Tracking the motion of the KV1.2 voltage sensor reveals the molecular perturbations caused by a de novo mutation in a case of epilepsy

KEY POINTS

KV1.2 channels, encoded by the KCNA2 gene, regulate neuronal excitability by conducting K+ upon depolarization. A new KCNA2 missense variant was discovered in a patient with epilepsy, causing amino acid substitution F302L at helix S4, in the KV1.2 voltage-sensing domain. Immunocytochemistry and flow cytometry showed that F302L does not impair KCNA2 subunit surface trafficking. Molecular dynamics simulations indicated that F302L alters the exposure of S4 residues to membrane lipids. Voltage clamp fluorometry revealed that the voltage-sensing domain of KV1.2-F302L channels is more sensitive to depolarization. Accordingly, KV1.2-F302L channels opened faster and at more negative potentials; however, they also exhibited enhanced inactivation: that is, F302L causes both gain- and loss-of-function effects. Coexpression of KCNA2-WT and -F302L did not fully rescue these effects. The proband's symptoms are more characteristic of patients with loss of KCNA2 function. Enhanced KV1.2 inactivation could lead to increased synaptic release in excitatory neurons, steering neuronal circuits towards epilepsy.

ABSTRACT

An exome-based diagnostic panel in an infant with epilepsy revealed a previously unreported de novo missense variant in KCNA2, which encodes voltage-gated K+ channel KV1.2. This variant causes substitution F302L, in helix S4 of the KV1.2 voltage-sensing domain (VSD). F302L does not affect KCNA2 subunit membrane trafficking. However, it does alter channel functional properties, accelerating channel opening at more hyperpolarized membrane potentials, indicating gain of function. F302L also caused loss of KV1.2 function via accelerated inactivation onset, decelerated recovery and shifted inactivation voltage dependence to more negative potentials. These effects, which are not fully rescued by coexpression of wild-type and mutant KCNA2 subunits, probably result from the enhancement of VSD function, as demonstrated by optically tracking VSD depolarization-evoked conformational rearrangements. In turn, molecular dynamics simulations suggest altered VSD exposure to membrane lipids. Compared to other encephalopathy patients with KCNA2 mutations, the proband exhibits mild neurological impairment, more characteristic of patients with KCNA2 loss of function. Based on this information, we propose a mechanism of epileptogenesis based on enhanced KV1.2 inactivation leading to increased synaptic release preferentially in excitatory neurons, and hence the perturbation of the excitatory/inhibitory balance of neuronal circuits.

Development and validation of an interpretable deep learning framework for Alzheimer's disease classification

Alzheimer's disease is the primary cause of dementia worldwide, with an increasing morbidity burden that may outstrip diagnosis and management capacity as the population ages. Current methods integrate patient history, neuropsychological testing and MRI to identify likely cases, yet effective practices remain variably applied and lacking in sensitivity and specificity. Here we report an interpretable deep learning strategy that delineates unique Alzheimer's disease signatures from multimodal inputs of MRI, age, gender, and Mini-Mental State Examination score. Our framework linked a fully convolutional network, which constructs high resolution maps of disease probability from local brain structure to a multilayer perceptron and generates precise, intuitive visualization of individual Alzheimer's disease risk en route to accurate diagnosis. The model was trained using clinically diagnosed Alzheimer's disease and cognitively normal subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (n = 417) and validated on three independent cohorts: the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) (n = 382), the Framingham Heart Study (n = 102), and the National Alzheimer's Coordinating Center (NACC) (n = 582). Performance of the model that used the multimodal inputs was consistent across datasets, with mean area under curve values of 0.996, 0.974, 0.876 and 0.954 for the ADNI study, AIBL, Framingham Heart Study and NACC datasets, respectively. Moreover, our approach exceeded the diagnostic performance of a multi-institutional team of practicing neurologists (n = 11), and high-risk cerebral regions predicted by the model closely tracked post-mortem histopathological findings. This framework provides a clinically adaptable strategy for using routinely available imaging techniques such as MRI to generate nuanced neuroimaging signatures for Alzheimer's disease diagnosis, as well as a generalizable approach for linking deep learning to pathophysiological processes in human disease.