Biology subjects

Koh, H. Y.

Publications and source records attributed to Koh, H. Y..

3 recordsLinked to original sources

bioRxiv2024

Zebrafish models of candidate human epilepsy-associated genes provide evidence of hyperexcitability

Hundreds of novel candidate human epilepsy-associated genes have been identified thanks to advancements in next-generation sequencing and large genome-wide association studies, but establishing genetic etiology requires functional validation. We generated a list of >2200 candidate epilepsy-associated genes, of which 81 were determined suitable for the generation of loss-of-function zebrafish models via CRISPR/Cas9 gene editing. Of those 81 crispants, 48 were successfully established as stable mutant lines and assessed for seizure-like swim patterns in a primary F2 screen. Evidence of seizure-like behavior was present in 5 (arfgef1, kcnd2, kcnv1, ubr5, wnt8b) of the 48 mutant lines assessed. Further characterization of those 5 lines provided evidence for epileptiform activity via electrophysiology in kcnd2 and wnt8b mutants. Additionally, arfgef1 and wnt8b mutants showed a decrease in the number of inhibitory interneurons in the optic tectum of larval animals. Furthermore, RNAseq revealed convergent transcriptional abnormalities between mutant lines, consistent with their developmental defects and hyperexcitable phenotypes. These zebrafish models provide strongest experimental evidence supporting the role of ARFGEF1, KCND2, and WNT8B in human epilepsy and further demonstrate the utility of this model system for evaluating candidate human epilepsy genes. HighlightsO_LIZebrafish models generated by CRISPR/Cas9 gene editing display seizure-like swim patterns in five candidate human epilepsy genes: arfgef1, kcnd2, kcnv1, ubr5, wnt8b. C_LIO_LILocal field potential abnormalities recorded from kcnd2 and wnt8b crispants provide additional evidence of hyperexcitability. C_LIO_LIArfgef1 and wnt8b mutant larvae have fewer inhibitory interneurons than wild type in the optic tectum. C_LIO_LICRISPR-generated mutants of epilepsy genes displayed convergent transcriptional dysregulation, consistent with developmental abnormalities and their hyperexcitability phenotype. C_LI

genetics↗

bioRxiv2023

Threshold of somatic mosaicism disrupting the brain function

Somatic mosaicism in a fraction of brain cells causes neurodevelopmental disorders, including childhood intractable epilepsy. However, the threshold for somatic mosaicism leading to brain dysfunction is unknown. In this study, we induced various mosaic burdens in mice of focal cortical dysplasia type II (FCD II), featuring mTOR somatic mosaicism and spontaneous behavioral seizures. Mosaic burdens ranged from approximately 1,000 to 40,000 neurons expressing the mTOR mutant in the somatosensory (SSC) or medial prefrontal (PFC) cortex. Surprisingly, just [~]8,000-9,000 neurons expressing the MTOR mutant were sufficient to trigger epileptic seizures. Mutational burden correlated with seizure frequency and onset, with a higher tendency for electrographic inter-ictal spikes and beta- and gamma-frequency oscillations in FCD II mice exceeding the threshold. Moreover, mutation-negative FCD II patients in deep sequencing of their bulky brain tissues revealed somatic mosaicism of mTOR pathway genes as low as 0.07% in resected brain tissues through ultra-deep targeted sequencing (up to 20 million reads). Thus, our study suggests that extremely low levels of somatic mosaicism can contribute to brain dysfunction.

genetics↗

bioRxiv2023

PSICHIC: physicochemical graph neural network for learning protein-ligand interaction fingerprints from sequence data

In drug discovery, determining the binding affinity and functional effects of small-molecule ligands on proteins is critical. Current computational methods can predict these protein-ligand interaction properties but often lose accuracy without high-resolution protein structures and falter in predicting functional effects. We introduce PSICHIC (PhySIcoCHemICal graph neural network), a framework uniquely incorporating physicochemical constraints to decode interaction fingerprints directly from sequence data alone. This enables PSICHIC to attain first-of-its-kind emergent capabilities in deciphering mechanisms underlying protein-ligand interactions, achieving state-of-the-art accuracy and interpretability. Trained on identical protein-ligand pairs without structural data, PSICHIC matched and even surpassed leading structure-based methods in binding affinity prediction. In a library screening for adenosine A1 receptor agonists, PSICHIC discerned functional effects effectively, ranking the sole novel agonist within the top three. PSICHICs interpretable fingerprints identified protein residues and ligand atoms involved in interactions. We foresee PSICHIC reshaping virtual screening and deepening our understanding of protein-ligand interactions.

bioinformatics↗