Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tuebingen, Hoppe-Seyler-Str. 3, D-72076 Tuebingen, Germany; Methods in Medical Informatics, Department of Computer Science, University of Tuebingen, Sand 14, D-72076 Tuebingen, Germany.
Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tuebingen, Hoppe-Seyler-Str. 3, D-72076 Tuebingen, Germany.
EBioMedicine. 2022 Jul;81:104115. doi: 10.1016/j.ebiom.2022.104115. Epub 2022 Jun 24.
Variants in genes encoding voltage-gated potassium channels are associated with a broad spectrum of neurological diseases including epilepsy, ataxia, and intellectual disability. Knowledge of the resulting functional changes, characterized as overall ion channel gain- or loss-of-function, is essential to guide clinical management including precision medicine therapies. However, for an increasing number of variants, little to no experimental data is available. New tools are needed to evaluate variant functional effects.
We catalogued a comprehensive dataset of 959 functional experiments across 19 voltage-gated potassium channels, leveraging data from 782 unique disease-associated and synthetic variants. We used these data to train a taxonomy-based multi-task learning support vector machine (MTL-SVM), and compared performance to several baseline methods.
MTL-SVM maintains channel family structure during model training, improving overall predictive performance (mean balanced accuracy 0·718 ± 0·041, AU-ROC 0·761 ± 0·063) over baseline (mean balanced accuracy 0·620 ± 0·045, AU-ROC 0·711 ± 0·022). We can obtain meaningful predictions even for channels with few known variants (KCNC1, KCNQ5).
Our model enables functional variant prediction for voltage-gated potassium channels. It may assist in tailoring current and future precision therapies for the increasing number of patients with ion channel disorders.
This work was supported by intramural funding of the Medical Faculty, University of Tuebingen (PATE F.1315137.1), the Federal Ministry for Education and Research (Treat-ION, 01GM1907A/B/G/H) and the German Research Foundation (FOR-2715, Le1030/16-2, He8155/1-2).
编码电压门控钾通道的基因变异与广泛的神经疾病有关，包括癫痫、共济失调和智力障碍。了解导致整体离子通道功能增益或损失的功能变化对于指导临床管理，包括精准医学治疗，至关重要。然而，对于越来越多的变异，几乎没有实验数据。需要新的工具来评估变体的功能影响。
我们编制了一个涵盖 19 种电压门控钾通道的 959 项功能实验的综合数据集，利用来自 782 个独特疾病相关和合成变异的数据。我们使用这些数据来训练基于分类法的多任务学习支持向量机（MTL-SVM），并将性能与几种基线方法进行比较。
MTL-SVM 在模型训练过程中保持通道家族结构，提高了整体预测性能（平均平衡准确率 0.718±0.041，AU-ROC 0.761±0.063），优于基线（平均平衡准确率 0.620±0.045，AU-ROC 0.711±0.022）。即使对于已知变体较少的通道（KCNC1，KCNQ5），我们也可以获得有意义的预测。
我们的模型可以对电压门控钾通道的功能变体进行预测。它可以帮助为越来越多的离子通道疾病患者定制当前和未来的精准治疗。
这项工作得到了图宾根大学医学系内部资金（PATE F.1315137.1）、联邦教育和研究部（Treat-ION，01GM1907A/B/G/H）和德国研究基金会（FOR-2715，Le1030/16-2，He8155/1-2）的支持。
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