Video_1_Efficient strategies based on behavioral and electrophysiological methods for epilepsy-related gene screening in the Drosophila model.MP4

IntroductionWith the advent of trio-based whole-exome sequencing, the identification of epilepsy candidate genes has become easier, resulting in a large number of potential genes that need to be validated in a whole-organism context. However, conducting animal experiments systematically and efficiently remains a challenge due to their laborious and time-consuming nature. This study aims to develop optimized strategies for validating epilepsy candidate genes using the Drosophila model.MethodsThis study incorporate behavior, morphology, and electrophysiology for genetic manipulation and phenotypic examination. We utilized the Gal4/UAS system in combination with RNAi techniques to generate loss-of-function models. We performed a range of behavioral tests, including two previously unreported seizure phenotypes, to evaluate the seizure behavior of mutant and wild-type flies. We used Gal4/UAS-mGFP flies to observe the morphological alterations in the brain under a confocal microscope. We also implemented patch-clamp recordings, including a novel electrophysiological method for studying synapse function and improved methods for recording action potential currents and spontaneous EPSCs on targeted neurons.ResultsWe applied different techniques or methods mentioned above to investigate four epilepsy-associated genes, namely Tango14, Klp3A, Cac, and Sbf, based on their genotype-phenotype correlation. Our findings showcase the feasibility and efficiency of our screening system for confirming epilepsy candidate genes in the Drosophila model.DiscussionThis efficient screening system holds the potential to significantly accelerate and optimize the process of identifying epilepsy candidate genes, particularly in conjunction with trio-based whole-exome sequencing.</p

Video_2_Efficient strategies based on behavioral and electrophysiological methods for epilepsy-related gene screening in the Drosophila model.MP4

IntroductionWith the advent of trio-based whole-exome sequencing, the identification of epilepsy candidate genes has become easier, resulting in a large number of potential genes that need to be validated in a whole-organism context. However, conducting animal experiments systematically and efficiently remains a challenge due to their laborious and time-consuming nature. This study aims to develop optimized strategies for validating epilepsy candidate genes using the Drosophila model.MethodsThis study incorporate behavior, morphology, and electrophysiology for genetic manipulation and phenotypic examination. We utilized the Gal4/UAS system in combination with RNAi techniques to generate loss-of-function models. We performed a range of behavioral tests, including two previously unreported seizure phenotypes, to evaluate the seizure behavior of mutant and wild-type flies. We used Gal4/UAS-mGFP flies to observe the morphological alterations in the brain under a confocal microscope. We also implemented patch-clamp recordings, including a novel electrophysiological method for studying synapse function and improved methods for recording action potential currents and spontaneous EPSCs on targeted neurons.ResultsWe applied different techniques or methods mentioned above to investigate four epilepsy-associated genes, namely Tango14, Klp3A, Cac, and Sbf, based on their genotype-phenotype correlation. Our findings showcase the feasibility and efficiency of our screening system for confirming epilepsy candidate genes in the Drosophila model.DiscussionThis efficient screening system holds the potential to significantly accelerate and optimize the process of identifying epilepsy candidate genes, particularly in conjunction with trio-based whole-exome sequencing.</p

Annual HFRS cases and annual moisture condition, 1991–2010.

<p>(<i>A</i>) Temporal dynamics of annual precipitation and HFRS cases. (<i>B</i>) Scatterplot of annual precipitation and HFRS cases. (<i>C</i>) Temporal dynamics of annual mean AH and HFRS cases. (<i>D</i>) Scatterplot of annual mean AH and HFRS cases. The thick solid straight lines are linear regressions of annual HFRS cases and moisture condition.</p

Geographic location of Changsha, China.

<p>Geographic location of Changsha, China.</p

Observed versus predicted HFRS cases in Changsha.

<p>(<i>A</i>) Temporal dynamics, and (<i>B</i>) scatterplot.</p

Wavelet power spectrum of HFRS incidence in Changsha.

<p>(<i>A</i>) Temporal variation in climatic variables and the number of hemorrhagic fever with renal syndrome (HFRS) cases in Changsha, 1991–2010. (<i>B</i>) The wavelet power spectrum of monthly number of HFRS cases by date of symptoms onset reported through the surveillance system in Changsha during the period 1991–2010 (square root transformed). The left panel illustrates the wavelet power spectrum for the different series (x-axia: time in year; y-axis: period in year). The power is coded from low values, in dark blue, to high values, in dark red. Statistically significant areas (threshold of 5% confidence interval) in wavelet power spectrum (left panels) are highlighted with dashed line; the cone of influence (region not influenced by edge effects) is also indicated. Finally, the right panels show the mean spectrm (solid line) with its significant threshold value of 5% (dashed line).</p

Maximum cross-correlation coefficients of monthly environmental variables and notifications of HFRS: Changsha, China, 1991–2010

*<p><i>p</i><0.01.</p>a<p>Chi-square test for cross-correlations.</p

Summary of the model obtained for Changsha.^a

<p>IRR, incidence rate ratio.</p>a<p>Dummy variables for month were included in the final model.</p