Biology subjects

Ahn, K.

Publications and source records attributed to Ahn, K..

6 recordsLinked to original sources

bioRxiv2026

Impact of impaired endogenous neurosteroidogenesis on outcomes following chronic alcohol exposure.

Alcohol use disorder is a major public health concern worldwide and there is a high comorbidity with psychiatric disorders. The basolateral amygdala (BLA) has been implicated in both mood and alcohol use disorders; however, the mechanisms contributing to the shared pathophysiology remain unknown. Extensive evidence indicates that ethanol modulates GABAergic signaling in the BLA, including actions on neurosteroid-sensitive, extrasynaptic {delta} subunit-containing GABAA receptors (GABAARs), which has been suggested to mediate many of the behavioral effects. In fact, several studies have suggested that 5-reduced neurosteroids, such as allopregnanolone, may mediate some of the behavioral effects of alcohol. Here we demonstrate that chronic intermittent ethanol (CIE) exposure impairs endogenous neurosteroidogenesis via downregulation of key neurosteroidogenic enzymes, 5-reductase type 1 and type 2. To examine the impact of impaired endogenous neurosteroidogenesis of the behavioral consequences of chronic alcohol exposure, including withdrawal-induced anxiety and increased alcohol consumption, we used CRISPR/Cas9 mediated knockdown of 5-reductase in the BLA. Reduced expression of 5-reductase in the BLA did not impact post-CIE alcohol intake or anxiety-like behaviors during withdrawal, perhaps because endogenous neurosteroidogenesis is already impaired following CIE. Therefore, we examined the impact of enhancing neurosteroid levels, treating mice post-CIE with SGE-516, a synthetic GABAAR positive allosteric modulator, which increased voluntary alcohol intake. These findings implicate endogenous neurosteroidogenesis in behavioral outcomes associated with withdrawal from chronic alcohol exposure. Further, this study suggests that targeting endogenous neurosteroidogenesis may be a novel and useful therapeutic target.

neuroscience↗

bioRxiv2024

Human immunodeficiency virus-1 induces and targets host genomic R-loops for viral genome integration

Although HIV-1 integration sites favor active transcription units in the human genome, high-resolution analysis of individual HIV-1 integration sites has shown that the virus can integrate into a variety of host genomic locations, including non-genic regions. The invisible infection by HIV-1 integrating into non-genic regions, challenging the traditional understanding of HIV-1 integration site selection, is more problematic because they are selected for preservation in the host genome during prolonged antiretroviral therapies. Here, we showed that HIV-1 integrates its viral genome into the vicinity of R-loops, a genomic structure composed of DNA- RNA hybrids. VSV-G-pseudotyped HIV-1 infection initiates the formation of R-loops in both genic and non-genic regions of the host genome and preferentially integrates into R-loop-rich regions. Using a HeLa cell model that can independently control transcriptional activity and R-loop formation, we demonstrated that the exogenous formation of R-loops directs HIV-1 integration-targeting sites. We also found that HIV-1 integrase proteins physically bind to the host genomic R-loops. These findings provide novel insights into the mechanisms underlying retroviral integration and the new strategies for antiretroviral therapy against HIV-1 latent infection.

molecular biology↗

bioRxiv2022

Proteomic discovery of chemical probes that perturb protein complexes in human cells

Most human proteins lack chemical probes, and several large-scale and generalizable small-molecule binding assays have been introduced to address this problem. How compounds discovered in such "binding-first" assays affect protein function, nonetheless, often remains unclear. Here, we describe a "function-first" proteomic strategy that uses size exclusion chromatography (SEC) to assess the global impact of electrophilic compounds on protein complexes in human cells. Integrating the SEC data with cysteine-directed activity-based protein profiling identifies changes in protein-protein interactions that are caused by site-specific liganding events, including the stereoselective engagement of cysteines in PSME1 and SF3B1 that disrupt the PA28 proteasome regulatory complex and stabilize a dynamic state of the spliceosome, respectively. Our findings thus show how multidimensional proteomic analysis of focused libraries of electrophilic compounds can expedite the discovery of chemical probes with site-specific functional effects on protein complexes in human cells.

biochemistry↗

bioRxiv2020

Pseudo-Location: A novel predictor for predicting pseudo-temporal gene expression patterns using spatial functional regression

Statement of withdrawalThe authors have withdrawn version 1 of this manuscript because a draft manuscript, which was still in the early stages of preparation and required major revisions including the replacement of the source RNA-seq datasets, was erroneously submitted. The authors do not wish this version to be cited as reference for this study. We will post a revised manuscript in the future. If you have any questions, please contact the corresponding author.

bioinformatics↗

bioRxiv2019

Subpopulation identification for single-cell RNA-sequencing data using functional data analysis

BackgroundIn single-cell RNA-sequencing (scRNA-seq) data analysis, a number of statistical tools in multivariate data analysis (MDA) have been developed to help analyze the gene expression data. This MDA approach is typically focused on examining discrete genomic units of genes that ignores the dependency between the data components. In this paper, we propose a functional data analysis (FDA) approach on scRNA-seq data whereby we consider each cell as a single function. To avoid a large number of dropouts (zero or zero-closed values) and reduce the high dimensionality of the data, we first perform a principal component analysis (PCA) and assign PCs to be the amplitude of the function. Then we use the index of PCs directly from PCA for the phase components. This approach allows us to apply FDA clustering methods to scRNA-seq data analysis. ResultsTo demonstrate the robustness of our method, we apply several existing FDA clustering algorithms to the gene expression data to improve the accuracy of the classification of the cell types against the conventional clustering methods in MDA. As a result, the FDA clustering algorithms achieve superior accuracy on simulated data as well as real data such as human and mouse scRNA-seq data. ConclusionsThis new statistical technique enhances the classification performance and ultimately improves the understanding of stochastic biological processes. This new framework provides an essentially different scRNA-seq data analytical approach, which can complement conventional MDA methods. It can be truly effective when current MDA methods cannot detect or uncover the hidden functional nature of the gene expression dynamics.

bioinformatics↗

bioRxiv2019

Deep transcriptome sequencing of subgenual anterior cingulate cortex reveals disorder-specific expression changes in major psychiatric disorders

How do differences in onset, symptoms, and treatment response arise between various mental illnesses despite substantial overlap of genetic risk factors? To address this question, we carried out deep RNA sequencing of human postmortem subgenual anterior cingulate cortex, a key component of limbic circuits linked to mental illness. Samples were obtained from 200 individuals diagnosed with bipolar disorder, schizophrenia, or major depression, and controls. Differential expression analysis in cases versus controls detected modest differences that were similar across disorders, although transcript-level differences were more pronounced. Case-case comparisons revealed greater expression differences between disorders, including many genes and transcripts that were expressed in opposite directions in each diagnostic group, compared to controls. Relative transcript abundances were associated with common genetic variants that accounted for disproportionate fractions of diagnosis-specific heritability. Inherited genetic risk factors shape the brain transcriptome and contribute to diagnostic differences between broad classes of mental illness.

genomics↗