An exploratory approach to identify microRNAs as circulatory biomarker candidates for epilepsy-associated psychiatric comorbidities in an electrical post-status epilepticus model

Abstract

Patients with epilepsy have a high risk of developing psychiatric comorbidities, and there is a particular need for early detection of these comorbidities. Here, in an exploratory, hypothesis-generating approach, we aimed to identify microRNAs as potential circulatory biomarkers for epilepsy-associated psychiatric comorbidities across different rat models of epilepsy. The identification of distress-associated biomarkers can also contribute to animal welfare assessment. MicroRNA expression profiles were analyzed in blood samples from the electrical post-status epilepticus (SE) model. Preselected microRNAs were correlated with behavioral and biochemical parameters in the electrical post-SE model, followed by quantitative real-time PCR validation in three additional well-described rat models of epilepsy. Six microRNAs (miR-376a, miR-429, miR-494, miR-697, miR-763, miR-1903) were identified showing a positive correlation with weight gain in the early post-insult phase as well as a negative correlation with social interaction, saccharin preference, and plasma BDNF. Real-time PCR validation confirmed miR-203, miR-429, and miR-712 as differentially expressed with miR-429 being upregulated across epilepsy models. While readouts from the electrical post-SE model suggest different microRNA candidates for psychiatric comorbidities, cross-model analysis argues against generalizability across models. Thus, further research is necessary to compare the predictive validity of rodent epilepsy models for detection and management of psychiatric comorbidities.

Figure 1

MicroRNA screening of 750 microRNAs. (a) rodent card A and (b) rodent card B. Heat maps representing up- (red) and down-regulation (green) of microRNAs in naïve control (n = 5), sham animals (n = 5) and animals with SE (n = 6). Comparison of animals with SE to sham animals revealed 48 significantly up- or down-regulated microRNAs with a minimum Ct-value of 30 in all samples. The heatmap was generated with Cluster3.0 (https://www.encodeproject.org/software/cluster/). cluster3 original website is http://bonsai.hgc.jp/~mdehoon/software/cluster/, original paper is https://pubmed.ncbi.nlm.nih.gov/14871861/

Figure 2

Principal component analysis (PCA) and correlation analysis of selected microRNAs (miR-148b-5p, miR-203, miR-342-3p, miR376a, miR-429, miR-494, miR-598, miR-697, miR-712, miR-763, miR-1903) in the electrical post-SE model. Illustration of the PCA of behavioral and biochemical parameters (a) and of expression data from selected microRNAs (b). Correlation matrix of the selected microRNAs and behavioral and biochemical parameters (c): the heat map illustrates the correlation (Spearman correlation coefficient) between the different parameters. The analysis identified a correlation between six microRNAs and weight gain in the early phase of epileptogenesis (positive correlation, blue) and with social interaction, saccharin preference and BDNF in the phase of epilepsy manifestation (negative correlation, red). All abbreviations are described in the Supplementary file. naïve naïve controls (n = 5), sham sham controls (n = 5), SE experimental animals with seizure history (n = 6), NB nestbuilding activity, BUR burrowing behavior, SI time animals spent in active social interaction, SP_percentage saccharin preference, OF open field, OF_distance open field distance moved in total, OF_rearing number of rearing postures in the open field, OF_immobile time the animal was immobile in the open field, OF_center time the animals spent in the center region of the open field, BWB black and white box, BWB_WB time the animals spent in the white box, BWB_entries number of transitions from black to white compartment, BWB_stretching number of stretching postures of the animal, BWB_LT latency to the first entry of the black box from the white box, EPM elevated plus maze, EPM_stretching number of stretching postures, EPM_head_dip number of the times the animal looked down from one of the open arms, EPM_closedarms time the animal spent in closed arms, EPM_openarms time the animal spent in open arms, EPM_open1_3 time the animal spent in the outer 1/3 of the open arms, Adrenal_glands weight of adrenal glands (g), BDNF brain-derived neurotrophic factor in pg/ml, Seizures_n number of seizures, Seizures_duration duration of seizures.

Figure 3

Correlation analysis between three selected microRNA candidates and selected behavioral and biochemical parameters from the electrical post-SE model. Analysis of correlation (Spearman) between the three microRNA candidates (out of eleven) with the strongest positive/negative Spearman correlation coefficient and the following behavioral and biochemical parameters: weight gain (a), social interaction (b), saccharin preference (c), and BDNF expression levels (d). dCT delta Ct (cycle threshold) value. For each analysis n = 12–16.

Figure 4

Validation of miR-429 expression in other epilepsy models. (a) MicroRNA miR-429 is upregulated in experimental animals (n = 40) when compared to sham animals (n = 42) across models (p = 0.0383). (b) Cross-model-correlation analysis of miR-429 expression levels and BDNF concentrations revealed a significantly positive correlation (Spearman correlation). (c) Correlation of miR-429 with several behavioral and biochemical parameters across models (positive correlation, blue; negative correlation, red). (d) Kindling model with focal seizures (sham/exp n = 12), (e) Kindling model with generalized seizures (sham n = 12, exp n = 11). (f) Chemical post-SE model (sham n = 12, exp n = 13) and (g) electrical post-SE model (sham n = 6, exp n = 4). In the kindling model with focal seizures (d) and in the chemical post-SE model (f), miR-429 expression levels were not increased in animals with seizure activity (p = 0.0628 and p = 0.0619, respectively; t test). sham sham controls, exp experimental animals with seizure history, dCT delta Ct (cycle threshold) value.