis may develop differently in different models. Finally,
given the lack of miRNA and mRNA datasets in epileptic patients matched
with valid controls, we could not verify whether the miRNA-mRNA
interactions identified in rats may be relevant for the human disease.
In addition, the comparison with mRNA datasets should be viewed as a
secondary outcome of the study and considered with caution. In this
study, this comparison is primarily based on the assumption that miRNAs
decrease levels of their mRNA targets ([209]Guo et al., 2010) and,
therefore, that target mRNAs will undergo changes in anticorrelation
with those of miRNAs. Although this is the best characterized mechanism
of miRNA action, it is becoming evident that miRNAs also have specific
nuclear functions, including transcriptional control of gene expression
and regulation of alternative splicing ([210]Catalanotto et al., 2016),
which may not lead to anticorrelation between miRNA and mRNA levels.
Therefore, we also performed a further analysis of direct correlation
between miRNAs and mRNAs. These analyses now require verification and
further studies to establish the exact patterns of interaction between
miRNAs and mRNAs in the epileptic tissue and their functional impact on
epileptogenesis and maintenance of an epileptic condition.
Conclusions
The present meta-analysis identified many significantly differentially
expressed miRNAs in epileptogenesis and chronic epilepsy, several of
which were not uncovered in the individual studies, highlighting the
additional information that can be gained by meta-analysis. Our results
also highlight the added value of meta-analysis of existing data and so
avoid unnecessary animal experimentation to generate new hypotheses on
miRNAs involved in epileptogenesis and chronic epilepsy. Our results
highlight a possible key role for a few miRNAs that are worthy of
further investigation. As it may be expected, however, the number and
heterogeneity of mRNAs identified by this meta-analysis suggest that
therapies focused on a single miRNA target may be not sufficient to
reverse or ameliorate the epileptogenic process.
Acknowledgments