Investigation of MicroRNA-134 as a Target against Seizures and SUDEP in a Mouse Model of Dravet Syndrome

Dravet syndrome (DS) is a catastrophic form of pediatric epilepsy mainly caused by noninherited mutations in the SCN1A gene. DS patients suffer severe and life-threatening focal and generalized seizures which are often refractory to available anti-seizure medication. Antisense oligonucleotides (ASOs) based approaches may offer treatment opportunities in DS. MicroRNAs are short noncoding RNAs that play a key role in brain structure and function by post-transcriptionally regulating gene expression, including ion channels. Inhibiting miRNA-134 (miR-134) using an antimiR ASO (Ant-134) has been shown to reduce evoked seizures in juvenile and adult mice and reduce epilepsy development in models of focal epilepsy. The present study investigated the levels of miR-134 and whether Ant-134 could protect against hyperthermia-induced seizures, spontaneous seizures and mortality (SUDEP) in F1.Scn1a(1/)tm1kea mice. At P17, animals were intracerebroventricular in-jected with 0.1–1 nmol of Ant-134 and subject to a hyperthermia challenge at postnatal day (P)18. A second cohort of P21 F1.Scn1a(1/)tm1kea mice received Ant-134 and were followed by video and EEG monitoring until P28 to track the incidence of spontaneous seizures and SUDEP. Hippocampal and cortical levels of miR-134 were similar between wild-type (WT) and F1.Scn1a(1/)tm1kea mice. Moreover, Ant-134 had no effect on hyperthermia-induced seizures, spontaneous seizures and SUDEP incidence were unchanged in Ant-134-treated DS mice. These findings suggest that targeting miR-134 does not have therapeutic applications in DS

Antioxidant activity elicited by low dose of caffeine attenuates pentylenetetrazol-induced seizures and oxidative damage in rats

AbstractAlthough caffeine supplementation has a beneficial effect on people with neurological disorders, its implications for oxidative damage related to seizures are not well documented. Thus the aim of this study was to investigate the effects of two weeks caffeine supplementation (6mg/kg; p.o.) on seizures and neurochemical alterations induced by pentylenetetrazol (PTZ 60mg/kg i.p.). Statistical analyses showed that long-term rather than single dose caffeine administration decreased the duration of PTZ-induced seizures in adult male Wistar rats as recorded by cortical electroencephalographic (EEG) and behavioral analysis. The quantification of EEG recordings also revealed that caffeine supplementation protected against a wave increase induced by PTZ. Neurochemical analyses revealed that caffeine supplementation increased glutathione (GSH) content per se and protected against the increase in the levels of thiobarbituric acid reactive substances (TBARS) and oxidized diclorofluoresceine diacetate (DCFH-DA). Also, caffeine prevent the decrease in GSH content and Na+, K+-ATPase activity induced by PTZ. Our data also showed that the infusion of L-buthionine sulfoximine (BSO; 3.2μmol/site i.c.v), an inhibitor of GSH synthesis, two days before injecting PTZ reversed the anticonvulsant effect caused by caffeine. BSO infusion also decreased GSH content and Na+, K+-ATPase activity. However, it increased DCFH-DA oxidation and TBARS per se and reversed the protective effect of caffeine. Results presented in this paper support the neuroprotective effects of low long-term caffeine exposure to epileptic damage and suggest that the increase in the cerebral GSH content caused by caffeine supplementation may provide a new therapeutic approach to the control of seizure

Identification of new features and evaluation of antimir-based seizure therapies in the Scn1a(+/-)tm1Kea mouse model of Dravet syndrome

Dravet syndrome (DS) is a rare and intractable form of paediatric epilepsy characterized by the early onset of seizures caused mainly by mutations in the SCN1A gene encoding the alpha subunit of Nav1.1 channels. Patients with DS suffer with several comorbidities and severe life-threatening seizures that are refractory to antiseizure medication. Several DS mouse models have been generated which have served as valuable tools for understanding underlying mechanisms and identifying new treatments. However, the discovery and preclinical development of new therapies requires full understanding of the epilepsy and behaviour phenotypes in DS mouse models. The Scn1a(+/-)tm1Kea mouse line has demonstrated translational value in identifying known and novel anti-seizure molecules. However, certain epilepsy-related features at the different stages of DS progression, long-term neuropsychiatric comorbidities, and the influence of sex on these parameters have not been fully explored in this DS mouse line. Among novel approaches being pursued for the treatment of drug-resistant epilepsy is the targeting of small noncoding RNAs called microRNA. One of these, microRNA-134 (miR-134), has been shown to target gene networks controlling neuronal microstructure and brain excitability. Levels of miR-134 have been found to be elevated in several rodent models and in human brain tissue resected from temporal lobe epilepsy (TLE) patients. Notably, the use of antisense oligonucleotides (locked nucleic acid (LNA), antimirs/antagomirs) targeting miR-134 (Ant-134) reduced evoked and spontaneous seizures and conferred a sustained neuroprotective effect in multiple rat and mouse models of epilepsy. Based on that, we hypothesized that antimirs might be protective in DS. In this thesis, we proposed three specific aims. First, to characterise the epilepsy phenotype over different developmental stages and associated comorbidities in F1.Scn1a(+/-)tm1Kea mice. Second, to functionally assess the role of miR-134 in the epilepsy phenotype of F1.Scn1a(+/-)tm1Kea mice. Finally, to characterise novel miRNAs that may be relevant for the treatment of DS. Experiments in the first chapter comprised a comprehensive characterisation of phenotypes in F1.Scn1a(+/-)tm1Kea mice. At P18, F1.Scn1a(+/-)tm1Kea mice experience sensitivity to hyperthermia-induced seizures. Between P21 and P28, EEG recordings in F1.Scn1a(+/-)tm1Kea mice combined with video monitoring revealed a high frequency of spontaneous recurrent seizures (SRS) and sudden unexplained death in epilepsy (SUDEP). Power spectral analyses of background EEG activity also revealed that low EEG power in multiple frequency bands was associated with SUDEP risk in F1.Scn1a(+/-)tm1Kea mice during the worsening stage of DS. Later, SRS and SUDEP rates stabilized and then declined in F1.Scn1a(+/-)tm1kea mice. The incidence of SRS ending with death in F1.Scn1a(+/-)tm1kea mice displayed variations with the time of day and sex, with female mice displaying higher numbers of severe seizures resulting in greater SUDEP risk. At ~6 month-old, F1.Scn1a(+/-)tm1kea mice displayed fewer behavioural impairments than expected including hyperactivity, impaired exploratory behaviour and poor nest building performance. These results reveal new features of this model that will optimize use and selection of phenotype assays for future studies on the mechanisms, diagnosis, and treatment of DS. Experiments in the second results chapter investigated the levels of miR-134 and whether Ant-134 could protect against hyperthermia-induced seizures, SRS and mortality (SUDEP) in F1.Scn1a(+/-)tm1kea mice. Hippocampal levels of miR-134 were similar between wildtype and F1.Scn1a(+/-)tm1kea mice. At P17, animals were intracerebroventricular (i.c.v) injected with 0.1 – 1 nmol of Ant-134 and subject to a hyperthermia challenge at P18. A second cohort of P21 F1.Scn1a(+/-)tm1kea mice received Ant-134 and were followed by video and EEG monitoring until P28 to track the incidence of spontaneous seizures and SUDEP. Ant-134 had no effect on hyperthermia-induced seizures, spontaneous seizures or SUDEP incidence. These findings suggest that targeting miR-134 does not have therapeutic applications in DS. In the final results chapter, we investigated a rational-targeting approach, selecting miRNAs that target the SCN1A transcript for inhibition. In addition, we performed small RNA sequencing of miRNAs in DS brain samples. Two miRNAs, miR-582-5p and miR-374a-5p were identified as potential conserved SCN1A/Scn1a targeting miRNAs and antimirs were designed against these. However, a combination of Anti-582-5p and Anti-374a-5p did not protect against hyperthermia-induced seizures in F1.Scn1a(+/-)tm1kea mice. Additionally, Scn1a transcript levels remained unchanged in the antimir-treated F1.Scn1a(+/-)tm1kea mice. Finally, sequencing of miRNAs bound to the RNA silencing component Ago revealed several differentially expressed miRNAs in F1.Scn1a(+/-)tm1kea mice, three of which are predicted to target the SCN1A transcript. Overall, the findings in this thesis enhance our understanding of the phenotypes of the Scn1a(+/-)tm1Kea DS mouse line and identify limitations as well as opportunities for a miRNA targeting approach that may be relevant in DS treatment.  </p

XGboost-based Method for Seizure Detection in Mouse Models of Epilepsy

The 2020 IEEE Signal Processing in Medicine and Biology Symposium, Virtual Conference, 5 December 2020Epilepsy is a chronic neurological disease which affects over 50 million people worldwide [1], caused by the disruption of the finely tuned inhibitory and excitatory balance in brain networks, manifesting clinically as seizures. Electroencephalographic (EEG) monitoring in rodent disease models of epilepsy is critical in the understanding of disease mechanisms and the development of anti-seizure drugs. However, the visual annotation of EEG traces is time-consuming, and is complicated by different models and seizure types. Automated annotation systems can help to solve these problems by reducing expert annotation time and increasing the throughput and reliability of seizure quantification. As machine learning is becoming increasingly popular for modelling sequential signals such as EEG, several researchers have tried machine learning to detect seizures in EEG traces from mouse models of epilepsy. Most existing work [2], [3] can only detect seizures in single mouse models of epilepsy and research on multiple mouse models has been limited to-date.European Commission - Seventh Framework Programme (FP7)Science Foundation IrelandFutureNeuro industry partner

Sexual dimorphism in epilepsy and comorbidities in Dravet syndrome mice carrying a targeted deletion of exon 1 of the Scn1a gene

Objective Dravet Syndrome (DS) is a catastrophic form of paediatric epilepsy associated with multiple comorbidities mainly caused by mutations in the SCN1A gene. DS progresses in three different phases termed febrile, worsening and stabilization stage. Mice that are haploinsufficient for Scn1a faithfully model each stage of DS, although various aspects have not been fully described, including the temporal appearance and sex differences of the epilepsy and comorbidities. The aim of the present study was to investigate the epilepsy landscape according to the progression of DS and the long-term co-morbidities in the Scn1a(+/-)tm1Kea DS mouse line that are not fully understood yet. Methods Male and female F1.Scn1a(+/+) and F1.Scn1a(+/-)tm1Kea mice were assessed in the hyperthermia model or monitored by video electroencephalogram (vEEG) and wireless video-EEG according to the respective stage of DS. Long-term comorbidities were investigated through a battery of behaviour assessments in ∼6 month-old mice. Results At P18, F1.Scn1a(+/-)tm1Kea mice showed the expected sensitivity to hyperthermia-induced seizures. Between P21 and P28, EEG recordings in F1.Scn1a(+/-)tm1Kea mice combined with video monitoring revealed a high frequency of SRS and SUDEP. Power spectral analyses of background EEG activity also revealed that low EEG power in multiple frequency bands was associated with SUDEP risk in F1.Scn1a(+/-)tm1Kea mice during the worsening stage of DS. Later, SRS and SUDEP rates stabilized and then declined in F1.Scn1a(+/-)tm1kea mice. SRS and SUDEP in F1.Scn1a(+/-)tm1kea mice displayed variations with the time of day and sex, with female mice displaying higher numbers of seizures and greater SUDEP risk. F1.Scn1a(+/-)tm1kea mice ∼6 month- old displayed fewer behavioural impairments than expected including hyperactivity, impaired exploratory behaviour and poor nest building performance. Significance These results reveal new features of this model that will optimize use and selection of phenotype assays for future studies on the mechanisms, diagnosis, and treatment of DS.Key point boxScn1a(+/-)tm1kea DS mouse model faithfully reproduces the three stages of DSSex of F1.Scn1a(+/-)tm1kea mice influences the epilepsy phenotypeF1.Scn1a(+/-)tm1kea develop some of the long-term comorbidities of DS</p

Life-span characterization of epilepsy and comorbidities in Dravet syndrome mice carrying a targeted deletion of exon 1 of the Scn1a gene

Objective: Dravet Syndrome (DS) is a catastrophic form of paediatric epilepsy associated with multiple comorbidities mainly caused by mutations in the SCN1A gene. DS progresses in three different phases termed febrile, worsening and stabilization stage. Mice that are haploinsufficient for Scn1a faithfully model each stage of DS, although various aspects have not been fully described, including the temporal appearance and sex differences of the epilepsy and comorbidities. The aim of the present study was to investigate the epilepsy landscape according to the progression of DS and the long-term co-morbidities in the Scn1a(+/-)tm1Kea DS mouse line that are not fully understood yet. Methods: Male and female F1.Scn1a(+/+) and F1.Scn1a(+/-)tm1Kea mice were assessed in the hyperthermia model or monitored by video electroencephalogram (vEEG) and wireless video-EEG according to the respective stage of DS. Long-term comorbidities were investigated through a battery of behaviour assessments in ~6 month-old mice. Results: At P18, F1.Scn1a(+/-)tm1Kea mice showed the expected sensitivity to hyperthermia-induced seizures. Between P21 and P28, EEG recordings in F1.Scn1a(+/-)tm1Kea mice combined with video monitoring revealed a high frequency of SRS and SUDEP (sudden unexpected death in epilepsy). Power spectral analyses of background EEG activity also revealed that low EEG power in multiple frequency bands was associated with SUDEP risk in F1.Scn1a(+/-)tm1Kea mice during the worsening stage of DS. Later, SRS and SUDEP rates stabilized and then declined in F1.Scn1a(+/-)tm1kea mice. Incidence of SRS ending with death in F1.Scn1a(+/-)tm1kea mice displayed variations with the time of day and sex, with female mice displaying higher numbers of severe seizures resulting in greater SUDEP risk. F1.Scn1a(+/-)tm1kea mice ~6 month-old displayed fewer behavioural impairments than expected including hyperactivity, impaired exploratory behaviour and poor nest building performance. Significance: These results reveal new features of this model that will optimize use and selection of phenotype assays for future studies on the mechanisms, diagnosis, and treatment of DS.</p

Life-span characterization of epilepsy and comorbidities in Dravet syndrome mice carrying a targeted deletion of exon 1 of the Scn1a gene

Objective: Dravet Syndrome (DS) is a catastrophic form of paediatric epilepsy associated with multiple comorbidities mainly caused by mutations in the SCN1A gene. DS progresses in three different phases termed febrile, worsening and stabilization stage. Mice that are haploinsufficient for Scn1a faithfully model each stage of DS, although various aspects have not been fully described, including the temporal appearance and sex differences of the epilepsy and comorbidities. The aim of the present study was to investigate the epilepsy landscape according to the progression of DS and the long-term co-morbidities in the Scn1a(+/-)tm1Kea DS mouse line that are not fully understood yet. Methods: Male and female F1.Scn1a(+/+) and F1.Scn1a(+/-)tm1Kea mice were assessed in the hyperthermia model or monitored by video electroencephalogram (vEEG) and wireless video-EEG according to the respective stage of DS. Long-term comorbidities were investigated through a battery of behaviour assessments in ~6 month-old mice. Results: At P18, F1.Scn1a(+/-)tm1Kea mice showed the expected sensitivity to hyperthermia-induced seizures. Between P21 and P28, EEG recordings in F1.Scn1a(+/-)tm1Kea mice combined with video monitoring revealed a high frequency of SRS and SUDEP (sudden unexpected death in epilepsy). Power spectral analyses of background EEG activity also revealed that low EEG power in multiple frequency bands was associated with SUDEP risk in F1.Scn1a(+/-)tm1Kea mice during the worsening stage of DS. Later, SRS and SUDEP rates stabilized and then declined in F1.Scn1a(+/-)tm1kea mice. Incidence of SRS ending with death in F1.Scn1a(+/-)tm1kea mice displayed variations with the time of day and sex, with female mice displaying higher numbers of severe seizures resulting in greater SUDEP risk. F1.Scn1a(+/-)tm1kea mice ~6 month-old displayed fewer behavioural impairments than expected including hyperactivity, impaired exploratory behaviour and poor nest building performance. Significance: These results reveal new features of this model that will optimize use and selection of phenotype assays for future studies on the mechanisms, diagnosis, and treatment of DS.</p

Genetic deletion of microRNA-22 blunts the inflammatory transcriptional response to status epilepticus and exacerbates epilepsy in mice

MicroRNAs perform important roles in the post-transcriptional regulation of gene expression. Sequencing as well as functional studies using antisense oligonucleotides indicate important roles for microRNAs during the development of epilepsy through targeting transcripts involved in neuronal structure, gliosis and inflammation. MicroRNA-22 (miR-22) has been reported to protect against the development of epileptogenic brain networks through suppression of neuroinflammatory signalling. Here, we used mice with a genetic deletion of miR-22 to extend these insights. Mice lacking miR-22 displayed normal behaviour and brain structure and developed similar status epilepticus after intraamygdala kainic acid compared to wildtype animals. Continuous EEG monitoring after status epilepticus revealed, however, an accelerated and exacerbated epilepsy phenotype whereby spontaneous seizures began sooner, occurred more frequently and were of longer duration in miR-22-deficient mice. RNA sequencing analysis of the hippocampus during the period of epileptogenesis revealed a specific suppression of inflammatory signalling in the hippocampus of miR-22-deficient mice. Taken together, these findings indicate a role for miR-22 in establishing early inflammatory responses to status epilepticus. Inflammatory signalling may serve anti-epileptogenic functions and cautions the timing of anti-inflammatory interventions for the treatment of status epilepticus

Detection of spontaneous seizures in EEGs in multiple experimental mouse models of epilepsy

Objective: Electroencephalography (EEG) is a key tool for non-invasive recording of brain activity and the diagnosis of epilepsy. EEG monitoring is also widely employed in rodent models to track epilepsy development and evaluate experimental therapies and interventions. Whereas automated seizure detection algorithms have been developed for clinical EEG, preclinical versions face challenges of inter-model differences and lack of EEG standardization, leaving researchers relying on time-consuming visual annotation of signals. Approach: In this study, a machine learning-based seizure detection approach, "Epi-AI", which can semi-automate EEG analysis in multiple mouse models of epilepsy was developed. Twenty-six mice with a total EEG recording duration of 6,451 hours were used to develop and test the Epi-AI approach. EEG recordings were obtained from two mouse models of kainic acid-induced epilepsy (Model I and III), a genetic model of Dravet syndrome (Model II) and a pilocarpine mouse model of epilepsy (Model IV). The Epi-AI algorithm was compared against two threshold-based approaches for seizure detection, a local Teager-Kaiser energy operator (TKEO) approach and a global Teager-Kaiser energy operator-discrete wavelet transform (TKEO-DWT) combination approach. Main results: Epi-AI demonstrated a superior sensitivity, 91.4% - 98.8%, and specificity, 93.1% - 98.8%, in Model I - III, to both of the threshold-based approaches which performed well on individual mouse models but did not generalise well across models. The performance of the TKEO approach in Model I - III ranged from 66.9% - 91.3% sensitivity and 60.8% - 97.5% specificity to detect spontaneous seizures when compared with expert annotations. The sensitivity and specificity of the TKEO-DWT approach were marginally better than the TKEO approach in Model I-III at 73.2% - 80.1% and 75.8% - 98.1%, respectively. When tested on EEG from Model IV which was not used in developing the Epi-AI approach, Epi-AI was able to identify seizures with 76.3% sensitivity and 98.1% specificity. Significance: Epi-AI has the potential to provide fast, objective and reproducible semi-automated analysis of multiple types of seizure in long-duration EEG recordings in rodents