Visually or auditorily induced seizures involve the activation of nonhippocampal brain areas and hippocampal removal does not alleviate seizures in a mouse model of temporal lobe epilepsy.

OBJECTIVE: Several studies have attributed epileptic activities in temporal lobe epilepsy (TLE) to the hippocampus; however, the participation of nonhippocampal neuronal networks in the development of TLE is often neglected. Here, we sought to understand how these nonhippocampal networks are involved in the pathology that is associated with TLE disease. METHODS: A kainic acid (KA) model of temporal lobe epilepsy was induced by injecting KA into dorsal hippocampus of C57BL/6J mice. Network activation after spontaneous seizure was assessed using c-Fos expression. Protocols to induce seizure using visual or auditory stimulation were developed, and seizure onset zone (SOZ) and frequency of epileptic spikes were evaluated using electrophysiology. The hippocampus was removed to assess seizure recurrence in the absence of hippocampus. RESULTS: Our results showed that cortical and hippocampal epileptic networks are activated during spontaneous seizures. Perturbation of these networks using visual or auditory stimulation readily precipitates seizures in TLE mice; the frequency of the light-induced or noise-induced seizures depends on the induction modality adopted during the induction period. Localization of SOZ revealed the existence of cortical and hippocampal SOZ in light-induced and noise-induced seizures, and the development of local and remote epileptic spikes in TLE occurs during the early stage of the disease. Importantly, we further discovered that removal of the hippocampi does not stop seizure activities in TLE mice, revealing that seizures in TLE mice can occur independent of the hippocampus. SIGNIFICANCE: This study has shown that the network pathology that evolves in TLE is not localized to the hippocampus; rather, remote brain areas are also recruited. The occurrence of light-induced or noise-induced seizures and epileptic discharges in epileptic mice is a consequence of the activation of nonhippocampal brain areas. This work therefore demonstrates the fundamental role of nonhippocampal epileptic networks in generating epileptic activities with or without the hippocampus in TLE disease.

In silico design of MHC class I high binding affinity peptides through motifs activation map.

BACKGROUND: Finding peptides with high binding affinity to Class I major histocompatibility complex (MHC-I) attracts intensive research, and it serves a crucial part of developing a better vaccine for precision medicine. Traditional methods cost highly for designing such peptides. The advancement of computational approaches reduces the cost of new drug discovery dramatically. Compared with flourishing computational drug discovery area, the immunology area lacks tools focused on in silico design for the peptides with high binding affinity. Attributed to the ever-expanding amount of MHC-peptides binding data, it enables the tremendous influx of deep learning techniques for modeling MHC-peptides binding. To leverage the availability of these data, it is of great significance to find MHC-peptides binding specificities. The binding motifs are one of the key components to decide the MHC-peptides combination, which generally refer to a combination of some certain amino acids at certain sites which highly contribute to the binding affinity. RESULT: In this work, we propose the Motif Activation Mapping (MAM) network for MHC-I and peptides binding to extract motifs from peptides. Then, we substitute amino acid randomly according to the motifs for generating peptides with high affinity. We demonstrated the MAM network could extract motifs which are the features of peptides of highly binding affinities, as well as generate peptides with high-affinities; that is, 0.859 for HLA-A*0201, 0.75 for HLA-A*0206, 0.92 for HLA-B*2702, 0.9 for HLA-A*6802 and 0.839 for Mamu-A1*001:01. Besides, its binding prediction result reaches the state of the art. The experiment also reveals the network is appropriate for most MHC-I with transfer learning. CONCLUSIONS: We design the MAM network to extract the motifs from MHC-peptides binding through prediction, which are proved to generate the peptides with high binding affinity successfully. The new peptides preserve the motifs but vary in sequences.