According to the World Health Organization (WHO), there are around 50 million people with epilepsy worldwide. Although drugs are available to control epileptic seizures, these only provide symptomatic relief. They cannot prevent the condition from worsening, and if people with epilepsy stop taking their medication, there is no lasting effect on the severity or frequency of their seizures.

Some epilepsy cases are also resistant to these drugs. This is particularly common in adults with temporal epilepsy, with 30% of people continuing to suffer with seizures despite receiving medication. Current treatments also have no effect on problems with learning, memory and mental health that sometimes accompany drug-resistant epilepsy.

Previous studies in animals have identified some potential treatments that could slow the progression of temporal epilepsy, but these have only been shown to work when used at a very early stage. Since most individuals with temporal epilepsy have already started having seizures when they are diagnosed (and it is difficult to predict who will develop the condition), these drugs are unlikely to be useful in practice.

Here, Casillas-Espinosa et al. set out to find if a novel drug called sodium selenate can stop the progression of epilepsy and reduce the severity of temporal epilepsy when the condition is fully advanced. To do this, they used an animal model of temporal epilepsy, where rats had been modified to develop spontaneous seizures, resistance to normal anti-seizure medications, and problems with learning and memory.

Casillas-Espinosa et al. found that sodium selenate not only reduced the number and severity of seizures in these model rats, but also improved their memory and learning ability. Several rats stopped having seizures altogether even after the treatment had stopped, indicating that sodium selenate had a long-lasting protective effect. Genetic analysis of the rats also revealed that shorter telomeres (special DNA sequences at the ends of chromosomes) correlated with increasing severity of the condition, suggesting that telomere length could help predict who might develop temporal epilepsy or respond best to treatment.

This study identifies sodium selenate as a potential treatment that could reverse the progression of temporal epilepsy, even in individuals with advanced symptoms. Later this year, sodium selenate will be trialled in people with drug-resistant temporal epilepsy to determine if the drug benefits humans in the same way. Casillas-Espinosa et al. hope that it will improve participants’ epilepsy and, ultimately, their quality of life.

Temporal lobe epilepsy (TLE) is the most common form of drug-resistant epilepsy in adults and is frequently accompanied by disabling neuropsychiatric and cognitive comorbidities (Hermann et al., 2008; Hermann et al., 2000; Kwan et al., 2011; Sharma et al., 2007; Tellez-Zenteno et al., 2007). People with epilepsy are often required to take multiple anti-seizure medications (ASMs) in an attempt to control their seizures. However, these treatments are ineffective at controlling seizures in more than 30% of cases (Hermann et al., 2008; Hermann et al., 2000; Kwan et al., 2011; Sharma et al., 2007; Tellez-Zenteno et al., 2007), and can cause significant adverse side effects (Alonso-Vanegas et al., 2013; Perucca and Gilliam, 2012; Taylor et al., 2011). Current ASMs are merely symptomatic and do not mitigate the severity, progression, or outcome of the epilepsy, and if the patient ceases the medication their seizures are just as severe and frequent as if the person has never taken the medication in the first place (Simonato et al., 2014). Despite decades of research and development, the more than 20 new ASMs that have been introduced to clinical practice have failed to reduce the proportion of people with epilepsy who have drug-resistant seizures (Chen et al., 2018). Moreover, the cognitive and neuropsychiatric comorbidities that are prevalent in these patients remain difficult to treat and are not improved by the ASM treatment (Hinnell et al., 2010; Valente and Busatto Filho, 2013).

Animal studies have identified compounds potentially capable of delaying epileptogenesis (i.e. the process by which a non-epileptic brain turns into an epileptic one), but to date, these have only been demonstrated to be effective when the treatment is initiated early in the disease process (i.e. immediately after the brain insult) (Casillas-Espinosa et al., 2019b; Liu et al., 2016; Pauletti et al., 2019; Rizzi et al., 2019; Zeng et al., 2009; Huang et al., 2019). Because the majority of TLE patients already have established epilepsy when they present to the clinic (Foster et al., 2019), and there are no reliable biomarkers to predict who will develop TLE, this treatment strategy has limited clinical utility (Simonato et al., 2021). Surgical resection of the epileptogenic zone is currently the only effective disease-modifying intervention in patients with chronic drug-resistant TLE, but is only suitable in selected patients, is not always successful, and can have significant complications (Téllez-Zenteno et al., 2005). It is estimated that <5% of patients with drug-resistant epilepsy are suitable for resective epilepsy surgery (Téllez-Zenteno et al., 2005). Thus, there is a compelling need to identify pharmacological DMTs that are effective in patients with chronic TLE. (O’Brien et al., 2013; Sharma et al., 2007; Simonato et al., 2013).

Hyperphosphorylated tau (h-tau) is a neuropathological hallmark of several neurodegenerative conditions and has recently been implicated in epileptogenesis (Casillas-Espinosa et al., 2020; Ittner and Götz, 2011; Johnstone et al., 2015; Liu et al., 2016; Sen et al., 2007; Shultz et al., 2015; Thom et al., 2011; van Eersel et al., 2010). For example, h-tau is present in the brains of rodents with TLE, (Corcoran et al., 2010b; Jones et al., 2012; Shultz et al., 2015) ,and deposits of h-tau have been identified in surgically resected tissue from patients with drug-resistant epilepsy (Puvenna et al., 2016; Sánchez et al., 2018; Sen et al., 2007; Tai et al., 2016; Thom et al., 2011). The primary tau phosphatase in the brain (i.e. an enzyme that dephosphorylates tau) is protein phosphatase 2 A (PP2A) (Corcoran et al., 2010a), a heterotrimer consisting of a catalytic C subunit, a scaffold A subunit, and a regulatory B subunit with variable isoforms (Janssens and Goris, 2001). In several models of epilepsy, activation of PP2A with sodium selenate has been shown to reduce h-tau, and early treatment imparts anti-epileptogenic effects, while also reducing behavioral deficits associated with the epilepsy (Liu et al., 2016). However, whether pharmacological intervention with sodium selenate in chronic epileptic animals is a DMT remains to be determined. Therefore, this study investigated the effect of sodium selenate treatment on epileptic and behavioral outcomes, as well as multi-omics network medicine and bioinformatics approaches to uncover relevant molecular pathways, in chronically epileptic rats. We used the well-validated kainic acid (KA) post- post-status epilepticus (SE) model of TLE, where animals develop resistance to drug treatment, behavioral, cognitive, and sensorimotor comorbidities analogous to human drug-resistant TLE (Casillas-Espinosa et al., 2019b; Thomson et al., 2020).

This study evaluated the effect of sodium selenate on established TLE, using the post-SE model of drug-resistant TLE. Ten-weeks post-SE, chronically epileptic rats were randomly assigned to receive either sodium selenate (1 mg/kg/day), levetiracetam (200 /mg/kg/day, a commonly prescribed ASM for TLE) (Mbizvo et al., 2012), or vehicle (saline 0.9%) continuously for four weeks. To evaluate the sustained effects of the treatments, video-EEG was acquired before, during, and up to eight weeks post-treatment, followed by behavioral tests to assess cognitive, and sensorimotor comorbidities. Targeted and untargeted proteomics and metabolomics were performed on brain tissue to investigate potential pathways and molecules associated with modified disease outcomes. Telomere length was investigated as a surrogate marker of disease severity and treatment response. The experimental paradigm is shown in Figure 7 of the Methods section.

Sodium selenate is disease-modifying with enduring effects that reduce the number of seizures in drug-resistant TLE rats

Video-electroencephalogram (vEEG) was used to examine the effects of the treatments before, during, and up to eight weeks post-treatment on the occurrence of spontaneous recurrent seizures (Figure 1). Vehicle and levetiracetam-treated animals showed a significant progressive increase in the number of seizures recorded over the course of the experiments [F (8, 124)=2.022, p<0.05]. vEEG analysis during continuous drug treatment (10–14 weeks post-SE) revealed that neither vehicle, sodium selenate, or levetiracetam significantly impacted the number [F (4, 62)=0.4781, p=0.751], duration [F (2, 43)=0.5030, p=0.608], or severity of the seizures [F (2, 43)=0.4563, p=0.636], experienced by post-SE rats, indicating that drug-resistant epilepsy is present 10 weeks after KA-induced SE (Figure 1A). However, following the cessation of treatment, one-way ANOVA showed a treatment effect reducing the number of seizures that the animal experienced [F (31, 31)=3.956, p<0.0001; Figure 1A]. Tukey’s post hoc analysis revealed that sodium selenate treatment significantly reduced the number of seizures eight weeks post-treatment in contrast to vehicle-treated animals (p<0.05). Interestingly, four of the twelve sodium selenate-treated animals did not manifest any seizures eight weeks after the drug washout, while all of the vehicle and levetiracetam-treated animals showed spontaneous seizures through the experiment. In contrast, Tukey’s post hoc showed that levetiracetam treatment did not significantly reduce the number of seizures in comparison to vehicle or selenate-treated animals. Average seizure duration [F (1, 15)=1.038, p=0.3243; Figure 1B] and seizure severity, assessed by the Racine scale (Racine, 1972) [F (1, 13)=0.4873, p=0.497; Figure 1C] were not significantly different between the three treatment groups. Figures showing the individual timepoints of the seizure analysis are found in Figure 1—figure supplement 1.

Figure 1 with 1 supplement see all

Download asset Open asset

Sodium selenate treatment improves spatial memory and recognition memory

Cognitive deficits are one of the most debilitating comorbidities seen in patients with TLE (Hermann et al., 2008; Hermann et al., 2000; Kwan et al., 2011; Sharma et al., 2007; Tellez-Zenteno et al., 2007). The novel object placement (NOP) evaluates spatial memory (Spanswick and Sutherland, 2010). Vehicle-treated animals had impaired spatial memory in comparison to shams, as they spent significantly less time in the novel location of the object (p<0.05, Figure 2A). Levetiracetam treatment did not improve spatial memory compared to vehicles. Animals that had been treated with sodium selenate, even after an eight week washout, spent significantly more time evaluating the novel location of the object than those treated with the vehicle, which is indicative of improved spatial memory (p<0.05). The novel object recognition (NOR) evaluates recognition memory (Spanswick and Sutherland, 2010). Both vehicle and levetiracetam-treated rats spent less time evaluating the novel object relative to sham rats (p<0.05, Figure 2B), which is indicative of impaired recognition memory. In contrast, sodium selenate treatment was also able to improve recognition memory in comparison to vehicle-treated rats (p<0.05). Interestingly, the performance of the sodium selenate-treated animals was comparable to shams in both the NOP and NOR. Figures showing the individual timepoints of the MWM are found in Figure 2—figure supplement 1.

Figure 2 with 2 supplements see all

Download asset Open asset

To further evaluate cognitive function, rats were tested in the Morris water maze (MWM) (Morris, 1984). During the acquisition session of the MWM, only the post-SE rats treated with vehicle and levetiracetam displayed significantly increased search times than their sham counterparts [subject x treatment effects: F(12, 164)=2.501, p<0.01; selenate animals p>0.05 Figure 2C]. One day after the water maze acquisition session, rats underwent MWM reversal session where the location of the escape platform was changed. Here, all of the post-SE rats cohorts took more time to find the hidden platform in comparison to shams [F(4, 157)=5.971 p<0.001 Figure 2D]. No differences in swim speed were observed between the different groups.

The protein phosphatase family is differentially expressed after treatment with sodium selenate

Sodium selenate has been described to activate PP2A subunit B and to reduce h-tau after traumatic brain injury and in different models of epilepsy (Liu et al., 2016; Shultz et al., 2015). However, to further understand the mechanism by which sodium selenate exerts its disease-modifying effects in our model of established chronic TLE, we utilized state-of-the-art, high-resolution data-independent acquisition mass-spectrometry (DIA-MS), untargeted proteomics and metabolomics analyzes were carried out in the somatosensory cortex and hippocampus to identify potential markers of TLE and on the proteome and metabolome. A total of 5615 and 5538 proteins have been identified and quantified across all samples in the somatosensory cortex and hippocampus, respectively, considering a false discovery rate (q-value) cut-off of 1%. Differential expression analyses using ANOVA revealed 24 proteins in the somatosensory cortex and 9 proteins in the hippocampus to be significantly dysregulated across all experimental conditions (Figure 3, Supplementary file 1 and Supplementary file 2). Notably, protein phosphatase 2 regulatory subunit B alpha (Ppp2r5a), heat shock protein family A member 2 (Hspa2), and lymphocyte cytosolic protein 1 (Lcp1) were differentially expressed between selenate and vehicle-treated post-SE groups in the somatosensory cortex (Tukey’s HSD, FDR <0.05, Figure 3A, Supplementary file 1). Heat shock protein family B member 1 (Hspb1), Ro690 Y RNA binding protein (Ro60), and DENN domain containing 11 (Dennd11) proteins were differentially expressed between selenate and vehicle treated groups in the hippocampus (Tukey’s HSD, FDR <0.05, Figure 3B, Supplementary file 2). To further examine the changes in the proteomic profile of selenate-treated rats we performed quantitative gene set enrichment analysis between all pairs of treatment groups. Figure 3—figure supplement 1 shows the top 25 differentially regulated pathways in the somatosensory cortex (Figure 3—figure supplement 1A) and hippocampus (Figure 3—figure supplement 1B). Interestingly in the somatosensory cortex of the selenate group, among the most significantly upregulated pathways were signaling cascades mediated by activated insulin-like growth factor receptors (IGFR) and fibroblast growth factor receptors (FGRF) such as the FGFR-mediated phosphatidylinositol 3-kinase (PI-3K) cascade activation and IGFR1 signaling cascades (FDR <0.05). Among the top downregulated pathways in the somatosensory cortex were beta-oxidation of fatty acids and iron uptake and transport.

Figure 3 with 3 supplements see all

Download asset Open asset

In addition to proteomic analyses, we also performed untargeted univariate metabolomic analyses on the same samples. However, we were not able to identify significant changes between the experimental groups in the somatosensory cortex, suggesting that selenate primarily induces proteomic alterations in this region whilst keeping the metabolome stable (Supplementary file 3). In contrast, six metabolites were significantly different in selenate-treated animals as shown in Supplementary file 4. Partial least squares-discriminant analysis (PLS-DA) and principal component analysis (PCA) for the proteomics and metabolomics analyses are shown in Figure 3—figure supplement 2.

Sodium selenate-treated animals have longer telomere lengths

Telomere length was investigated as a surrogate marker of epilepsy severity and response to pharmacological treatment (Chan et al., 2020; Galletly et al., 2017; Kang et al., 2020; Lukens et al., 2009). Ear biopsies were taken 25 weeks post-SE to evaluate the effects of chronic epilepsy on telomere length. We found that TLE resulted in telomere shortening in vehicle-treated rats (p<0.001), and this was not prevented by levetiracetam treatment (p<0.01). However, sodium selenate treatment was able to reverse telomere shortening seen in post-SE animals treated with the vehicle (p<0.01), with the treated rats having telomere lengths similar to sham animals (Figure 4).

Figure 4

Download asset Open asset

Network medicine integration highlights discrete protein-metabolite modules correlate with treatment and pre-clinical outcomes

Network medicine integration was performed to investigate pathways correlated with the TLE phenotype. We integrated the untargeted multi-omics (proteomics and metabolomics) datasets with the targeted proteomics and phosphoproteomics (PP2A, tau, and h-tau), TLE phenotype (seizures, cognitive, and sensorimotor outcomes), and telomere length studies to investigate protein-metabolite groups/pathways correlated with the response to selenate treatment. Weighted correlation network analysis identified 20 and 17 protein-metabolite modules, respectively, in the hippocampal and cortical multi-omic datasets. Several protein-metabolite modules showed significant positive and negative correlations with multiple treatment groups and various clinical measures (Figure 5). Figure 5C In the hippocampal network (Figure 5A), the Purple module shows significant inverse correlation with selenate treatment group, and is enriched for proteins and metabolites involved in aminoacyl-tRNA biosynthesis (FDR = 0.0059) and Lysine degradation (FDR = 0.021) pathways. This module is also inversely correlated with levetiracetam-treated group, but shows no correlation with the vehicle group. The Yellow module is significantly (p=0.02) correlated with the selenate-treated group and has no significant correlations with other groups. This module is enriched for proteins and metabolites involved in complement cascade, oxidative phosphorylation and other metabolic pathways.

Figure 5

Download asset Open asset

In the somatosensory cortex correlation network (Figure 5B), the Tan (p=0.002), Magenta (p=0.002), Yellow (p=0.02), and Light-cyan (p=0.02) modules were significantly correlated with the selenate treated group and showed no correlation with the vehicle-treated group (Figure 5B). The magenta module does not annotate to any significant pathways; however, the Yellow module is enriched for proteins and metabolites involved in oxidative phosphorylation and other metabolic pathways, and the Tan module annotates to the insulin signaling pathway. Lastly, proteins and metabolites in the Light-cyan module are enriched for fatty acid biosynthesis pathways.

Current pharmacological treatments for TLE, ASMs, are merely symptomatic, attenuating seizures when taken on an ongoing basis without any sustained disease-modifying effects on the propensity to have seizures, or on its disabling associated neuropsychiatric, cognitive, and sensorimotor comorbidities (Brooks-Kayal et al., 2013; Pitkänen et al., 2013; Saletti et al., 2019). Once these treatments are discontinued (or the patient fails to take doses), the seizures relapse to at least the same severity and frequency as pre-treatment (Coan and Cendes, 2013; Kwan et al., 2011). Here, we report for the first time a pharmacological treatment that has persistent disease-modifying effects in an animal model of chronic drug-resistant TLE. Sodium selenate treatment was shown to have an enduring effect, mitigating the established epileptic state, and reducing the frequency of seizures and cognitive comorbidities. Our group has previously shown that treatment with sodium selenate in epilepsy models reduces h-tau, increases PP2A, and mitigates the development of epilepsy when delivered immediately after an epileptogenic brain injury before the occurrence of spontaneous recurrent seizures (i.e. an anti-epileptogenic effect) (Jones et al., 2012; Liu et al., 2016). However, the most common clinical scenario is that patients present to the clinic with established TLE, weeks to years after the epileptogenic brain insult, and in some patients, there is no identifiable brain insult (Foster et al., 2019).

The data from this study demonstrates that four weeks of sodium selenate treatment is associated with an enduring effect on mitigating the chronic epileptic state, reducing the frequency of seizures, neurocognitive, sensorimotor and neurobehavioral comorbidities, and biomarkers of disease severity. Strikingly, 4 of 12 animals (33%), did not have any seizures at all at this final timepoint eight weeks following the cessation of the treatment. In contrast, all levetiracetam and vehicle-treated animals showed spontaneous seizures at the end of the study. It is important to note that, analogous to human chronic TLE, spontaneous remission of seizures is not part of the natural history of the post-KA SE rat model of chronic TLE (Van Nieuwenhuyse et al., 2015a; Williams et al., 2009).

Behavioral comorbidities are considered an integral part of the TLE disease (Brooks-Kayal et al., 2013; Mazarati et al., 2018). Patients with chronic and drug-resistant TLE have an increased prevalence of psychiatric conditions, memory, and learning disabilities (Kandratavicius et al., 2012; Ott et al., 2003; Stafstrom et al., 1993; Stores, 1971; Tellez-Zenteno et al., 2007). Cognitive impairment is the most common comorbidity that afflicts people with TLE and tends to resist treatment, even after epilepsy surgery (Hermann et al., 2000). In fact, in a proportion of cases, the cognitive dysfunction progresses despite successful seizure control (Amlerova et al., 2014; Berg et al., 2012; Smith et al., 2014; Taylor et al., 2010). In keeping with the human condition, cognitive deficits have been demonstrated as a comorbidity in the post-SE animal model (Detour et al., 2005; Pearson et al., 2014; Chauvière et al., 2009). Here, we found that animals treated with sodium selenate had improved spatial memory and recognition memory, comparable to non-epileptic controls. This is the first time that a treatment has been described to reverse memory deficits in established chronic TLE.

Sensorimotor deficits are another significant comorbidity in people with TLE (Girardi-Schappo et al., 2021; Gandelman-Marton et al., 2006), and are not necessarily associated with the presence of seizures or with the use of ASMs (Gandelman-Marton et al., 2006; Petty et al., 2010; Shiek Ahmad et al., 2015). A study in patients with drug-resistant TLE found abnormal postural instability and an increased risk of falls than healthy controls (Gandelman-Marton et al., 2006). Interestingly, transgenic mouse models that expressed h-tau have been described to not only present cognitive dysfunction but also sensorimotor impairments (Kreilaus et al., 2021; Leroy et al., 2007; Lewis et al., 2000). Here, we found that sodium selenate treatment mitigated the sensorimotor deficits in chronically epileptic rats in the post-KA SE model of TLE.

The results from this study add to the growing literature that indicates that pathological h-tau is a key pathophysiological driver of drug-resistant TLE and associated comorbidities. To further test this hypothesis, we performed untargeted proteomics using DIA-MS and targeted h-tau and PP2A protein analysis using Western Blots. Our data indicate that the persistent disease-modifying effects of sodium selenate in drug-resistant TLE correlate to the increased the expression of the B catalytic subunit of PP2A and the reduction of h-tau. Evidence has shown that the PP2A B subunit is specifically involved in the dephosphorylation of tau (Barnes et al., 1995; Corcoran et al., 2010a) and PP2A expression and activity are potentiated by sodium selenate (Corcoran et al., 2010a; Jones et al., 2012; Shultz et al., 2015).

In accordance with animal models (Corcoran et al., 2010b; Jones et al., 2012; Shultz et al., 2015), increased levels and deposits of h-tau have been described in surgically resected tissue from patients with drug-resistant TLE (Puvenna et al., 2016; Sánchez et al., 2018; Sen et al., 2007; Tai et al., 2016; Thom et al., 2011). In resected tissue from patients with drug-resistant TLE, PP2A protein expression is also significantly decreased, but remains unchanged in Alzheimer’s disease patients, indicating that PP2A alterations may be an epilepsy-specific pathology (Gourmaud et al., 2020). There is also building evidence that tau-based mechanisms enhance neuronal excitability. Alterations in tau phosphorylation have been implicated in changing the excitation/inhibition balance in the neurons (Palop et al., 2007). Unstable microtubule assembly at axonal segments, as seen with the presence of h-tau, dysregulates the resting membrane potential and thereby generations of action potentials (Matsumoto and Sakai, 1979). Tau is transported into the cell via Fyn kinase where it influences N-methyl-D-aspartate (NMDA) receptor-mediated excitotoxicity (van Eersel et al., 2010) and changes in tau has been correlated with dysfunction of axonal NMDA receptors which are all considered mechanisms that promote the expression of seizures (Liu et al., 2017).

Pathological h-tau, has been proposed as one of the main culprits underlying cognitive deficits across different neurodegenerative disorders such as Alzheimer’s disease, TBI, and epilepsy (Casillas-Espinosa et al., 2020). Accumulation of h-tau in temporal lobe brain tissue from patients with drug-resistant TLE is associated with cognitive decline (Gourmaud et al., 2020; Thom et al., 2011). Furthermore, tau pathology has been associated with a decline in verbal learning, recall, and graded naming test scores in different neurodegenerative disorders (Tai et al., 2016) and in animal models that overexpressed h-tau (Kreilaus et al., 2021; Leroy et al., 2007; Lewis et al., 2000).

It is interesting that for some of the animals, the effects of sodium selenate were only observed several weeks after cessation of treatment, and the magnitude of the decrease in seizure expression progressively increased over the eight weeks of follow-up post-treatment. One possibility could be that the processes required to mitigate epileptic activity in neurons take time, particularly in chronically epileptic animals. However, it is also possible that the effects of sodium selenate extend beyond the described modification of h-tau and PP2A.

Dimension reduction is crucial for hypothesis generation to elucidate pathological mechanisms and changes in biological pathways as a response to treatment. To further explore sodium selenate’s mechanism of action, we performed a network medicine integration of our untargeted/targeted multi-omics datasets, TLE outcomes (seizures and behavior), and molecular studies (western blots and telomere length analyses) to evaluate the correlation coefficients of single modules between the different treatment groups (e.g. Selenate and Vehicle). By mapping significant genes/proteins together with metabolites to biological pathways, our network integration approach allowed for the reduction in experimental and biological noise and higher confidence levels, thereby generating potential new disease modules (proteins and metabolites and the cellular pathways they are involved in). We found modules that constitute the ‘healthy state,’ which operate in a concerted fashion in a healthy brain but are lost in the setting of TLE. For example, the Green Module is positively correlated to epilepsy phenotype, poor cognitive performance, poor balance, shorter telomeres, decreased PP2A, increased h-tau expression, and cardiomyopathies, which are associated with several epilepsy syndromes (Devinsky et al., 2018; Gilchrist, 1963; Naggar et al., 2014; Stöllberger et al., 2011; Surges and Sander, 2012). Of particular interest are the Yellow Module in both the hippocampal and somatosensory cortex correlation networks as well as Tan and Light-cyan Modules in the somatosensory cortex. All of the above-mentioned modules positively correlated with selenate treatment. The Yellow Module is enriched for pathways involved in ATP metabolism, oxidative phosphorylation, and several inflammatory systems. The Tan Module is enriched for insulin signaling pathways and the Light-cyan Module annotates to fatty acid metabolism pathways. It is important to note that ‘enrichment’ does not mean ‘increase’ or ‘upregulation.’ The enrichment in insulin signaling, for example, highlights the involvement of this pathway in the epilepsy phenotype and selenate treatment, but unfortunately provides no information about the direction of change (upregulation or downregulation). In an attempt to capture changes in the regulation of biological pathways that may be influenced by sodium selenate treatment, we conducted a quantitative enrichment analysis on a pathway level (PADOG) between the selenate and vehicle groups, utilizing the Reactome pathway database resource. Based on the PADOG analysis results, there is significant upregulation of IGFR1 and FGFR signaling cascades and FGFR-mediated PI-3K cascade activation in the selenate-treated group compared to the vehicle. Given the neuroprotective role of PI-3K signaling in several epilepsy syndromes, it is conceivable that this is one of the potential mechanisms by which sodium selenate exerts its long-term disease-modifying effect. The strong correlation of the Tan Module, which annotates to insulin signaling pathways to selenate treatment only further highlights the involvement of insulin signaling in the potential mechanism of action of this treatment. The downregulation of fatty acid oxidation processes in the selenate group suggests a shift from hyperactive neurons with high-energy demand (like in epileptic seizures) towards neurons with lower energy demand.

To our knowledge, this analysis is the first attempt at the integration of metabolomic and proteomic data in an epilepsy network medicine paradigm. We demonstrate that multi-omic, data-driven correlation networks can be used for agnostic, hypothesis-free examination of disease states as well as the identification of specific protein-metabolite modules associated with different states and treatments. Further investigation of these pathways as potential mechanisms involved in the mode of action of sodium selenate can be instrumental in understanding and reversing the drug resistance of TLE.

Prognostic biomarkers are used to predict disease characteristics and identify disease recurrence or progression in patients who have the disease of interest. Unfortunately, no such prognostic biomarkers for TLE currently exist (Simonato et al., 2021). Telomere length has emerged as a potential prognostic biomarker of chronic neurodegenerative conditions (Chan et al., 2020; Kang et al., 2020; Galletly et al., 2017; Lukens et al., 2009). Telomeres are repetitive non-coding DNA-tandem sequences of TTAGGG found at the end of eukaryotic chromosomes (Hehar and Mychasiuk, 2016). Telomere length has been used as a biological marker for cellular aging and has been described in many neurological disorders (Hehar and Mychasiuk, 2016; Koppelstaetter et al., 2009; Sun et al., 2019; Wright et al., 2018), including TLE (Miranda et al., 2020). Here, we analyzed telomere length from ear biopsies, as previous evidence has shown a strong correlation between telomere length in the brain and peripheral skin cells (Hehar and Mychasiuk, 2016). We found that telomere length was significantly reduced in TLE animals treated with vehicle or levetiracetam, and sodium selenate-treated animals had longer telomeres. The magnitude of the reduced telomere length was strongly associated with the number of seizures and more pronounced cognitive deficits in our drug-resistant TLE model. Overall, our data highlights the potential of telomere length as a prognostic biomarker of disease severity, treatment response, and TLE disease modification, which has the potential to be used in further trials of DMT.

Future studies should incorporate multi-omics and telomere length analysis integration performed at different timepoints from brain tissue and biofluids (blood and CSF) to map out the development, progression of epilepsy, and treatment response in this model. Unlike individual markers, pathway/network-based prognostic markers should be inherently more reliable since they provide the biological interpretation for the association between multiple molecules and the disease characteristics (seizures, behavior comorbidities, development of TLE) (Zou et al., 2013). Similarly, this approach could help to increase our understanding of the mechanism of action of sodium selenate as a disease-modifying treatment for established drug-resistant TLE.

All the metrodology and results form this work have been included in the manuscrit or as part of the supplementary material. Data has been deposited in Dryad https://doi.org/10.5061/dryad.37pvmcvnd.

1. 1. Casillas-Espinosa PM

   (2022) Dryad Digital Repository

   EEG and multi-omics data.

   https://doi.org/10.5061/dryad.37pvmcvnd

1. 1. Alonso-Vanegas MA
   2. Cisneros-Franco JM
   3. Castillo-Montoya C
   4. Martínez-Rosas AR
   5. Gómez-Pérez ME
   6. Rubio-Donnadieu F

   (2013) Self-Reported quality of life in pharmacoresistant temporal lobe epilepsy: correlation with clinical variables and memory evaluation

   Epileptic Disorders 15:263–271.

   https://doi.org/10.1684/epd.2013.0590

   • PubMed
   • Google Scholar
2. 1. Amlerova J
   2. Cavanna AE
   3. Bradac O
   4. Javurkova A
   5. Raudenska J
   6. Marusic P

   (2014) Emotion recognition and social cognition in temporal lobe epilepsy and the effect of epilepsy surgery

   Epilepsy & Behavior 36:86–89.

   https://doi.org/10.1016/j.yebeh.2014.05.001

   • PubMed
   • Google Scholar
3. 1. Bao F
   2. Shultz SR
   3. Hepburn JD
   4. Omana V
   5. Weaver LC
   6. Cain DP
   7. Brown A

   (2012) A CD11d monoclonal antibody treatment reduces tissue injury and improves neurological outcome after fluid percussion brain injury in rats

   Journal of Neurotrauma 29:2375–2392.

   https://doi.org/10.1089/neu.2012.2408

   • PubMed
   • Google Scholar
4. 1. Barker-Haliski ML
   2. Friedman D
   3. French JA
   4. White HS

   (2015) Disease modification in epilepsy: from animal models to clinical applications

   Drugs 75:749–767.

   https://doi.org/10.1007/s40265-015-0395-9

   • PubMed
   • Google Scholar
5. 1. Barnes GN
   2. Slevin JT
   3. Vanaman TC

   (1995) Rat brain protein phosphatase 2A: an enzyme that may regulate autophosphorylated protein kinases

   Journal of Neurochemistry 64:340–353.

   https://doi.org/10.1046/j.1471-4159.1995.64010340.x

   • PubMed
   • Google Scholar
6. 1. Berg AT
   2. Zelko FA
   3. Levy SR
   4. Testa FM

   (2012) Age at onset of epilepsy, pharmacoresistance, and cognitive outcomes: a prospective cohort study

   Neurology 79:1384–1391.

   https://doi.org/10.1212/WNL.0b013e31826c1b55

   • PubMed
   • Google Scholar
7. 1. Bhandare AM
   2. Kapoor K
   3. Powell KL
   4. Braine E
   5. Casillas-Espinosa P
   6. O’Brien TJ
   7. Farnham MMJ
   8. Pilowsky PM

   (2017) Inhibition of microglial activation with minocycline at the intrathecal level attenuates sympathoexcitatory and proarrhythmogenic changes in rats with chronic temporal lobe epilepsy

   Neuroscience 350:23–38.

   https://doi.org/10.1016/j.neuroscience.2017.03.012

   • PubMed
   • Google Scholar
8. 1. Brady RD
   2. Wong KR
   3. Robinson DL
   4. Mychasiuk R
   5. McDonald SJ
   6. D’Cunha RA
   7. Yamakawa GR
   8. Sun M
   9. Wark JD
   10. Lee PVS
   11. O’Brien TJ
   12. Casillas-Espinosa PM
   13. Shultz SR

   (2019) Bone health in rats with temporal lobe epilepsy in the absence of anti-epileptic drugs

   Frontiers in Pharmacology 10:1278.

   https://doi.org/10.3389/fphar.2019.01278

   • PubMed
   • Google Scholar
9. 1. Brandt C
   2. Glien M
   3. Gastens AM
   4. Fedrowitz M
   5. Bethmann K
   6. Volk HA
   7. Potschka H
   8. Löscher W

   (2007) Prophylactic treatment with levetiracetam after status epilepticus: lack of effect on epileptogenesis, neuronal damage, and behavioral alterations in rats

   Neuropharmacology 53:207–221.

   https://doi.org/10.1016/j.neuropharm.2007.05.001

   • PubMed
   • Google Scholar
10. 1. Broadbent NJ
    2. Squire LR
    3. Clark RE

    (2004) Spatial memory, recognition memory, and the hippocampus

    PNAS 101:14515–14520.

    https://doi.org/10.1073/pnas.0406344101

    • PubMed
    • Google Scholar
11. 1. Brooks-Kayal AR
    2. Bath KG
    3. Berg AT
    4. Galanopoulou AS
    5. Holmes GL
    6. Jensen FE
    7. Kanner AM
    8. O’Brien TJ
    9. Whittemore VH
    10. Winawer MR
    11. Patel M
    12. Scharfman HE

    (2013) Issues related to symptomatic and disease-modifying treatments affecting cognitive and neuropsychiatric comorbidities of epilepsy

    Epilepsia 54 Suppl 4:44–60.

    https://doi.org/10.1111/epi.12298

    • PubMed
    • Google Scholar
12. 1. Cardoso BR
    2. Roberts BR
    3. Malpas CB
    4. Vivash L
    5. Genc S
    6. Saling MM
    7. Desmond P
    8. Steward C
    9. Hicks RJ
    10. Callahan J
    11. Brodtmann A
    12. Collins S
    13. Macfarlane S
    14. Corcoran NM
    15. Hovens CM
    16. Velakoulis D
    17. O’Brien TJ
    18. Hare DJ
    19. Bush AI

    (2019) Supranutritional sodium selenate supplementation delivers selenium to the central nervous system: results from a randomized controlled pilot trial in Alzheimer’s disease

    Neurotherapeutics 16:192–202.

    https://doi.org/10.1007/s13311-018-0662-z

    • PubMed
    • Google Scholar
13. 1. Carter RJ
    2. Lione LA
    3. Humby T
    4. Mangiarini L
    5. Mahal A
    6. Bates GP
    7. Dunnett SB
    8. Morton AJ

    (1999) Characterization of progressive motor deficits in mice transgenic for the human Huntington’s disease mutation

    The Journal of Neuroscience 19:3248–3257.

    https://doi.org/10.1523/JNEUROSCI.19-08-03248.1999

    • PubMed
    • Google Scholar
14. 1. Carter RJ
    2. Morton J
    3. Dunnett SB

    (2001) Motor coordination and balance in rodents

    Current Protocols in Neuroscience 15:12.

    https://doi.org/10.1002/0471142301.ns0812s15

    • Google Scholar
15. 1. Casillas-Espinosa PM
    2. Hicks A
    3. Jeffreys A
    4. Snutch TP
    5. O’Brien TJ
    6. Powell KL

    (2015) Z944, a novel selective T-type calcium channel antagonist delays the progression of seizures in the amygdala kindling model

    PLOS ONE 10:e0130012.

    https://doi.org/10.1371/journal.pone.0130012

    • PubMed
    • Google Scholar
16. 1. Casillas-Espinosa PM
    2. Sargsyan A
    3. Melkonian D
    4. O’Brien TJ

    (2019a) A universal automated tool for reliable detection of seizures in rodent models of acquired and genetic epilepsy

    Epilepsia 60:783–791.

    https://doi.org/10.1111/epi.14691

    • PubMed
    • Google Scholar
17. 1. Casillas-Espinosa PM
    2. Shultz SR
    3. Braine EL
    4. Jones NC
    5. Snutch TP
    6. Powell KL
    7. O’Brien TJ

    (2019b) Disease-modifying effects of a novel T-type calcium channel antagonist, Z944, in a model of temporal lobe epilepsy

    Progress in Neurobiology 182:101677.

    https://doi.org/10.1016/j.pneurobio.2019.101677

    • PubMed
    • Google Scholar
18. 1. Casillas-Espinosa PM
    2. Andrade P
    3. Santana-Gomez C
    4. Paananen T
    5. Smith G
    6. Ali I
    7. Ciszek R
    8. Ndode-Ekane XE
    9. Brady RD
    10. Tohka J
    11. Hudson MR
    12. Perucca P
    13. Braine EL
    14. Immonen R
    15. Puhakka N
    16. Shultz SR
    17. Jones NC
    18. Staba RJ
    19. Pitkänen A
    20. O’Brien TJ

    (2019c) Harmonization of the pipeline for seizure detection to phenotype post-traumatic epilepsy in a preclinical multicenter study on post-traumatic epileptogenesis

    Epilepsy Research 156:106131.

    https://doi.org/10.1016/j.eplepsyres.2019.04.011

    • Google Scholar
19. 1. Casillas-Espinosa PM
    2. Ali I
    3. O’Brien TJ

    (2020) Neurodegenerative pathways as targets for acquired epilepsy therapy development

    Epilepsia Open 5:138–154.

    https://doi.org/10.1002/epi4.12386

    • PubMed
    • Google Scholar
20. 1. Cawthon RM

    (2002) Telomere measurement by quantitative PCR

    Nucleic Acids Research 30:e47.

    https://doi.org/10.1093/nar/30.10.e47

    • PubMed
    • Google Scholar
21. 1. Chan D
    2. Martin-Ruiz C
    3. Saretzki G
    4. Neely D
    5. Qiu W
    6. Kunadian V

    (2020) The association of telomere length and telomerase activity with adverse outcomes in older patients with non-ST-elevation acute coronary syndrome

    PLOS ONE 15:e0227616.

    https://doi.org/10.1371/journal.pone.0227616

    • PubMed
    • Google Scholar
22. 1. Chauvière L
    2. Rafrafi N
    3. Thinus-Blanc C
    4. Bartolomei F
    5. Esclapez M
    6. Bernard C

    (2009) Early deficits in spatial memory and theta rhythm in experimental temporal lobe epilepsy

    The Journal of Neuroscience 29:5402–5410.

    https://doi.org/10.1523/JNEUROSCI.4699-08.2009

    • PubMed
    • Google Scholar
23. 1. Chen Z
    2. Brodie MJ
    3. Liew D
    4. Kwan P

    (2018) Treatment outcomes in patients with newly diagnosed epilepsy treated with established and new antiepileptic drugs: a 30-year longitudinal cohort study

    JAMA Neurology 75:279–286.

    https://doi.org/10.1001/jamaneurol.2017.3949

    • PubMed
    • Google Scholar
24. 1. Coan AC
    2. Cendes F

    (2013) Epilepsy as progressive disorders: what is the evidence that can guide our clinical decisions and how can neuroimaging help?

    Epilepsy & Behavior 26:313–321.

    https://doi.org/10.1016/j.yebeh.2012.09.027

    • PubMed
    • Google Scholar
25. 1. Corcoran NM
    2. Hovens CM
    3. Michael M
    4. Rosenthal MA
    5. Costello AJ

    (2010a) Open-label, phase I dose-escalation study of sodium selenate, a novel activator of PP2A, in patients with castration-resistant prostate cancer

    British Journal of Cancer 103:462–468.

    https://doi.org/10.1038/sj.bjc.6605798

    • PubMed
    • Google Scholar
26. 1. Corcoran NM
    2. Martin D
    3. Hutter-Paier B
    4. Windisch M
    5. Nguyen T
    6. Nheu L
    7. Sundstrom LE
    8. Costello AJ
    9. Hovens CM

    (2010b) Sodium selenate specifically activates PP2A phosphatase, dephosphorylates tau and reverses memory deficits in an alzheimer’s disease model

    Journal of Clinical Neuroscience 17:1025–1033.

    https://doi.org/10.1016/j.jocn.2010.04.020

    • PubMed
    • Google Scholar
27. 1. Creek DJ
    2. Anderson J
    3. McConville MJ
    4. Barrett MP

    (2012) Metabolomic analysis of trypanosomatid protozoa

    Molecular and Biochemical Parasitology 181:73–84.

    https://doi.org/10.1016/j.molbiopara.2011.10.003

    • PubMed
    • Google Scholar
28. 1. Detour J
    2. Schroeder H
    3. Desor D
    4. Nehlig A

    (2005) A 5-month period of epilepsy impairs spatial memory, decreases anxiety, but spares object recognition in the lithium-pilocarpine model in adult rats

    Epilepsia 46:499–508.

    https://doi.org/10.1111/j.0013-9580.2005.38704.x

    • PubMed
    • Google Scholar
29. 1. Devinsky O
    2. Kim A
    3. Friedman D
    4. Bedigian A
    5. Moffatt E
    6. Tseng ZH

    (2018) Incidence of cardiac fibrosis in SUDEP and control cases

    Neurology 91:e55–e61.

    https://doi.org/10.1212/WNL.0000000000005740

    • PubMed
    • Google Scholar
30. 1. Dezsi G
    2. Ozturk E
    3. Stanic D
    4. Powell KL
    5. Blumenfeld H
    6. O’Brien TJ
    7. Jones NC

    (2013) Ethosuximide reduces epileptogenesis and behavioral comorbidity in the GAERS model of genetic generalized epilepsy

    Epilepsia 54:635–643.

    https://doi.org/10.1111/epi.12118

    • PubMed
    • Google Scholar
31. 1. Fabregat A
    2. Jupe S
    3. Matthews L
    4. Sidiropoulos K
    5. Gillespie M
    6. Garapati P
    7. Haw R
    8. Jassal B
    9. Korninger F
    10. May B
    11. Milacic M
    12. Roca CD
    13. Rothfels K
    14. Sevilla C
    15. Shamovsky V
    16. Shorser S
    17. Varusai T
    18. Viteri G
    19. Weiser J
    20. Wu G
    21. Stein L
    22. Hermjakob H
    23. D’Eustachio P

    (2018) The reactome pathway knowledgebase

    Nucleic Acids Research 46:D649–D655.

    https://doi.org/10.1093/nar/gkx1132

    • PubMed
    • Google Scholar
32. 1. Foster E
    2. Carney P
    3. Liew D
    4. Ademi Z
    5. O’Brien T
    6. Kwan P

    (2019) First seizure presentations in adults: beyond assessment and treatment

    Journal of Neurology, Neurosurgery, and Psychiatry 90:1039–1045.

    https://doi.org/10.1136/jnnp-2018-320215

    • PubMed
    • Google Scholar
33. 1. Galletly C
    2. Dhillon VS
    3. Liu D
    4. Balzan RP
    5. Hahn LA
    6. Fenech MF

    (2017) Shorter telomere length in people with schizophrenia: a preliminary study from Australia

    Schizophrenia Research 190:46–51.

    https://doi.org/10.1016/j.schres.2017.03.007

    • PubMed
    • Google Scholar
34. 1. Gandelman-Marton R
    2. Arlazoroff A
    3. Dvir Z

    (2006) Balance performance in adult epilepsy patients

    Seizure 15:582–589.

    https://doi.org/10.1016/j.seizure.2006.08.002

    • PubMed
    • Google Scholar
35. 1. Gilchrist L

    (1963)

    Cardiomyopathy, temporal lobe epilepsy and pregnancy

    Proceedings of the Royal Society of Medicine 56:923.

    • PubMed
    • Google Scholar
36. 1. Girardi-Schappo M
    2. Fadaie F
    3. Lee HM
    4. Caldairou B
    5. Sziklas V
    6. Crane J
    7. Bernhardt BC
    8. Bernasconi A
    9. Bernasconi N

    (2021) Altered communication dynamics reflect cognitive deficits in temporal lobe epilepsy

    Epilepsia 62:1022–1033.

    https://doi.org/10.1111/epi.16864

    • PubMed
    • Google Scholar
37. 1. Gourmaud S
    2. Shou H
    3. Irwin DJ
    4. Sansalone K
    5. Jacobs LM
    6. Lucas TH
    7. Marsh ED
    8. Davis KA
    9. Jensen FE
    10. Talos DM

    (2020) Alzheimer-like amyloid and tau alterations associated with cognitive deficit in temporal lobe epilepsy

    Brain 143:191–209.

    https://doi.org/10.1093/brain/awz381

    • PubMed
    • Google Scholar
38. 1. Harutyunyan A
    2. Chong D
    3. Li R
    4. Shah AD
    5. Ali Z
    6. Huang C
    7. Barlow CK
    8. Perucca P
    9. O’Brien TJ
    10. Jones NC
    11. Schittenhelm RB
    12. Anderson A
    13. Casillas-Espinosa PM

    (2022) An integrated multi-omic network analysis identifies seizure-associated dysregulated pathways in the GAERS model of absence epilepsy

    International Journal of Molecular Sciences 23:6063.

    https://doi.org/10.3390/ijms23116063

    • PubMed
    • Google Scholar
39. 1. Hehar H
    2. Mychasiuk R

    (2016) The use of telomere length as a predictive biomarker for injury prognosis in juvenile rats following a concussion/mild traumatic brain injury

    Neurobiology of Disease 87:11–18.

    https://doi.org/10.1016/j.nbd.2015.12.007

    • PubMed
    • Google Scholar
40. 1. Hermann BP
    2. Seidenberg M
    3. Bell B
    4. Woodard A
    5. Rutecki P
    6. Sheth R

    (2000) Comorbid psychiatric symptoms in temporal lobe epilepsy: association with chronicity of epilepsy and impact on quality of life

    Epilepsy & Behavior 1:184–190.

    https://doi.org/10.1006/ebeh.2000.0066

    • PubMed
    • Google Scholar
41. 1. Hermann B
    2. Seidenberg M
    3. Jones J

    (2008) The neurobehavioural comorbidities of epilepsy: can a natural history be developed?

    The Lancet. Neurology 7:151–160.

    https://doi.org/10.1016/S1474-4422(08)70018-8

    • PubMed
    • Google Scholar
42. 1. Hinnell C
    2. Williams J
    3. Metcalfe A
    4. Patten SB
    5. Parker R
    6. Wiebe S
    7. Jetté N

    (2010) Health status and health-related behaviors in epilepsy compared to other chronic conditions -- a national population-based study

    Epilepsia 51:853–861.

    https://doi.org/10.1111/j.1528-1167.2009.02477.x

    • PubMed
    • Google Scholar
43. 1. Huang YZ
    2. He X-P
    3. Krishnamurthy K
    4. McNamara JO

    (2019) TrkB-shc signaling protects against hippocampal injury following status epilepticus

    The Journal of Neuroscience 39:4624–4630.

    https://doi.org/10.1523/JNEUROSCI.2939-18.2019

    • PubMed
    • Google Scholar
44. 1. Ittner LM
    2. Götz J

    (2011) Amyloid-Β and tau -- a toxic PAS de deux in Alzheim’r's disease

    Nature Reviews. Neuroscience 12:65–72.

    https://doi.org/10.1038/nrn2967

    • PubMed
    • Google Scholar
45. 1. Janssens V
    2. Goris J

    (2001) Protein phosphatase 2A: a highly regulated family of serine/threonine phosphatases implicated in cell growth and signalling

    The Biochemical Journal 353:417–439.

    https://doi.org/10.1042/0264-6021:3530417

    • PubMed
    • Google Scholar
46. 1. Jassal B
    2. Matthews L
    3. Viteri G
    4. Gong C
    5. Lorente P
    6. Fabregat A
    7. Sidiropoulos K
    8. Cook J
    9. Gillespie M
    10. Haw R
    11. Loney F
    12. May B
    13. Milacic M
    14. Rothfels K
    15. Sevilla C
    16. Shamovsky V
    17. Shorser S
    18. Varusai T
    19. Weiser J
    20. Wu G
    21. Stein L
    22. Hermjakob H
    23. D’Eustachio P

    (2020) Reactome pathway knowledgebase

    Nucleic Acids Research 48:D498–D503.

    https://doi.org/10.1093/nar/gkz1031

    • PubMed
    • Google Scholar
47. 1. Johnstone VPA
    2. Wright DK
    3. Wong K
    4. O’Brien TJ
    5. Rajan R
    6. Shultz SR

    (2015) Experimental traumatic brain injury results in long-term recovery of functional responsiveness in sensory cortex but persisting structural changes and sensorimotor, cognitive, and emotional deficits

    Journal of Neurotrauma 32:1333–1346.

    https://doi.org/10.1089/neu.2014.3785

    • PubMed
    • Google Scholar
48. 1. Jones NC
    2. Salzberg MR
    3. Kumar G
    4. Couper A
    5. Morris MJ
    6. O’Brien TJ

    (2008) Elevated anxiety and depressive-like behavior in a rat model of genetic generalized epilepsy suggesting common causation

    Experimental Neurology 209:254–260.

    https://doi.org/10.1016/j.expneurol.2007.09.026

    • PubMed
    • Google Scholar
49. 1. Jones NC
    2. Nguyen T
    3. Corcoran NM
    4. Velakoulis D
    5. Chen T
    6. Grundy R
    7. O’Brien TJ
    8. Hovens CM

    (2012) Targeting hyperphosphorylated tau with sodium selenate suppresses seizures in rodent models

    Neurobiology of Disease 45:897–901.

    https://doi.org/10.1016/j.nbd.2011.12.005

    • PubMed
    • Google Scholar
50. 1. Kandratavicius L
    2. Lopes-Aguiar C
    3. Bueno-Júnior LS
    4. Romcy-Pereira RN
    5. Hallak JEC
    6. Leite JP

    (2012) Psychiatric comorbidities in temporal lobe epilepsy: possible relationships between psychotic disorders and involvement of limbic circuits

    Revista Brasileira de Psiquiatria 34:454–466.

    https://doi.org/10.1016/j.rbp.2012.04.007

    • PubMed
    • Google Scholar
51. 1. Kang JI
    2. Mueller SG
    3. Wu GWY
    4. Lin J
    5. Ng P
    6. Yehuda R
    7. Flory JD
    8. Abu-Amara D
    9. Reus VI
    10. Gautam A
    11. Hammamieh R
    12. Doyle FJ
    13. Jett M
    14. Marmar CR
    15. Mellon SH
    16. Wolkowitz OM
    17. PTSD Systems Biology Consortium

    (2020) Effect of combat exposure and posttraumatic stress disorder on telomere length and amygdala volume

    Biological Psychiatry. Cognitive Neuroscience and Neuroimaging 5:678–687.

    https://doi.org/10.1016/j.bpsc.2020.03.007

    • PubMed
    • Google Scholar
52. 1. Kolb B
    2. Whishaw IQ

    (1985) Earlier is not always better: behavioral dysfunction and abnormal cerebral morphogenesis following neonatal cortical lesions in the rat

    Behavioural Brain Research 17:25–43.

    https://doi.org/10.1016/0166-4328(85)90005-1

    • PubMed
    • Google Scholar
53. 1. Koppelstaetter C
    2. Kern G
    3. Mayer G

    (2009) Biomarkers of aging with prognostic and predictive value in non-oncological diseases

    Current Medicinal Chemistry 16:3469–3475.

    https://doi.org/10.2174/092986709789057644

    • PubMed
    • Google Scholar
54. 1. Kreilaus F
    2. Masanetz R
    3. Watt G
    4. Przybyla M
    5. Ittner A
    6. Ittner L
    7. Karl T

    (2021) The behavioural phenotype of 14-month-old female TAU58/2 transgenic mice

    Behavioural Brain Research 397:112943.

    https://doi.org/10.1016/j.bbr.2020.112943

    • PubMed
    • Google Scholar
55. 1. Kwan P
    2. Schachter SC
    3. Brodie MJ

    (2011) Drug-Resistant epilepsy

    The New England Journal of Medicine 365:919–926.

    https://doi.org/10.1056/NEJMra1004418

    • PubMed
    • Google Scholar
56. 1. Langfelder P
    2. Horvath S

    (2008) WGCNA: an R package for weighted correlation network analysis

    BMC Bioinformatics 9:559.

    https://doi.org/10.1186/1471-2105-9-559

    • PubMed
    • Google Scholar
57. 1. Leroy K
    2. Bretteville A
    3. Schindowski K
    4. Gilissen E
    5. Authelet M
    6. De Decker R
    7. Yilmaz Z
    8. Buée L
    9. Brion JP

    (2007) Early axonopathy preceding neurofibrillary tangles in mutant tau transgenic mice

    The American Journal of Pathology 171:976–992.

    https://doi.org/10.2353/ajpath.2007.070345

    • PubMed
    • Google Scholar
58. 1. Lewis J
    2. McGowan E
    3. Rockwood J
    4. Melrose H
    5. Nacharaju P
    6. Van Slegtenhorst M
    7. Gwinn-Hardy K
    8. Paul Murphy M
    9. Baker M
    10. Yu X
    11. Duff K
    12. Hardy J
    13. Corral A
    14. Lin WL
    15. Yen SH
    16. Dickson DW
    17. Davies P
    18. Hutton M

    (2000) Neurofibrillary tangles, amyotrophy and progressive motor disturbance in mice expressing mutant (P301L) tau protein

    Nature Genetics 25:402–405.

    https://doi.org/10.1038/78078

    • PubMed
    • Google Scholar
59. 1. Li C
    2. Silva J
    3. Ozturk E
    4. Dezsi G
    5. O’Brien TJ
    6. Renoir T
    7. Jones NC

    (2018) Chronic fluoxetine treatment accelerates kindling epileptogenesis in mice independently of 5-HT2A receptors

    Epilepsia 59:e114–e119.

    https://doi.org/10.1111/epi.14435

    • PubMed
    • Google Scholar
60. 1. Liu SJ
    2. Zheng P
    3. Wright DK
    4. Dezsi G
    5. Braine E
    6. Nguyen T
    7. Corcoran NM
    8. Johnston LA
    9. Hovens CM
    10. Mayo JN
    11. Hudson M
    12. Shultz SR
    13. Jones NC
    14. O’Brien TJ

    (2016) Sodium selenate retards epileptogenesis in acquired epilepsy models reversing changes in protein phosphatase 2A and hyperphosphorylated tau

    Brain 139:1919–1938.

    https://doi.org/10.1093/brain/aww116

    • PubMed
    • Google Scholar
61. 1. Liu X
    2. Ou S
    3. Yin M
    4. Xu T
    5. Wang T
    6. Liu Y
    7. Ding X
    8. Yu X
    9. Yuan J
    10. Huang H
    11. Zhang X
    12. Tan X
    13. Chen L
    14. Chen Y

    (2017)

    N-Methyl-D-Aspartate receptors mediate epilepsy-induced axonal impairment and tau phosphorylation via activating glycogen synthase kinase-3β and cyclin-dependent kinase 5

    Discovery Medicine 23:221–234.

    • PubMed
    • Google Scholar
62. 1. Löscher W
    2. Hönack D

    (1993) Profile of ucb L059, a novel anticonvulsant drug, in models of partial and generalized epilepsy in mice and rats

    European Journal of Pharmacology 232:147–158.

    https://doi.org/10.1016/0014-2999(93)90768-d

    • PubMed
    • Google Scholar
63. 1. Löscher W
    2. Hönack D
    3. Rundfeldt C

    (1998)

    Antiepileptogenic effects of the novel anticonvulsant levetiracetam (ucb L059) in the kindling model of temporal lobe epilepsy

    The Journal of Pharmacology and Experimental Therapeutics 284:474–479.

    • PubMed
    • Google Scholar
64. 1. Lukens JN
    2. Van Deerlin V
    3. Clark CM
    4. Xie SX
    5. Johnson FB

    (2009) Comparisons of telomere lengths in peripheral blood and cerebellum in Alzheimer’s disease

    Alzheimer’s & Dementia 5:463–469.

    https://doi.org/10.1016/j.jalz.2009.05.666

    • PubMed
    • Google Scholar
65. 1. Malpas CB
    2. Vivash L
    3. Genc S
    4. Saling MM
    5. Desmond P
    6. Steward C
    7. Hicks RJ
    8. Callahan J
    9. Brodtmann A
    10. Collins S
    11. Macfarlane S
    12. Corcoran NM
    13. Hovens CM
    14. Velakoulis D
    15. O’Brien TJ

    (2016) A phase IIA randomized control trial of VEL015 (sodium selenate) in mild-moderate Alzheimer’s disease

    Journal of Alzheimer’s Disease 54:223–232.

    https://doi.org/10.3233/JAD-160544

    • PubMed
    • Google Scholar
66. 1. Matsumoto G
    2. Sakai H

    (1979) Microtubules inside the plasma membrane of squid giant axons and their possible physiological function

    The Journal of Membrane Biology 50:1–14.

    https://doi.org/10.1007/BF01868784

    • PubMed
    • Google Scholar
67. 1. Mazarati A
    2. Jones NC
    3. Galanopoulou AS
    4. Harte-Hargrove LC
    5. Kalynchuk LE
    6. Lenck-Santini P-P
    7. Medel-Matus J-S
    8. Nehlig A
    9. de la Prida LM
    10. Sarkisova K
    11. Veliskova J

    (2018) A companion to the preclinical common data elements on neurobehavioral comorbidities of epilepsy: a report of the TASK3 behavior Working group of the ILAE/AES joint translational Task force

    Epilepsia Open 3:24–52.

    https://doi.org/10.1002/epi4.12236

    • PubMed
    • Google Scholar
68. 1. Mbizvo GK
    2. Dixon P
    3. Hutton JL
    4. Marson AG

    (2012) Levetiracetam add-on for drug-resistant focal epilepsy: an updated cochrane review

    The Cochrane Database of Systematic Reviews 2012:CD001901.

    https://doi.org/10.1002/14651858.CD001901.pub2

    • PubMed
    • Google Scholar
69. 1. Miranda DM
    2. Rosa DV
    3. Costa BS
    4. Nicolau NF
    5. Magno LAV
    6. de Paula JJ
    7. Romano-Silva MA

    (2020) Telomere shortening in patients with drug-resistant epilepsy

    Epilepsy Research 166:106427.

    https://doi.org/10.1016/j.eplepsyres.2020.106427

    • PubMed
    • Google Scholar
70. 1. Morris R

    (1984) Developments of a water-maze procedure for studying spatial learning in the rat

    Journal of Neuroscience Methods 11:47–60.

    https://doi.org/10.1016/0165-0270(84)90007-4

    • PubMed
    • Google Scholar
71. 1. Morris RG

    (1989) Synaptic plasticity and learning: selective impairment of learning rats and blockade of long-term potentiation in vivo by the N-methyl-D-aspartate receptor antagonist AP5

    The Journal of Neuroscience 9:3040–3057.

    https://doi.org/10.1523/JNEUROSCI.09-09-03040.1989

    • PubMed
    • Google Scholar
72. 1. Naggar I
    2. Lazar J
    3. Kamran H
    4. Orman R
    5. Stewart M

    (2014) Relation of autonomic and cardiac abnormalities to ventricular fibrillation in a rat model of epilepsy

    Epilepsy Research 108:44–56.

    https://doi.org/10.1016/j.eplepsyres.2013.10.018

    • PubMed
    • Google Scholar
73. 1. Ndode-Ekane XE
    2. Santana-Gomez C
    3. Casillas-Espinosa PM
    4. Ali I
    5. Brady RD
    6. Smith G
    7. Andrade P
    8. Immonen R
    9. Puhakka N
    10. Hudson MR
    11. Braine EL
    12. Shultz SR
    13. Staba RJ
    14. O’Brien TJ
    15. Pitkänen A

    (2019) Harmonization of lateral fluid-percussion injury model production and post-injury monitoring in a preclinical multicenter biomarker discovery study on post-traumatic epileptogenesis

    Epilepsy Research 151:7–16.

    https://doi.org/10.1016/j.eplepsyres.2019.01.006

    • PubMed
    • Google Scholar
74. 1. O’Brien TJ
    2. Ben-Menachem E
    3. Bertram EH
    4. Collins SD
    5. Kokaia M
    6. Lerche H
    7. Klitgaard H
    8. Staley KJ
    9. Vaudano E
    10. Walker MC
    11. Simonato M

    (2013) Proposal for a “ phase II” multicenter trial model for preclinical new antiepilepsy therapy development

    Epilepsia 54 Suppl 4:70–74.

    https://doi.org/10.1111/epi.12300

    • PubMed
    • Google Scholar
75. 1. Ott D
    2. Siddarth P
    3. Gurbani S
    4. Koh S
    5. Tournay A
    6. Shields WD
    7. Caplan R

    (2003) Behavioral disorders in pediatric epilepsy: unmet psychiatric need

    Epilepsia 44:591–597.

    https://doi.org/10.1046/j.1528-1157.2003.25002.x

    • PubMed
    • Google Scholar
76. 1. Palop JJ
    2. Chin J
    3. Roberson ED
    4. Wang J
    5. Thwin MT
    6. Bien-Ly N
    7. Yoo J
    8. Ho KO
    9. Yu GQ
    10. Kreitzer A
    11. Finkbeiner S
    12. Noebels JL
    13. Mucke L

    (2007) Aberrant excitatory neuronal activity and compensatory remodeling of inhibitory hippocampal circuits in mouse models of Alzheimer’s disease

    Neuron 55:697–711.

    https://doi.org/10.1016/j.neuron.2007.07.025

    • PubMed
    • Google Scholar
77. 1. Pang Z
    2. Chong J
    3. Zhou G
    4. de Lima Morais DA
    5. Chang L
    6. Barrette M
    7. Gauthier C
    8. Jacques P-É
    9. Li S
    10. Xia J

    (2021) MetaboAnalyst 5.0: narrowing the gap between RAW spectra and functional insights

    Nucleic Acids Research 49:W388–W396.

    https://doi.org/10.1093/nar/gkab382

    • PubMed
    • Google Scholar
78. 1. Pauletti A
    2. Terrone G
    3. Shekh-Ahmad T
    4. Salamone A
    5. Ravizza T
    6. Rizzi M
    7. Pastore A
    8. Pascente R
    9. Liang L-P
    10. Villa BR
    11. Balosso S
    12. Abramov AY
    13. van Vliet EA
    14. Del Giudice E
    15. Aronica E
    16. Patel M
    17. Walker MC
    18. Vezzani A

    (2019) Targeting oxidative stress improves disease outcomes in a rat model of acquired epilepsy

    Brain 142:e39.

    https://doi.org/10.1093/brain/awz130

    • PubMed
    • Google Scholar
79. Book

    1. Paxinos G.
    2. Watson C.

    (1982)

    The rat brain in stereotaxic coordinates

    Sydney: Academic Press.

    • Google Scholar
80. 1. Pearson JN
    2. Schulz KM
    3. Patel M

    (2014) Specific alterations in the performance of learning and memory tasks in models of chemoconvulsant-induced status epilepticus

    Epilepsy Research 108:1032–1040.

    https://doi.org/10.1016/j.eplepsyres.2014.04.003

    • PubMed
    • Google Scholar
81. 1. Percie du Sert N
    2. Hurst V
    3. Ahluwalia A
    4. Alam S
    5. Avey MT
    6. Baker M
    7. Browne WJ
    8. Clark A
    9. Cuthill IC
    10. Dirnagl U
    11. Emerson M
    12. Garner P
    13. Holgate ST
    14. Howells DW
    15. Karp NA
    16. Lazic SE
    17. Lidster K
    18. MacCallum CJ
    19. Macleod M
    20. Pearl EJ
    21. Petersen OH
    22. Rawle F
    23. Reynolds P
    24. Rooney K
    25. Sena ES
    26. Silberberg SD
    27. Steckler T
    28. Würbel H

    (2020) The ARRIVE guidelines 2.0: updated guidelines for reporting animal research

    PLOS Biology 18:e3000410.

    https://doi.org/10.1371/journal.pbio.3000410

    • PubMed
    • Google Scholar
82. 1. Perucca P
    2. Gilliam FG

    (2012) Adverse effects of antiepileptic drugs

    The Lancet. Neurology 11:792–802.

    https://doi.org/10.1016/S1474-4422(12)70153-9

    • PubMed
    • Google Scholar
83. 1. Petty SJ
    2. Hill KD
    3. Haber NE
    4. Paton LM
    5. Lawrence KM
    6. Berkovic SF
    7. Seibel MJ
    8. O’Brien TJ
    9. Wark JD

    (2010) Balance impairment in chronic antiepileptic drug users: a twin and sibling study

    Epilepsia 51:280–288.

    https://doi.org/10.1111/j.1528-1167.2009.02254.x

    • PubMed
    • Google Scholar
84. 1. Pitkänen A
    2. Kharatishvili I
    3. Narkilahti S
    4. Lukasiuk K
    5. Nissinen J

    (2005) Administration of diazepam during status epilepticus reduces development and severity of epilepsy in rat

    Epilepsy Research 63:27–42.

    https://doi.org/10.1016/j.eplepsyres.2004.10.003

    • PubMed
    • Google Scholar
85. 1. Pitkänen A
    2. Nehlig A
    3. Brooks-Kayal AR
    4. Dudek FE
    5. Friedman D
    6. Galanopoulou AS
    7. Jensen FE
    8. Kaminski RM
    9. Kapur J
    10. Klitgaard H
    11. Löscher W
    12. Mody I
    13. Schmidt D

    (2013) Issues related to development of antiepileptogenic therapies

    Epilepsia 54 Suppl 4:35–43.

    https://doi.org/10.1111/epi.12297

    • PubMed
    • Google Scholar
86. 1. Puvenna V
    2. Engeler M
    3. Banjara M
    4. Brennan C
    5. Schreiber P
    6. Dadas A
    7. Bahrami A
    8. Solanki J
    9. Bandyopadhyay A
    10. Morris JK
    11. Bernick C
    12. Ghosh C
    13. Rapp E
    14. Bazarian JJ
    15. Janigro D

    (2016) Is phosphorylated tau unique to chronic traumatic encephalopathy? phosphorylated tau in epileptic brain and chronic traumatic encephalopathy

    Brain Research 1630:225–240.

    https://doi.org/10.1016/j.brainres.2015.11.007

    • PubMed
    • Google Scholar
87. 1. Racine RJ

    (1972) Modification of seizure activity by electrical stimulation. II. motor seizure

    Electroencephalography and Clinical Neurophysiology 32:281–294.

    https://doi.org/10.1016/0013-4694(72)90177-0

    • PubMed
    • Google Scholar
88. 1. Rizzi M
    2. Brandt C
    3. Weissberg I
    4. Milikovsky DZ
    5. Pauletti A
    6. Terrone G
    7. Salamone A
    8. Frigerio F
    9. Löscher W
    10. Friedman A
    11. Vezzani A

    (2019) Changes of dimension of EEG/ecog nonlinear dynamics predict epileptogenesis and therapy outcomes

    Neurobiology of Disease 124:373–378.

    https://doi.org/10.1016/j.nbd.2018.12.014

    • PubMed
    • Google Scholar
89. 1. Saletti PG
    2. Ali I
    3. Casillas-Espinosa PM
    4. Semple BD
    5. Lisgaras CP
    6. Moshé SL
    7. Galanopoulou AS

    (2019) In search of antiepileptogenic treatments for post-traumatic epilepsy

    Neurobiology of Disease 123:86–99.

    https://doi.org/10.1016/j.nbd.2018.06.017

    • PubMed
    • Google Scholar
90. 1. Sánchez MP
    2. García-Cabrero AM
    3. Sánchez-Elexpuru G
    4. Burgos DF
    5. Serratosa JM

    (2018) Tau-Induced pathology in epilepsy and dementia: notions from patients and animal models

    International Journal of Molecular Sciences 19:1092.

    https://doi.org/10.3390/ijms19041092

    • PubMed
    • Google Scholar
91. 1. Santana-Gomez C
    2. Andrade P
    3. Hudson MR
    4. Paananen T
    5. Ciszek R
    6. Smith G
    7. Ali I
    8. Rundle BK
    9. Ndode-Ekane XE
    10. Casillas-Espinosa PM
    11. Immonen R
    12. Puhakka N
    13. Jones N
    14. Brady RD
    15. Perucca P
    16. Shultz SR
    17. Pitkänen A
    18. O’Brien TJ
    19. Staba R

    (2019) Harmonization of pipeline for detection of hfos in a rat model of post-traumatic epilepsy in preclinical multicenter study on post-traumatic epileptogenesis

    Epilepsy Research 156:106110.

    https://doi.org/10.1016/j.eplepsyres.2019.03.008

    • Google Scholar
92. 1. Sarkisova KY
    2. Midzianovskaia IS
    3. Kulikov MA

    (2003) Depressive-Like behavioral alterations and c-fos expression in the dopaminergic brain regions in WAG/Rij rats with genetic absence epilepsy

    Behavioural Brain Research 144:211–226.

    https://doi.org/10.1016/s0166-4328(03)00090-1

    • PubMed
    • Google Scholar
93. 1. Scheltema RA
    2. Jankevics A
    3. Jansen RC
    4. Swertz MA
    5. Breitling R

    (2011) PeakML/mzmatch: a file format, Java library, R library, and tool-chain for mass spectrometry data analysis

    Analytical Chemistry 83:2786–2793.

    https://doi.org/10.1021/ac2000994

    • PubMed
    • Google Scholar
94. 1. Schouten M
    2. Bielefeld P
    3. Fratantoni SA
    4. Hubens CJ
    5. Piersma SR
    6. Pham TV
    7. Voskuyl RA
    8. Lucassen PJ
    9. Jimenez CR
    10. Fitzsimons CP

    (2016) Multi-omics profile of the mouse dentate gyrus after kainic acid-induced status epilepticus

    Scientific Data 3:160068.

    https://doi.org/10.1038/sdata.2016.68

    • PubMed
    • Google Scholar
95. 1. Sen A
    2. Thom M
    3. Martinian L
    4. Harding B
    5. Cross JH
    6. Nikolic M
    7. Sisodiya SM

    (2007) Pathological tau tangles localize to focal cortical dysplasia in older patients

    Epilepsia 48:1447–1454.

    https://doi.org/10.1111/j.1528-1167.2007.01107.x

    • PubMed
    • Google Scholar
96. 1. Sharma AK
    2. Reams RY
    3. Jordan WH
    4. Miller MA
    5. Thacker HL
    6. Snyder PW

    (2007) Mesial temporal lobe epilepsy: pathogenesis, induced rodent models and lesions

    Toxicologic Pathology 35:984–999.

    https://doi.org/10.1080/01926230701748305

    • PubMed
    • Google Scholar
97. 1. Shiek Ahmad B
    2. Wark JD
    3. Petty SJ
    4. O’Brien TJ
    5. Gorelik A
    6. Sambrook PN
    7. Hill KD

    (2015) Changes in balance function with chronic antiepileptic drug therapy: a twin and sibling study

    Epilepsia 56:1714–1722.

    https://doi.org/10.1111/epi.13136

    • PubMed
    • Google Scholar
98. 1. Shultz SR
    2. Wright DK
    3. Zheng P
    4. Stuchbery R
    5. Liu SJ
    6. Sashindranath M
    7. Medcalf RL
    8. Johnston LA
    9. Hovens CM
    10. Jones NC
    11. O’Brien TJ

    (2015) Sodium selenate reduces hyperphosphorylated tau and improves outcomes after traumatic brain injury

    Brain 138:1297–1313.

    https://doi.org/10.1093/brain/awv053

    • PubMed
    • Google Scholar
99. 1. Simonato M.
    2. French JA
    3. Galanopoulou AS
    4. O’Brien TJ

    (2013) Issues for new antiepilepsy drug development

    Current Opinion in Neurology 26:195–200.

    https://doi.org/10.1097/WCO.0b013e32835efe29

    • PubMed
    • Google Scholar
100. 1. Simonato M
     2. Brooks-Kayal AR
     3. Engel J
     4. Galanopoulou AS
     5. Jensen FE
     6. Moshé SL
     7. O’Brien TJ
     8. Pitkanen A
     9. Wilcox KS
     10. French JA

     (2014) The challenge and promise of anti-epileptic therapy development in animal models

     The Lancet. Neurology 13:949–960.

     https://doi.org/10.1016/S1474-4422(14)70076-6

     • PubMed
     • Google Scholar
101. 1. Simonato M.
     2. Agoston DV
     3. Brooks-Kayal A
     4. Dulla C
     5. Fureman B
     6. Henshall DC
     7. Pitkänen A
     8. Theodore WH
     9. Twyman RE
     10. Kobeissy FH
     11. Wang KK
     12. Whittemore V
     13. Wilcox KS

     (2021) Identification of clinically relevant biomarkers of epileptogenesis-a strategic roadmap

     Nature Reviews. Neurology 17:231–242.

     https://doi.org/10.1038/s41582-021-00461-4

     • PubMed
     • Google Scholar
102. 1. Smith ML
     2. Olds J
     3. Snyder T
     4. Elliott I
     5. Lach L
     6. Whiting S

     (2014) A follow-up study of cognitive function in young adults who had resective epilepsy surgery in childhood

     Epilepsy & Behavior 32:79–83.

     https://doi.org/10.1016/j.yebeh.2014.01.006

     • PubMed
     • Google Scholar
103. 1. Spanswick SC
     2. Sutherland RJ

     (2010) Object/context-specific memory deficits associated with loss of hippocampal granule cells after adrenalectomy in rats

     Learning & Memory 17:241–245.

     https://doi.org/10.1101/lm.1746710

     • PubMed
     • Google Scholar
104. 1. Srivastava PK
     2. Bagnati M
     3. Delahaye-Duriez A
     4. Ko J-H
     5. Rotival M
     6. Langley SR
     7. Shkura K
     8. Mazzuferi M
     9. Danis B
     10. van Eyll J
     11. Foerch P
     12. Behmoaras J
     13. Kaminski RM
     14. Petretto E
     15. Johnson MR

     (2017) Genome-Wide analysis of differential RNA editing in epilepsy

     Genome Research 27:440–450.

     https://doi.org/10.1101/gr.210740.116

     • PubMed
     • Google Scholar
105. 1. Stafstrom CE
     2. Chronopoulos A
     3. Thurber S
     4. Thompson JL
     5. Holmes GL

     (1993) Age-Dependent cognitive and behavioral deficits after kainic acid seizures

     Epilepsia 34:420–432.

     https://doi.org/10.1111/j.1528-1157.1993.tb02582.x

     • PubMed
     • Google Scholar
106. 1. Stöllberger C
     2. Wegner C
     3. Finsterer J

     (2011) Seizure-associated takotsubo cardiomyopathy

     Epilepsia 52:e160–e167.

     https://doi.org/10.1111/j.1528-1167.2011.03185.x

     • PubMed
     • Google Scholar
107. 1. Stores G

     (1971) Cognitive function in children with epilepsy

     Developmental Medicine and Child Neurology 13:390–393.

     https://doi.org/10.1111/j.1469-8749.1971.tb03280.x

     • PubMed
     • Google Scholar
108. 1. Sun M
     2. Brady RD
     3. Casillas-Espinosa PM
     4. Wright DK
     5. Semple BD
     6. Kim HA
     7. Mychasiuk R
     8. Sobey CG
     9. O’Brien TJ
     10. Vinh A
     11. McDonald SJ
     12. Shultz SR

     (2019) Aged rats have an altered immune response and worse outcomes after traumatic brain injury

     Brain, Behavior, and Immunity 80:536–550.

     https://doi.org/10.1016/j.bbi.2019.04.038

     • PubMed
     • Google Scholar
109. 1. Surges R
     2. Sander JW

     (2012) Sudden unexpected death in epilepsy: mechanisms, prevalence, and prevention

     Current Opinion in Neurology 25:201–207.

     https://doi.org/10.1097/WCO.0b013e3283506714

     • PubMed
     • Google Scholar
110. 1. Tai XY
     2. Koepp M
     3. Duncan JS
     4. Fox N
     5. Thompson P
     6. Baxendale S
     7. Liu JYW
     8. Reeves C
     9. Michalak Z
     10. Thom M

     (2016) Hyperphosphorylated tau in patients with refractory epilepsy correlates with cognitive decline: a study of temporal lobe resections

     Brain 139:2441–2455.

     https://doi.org/10.1093/brain/aww187

     • PubMed
     • Google Scholar
111. 1. Tautenhahn R
     2. Böttcher C
     3. Neumann S

     (2008) Highly sensitive feature detection for high resolution LC/MS

     BMC Bioinformatics 9:504.

     https://doi.org/10.1186/1471-2105-9-504

     • PubMed
     • Google Scholar
112. 1. Taylor J
     2. Kolamunnage-Dona R
     3. Marson AG
     4. Smith PEM
     5. Aldenkamp AP
     6. Baker GA
     7. SANAD study group

     (2010) Patients with epilepsy: cognitively compromised before the start of antiepileptic drug treatment?

     Epilepsia 51:48–56.

     https://doi.org/10.1111/j.1528-1167.2009.02195.x

     • PubMed
     • Google Scholar
113. 1. Taylor RS
     2. Sander JW
     3. Taylor RJ
     4. Baker GA

     (2011) Predictors of health-related quality of life and costs in adults with epilepsy: a systematic review

     Epilepsia 52:2168–2180.

     https://doi.org/10.1111/j.1528-1167.2011.03213.x

     • PubMed
     • Google Scholar
114. 1. Téllez-Zenteno JF
     2. Dhar R
     3. Wiebe S

     (2005) Long-Term seizure outcomes following epilepsy surgery: a systematic review and meta-analysis

     Brain 128:1188–1198.

     https://doi.org/10.1093/brain/awh449

     • PubMed
     • Google Scholar
115. 1. Tellez-Zenteno JF
     2. Patten SB
     3. Jetté N
     4. Williams J
     5. Wiebe S

     (2007) Psychiatric comorbidity in epilepsy: a population-based analysis

     Epilepsia 48:2336–2344.

     https://doi.org/10.1111/j.1528-1167.2007.01222.x

     • PubMed
     • Google Scholar
116. 1. Thom M
     2. Liu JYW
     3. Thompson P
     4. Phadke R
     5. Narkiewicz M
     6. Martinian L
     7. Marsdon D
     8. Koepp M
     9. Caboclo L
     10. Catarino CB
     11. Sisodiya SM

     (2011) Neurofibrillary tangle pathology and Braak staging in chronic epilepsy in relation to traumatic brain injury and hippocampal sclerosis: a post-mortem study

     Brain 134:2969–2981.

     https://doi.org/10.1093/brain/awr209

     • PubMed
     • Google Scholar
117. 1. Thomson KE
     2. Metcalf CS
     3. Newell TG
     4. Huff J
     5. Edwards SF
     6. West PJ
     7. Wilcox KS

     (2020) Evaluation of subchronic administration of antiseizure drugs in spontaneously seizing rats

     Epilepsia 61:1301–1311.

     https://doi.org/10.1111/epi.16531

     • PubMed
     • Google Scholar
118. 1. Valente KDR
     2. Busatto Filho G

     (2013) Depression and temporal lobe epilepsy represent an epiphenomenon sharing similar neural networks: clinical and brain structural evidences

     Arquivos de Neuro-Psiquiatria 71:183–190.

     https://doi.org/10.1590/s0004-282x2013000300011

     • PubMed
     • Google Scholar
119. 1. van Eersel J
     2. Ke YD
     3. Liu X
     4. Delerue F
     5. Kril JJ
     6. Götz J
     7. Ittner LM

     (2010) Sodium selenate mitigates tau pathology, neurodegeneration, and functional deficits in alzheimer’s disease models

     PNAS 107:13888–13893.

     https://doi.org/10.1073/pnas.1009038107

     • PubMed
     • Google Scholar
120. 1. Van Nieuwenhuyse B
     2. Raedt R
     3. Delbeke J
     4. Sprengers M
     5. Dauwe I
     6. Gadeyne S
     7. Wadman W
     8. Boon P
     9. Vonck K

     (2015a)

     Hippocampal dbs affects disease development in the ka rat model for tle

     Epilepsy Currents 15:577.

     • Google Scholar
121. 1. Van Nieuwenhuyse B
     2. Raedt R
     3. Sprengers M
     4. Dauwe I
     5. Gadeyne S
     6. Carrette E
     7. Delbeke J
     8. Wadman WJ
     9. Boon P
     10. Vonck K

     (2015b) The systemic kainic acid rat model of temporal lobe epilepsy: long-term EEG monitoring

     Brain Research 1627:1–11.

     https://doi.org/10.1016/j.brainres.2015.08.016

     • PubMed
     • Google Scholar
122. 1. Vivash L
     2. Malpas CB
     3. Churilov L
     4. Walterfang M
     5. Brodtmann A
     6. Piguet O
     7. Ahmed RM
     8. Bush AI
     9. Hovens CM
     10. Kalincik T
     11. Darby D
     12. Velakoulis D
     13. O’Brien TJ

     (2020) A study protocol for a phase II randomised, double-blind, placebo-controlled trial of sodium selenate as a disease-modifying treatment for behavioural variant frontotemporal dementia

     BMJ Open 10:e040100.

     https://doi.org/10.1136/bmjopen-2020-040100

     • PubMed
     • Google Scholar
123. 1. Whishaw IQ
     2. Jarrard LE

     (1995) Similarities vs. differences in place learning and circadian activity in rats after fimbria-fornix section or ibotenate removal of hippocampal cells

     Hippocampus 5:595–604.

     https://doi.org/10.1002/hipo.450050610

     • PubMed
     • Google Scholar
124. 1. Williams PA
     2. White AM
     3. Clark S
     4. Ferraro DJ
     5. Swiercz W
     6. Staley KJ
     7. Dudek FE

     (2009) Development of spontaneous recurrent seizures after kainate-induced status epilepticus

     The Journal of Neuroscience 29:2103–2112.

     https://doi.org/10.1523/JNEUROSCI.0980-08.2009

     • PubMed
     • Google Scholar
125. 1. Wright DK
     2. O’Brien TJ
     3. Mychasiuk R
     4. Shultz SR

     (2018) Telomere length and advanced diffusion MRI as biomarkers for repetitive mild traumatic brain injury in adolescent rats

     NeuroImage. Clinical 18:315–324.

     https://doi.org/10.1016/j.nicl.2018.01.033

     • PubMed
     • Google Scholar
126. 1. Zeng L-H
     2. Rensing NR
     3. Wong M

     (2009) The mammalian target of rapamycin signaling pathway mediates epileptogenesis in a model of temporal lobe epilepsy

     The Journal of Neuroscience 29:6964–6972.

     https://doi.org/10.1523/JNEUROSCI.0066-09.2009

     • PubMed
     • Google Scholar
127. 1. Zhang G
     2. He P
     3. Tan H
     4. Budhu A
     5. Gaedcke J
     6. Ghadimi BM
     7. Ried T
     8. Yfantis HG
     9. Lee DH
     10. Maitra A
     11. Hanna N
     12. Alexander HR
     13. Hussain SP

     (2013) Integration of metabolomics and transcriptomics revealed a fatty acid network exerting growth inhibitory effects in human pancreatic cancer

     Clinical Cancer Research 19:4983–4993.

     https://doi.org/10.1158/1078-0432.CCR-13-0209

     • PubMed
     • Google Scholar
128. 1. Zou J
     2. Zheng MW
     3. Li G
     4. Su ZG

     (2013) Advanced systems biology methods in drug discovery and translational biomedicine

     BioMed Research International 2013:742835.

     https://doi.org/10.1155/2013/742835

     • PubMed
     • Google Scholar

Decision letter after peer review:

Thank you for submitting your article "Disease-modifying effects of sodium selenate in a rat model of drug-resistant, temporal lobe epilepsy" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Christian Büchel as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Summary of findings and potential significance:

Sodium selenate reduced seizures when administered after initiation of epilepsy, complementing earlier work showing efficacy if administered before initiation. Data were compared to Levitiracetam and vehicle-treated animals. Sodium selenate further improved cognitive impairments. It also persistently reduced phospho-tau and increased PP2A protein expression, and reversed TLE-associated telomere-shortening. Finally, proteome and metabolome data from the animal model of epilepsy is discussed and provide initial insights into the effects of sodium selenate treatment on molecular pathology.

Essential revisions:

1) Address novelty/significance by providing evidence PP2A, tau, and telomeres are critical to the hypothesized mechanisms.

2) Clarify and extend molecular data as described by Reviewer 2.

3) Address lesser concerns itemized below.

Reviewer #1 (Recommendations for the authors):

To improve the paper, the authors should consider how additional experiments could make it novel and a significant advance. Defining the mechanism would be an example. Additional endpoints typically studied or additional models might be useful if they shed light on the mechanism. Clinical relevance would be increased by human data.

Reviewer #2 (Recommendations for the authors):

The multi-omics analysis appears to be the weakest part of the manuscript. Given the interindividual variability and complexity of proteomic and metabolomic data, an n of 3 per group is small. In the Materials and methods section, the authors mention a power analysis that suggested a group size of 8. Please comment.

The authors state that network medicine integration highlights discrete protein-metabolite modules that correlate with treatment and pre-clinical outcomes. This part needs much more attention and clarification. There is no individual discussion of the proteomic and metabolomic data including some supporting Figures (e.g., multivariate analysis/PCA, volcano plot, hierarchical clustering). Results from this section should not be "hidden" in the supplements.

Figure 5 is not intuitive. Please consider using different labels with a clear association to their gene/protein context, e.g, metabolism instead of the red module. Please provide more specific information on module size, gene content, and associations. A network graph (node size = module size, color code of nodes = Spearman correlation coefficient) may be used to help visualization. Are there any proteins or modules that distinguish treatment groups?

Figures 1-4: Please stick to a uniform color code across all figures, so that, e.g., Lev or Sodium selenate treated animals always have the same color in all graphs. The authors further may consider changing the labeling to support a more intuitive understanding of the figures, e.g., sham-control, post-SE+vehicle, post-SE+Lev, post-SE+Sodium selenate

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Disease-modifying effects of sodium selenate in a rat model of drug-resistant, temporal lobe epilepsy" for further consideration by eLife. Your revised article has been evaluated by Christian Büchel (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

The strength of the manuscript continues to be the persistent nature of sodium selenate to reduce seizures, in the KA post-SE model, even after treatment has stopped. The limitations of the paper continue to be the lack of evidence that the changes in molecules like PPAR and h-tau, or changes in telomeres, mediate the effects of selenate. The authors were asked to provide such evidence or make their conclusions more cautious and they do not appear to have done so. However, the data about PPAR and other molecules are interesting. The authors just need to refrain from conclusions about cause-effect. For example, the conclusions state that h-tau mediates the effects of selenate and that was not shown. Other statements along these lines also require revision.

The authors have improved the section about transcriptomics, but here there are mainly correlative data also, so while improved, there is a limit to the impact of the work.

Methodological concerns are:

Only 1 week of seizure analysis by video-EEG. The standard is 2 weeks and many investigators use a longer time because seizures in the post-SE models are not consistent from week-to-week. One reason is seizures cluster so that one week there can be very few seizures and the next week the opposite. This may have been the reason for the variability in the graphs. On the other hand, selenate treatment led to a marked reduction and lower variance by the end of the experimental period, making it unlikely that selenate lacked effects. One issue that should be addressed is how the authors handled the inequality of variance in their statistics. Usually it requires data transformation or non-parametric statistics.

Recommendations:

1. Remove all statements that PPAR, tau, telomeres, (etc.) and transcriptomics data caused effects of selenate. These data are only correlative.

2. Revise statistics so that where variance is unequal in the different groups all data are either transformed (common methods are to take the log or reciprocal of all data points) to resolve the heterogeneity (making parametric statistics possible to use), or (when transformation does not resolve heterogeneity of variance) non-parametric statistics are used. To test whether variance is unequal, Bartlett's test and Brown-Forsythe are used.

Essential revisions:

1) Address novelty/significance by providing evidence PP2A, tau, and telomeres are critical to the hypothesized mechanisms.

We thank the reviewers and editors for their comments. While previous work from our group has described that sodium selenate activates the PP2A subunit B (Corcoran et al., 2010) and reduces phosphorylated tau after traumatic brain injury and administration of KA but before the occurrence of spontaneous seizures (i.e. before epilepsy has been established) (Liu et al., 2016, Shultz et al., 2015). Here, we show that sodium selenate is disease-modifying if given in the chronic established epilepsy state (i.e. 10 weeks post-KA administration). This is of great potential clinical relevance, as most commonly patients present to the neurologist in practice after the epilepsy has been established, not in the pre-epileptic state.

To further investigate the mechanism by which sodium selenate exerts its disease-modifying effects, we evaluated the proteomics and metabolomics from the somatosensory cortex and hippocampus of our TLE rat model. Our untargeted protein investigation found that phosphatase 2 regulatory subunit B α (Ppp2r5a), heat shock protein family A member 2 (Hspa2) and lymphocyte cytosolic protein 1 (Lcp1) were differentially expressed in the somatosensory cortex between selenate and vehicle-treated post-SE rats (Tukey’s HSD, FDR<0.05, Figure 3A, Supplementary File 1). We utilised semi-automated western blots to further validate the modification of the h-tau pathway by sodium selenate in chronically epileptic rats, and found that sodium selenate treatment persistently reduced h-tau compared to vehicle and levetiracetam treated animals (p <0.01 for both comparisons, Figure 3—figure supplement 3A). Similarly, PP2A protein expression was significantly increased in selenate-treated animals compared to vehicle (p <0.01), levetiracetam (p <0.01) and shams (p <0.05, Figure 3—figure supplement 3B).

Also novel is the analyses investigating the effect of sodium selenate treatment in chronically epileptic rats on telomere length. Telomere length has emerged as a potential prognostic biomarker of chronic neurodegenerative conditions (Chan et al., 2020, Kang et al., 2020, Galletly et al., 2017, Lukens et al., 2009). Our hypothesis was that selenate could be a modifier of neurodegeneration pathways, therefore we would anticipate that selenate treatment would prevent telomere shortening. We found that telomere length was significantly reduced in TLE animals treated with vehicle or levetiracetam, and that treatment with sodium selenate mitigated telomere length shortening. The magnitude of the reduced telomere length was strongly associated with the number of seizures the rats manifest, and more pronounced cognitive deficits, in our drug-resistant TLE model. Overall, our data highlights the potential of telomere length as a prognostic biomarker of disease severity and treatment response. Moreover, telomere length has the potential to be used in further trials of DMT (Pg 29-30).

However, unlike individual markers, pathway/network-based prognostic markers should be inherently more reliable since they provide the biological interpretation for the association between multiple molecules and the disease characteristics (seizures, behaviour comorbidities, development of TLE) (Zou et al., 2013) (Pg 30, lines 10-18). Therefore, in an attempt to shed light on the mechanisms by which sodium selenate is disease-modifying in established drug-resistant TLE, we performed a network medicine integration (Pg 27-29) of our untargeted/targeted multi-omics datasets, TLE outcomes (seizures and behaviour) and molecular studies (western blots and telomere length analyses), to evaluate the correlation coefficients of single modules between the different treatment groups. By mapping significant genes/proteins together with metabolites to biological pathways, our network integration approach allowed for a reduction in experimental and biological noise and higher confidence levels, thereby generating potential new disease modules (proteins and metabolites and the cellular pathways they are involved in).

We found modules that constitute the “healthy state”, which operate in a concerted fashion in a healthy brain but are lost in the setting of TLE. The Green Module is positively correlated to epilepsy phenotype, poor cognitive performance, poor balance, shorter telomeres, decreased PP2A, increased h-tau expression, and cardiomyopathies, which have been shown to be associated with several epilepsy syndromes. Of particular interest are the Yellow-Module in both the hippocampal and somatosensory cortex correlation networks as well as Tan and Light-cyan Modules in the somatosensory cortex. All of the above-mentioned modules positively correlated with selenate treatment. The Yellow Module is enriched for pathways involved in ATP metabolism, oxidative phosphorylation, and several inflammatory systems. The Tan Module is enriched for insulin signalling pathways and the Light-cyan Module annotates to fatty acid metabolism pathways. It is important to note that “enrichment” does not mean “increase” or “upregulation”. The enrichment in insulin signalling, for example, highlights the involvement of this pathway in the epilepsy phenotype and selenate treatment, but unfortunately provides no information about the direction of change (upregulation or downregulation). To capture changes in the regulation of biological pathways that may be influenced by sodium selenate treatment we conducted a quantitative enrichment analysis on a pathway level (PADOG) between the selenate and vehicle groups, utilizing the Reactome pathway database resource. Based on the PADOG analysis results, there is significant upregulation of IGFR1 and FGFR signalling cascades and FGFR-mediated PI-3K cascade activation in the selenate treated group compared to vehicle. Given the neuroprotective role of PI-3K signalling in several epilepsy syndromes, it is conceivable that this is one of the potential mechanisms by which sodium selenate exerts its long-term disease-modifying effect. The strong correlation of the Tan Module, which annotates to insulin signalling pathways to selenate treatment only further highlights the involvement of insulin signalling in the potential mechanism of action of this treatment. The downregulation of fatty acid oxidation processes in the selenate group suggests a shift from hyperactive neurons with high-energy demand (like in epileptic seizures) towards neurons with lower energy demand.

Our investigation supports our hypothesis about the importance of the molecules associated with the tau pathway and the potential value of telomere length as a surrogate marker of disease progression and response to treatment. However, future studies should incorporate multi-omics and telomere length analysis integration performed at different time points from brain tissue and biofluids (blood and CSF) to map out the development and progression of epilepsy and treatment response in this model (Pg 30). Further investigation of these pathways as potential mechanisms involved in the mode of action of sodium selenate can be instrumental in understanding and reversing the drug resistance of TLE. To our knowledge, this analysis is the first attempt of integration of metabolomic and proteomic data in an epilepsy network medicine paradigm.

Reviewer #1 (Recommendations for the authors):

To improve the paper, the authors should consider how additional experiments could make it novel and a significant advance. Defining the mechanism would be an example. Additional endpoints typically studied or additional models might be useful if they shed light on the mechanism. Clinical relevance would be increased by human data.

Thanks for the insight, however, as described above, our current work is highly novel and clinically relevant, representing a major advance in the field being the first demonstration of a pharmacological disease-modifying therapy in a model of chronic, established epilepsy. Nevertheless, we have included ideas for future experiments to build up on our molecular findings and further dissect the mechanism of action of sodium selenate (Pg 29-30)

Reviewer #2 (Recommendations for the authors):

The multi-omics analysis appears to be the weakest part of the manuscript. Given the interindividual variability and complexity of proteomic and metabolomic data, an n of 3 per group is small. In the Materials and methods section, the authors mention a power analysis that suggested a group size of 8. Please comment.

The power calculation we provided relates to the epilepsy/behavioural outcomes only and we have now clarified this in the manuscript. We acknowledge that 3 animals per group for the multi-omics analysis may be considered a limitation of our study, but unfortunately, these were the only tissue specimen available for this analysis. We believe that by combining data from two experimental modalities, using an early integration analysis approach, we optimise the opportunity to identify meaningful biological insights. We cite Schouten et al. work (Pg 47), as an example of the utility of a multi-omics approach using a similar sample size (Schouten et al., 2016),

The authors state that network medicine integration highlights discrete protein-metabolite modules that correlate with treatment and pre-clinical outcomes. This part needs much more attention and clarification. There is no individual discussion of the proteomic and metabolomic data including some supporting Figures (e.g., multivariate analysis/PCA, volcano plot, hierarchical clustering). Results from this section should not be "hidden" in the supplements.

We thank the reviewer for the comment. We decided to discuss only the multi-omics integration to facilitate the readability of the main manuscript. The discussion of the multivariate analysis/PCA, volcano plot, and hierarchical clustering as suggested by the reviewer, is interesting but perhaps is best reserved for a more technical paper. Our aim is to demonstrate that sodium selenate has a disease modifying effect in animals with established chronic epilepsy, and to use multi-omics network medicine and bioinformatics approaches to uncover relevant molecular pathways correlated to the response to selenate treatment and the epilepsy phenotype. However, we have now provided partial least squares-discriminant analysis (PLS-DA) and principal component analysis (PCA) plots for each proteomics metabolomics study from each brain region (Figure 3—figure supplement 2). The results of the univariate analysis on Pg 14-16, Figure 3 and we have now added the top 25 differentially expressed proteins as Figure 3—figure supplement 1. The rest of the omics data and all the data is included in the supplementary material or accessible through the public database Dryad.

Figure 5 is not intuitive. Please consider using different labels with a clear association to their gene/protein context, e.g, metabolism instead of the red module. Please provide more specific information on module size, gene content, and associations. A network graph (node size = module size, color code of nodes = Spearman correlation coefficient) may be used to help visualization. Are there any proteins or modules that distinguish treatment groups?

To address this concern, we have now labelled each module by its corresponding representative enriched pathway (Figure 5C and 5D, Pg 22-23). We thank the reviewer for the visualization suggestion, but feel this option would not work well due to the size of the modules identified in our analyses.

Figures 1-4: Please stick to a uniform color code across all figures, so that, e.g., Lev or Sodium selenate treated animals always have the same color in all graphs. The authors further may consider changing the labeling to support a more intuitive understanding of the figures, e.g., sham-control, post-SE+vehicle, post-SE+Lev, post-SE+Sodium selenate

Thanks for the comment, we have modified the figures, maintaining consistency and have changed the labelling as suggested by the reviewer.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

Recommendations:

1. Remove all statements that PPAR, tau, telomeres, (etc.) and transcriptomics data caused effects of selenate. These data are only correlative.

We thank the reviewers and editors for their comments. We have removed and clarified the meaning of those statements throughout the manuscript as suggested by the reviewer. Changes have been incorporated on page 1-2; page 17 line 3; page 23 line 19-20, 24; page 24 line 1-2, page 25 lines 5-11, and page 29 lines 1-3, 18-19.

2. Revise statistics so that where variance is unequal in the different groups all data are either transformed (common methods are to take the log or reciprocal of all data points) to resolve the heterogeneity (making parametric statistics possible to use), or (when transformation does not resolve heterogeneity of variance) non-parametric statistics are used. To test whether variance is unequal, Bartlett's test and Brown-Forsythe are used.

We appreciate the feedback from the reviewers. We have reviewed all the statistics and modified the statistical analysis as requested. All EEG parameters and MWM were normally distributed as per the Shapiro-Wilk test. EEG data were compared using a two-way repeated measure ANOVA. Variance was assessed using the Brown-Forsythe test. For non-normally distributed data or where variance was unequal, a log10 transformation was used (i.e. SPT, NOP) to resolve the heterogeneity. Normally distributed, (OF, EPM, NOR, BT, TL and PP2A western blot) data were analyzed using one-way ANOVA with Tukey post hoc. The data that did not fit a normal or Gaussian distribution (NOP, NOR, SPT, h-tau/total tau western blot) was unequal after a log10 transformation (SPT) the non-parametric Kruskal-Wallis test with Dunn’s post hoc was used instead. Changes are reflected in the figures and the statistical analysis subsection (page 45-46).

Author details

1. Pablo M Casillas-Espinosa

   1. Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Melbourne, Australia
   2. Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
   3. Monash Proteomics & Metabolomics Facility and Monash Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   pablo.casillas-espinosa@monash.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6199-9415

2. Alison Anderson

   1. Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Melbourne, Australia
   2. Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia

   Contribution

   Data curation, Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

3. Anna Harutyunyan

   1. Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Melbourne, Australia
   2. Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia

   Contribution

   Formal analysis, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6165-5639

4. Crystal Li

   Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia

   Contribution

   Data curation, Investigation

   Competing interests

   No competing interests declared

5. Jiyoon Lee

   Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Melbourne, Australia

   Contribution

   Data curation

   Competing interests

   No competing interests declared

6. Emma L Braine

   1. Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Melbourne, Australia
   2. Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia

   Contribution

   Data curation

   Competing interests

   No competing interests declared

7. Rhys D Brady

   1. Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Melbourne, Australia
   2. Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

8. Mujun Sun

   Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia

   Contribution

   Investigation

   Competing interests

   No competing interests declared

9. Cheng Huang

   Department of Neurology, The Alfred Hospital, Commercial Road,, Melbourne, Victoria, Australia

   Contribution

   Data curation, Investigation

   Competing interests

   No competing interests declared

10. Christopher K Barlow

    Department of Neurology, The Alfred Hospital, Commercial Road,, Melbourne, Victoria, Australia

    Contribution

    Data curation

    Competing interests

    No competing interests declared

11. Anup D Shah

    Department of Neurology, The Alfred Hospital, Commercial Road,, Melbourne, Victoria, Australia

    Contribution

    Data curation, Methodology

    Competing interests

    No competing interests declared

12. Ralf B Schittenhelm

    Department of Neurology, The Alfred Hospital, Commercial Road,, Melbourne, Victoria, Australia

    Contribution

    Formal analysis, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

13. Richelle Mychasiuk

    Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia

    Contribution

    Data curation, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

14. Nigel C Jones

    1. Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Melbourne, Australia
    2. Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia

    Contribution

    Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

15. Sandy R Shultz

    1. Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Melbourne, Australia
    2. Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia

    Contribution

    Data curation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-2525-8775

16. Terence J O'Brien

    1. Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Melbourne, Australia
    2. Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
    3. Monash Proteomics & Metabolomics Facility and Monash Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia

    Contribution

    Conceptualization, Supervision, Visualization, Writing – review and editing

    For correspondence

    Terence.OBrien@monash.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7198-8621

• 1,432

  Page views

• 183

  Downloads

• 5

  Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.