WOREE syndrome caused by human germline biallelic mutations in WWOX is a neurodevelopmental disorder characterized by intractable epilepsy, severe developmental delay, ataxia and premature death at the age of 2–4 years. The underlying mechanisms of WWOX actions are poorly understood. In the current study, we show that specific neuronal deletion of murine Wwox produces phenotypes typical of the Wwox-null mutation leading to brain hyperexcitability, intractable epilepsy, ataxia and postnatal lethality. A significant decrease in transcript levels of genes involved in myelination was observed in mouse cortex and hippocampus. Wwox-mutant mice exhibited reduced maturation of oligodendrocytes, reduced myelinated axons and impaired axonal conductivity. Brain hyperexcitability and hypomyelination were also revealed in human brain organoids with a WWOX deletion. These findings provide cellular and molecular evidence for myelination defects and hyperexcitability in the WOREE syndrome linked to neuronal function of WWOX.
The School Malaise Trap Program (SMTP) provides a technologically sophisticated and scientifically relevant educational experience that exposes students to the diversity of life, enhancing their understanding of biodiversity while promoting environmental stewardship. Since 2013, the SMTP has allowed 15,000 students at 350 primary and secondary schools to explore insect diversity in Canadian schoolyards. Students at each school collected hundreds of insects for an analysis of DNA sequence variation that enabled their rapid identification to a species. Through this hands-on approach, they participated in a learning exercise that conveys a real sense of scientific discovery. As well, the students contributed valuable data to the largest biodiversity genomics initiative ever undertaken: the International Barcode of Life project. To date, the SMTP has sequenced over 80,000 insect specimens, which includes representatives of 7,990 different species, nearly a tenth of the Canadian fauna. Both surprisingly and importantly, the collections generated the first DNA barcode records for 1,288 Canadian species.
Phase-amplitude coupling analysis shows that a state of postictal generalized EEG suppression has increased delta-gamma coupling. These coupling features, used with an unsupervised hidden Markov model, reliably differentiated four substates in seizure episodes. A sudden unexpected death in epilepsy case study showed coupling activity similar to a postictal state. Postictal generalized EEG suppression is the state of suppression of electrical activity at the end of a seizure. Prolongation of this state has been associated with increased risk of sudden unexpected death in epilepsy, making characterization of underlying electrical rhythmic activity during postictal suppression an important step in improving epilepsy treatment. Phase-amplitude coupling in EEG reflects cognitive coding within brain networks and some of those codes highlight epileptic activity; therefore, we hypothesized that there are distinct phase-amplitude coupling features in the postictal suppression state that can provide an improved estimate of this state in the context of patient risk for sudden unexpected death in epilepsy. We used both intracranial and scalp EEG data from eleven patients (six male, five female; age range 21–41 years) containing 25 seizures, to identify frequency dynamics, both in the ictal and postictal EEG suppression states. Cross-frequency coupling analysis identified that during seizures there was a gradual decrease of phase frequency in the coupling between delta (0.5-4 Hz) and gamma (30+ Hz), which was followed by an increased coupling between the phase of 0.5-1.5 Hz signal and amplitude of 30-50 Hz signal in the postictal state as compared to the pre-seizure baseline. This marker was consistent across patients. Then, using these postictal-specific features, an unsupervised state classifier – a hidden Markov model – was able to reliably classify four distinct states of seizure episodes, including a postictal suppression state. Furthermore, a connectome analysis of the postictal suppression states showed increased information flow within the network during postictal suppression states as compared to the pre-seizure baseline, suggesting enhanced network communication. When the same tools were applied to the EEG of an epilepsy patient who died unexpectedly, ictal coupling dynamics disappeared and postictal phase-amplitude coupling remained constant throughout. Overall, our findings suggest that there are active postictal networks, as defined through coupling dynamics, that can be used to objectively classify the postictal suppression state; furthermore, in a case study of sudden unexpected death in epilepsy, the network does not show ictal-like phase-amplitude coupling features despite the presence of convulsive seizures, and instead demonstrates activity similar to postictal. The postictal suppression state is a period of elevated network activity as compared to the baseline activity which can provide key insights into the epileptic pathology.
In the epileptic brain, phase amplitude cross-frequency coupling (CFC) features have been used to objectively classify seizure-related states, and the inter-seizure state has been demonstrated as being random, in contrast to the seizure state being predictable; however, the excitatory and inhibitory networks underlying their dynamics remain unclear. Therefore, the objectives of this study are to classify the dynamics of seizure sub-states labeling seizure-like event (SLE) onset and termination intervals using CFC features and to obtain their underlying excitatory/inhibitory cellular correlates. SLEs were induced in mouse neocortical brain slices using a low-magnesium perfusate, and were recorded in Layer II/III using simultaneous local field potential (LFP) and whole-cell voltage clamp electrodes. Classification of onset and termination of SLE transitions was investigated using CFC features in conjunction with an unsupervised two-state hidden Markov model (HMM). γ-Distributions of their durations indicated that both are predictable. Furthermore, omitting 4 Hz from the HMM classifier switched both SLE sub-states from statistically deterministic to random without changing the dynamics of the SLE state. These results were generalized to 4-aminopyridine (4-AP)-induced SLEs and human seizure traces. Only during these sub-states, both excitatory and inhibitory currents coupled with the field. Where excitatory currents phase locked to a broad range of frequencies between 1 and 12 Hz, inhibitory currents dominantly phase locked at 4 Hz. We conclude that inhibition underlies the predictability of neocortical CFC-defined SLE transition sub-states.
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