Autism-associated mutations in KV7 channels induce gating pore current

2021 ◽  
Vol 118 (45) ◽  
pp. e2112666118
Author(s):  
Tamer M. Gamal El-Din ◽  
Timothy Lantin ◽  
Christopher W. Tschumi ◽  
Barbara Juarez ◽  
Meagan Quinlan ◽  
...  

Autism spectrum disorder (ASD) adversely impacts >1% of children in the United States, causing social interaction deficits, repetitive behaviors, and communication disorders. Genetic analysis of ASD has advanced dramatically through genome sequencing, which has identified >500 genes with mutations in ASD. Mutations that alter arginine gating charges in the voltage sensor of the voltage-gated potassium (KV) channel KV7 (KCNQ) are among those frequently associated with ASD. We hypothesized that these gating charge mutations would induce gating pore current (also termed ω-current) by causing an ionic leak through the mutant voltage sensor. Unexpectedly, we found that wild-type KV7 conducts outward gating pore current through its native voltage sensor at positive membrane potentials, owing to a glutamine in the third gating charge position. In bacterial and human KV7 channels, gating charge mutations at the R1 and R2 positions cause inward gating pore current through the resting voltage sensor at negative membrane potentials, whereas mutation at R4 causes outward gating pore current through the activated voltage sensor at positive potentials. Remarkably, expression of the KV7.3/R2C ASD-associated mutation in vivo in midbrain dopamine neurons of mice disrupts action potential generation and repetitive firing. Overall, our results reveal native and mutant gating pore current in KV7 channels and implicate altered control of action potential generation by gating pore current through mutant KV7 channels as a potential pathogenic mechanism in autism.

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Benjamin Grieb ◽  
Sivaranjan Uppala ◽  
Gal Sapir ◽  
David Shaul ◽  
J. Moshe Gomori ◽  
...  

AbstractDirect and real-time monitoring of cerebral metabolism exploiting the drastic increase in sensitivity of hyperpolarized 13C-labeled metabolites holds the potential to report on neural activity via in-cell metabolic indicators. Here, we followed the metabolic consequences of curbing action potential generation and ATP-synthase in rat cerebrum slices, induced by tetrodotoxin and oligomycin, respectively. The results suggest that pyruvate dehydrogenase (PDH) activity in the cerebrum is 4.4-fold higher when neuronal firing is unperturbed. The PDH activity was 7.4-fold reduced in the presence of oligomycin, and served as a pharmacological control for testing the ability to determine changes to PDH activity in viable cerebrum slices. These findings may open a path towards utilization of PDH activity, observed by magnetic resonance of hyperpolarized 13C-labeled pyruvate, as a reporter of neural activity.


2020 ◽  
Author(s):  
Haishuai Wang ◽  
Paul Avillach

BACKGROUND In the United States, about 3 million people have autism spectrum disorder (ASD), and around 1 out of 59 children are diagnosed with ASD. People with ASD have characteristic social communication deficits and repetitive behaviors. The causes of this disorder remain unknown; however, in up to 25% of cases, a genetic cause can be identified. Detecting ASD as early as possible is desirable because early detection of ASD enables timely interventions in children with ASD. Identification of ASD based on objective pathogenic mutation screening is the major first step toward early intervention and effective treatment of affected children. OBJECTIVE Recent investigation interrogated genomics data for detecting and treating autism disorders, in addition to the conventional clinical interview as a diagnostic test. Since deep neural networks perform better than shallow machine learning models on complex and high-dimensional data, in this study, we sought to apply deep learning to genetic data obtained across thousands of simplex families at risk for ASD to identify contributory mutations and to create an advanced diagnostic classifier for autism screening. METHODS After preprocessing the genomics data from the Simons Simplex Collection, we extracted top ranking common variants that may be protective or pathogenic for autism based on a chi-square test. A convolutional neural network–based diagnostic classifier was then designed using the identified significant common variants to predict autism. The performance was then compared with shallow machine learning–based classifiers and randomly selected common variants. RESULTS The selected contributory common variants were significantly enriched in chromosome X while chromosome Y was also discriminatory in determining the identification of autistic from nonautistic individuals. The ARSD, MAGEB16, and MXRA5 genes had the largest effect in the contributory variants. Thus, screening algorithms were adapted to include these common variants. The deep learning model yielded an area under the receiver operating characteristic curve of 0.955 and an accuracy of 88% for identifying autistic from nonautistic individuals. Our classifier demonstrated a significant improvement over standard autism screening tools by average 13% in terms of classification accuracy. CONCLUSIONS Common variants are informative for autism identification. Our findings also suggest that the deep learning process is a reliable method for distinguishing the diseased group from the control group based on the common variants of autism.


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