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. Author manuscript; available in PMC: 2026 Apr 21.
Published in final edited form as: Mol Cell Neurosci. 2026 Apr 11;137:104092. doi: 10.1016/j.mcn.2026.104092

Severity-dependent proteomic alterations in the rat hippocampus following pilocarpine-induced status epilepticus

Surabhi Soni 1,#, Yibo Li 1,#, Season K Wyatt-Johnson 2, Uma K Aryal 3,4, Amy L Brewster 1,2,*
PMCID: PMC13094721  NIHMSID: NIHMS2165429  PMID: 41974393

Abstract

Status epilepticus (SE) is a prolonged seizure state that can induce lasting hippocampal damage and promote the development of spontaneous seizures and cognitive deficits. The severity and duration of SE affect long-term outcomes; however, many studies rely on behavioral assessments such as the Racine scale, which may miss subclinical or non-convulsive seizures and leave the molecular effects of mild SE unclear. Here, we examined whether behavioral seizure severity in the pilocarpine rat model of acquired epilepsy correlates with distinct hippocampal proteomic changes. Adult male rats were classified as control, mild SE, or severe SE following pilocarpine-induced seizures, and hippocampal tissue was analyzed using mass spectrometry-based proteomics. Partial least squares discriminant analysis revealed distinct proteomic signatures across groups. Severe SE was associated with widespread changes (129 differentially expressed proteins [DEPs]) involving synaptic structure, RNA regulation, and metabolism, whereas mild SE showed fewer alterations (81 DEPs) related primarily to synaptic organization and endocytosis. Comparison of severe and mild SE identified 76 DEPs enriched in pathways linked to synaptic plasticity and neurodegeneration, with 23 proteins exhibiting a stepwise expression pattern across seizure severities. Glial and inflammatory proteins emerged as candidate markers of seizure burden. These findings demonstrate that behavioral seizure severity aligns with specific hippocampal proteomic profiles and that even lower-severity SE induces molecular changes relevant to epileptogenic progression.

Introduction

A prolonged and ongoing seizure lasting more than five minutes can be life-threatening and, if not promptly controlled, may result in long-term neurological consequences. This condition, known as status epilepticus (SE), is defined by the International League Against Epilepsy (ILAE) as “a condition resulting either from the failure of the mechanisms responsible for seizure termination or from the initiation of mechanisms, which lead to abnormally prolonged seizures [after time point t1 (i.e., 5 mins)]. It is a condition which can have long-term consequences [after time point t2 (i.e., 30 mins)], including neuronal death, neuronal injury, and alteration of neuronal networks, depending on the type and duration (1).” In clinical settings, SE episodes that are successfully terminated with anti-seizure medications (ASMs) within 30 minutes are associated with lower morbidity and mortality (2). In contrast, SE that persists beyond 30 minutes increases the risk of developing epilepsy along with additional long-term neurological problems, including cognitive impairment (2).

Evidence from pre-clinical models further supports that, beyond its immediate clinical risks, SE induces widespread molecular, cellular, structural, and physiological changes in susceptible brain areas such as the hippocampus (3–5), which can lead to the generation of hyperexcitable neural circuits that promote spontaneous recurrent seizures (SRS) and impair hippocampal-dependent cognitive functions (6, 7). In rodent models, SE can be induced using chemoconvulsants such as the muscarinic receptor agonist pilocarpine or the kainate receptor agonist kainic acid, and later terminated pharmacologically with ASMs such as diazepam (6). Prolonged SE episodes lasting more than 60 minutes are associated with greater neuronal injury and an increased risk for subsequent development of SRS and cognitive decline (7–10). Because these models recapitulate key aspects of SE pathophysiology and can lead to acquired temporal lobe epilepsy (TLE) similar to that observed in humans (6, 7), chemoconvulsant-induced SE paradigms are widely used in mechanistic studies aimed at identifying causal pathways and therapeutic targets.

Although EEG is the most objective method for determining seizure severity and confirming SE onset in experimental models, many studies rely on behavioral scoring systems such as the Racine scale (6, 7, 11). This widely used and accepted scale categorizes seizure severity from mild facial automatisms (Stage 1) to generalized tonic-clonic seizures with loss of posture (Stage 6), which is the most severe stage (11). While the pathological consequences of prolonged, high-severity SE are well documented in our work and others (3–5, 12–16), considerably less is known about the molecular impact of prolonged lower-severity seizures (i.e., continuous level 3) that do not meet the behavioral thresholds typically used to define severe SE (e.g., ≥ Stage 4 for ≥30 minutes) in rodents. This knowledge gap limits our ability to determine whether mild but sustained seizure activity engages pathological mechanisms distinct from, or overlapping with, those triggered by severe SE. To address this, we investigated how behavioral seizure severity, operationally defined as sustained Racine Stage 3 (“mild SE”) versus ≥ Stage 4 (“severe SE”) for one hour, shapes the hippocampal proteome, a brain region highly vulnerable to SE-induced cellular, molecular, and physiological alterations.

Materials and Methods

Animals:

Sprague Dawley rats (males, 8–9 weeks old, 175–200g; Envigo) were kept at room temperature with unrestricted access to food and water (24 hours a day, 7 days a week). The rooms followed a 12-hour light-dark cycle (6:00–18:00). Rats were housed in pairs at the Psychological Sciences animal facility at Purdue University. All procedures adhered to Protocol #1309000927, approved by the Purdue Animal Care and Use Committee, and complied with NIH and institutional guidelines (17).

Pilocarpine-induced SE:

Rats (8–9 weeks old) were pretreated with scopolamine methyl bromide (1 mg/kg; Cat# S8502–1G, Sigma-Aldrich) 30 minutes before receiving pilocarpine (280 mg/kg; i.p.; Cat# P6503–10G, Sigma-Aldrich) following previously described protocols (3, 12, 14). Control rats received 0.9% saline (i.p.). Seizures were scored according to a modified Racine scale as follows: 1 = rigid posture, mouth movements; 2 = tail clonus; 3 = partial body clonus with head bobbing; 4 = rearing with severe whole-body clonic seizures while retaining posture; 5 = rearing and falling; and 6 = tonic-clonic seizures with jumping or loss of posture. Rats that progressed to stage 3 and maintained these seizures for up to 1 hour were classified as the mild SE group. Rats that reached stage 4 or higher and sustained those behavioral seizure manifestations for 1 hour were classified as the severe SE group. At 1 hour from seizure onset, SE was terminated with diazepam (10 mg/kg, i.p.; Hospira, Inc.). All animals showed a similar reduction in behavioral seizures following Diazepam administration. Following SE induction, all rats received 0.9% saline (i.p.) for hydration and were given supplemental nutrition (chocolate Ensure and Kellogg’s Fruit Loops) for up to three days post-SE. Body weight was measured daily over 10 days. Final rat counts: control, n = 3; mild SE, n = 5; severe SE, n = 6.

Tissue preparation:

Two weeks after pilocarpine-induced seizures, rats were euthanized with a lethal dose of Beuthanasia (200 mg/kg, i.p.) and transcardially perfused with ice-cold 1X phosphate-buffered saline (PBS; 137 mM NaCl, 2.7 mM KCl, 4.3 mM Na2HPO4, 1.47 mM KH2PO4, pH 7.4). Hippocampi were immediately dissected, frozen, and stored at −80 °C for proteomic analysis. The two-week time point after pilocarpine was selected based on the robust hippocampal neuronal, synaptodendritic, inflammatory, and glial changes previously observed following SE in our previous studies (3–5, 16).

Proteomics sample preparation and data analysis:

Hippocampal tissues were processed at the Purdue Proteomics Core Facility at the Bindley Bioscience Center, as previously described (18). Briefly, tissues were suspended in 100 mM ABC and homogenized in Precellys homogenization vials (Bertin Technologies SAS, France) for 90 seconds at 6500 rpm. Protein concentration was measured with the bicinchoninic acid assay (Pierce Chemical Co., USA). Fifty μg of protein were precipitated at −20 °C with acetone overnight. Then, samples were centrifuged at 13,500 × g for 15 minutes at 4 °C. The pellets were briefly dried in a vacuum centrifuge without heat on, resuspended in 10 μL of 10 mM DTT in 8 M urea, and incubated at 37 °C for 1 hour. An equal volume of alkylating mixture (195 mL acetonitrile, 1 mL triethyl phosphate (TEP), 4 mL Iodoethanol) was added. Samples were incubated at 37 °C for an additional hour, dried in a vacuum centrifuge, and digested with Lys-C/Trypsin in 50 mM ammonium bicarbonate (ABC) at a 1:25 enzyme-to-protein ratio using a barocycler (60 cycles: 20 kPSI for 50 seconds and 1 ATM for 10 seconds, at 50 °C). Digested peptides were desalted using C18 Silica MicroSpin Columns (The Nest Group, Inc., USA). Eluted and cleaned peptides were dried in SpeedVac and resuspended in 3% acetonitrile/96.9% MilliQ, and 0.1% formic acid (FA) at a final concentration of 1mg/ml.

Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) analysis:

Peptides were analyzed in a Dionex UltiMate 3000 RSLC nano System (Thermo Fisher Scientific, Odense, Denmark) coupled with an Orbitrap Fusion Lumos Tribrid Mass Spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) (19). Reverse phase peptide separation was accomplished using a trap column (300 mm ID ´ 5 mm) packed with 5 mm 100 Å PepMap C18 medium coupled to a 50-cm long × 75 μm inner diameter analytical column packed with 2 μm 100 Å PepMap C18 silica (Thermo Fisher Scientific) at 50°C. Mobile phase solvent A: 2% acetonitrile (ACN), 98% water, and 0.1% Formic Acid (FA). Mobile phase solvent B: 80% ACN, 20% water, and 0.1% FA. One mg of peptide sample was loaded to the trap column in a loading buffer (3% acetonitrile, 0.1% FA) at a flow rate of 5 mL/min for 5 min and eluted from the analytical column at a flow rate of 200 nL/min using a 160-min LC gradient as: 6.5 to 27% of solvent B in 82 min, 27–40% of B in next 8 min, 40–100% of B in 7 min at which point the gradient was held at 100% of B for 7 min before reverting to 2% of B, and hold at 2% of B for next 15 min for column equilibration. The column was washed and equilibrated by using three 30-minute LC gradients before injecting the following samples. All data were acquired on an Orbitrap mass analyzer and collected using an HCD fragmentation scheme. For MS scans, the scan range was 350–1600 m/z at a resolution of 120,000. The automatic gain control (AGC) target was set to 4 × 105, the maximum injection time was 50 ms, dynamic exclusion lasted 30 seconds, and the intensity threshold was 5.0 × 104. MS data were acquired in Data Dependent mode with a cycle time of 5s/scan. MS/MS data were collected at a resolution of 15,000.

Data Analysis:

LC-MS/MS data were analyzed using MaxQuant software (version 1.6.3.3) against the combined non-redundant protein sequence database from Uniprot (www.uniprot.org) for rat, for protein identification and label-free relative quantitation. The following parameters were used for database searches: precursor mass tolerance of 10 ppm; enzyme specificity of trypsin/Lys-C, allowing up to 2 missed cleavages; oxidation of methionine (M) as a variable modification, and iodoethanol (C) as a fixed modification. False discovery rate (FDR) of peptide spectral match (PSM) and protein identification was set to 0.01. Proteins with LFQ # 0 and MS/MS (spectral counts) ≥ 2 were considered true identifications and used for downstream statistical analysis and data visualization.

Bioinformatics analysis:

For statistical analyses, Label-Free Quantification (LFQ) intensities were employed, and the dataset was limited to proteins quantified in at least half of the samples in each condition. The remaining missing values were imputed by using K-nearest neighbors (KNN) with k = 10 (impute R package), which may slightly reduce the dynamic range of low-abundance proteins but is expected to have minimal impact given the low missingness. Data were then normalized for downstream analysis. Volcano plot, Partial Least Squares Discriminant Analysis (PLS-DA), and hierarchical cluster analysis were performed using R software (version 4.4.0). For hierarchical clustering, Euclidean distance was used as the distance measure, and complete linkage was used to combine clusters. The ShinyGO 0.76, a web-based tool (http://bioinformatics.sdstate.edu/go/), was used to perform enrichment analysis of differentially expressed proteins (DEPs), enabling the study of gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The GO classification consisted of biological processes (BP), molecular functions (MF), and cellular components (CC). P-value < 0.05 was set as the threshold of significance. For KEGG pathway enrichment analysis, DEP information was mapped to the KEGG database to identify enriched pathways. Spearman correlation coefficients (ρ) and corresponding p-values were calculated in R (version 4.4.0) using the cor.test() function, with a two-tailed significance threshold. Protein abundance values were log2-transformed and median-normalized across samples before correlation analysis to minimize technical variation and improve comparability across animals.

Statistical analysis:

The differences between the two groups were evaluated using the Student’s t-test, uncorrected. FDR correction (Benjamini-Hochberg) was performed and was included in the supplementary files. The plots were generated in R (version 4.4.0) and GraphPad Prism software (version 10.2.0). Differences were considered statistically significant at p < 0.05. Significantly altered proteins are included in Supplementary Table 1.

Results

The Racine scale is a standardized scoring system used to classify the severity of behavioral seizures in rodent models of epilepsy, especially during and after chemically induced SE. It was first developed in the 1970s by Ronald Racine and remains widely used in epilepsy research to evaluate seizure progression in rodent models of SE (6, 7, 11). The Racine scale includes the following stages: Stage 1 involves rigid posture and mouth movements; Stage 2 includes tail clonus; Stage 3 features partial body clonus and head nodding; Stage 4 is characterized by rearing (standing on hind limbs) with forelimb clonus; Stage 5 involves rearing and falling; and Stage 6 refers to tonic-clonic seizures with jumping or loss of posture (Fig. 1B). This scale helps researchers estimate seizure severity and progression after the chemoconvulsant administration. Although various modifications and expanded versions have been developed to incorporate additional behaviors or seizure types, the original Racine scale remains a key tool for assessing seizure severity in studies using rats and mice. Across our studies, about 90% of pilocarpine-treated rats develop and maintain seizures above Racine scale 4, which we classify as severe SE (Fig. 1C). These animals are included in subsequent analyses (3–5, 12, 14, 15). However, during our studies, we also noticed that a small group of animals (~5%) that received the same pilocarpine dose initially reached Racine scale 3–4 but did not progress to higher levels. Instead, their behavioral seizures remained at Racine scale 3 consistently for at least one hour (Fig. 1C), which we classified as mild SE. When we compared daily body weight among the mild SE, severe SE, and control groups, we found that rats in the severe SE group showed significantly reduced body weight between days 2 and 6 after pilocarpine treatment (p < 0.05). In contrast, rats in the mild SE and control groups did not lose weight and exhibited comparable weight gain throughout the monitoring period (Fig. 1F).

Figure 1.

Figure 1.

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Characterization of seizure induction. (A) Experimental timeline. Pilocarpine was administered on Day 0 to induce seizures. Body weight was monitored daily for 10 days, and tissues were collected on Day 14. (B) Schematic representation of Racine seizure scale stages. Stage 1: Rigid posture, mouth moving; Stage 2: Tail clonus; Stage 3: Partial body clonus, head nodding; Stage 4: Rearing (standing on hind limbs) with forelimb clonus; Stage 5: Rearing and falling; Stage 6: Tonic-clonic seizure with jumping or loss of posture. (C) Seizure scores over time following pilocarpine injection in the mild SE and severe SE groups are shown. (D-E) Quantification of latency to reach Racine Stage 3 (D) and Stage 4 (E) in mild SE and severe SE groups. (F) Body weight trajectories during the 10 days following pilocarpine injections. Abbreviations: mild SE, mSE; severe SE, sSE.

To determine whether the observed differences in behavioral seizure severity are reflected in hippocampal alterations, we analyzed the hippocampal proteome at 2 weeks after SE (Fig. 2A), when we had previously found robust and significant changes in dendritic and glial markers (3–5, 12, 13, 16). Partial least squares discriminant analysis (PLS-DA) of the proteomic data revealed clear separation among the three groups (Fig. 2B), and there were significant differences in both component 1 (Fig. 2C) and component 2 (Fig. 2D) across all groups, suggesting distinct proteomic profiles associated with SE severity as measured by the Racine scale.

Figure 2. Diagram of the experimental workflow.

Figure 2.

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(A) Rat hippocampal tissues were collected from each group (control, n = 3; mild SE, n = 5; severe SE, n = 6). Proteins were quantified using liquid chromatography-tandem mass spectrometry (LC-MS), followed by statistical analysis, functional enrichment, and pathway analysis to identify differentially expressed proteins and enriched pathways. (B) PLS-DA plot showing separation of Control, mild SE, and severe SE groups based on proteomic expression data. Each point represents an individual sample, and group clustering indicates distinct molecular signatures associated with seizure severity. Ellipses represent 95% confidence intervals for each group. (C-D) Component 1 and Component 2 scores from the PLS-DA showing significant differences among control, mild SE, and severe SE groups. Abbreviations: Control, C; mild SE, mSE; severe SE, sSE.

A direct comparison between the control and severe SE groups revealed distinct global proteomic profiles (Fig. 3A). A total of 129 DEPs were identified, with 72 up-regulated and 57 down-regulated in the severe SE group relative to the control group (Fig. 3B). The heatmap of DEPs comparing the severe SE and control groups, showed a hierarchical group-specific clustering (Fig. 3C). GO enrichment analysis of the DEPs revealed significant changes in the abundance of proteins associated with cytoskeletal protein binding, cortical cytoskeleton, synapses, and regulation of mRNA metabolic processes (Fig. 3D). KEGG pathway enrichment analysis indicated the involvement of pathways such as PI3K-Akt signaling, phagosome formation, and metabolic processes including cysteine and methionine metabolism (Fig. 3E). These results suggest that severe SE induces widespread long-lasting alterations in the hippocampal proteome with notable changes involving the regulation of RNA and synaptic structures as well as in metabolic and intracellular signaling pathways.

Figure 3. Proteomic differences between the Control and severe SE groups.

Figure 3.

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(A) PLS-DA plot shows clear separation between Control and severe SE groups based on global protein expression profiles. Each point represents an individual sample. Ellipses denote 95% confidence intervals. (B) Volcano plot displaying differentially expressed proteins between the Control and severe SE groups. Red and blue dots indicate significantly up- and downregulated proteins, respectively (p < 0.05). (C) Heatmap of differentially expressed proteins clustered by expression pattern across samples. Rows represent proteins; columns represent individual samples. (D) Gene Ontology (GO) enrichment analysis of DEPs, categorized by Biological Process (BP), Cellular Component (CC), and Molecular Function (MF), the top 10 are shown. (E) KEGG pathway enrichment analysis of DEPs showing the top 10 significantly enriched pathways. Abbreviations: Control, C; severe SE, sSE.

Next, we compared the control and mild SE groups. PLS-DA analysis revealed a clear separation between the two groups (Fig. 4A), suggesting distinct hippocampal proteomic profiles. A total of 81 DEPs were identified, with 63 up-regulated and 18 down-regulated in the mild SE group (Fig. 4B). The heatmap of these DEPs also showed distinct expression patterns between these groups (Fig. 4C). GO enrichment analysis showed that most of the altered proteins were associated with biological processes related to synaptic organization, protein localization, and cytoskeleton regulation, and molecular functions involved in cytoskeletal, receptor, and nucleotide binding functions (Fig. 4D). KEGG pathway analysis identified endocytosis as the only significantly enriched pathway (Fig. 4E).

Figure 4. Proteomic differences between the Control and mild SE groups.

Figure 4.

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(A) PLS-DA plot showing clear separation between Control and mild SE groups based on global protein expression profiles. Each point represents an individual sample. Ellipses denote 95% confidence intervals. (B) Volcano plot displaying differentially expressed proteins between the Control and mild SE groups. Red and blue dots indicate significantly up- and downregulated proteins, respectively (p < 0.05). (C) Heatmap of differentially expressed proteins clustered by expression pattern across samples. Rows represent proteins; columns represent individual samples. (D) Gene Ontology (GO) enrichment analysis of DEPs, categorized by Biological Process (BP), Cellular Component (CC), and Molecular Function (MF), the top 10 are shown. (E) KEGG pathway enrichment analysis of DEPs. Abbreviations: Control, C; mild SE, mSE.

To study proteomic changes related to SE severity, we compared the mild SE group with the severe SE group. (Fig. 5). PLS-DA analysis showed a clear and significant separation between the groups (Fig. 5A). Statistical analysis (p < 0.05) identified 76 DEPs, with 37 up-regulated and 39 down-regulated in the severe SE group (Fig. 5B), as evidenced by distinct clustering and expression patterns in the heatmaps (Fig. 5C). These differences may reflect a molecular transition that may contribute to epileptogenesis in this model. GO enrichment analysis revealed that DEPs are associated with synaptic function and plasticity (BP), enriched in synaptic structures (CC), as well as with actin, receptor, calcium, and adhesion-related binding activities (MF) (Fig. 5D). KEGG pathway analysis revealed enrichment in synaptic and neurodegenerative pathways, including mitophagy, ubiquitin-mediated proteolysis, cell adhesion, and diseases such as Alzheimer’s and Parkinson’s diseases, and amyotrophic lateral sclerosis (Fig. 5E). These findings suggest that progressing to, and sustaining, a severe SE episode at Racine scale level 4 or above leads to widespread synaptic remodeling and activation of neurodegenerative pathways, compared with sustained Racine scale level 3 seizures.

Figure 5. Proteomic differences between the mild SE and severe SE groups.

Figure 5.

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(A) PLS-DA plot showing clear separation between mild SE and severe SE groups based on global protein expression profiles. Each point represents an individual sample. Ellipses denote 95% confidence intervals. (B) Volcano plot displaying differentially expressed proteins between severe SE and mild SE groups. Red and blue dots indicate significantly up- and downregulated proteins, respectively (p < 0.05). (C) Heatmap of differentially expressed proteins clustered by expression pattern across samples. Rows represent proteins; columns represent individual samples. (D) Gene Ontology (GO) enrichment analysis of DEPs, categorized by Biological Process (BP), Cellular Component (CC), and Molecular Function (MF), the top 10 are shown. (E) KEGG pathway enrichment analysis of DEPs showing the top 10 significantly enriched pathways. (F) Venn diagrams showing 14 up-regulated proteins were commonly altered in both the Control vs severe SE and mild SE vs severe SE comparisons. (G) Venn diagrams showing 9 down-regulated proteins commonly altered in both Control vs severe SE and mild SE vs severe SE comparisons. (H) Heatmap of the 23 overlapped proteins (14 up-regulated and 9 down-regulated) identified in both comparison analyses. Rows represent proteins and columns represent individual samples from the Control, mild SE, and severe SE groups. Protein expression values are Z-score normalized. Abbreviations: mild SE, mSE; severe SE, sSE.

To identify proteins consistently associated with SE severity and potential epileptogenesis, we compared DEPs between control vs. severe SE and mild SE vs. severe SE groups. We identified 14 proteins that were significantly upregulated (Fig. 5F) and 9 that were significantly downregulated (Fig. 5G) exclusively in the severe SE group. Proteins with increased abundance in severe SE included GFAP (Glial Fibrillary Acidic Protein), LGALS1 (Galectin-1), NCAN (Neurocan), and ANXA5 (Annexin A5), which provide structural support in glial cells and can promote astrocyte activation and immune-matrix signaling (LGALS1), remodel the extracellular matrix and regulate synaptic stability and plasticity (NCAN), and reflect membrane stress, calcium dysregulation, and apoptosis-associated signaling (ANXA5). Collectively, increased levels of these proteins suggest ongoing SE-induced reactive astrogliosis, extracellular matrix reorganization, and neuroinflammation. These processes are known to enhance neuronal hyperexcitability and contribute to seizure propagation and persistence in epilepsy (20–22). In addition, increases in MSN (Moesin), CNN3 (Calponin 3), TLN1 (Talin 1), and MYL6 (Myosin Light Chain 6), which support actin-based adhesion, contraction, and motility, indicate cytoskeletal reorganization and mechanotransductive signaling (23). Together, changes in these proteins in the hippocampus suggest that reactive astrocytes or glial cells undergo pronounced structural remodeling in response to severe SE. Conversely, downregulated proteins specific to severe SE included DNM1 (Dynamin 1), SV2A (Synaptic Vesicle Glycoprotein 2A), PHACTR1 (Phosphatase and Actin Regulator 1), ACTN2 (Alpha-Actinin 2), and CLVS2 (Clavesin 2), all of which are linked to synaptic structure and function, may contribute to the extensive synaptodendritic loss observed in the hippocampus two weeks post-SE in this model (4, 5, 12, 15).

To further explore the relationship between protein abundance and SE severity, we performed Spearman correlations between 33 top proteins (C vs. severe SE, p < 0.05, |log2FC| > 0.5) and four seizure metrics: maximum Racine score, cumulative seizure duration, and times to stages 3 and 4. The heatmap (Fig. 6A) highlights these relationships. GFAP, VIM (Vimentin), and NCAN showed strong positive correlations with seizure severity, linking higher levels to more severe SE. GFAP, VIM, and NCAN correlated strongly with both seizure duration (Fig. 6B–D) and Racine score (Fig. 6E–G). In contrast, SRR (Serine Racemase), ACTN2, and CALB2 (calretinin), negatively correlated with seizure parameters (Fig. 6H–J), suggesting that reduced hippocampal levels of proteins involved in synaptic organization, cytoskeletal stability, and calcium buffering may be associated with altered neuronal homeostasis and increased network excitability that may be pro-epileptogenic. Taken together, these proteomic findings suggest that proteins linked to gliosis, neuroinflammation, structural remodeling, and neuroprotection are associated with seizure severity in this SE rat model.

Figure 6. Correlations between the top 33 DEPs and seizure parameters.

Figure 6.

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(A) Heatmap showing Spearman correlation coefficients (ρ values) between the normalized abundance of the top 33 proteins (C vs severe SE, p < 0.05, |log2FC| > 0.5) and four seizure metrics: maximum Racine score, cumulative duration, and times to stages 3 and 4. Red indicates positive correlations; blue indicates negative correlations. (B-J) Scatter plots show the relationship between seizure severity parameters and the abundance of the proteins GFAP, VIM, NCAN, ACTN2, SRR, and CALB2. Each panel represents one protein, and each triangle represents one animal (Green dots represent mSE, while red dots represent sSE).

Discussion

This study employed a well-characterized pilocarpine-induced SE rat model to define hippocampal proteomic signatures across three behavioral outcomes: no seizures (Control), mild SE, and severe SE. Prior work has largely compared severe, sustained SE to controls, leaving the molecular impact of shorter, non-progressing seizures (Racine ≤3) poorly defined. Inclusion of the mild SE group enabled detection of intermediate proteomic changes and revealed a graded molecular response aligned with seizure severity. PLS-DA demonstrated clear biological separation among all three groups, indicating that behavioral classification reflects distinct hippocampal proteomic states. Severe SE produced the most extensive alterations, with 129 DEPs enriched in pathways related to synaptic architecture, RNA regulation, and metabolism. Mild SE was associated with 81 DEPs involving synaptic organization and endocytosis, representing a distinct molecular profile rather than a reduced version of the severe SE response. Direct comparison of severe versus mild SE identified 76 DEPs enriched in synaptic plasticity and neurodegeneration pathways, suggesting these proteins may mark early mechanistic transitions in epileptogenesis.

Our comparison of SE and control groups aligns with previous proteomic reports in SE models, which show distinct alterations between the groups (24–26). A study by Bitsika et al. conducted a hippocampal proteomic analysis 30 days after intrahippocampal kainate-induced SE in male mice and identified 175 differentially expressed proteins (97 upregulated and 78 downregulated) (24), which match the expression patterns observed in the pilocarpine model. Following GO analysis, this study reported that proteins related to neuronal projection (e.g., Purkinje cell protein 4, Tenascin), synaptic plasticity, and synaptic organization (e.g., Map1b/a, Paralemmin-1, MAPK) were consistently downregulated, consistent with our observations following pilocarpine-induced SE. The study also highlighted pronounced alterations in astrocyte- and glia-related proteins accompanying the development of chronic epilepsy (24). Further supporting the involvement of glial and neuronal remodeling, Li et al. performed a proteomic analysis of the hippocampal dentate gyrus in male mice four weeks after pilocarpine-induced SE (25). They identified SE-driven changes in proteins associated with metabolic, synaptic, structural, and transcriptional regulatory pathways. Specifically, within the dentate gyrus, metabolism-related (e.g., L-lactate dehydrogenase B, pyridoxal phosphatase) and transcription-related proteins (e.g., nascent polypeptide-associated complex alpha subunit isoform b, heterogeneous nuclear ribonucleoprotein D-like) were downregulated, whereas structural (e.g., profilin-1, vimentin) and synaptic proteins (e.g., synuclein) were upregulated. Together, these results further emphasize the coordinated metabolic, structural, and glial changes accompanying the progression to chronic epilepsy.

Interesting differences were noted in synaptic and metabolic pathway changes between the above-mentioned proteomic studies using whole hippocampal homogenates (24) and those using dissected dentate gyrus (DG)(25). The loss of synaptic elements in whole hippocampal homogenates may reflect the pronounced loss of synaptodendritic structures within the CA1-CA3 regions, which has been widely reported after SE across models of acquired epilepsies (27). In contrast, analysis of the DG alone showed increases in synaptic-related pathways (25), which may be due to enhanced connectivity among granule cells and mossy cells, and to mossy fiber sprouting associated with epilepsy progression in rodent models (28–32). These findings support the idea that using the entire hippocampus in omics studies can dilute region-specific effects. Thus, while the proteomic findings support broad changes across multiple pathways, identifying which changes may cause post-SE epilepsy and which are secondary to the SE-induced injury requires further region-specific cell-level analysis.

By incorporating a mild SE cohort, this study extends prior work by capturing a frequently overlooked intermediate seizure phenotype. Proteomic analysis of this group showed enrichment in synaptic organization and endocytosis pathways. These shifts suggest that even mild seizures can disturb synaptic trafficking and cytoskeletal regulation, though such changes alone may not be sufficient to drive epileptogenesis. We also identified 23 proteins uniquely associated with the severe SE phenotype. In particular, GFAP, VIM, and NCAN demonstrated strong positive correlations with pilocarpine-induced seizure duration and higher Racine scale scores. This aligns with previous studies that have established GFAP and VIM as markers of astrocyte activation and reactive gliosis, hallmarks of seizure-induced neuroinflammation and damage in both SE and chronic epilepsy models (20, 33). NCAN, an extracellular matrix (ECM) protein, has also been implicated in seizure-related ECM remodeling, a process increasingly recognized as contributing to epileptogenic mechanisms (34–36). While we did not directly quantify neuronal loss or gliosis histologically in the present study, the proteomic signatures observed are consistent with well-characterized structural, glial, and inflammatory responses to severe SE in this same model (3–5, 12, 15). Importantly, using the remaining homogenates from the proteomic analysis, we confirmed by immunoblotting that SE-induced increases in GFAP protein levels occurred in the sSE group but not in the mSE group, compared with the control group (Supplementary Figure 1), thus supporting the proteomic results.

The finding of significant negative correlations between seizure severity and the expression of ACTN2, SRR, and CALB2 suggests that these proteins may serve as molecular indicators of SE-induced neuronal alterations that could contribute to epileptogenesis. ACTN2, a cytoskeletal protein involved in synaptic structure, was significantly downregulated as seizure severity increased. This aligns with previous findings of reduced α-actinin-2 levels during dendritic remodeling in the DG gyrus after SE (37), consistent with a role in synaptic destabilization associated with epileptogenic processes. SRR, which synthesizes D-serine, also showed a strong negative correlation with seizure severity. This is consistent with reports that exogenous D-serine mitigates neuronal loss and inflammation in epilepsy models (38), suggesting that reduced SRR may impair endogenous neuroprotection in a pro-epileptogenic manner. CALB2, a calcium-binding protein, was similarly decreased with increasing seizure severity, consistent with evidence of selective calretinin-positive neuron loss following kainic acid-induced SE (39). Collectively, these patterns indicate that ACTN2, SRR, and CALB2 alterations may reflect seizure burden and associated synaptic vulnerability, potentially contributing to mechanisms underlying epileptogenesis.

Some limitations of this study include the absence of continuous EEG monitoring, the exclusive use of male subjects, and the restriction of analyses to the hippocampus. Without EEG, spontaneous seizures and ongoing electrographic ictal or interictal activity could not be assessed; thus, rats in the severe SE group may have experienced additional epileptiform activity that contributed to the pronounced proteomic changes observed at 2 weeks post-SE, whereas the epileptic status of the mild SE group remains uncertain. Continuous EEG would be necessary to determine the relative epileptogenic potential of mild versus severe SE and to better link molecular changes to seizure activity. The use of only male animals reflects reliance on existing samples generated in prior studies, consistent with NIH 3Rs principles (reduction, refinement, and replacement), allowing us to maximize data yield without additional animal use. This limits the generalizability of our current findings to females, as growing evidence indicates sex-specific transcriptomic and proteomic responses in epilepsy (18, 40–42), suggesting that SE may induce distinct hippocampal changes in females that remain uncharacterized. Future research including both sexes will be crucial to establish whether seizure severity produces similar molecular signatures in female animals and to deepen our understanding of how sex-specific mechanisms affect early epileptogenic processes. Finally, the focus on the hippocampus precludes assessment of extra-hippocampal and peripheral changes (43), which may also contribute to the mechanisms of epilepsy and warrant future investigation.

In addition to these experimental and design constraints, there are analytical limitations inherent to the proteomic approach that need to be considered. After multiple-comparison adjustment, few proteins remained statistically significant, so analyses used uncorrected values. This is common in discovery-based proteomics of complex tissues with small sample sizes, where strict FDR correction can overlook biologically relevant changes. In our previous work using a similar workflow, proteins that did not survive FDR were validated by independent methods, such as western blotting, to support their relevance (18). Importantly, our findings align with other proteomic studies in comparable rodent SE models. We also recognize that GO/KEGG enrichment analyses can be biased toward well-annotated proteins and may underrepresent poorly characterized DEPs, thereby potentially missing biological insights from less-studied proteins. Therefore, these results should be viewed as a general overview of functional trends rather than a complete picture of all underlying biology.

A critical finding reinforced by this study is the need for clearly defined, standardized criteria to classify experimental groups according to seizure severity following pilocarpine-induced SE. Relying only on behavioral signs using the Racine scale alone might be inadequate, as behavioral severity does not always mirror the actual electrographic seizure burden. Accordingly, experimental protocols should explicitly specify which Racine scale stages are used to distinguish mild from severe SE for downstream analyses. EEG studies support the association between SE severity and differences in electrographic activity patterns and hippocampal molecular profiles in rodent models of SE and acquired TLE (44–48). Therefore, analyzing animals that experience sustained mild versus severe SE as separate groups, when classification is based solely on behavioral criteria, may reduce within-group variability and make it easier to interpret experimental results, whereas combining animals across different SE severities is likely to mask severity-dependent effects. The significance of precisely measuring seizure burden and its dynamics is reinforced by other epilepsy models. For instance, neocortical injury-induced SE exhibits increasing and widespread network activation during extended seizures (49). Additionally, research using the kainic acid model of SE-induced acquired epilepsy reveals that repeated seizures can alter the features of seizure clusters over time (50). These findings highlight that seizure severity and timing patterns are crucial factors in the development of epileptogenic changes.

In summary, this study demonstrates that seizure severity in an initial pilocarpine-induced SE episode is reflected in distinct hippocampal proteomic signatures, with mild and severe SE exhibiting potentially graded alterations in synaptic, metabolic, and glial processes that may underlie differential trajectories of epileptogenesis in male rats.

Supplementary Material

Supplementary Table 1

Supplementary Table 1: Significantly altered proteins in each comparison.

Supplementary Figure 1

Supplementary Figure 1: GFAP protein levels are increased in the hippocampus following severe status epilepticus (sSE).

Supplementary Table 2

Supplementary Table 2: Correlation between 33 selected top proteins and four seizure metrics.

Acknowledgments:

The author(s) acknowledge the use of the facilities of the Bindley Bioscience Center, a core facility of the NIH-funded Indiana Clinical and Translational Sciences Institute. This research was supported by NS096234 (ALB); Hamilton Undergraduate Research Scholars Program (ALB & SS), Southern Methodist University.

Footnotes

Conflicts of Interest: None.

Declaration of generative AI and AI-assisted technologies in the manuscript preparation process: During the preparation of this work, the author(s) used ChatGPT and Grammarly to correct grammar. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

Data availability:

Raw data from this study are available from the Texas Data Repository (Accession No. UBOM3D).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Table 1

Supplementary Table 1: Significantly altered proteins in each comparison.

NIHMS2165429-supplement-Supplementary_Table_1.xlsx (56.4KB, xlsx)
Supplementary Figure 1

Supplementary Figure 1: GFAP protein levels are increased in the hippocampus following severe status epilepticus (sSE).

NIHMS2165429-supplement-Supplementary_Figure_1.pdf (173.9KB, pdf)
Supplementary Table 2

Supplementary Table 2: Correlation between 33 selected top proteins and four seizure metrics.

NIHMS2165429-supplement-Supplementary_Table_2.csv (2.1KB, csv)

Data Availability Statement

Raw data from this study are available from the Texas Data Repository (Accession No. UBOM3D).

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