Biology subjects

Kang, H.-C.

Publications and source records attributed to Kang, H.-C..

4 recordsLinked to original sources

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↗

bioRxiv2022

Comprehensive multiomic profiling of somatic mutations in malformations of cortical development

Malformations of cortical development (MCD) are neurological conditions displaying focal disruption of cortical architecture and cellular organization arising during embryogenesis, largely from somatic mosaic mutations. Identifying the genetic causes of MCD has been a challenge, as mutations remain at low allelic fractions in brain tissue resected to treat epilepsy. Here, we report a genetic atlas from 317 brain resections, identifying 69 mutated genes through intensive profiling of somatic mutations, combining whole-exome and targeted-amplicon sequencing with functional validation and single-cell sequencing. Genotype-phenotype correlation analysis elucidated specific MCD gene sets associating distinct pathophysiological and clinical phenotypes. The unique spatiotemporal expression patterns identified by comparing single-nucleus transcriptional sequences of mutated genes in control and patient brains implicate critical roles in excitatory neurogenic pools during brain development, and in promoting neuronal hyperexcitability after birth.

genetics↗

bioRxiv2020

Successful Correction of ALD Patient-derived iPSCs Using CRISPR/Cas9

X-linked adrenoleukodystrophy (ALD) caused by the ABCD1 mutation, is the most common inherited peroxisomal disease. It is characterized by three phenotypes: inflammatory cerebral demyelination, progressive myelopathy, and adrenal insufficiency, but there is no genotype-phenotype correlation. Hematopoietic stem cell transplantation can only be used in a few patients in the early phase of cerebral inflammation; therefore, most affected patients have no curative option. Previously, we reported the generation of an ALD patient-derived iPSC model and its differentiation to oligodendrocytes. In this study, we have performed the first genome editing of ALD patient-derived iPSCs using homology-directed repair (HDR). The mutation site, c.1534G>A [GenBank: NM_000033.4], was corrected by introducing ssODN and the CRISPR/Cas9 system. The cell line exhibited normal ALD protein expression following genome editing. We differentiated the intermediate oligodendrocytes from mutation-corrected iPSCs and the metabolic derangement of ALD tended to correct but was not statistically significant. Mutation-corrected iPSCs from ALD patient can be used in research into the pathophysiology of and therapeutics for ALD.

neuroscience↗

bioRxiv2017

Accurate detection of low-level somatic mutations with technical replication for next-generation sequencing

Accurate genome-wide detection of somatic mutations with low variant allele frequency (VAF, <1%) has proven difficult, for which generalized, scalable methods are lacking. Herein, we describe a new computational method, called RePlow that we developed to detect low-VAF somatic mutations based on simple, library-level replicates for next-generation sequencing on any platform. Through joint analysis of replicates, RePlow is able to remove prevailing background errors in next-generation sequencing analysis, facilitating remarkable improvement in the detection accuracy for low-VAF somatic mutations (up to [~]99% reduction in false positives). The method was validated in independent cancer panel and brain tissue sequencing data. Our study suggests a new paradigm with which to exploit an overwhelming abundance of sequencing data for accurate variant detection.

genomics↗