Biology subjects

Kim, D.-G.

Publications and source records attributed to Kim, D.-G..

9 recordsLinked to original sources

bioRxiv2026

Brain-wide Cerebrospinal Fluid Recirculation Collapse in Alzheimers Disease

Cerebrospinal fluid (CSF) flow and the glymphatic system are essential for maintaining brain homeostasis, yet the precise mechanisms driving their profound failure in Alzheimers disease (AD) remain largely elusive. Here, we reveal that macroscopic CSF propagates dorsally via the perivascular spaces (PAS) of major ventral arteries--the Circle of Willis (CW), middle cerebral artery (MCA), and olfactory artery (OlfA)--which undergo severe structural degradation in an AD mouse model. High-resolution imaging of the dorsal cortex demonstrates that this structural collapse induces a severe influx blockade; in AD, CSF tracers form massive aggregates within the PAS, physically decoupling the gliovascular interface, while tracers entering the parenchyma are pathologically sequestered by amyloid plaques. Furthermore, we identify an active, cholesterol-mediated clearance mechanism responding to exogenous CSF tracer infiltration. While healthy brains rapidly synthesize and efficiently clear this cholesterol via vascular efflux within 120 minutes, the AD model exhibits blunted cholesterol mobilization and a complete failure of capillary efflux. This terminal bottleneck forces uncleared cholesterol to retrogradely accumulate into pathological aggregates within perivascular pericytes and neuronal somata. Collectively, our findings demonstrate that AD dismantles the entire CSF recirculation loop--from macroscopic ventral entry to terminal microvascular efflux--highlighting pericytic and neuronal lipidosis as fundamental drivers of brain fluid stagnation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/755971v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@1c62a5eorg.highwire.dtl.DTLVardef@107919eorg.highwire.dtl.DTLVardef@1e921baorg.highwire.dtl.DTLVardef@a549ad_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2026

Divergent transcriptomic transition programs in Alzheimer disease: immune priming and synaptic collapse in females versus organelle membrane bifurcation in males

Alzheimers disease (AD) disproportionately affects women, who account for approximately two-thirds of prevalent cases. Despite decades of research, the mechanistic basis for this profound sex disparity remains poorly resolved. Prior transcriptomic studies have predominantly used pooled or female-enriched cohorts, obscuring whether the transition from normal cognition (NCI) to AD follows a common molecular program across sexes or reflects fundamentally divergent biology. Here, we analyzed bulk RNA-sequencing data from the dorsolateral prefrontal cortex of 624 individuals (401 females, 223 males) in the ROSMAP cohort using a transition-aware framework that separates variance instability along the disease axis from mean expression changes. We demonstrate that female and male brains exhibit structurally distinct transcriptomic transition programs. Females display a sequential, multi-tier architecture: interferon and immune variance priming (628 genes) is detectable early at the NCI-to-mild cognitive impairment (MCI) interval, which structurally precedes a massive mean-level synaptic and neuropeptide loss (8,935 genes) in AD. The female NCI-to-MCI interval alone produces 1,249 variance bifurcation events entirely absent in males. Conversely, males exhibit no early immune priming or powered mean-level changes. Instead, they collapse into a single large variance bifurcation pool (8,237 genes) heavily enriched for system-wide organelle membrane fusion, post-translational modification, and intracellular trafficking. These findings reveal that the AD transcriptomic transition is not a unitary program quantitatively modified by sex, but two distinct biological trajectories. This fundamental divergence motivates sex-stratified mechanistic models and independent biomarker development for each sex, cautioning against analytical pooling in transition-stage cohorts.

bioinformatics↗

bioRxiv2026

Transcriptomic phase transitions along the Alzheimer's disease continuum using riemmaninan tensor model: a bifurcation-intermediate variance framework on the ROSMAP cohort

Alzheimers disease (AD) progression involves systemic network transitions. To capture these using ROSMAP bulk RNA-seq (n=624), we focused on the geometry of the covariance structure, performing a Riemannian (Log-Euclidean) analysis of stage-wise covariance matrices as points on the manifold of symmetric positive-definite (SPD) matrices. On the SPD manifold the three stages were non-collinear: geodesic distances were non-uniform and MCI was displaced from the NCI-AD chord, while the von Neumann entropy of the covariance structure dipped at MCI (S = 2.760, 2.639, 2.647 for normal cognitive intact NCI, mild cognitive impairment MCI, AD) and the path-curvature profile reached a minimum there -- together identifying MCI as a saddle/bifurcation state. The differential covariance spectrum (CAD - CNCI) separated AD-amplified ("structural collapse") from AD-suppressed ("protective loss") modes. Ultimately, second-order statistics analyzed through Riemannian geometry, rather than Euclidean summaries, reveal AD progression structure invisible to mean-level analysis.

bioinformatics↗

bioRxiv2026

Riemannian Tensor Analysis Identifies a Bifurcation State in the Single-Cell Transcriptomic Landscape of Parkinson's Disease

The transition from healthy aging to Parkinsons disease (PD) involves highly volatile transcriptomic rewiring that remains invisible to conventional mean-based analyses. To decode this critical tipping point, we integrated single-cell RNA-sequencing with a novel Log-Euclidean Riemannian tensor framework. By conceptualizing distinct transcriptomic states as symmetric positive definite (SPD) covariance tensors, we bypassed Euclidean geometric artifacts to accurately map the macroscopic network architecture of the human prefrontal cortex. Our analysis identified a highly unstable, intermediate bifurcation (BIF) state. Thermodynamic and topological validation demonstrated that this BIF state operates as a definitive geometric saddle point, characterized by maximal von Neumann entropy and positioned perfectly equidistant between the healthy (HC) and pathological (PD) Frechet means on the non-Euclidean manifold. Furthermore, spectral deconstruction of differential covariance networks [Formula] isolated the exact topological drivers of this transition--revealing the catastrophic structural collapse of core synaptic and electrophysiological hubs prior to overt pathological commitment. Ultimately, this ab initio geometric framework fundamentally redefines PD progression, providing a quantitative blueprint to intercept neurodegenerative trajectories during their final reversible window.

systems biology↗

bioRxiv2025

Isolation of Pure Disease Specific Aging Trajectories in Spatial Transcriptomics via the Delta Delta Method

Disentangling normal aging from disease driven transcriptional change remains a major obstacle for spatial genomics. We introduce the Delta-Delta ({Delta}{Delta}) Method, a contrastive trajectory framework that resolves a four-dimensional progression (genes x cell types x brain regions x time) by subtracting the wild type (WT) aging trajectory from the transgenic (TG) trajectory to yield a pure disease trajectory ({Delta}{Delta}log2FC). The method is platform agnostic, integrates with common spatial transcriptomics workflows, and outputs direction and speed of change summaries, enriched pathways, and region and cell type specific maps. In a demonstration using G2-3 -synuclein TG mice and age matched WT controls at 6 and 10 months across hippocampus and midbrain, {Delta}{Delta} uncovered opposite regional dynamics in glutamatergic neurons and a convergent enrichment of RNA splicing pathways, corroborated by alternative splicing analyses. By explicitly modeling time while controlling for aging within each region and cell type, the {Delta}{Delta} Method isolates disease specific molecular programs that are obscured in conventional bulk or single cell analyses, and provides a generalizable framework for trajectory aware mechanistic target prioritization in neurodegeneration and other progressive conditions.

bioinformatics↗

bioRxiv2025

Influenza A Virus infection is associated with TDP-43 pathology and neuronal damage in the brain

Viral pandemics such as COVID-19 have demonstrated long-term neurological consequences, including memory impairment and depression, emphasizing the importance of understanding virus-brain interactions [1]. Similar concerns have been raised for Influenza A virus (IAV), which has been implicated in neurodegenerative disorders [2, 3]. In this study, we investigated the neuropathological effects of highly pathogenic avian influenza (HPAI) H5N1 and H5N8 strains in a mouse model. Although viral RNA was detected in the brain post-infection, no viral proteins were found, suggesting limited or transient brain replication. Despite this, infected brains showed significant neuronal damage, including axonal loss and nuclear condensation, as evidenced by immunofluorescence and Nissl staining. We also observed pathological changes in TDP-43, including conformational alterations and increased phosphorylation, which required antigen retrieval for detection--features reminiscent of those found in frontotemporal dementia and amyotrophic lateral sclerosis [4, 5]. Transcriptomic analysis further revealed strain-specific host responses, including activation of interferon-related genes and downregulation of microtubule-associated pathways. These findings suggest that IAV infection can trigger hallmarks of neurodegeneration in the absence of persistent viral protein expression, possibly through host-driven mechanisms. Our results underscore the need for further investigation into virus-induced molecular pathways contributing to neurodegenerative disease.

neuroscience↗

bioRxiv2025

EZH1 Q571R-mediated chromatin compaction and its oncogenic potential in thyroid cancer

Dysregulation of Polycomb Repressive Complex 2 (PRC2) is established to contribute to cancer. Of its catalytic subunits, EZH1 and EZH2, EZH2 mutations in cancer have been extensively identified and studied, but the role of EZH1 in cancer remains largely unexplored. The recurrent presence of the EZH1Q571R mutation in follicular thyroid cancer suggests its involvement in tumor progression. Using EZH1 ChIP-seq, ATAC-seq, and H3K27me3 CUT&Tag, we demonstrated that EZH1Q571R enhances chromatin binding and compaction and stimulates PRC2-EZH1 catalytic activity, leading to increased H3K27me3 levels and repression of tumor suppressor genes. Purified PRC2-EZH1Q571R demonstrated a significant increase in histone methyltransferase activity compared to PRC2-EZH1WT via enhanced nucleosome binding and DNA compaction. Notably, this effect is particularly observed with EZH1Q571R but only to a lesser extent with the corresponding EZH2Q570R mutation, highlighting it as an EZH1-specific mechanism. We additionally demonstrated PRC2-EZH1Q571R to efficiently methylate H3K27 in pre-existing H3K36me2/3 nucleosomes, disrupting chromatin homeostasis. Our findings provide key insights into the molecular pathogenesis of EZH1Q571R-driven follicular thyroid cancer.

molecular biology↗

bioRxiv2022

AVATAR: AI Vision Analysis for Three-dimensional Action in Real-time

Artificial intelligence (AI) is an emerging tool for high-resolution behavioural analysis and conduction of human-free behavioural experiments. Here, we applied an AI-based system, AVATAR, which automatically virtualises 3D motions from the detection of 9 body parts. This allows quantification, classification and detection of specific action sequences in real-time and facilitates closed-loop manipulation, triggered by the onset of specific behaviours, in freely moving mice.

neuroscience↗

bioRxiv2021

Robust Characterization of Non-Invasive Post-Traumatic Osteoarthritis Mouse Model

ObjectiveBiochemical and molecular changes involved in the pathophysiology of post-traumatic arthritis (PTOA) have not been fully understood. This study used non-invasive mouse models to study biochemical, biomechanical and pain-related behavior changes induced in mice following repetitive mechanical knee loading. Mouse models were used to reflect the effects of the early stages of PTOA in humans. MethodsForty-eight twelve week old male mice were obtained for three groups: normal control without mechanical loading, trauma (24 hours after loading), and PTOA (early OA) groups. For the non-invasive PTOA mouse model, cyclic comprehensive loading (9 N) was applied on the left knee joint of each mouse. Biochemical and molecular changes induced by mechanical loading were analyzed after loading was completed. Blood and cartilage were collected and further examined using gene expression analysis. Grading of the tissue sections was completed using the osteoarthritis research society international (OARSI) scale. Biomechanical features of mechanically loaded knee joints were determined after 24 hours (Trauma) and three weeks (PTOA) post-mechanical loading sessions to examine the development of PTOA, respectively. ResultsThe loaded left knee joint showed a greater ROS/RNS signal than the right knee that was not loaded. There was an increase in cartilage damage and MMP activity in the affected knee as the intensity of MabCII680 and MMP750 signal increased in the mechanical loaded joints as compared to unloaded control knee joints. There was also an increase in the difference of viscoelastic energy dissipation ability (tan {delta}) in PTOA. The OA score increased significantly in mechanically loaded knee joints. ConclusionThis study showed that biomechanical, biochemical, and behavioral characteristics of the murine PTOA groups are significantly different from the control group. These results validate that the current mouse model can be used for translational studies to examine PTOA.

animal behavior and cognition↗